Neurons in the brain form an intricate network that follows a precise template. For example, in a young frog embryo, the neurons from the eyes send out thin structures, called axons, which navigate along a well-defined path and eventually connect with the visual centres of the brain. This journey requires the axons to take a sharp turn so they can wire with the right brain structures.

Axons find their paths not only by following chemical signals but also by reacting to the stiffness of their environment. In an older frog embryo for instance, the brain is stiffer at the front, and softer at the back. As neurons from the eyes make their way through the brain, they turn to follow this gradient, moving away from stiffer areas towards the softer regions.

Here, Thompson, Pillai et al. investigate when and how this stiffness gradient is established in frogs. To do so, a new technique was developed. Called time-lapse in vivo atomic force microscopy, the method measures how brain stiffness changes over time in a live embryo, while also taking images of the growing axons.

The experiments show that the stiffness gradient arose within tens of minutes, just as the first ‘pioneering’ axons from the eyes began to grow across the brain. These axons then responded to the gradient, turning towards the softer tissue. Changes in the number of cells in the underlying brain tissue governed the formation of the gradient, with rapidly stiffening areas containing more cells than those that remained soft. In fact, using drugs that stop cells from dividing reduced both the mechanical gradient and the turning response of the axons.

The technique developed by Thompson, Pillai et al. is a useful tool that can help elucidate how variations in stiffness control the brain wiring process. It could also be used to look into how other developmental or regenerative processes, such as the way neurons reconnect after injuries to the brain or spinal cord, may be regulated by mechanical tissue properties.

During embryonic development, many biological processes are regulated by tissue mechanics, including cell migration (Barriga et al., 2018), neuronal growth (Koser et al., 2016), and large-scale tissue remodelling (Butler et al., 2009; Munjal et al., 2015). Recent measurements at specific time points suggested that tissue mechanics change during developmental (Koser et al., 2016; Iwashita et al., 2014; Majkut et al., 2013) and pathological (Murphy et al., 2011; Moeendarbary et al., 2017) processes, which might significantly impact cell function. Furthermore, several approaches have recently been developed to measure in vivo tissue stiffness, including atomic force microscopy (Barriga et al., 2018; Koser et al., 2016; Gautier et al., 2015), magnetic resonance elastography (Sack et al., 2008), Brillouin microscopy (Scarcelli and Yun, 2012), and magnetically responsive ferrofluid microdroplets (Serwane et al., 2017). However, the precise spatiotemporal dynamics of tissue mechanics remains poorly understood, and how cells respond to changes in local tissue stiffness in vivo is largely unknown.

To enable time-resolved measurements of developmental tissue mechanics, we here developed time-lapse in vivo atomic force microscopy (tiv-AFM), a method that combines sensitive upright epifluorescence imaging of opaque samples, such as frog embryos, with iterated AFM indentation measurements of in vivo tissue at cellular resolution and at a time scale of tens of minutes (Figure 1). A fluorescence zoom stereomicroscope equipped with an sCMOS camera (quantum yield 82%) was custom-fitted above a bio-AFM set-up (Figure 1—figure supplement 1), which had a transparent pathway along the area of the cantilever. To cope with the long working distance required for imaging through the AFM head, the microscope was fitted with a 0.125 NA/114 mm WD objective. The AFM was set up on an automated motorised stage containing a temperature-controlled sample holder to maintain live specimens at optimal conditions during the experimental time course. (Figure 1a,b) (see Materials and methods for details).

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We tested tiv-AFM using the developing Xenopus embryo brain during outgrowth of the optic tract (OT) as a model (Figure 1c). In the OT, retinal ganglion cell (RGC) axons grow in a bundle across the brain surface, making a stereotypical turn in the caudal direction en route that directs them to their target, the optic tectum (McFarlane and Lom, 2012). We previously demonstrated that by later stages of OT outgrowth (i.e. when axons had reached their target), a local stiffness gradient lies orthogonal to the path of OT axons, with the stiffer region rostral to the OT and softer region caudal to it (Koser et al., 2016). This gradient strongly correlated with axon turning, with the OT routinely turning caudally towards softer tissue (Koser et al., 2016). We therefore wanted to determine when this stiffness gradient first developed, whether its emergence preceded OT axon turning, and what the origin of the stiffness gradient was.

To answer these questions, we performed iterated tiv-AFM measurements of the embryonic brain in vivo at early-intermediate stages, that is, just before and during turn initiation by the first ‘pioneer’ OT axons. The apparent elastic modulus K, which is a measure of the tissue’s elastic stiffness, was assessed in an ~150 by 250 µm raster at 20 µm resolution every ~35 min, producing a sequence of ‘stiffness maps’ of the area (Figure 2—figure supplement 1). The applied force (F = 10 nN) and cantilever probe (r = 18.64 µm) were chosen to measure the stiffness mainly of the top ~20–30 µm of the tissue, within which RGC axons grow (Holt, 1989; Harris et al., 1987). To reduce noise, raw AFM data were interpolated and smoothed in x-, y-, and time dimensions using an algorithm based on the discrete cosine transform (Figure 2a,b, see Materials and methods for details) (Garcia, 2010; Garcia, 2011). Simultaneously, we recorded optical time-lapse images of fluorescently labelled RGC axons growing through the region of interest (Figure 2a, Figure 2—figure supplement 1).

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To assess whether repeated AFM measurements of the Xenopus brain affect RGC axon growth, we first conducted time-lapse AFM measurements on one group of embryos while we exposed another group to the same conditions without making force measurements. At the end of the experiments (i.e., at stage 37/38), OTs were labelled with DiI (Wizenmann et al., 2009), and their elongation and turning angles measured. We did not find any significant differences between the groups (Figure 2—figure supplement 2), suggesting that repeated AFM measurements do not affect axon growth.

We then performed tiv-AFM measurements of developing Xenopus brains. Early in the time-lapse sequence (i.e. prior to axon turning), the stiffness of the brain was similar on both sides of the OT. However, over the time course of the measurements a stiffness gradient arose, mostly due to rising stiffness of tissue rostral to the OT (Figure 2a,b). Visual inspection of the fold-change in tissue stiffness from one time point to the next indicated that significant changes in tissue mechanics were already occurring approximately 40–80 min after the onset of measurements (Figure 2b), that is before axons started turning caudally, suggesting that the tissue stiffness gradient was established prior to axon turning.

To test this hypothesis, we quantified the temporal evolution of the stiffness gradient in a small region immediately in front of the advancing OT (Figure 2—figure supplement 1a). At the beginning of each time point in the sequence of tiv-AFM maps, we calculated the angle through which axons turned (‘OT turn angle’). For each animal, minimum and maximum absolute values were rescaled to 0 and 1, respectively (Figure 2c). The projected appearance of the stiffness gradient preceded the projected onset of axon turning on average by 18 min (Figure 2d,e), indicating that axons indeed turned after the stiffness gradient was established, which is consistent with a role for mechanical gradients in helping to guide OT axons caudally (Koser et al., 2016). Based on the first time point at which we detected axon turning in each animal, our data suggested that a stiffness gradient of at least (0.9 ± 0.4) Pa/µm (mean ± SEM) was required for axons to change their growth direction. In line with this idea, RGC axons from heterochronic eye primordia transplants growing through Xenopus brains at stages before the stiffness gradient is established grow rather straight and do not turn caudally in the mid-diencephalon (Cornel and Holt, 1992).

We have previously shown that tissue stiffness scales with local cell body density (Barriga et al., 2018; Koser et al., 2015), and that in Xenopus embryo brains local stiffness gradients at later developmental stages (39-40) correlate with a gradient in cell density (Koser et al., 2016). To determine if changes in cell densities are driving changes in tissue stiffness, and thus parallel the evolution of the stiffness gradient at earlier stages, we assayed cell densities using DAPI labelling of nuclei in whole-mounted brains with fluorescently labelled OTs, beginning at the morphological stage corresponding to the start of tiv-AFM measurements (33/34) and repeated for the two subsequent stages encompassing OT turning (35/36 and 37/38).

While at the first stage cell densities on both sides of the OT were similar, a clear difference in nuclear densities rostral and caudal to the OT developed at later stages (Figure 3a). Cell densities at the two later stages were significantly higher in the region rostral to the OT (i.e. where tissue was stiffer) than caudal to it, and the overall magnitude of the cell density gradient significantly rose over time (Figure 3b). Plotting the stage-specific gradient in cell body densities against the stiffness gradient revealed a strong linear correlation between them (Pearson’s correlation coefficient ρ=0.97) (Figure 3c).

Figure 3

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To test if local cell densities drive the evolution of the stiffness gradient during OT turning, we repeated both nuclear staining and tiv-AFM measurements on embryos treated with the mitotic blocker BI2536 (Lénárt et al., 2007), which inhibits Polo-like kinase 1 and has previously been used to inhibit in vivo cell proliferation in the embryonic retina (Weber et al., 2014). BI2536 also triggered mitotic arrest in brains of developing Xenopus embryos, as the number of phosphorylated histone H3 (pH3)-positive cells (Hugle et al., 2015) was significantly higher in treated compared to control brains, and the cross-sectional area of treated brains was significantly decreased at later stages, indicating a decrease in total cell number (Figure 4a–c). Inspection of the stage-dependent distribution of cells in the developing Xenopus brain suggested that in BI2536-treated brains, the nuclear density was decreased particularly rostral to the OT (Figure 4d, Figure 4—figure supplement 1). In tiv-AFM experiments, blocking cell proliferation significantly attenuated the increase of both the stiffness gradient and the OT turn angle over the time course of the experiment (Figure 4e,f), suggesting that the gradient in cell densities strongly contributed to the stiffness gradient, which in turn helps instruct axon growth.

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The absence of an increase in tissue stiffness near the advancing OT in BI2536-treated brains was furthermore accompanied by a decrease in OT elongation (Figure 4g). Similarly, OT elongation decreased when brains were softened by manipulating the extracellular matrix (Koser et al., 2016), confirming that tissue stiffness is involved in regulating axon growth in vivo.

We obtained similar results using a different mitotic blocker, hydroxyurea/aphidicolin (HUA), which inhibits DNA replication (Gilman et al., 1980; Ikegami et al., 1978) and has previously been used to block cell division in Xenopus embryos (Harris and Hartenstein, 1991). HUA also decreased the gradient in nuclear densities (mainly by reducing nuclear densities rostral to the OT), decreased brain stiffness, and generated defects in OT outgrowth (Figure 4—figure supplement 2).

In order to test if the mitotic blocker BI2536 perturbs neuronal mechanosensing, we cultured eye primordia on laminin-coated polyacrylamide substrates of different stiffnesses (Koser et al., 2016), exposed the outgrowing RGC axons to the drug, and quantified axon growth as a function of substrate stiffness. While RGC axon growth on stiff substrates with a shear modulus G’~5,500 Pa was not altered by the presence of BI2536 if compared to control conditions (Figure 4—figure supplement 3e), axons grew longer on soft substrates of G’~200 Pa when they were exposed to 50 nM BI2536 but not when they were exposed to 500 nM (Figure 4—figure supplement 3f), suggesting that the drug might partially impact axon growth on very soft substrates (although no significant differences were observed when axons were grown on slightly stiffer substrates of G’~300 Pa, Figure 4—figure supplement 3d). However, similar to control conditions, axons exposed to different concentrations of BI2536 grew significantly longer on stiff substrates compared to softer substrates. Hence, RGC axons responded to substrate stiffness despite the presence of the mitotic blocker (Figure 4—figure supplement 3g).

Our data show that, during early embryonic development, local tissue stiffness may change significantly within only tens of minutes (Figure 2), leading to heterogeneous stiffness distributions which, in the developing Xenopus brain, impact RGC axon growth. These stiffness heterogeneities were largely governed by differential cell proliferation (Figures 3 and 4), which is in agreement with previously published correlations between cell body densities and tissue stiffness (Barriga et al., 2018; Koser et al., 2016; Koser et al., 2015; Weber et al., 2017). Mitotic blockers almost completely abolished the rise of the stiffness gradient (Figure 4), which was accompanied by a decrease in turning angle of the OT, emphasizing the importance of the control of local cell proliferation for mechanosensitive cellular processes in vivo.

As changes in substrate stiffness have been shown to promote cell proliferation in vitro (Georges and Janmey, 2005), an increase in cell density might lead to a mechanical positive feedback loop, facilitating further cell proliferation. Perturbing cell division, on the other hand, might alter not only local tissue stiffness but also topological cues in the tissue, which may also provide important signals regulating axon growth. Having fewer cell bodies rostral to the OT might decrease the amount of steric hindrance and provide more space for axons to grow into, contributing to the reduction in OT turning angle (Figure 4e).

Our analysis of the correlation between local cell body density gradients and stiffness gradients suggests that other structures may contribute to the stiffness gradient as well (Figure 3c), although perhaps to a smaller degree. Potential candidates include radial glial cells (MacDonald et al., 2015) as well as components of the extracellular matrix (Moeendarbary et al., 2017).

Tiv-AFM allows simultaneous time-lapse measurements of tissue mechanics in vivo and optical monitoring of fluorescently labelled structures at the surface of otherwise optically opaque samples, at length and time scales that are relevant for developmental processes. It enabled us for the first time to trace the in vivo mechanical properties of the embryonic Xenopus brain as the embryo developed, and to relate changes in tissue mechanics to a key event in axon pathfinding. As in all other AFM applications, in tiv-AFM forces are applied to sample surfaces, restricting it to biological processes that occur within tens of micrometers away from the surface, such as OT elongation in developing Xenopus embryos. Mapping of the whole Xenopus brain at cellular resolution takes about half an hour, which defines the maximum temporal resolution that can currently be achieved. Future developments of alternative approaches may enable measurements within tissues at even higher rates. However, tiv-AFM in its current form revealed that significant changes in tissue stiffness occur in vivo within tens of minutes, and that these changes have significant implications for a biological process, the outgrowth of RGC axons along the developing embryonic brain.

More broadly, tiv-AFM can also be easily adapted for in vivo applications in other small organisms, or alternatively in tissues ex vivo. It can be used to study cellular responses to a range of mechanical stimuli via the AFM in vivo, such as sustained compression (Barriga et al., 2018; Koser et al., 2016), or to track the temporal mechanical response of tissues or organs to different pharmacological treatments (such as the mitotic inhibitor used here). Additionally, the setup is very versatile and can be further expanded, for example, by combining it with calcium imaging to investigate how cellular activity is regulated by changes in tissue stiffness during development and pathology. Tiv-AFM will greatly expand the range of bio-AFM experiments possible, allowing for more scope both for a detailed characterisation of in vivo tissue mechanics during development and disease progression, and for testing how mechanics shapes cell behaviour and function.

All data generated or analysed during this study are included in the manuscript and supporting files. Codes used for Sholl analysis post-processing, OT elongation, motorized stage control, processing of AFM raw data, mapping of stiffness maps onto brains and OT and local tissue stiffness gradient calculations can be found at https://github.com/FranzeLab/AFM-data-analysis-and-processing, https://github.com/FranzeLab/Image-processing-and-analysis and https://github.com/FranzeLab/Instrument-Control (copies archived at https://github.com/elifesciences-publications/Image-processing-and-analysis, https://github.com/elifesciences-publications/Instrument-Control and https://github.com/elifesciences-publications/AFM-data-analysis-and-processing).

1. 1. Barriga EH
   2. Franze K
   3. Charras G
   4. Mayor R

   (2018) Tissue stiffening coordinates morphogenesis by triggering collective cell migration in vivo

   Nature 554:523–527.

   https://doi.org/10.1038/nature25742

   • PubMed
   • Google Scholar
2. 1. Butler LC
   2. Blanchard GB
   3. Kabla AJ
   4. Lawrence NJ
   5. Welchman DP
   6. Mahadevan L
   7. Adams RJ
   8. Sanson B

   (2009) Cell shape changes indicate a role for extrinsic tensile forces in Drosophila germ-band extension

   Nature Cell Biology 11:859–864.

   https://doi.org/10.1038/ncb1894

   • PubMed
   • Google Scholar
3. 1. Chien CB
   2. Rosenthal DE
   3. Harris WA
   4. Holt CE

   (1993) Navigational errors made by growth cones without filopodia in the embryonic Xenopus brain

   Neuron 11:237–251.

   https://doi.org/10.1016/0896-6273(93)90181-P

   • PubMed
   • Google Scholar
4. 1. Christ AF
   2. Franze K
   3. Gautier H
   4. Moshayedi P
   5. Fawcett J
   6. Franklin RJ
   7. Karadottir RT
   8. Guck J

   (2010) Mechanical difference between white and gray matter in the rat cerebellum measured by scanning force microscopy

   Journal of Biomechanics 43:2986–2992.

   https://doi.org/10.1016/j.jbiomech.2010.07.002

   • PubMed
   • Google Scholar
5. 1. Cornel E
   2. Holt C

   (1992) Precocious pathfinding: retinal axons can navigate in an axonless brain

   Neuron 9:1001–1011.

   https://doi.org/10.1016/0896-6273(92)90061-H

   • PubMed
   • Google Scholar
6. 1. Crick SL
   2. Yin FC

   (2007) Assessing micromechanical properties of cells with atomic force microscopy: importance of the contact point

   Biomechanics and Modeling in Mechanobiology 6:199–210.

   https://doi.org/10.1007/s10237-006-0046-x

   • PubMed
   • Google Scholar
7. 1. Das T
   2. Payer B
   3. Cayouette M
   4. Harris WA

   (2003) In vivo time-lapse imaging of cell divisions during neurogenesis in the developing zebrafish retina

   Neuron 37:597–609.

   https://doi.org/10.1016/S0896-6273(03)00066-7

   • PubMed
   • Google Scholar
8. 1. Ferreira TA
   2. Blackman AV
   3. Oyrer J
   4. Jayabal S
   5. Chung AJ
   6. Watt AJ
   7. Sjöström PJ
   8. van Meyel DJ

   (2014) Neuronal morphometry directly from bitmap images

   Nature Methods 11:982–984.

   https://doi.org/10.1038/nmeth.3125

   • PubMed
   • Google Scholar
9. Software

   1. Franze K

   (2018a) AFM data analysis and processing, version 87972c5

   Github.

   https://github.com/FranzeLab/AFM-data-analysis-and-processing
10. Software

    1. Franze K

    (2018b) Image processing and analysis, version 55e5275

    Github.

    https://github.com/FranzeLab/Image-processing-and-analysis
11. Software

    1. Franze K

    (2018c) Instrument Control, version 240c841

    Github.

    https://github.com/FranzeLab/Instrument-Control
12. 1. Garcia D

    (2010) Robust smoothing of gridded data in one and higher dimensions with missing values

    Computational Statistics & Data Analysis 54:1167–1178.

    https://doi.org/10.1016/j.csda.2009.09.020

    • PubMed
    • Google Scholar
13. 1. Garcia D

    (2011) A fast all-in-one method for automated post-processing of PIV data

    Experiments in Fluids 50:1247–1259.

    https://doi.org/10.1007/s00348-010-0985-y

    • PubMed
    • Google Scholar
14. 1. Gautier HO
    2. Thompson AJ
    3. Achouri S
    4. Koser DE
    5. Holtzmann K
    6. Moeendarbary E
    7. Franze K

    (2015) Atomic force microscopy-based force measurements on animal cells and tissues

    Methods in Cell Biology 125:.

    https://doi.org/10.1016/bs.mcb.2014.10.005

    • PubMed
    • Google Scholar
15. 1. Georges PC
    2. Janmey PA

    (2005) Cell type-specific response to growth on soft materials

    Journal of Applied Physiology 98:1547–1553.

    https://doi.org/10.1152/japplphysiol.01121.2004

    • PubMed
    • Google Scholar
16. Book

    1. Gilman AG
    2. Goodman LS
    3. Gilman A

    (1980)

    The Pharmacological Basis of Therapeutics

    Macmillan.

    • Google Scholar
17. Book

    1. Gurdon JB
    2. Lemaire P
    3. Mohun TJ

    (1997)

    Methods in Cell Biology, 52

    Emerson CP, Sweeney HL, editors. Elsevier.

    • Google Scholar
18. 1. Harris WA
    2. Holt CE
    3. Bonhoeffer F

    (1987)

    Retinal axons with and without their somata, growing to and arborizing in the tectum of Xenopus embryos: a time-lapse video study of single fibres in vivo

    Development 101:123–133.

    • PubMed
    • Google Scholar
19. 1. Harris WA
    2. Hartenstein V

    (1991) Neuronal determination without cell division in Xenopus embryos

    Neuron 6:499–515.

    https://doi.org/10.1016/0896-6273(91)90053-3

    • PubMed
    • Google Scholar
20. 1. Hertz H

    (1882)

    Uber die Beruhrung fester elastischer Korper

    Journal Fur Die Reine Und Angewandte Mathematik 92:156–171.

    • Google Scholar
21. 1. Holt CE

    (1989) A single-cell analysis of early retinal ganglion cell differentiation in Xenopus: from soma to axon tip

    The Journal of Neuroscience 9:3123–3145.

    https://doi.org/10.1523/JNEUROSCI.09-09-03123.1989

    • PubMed
    • Google Scholar
22. 1. Hugle M
    2. Belz K
    3. Fulda S

    (2015) Identification of synthetic lethality of PLK1 inhibition and microtubule-destabilizing drugs

    Cell Death & Differentiation 22:1946–1956.

    https://doi.org/10.1038/cdd.2015.59

    • PubMed
    • Google Scholar
23. 1. Hutter JL
    2. Bechhoefer J

    (1993) Calibration of atomic‐force microscope tips

    Review of Scientific Instruments 64:1868–1873.

    https://doi.org/10.1063/1.1143970

    • Google Scholar
24. 1. Ikegami S
    2. Taguchi T
    3. Ohashi M
    4. Oguro M
    5. Nagano H
    6. Mano Y

    (1978) Aphidicolin prevents mitotic cell division by interfering with the activity of DNA polymerase-alpha

    Nature 275:458–460.

    https://doi.org/10.1038/275458a0

    • PubMed
    • Google Scholar
25. 1. Irie A
    2. Yates EA
    3. Turnbull JE
    4. Holt CE

    (2002)

    Specific heparan sulfate structures involved in retinal axon targeting

    Development 129:61–70.

    • PubMed
    • Google Scholar
26. 1. Iwashita M
    2. Kataoka N
    3. Toida K
    4. Kosodo Y

    (2014) Systematic profiling of spatiotemporal tissue and cellular stiffness in the developing brain

    Development 141:3793–3798.

    https://doi.org/10.1242/dev.109637

    • PubMed
    • Google Scholar
27. 1. Kanekar S
    2. Perron M
    3. Dorsky R
    4. Harris WA
    5. Jan LY
    6. Jan YN
    7. Vetter ML

    (1997) Xath5 participates in a network of bHLH genes in the developing Xenopus retina

    Neuron 19:981–994.

    https://doi.org/10.1016/S0896-6273(00)80391-8

    • PubMed
    • Google Scholar
28. 1. Koser DE
    2. Moeendarbary E
    3. Hanne J
    4. Kuerten S
    5. Franze K

    (2015) CNS cell distribution and axon orientation determine local spinal cord mechanical properties

    Biophysical Journal 108:2137–2147.

    https://doi.org/10.1016/j.bpj.2015.03.039

    • PubMed
    • Google Scholar
29. 1. Koser DE
    2. Thompson AJ
    3. Foster SK
    4. Dwivedy A
    5. Pillai EK
    6. Sheridan GK
    7. Svoboda H
    8. Viana M
    9. Costa LD
    10. Guck J
    11. Holt CE
    12. Franze K

    (2016) Mechanosensing is critical for axon growth in the developing brain

    Nature Neuroscience 19:1592–1598.

    https://doi.org/10.1038/nn.4394

    • PubMed
    • Google Scholar
30. 1. Lénárt P
    2. Petronczki M
    3. Steegmaier M
    4. Di Fiore B
    5. Lipp JJ
    6. Hoffmann M
    7. Rettig WJ
    8. Kraut N
    9. Peters JM

    (2007) The small-molecule inhibitor BI 2536 reveals novel insights into mitotic roles of polo-like kinase 1

    Current Biology 17:304–315.

    https://doi.org/10.1016/j.cub.2006.12.046

    • PubMed
    • Google Scholar
31. 1. MacDonald RB
    2. Randlett O
    3. Oswald J
    4. Yoshimatsu T
    5. Franze K
    6. Harris WA

    (2015) Müller glia provide essential tensile strength to the developing retina

    The Journal of Cell Biology 210:1075–1083.

    https://doi.org/10.1083/jcb.201503115

    • PubMed
    • Google Scholar
32. 1. Majkut S
    2. Idema T
    3. Swift J
    4. Krieger C
    5. Liu A
    6. Discher DE

    (2013) Heart-specific stiffening in early embryos parallels matrix and myosin expression to optimize beating

    Current Biology 23:2434–2439.

    https://doi.org/10.1016/j.cub.2013.10.057

    • PubMed
    • Google Scholar
33. 1. McFarlane S
    2. Lom B

    (2012) The Xenopus retinal ganglion cell as a model neuron to study the establishment of neuronal connectivity

    Developmental Neurobiology 72:520–536.

    https://doi.org/10.1002/dneu.20928

    • PubMed
    • Google Scholar
34. 1. Moeendarbary E
    2. Weber IP
    3. Sheridan GK
    4. Koser DE
    5. Soleman S
    6. Haenzi B
    7. Bradbury EJ
    8. Fawcett J
    9. Franze K

    (2017) The soft mechanical signature of glial scars in the central nervous system

    Nature Communications 8:14787.

    https://doi.org/10.1038/ncomms14787

    • PubMed
    • Google Scholar
35. 1. Munjal A
    2. Philippe JM
    3. Munro E
    4. Lecuit T

    (2015) A self-organized biomechanical network drives shape changes during tissue morphogenesis

    Nature 524:351–355.

    https://doi.org/10.1038/nature14603

    • PubMed
    • Google Scholar
36. 1. Murphy MC
    2. Huston J
    3. Jack CR
    4. Glaser KJ
    5. Manduca A
    6. Felmlee JP
    7. Ehman RL

    (2011) Decreased brain stiffness in Alzheimer's disease determined by magnetic resonance elastography

    Journal of Magnetic Resonance Imaging 34:494–498.

    https://doi.org/10.1002/jmri.22707

    • PubMed
    • Google Scholar
37. 1. Nieuwkoop PD
    2. Faber J

    (1958)

    Copeia

    65, Normal Table of Xenopus Laevis (Daudin), Copeia, 1958, UK, Taylor and Francis.

    • Google Scholar
38. 1. Poggi L
    2. Vitorino M
    3. Masai I
    4. Harris WA

    (2005) Influences on neural lineage and mode of division in the zebrafish retina in vivo

    The Journal of Cell Biology 171:991–999.

    https://doi.org/10.1083/jcb.200509098

    • PubMed
    • Google Scholar
39. 1. Sack I
    2. Beierbach B
    3. Hamhaber U
    4. Klatt D
    5. Braun J

    (2008) Non-invasive measurement of brain viscoelasticity using magnetic resonance elastography

    NMR in Biomedicine 21:265–271.

    https://doi.org/10.1002/nbm.1189

    • PubMed
    • Google Scholar
40. 1. Scarcelli G
    2. Yun SH

    (2012) In vivo Brillouin optical microscopy of the human eye

    Optics Express 20:9197–9202.

    https://doi.org/10.1364/OE.20.009197

    • PubMed
    • Google Scholar
41. 1. Serwane F
    2. Mongera A
    3. Rowghanian P
    4. Kealhofer DA
    5. Lucio AA
    6. Hockenbery ZM
    7. Campàs O

    (2017) In vivo quantification of spatially varying mechanical properties in developing tissues

    Nature Methods 14:181–186.

    https://doi.org/10.1038/nmeth.4101

    • PubMed
    • Google Scholar
42. 1. Weber IP
    2. Ramos AP
    3. Strzyz PJ
    4. Leung LC
    5. Young S
    6. Norden C

    (2014) Mitotic position and morphology of committed precursor cells in the zebrafish retina adapt to architectural changes upon tissue maturation

    Cell Reports 7:386–397.

    https://doi.org/10.1016/j.celrep.2014.03.014

    • PubMed
    • Google Scholar
43. 1. Weber IP
    2. Yun SH
    3. Scarcelli G
    4. Franze K

    (2017) The role of cell body density in ruminant retina mechanics assessed by atomic force and Brillouin microscopy

    Physical Biology 14:065006.

    https://doi.org/10.1088/1478-3975/aa6d18

    • PubMed
    • Google Scholar
44. 1. Wizenmann A
    2. Brunet I
    3. Lam J
    4. Sonnier L
    5. Beurdeley M
    6. Zarbalis K
    7. Weisenhorn-Vogt D
    8. Weinl C
    9. Dwivedy A
    10. Joliot A
    11. Wurst W
    12. Holt C
    13. Prochiantz A

    (2009) Extracellular Engrailed participates in the topographic guidance of retinal axons in vivo

    Neuron 64:355–366.

    https://doi.org/10.1016/j.neuron.2009.09.018

    • PubMed
    • Google Scholar
45. 1. Zolessi FR
    2. Poggi L
    3. Wilkinson CJ
    4. Chien CB
    5. Harris WA

    (2006) Polarization and orientation of retinal ganglion cells in vivo

    Neural Development 1:2.

    https://doi.org/10.1186/1749-8104-1-2

    • PubMed
    • Google Scholar

Author details

1. Amelia J Thompson

   Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Eva K Pillai

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3912-3652

2. Eva K Pillai

   Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Amelia J Thompson

   Competing interests

   No competing interests declared

3. Ivan B Dimov

   Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Software, Formal analysis, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

4. Sarah K Foster

   Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

5. Christine E Holt

   Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Resources, Supervision, Writing—review and editing

   Competing interests

   No competing interests declared

6. Kristian Franze

   Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, United Kingdom

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   kf284@cam.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8425-7297

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