Correlative all-optical quantification of mass density and mechanics of subcellular compartments with fluorescence specificity

  1. Raimund Schlüßler  Is a corresponding author
  2. Kyoohyun Kim  Is a corresponding author
  3. Martin Nötzel
  4. Anna Taubenberger
  5. Shada Abuhattum
  6. Timon Beck
  7. Paul Müller
  8. Shovamaye Maharana
  9. Gheorghe Cojoc
  10. Salvatore Girardo
  11. Andreas Hermann
  12. Simon Alberti
  13. Jochen Guck  Is a corresponding author
  1. Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität, Germany
  2. Max Planck Institute for the Science of Light and Max-Planck-Zentrum für Physik und Medizin, Germany
  3. Department of Microbiology and Cell Biology, Indian Institute of Science, India
  4. Translational Neurodegeneration Section "Albrecht Kossel", University Rostock, and German Center for Neurodegenerative Diseases (DZNE), Germany
  5. Physics of Life, Technische Universität Dresden, Germany

Abstract

Quantitative measurements of physical parameters become increasingly important for understanding biological processes. Brillouin microscopy (BM) has recently emerged as one technique providing the 3D distribution of viscoelastic properties inside biological samples − so far relying on the implicit assumption that refractive index (RI) and density can be neglected. Here, we present a novel method (FOB microscopy) combining BM with optical diffraction tomography and epifluorescence imaging for explicitly measuring the Brillouin shift, RI, and absolute density with specificity to fluorescently labeled structures. We show that neglecting the RI and density might lead to erroneous conclusions. Investigating the nucleoplasm of wild-type HeLa cells, we find that it has lower density but higher longitudinal modulus than the cytoplasm. Thus, the longitudinal modulus is not merely sensitive to the water content of the sample − a postulate vividly discussed in the field. We demonstrate the further utility of FOB on various biological systems including adipocytes and intracellular membraneless compartments. FOB microscopy can provide unexpected scientific discoveries and shed quantitative light on processes such as phase separation and transition inside living cells.

Editor's evaluation

This is an important and timely contribution that introduces a new approach that combines Brillouin microscopy with fluorescence (FOB) to measure the mechanical properties in terms of longitudinal moduli for viscoelastic materials in cells. This approach has many promising applications, which the authors articulate, and could be important as a new and complementary modality for investigating the mechanical properties of soft materials, specifically membrane-bound and membrane-less organelles.

https://doi.org/10.7554/eLife.68490.sa0

Introduction

The mechanical properties of tissues, single cells, and intracellular compartments are linked to their function, in particular during migration and differentiation, and as a response to external stress (Engler et al., 2006; Provenzano et al., 2006; Lo et al., 2000). Hence, characterizing mechanical properties in vivo has become important for understanding cell physiology and pathology, for example, during development or cancer progression (Mammoto et al., 2013; Jansen et al., 2015; Mohammed et al., 2019). To measure the mechanical properties of biological samples, many techniques are available. These include atomic force microscopy (Christ et al., 2010; Koser et al., 2015; Gautier et al., 2015; Franze et al., 2013), micropipette aspiration (Maitre et al., 2012), and optical traps (Wu et al., 2018a; Litvinov et al., 2002; Bambardekar et al., 2015; Guck et al., 2001). These techniques can access the rheological properties of a sample and their changes under various pathophysiological conditions. Yet, most of them require physical contact between probe and sample surface and none of them allows to obtain spatially resolved distributions of the mechanical properties inside the specimens.

Brillouin microscopy has emerged as a novel microscopy technique to provide label-free, noncontact, and spatially resolved measurements of the mechanical properties inside biological samples (Scarcelli and Yun, 2008; Scarcelli et al., 2015; Antonacci et al., 2018; Prevedel et al., 2019). The technique is based on Brillouin light scattering that arises from the inelastic interaction between the incident photons and collective fluctuations of the molecules (acoustic phonons) (Brillouin, 1922; Boyd, 2008). The Brillouin shift measured is related to the longitudinal modulus, refractive index (RI), and absolute density, and the Brillouin peak linewidth is associated with the viscosity of the sample (see Materials and methods). The longitudinal modulus characterizes the compressibility of a sample and is in the GPa range for common biological samples (Prevedel et al., 2019). While the longitudinal modulus is theoretically related to the more commonly used Young’s modulus by the Poisson’s ratio, a conversion between the two moduli is generally not possible since the Poisson’s ratio is frequency dependent and normally unknown. However, multiple studies found empirical correlations between the longitudinal modulus and the Young’s modulus (Scarcelli et al., 2011; Scarcelli et al., 2015; Schlüßler et al., 2018). Furthermore, the longitudinal modulus takes into account all instrument properties like wavelength or scattering angle, and does not need normalization for comparability between different setups as the Brillouin shift does (Antonacci et al., 2020). So far, conventional Brillouin microscopy does not consider the contribution of heterogeneous RI and absolute density distributions to the longitudinal modulus. Most studies either assume a homogeneous RI distribution (Scarcelli and Yun, 2008; Scarcelli et al., 2011; Antonacci and Braakman, 2016), argument that the RI and absolute density trivially cancel out (Scarcelli et al., 2012; Scarcelli et al., 2015; Antonacci et al., 2018), or use RI values obtained separately by other imaging setups (Schlüßler et al., 2018). Other approaches to calculate the longitudinal modulus measure the mass density of the sample, but still rely on a priori knowledge of the RI (Liu et al., 2019; Remer et al., 2020). These simplifications may result in an inaccurate calculation of the longitudinal modulus since the RI distribution might not be homogenous throughout the sample, RI and density might not be coupled, hence, not cancel out, or the sample preparations necessary for separate RI measurements could influence the RI measured. Only recently serial Brillouin measurements of samples illuminated under different illumination angles allowed measuring the RI value inside the focal volume as well (Fiore et al., 2019). However, this technique requires illuminating the sample from two different directions, which doubles the acquisition time and decreases the spatial resolution of the measurement when compared to a setup only acquiring the Brillouin shift.

Optical diffraction tomography (ODT) has been utilized for measuring the three-dimensional (3D) RI distribution of various specimens (Sung et al., 2009; Cotte et al., 2013; Kim et al., 2016a). Employing quantitative phase imaging, ODT can reconstruct the 3D RI distribution of living biological samples from the complex optical fields measured under different illumination angles. Given the RI, the mass density and protein concentration of most biological samples can be calculated using a two-substance mixture model (see Materials and methods) (Barer, 1952; Popescu et al., 2008; Zangle and Teitell, 2014). Protein concentrations acquired with ODT were shown to agree well with results from volume-based measurements and did not suffer from differences in the quantum yield of fluorescent dyes between dilute and condensed phase as it might happen for fluorescence intensity ratio measurements (McCall et al., 2020). However, using the two-substance mixture model requires knowledge of the refraction increment, which depends on the material composition and takes on values of 0.173-0.215 ml/g with an average of 0.190 ml/g for different human proteins (Zhao et al., 2011; Theisen, 2000) and can go down to 0.135-0.138 ml/g for phospholipids (Erbe and Sigel, 2007; Mashaghi et al., 2008). Furthermore, the two-substance mixture model does not apply to cell compartments mainly filled with a single substance, for example, lipid droplets in adipocytes. Hence, specificity by, for example, fluorescence imaging is necessary to determine whether the two-substance mixture model is appropriate and which refraction increment should be used to calculate the absolute density of a certain cell region.

Here, we present a combined optical system for epifluorescence, ODT, and Brillouin microscopy (FOB microscopy), which can provide the correct longitudinal modulus from colocalized measurements of the Brillouin shift and RI distributions and the subsequently calculated absolute densities of a sample. The principal function of the FOB microscope is demonstrated by measurements of cell phantoms made of biconstituent polymers with known mechanical properties. We further applied the setup to HeLa cells and adipocytes. First, we investigated two condensates that form by physical process of phase separation – nucleoli in the nucleus and stress granules (SGs) in the cytoplasm (Alberti and Dormann, 2019). Nucleoli in HeLa cells showed a higher RI (n=1.3618±0.0004) and longitudinal modulus (M=2.487±0.005GPa) than the cytoplasm (n=1.3545±0.0004,M=2.410±0.005GPa), whereas the nucleoplasm had a lower RI (n=1.3522±0.0004) than the cytoplasm while still showing a higher longitudinal modulus (M=2.448±0.005GPa). The RI of the cytoplasm and nucleoplasm decreased after stressing HeLa cells with arsenite, but we found no statistically significant difference of either the RI or longitudinal modulus of SGs to the surrounding cytoplasm. By contrast, poly-Q aggregates formed by overexpressing the aggregation-prone exon 1 of Q103 huntingtin exhibited a 2.5% higher RI and 20.0% higher longitudinal modulus compared to the surrounding compartment. Moreover, unlike water-based cellular condensates and aggregates, lipid droplets inside adipocytes showed higher RI and Brillouin shift, but lower longitudinal modulus than the cytoplasm when taking into account their absolute density. These data illustrates that in order to correctly calculate the longitudinal modulus the RI as well as the absolute density have to be taken into account. In summary, the presented setup could provide measurement data necessary for a deeper understanding of pathophysiological processes related to cell mechanics and condensates that form by the process of phase separation.

Results

Optical setup

FOB microscopy combines ODT with Brillouin microscopy and epifluorescence imaging (Figure 1a). The three imaging modalities are sequentially applied to quantitatively map the RI, the Brillouin shift, and the epifluorescence intensity distribution inside a given sample. These parameters allow to, for example, infer the mass density and dry mass of the sample, and provide specificity to fluorescently labeled structures. Given the fluorescence specificity, it is furthermore possible to localize subcellular organelles of interest and to determine whether for a certain region the two-substance mixture model can be applied to calculate the local absolute density, or if the literature value of the absolute density has to be used (e.g., in lipid droplets). Finally, with the combination of RI, absolute density, and Brillouin shift distributions, the longitudinal modulus can be calculated.

Figure 1 with 1 supplement see all
Combined fluorescence, optical diffraction tomography (ODT), and Brillouin microscopy.

(a) Optical setup. The beam paths for epifluorescence/brightfield imaging, ODT, and Brillouin microscopy are shown in light yellow, dark green, and light green, respectively. The laser light illuminating the sample is collimated in ODT mode and focused in Brillouin mode. A moveable mirror enables to switch between the two modes. The Brillouin scattered light is guided to the spectrometer by a single-mode fiber, which acts as confocal pinhole. The light transmitted through the sample interferes with a reference beam. AOM, acousto-optic modulator; CL, cylindrical lens; LED, light-emitting diode; VIPA, virtually imaged phased array. (b–e) Quantitative and spatially resolved maps of a cell phantom consisting of a polydimethylsiloxane (PDMS) bead (indicated by the white arrows) inside a polyacrylamide (PAA) bead stained with Alexa 488 (green fluorescence in b) acquired with the FOB microscope. (b) Epifluorescence intensities, (c) refractive indices, (d) Brillouin shifts, and (e) calculated longitudinal moduli. The size of the Brillouin map is 41 × 41 pixel, resulting in an acquisition duration of 30 min. Scale bars 10 µm.

For ODT, the sample is illuminated with a plane wave under different incident angles. To illuminate the sample under different angles, a dual-axis galvanometer mirror tilts the illumination beam. The transmitted light interferes with a reference beam and creates a spatially modulated hologram on a camera from which the phase delay and finally the RI of the sample are calculated with a resolution of 0.25 μm within the lateral plane and 0.5 μm in the axial direction (Figure 1c). Epifluorescence microscopy captures the fluorescence emission intensity image (Figure 1b) with the same camera used for the ODT acquisition.

For Brillouin microscopy, a moveable mirror guides the incident light to an additional lens, which leads to a focus in the sample with a size of 0.4 μm in the lateral plane and approximately 1 μm in axial direction. The focus is translated by the galvanometer mirror to scan the whole sample. The Brillouin scattered light is collected in the backscattering configuration and guided to a two-stage virtually imaged phased array (VIPA) spectrometer (Scarcelli and Yun, 2008). The Brillouin shift (Figure 1d) is extracted from the recorded Brillouin spectrum, and the longitudinal modulus (Figure 1e) is calculated from the Brillouin shift, RI, and absolute density distributions acquired (see Materials and methods).

Validation of the setup with cell phantoms

To validate the basic performance of the combined FOB microscopy setup, we acquired the RI and Brillouin shift of an artificial cell phantom with known material properties. The phantom consists of a polydimethylsiloxane (PDMS) bead embedded in a polyacrylamide (PAA) bead (Figure 1b–e), which was fluorescently labeled with Alexa 488 (see Materials and methods). The material properties of the two components of the phantom are expected to be homogeneous, so that the standard deviation (SD) of the values measured can be used as an estimate of the setups’ measurement uncertainty. The RI of the embedded PDMS bead was measured as 1.3920 ± 0.0080 (mean value ± SD) (Figure 1c). This was slightly lower than the values reported for bulk PDMS with the RI of 1.416 (Meichner et al., 2015), which could be due to the swelling of the PDMS beads during the fabrication process (Wang et al., 2015). The RI of the PAA bead (1.3485 ± 0.0024) was substantially lower than that of the PDMS bead and was close to the previously reported value (Girardo et al., 2018). In contrast, the Brillouin shift of the PDMS bead (7.279 ± 0.043 GHz) was lower than for the PAA bead (7.574 ± 0.020 GHz) (Figure 1d). In order to calculate the longitudinal modulus, the absolute density of the PAA bead (1.019 ± 0.001 g/ml) was calculated from the measured RI by applying a two-substance mixture model (see Materials and methods). However, this model cannot be applied for the PDMS bead since the bead does not contain a fluid phase. Hence, the area of the PDMS bead was segmented based on the RI and fluorescence intensity (Figure 1b), and the literature value for the absolute density of PDMS (1.03 g/ml) was used (see Figure 1—figure supplement 1; Rahman et al., 2012). The resulting longitudinal modulus is shown in Figure 1e. We found values of 2.022 ± 0.030 GPa for the PDMS bead and 2.274 ± 0.012 GPa for the PAA bead. The results are consistent with previous measurements of the speed of sound in PDMS (Cafarelli et al., 2017) and the longitudinal modulus of PAA (Schlüßler et al., 2018) when taking into account the absolute density of the dry fraction (i.e., (Equation 2)). Our finding clearly demonstrates the strength of the presented FOB setup to provide local RI and absolute density distributions for correctly calculating longitudinal modulus from the Brillouin shift measured.

Cell nucleoplasm has lower absolute density but higher longitudinal modulus than cytoplasm

The FOB microscope can also provide the much needed quantitative insight into a biological phenomenon that has recently captured the imagination of physicists and biologists alike – the formation of membraneless compartments by liquid-liquid phase separation (LLPS) (Brangwynne et al., 2011). One such membraneless compartment is the nucleolus, a region within the nucleus where ribosomal subunits are synthesized. Here, we recorded the epifluorescence, Brillouin shift, and RI distributions of 139 HeLa cells in which a nucleolar marker protein NIFK was tagged with GFP and the nuclei were stained with Hoechst (see Materials and methods). In order to evaluate the mechanical properties of the cytoplasm, nucleoplasm, and nucleoli separately, we segmented the different compartments of the cells based on the RI and the two-channel epifluorescence intensity maps (Figure 2a, see Materials and methods).

Figure 2 with 1 supplement see all
Cell nucleoplasm has lower refractive index (RI) but higher longitudinal modulus than cytoplasm.

(a–c) Representative maps of the (a) epifluorescence intensity distribution, (b) longitudinal moduli, and (c) RIs of a HeLa cell. Nuclei are stained with Hoechst (blue), and the nucleolar protein in the nucleoli is labeled with GFP (green). Quantitative analysis of (d) the RI and (e) the calculated longitudinal modulus taking into account the Brillouin shifts, RIs, and absolute densities of 139 HeLa cells. The size of the Brillouin map is 21 × 21 pixel, resulting in an acquisition duration of 8 min . Scale bars 10 µm. ***p<0.001, ****p<0.0001.

Figure 2—source data 1

Refractive index, longitudinal modulus, Brillouin shift, and absolute density values of different compartments in HeLa cells.

https://cdn.elifesciences.org/articles/68490/elife-68490-fig2-data1-v2.xlsx

As shown in Figure 2b and d, the nucleoplasm of HeLa cells exhibited a statistically significantly lower RI than the cytoplasm (Kruskal−Wallis pncyto,nnp=9×104), with values of 1.3522 ± 0.0004 (mean value ± SEM) (nucleoplasm) and 1.3545 ± 0.0004 (cytoplasm), which is consistent with previous studies (Schürmann et al., 2016; Kim and Guck, 2020). Since the RI of the HeLa cells measured is linearly proportional to their mass density (Kim and Guck, 2020), we applied the two-substance mixture model and used a global refraction increment of 0.190 ml/g, which is valid for protein and nucleic acid (Zhao et al., 2011; Zangle and Teitell, 2014), to calculate the absolute densities of each cell and its compartments. The resulting absolute densities are shown in Figure 2—figure supplement 1b and d. We found that the nucleoplasm had a lower absolute density (1.0207 ± 0.0005 g/ml) than the cytoplasm (1.0234 ± 0.0006 g/ml). Here, the perinuclear cytoplasm also contains many lipid-rich membrane-bound organelles, and the RI increment of phospholipids (0.135-0.138 ml/g, Erbe and Sigel, 2007; Mashaghi et al., 2008) is lower than that of protein and nucleic acid. Hence, the calculated absolute density of the cytoplasm could be underestimated and the absolute density difference between cytoplasm and nucleoplasm might be even more pronounced.

Interestingly, the Brillouin shift of the nucleoplasm (7.872 ± 0.007 GHz) was statistically significantly higher than the value of the cytoplasm (7.811 ± 0.008 GHz) (pνB,cyto,νB,np=2×106, Figure 2—figure supplement 1b and d). The longitudinal moduli of the nucleoplasm (2.448 ± 0.005 GPa) and cytoplasm (2.410 ± 0.005 GPa) followed the same trend as the Brillouin shifts (pMcyto,Mnp=7×107, Figure 2c and e). Moreover, the nucleoli, where ribosomal subunits are synthesized, exhibited statistically significantly higher RI (n=1.3618±0.0004), Brillouin shift (νB=7.938±0.008GHz), and longitudinal modulus (M=2.487±0.005GPa) than either nucleoplasm or cytoplasm. We further found that the Brillouin peak linewidth of the cytoplasm and nucleoplasm is not statistically significantly different, but shows a statistically significant increase in the nucleoli (Figure 2—figure supplement 1). This indicates a higher viscosity and a less fluid-like behavior in the nucleoli compared to the cytoplasm and nucleoplasm. A full list of the resulting RI, Brillouin shifts, absolute densities, longitudinal moduli and linewidths, and the corresponding p-values when comparing between different cell compartments can be found in Supplementary files 1 and 2.

These findings imply that membraneless compartments formed by phase separation, in this case the nucleolus, can maintain a higher absolute density and distinct compressibility (here, higher longitudinal modulus) in spite of the thermodynamic instability inherent in this state.

polyQ aggregates have higher absolute density and longitudinal modulus than cytoplasm

To compare the properties of physiological condensates with a densely packed protein aggregate, we overexpressed an expanded version of the aggregation-prone exon 1 of huntingtin with 103 consecutive glutamines (Lieberman et al., 2019; Norrbacka et al., 2019; Bäuerlein et al., 2017). Q103 phase separates into liquid droplets in cells and these droplets rapidly convert into a solid-like state (Yang and Yang, 2020), meaning that they do not recover from photobleaching when subjected to fluorescence recovery after photobleaching (FRAP) experiments (Kroschwald et al., 2015). Here, we observe polyglutamine (polyQ) aggregates labeled with GFP in transiently transfected wild-type HeLa cells. We used the FOB microscope to measure the mechanical properties of polyQ granules in 22 different cells.

The polyQ aggregates showed a strong fluorescence signal in the GFP channel (Figure 3a). We hence used the fluorescence intensity to segment the aggregates from the peripheral cytoplasm in order to quantitatively compare cytoplasm and aggregates (Figure 3b and c). The RI (1.3856 ± 0.0018) and the longitudinal modulus (3.051 ± 0.029 GPa) of the aggregates were statistically significantly higher (p<0.0001) than the RI (1.3506 ± 0.0013) and longitudinal modulus (2.442 ± 0.009 GPa) of the peripheral cytoplasm (Figure 3d and f and Supplementary file 3). Using the RI measured, we find a protein concentration of 255.8 ± 9.4 mg/ml in the polyQ aggregates, a fourfold higher concentration than the value of 65 mg/ml previously measured with ODT in a G3BP1 in vitro system (Guillén-Boixet et al., 2020). Our results show that FOB microscopy can quantify the physical properties of cytoplasmic membraneless condensates – in principle.

Polyglutamine (polyQ) aggregates have a higher refractive index, Brillouin shift, and longitudinal modulus than the peripheral cytoplasm.

(a–c) Representative maps of (a) the epifluorescence intensity distribution, (b) the refractive indices, and (c) the longitudinal moduli of a HeLa cell transfected with a plasmid encoding HttQ103. The polyQ aggregates are labeled with GFP (green). Quantitative analysis of (d) the refractive index, (e) the Brillouin shift, and (f) the calculated longitudinal modulus taking into account the Brillouin shifts, refractive indices, and absolute densities of 22 polyQ granules. The size of the Brillouin map is 37 × 37 pixel, resulting in an acquisition duration of 23 min. Scale bars µm. ****p<0.0001.

Figure 3—source data 1

Refractive index, Brillouin shift, and longitudinal modulus values of polyglutamine (polyQ) aggregates and their periphery.

https://cdn.elifesciences.org/articles/68490/elife-68490-fig3-data1-v2.xlsx

FUS-GFP SGs in living P525L HeLa cells show RI and longitudinal modulus similar to the surrounding cytoplasm

Recently, another type of condensates formed by LLPS − RNA and protein (RNP) granules, such as SGs − has received much attention due to its linkage to neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (Patel et al., 2015; Alberti and Hyman, 2016). It is also increasingly recognized that the mechanical properties of these compartments influence their functions and involvement in disease (Jawerth et al., 2018; Nötzel et al., 2018). Fused in sarcoma (FUS) protein, an RNA-binding protein involved in DNA repair and transcription, is one example of a protein that localizes to SGs (Patel et al., 2015). Purified FUS protein is able to phase separate into liquid condensates in vitro, and this property is important for FUS to localize to SGs. Disease-linked mutations in FUS have been shown to promote a conversion of reconstituted liquid FUS droplets from a liquid to a solid state, suggesting that an aberrant liquid to solid transition of FUS protein promotes disease.

Conventionally, the mechanical changes of SGs have been indirectly characterized by FRAP or observing fusion events of liquid droplets (Brangwynne et al., 2009). Recently, Brillouin microscopy was used to measure the Brillouin shift of SGs in chemically fixed P525L HeLa cells expressing mutant RFP-tagged FUS under doxycycline exposure (Antonacci et al., 2018). P525L HeLa cells are used as a disease model for ALS and form SGs under arsenite stress conditions. It was shown that the Brillouin shift of SGs induced by arsenite treatment with mutant RFP-FUS is statistically significantly higher than the Brillouin shift of SGs without mutant RFP-FUS. Furthermore, the Brillouin shift of mutant RFP-FUS SGs was reported to be statistically significantly higher than the value of the surrounding cytoplasm (Antonacci et al., 2018).

Here, we applied the FOB setup to P525L HeLa cells that express GFP-tagged FUS and quantified the RI distributions, epifluorescence intensities, and Brillouin shifts of the nucleoplasm, cytoplasm, and SGs. As not all HeLa cells were GFP-positive (see Figure 4a), we only selected the ones with a clear signal in the GFP channel. The cells were measured under control conditions after oxidative stress conditions when exposed to 5 mM sodium arsenite NaAsO2 30 min prior to the measurements and after chemical fixation after oxidative stress. Since the SGs are not static, and assemble and disassemble dynamically in living cells, acquiring the Brillouin shift map of a complete cell would be too slow, which was the reason for the chemical fixation of the cells in a previous study (Antonacci et al., 2018). During the approximate duration of 20-30 min of a whole-cell measurement, SGs moved substantially or even disassembled and, hence, did not colocalize with their epifluorescence signal acquired before. Furthermore, the P525L FUS-GFP HeLa cells reacted sensitively to the exposure to green laser light and suffered from cell death during a whole-cell measurement. We therefore did not acquire Brillouin shift maps of complete P525L HeLa cells, but only of small regions of 5 μm × 5 μm around the SGs or the corresponding regions in the cytoplasm of the control cells. This reduced the measurement duration to less than 2 min, allowing us to colocalize the SGs Brillouin shift and epifluorescence signal and preventing cell death during the acquisition (see Figure 4—figure supplement 1). Hence, all measurements presented here stem from living cells. The positions for the Brillouin shift measurements of the different compartments were chosen manually based on the epifluorescence and brightfield intensities (see Figure 4a–f). In total, we measured over 100 different cells, with the number of values per compartment and condition varying from 32 to 42, as shown in Figure 4g and h.

Figure 4 with 2 supplements see all
FUS-GFP-labeled stress granules induced by oxidative stress in living P525L FUS HeLa cells show a similar refractive index (RI), and longitudinal modulus as the peripheral cytoplasm.

Representative example of (a–c) the fluorescence intensity and (d−f) the RI distribution under control conditions without arsenite, with arsenite, and with arsenite after fixation, respectively. Quantitative analysis of (g) the RI and (h) the calculated longitudinal modulus taking into account the Brillouin shift and RI. Scale bars 10 µm. *p<0.05, **p<0.01, ***p<0.001, ****p<0.0001.

Figure 4—source data 1

Refractive index and longitudinal modulus values of cytoplasm, nucleoplasm, and stress granules (SGs) in P525L HeLa cells after different treatments.

https://cdn.elifesciences.org/articles/68490/elife-68490-fig4-data1-v2.xlsx

We found that the RI of the cytoplasm measured in the region of SG formation was statistically significantly lower than the RI of the nucleoplasm for all conditions (control [c], arsenite [a], and arsenite fixed [f]) tested (pc=0.034,pa=0.035,pf=5×106, Figure 4g and Supplementary file 4). However, when segmenting the RI of the whole cell and not only taking into account the RI of the manually selected regions for which we also performed measurements of the Brillouin shift, we found a slightly, although not statistically significantly, lower RI in the nucleoplasm than in the cytoplasm (Figure 4—figure supplement 2). Hence, we think the higher RI of the nucleoplasm is a result of the manual selection of the measurement positions in the region of SG formation near the cell boundary. As for wild-type HeLa cells, the longitudinal modulus of the nucleoplasm was statistically significantly higher than the modulus of the cytoplasm for all conditions (pc=1×104,pa=8×106,pf=1×1011, Figure 4h and Supplementary file 4). While the GFP-tagged FUS of the control cells was mainly located in the nucleoplasm (Figure 4a), after arsenite treatment, the FUS was relocated from the nucleoplasm and aggregated in SGs within the cytoplasm (Figure 4b). This was accompanied by a statistically significant decrease of the RI of both the peri-SG cytoplasm (na,pericyto=1.3456±0.0007,p=0.019) and the nucleoplasm (na,nucleo=1.3476±0.0006,p=0.017) as well as of the longitudinal modulus of the peri-SG cytoplasm (Ma,pericyto=2.329±0.009GPa,p=0.036). Furthermore, we found no statistically significant difference of neither the RI (na,SG=1.3443±0.0006) nor the longitudinal modulus (Ma,SG=2.345±0.009GPa) of SGs to the respective values in the peri-SG cytoplasm. However, after chemical fixation the longitudinal modulus of the SGs (Mf,SG=2.357±0.006GPa) was statistically significantly higher than the longitudinal modulus of the cytoplasm (Mf,pericyto=2.331±0.006GPa,p=0.030).

Altogether, in P525L HeLa cells expressing FUS-GFP, the RI and longitudinal modulus of the nucleoplasm of the control, arsenite-treated, and fixed cells was statistically significantly higher than the respective values in the cytoplasm. Interestingly, SGs showed no statistically significant differences to the peri-SG cytoplasm in living, arsenite-treated cells, but had a statistically significantly higher longitudinal modulus in arsenite-treated and chemically fixed cells. This is consistent to previous studies showing a higher longitudinal modulus of SGs compared to the cytoplasm in chemically fixed P525L HeLa cells (Antonacci et al., 2018) and that fixation can substantially alter the mechanical (Braet et al., 1998) as well as the optical properties (Su et al., 2014) of biological samples.

Mechanical characterization of lipid droplets in adipocytes requires precise RI and density

Most biological cells can be thought of as a mixture of ions and macromolecules such as proteins, nucleic acids, and sugars dissolved in water, for which the two-substance mixture model (Barer, 1952; Popescu et al., 2008; Zangle and Teitell, 2014) is appropriate to describe the relationship between the RI and the absolute density. However, this is not the case for special compartments in certain cell types. The lipid droplets within adipocytes are not composed of a water-based solution and cannot be characterized by the two-substance mixture model. To overcome this problem, we exploit the specificity to fluorescently labeled structures of the FOB setup to identify and segment the lipid droplets. Since previous mass spectroscopy studies on adipocyte cell culture models have identified palmitoyl triacylglycerides as predominant lipid species (Gouw and Vlugter, 1966; Liaw et al., 2016), we use an absolute density value of 0.8932 g/ml for calculating the longitudinal moduli of the lipid droplets.

Here, we observed Simpson–Golabi–Behmel syndrome (SGBS) adipocytes (Wabitsch et al., 2001) (N=3) whose nucleus and lipid droplets were stained with Hoechst and Nile red, respectively, on day 11 of adipogenic differentiation. The lipid droplets were clearly visible in the fluorescence intensity (Figure 5a) and showed a high mean RI value of 1.409 ± 0.004 (Figure 5c). The Brillouin shift of the lipid droplets of 8.25 ± 0.02 GHz was also statistically significantly higher than the Brillouin shift of the surrounding cytoplasm of 7.81 ± 0.02 GHz (Figure 5d). Hence, one could expect that the longitudinal modulus shows a similar trend as the Brillouin shift as it does for samples described by the two-substance mixture model. However, the longitudinal modulus of the lipid droplets (2.161 ± 0.005 GPa) was lower than that of the cytoplasm (2.398 ± 0.009 GPa) when the measured RI and extracted absolute density distributions were considered (Figure 5e). The longitudinal modulus of lipid droplets being lower than that of cytoplasm was consistent with previous measurement data of the speed of sound of triacylglycerides that is lower than that of water (Gouw and Vlugter, 2006). In order to demonstrate the effect of the RI and absolute density on the calculation of the longitudinal modulus, we calculated the longitudinal modulus under the assumption of a homogeneous RI (1.337) and absolute density (1 g/ml) distribution instead of the values measured, as it would likely be done for a stand-alone Brillouin microscope. The longitudinal modulus of lipid droplets without considering the RI and absolute densities measured results to 2.717 ± 0.022 GPa, which was 26% higher than the correctly calculated longitudinal modulus (Figure 5f). Our finding clearly demonstrates that the local distribution of RI and absolute density can contribute considerably to the extraction of the longitudinal modulus of the samples, especially for compartments that cannot be described by the water-based two-substance mixture model.

Despite a higher refractive index (RI) and Brillouin shift, the longitudinal modulus of lipid droplets is lower than that of the surrounding cytoplasm.

(a–d) Representative maps of the (a) epifluorescence intensities, (b) brightfield intensities, (c) RIs, and (d) Brillouin shifts of an adipocyte cell. The nucleus is stained with Hoechst (blue in a) and lipid droplets are stained with Nile red (red in a). (e) Longitudinal modulus calculated from the RIs, absolute densities, and Brillouin shifts. (f) Deviation of the longitudinal modulus calculated with a homogeneous RI and absolute density value when compared to the precise longitudinal modulus in (e). Quantitative analysis of (g) the RI, (h) the Brillouin shift, and (i) the calculated longitudinal modulus taking into account the Brillouin shifts, refractive indices, and absolute densities of N=3 adipocytes. The size of the Brillouin map is 57 × 41 pixel, resulting in an acquisition duration of 40 min. Scale bars 10 µm.

Discussion

In this report, we experimentally demonstrated a combined epifluorescence, ODT, and Brillouin (FOB) microscopy setup. The colocalized measurements and the subsequent image analysis of the epifluorescence intensities and the RI distributions acquired by the FOB setup allowed to identify regions of different material or molecular composition. This enabled us to extract the correct absolute density from either the RI measured by applying the two-substance mixture model or from the literature in case the two-substance mixture model is not applicable. In combination with the Brillouin shift distributions measured, it was possible to accurately calculate the longitudinal moduli of a specimen. While in principle similar measurements would be possible with two separate setups individually acquiring Brillouin shift and RI, the combined setup simplifies sample handling, eliminates the necessity to locate the same cell or sample region on different setups, and substantially reduces the time between the acquisition of the different modalities from multiple minutes to a few seconds. The last point is especially important for the analysis of dynamic processes such as the formation of SGs, which otherwise would not be captured adequately. We demonstrated the working principle of the setup using an artificial cell phantom consisting of a PDMS bead embedded in a PAA bead, for which the acquired longitudinal moduli values are consistent with previous studies only when we consider the RI and the absolute density of the PDMS and PAA bead.

The setup was also applied to investigate the physical and mechanical properties of intracellular compartments in HeLa cells including nucleoplasm, cytoplasm, and nucleoli. We found that the nucleoplasm has a lower RI and absolute density than the cytoplasm while showing a higher Brillouin shift and longitudinal modulus. We further measured a statistically significantly higher peak linewidth, that is, viscosity in the nucleoli compared to the other compartments. The nucleus is described as a ‘network of chromatin and other intranuclear components surrounded by a cytosol fluid’ by Wachsmuth et al., 2000. This chromatin network was suggested to be responsible for the nucleus’ mechanical response in the GHz frequency range as tested with Brillouin microscopy (Zouani et al., 2014), and it was shown that the mass density in the cytoskeleton network is higher than in the chromatin network although the Brillouin shift behaves in the opposite way (Liu et al., 2019). Hence, it seems reasonable to assume that the chromatin network in the nucleoplasm leads to the higher longitudinal modulus observed in this compartment compared to the cytoplasm, even though the mass density in the nucleoplasm is lower than in the cytoplasm. Further analysis, for example, testing the response of the longitudinal modulus to chromatin (de)condensation, can be performed to further consolidate this idea. Furthermore, nucleoli, which are formed by LLPS in the nucleoplasm, and polyQ aggregates, which undergo a rapid liquid-to-solid transition in the cytoplasm, exhibit a statistically significantly higher RI and longitudinal modulus than either nucleoplasm or cytoplasm. However, SGs in P525L HeLa cells, which are also formed by LLPS, did not show statistically significant differences in terms of RI or longitudinal modulus compared to the surrounding cytoplasm in living cells, but showed a higher longitudinal modulus compared to the cytoplasm after chemical fixation. Hence, it seems that not every condensation process is accompanied by changes of the RI, absolute density, or longitudinal modulus. Further investigation is required to reveal the underlying mechanism of how nucleoli consisting of proteins and nucleic acids maintain a higher density and longitudinal modulus than the surrounding nucleoplasm despite the dynamic behavior of compartments formed by LLPS (Caragine et al., 2019).

Currently, there is a vivid debate whether the Brillouin shift mainly depends on the water content of the specimen, not on its mechanical properties (Wu et al., 2017; Wu et al., 2018b; Scarcelli and Yun, 2018; Bailey et al., 2019). If we followed the idea that the water content dominates the Brillouin shift, samples with a higher water content would exhibit a lower Brillouin shift. As the RI of the cytoplasm and the nucleoplasm of the HeLa cells measured here is linearly proportional to the mass density of macromolecules in water solution (Barer, 1952; Popescu et al., 2008) and the refraction increments of both compartments are similar (Zhao et al., 2011; Zangle and Teitell, 2014), the lower RI of the nucleoplasm compared to the cytoplasm indicates that the nucleoplasm has a higher water content than the cytoplasm. However, the nucleoplasm exhibits a higher Brillouin shift and longitudinal modulus than the surrounding cytoplasm. Hence, this result indicates that the Brillouin shift and the longitudinal modulus are not only governed by the water content, but are at least substantially influenced by the mechanical properties of the specimen.

An important aspect of the calculation of the longitudinal moduli is the extraction of the densities of the samples. For samples or compartments that can be described by the two-substance mixture model, we exploited the linear relation between the RI and the mass density to calculate the absolute density value (Barer, 1952; Popescu et al., 2008; Zangle and Teitell, 2014). However, as the partial specific volume of the dry fraction is unknown, this approach might overestimate the absolute density by approximately 10% (see Materials and methods). We find that in all samples measured here where the two-substance mixture model can be applied, neglecting the contribution of RI and density to the longitudinal modulus still yields a similar tendency for the longitudinal modulus and Brillouin shift (i.e., a high Brillouin shift means a high longitudinal modulus and vice versa), but doing so might lead to a systematic error for the longitudinal modulus. For cell compartments mainly containing a single substance, where this model cannot be applied, for example, lipid droplets in adipocytes, we used the specificity provided by the epifluorescence imaging to identify the respective regions and employed the literature value for the absolute density in this region. Using this approach, we found that although the RI and Brillouin shift of the lipid compartments in adipocytes are higher than those values of the cytoplasm, the resulting longitudinal modulus is actually lower when taking into account the RI and absolute density distribution. This illustrates that RI and absolute density do not cancel out for every cell and compartment – an implicit assumption in many studies acquiring only the Brillouin shift – and that RI and absolute density have to be known in order to precisely calculate the longitudinal modulus.

However, both the calculation of the absolute density from the RI and the identification of regions not described by the two-substance mixture model rely on the knowledge of the molecular composition of the sample. In order to calculate the absolute density from the RI, the refraction increment has to be known, which, albeit comparable for proteins and nucleic acids, might slightly vary between different cell compartments depending on their composition. Obviously, the composition also plays an important role when selecting the correct literature value for the absolute density of compartments where the two-substance mixture model is not applicable. As the molecular composition cannot be resolved exactly by the FOB microscope, we used the refraction increment or absolute density of the constituent likely predominant in a certain compartment. This might lead to a slight deviation of the absolute density from the exact value, for example, in the membrane-rich perinuclear region of HeLa cells where the absolute density might be underestimated. To overcome this issue and use the appropriate refraction increment or absolute density for a mixture of different proteins, nucleic acids, or phospholipids, more sophisticated labeling and staining of different molecules and the use of several fluorescence channels might allow identifying multiple substances. Also, the absolute concentration of different molecules could be directly measured from the intensity of Raman scattering signals (Oh et al., 2019), an imaging extension that could be added for future studies to the FOB setup presented here (Traverso et al., 2015; Mattana et al., 2018).

Further improvements of the setup could include moving to a laser source with a wavelength of 660 nm or longer to reduce cell damage due to phototoxicity (Nikolić and Scarcelli, 2019). This would allow a higher laser power at the sample plane for Brillouin microscopy, which reduces the acquisition time and could help analyzing dynamic processes. To correlate the RI value and Brillouin frequency shift of the samples at the same wavelength, the FOB setup uses the same laser source for ODT and Brillouin mode. Brillouin spectroscopy requires a laser with an extremely narrow linewidth that, hence, has a high temporal coherence length. While the coherent nature of the laser illumination makes ODT susceptible to speckle noise, the ODT system used achieves an RI uncertainty of 4.15×10-5 (which corresponds to a difference in protein concentration of 0.22 mg/ml) (Kim and Guck, 2020) that is sufficient to pick up the RI differences between the various regions of the cells characterized here. The speckle noise could be further reduced by using a dynamic diffuser (Choi et al., 2011) or numerical filtering approaches (Bianco et al., 2018). Furthermore, the setup could be enhanced to measure not only longitudinal phonons, but also transverse phonons, which are related to the shear modulus and can generally not propagate in liquids. Hence, this could help to discriminate liquid-like versus solid-like materials (Kim et al., 2016b; Prevedel et al., 2019). The limitation of ODT to weakly scattering samples like single cells or beads could be overcome by the implementation of tomogram reconstruction algorithms taking into account multiple light scattering in the sample (Lim et al., 2019; Chowdhury et al., 2019). This would enable the setup to measure tissues and organisms.

Although Brillouin microscopy was introduced to biology more than a decade ago (Scarcelli and Yun, 2008), its relevance to biological questions is sometimes still viewed skeptically in the field of mechanobiology. This is mainly due to the fact that the main quantity measured – the longitudinal modulus – relates to the rarely acquired compressibility in a frequency range to which cells might not be sensitive. However, multiple studies observed changes of the longitudinal modulus due to biophysical processes. The modulus changes due to the inhibition of actin polymerization (Scarcelli et al., 2015; Antonacci and Braakman, 2016) and after spinal cord injury (Schlüßler et al., 2018), after UV-induced polymer crosslinking (Scarcelli and Yun, 2008), or actin polymerization into a gel (Scarcelli et al., 2015). For various samples, for example, cells under osmotic shock, bovine lenses, or zebrafish tissue, phenomenological correlations with the Young’s modulus have been found (Scarcelli and Yun, 2011; Scarcelli et al., 2015; Schlüßler et al., 2018), implying that for these tissues the two moduli could serve as a proxy for each other. Furthermore, Brillouin microscopy gives access to other quantities besides the compressibility. The viscosity and shear modulus of a sample are also accessible by Brillouin microscopy by evaluating the peak linewidth and observing transverse phonons (Antonacci et al., 2018; Kim et al., 2016b; Prevedel et al., 2019) and can be influenced, for example, by liquid-to-solid phase transitions. For the observed, physically real variations of the longitudinal modulus and viscosity to affect biophysical processes, it is moreover not necessary that cells or organisms are able to sense these mechanical differences directly in the first place, as would be relevant in the context of mechanosensing. It seems entirely possible that the variations in the longitudinal modulus at GHz frequencies detected in cells will turn out to be reporting on local changes in intermolecular interactions, water mobility and hydration shells, and other aspects relevant for emergent, supramolecular processes, which are important in their own right. We thus believe that Brillouin microscopy can contribute to open questions in biology, but further studies are necessary to finally establish its relevance.

In conclusion, the FOB setup allows a precise calculation of the longitudinal modulus from the measured RI and Brillouin shift even for samples with a heterogeneous RI and absolute density distribution. This enables quantitative measurements of the mechanical properties of single cells and their compartments and might potentially contribute to a better understanding of physiological and pathological processes such as phase separation and transition in cells as a response to external stress.

Materials and methods

Optical setup

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The FOB microscope setup combines ODT, Brillouin microscopy, and epifluorescence imaging in the same optical system. It allows to obtain quantitative maps of the RIs, the Brillouin shifts, and the fluorescence and brightfield intensities of a sample.

In order to acquire the 3D RI distribution, ODT employing Mach−Zehnder interferometry was applied. Besides small modifications necessary for the combination with Brillouin microscopy, the ODT part of the setup is identical to the one presented in Abuhattum et al., 2018. As laser source, a frequency-doubled Nd-YAG laser (Torus 532, Laser Quantum Ltd, UK) with a wavelength of 532 nm and a maximum output power of 750 mW is used for both ODT and Brillouin microscopy. The laser was chosen as it offers a very low amplified spontaneous emission intensity of 110 dB necessary for Brillouin measurements. The main beam of the laser is coupled into a single-mode fiber and split into two beams by a 2×2 fiber coupler. One beam is used as the reference for the Mach−Zehnder interferometer. The other beam is collimated and demagnified through a tube lens with a focal length of 175 mm and a ×40/1.0 NA water dipping objective lens (Carl Zeiss AG, Germany) and illuminates the sample in a custom-built inverted microscope. To allow to reconstruct a 3D RI tomogram of the sample, the sample is illuminated under 150 different incident angles. The illumination angles are generated by a dual-axis galvanometer mirror (GVS012/M, Thorlabs Inc, USA), which is placed at the conjugate plane of the sample and diffracts the illumination beam. The diffracted beam is collected by a ×63/1.2 NA water immersion objective lens (Carl Zeiss AG) and a tube lens with a focal length of 200 mm. The sample and the reference beam then interfere at the image plane of a CCD camera (FL3-U3-13Y3M-C, FLIR Systems Inc, USA), which records the generated spatially modulated hologram of the sample. In some cases, the hologram additionally shows parasitic interference patterns originating from the protective window in front of the CCD camera (e.g., Figure 2b; this is a general limitation of the ODT setup due to the coherent nature of the laser source). The setup achieves a spatial resolution of 0.25 μm within the lateral plane and 0.5 μm in the axial direction.

In order to switch to Brillouin microscopy mode, a motorized mirror is moved into the beam path guiding the light towards an additional lens with a focal length of 300 mm. In combination with the upper tube lens, this ensures a collimated beam before the microscope objective and effectively creates a laser focus at the sample plane. Hence, in Brillouin mode the galvanometer mirrors are located at the Fourier conjugate plane of the sample and can move the laser focus in the sample plane (Figure 1a, inset). This allows to scan the laser focus over the sample by adjusting the galvanometer voltage. The relation between the applied galvanometer voltage and the resulting focus position is calibrated by acquiring images of the laser foci with the ODT camera and extracting the foci positions for different galvanometer voltages. The Brillouin scattered light is collected in the backscattering configuration with the same objective used for ODT and coupled into a single-mode fiber that acts as a pinhole confocal to the illumination fiber and delivers the light to a two-stage VIPA Brillouin spectrometer (Scarcelli and Yun, 2011; Scarcelli et al., 2015). This results in a spatial resolution of 0.4 μm within the lateral plane and approximately 1 μm in the axial direction. In the spectrometer, the beam is collimated and passes through the iodine absorption cell, which blocks the Rayleigh scattered and reflected light. The beam is then guided to two VIPA interferometers (OP-6721-3371-2, Light Machinery, Canada) with 30 GHz free spectral range and a spectral resolution of approximately δν=350MHz, which is comparable to values reported for other VIPA-based setups (Antonacci et al., 2013) but lower than the spectral resolution achievable with stimulated Brillouin scattering setups of around 100 MHz (Remer et al., 2020). The Brillouin spectrum is imaged with an sCMOS camera (Neo 5.5, Andor, USA) with a typical exposure time of 0.5 s at a laser power of 10 mW at the sample.

Furthermore, the laser frequency is stabilized to the absorption maximum of a transition line of molecular iodine by controlling the laser cavity temperature. This allows to attenuate the intensity of the Rayleigh scattered light entering the Brillouin spectrometer, eliminates potential laser frequency drifts (Meng et al., 2014; Schlüßler et al., 2018), and simplifies the mechanical alignment of the spectrometer as no masks for blocking the elastically scattered light are necessary. To generate an error signal for the frequency stabilization loop, a small fraction of the laser light is frequency shifted by 350 MHz by an acousto-optic modulator (AOM 3350-125, EQ Photonics GmbH, Germany) and guided through an absorption cell (TG-ABI-Q, Precision Glass Blowing, USA) filled with iodine I2. The beam intensity is measured before and after the absorption cell by two photodetectors (PDA36A2, Thorlabs Inc) and a data acquisition card (PicoScope 2205A, Pico Technology, UK). The quotient of both intensities is a measure for the absorption due to the iodine vapor. The laser cavity temperature is then controlled with a custom C++ software LQTControl to achieve an absorption of 50% for the frequency-shifted stabilization beam, which leads to maximum absorption for the not-shifted main beam.

To realize epifluorescence imaging, an incoherent beam from a white light halogen lamp (DC-950, Dolan-Jenner Industries Inc, USA) is coupled into the setup by a three-channel dichroic mirror (FF409/493/596-Di01−25 × 36, Semrock, USA). The bandwidth of the excitation and emission beam is selected by two motorized filter sliders equipped with band-pass filters in front of the halogen lamp and the CCD camera. A white light LED (Thorlabs, USA) coupled into the Brillouin illumination path allows to observe a brightfield image of the sample during Brillouin acquisition. Since fluorescence imaging and ODT use the same objective, the acquired fluorescence images are guaranteed to focus the central plane of the acquired RI tomogram.

The two cameras and all moveable optical devices of the setup are controlled with a custom acquisition program written in C++. The software allows to control all three imaging modalities and stores the acquired data as an HDF5 file.

Refractive index tomogram reconstruction

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From the spatially modulated holograms recorded, the complex optical field of the light scattered by the sample is retrieved by a field retrieval algorithm based on the Fourier transform (Cuche et al., 2000). The RI tomogram of the sample is reconstructed from the retrieved optical fields with various incident angles via the Fourier diffraction theorem. The detailed procedure for the tomogram reconstruction is presented in Kim et al., 2014; Müller et al., 2015. The field retrieval and tomogram reconstruction were performed by custom-made MATLAB (The MathWorks, Natick) scripts. From the reconstructed RI tomograms, subcellular compartments are segmented based on the RI and epi-fluorescence signals. First, cell regions are segmented from background by applying the Otsu’s thresholding method, and the watershed algorithm is used to determine individual cells in the RI tomograms. Then, epifluorescence images of the fluorescence-labeled subcellular compartments (e.g., nuclei, polyQ aggregates in HeLa cells, nuclei and lipid droplets in adipocytes) are colocalized with the RI tomograms to segment the compartments. In the nuclei of the HeLa cells, the RI tomogram regions having higher RI values than the surrounding nucleoplasm are segmented by the Otsu’s thresholding method and identified as nucleoli. The detailed segmentation procedure is described elsewhere (Schürmann et al., 2016; Kim and Guck, 2020), and the source code for the segmentation can be found at https://github.com/OpticalDiffractionTomography/NucleiAnalysis.

Brillouin shift evaluation

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To evaluate the Brillouin shift νB, a custom MATLAB program is used. Details of the evaluation process can be found in Schlüßler et al., 2018.

Calculation of the longitudinal modulus

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The longitudinal modulus M is determined by

(1) M=ρ(λνB2ncos(Θ/2))2

where the wavelength λ of the laser source and the scattering angle Θ are known parameters of the setup. The RI n and the Brillouin shift νB of the sample are measured using the FOB microscope. The absolute density ρ can be calculated for the majority of biological samples from the RI assuming a two-substance mixture. The absolute density is given by Barer, 1952; Davies and WilkinsNS, 1952; Zangle and Teitell, 2014; Popescu et al., 2008; Schlüßler et al., 2018

(2) ρ=n-nfluidα+ρfluid(1-ρdryν¯dry).

with the RI nfluid of the medium, the refraction increment α (α=0.190mL/g for proteins and nucleic acid [Zhao et al., 2011; Zangle and Teitell, 2014; Biswas et al., 2021]), the absolute density ρfluid of the medium, the absolute density ρdry, and the partial specific volume ν¯dry of the dry fraction. In case of ρdry1ν¯dry, this can be simplified to

(3) ρn-nfluidα+ρfluid.

This simplification leads to an overestimation of the absolute density and, hence, the longitudinal modulus, of around 10% for , for example, HeLa cells, which we believe to be acceptable.

For certain cell types, for example, adipocyte cells, the two-substance mixture model cannot be applied for all cell compartments, that is, the lipid droplets inside these cells do only consist of lipids. Applying the two-substance model here leads to an unphysiological overestimation of the absolute density. Hence, in special cases the absolute density cannot be inferred from the RI and has to be estimated from the literature. This is possible with the FOB microscope since fluorescence labeling of the lipid droplets allows to identify cell regions governed by, for example, lipids.

In order to calculate the longitudinal modulus and visualize the measurement results of the FOB microscope, a custom MATLAB program FOBVisualizer is used. The software allows to adjust the spatial alignment of the Brillouin and ODT measurements by cross-correlating the two-dimensional maps acquired by both modalities and shifting the Brillouin maps towards the highest correlation coefficient.

Statistical analysis

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For the statistical analysis of the RI and longitudinal modulus differences between cytoplasm, nucleoplasm, and nucleoli (Figures 24), the Kruskal−Wallis test in combination with a least significant difference post-hoc test was used. Asterisks indicate the significance levels: *p<0.05, **p<0.01, ***p<0.001, and ****p<0.0001. In box-and-whisker plots, the center lines indicate the medians, the edges of the boxes define the 25th and 75th percentiles, the red plus signs represent data points outside the ±2.7σ range, which are considered outliers, and the whiskers extend to the most extreme data value that is not an outlier.

Cell phantom preparation

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Artificial cell phantoms, consisting of PDMS (Dow Corning Sylgard 184) particles embedded in larger PAA microgel beads, were produced as follows. The PDMS particles were generated by vortex-mixing a solution of 1 g PDMS (10:1 w/w, base/curing agent) dispersed in 10 ml of 2% w/v poly(ethylene glycol) monooleate (Merck Chemicals GmbH, Germany) aqueous solution. After mixing, the emulsion was kept overnight in an oven at 75°C to allow the polymerization of the PDMS droplets. The size dispersion of the PDMS particle was reduced by centrifugation and removing all particles with a diameter larger than 5 μm. The final solution, containing PDMS particles with a diameter lower than 5 μm, was washed three times in Tris-buffer (pH 7.48) and resuspended in 1% w/v Pluronic F-127 (Merck Chemicals GmbH) Tris-buffer.

1 µl of concentrated PDMS particles were added to 1001 PAA pre-gel mixture with a total monomer concentration of 11.8% w/v. This solution was used as a dispersed phase in a flow-focusing microfluidic device to produce PAAm microgel beads, as previously described in Girardo et al., 2018, embedding PDMS particles. N-hydroxysuccinimide ester (0.1% w/v, Merck Chemicals GmbH) was added to the oil solution to functionalize the phantoms with Alexa 488. Precisely, 100 μl of Alexa Fluor hydrazide 488 (Thermo Fisher Scientific, Germany) in deionized water (1 mg/ml) was added to 100 μl phantom pellet and incubated overnight at 4°C. The unbonded fluorophores were removed by three washings in PBS. The final functionalized phantoms were stored in PBS at 4°C.

Cell preparation

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The stable HeLa cell line expressing GFP fused to the N terminus of NIFK (nucleolar protein interacting with the FHA domain of MKI67) was kindly provided by the lab of Anthony Hyman (Max Planck Institute of Molecular Cell Biology and Genetics). The cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) (31966-021, Thermo Fisher), high glucose with GlutaMax medium (61965-026, Gibco) under standard conditions at 37°C and 5% CO2. The culture medium was supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin. The cells were subcultured in a glass-bottom Petri dish (FluoroDish, World Precision Instruments Germany GmbH) 1 day prior to the measurement, and the culture medium was exchanged to Leibovitz’s L-15 medium without phenol red (21083027, Thermo Fisher Scientific) prior to imaging. For staining nuclei, the cells were stained with Hoechst (1 dilution) for 10 min and washed with fresh Leibovitz’s L-15 medium prior to imaging.

The wild-type HeLa cells transiently expressing amyloid (Q103-GFP) aggregates were cultured in DMEM (31966-021, Thermo Fisher), high glucose with GlutaMax medium (61965-026, Gibco) under standard conditions at 37°C and 5% CO2. The culture medium was supplemented with 10% FBS and 1% penicillin-streptomycin. The cells were subcultured in a glass-bottom Petri dish (FluoroDish, World Precision Instruments Germany GmbH) 2 days prior to the measurement. One day prior to the measurement, the cells were transiently transfected with pcDNA3.1-Q103-GFP using Lipofectamine 2000 (Invitrogen, Carlsbad, CA). Directly before the imaging, the culture medium was exchanged to Leibovitz’s L-15 medium without phenol red (21083027, Thermo Fisher Scientific).

The HeLa cells FUS-GFP WT (wild-type) and FUS-GFPP525L (disease model for ALS) were kindly provided by the lab of Anthony Hyman (Max Planck Institute of Molecular Cell Biology and Genetics). The cells were cultured in 89% DMEM supplemented with 10% FBS (Sigma-Aldrich; F7524) and 1% penicillin-streptomycin under standard conditions at 37°Cand 5% CO2. One day before the experiment, the cells were transferred to a 35 mm glass-bottom Petri dish (FluoroDish, World Precision Instruments Germany GmbH). 30 min prior to the measurements, the culture medium was exchanged to Leibovitz’s L-15 medium without phenol red (21083027, Thermo Fisher Scientific) and the noncontrol samples were treated with 5 mM sodium arsenite. For fixation, the arsenite-treated cells were washed with PBS, fixed with 4% paraformaldehyde for 10 min at room temperature, washed with PBS twice, and left in PBS for FOB microscopy measurements.

Adipocyte preparation

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SGBS preadipocytes were cultured and differentiated as described previously (Wabitsch et al., 2001; Fischer-Posovszky et al., 2008). For regular cell culture, cells were maintained in DMEM/nutrient F-12 Ham (Thermo Fisher) supplemented with 4 μM panthotenic, 8 μM biotin (Pan/Bio), 100 U/ml penicillin/100 μg/ml streptomycin (=OF-medium) with 10% FBS (OF-medium + FBS, Thermo Fisher) at 37°C in T75 flasks. For adipogenic differentiation, cells were washed with PBS, detached using TrypLE Express (Thermo Fisher), and seeded onto glass-bottom Petri dishes (FluoroDish, World Precision Instruments Germany GmbH, 35 mm, 105 cells). After 24 hr, cells were washed three times with serum-free OF-medium, and differentiation medium was added, consisting of OF-medium complemented with 10 μg/ml human transferrin (Sigma-Aldrich), 20 nM human insulin (Sigma-Aldrich), 2 μM rosiglitazone (Cayman), 100 nM dexamethasone (Sigma-Aldrich), 250 μM 3-isobutyl-1-methylxantine IBMX (Sigma-Aldrich), 100 nM cortisol (Sigma-Aldrich), and 0.2 nM triiodothyronine T3 (Sigma-Aldrich). On day 4, the medium was exchanged to OF-medium supplemented with only transferrin, insulin, cortisol, T3 (concentrations as above). The medium was replaced every fourth day. Cells were probed on day 11 of adipogenic differentiation.

Cell lines

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HeLa WT cells were a kind gift of F. Buchholz (Technische Universitat Dresden, Germany). The HeLa cell line transfected with GFP:NIFK, HeLa WT FUS-GFP, and HeLa P525L FUS-GFP was a kind gift of A. Hyman (Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany). SGBS adipocytes were a kind gift of Prof. Martin Wabitsch (Centre for Hormonal Disorders in Children and Adolescents – Ulm University Hospital).

No authentication was performed. All cell lines tested negative for mycoplasma contamination. No commonly misidentified cell lines were used.

Code availability

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The source code of LQTControl, the program to stabilize the laser cavity temperature, is open source and can be found on GitHub (https://github.com/BrillouinMicroscopy/LQTControl; Schlüßler, 2018). The same is true for BrillouinAcquisition, the program for controlling and data acquisition of the FOB microscope (https://github.com/BrillouinMicroscopy/BrillouinAcquisition; Schlüßler, 2017), BrillouinEvaluation, used for evaluating Brillouin data (https://github.com/BrillouinMicroscopy/BrillouinEvaluation; Schlüßler, 2016), and FOBVisualizer, used for viewing FOB microscopy data (https://github.com/BrillouinMicroscopy/FOBVisualizer ; Schlüßler, 2019). The MATLAB scripts for cell segmentation and ODT reconstruction can be found under https://github.com/OpticalDiffractionTomography/NucleiAnalysis (Kim, 2022a copy archived at swh:1:rev:ee5da0592cb893bac393f5cd19c223a518495c4a) and https://github.com/OpticalDiffractionTomography/ODT_Reconstruction (Kim, 2022b copy archived at swh:1:rev:e9a63082bb55564fc3edbcf48108e1e4b6f12458), respectively.

Data availability

The data sets generated during and/or analyzed during the current study are available from figshare under the following link: https://doi.org/10.6084/m9.figshare.c.5347778.

The following data sets were generated

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Decision letter

  1. Rohit V Pappu
    Reviewing Editor; Washington University in St Louis, United States
  2. Anna Akhmanova
    Senior Editor; Utrecht University, Netherlands

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Combined fluorescence, optical diffraction tomography and Brillouin microscopy" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Anna Akhmanova as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) One of the reviewers asked for a deeper discussion of the biological relevance of the longitudinal modulus measurement at high frequencies. Are these high frequencies likely to be of importance? Perhaps in analogy with THz spectroscopy that is useful for probing the frequency dependence of dielectric responses, these high frequency longitudinal moduli might be of import for querying the elastic and / or dielectric responses. Please add a relevant discussion.

2) One of the reviewers asks for a deeper, technical discussion of the choice of the diffraction tomography method, specifically the off-axis holography with laser illumination approach. Are there issues of phase instability and speckles and how are these alleviated? Addition of data in the SI to address this point would be very helpful.

3) Please add a discussion to address the concern of independent calibration methods raised by reviewer 3. The issue is two-fold: How well does the method work with samples / condensates that are of lower density? The reconstituted G3BP1/2 based stress granule system, already studied in the Alberti lab, would be a good target because the protein is only 7-8 fold more concentrated in the stress granule facsimiles. Additionally, the addition of FRAP data as a comparative standard would be useful.

4) In addition, please make revisions to address the series of points raised by reviewer 1. And please make these changes in a way that is readily visible.

Reviewer #1 (Recommendations for the authors):

In this interesting study by Schlüßler et al., the authors combined Brillouin microscopy with ODT and epi-fluorescence imaging and applied the same to study physical properties of biological materials including nucleoplasm, cytoplasm, phase-separated organelles, and adipocytes. The results are largely convincing and offer interesting insights into the material properties of these materials. There is a critical need for new methodologies to study the physical properties of biomolecular condensates in living cells under normal and pathological conditions. Hence, the current study can be significant. However, I have noted several points after carefully reading the manuscript, which is described below.

1. Abstract: "Investigating the cell nucleus, we find that it has lower density but higher longitudinal modulus" compared to what exactly?

2. Abstract: The authors state that they have measured "absolute density with molecular specificity". Is this true? The discussion and conclusion section (p14, lines # 417-427) nicely summarizes the potential limitations of the current approach and offers future strategies on the same issue to improve upon the current foundation.

3. P2. Line #55 – 63. The authors build their case of employing a new integrative approach (FOB microscopy) for correct longitudinal modulus measurements, however, it is not quite clear how simplifications such as using measured mass density with a priori knowledge of RI, or obtaining RI values from separate measurements lead to erroneous results of longitudinal modulus.

4. In my opinion, the description of the results obtained in this study by the application of FOB microscopy as stated at the end of the introduction section is rather qualitative and can be improved.

5. Images of RI in Figure 2b, 3b, 4b and d have concentric rings throughout the images. What are these rings? If the RI is calculated from the image, then are these rings artifacts of image processing? Please explain.

6. Page 7. The authors state that the Brillouin shift of the nucleoplasm is "significantly" different than the cytoplasm. I have a question regarding the use of the word "significantly" here. The authors show a moduli difference of 0.038 GPa. While this may be significant in the absolute sense, it's may not be a huge difference relative to the measured moduli of the two materials. The same thing could be said for the refractive index differences, but in that case, the range of known refractive indices is small. So, please explain.

7. The authors show that the RI and the density of the cytoplasm and nucleoplasm are comparable to water. I am curious to see what value of the longitudinal modulus will they find if they measure the same for pure water samples. For the correct modulus to be obtained, wouldn't the Brillouin shift be very different from that of the cytoplasm and nucleoplasm? In such a case, is it reasonable to assume water values for the density and the refractive index and measure only the Brillouin shift to obtain the moduli?

8. The authors state that "These findings imply that membrane-less compartments formed by phase separation……in spite of the thermodynamic instability inherent in this state" This information is nice and needed. But the authors need to explain why one should care about this longitudinal modulus, what does it represent, and how does it affect the material properties? In other words, the meaning of this modulus and its relation to the mechanical behavior of these materials is not clear. A discussion is enough to address this point.

9. On the previous point: The authors need to comment on these extremely large longitudinal moduli, as reported for polyQ aggregates and others. Previous studies showed that the elastic modulus of the ECM is about 1 kPa (additionally see table 1 in this report: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4553184/), so what does a value of 1 GPa for the longitudinal modulus mean? a good discussion of the meaning of the longitudinal modulus and its relation to the material properties and other moduli will be helpful.

10. Page 10, lines 265-268; I see that "significantly" is used here to describe statistical significance, and maybe it's used in the same sense in the other cases too. However, the authors need to specify as it can be easily misunderstood as a significant difference in magnitude and not in statistical similarity.

11. Page 10, lines 270-272; I am having trouble in understanding why the authors are using the standard error of the mean as opposed to the standard deviation as a measure of uncertainty. This gives an impression that the technique has superior accuracy. the variability shown in the plots is much larger than these errors (orders of magnitudes). Is this variability coming from the cell-to-cell and condensate to condensate variation or is it coming from the measurement itself? This data is not shown for the phantom cell controls (please include it in the SI). If this variability is present in that system as well, the authors may try a different system for the control to assess the inherent error of the technique, such as an aqueous two-phase system (e.g. peg and dextran). These systems are also closer to the intracellular fluid, so they may serve as better control. This would help the readers assess the accuracy of the technique itself. Alternatively, the authors should give a convincing argument for why are the SEMs a better measure of the variability of the measurement (or the uncertainty)? It's also crucial to understand the source of the variability in the refractive indices and moduli. If it is coming from the instrument, then SEMs should not be used. If it is coming from the cell-to-cell variation, then the authors may need to show data on a control system that does not have variations so that the readers can understand the level of accuracy of these measurements. If it is indeed from cell-to-cell variation and not from the experimental technique, then the use of SEMs is reasonable.

12. Are the errors in the case of FUS and polyQ condensates estimated from different condensates in the same cell or from one condensate in multiple cells? this is important to judge the accuracy of this measurement and the possible effect of reducing the ROI, would reducing the ROI of the Brillouin experiment on polyQ aggregates give the same result?

13. Regarding the difference in the RI and longitudinal moduli of the nucleoplasm, cytoplasm, and nucleolus, could the authors be able to provide a physical basis of such differences based on what is known about these materials? This will be useful to support the claim that FOB microscopy can be used to study "physiological and pathological processes such as phase separation".

Reviewer #2 (Recommendations for the authors):

1. Maybe the authors can discuss the biological relevance of the longitudinal modulus measurement at such high frequencies

2. The authors can also discuss their choice of the diffraction tomography method. It is known that the traditional off-axis holography with lase illumination suffers from phase instability and speckles. In recent years, common-path and white light-based methods have been developed to alleviate these issues.

Reviewer #3 (Recommendations for the authors):

One inherent concern is that the Brillouin scattering depends heavily on the density of the medium. Therefore, while the in vitro samples can be measured in a controlled buffer, cellular milieu will add significant level of "background noise" that prevents measuring precise difference between different compartments of cells. For example, this may have produced very similar parameter for P525L-FUS vs. cytoplasm.

Another major concern, as stated above is the excessive perturbation required to perform the whole cell measurement, which the authors reported resulted in cell death.

The proof of principle was tested on what the authors call "phantom cell" which are polymers, PDMS and PAA. Although they were used due to the known mechanical property, they are not biological molecules and PDMS does not even contain a fluid phase. In addition, the RI measured by FOB did not match the known values. The differences seen here appears to be quite small as the authors claim, but they are significantly higher than other RI values authors measure in later samples. For example, Figure 2d the negligible difference between cytoplasm and nucleoplasm is stated to be significantly different.

Overall, the only significant difference measured seem to be in the cases of nucleoli, polyQ and lipid which are already known to form a more dense phase. Again, the lack of comparison to orthogonal method such as FRAP, fusion/diffusion kinetic makes it difficult to assess the validity of even the obvious cases.

The result obtained on sodium arsenite and P525L seem inconsistent with what is known from other literature. Again, the clear difference between these conditions (oxidative and mutant) may have been masked by the cellular density background.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for submitting your article "Combined fluorescence, optical diffraction tomography and Brillouin microscopy" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Anna Akhmanova as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Gabriel Popescu (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) Reviewer 1 raises important questions regarding the measurements of longitudinal moduli and their biological relevance. Please address this explicitly.

2) The reviewer raises other important issues, specifically regarding the limitations of the FOB method that need to be addressed more thoroughly.

Please respond to this reviewer's concerns, and please do so fully and thoroughly. I agree that the title of the manuscript should be changed. As it stands, the average reader of eLife is not likely to know what the combination of techniques are intended for in the biological context. Please specify the intended applications in the title.

Reviewer #1 (Recommendations for the authors):

In the original manuscript, this reviewer and other reviewers raised important questions regarding two critical points.

The first point was: how measuring longitudinal modulus at GHz frequencies helps to understand the biological process as the authors state in their manuscript (line 439-441 in the manuscript). The authors made minimal attempt to address this, and in fact, they now have included a brief discussion on the limitations of Brillouin microscopy. There are some interesting studies in the literature which the authors cite in their argument, but that is not sufficient to make a case of how measuring longitudinal modulus at GHz frequencies helps to understand the biological process. Therefore, the response to this point remains inadequate.

The second point was pertaining to the understanding of the physics of sub-cellular mechanics. To this end, I had a question (point # 13) in the first round of review. The response again in point # 13 is superficial in my opinion and needs better considerations and/or new data.

Another important point is that the authors should include a subsection in the discussion pertaining to the limitations of FOB and data interpretation. For example, the argument presented in point # 3 should be included under the "limitations" section. The same goes for point # 7. As stated multiple times in their response letter, if we are not measuring physiologically relevant moduli and no clear conclusions can be made about the physical interpretation of the data, I share the same concern about reviewer 2 regarding how big of an impact this study will have in the field. The Nat Methods paper (Prevedel et. al 2019) that the authors cite articulates the limitations.

Overall, I am a bit surprised by the brevity of the responses by the authors from the first round of review and do not think that the revision rigorously addressed the reviewers' concerns. I also don't think the changes made in the revised manuscript are properly marked (I looked at the PDF generated by the system).

An important point which I missed in the first round of review: The title of the paper reads strange. The combination of techniques in this context is done to address a set of key biological questions, and the combination itself sounds very technical as the title of the paper.

Reviewer #2 (Recommendations for the authors):

The authors addressed my concerns thoroughly. I look forward to this publication in print.

Reviewer #3 (Recommendations for the authors):

The authors responded to all of criticisms appropriately.

https://doi.org/10.7554/eLife.68490.sa1

Author response

Essential revisions:

1) One of the reviewers asked for a deeper discussion of the biological relevance of the longitudinal modulus measurement at high frequencies. Are these high frequencies likely to be of importance? Perhaps in analogy with THz spectroscopy that is useful for probing the frequency dependence of dielectric responses, these high frequency longitudinal moduli might be of import for querying the elastic and / or dielectric responses. Please add a relevant discussion.

We added a section discussing the relevance of Brillouin microscopy for biological questions to the introduction. Please also see our answers to issue #8 of reviewer number 1 and to the public review of reviewer number 2 for details.

2) One of the reviewers asks for a deeper, technical discussion of the choice of the diffraction tomography method, specifically the off-axis holography with laser illumination approach. Are there issues of phase instability and speckles and how are these alleviated? Addition of data in the SI to address this point would be very helpful.

Please see our comment to the respective point raised by reviewer 2. In short, ODT and Brillouin share the same illumination source, in order to measure the refractive index (RI) and Brillouin shift at the same wavelength and not be influenced by possible dispersion. Since Brillouin requires a light source with a very narrow linewidth and, hence, a high temporal coherence length, speckle noise for ODT results. However, ODT still achieves a RI uncertainty of 4.15 × 10-5 (corresponding to a difference in protein concentration of 0.22 mg/ml, Kim, K. and Guck, J. Biophys. J. 119, 1946– 1957 (2020)), which is more than sufficient to pick up the RI differences in the study at hand.

We added a section regarding this issue to the discussion.

3) Please add a discussion to address the concern of independent calibration methods raised by reviewer 3. The issue is two-fold: How well does the method work with samples / condensates that are of lower density? The reconstituted G3BP1/2 based stress granule system, already studied in the Alberti lab, would be a good target because the protein is only 7-8 fold more concentrated in the stress granule facsimiles. Additionally, the addition of FRAP data as a comparative standard would be useful.

We addressed the concerns of reviewer 3 and now better reference and discuss publications showing independent reference measurements. Refractive index and protein concentration measurements in G3BP1 using ODT have been done and published by our group in Guillén-Boixet, J. et al. RNA-Induced Conformational Switching and Clustering of G3BP Drive Stress Granule Assembly by Condensation. Cell 181, 346-361.e17 (2020). We found a refractive index of ~1.35 and a protein concentration of 65 mg/ml for G3BP1 droplets. We furthermore confirmed protein concentration measurements done with ODT by volume-based measurements in McCall, P. M. et al. Quantitative phase microscopy enables precise and efficient determination of biomolecular condensate composition. bioRxiv 2020.10.25.352823 (2020) doi:10.1101/2020.10.25.352823. We do not see any particular reason why samples of lower density would cause problems, since e.g. the cytoplasm of HeLa cells exhibits densities comparable to the G3BP1 system and the PAA of the cell phantom measured shows even lower values.

FRAP measurements in polyQ aggregates have been shown by us in Kroschwald, S. et al. Promiscuous interactions and protein disaggregases determine the material state of stress-inducible RNP granules. eLife 4, e06807 (2015). We found that polyQ aggregates show a solid-like behavior and did not recover from photo-bleaching.

4) In addition, please make revisions to address the series of points raised by reviewer 1. And please make these changes in a way that is readily visible.

We addressed all points raised by reviewer 1 (see our comments to the respective points).

Reviewer #1 (Recommendations for the authors):

In this interesting study by Schlüßler et al., the authors combined Brillouin microscopy with ODT and epi-fluorescence imaging and applied the same to study physical properties of biological materials including nucleoplasm, cytoplasm, phase-separated organelles, and adipocytes. The results are largely convincing and offer interesting insights into the material properties of these materials. There is a critical need for new methodologies to study the physical properties of biomolecular condensates in living cells under normal and pathological conditions. Hence, the current study can be significant. However, I have noted several points after carefully reading the manuscript, which is described below.

1. Abstract: "Investigating the cell nucleus, we find that it has lower density but higher longitudinal modulus" compared to what exactly?

Thank you very much for this comment. The quoted sentence is indeed worded inaccurately. We changed it to now read:

“Investigating the nucleoplasm of wild-type HeLa cells, we find that it has lower density but higher longitudinal modulus than the cytoplasm.”

2. Abstract: The authors state that they have measured "absolute density with molecular specificity". Is this true? The discussion and conclusion section (p14, lines # 417-427) nicely summarizes the potential limitations of the current approach and offers future strategies on the same issue to improve upon the current foundation.

While we think that “molecular specificity” is not wrong, we agree that it might be misleading (after all, we did not measure the density of single molecules). We adjusted the respective sentence in the abstract to now read “absolute density with specificity to fluorescently labeled structures” and adjusted the usage of “molecular specificity” elsewhere.

3. P2. Line #55 – 63. The authors build their case of employing a new integrative approach (FOB microscopy) for correct longitudinal modulus measurements, however, it is not quite clear how simplifications such as using measured mass density with a priori knowledge of RI, or obtaining RI values from separate measurements lead to erroneous results of longitudinal modulus.

Using an a priori value for the RI might lead to a systematic error of the longitudinal modulus calculated, in case the assumed RI deviates from the RI actually present in the sample. Furthermore, if the RI distribution is not homogenous, neglecting the RI contribution will result in a relative error between the compartments analyzed. Whether or not this will lead to wrong trends and conclusions depends on how large the differences of the Brillouin shift and RI between the different compartments actually are (which remains unknown if not measured). For the wildtype HeLa cells measured in the manuscript, assuming a constant value for the RI would still give the correct trend for the longitudinal modulus, i.e. the modulus of the nucleoplasm would still be higher than for the cytoplasm. However, in case the differences in Brillouin shift would not be as pronounced, neglecting possible differences of the RI might conceal differences in the longitudinal modulus actually present (or introduce differences in other cases). Moreover, in case RI and density are not coupled by the two-substance mixture model (e.g. for lipid droplets in adipocytes), RI and density might not cancel out trivially, which can then also lead to systematic errors of the longitudinal modulus if the RI is neglected.

In the introduction, we now give a short summary of the reasons for deviating values of the longitudinal modulus when using these simplifications:

“These simplifications may result in an inaccurate calculation of the longitudinal modulus, since the RI distribution might not be homogenous throughout the sample, RI and density might not be coupled, hence, not cancel out, or the sample preparations necessary for separate RI measurements could influence the RI measured.”

4. In my opinion, the description of the results obtained in this study by the application of FOB microscopy as stated at the end of the introduction section is rather qualitative and can be improved.

We now give quantitative values for the RI and longitudinal modulus of cytoplasm, nucleoplasm and nucleoli in HeLa cells and a quantitative value for the difference of RI and modulus between poly-Q aggregates and cytoplasm. However, for the full list of values measured, we think the Results section is better suited.

5. Images of RI in Figure 2b, 3b, 4b and d have concentric rings throughout the images. What are these rings? If the RI is calculated from the image, then are these rings artifacts of image processing? Please explain.

The artefacts are parasitic interference patterns originated from the protective window in front of the imaging sensor of the ODT camera. This is inevitable due to the coherent nature of the laser illumination. The artifacts have a low amplitude (∆n < 0.002) and are averaged out during the quantitative analysis calculating the mean RI of each component.

6. Page 7. The authors state that the Brillouin shift of the nucleoplasm is "significantly" different than the cytoplasm. I have a question regarding the use of the word "significantly" here. The authors show a moduli difference of 0.038 GPa. While this may be significant in the absolute sense, it's may not be a huge difference relative to the measured moduli of the two materials. The same thing could be said for the refractive index differences, but in that case, the range of known refractive indices is small. So, please explain.

Throughout the manuscript, “significant(ly)” is now meant in the statistical sense. To make this clear, we now use “statistically significant(ly)” and changed the other four occurrences of “significant” to “substantially”.

7. The authors show that the RI and the density of the cytoplasm and nucleoplasm are comparable to water. I am curious to see what value of the longitudinal modulus will they find if they measure the same for pure water samples. For the correct modulus to be obtained, wouldn't the Brillouin shift be very different from that of the cytoplasm and nucleoplasm? In such a case, is it reasonable to assume water values for the density and the refractive index and measure only the Brillouin shift to obtain the moduli?

The longitudinal modulus of a pure water sample is around 2.2 GPa with a Brillouin shift of approximately 7.4 GHz, which is substantially different to the values measured for cytoplasm (2.410 GPa and 7.811 GHz) and nucleoplasm (2.448 GPa and 7.872 GHz) in HeLa cells. For HeLa cells, it would indeed be sufficient to assume water values for density and RI to yield the correct trend for the longitudinal modulus. However, in this case, a systematic underestimation of the longitudinal modulus would result and, obviously, the differences in density and RI of the different compartments would remain unknown.

We think that this point is already discussed sufficiently in the discussion (page 15, line 446 ff.): “We find that in all samples measured here where the two-substance mixture model can be applied, neglecting the contribution of RI and density to the longitudinal modulus still yields a similar tendency for the longitudinal modulus and Brillouin shift (i.e. a high Brillouin shift means a high longitudinal modulus and vice versa), but doing so might lead to a systematic error for the longitudinal modulus.”

8. The authors state that "These findings imply that membrane-less compartments formed by phase separation […] in spite of the thermodynamic instability inherent in this state" This information is nice and needed. But the authors need to explain why one should care about this longitudinal modulus, what does it represent, and how does it affect the material properties? In other words, the meaning of this modulus and its relation to the mechanical behavior of these materials is not clear. A discussion is enough to address this point.

The longitudinal modulus is a measure for the compressibility of the sample. It is measured by evaluating the frequency shift of incident photons due to the interaction with longitudinal photons (acoustic excitations) intrinsic to the sample. Since cells consist of liquids (water) mainly, and liquids are generally very hard to compress, the longitudinal modulus of biological samples is very high (in the GPa range) when compared to the elastic modulus. Multiple publications empirically found a correlation of the longitudinal modulus to the elastic modulus (Young’s modulus). However, this correlation does not necessarily imply a causal relation between trends of the two moduli.

We enhanced the introduction and now briefly introduce the physical meaning of the longitudinal modulus and mention the correlation to the elastic modulus. We added the following section:

“The longitudinal modulus characterizes the compressibility of a sample and is in the GPa range for common biological samples (Prevedel et al., 2019). […] However, multiple studies found empirical correlations between the longitudinal modulus and the Young’s modulus (Scarcelli et al. (2011, 2015); Schlüßler et al. (2018)).”

Since previous publications already discussed this in more detail, we do not think that an extensive review would be constructive here (see e.g. Prevedel et al. “Brillouin microscopy: an emerging tool for Mechanobiology”. Nature Methods. 2019; 16(10):969–977. doi: 10.1038/s41592-0190543-3).

9. On the previous point: The authors need to comment on these extremely large longitudinal moduli, as reported for polyQ aggregates and others. Previous studies showed that the elastic modulus of the ECM is about 1 kPa (additionally see table 1 in this report: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4553184/), so what does a value of 1 GPa for the longitudinal modulus mean? a good discussion of the meaning of the longitudinal modulus and its relation to the material properties and other moduli will be helpful.

Please see our answer to the previous point #8.

10. Page 10, lines 265-268; I see that "significantly" is used here to describe statistical significance, and maybe it's used in the same sense in the other cases too. However, the authors need to specify as it can be easily misunderstood as a significant difference in magnitude and not in statistical similarity.

We now use “statistically significant(ly)” wherever appropriate (see our answer to question number 6).

11. Page 10, lines 270-272; I am having trouble in understanding why the authors are using the standard error of the mean as opposed to the standard deviation as a measure of uncertainty. This gives an impression that the technique has superior accuracy. the variability shown in the plots is much larger than these errors (orders of magnitudes). Is this variability coming from the cell-to-cell and condensate to condensate variation or is it coming from the measurement itself? This data is not shown for the phantom cell controls (please include it in the SI). If this variability is present in that system as well, the authors may try a different system for the control to assess the inherent error of the technique, such as an aqueous two-phase system (e.g. peg and dextran). These systems are also closer to the intracellular fluid, so they may serve as better control. This would help the readers assess the accuracy of the technique itself. Alternatively, the authors should give a convincing argument for why are the SEMs a better measure of the variability of the measurement (or the uncertainty)? It's also crucial to understand the source of the variability in the refractive indices and moduli. If it is coming from the instrument, then SEMs should not be used. If it is coming from the cell-to-cell variation, then the authors may need to show data on a control system that does not have variations so that the readers can understand the level of accuracy of these measurements. If it is indeed from cell-to-cell variation and not from the experimental technique, then the use of SEMs is reasonable.

The uncertainties for the measurements in cells (HeLa wild-type, FUS stress granules, polyQ and adipocytes) are given as SEM because the single values averaged are the result from measurements in different cells. Therefore, the use of SEMs is reasonable, as the uncertainty is due to cell-to-cell variation.

However, the error given for the cell phantom measurement should actually be given as the standard deviation instead of the SEM, since only data from a single cell phantom is shown in the manuscript and the uncertainties are due to the setup and not due to variations of the sample measured. This was intended to demonstrate the accuracy of the technique itself.

We added the following sentence to the respective section “The material properties of the two components of the phantom are expected to be homogeneous so that the standard deviation (SD) of the values measured can be used as an estimate of the setups measurement uncertainty.” and corrected the uncertainty values given for the phantom measurement, so that the accuracy of the setup can now be judged correctly. Thank you very much for pointing out this problem.

12. Are the errors in the case of FUS and polyQ condensates estimated from different condensates in the same cell or from one condensate in multiple cells? this is important to judge the accuracy of this measurement and the possible effect of reducing the ROI, would reducing the ROI of the Brillouin experiment on polyQ aggregates give the same result?

The errors are given as SEM and result from measurements of a single compartment/stress granule or aggregate per cell. In total, we measured 22 different cells in case of polyQ aggregates and 90 different cells in case of FUS stress granules. We added a sentence to clarify this in the FUS stress granule section:

“In total, we measured 90 different cells, with the number of cells per compartment and condition varying between 13 and 22, as shown in Figure 4e and f.”

Reducing the region-of-interest (ROI) of the Brillouin measurements of polyQ aggregates might slightly change the quantitative values measured, in case only a specific region of the aggregate is targeted (e.g. the edge), since the finite spatial resolution of the setup leads to lower Brillouin shifts close to the surface of the aggregates. However, we would consider this a biased measurement and we prevent this by discarding measurement values close to the interface between aggregate and periphery when segmenting the polyQ aggregates. Hence, the results shown should not be affected by the chosen ROI.

13. Regarding the difference in the RI and longitudinal moduli of the nucleoplasm, cytoplasm, and nucleolus, could the authors be able to provide a physical basis of such differences based on what is known about these materials? This will be useful to support the claim that FOB microscopy can be used to study "physiological and pathological processes such as phase separation".

Unfortunately, we do not currently have a model that would explain the differences in RI and longitudinal moduli of the different compartments in HeLa cells. As written in the manuscript, we do think that the chromatin network of the nuclei has an influence on the measured values (see Liu L, Plawinski L, Durrieu MC, Audoin B. Label-Free Multi-Parametric Imaging of Single Cells: Dual Picosecond Optoacoustic Microscopy. Journal of Biophotonics. 2019; 12(8):e201900045. doi: 10.1002/jbio.201900045.), but a verified conclusion would require substantially more work on this question.

However, in order to further support the claim that FOB can be used to study "physiological and pathological processes such as phase separation", we now highlight the possibility of Brillouin microscopy to probe not only longitudinal phonons but also transverse phonons in the sample. Transverse phonons are related to the shear modulus of the sample and cannot propagate in liquids. Thus, measuring transverse phonons could help to discriminate liquid-like versus solid-like materials and analyze phase separations and transitions.

We added this section to the discussion:

“Furthermore, the setup could be enhanced to measure not only longitudinal phonons, but also transverse phonons, which are related to the shear modulus and can generally not propagate in liquids. Hence, this could help to discriminate liquid-like versus solid-like materials.”

Reviewer #2 (Recommendations for the authors):

1. Maybe the authors can discuss the biological relevance of the longitudinal modulus measurement at such high frequencies.

Please see our comment to issue #8 raised by reviewer 1. The mentioned relations have been described already in previous publications such as the excellent review by Prevedel et al. “Brillouin microscopy: an emerging tool for Mechanobiology”. Nature Methods. 2019; 16(10):969–977. doi: 10.1038/s41592-019-0543-3 and an extensive discussion is not exactly the scope of the manuscript at hand. However, in order to briefly discuss the biological relevance and to give further references to the interested reader, we enhanced the introduction by the following section:

“The longitudinal modulus characterizes the compressibility of a sample and is in the GPa range for common biological samples (Prevedel et al., 2019). […] However, multiple studies found empirical correlations between the longitudinal modulus and the Young’s modulus (Scarcelli et al. (2011, 2015); Schlüßler et al. (2018)).”

2. The authors can also discuss their choice of the diffraction tomography method. It is known that the traditional off-axis holography with lase illumination suffers from phase instability and speckles. In recent years, common-path and white light-based methods have been developed to alleviate these issues.

As the reviewer pointed out, the traditional off-axis holography with laser illumination encounters phase instability and speckles. Nonetheless, the refractive index precision of the current ODT system is achieved as 4.15 × 10-5 (corresponding to a difference in protein concentration of 0.22 mg/ml), which is measured from the standard error of the time series of RI tomograms (Kim, K. and Guck, J. Biophys. J. 119, 1946–1957 (2020)). Hence, this measurement uncertainty is sufficient to pick up the RI differences between the various regions of the cell characterized in the study at hand.

Although common-path interferometry or shearing interferometry can enhance the phase stability, ODT using common-path interferometry requires additional descanning of scattered fields with complicated optical setups (Kim, Y. et al. Sci. Rep. 4, 6659 (2014), Chowdhury, S. et al., Optica 4, 537 (2017)), and ODT using shearing interferometry has limited field-of-view and requires low sample density in order to fulfill the imaging condition (Kim, K. et al. Opt. Lett. 39, 6935 (2014)., Guo, R. et al., Biomed. Opt. Express 12, 1869 (2021)). Hence, we stick to the MachZehnder interferometry with a proper enclosure to measure a large field-of-view using a simple optical setup with still reasonable phase stability and RI precision.

Using temporally incoherent illumination including white light or supercontinuum laser can reduce the speckle noise in digital holographic microscopy. However, in this study, ODT should use the same laser beam for Brillouin microscopy to correlate the RI value and Brillouin frequency shift of the samples at the same wavelength. For that reason, ODT uses the same laser beam for Brillouin microscopy, which requires extremely narrow laser linewidth, therefore, extended temporal coherency. The speckle noise in the current ODT configuration can be reduced by other methods such as using a dynamic diffuser (Choi, Y. et al., Opt. Lett. 36, 2465 (2011)) or numerical filtering approaches (Bianco, V. et al. Light Sci. Appl. 7, 48 (2018)).

In the revised manuscript, we added this section to the discussion:

“To correlate the RI value and Brillouin frequency shift of the samples at the same wavelength, the FOB setup uses the same laser source for ODT and Brillouin mode. […] The speckle noise could be further reduced by using a dynamic diffuser (Choi, Y. et al., Opt. Lett. 36, 2465 (2011)) or numerical filtering approaches (Bianco, V. et al. Light Sci. Appl. 7, 48 (2018)).”

Reviewer #3 (Recommendations for the authors):

One inherent concern is that the Brillouin scattering depends heavily on the density of the medium. Therefore, while the in vitro samples can be measured in a controlled buffer, cellular milieu will add significant level of "background noise" that prevents measuring precise difference between different compartments of cells. For example, this may have produced very similar parameter for P525L-FUS vs. cytoplasm.

Indeed, measuring technical reference samples such as methanol or in vitro samples in controlled environments generates substantially less noisy data than measuring biological samples in vivo, primarily due to cell-to-cell variability. However, this limitation affects virtually every measurement technique and can be overcome by averaging multiple samples and measurements.

Furthermore, the confocality of the Brillouin microscopy setup ensures that photons interacting with the culture medium do not reach the Brillouin spectrometer and only photons, which are Brillouin scattered in the probed sample volume contribute to the measured Brillouin shift. In addition, the combination of Brillouin microscopy and ODT explicitly enables to measure the density of the probed sample volume so that differences of the local cell density are taken into account when calculating the longitudinal modulus. Hence, variations in the local cell density do not lead to “background noise” that would conceal differences of the longitudinal modulus between different cell compartment. This can be seen by the shown differences of the longitudinal modulus and refractive index between different compartments (cytoplasm, nucleoplasm and nucleoli) and polyQ aggregates in HeLa cells which could be extracted by the presented setup.

Another major concern, as stated above is the excessive perturbation required to perform the whole cell measurement, which the authors reported resulted in cell death.

Thank you for this comment. It is true that due to the low scattering efficiency of Brillouin scattering, Brillouin microscopy needs high illumination powers (e.g. when compared to Confocal fluorescence microscopy or ODT). Typically, we use 10 mW of laser power at the laser focus. As reported in the manuscript, HeLa cells with GFP-labeled FUS reacted with cell death when a large area of the cell, especially when including the nucleus, was exposed to laser illumination. Therefore, we adjusted the laser scanning scheme for these cells in a way that only a small area of the cell (the GFP-FUS stress granules and an adjacent area of the cytoplasm) was exposed. This effectively prevented cell death. Other cell types (wild-type HeLa, polyQ transfected wild-type HeLa and adipocytes) were not affected by the laser illumination. Hence, the data shown in the manuscript does not include values from cells suffering from cell death.

We adjusted the respective section to make this clearer. The section now reads:

“We therefore did not acquire Brillouin shift maps of complete P525L HeLa cells, but only of small regions of 5 µm by 5 µm around the SGs or the corresponding regions in the cytoplasm of the control cells. […] Hence, all measurements presented here stem form living cells.”

Furthermore, photo toxicity is highly dependent on the energy of the photons the sample is exposed to, hence, the wavelength of the laser used. Less energetic photons cause substantially less photo toxicity. To demonstrate this, we added a comparison of P525L HeLa GFP-FUS cells before and after full-cell and local Brillouin measurements with the FOB setup at 532 nm and a stand-alone Brillouin microscope using a wavelength of 780 nm, respectively. As shown in the Figure 4—figure supplement 1 in the revised manuscript, a scan of the full cell with a wavelength of 532 nm causes cell death. In difference, a measurement localized to a small area within the cytoplasm or stress granule, as it is performed in the manuscript for P525L HeLa cells, does not cause cell death. Furthermore, when using an excitation wavelength of 780 nm, also a full-cell scan does not cause any visible damage or cell death. Hence, cells presented in the manuscript are not damaged by the measurements and cell death is not a principle effect of the proposed method, but only of the wavelength of the laser used here. This laser was selected, because it is known to work well for ODT and requires no further technical effort for cleaning the laser emission spectrum for Brillouin microscopy illumination. Future FOB setups will use a higher wavelength of 780 nm as it is successfully used for Brillouin microscopy already by our stand-alone Brillouin setup (Schlüßler et al. “Mechanical Mapping of Spinal Cord Growth and Repair in Living Zebrafish Larvae by Brillouin Imaging”. Biophysical Journal. 2018) and is known to work for ODT (see Shin, S. et al. Common-path diffraction optical tomography with a low-coherence illumination for reducing speckle noise. Proc. SPIE 9336, 933629, 2015).

The proof of principle was tested on what the authors call "phantom cell" which are polymers, PDMS and PAA. Although they were used due to the known mechanical property, they are not biological molecules and PDMS does not even contain a fluid phase. In addition, the RI measured by FOB did not match the known values. The differences seen here appears to be quite small as the authors claim, but they are significantly higher than other RI values authors measure in later samples. For example, Figure 2d the negligible difference between cytoplasm and nucleoplasm is stated to be significantly different.

While it is true that we find a systematic difference (underestimation) of the refractive index of PDMS measured by FOB compared to the literature value measured with a different technique, this systematic difference does not invalidate or influence comparisons between data sets solely acquired by FOB. This is because all FOB measurements would be affected by the same systematic difference, which then cancels out when comparing FOB data to FOB data. Hence, the reported statistically significant differences are valid even though FOB might have a systematic difference to literature data larger than the measured differences between cell compartments.

Furthermore, as discussed in the manuscript, the systematic difference of the refractive index of PDMS measured with FOB might be explained by a swelling of the PDMS beads during the fabrication process, which could reduce the refractive index.

Overall, the only significant difference measured seem to be in the cases of nucleoli, polyQ and lipid which are already known to form a more dense phase. Again, the lack of comparison to orthogonal method such as FRAP, fusion/diffusion kinetic makes it difficult to assess the validity of even the obvious cases.

Thank you for this comment. Indeed, the verification of values measured with Brillouin microscopy is an important yet difficult issue to address, since no other technique we know of measures the longitudinal modulus of cells in vivo. However, Brillouin microscopy has been shown to be sensitive to e.g. actin polymerization, and correlations of the longitudinal modulus to the elastic modulus have been found. Hence, orthogonal techniques such as AFM have been used to show the validity of the results acquired by Brillouin microscopy. Furthermore, protein concentration measurements by ODT have been verified by a volume-based measurement approach by our group (McCall et al. “Quantitative Phase Microscopy Enables Precise and Efficient Determination of Biomolecular Condensate Composition” bioRxiv, 2020 Oct; p. 2020.10.25.352823. doi: 10.1101/2020.10.25.352823). FRAP measurements have also been done already on polyQ aggregates by our group and published in eLife (see Kroschwald et al. “Promiscuous Interactions and Protein Disaggregases Determine the Material State of Stress-Inducible RNP Granules” eLife. 2015 Aug; 4:e06807. doi: 10.7554/eLife.06807.). These measurements show that polyQ aggregates show solid-like properties, as the fluorescence intensity did not recover.

We think that separately repeating these comparison measurements would not yield substantial new insight and referencing the published results should be sufficient. To make these relations more clear, we added the following sentences to the introduction:

“Protein concentrations acquired with ODT were shown to agree well with results from volume-based measurements and did not suffer from differences in the quantum yield of fluorescent dyes between dilute and condensed phase as it might happen for fluorescence intensity ratio measurements (McCall et al., 2020).”

The result obtained on sodium arsenite and P525L seem inconsistent with what is known from other literature. Again, the clear difference between these conditions (oxidative and mutant) may have been masked by the cellular density background.

As stated above, “cellular density background” is not expected to mask differences in the mechanical properties of cells measured by FOB. While the differences between our measurements in P525L HeLa cells and the literature data really seemed inconsistent at first glance (although thought to be explainable by the differences in culture conditions and cell type), we now additionally performed measurements on fixed, sodium arsenite treated cells, which resolved this discrepancy. We find that in sodium arsenite treated, fixed cells SGs show a statistically significantly higher longitudinal modulus than the surrounding peri-SG cytoplasm. This is in agreement to the literature results. We adjusted the respective section. Please refer to the manuscript section “GFP-FUS stress granules in living P525L HeLa cells show RI and longitudinal modulus similar to the surrounding cytoplasm” on page 9 ff. and especially page 12 lines 333 to 349 for the changes made.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Essential revisions:

1) Reviewer 1 raises important questions regarding the measurements of longitudinal moduli and their biological relevance. Please address this explicitly.

2) The reviewer raises other important issues, specifically regarding the limitations of the FOB method that need to be addressed more thoroughly.

Please see our detailed responses to the reviewers concerns below.

Please respond to this reviewer's concerns, and please do so fully and thoroughly. I agree that the title of the manuscript should be changed. As it stands, the average reader of eLife is not likely to know what the combination of techniques are intended for in the biological context. Please specify the intended applications in the title.

We understand the concern of the reviewer and editor that the current title is too technical for the audience of eLife. We adjusted the title, which now reads:

“Correlative all-optical quantification of mass density and mechanics of sub-cellular compartments with fluorescence specificity”

Reviewer #1 (Recommendations for the authors):

In the original manuscript, this reviewer and other reviewers raised important questions regarding two critical points.

The first point was: how measuring longitudinal modulus at GHz frequencies helps to understand the biological process as the authors state in their manuscript (line 439-441 in the manuscript). The authors made minimal attempt to address this, and in fact, they now have included a brief discussion on the limitations of Brillouin microscopy. There are some interesting studies in the literature which the authors cite in their argument, but that is not sufficient to make a case of how measuring longitudinal modulus at GHz frequencies helps to understand the biological process. Therefore, the response to this point remains inadequate.

The physiological relevance of the longitudinal modulus in the GHz range, i.e. whether cells are sensitive or react to changes of the high-frequency compressibility, is indeed still under debate and not yet proven. We agree with Prevedel et al. that this is “a fact that needs to be considered in the biological interpretation of results obtained by Brillouin microscopy” and think that further investigations are necessary to either demonstrate or refute the relevance of Brillouin microscopy for biology. Because of this, we strongly believe that studies such as ours help to delineate what can be detected by Brillouin microscopy and contribute to the scientific enterprise of uncovering the unknown (rather than only confirming what is known). Having said this, there are already several studies implying biological relevance of the longitudinal modulus:

– Phenomenological correlations between the Young’s and the longitudinal modulus have been found e.g. for cells under osmotic shock, for porcine and bovine lenses, for hydrogels and in zebrafish tissue. This indicates that for these samples the longitudinal modulus could serve as a proxy of the Young’s modulus, which is widely accepted as relevant for biology. See Scarcelli et al. 2011, 2015; Schlüßler et al. 2018.

– The longitudinal modulus of cells changes due to inhibition of actin polymerization with cytochalasin D or latrunculin-A, and increases with increasing substrate rigidity (Scarcelli et al. 2015; Antonacci, Braakman 2016). The modulus first decreases and subsequently increases with progressing recovery after spinal cord injury in zebrafish tissue (Schlüßler et al. 2018). Hence, various biophysical influences were shown to affect the longitudinal modulus.

– UV-induced crosslinking of polymer and actin polymerization into a gel are accompanied by an increase of the longitudinal modulus measured. See Scarcelli et al. 2008; Scarcelli et al. 2015.

It is clear that these observations do not prove that cells or organisms experience (as in: are sensitive to and respond to) changes of the longitudinal modulus. However, the measured differences of the longitudinal modulus are physically real and could influence biophysical processes directly, even without the cells or organisms being able to sense these differences in the first place. For example, variations in the longitudinal modulus at GHz frequencies detected in cells could be reporting on local changes in intermolecular interactions, water mobility and hydration shells, and other aspects relevant for emergent, supramolecular processes, which are important in their own right.

Furthermore, liquid-to-solid phase transitions not only affect the longitudinal modulus, but can also lead to variations of the samples’ viscosity or shear modulus. These quantities can also be detected by Brillouin microscopy – by evaluating the peak linewidth and detecting transverse phonons (which cannot propagate in liquids), respectively. Hence, the longitudinal modulus is not the only material property provided by Brillouin microscopy that could be used to observe phase transitions in cells. See Antonacci et al. 2018; Kim et al. 2016b; Prevedel et al. 2019.

Most of these points have already been comprehensively discussed in previous dedicated publications and are summarized well e.g. in Prevedel et al.. However, in order to enable a critical assessment of the setup presented in the manuscript for readers not familiar with Brillouin microscopy, we added a section covering these points to the discussion:

“Although Brillouin microscopy was introduced to biology more than a decade ago (Scarcelli and Yun, 2008), its relevance to biological questions is sometimes still viewed skeptically in the field of mechanobiology. […] We thus believe that Brillouin microscopy can contribute to open questions in biology, but further studies are necessary to finally establish its relevance.”

We further added an evaluation of the linewidths of the different HeLa cell compartments and found a statistically significantly higher linewidth in the nucleoli compared to the cyto- and nucleoplasms.

We also slightly toned down the mentioned conclusion of the manuscript to now read:

“This enables quantitative measurements of the mechanical properties of single cells and their compartments and might potentially contribute to a better understanding of physiological and pathological processes such as phase separation and transition in cells as a response to external stress.”

The second point was pertaining to the understanding of the physics of sub-cellular mechanics. To this end, I had a question (point # 13) in the first round of review. The response again in point # 13 is superficial in my opinion and needs better considerations and/or new data.

As a reminder, the original question asked in the first round of review was:

“Regarding the difference in the RI and longitudinal moduli of the nucleoplasm, cytoplasm, and nucleolus, could the authors be able to provide a physical basis of such differences based on what is known about these materials? This will be useful to support the claim that FOB microscopy can be used to study "physiological and pathological processes such as phase separation".”

To the authors’ best knowledge there are no publications available that analytically or computationally model the longitudinal modulus of the cytoplasm or nucleus of cells in the GHz range, similar to how it was done for elastic modulus data in the kHz range acquired e.g. by atomic force microscopy, micropipette aspiration or constricted migration (see Hobson, C. M. and Stephens, A. D. Modeling of Cell Nuclear Mechanics: Classes, Components, and Applications. Cells 9, 1623 (2020) for a good overview of the modeling of cell nuclear mechanics). Hence, theoretical insight into the behavior of the longitudinal modulus is still rather limited.

However, the nucleus has been described as a “network of chromatin and other intranuclear components surrounded by a cytosol-like fluid” (Wachsmuth et al., Anomalous diffusion of fluorescent probes inside living cell nuclei investigated by spatially-resolved fluorescence correlation spectroscopy. Journal of Molecular Biology 298, 677–689, 2000) and can display rather unusual mechanical properties, for example having a negative Poisson ratio (Pagliara et al., Auxetic nuclei in embryonic stem cells exiting pluripotency. Nature Materials 13, 638–644, 2014). Later publications attributed the nucleus’ mechanical response in the GHz frequency range to the chromatin network (Zouani et al., Universality of the network-dynamics of the cell nucleus at high frequencies. Soft Matter 10, 8737–8743, 2014) and implied that the mass density in the cytoskeleton network could be higher than in the chromatin network although the Brillouin shift behaves in the opposite way (Liu et al., Label-free multi-parametric imaging of single cells: dual picosecond optoacoustic microscopy. Journal of Biophotonics 12, e201900045, 2019).

It is also not required that mass density scales with longitudinal modulus. As a Gedankenexperiment, a non-crosslinked polymer network, with crosslinking molecules already present in the mix but not engaged, will have the same mass density, but lower elastic modulus, compared to the same network with the crosslinking molecules engaged. The uncoupling of mass density from elastic modulus is even more easily imaginable for a topologically constrained polymer network such as the nuclear chromatin. The topological constraint restricts the packing density, which leads to relatively low mass density, but the polymer network can still have a relatively robust elastic resistance to deformation or compression.

Hence, we think that the chromatin network in the nucleoplasm could very well explain the higher longitudinal modulus of the nucleoplasm compared to the cytoplasm, even though the mass density in the nucleoplasm is lower than in the cytoplasm. Future measurements could further test this explanation by e.g. probing the response of the longitudinal modulus to chromatin decondensation due to trichostatin A (TSA) treatment.

We amended the discussion accordingly:

“The nucleus is described as a "network of chromatin and other intranuclear components surrounded by a cytosol fluid" by Wachsmuth et al. (2000). […] Further analysis, e.g. testing the response of the longitudinal modulus to chromatin (de)condensation, can be performed to further consolidate this idea.”

Another important point is that the authors should include a subsection in the discussion pertaining to the limitations of FOB and data interpretation. For example, the argument presented in point # 3 should be included under the "limitations" section. The same goes for point # 7. As stated multiple times in their response letter, if we are not measuring physiologically relevant moduli and no clear conclusions can be made about the physical interpretation of the data, I share the same concern about reviewer 2 regarding how big of an impact this study will have in the field. The Nat Methods paper (Prevedel et. al 2019) that the authors cite articulates the limitations.

Unfortunately, we cannot fully follow the reviewer’s intention here. The arguments presented in points #3 and #7 do not discuss limitations of the FOB setup. These points rather demonstrate that the assumptions necessary when the refractive index is not directly measured (hence, when doing standalone Brillouin microscopy instead of FOB) can lead to systematic errors of the longitudinal modulus (although still yielding similar tendencies for Brillouin shift and modulus). These systematic errors of the longitudinal modulus do not occur when using the FOB setup. Hence, we think that this is an argument to the advantage of FOB microscopy, which is already sufficiently discussed in the introduction and discussion of the manuscript.

However, the FOB setup has limitations that we cover in the discussion:

– True molecular specificity including the absolute concentration of different molecules is not feasible with fluorescence imaging only, but can be achieved by Raman scattering microscopy. This enhancement could be added to the FOB microscope in a straightforward way.

– The Brillouin microscopy modality requires relatively long acquisition times in the range of 100 ms to 1 s per point with illumination powers in the 10 mW range. In combination with the currently used green laser source, this leads to phototoxic effects for cells. Moving to near-infrared would reduce the phototoxicity and allow higher illumination powers, hence, reducing the acquisition time.

– The refractive index measurement with ODT only works for weakly scattering samples such as single cells or beads. Thicker samples such as tissues and organisms cannot be measured with the current implementation. However, future improvements of the tomogram reconstruction taking into account multiple scattering could overcome this.

– The biological relevance of the longitudinal modulus we discuss in a separate section of the discussion now.

Overall, I am a bit surprised by the brevity of the responses by the authors from the first round of review and do not think that the revision rigorously addressed the reviewers' concerns.

We certainly did not take the reviewers’ comments lightly, as they were of relevance, helpful and interesting. Nor do we think we superficially discussed the concerns raised. If brevity provides convincing clarification of the points raised, why elaborate? After all, we covered all but two questions from the first round of review to the satisfaction of this reviewer, and all concerns of the other two reviewers. The remaining two points under discussion were very profound and very difficult to answer, especially considering that they are also heavily debated within the Brillouin microscopy community as a whole. Now, with this second opportunity for improvement, we hope that we also provided satisfactory clarifications and amendments to settle them for the purpose of this present manuscript.

An important point which I missed in the first round of review: The title of the paper reads strange. The combination of techniques in this context is done to address a set of key biological questions, and the combination itself sounds very technical as the title of the paper.

We understand the concern of the reviewer and editor that the current title is too technical for the audience of eLife. We adjusted the title, which now reads:

“Correlative all-optical quantification of mass density and mechanics of sub-cellular compartments with fluorescence specificity”

https://doi.org/10.7554/eLife.68490.sa2

Article and author information

Author details

  1. Raimund Schlüßler

    Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität, Dresden, Germany
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Kyoohyun Kim
    For correspondence
    raimund.schluessler@tu-dresden.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3752-2382
  2. Kyoohyun Kim

    1. Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität, Dresden, Germany
    2. Max Planck Institute for the Science of Light and Max-Planck-Zentrum für Physik und Medizin, Erlangen, Germany
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Raimund Schlüßler
    For correspondence
    kyoohyun.kim@mpl.mpg.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1808-775X
  3. Martin Nötzel

    Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität, Dresden, Germany
    Contribution
    Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6442-9899
  4. Anna Taubenberger

    Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität, Dresden, Germany
    Contribution
    Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
  5. Shada Abuhattum

    1. Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität, Dresden, Germany
    2. Max Planck Institute for the Science of Light and Max-Planck-Zentrum für Physik und Medizin, Erlangen, Germany
    Contribution
    Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  6. Timon Beck

    1. Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität, Dresden, Germany
    2. Max Planck Institute for the Science of Light and Max-Planck-Zentrum für Physik und Medizin, Erlangen, Germany
    Contribution
    Formal analysis, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  7. Paul Müller

    1. Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität, Dresden, Germany
    2. Max Planck Institute for the Science of Light and Max-Planck-Zentrum für Physik und Medizin, Erlangen, Germany
    Contribution
    Formal analysis, Software, Writing – review and editing
    Competing interests
    No competing interests declared
  8. Shovamaye Maharana

    1. Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität, Dresden, Germany
    2. Department of Microbiology and Cell Biology, Indian Institute of Science, Bengaluru, India
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  9. Gheorghe Cojoc

    Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität, Dresden, Germany
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  10. Salvatore Girardo

    1. Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität, Dresden, Germany
    2. Max Planck Institute for the Science of Light and Max-Planck-Zentrum für Physik und Medizin, Erlangen, Germany
    Contribution
    Funding acquisition, Methodology, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
  11. Andreas Hermann

    Translational Neurodegeneration Section "Albrecht Kossel", University Rostock, and German Center for Neurodegenerative Diseases (DZNE), Rostock/Greifswald, Germany
    Contribution
    Conceptualization, Funding acquisition, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7364-7791
  12. Simon Alberti

    1. Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität, Dresden, Germany
    2. Physics of Life, Technische Universität Dresden, Dresden, Germany
    Contribution
    Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4017-6505
  13. Jochen Guck

    1. Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität, Dresden, Germany
    2. Max Planck Institute for the Science of Light and Max-Planck-Zentrum für Physik und Medizin, Erlangen, Germany
    3. Physics of Life, Technische Universität Dresden, Dresden, Germany
    Contribution
    Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing – review and editing
    For correspondence
    jochen.guck@mpl.mpg.de
    Competing interests
    No competing interests declared

Funding

Deutsche Forschungsgemeinschaft (419138906)

  • Simon Alberti
  • Jochen Guck

Volkswagen Foundation (92847)

  • Simon Alberti
  • Jochen Guck

Alexander von Humboldt-Stiftung

  • Jochen Guck

NOMIS Stiftung

  • Andreas Hermann

Hermann und Lilly Schilling-Stiftung

  • Andreas Hermann

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Anthony Hyman from the Max Planck Institute of Molecular Cell Biology and Genetics for providing the stable HeLa cell lines, Prof. Martin Wabitsch from the Centre for Hormonal Disorders in Children and Adolescents – Ulm University Hospital for providing the SGBS preadipocytes, and the Center for Molecular and Cellular Bioengineering Light Microscopy Facility (partly funded by the State of Saxony and the European Fund for Regional Development – EFRE) for technical support. Financial support from the Deutsche Forschungsgemeinschaft (SPP 2191-Molecular mechanisms of functional phase separation, grant agreement number 419138906 to SAl and JG), Volkswagen Stiftung (grant agreement number 92847 to SAl and JG), the Alexander von Humboldt-Stiftung (Alexander von Humboldt-Professorship to JG) are gratefully acknowledged. AH is supported by the NOMIS foundation and the Hermann und Lilly Schilling-Stiftung fur medizinische Forschung im Stifterverband.

Senior Editor

  1. Anna Akhmanova, Utrecht University, Netherlands

Reviewing Editor

  1. Rohit V Pappu, Washington University in St Louis, United States

Publication history

  1. Preprint posted: October 30, 2020 (view preprint)
  2. Received: March 17, 2021
  3. Accepted: January 8, 2022
  4. Accepted Manuscript published: January 10, 2022 (version 1)
  5. Version of Record published: February 4, 2022 (version 2)

Copyright

© 2022, Schlüßler et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Raimund Schlüßler
  2. Kyoohyun Kim
  3. Martin Nötzel
  4. Anna Taubenberger
  5. Shada Abuhattum
  6. Timon Beck
  7. Paul Müller
  8. Shovamaye Maharana
  9. Gheorghe Cojoc
  10. Salvatore Girardo
  11. Andreas Hermann
  12. Simon Alberti
  13. Jochen Guck
(2022)
Correlative all-optical quantification of mass density and mechanics of subcellular compartments with fluorescence specificity
eLife 11:e68490.
https://doi.org/10.7554/eLife.68490

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