1. Cell Biology
  2. Immunology and Inflammation
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Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells

  1. Moosung Lee
  2. Young-Ho Lee
  3. Jinyeop Song
  4. Geon Kim
  5. YoungJu Jo
  6. HyunSeok Min
  7. Chan Hyuk Kim  Is a corresponding author
  8. YongKeun Park  Is a corresponding author
  1. Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Republic of Korea
  2. KAIST Institute for Health Science and Technology, Republic of Korea
  3. Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Republic of Korea
  4. Curocell Inc, Republic of Korea
  5. Tomocube Inc, Republic of Korea
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Cite this article as: eLife 2020;9:e49023 doi: 10.7554/eLife.49023

Abstract

The immunological synapse (IS) is a cell-cell junction between a T cell and a professional antigen-presenting cell. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniques, most of which rely on fluorescence microscopy, have been used to study the dynamics of IS. However, the inherent limitations associated with the fluorescence-based imaging, such as photo-bleaching and photo-toxicity, prevent the long-term assessment of dynamic changes of IS with high frequency. Here, we propose and experimentally validate a label-free, volumetric, and automated assessment method for IS dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed method enables an automatic and quantitative spatiotemporal analysis of IS kinetics of morphological and biochemical parameters associated with IS dynamics, providing a new option for immunological research.

Introduction

Understanding the immune response at the cellular scale requires knowledge regarding interactions between immune cells and their microenvironment. For T lymphocyte, which is one of the major immune cell types involved in adaptive immune responses, it communicates with antigen-presenting target cells via the formation of a nanoscale cell-cell junction, which is called the immunological synapse (IS). Specifically, the engagement of T cell receptor (TCR) by peptide-loaded MHC complex (pMHC) presented on the target cells leads to the formation of IS that coordinates the downstream signaling events required for the initial activation of T cells (Bromley et al., 2001; Basu and Huse, 2017). Previous studies have shown that the TCR-mediated IS comprises segregated concentric rings of supramolecular activating clusters, which are crucial for stabilization of the IS as well as the secretion of lytic granules (Basu and Huse, 2017; Monks et al., 1998).

Alternatively, recent studies have highlighted the IS structures formed by chimeric antigen receptor (CAR), a synthetic fusion protein comprised of an extracellular targeting and hinge domain, a transmembrane domain, and intracellular signaling domains (Lee and Kim, 2019; Mukherjee et al., 2017). The extracellular targeting domain of CAR is typically adopted single-chain variable fragment (scFv) of monoclonal antibodies, allowing the CAR-T cells to recognize various types of surface antigens independent of its MHC restriction. In particular, CD19-specific CAR-T cells have demonstrated remarkable anti-cancer efficacy in patients with B-cell malignancies (van der Stegen et al., 2015). Although CAR/antigen and TCR/pMHC complexes have different IS structures (Davenport et al., 2018), the distinct dynamics and mechanochemical properties of the IS driven by these complexes remain understudied.

A variety of imaging techniques have been used to reveal the hierarchical details of the IS structures and their relevant functions. For instance, electron microscopy and single-molecule localization microscopy have resolved the spatial distributions of subcellular IS compartments beyond optical diffraction limits (Choudhuri et al., 2014; Hu et al., 2016). However, assessing dynamical changes in IS formation requires rapid and continuous imaging of immune cells. Fluorescence microscopy is useful in this regard (Balagopalan et al., 2011). Such fluorescence-based techniques have the advantage of chemical specificity. However, they are limited due to photo-bleaching and photo-toxicity, which necessitates the use of complementary label-free, rapid three-dimensional (3D) microscopy methods to assess long-term dynamic changes in IS morphologies (Skylaki et al., 2016). Recently developed lattice light-sheet microscopy enables long-term high-speed volumetric imaging (Chen et al., 2014), and thus can be potentially used for the study of the dynamics of CAR-T and the structures of IS. However, fluorescence labeling process requires complicated and time-consuming sample preparation, especially when multiplex fluorescence imaging is needed.

The development of label-free IS imaging has been limited to phase-contrast and differential interference contrast microscopy. The aim is to develop quantitative phase imaging (QPI) as a quantitative label-free imaging method to studying IS (Park et al., 2018a). Optical diffraction tomography (ODT) is a promising 3D QPI technique for imaging the 3D refractive index (RI) distribution of cells at a sub-micrometer spatial resolution (Kim et al., 2016a). Unlike nonlinear scanning microscopy that requires a long acquisition time due to weak signal intensities (Zumbusch et al., 1999; Freudiger et al., 2008; Squier et al., 1998; Zipfel et al., 2003), ODT enables fast 3D imaging via holographic recording. Also, because the reconstructed RI profile correlates with total cellular protein densities, ODT enables quantitative, photobleaching-free analyses of cell dynamics.

ODT has been actively used to study single-cell morphology (Yoon et al., 2017; Kim et al., 2018; Park et al., 2008; Yang et al., 2017). However, it has not yet been used to study cell–cell interactions that include immune responses. One of the primary reasons is the lack of an accurate 3D segmentation framework to distinguish interacting cell-to-cell interfaces, which is also a problem with other microscopy methods (Uchida, 2013). Manual marking is the most primitive segmentation strategy. It is effective but is too laborious and difficult for time-resolved volumetric segmentation. To overcome this barrier, automatic segmentation has been developed based on basic algorithms that include intensity thresholding, filtering, morphological operations, region accumulation, and deformable models (Dimopoulos et al., 2014). However, these methods often result in poor segmentation, particularly for adjoining cell segmentations, which occur in immune responses. To accurately and precisely segment immunologically interacting cells in an automated manner, a novel computational framework is needed.

Here, we present DeepIS, a computational framework for the systematic, label-free analysis of 3D IS dynamics of immune cells in ODT. Our framework is based on deep convolutional neural network (DCNN) that distinguishes adjoining immune cells, target cells, and IS surfaces from the obtained RI tomogram. The proposed framework enables the general, high-throughput, and automated segmentation of more than 1000 immune-target cell pairs. To validate the method, we applied this method to study the dynamics of CAR/antigen-mediated IS formed between CD19-specific CAR-engineered T cells (CART19) and CD19-positive K562 cancer cells (K562-CD19). The combined use of high-speed imaging capability of ODT enabled 3D high-speed CAR IS tracking in which a tomogram was measured every 3 to 8 s for a prolonged period of time (300 s to 10 min depending on the cell type). Exploiting the linear proportion between RI and protein density (Barer et al., 1953), we also demonstrate quantitative analyses of CAR IS kinetics using the morphological and biochemical properties. The results suggest that DeepIS offers a new analytical approach to immunological research.

Results

3D time-lapse RI measurement of the CART19 and K562-CD19 cell conjugates using optical diffraction tomography

In order to perform ODT experiments in our study, we employed an experimental setup which is based on off-axis holography equipped with a high-speed illumination scanner using a digital micro-mirror device (DMD; DLP6500FLQ DLP 0.65 1080 p Type A DMD, Texas Instrument) (Figure 1). The setup enables the high-speed acquisition of a single tomogram within 500 milliseconds (Figure 1a; Shin et al., 2015; Lee et al., 2017). A 1 × 2 single-mode FC/APC fiber coupler was utilized to split a coherent, monochromatic laser (λ = 532 nm) into a sample and reference arms. The DMD was then placed onto the sample plane of the sample arm to control the illumination angle of the first-order diffracted beam striking the sample. To scan the illuminations at large tilt angles, a 4-f array consisting of a tube lens (Lens 1, f = 250 mm) and a condenser objective (UPLASAPO 60XW, Olympus Inc, Japan) magnified the illumination angle. The light scattered by live cells in a live-cell chamber (TomoChamber, Tomocube Inc, Republic of Korea) was then transmitted through the other 4 f array formed by an objective lens (UPLASAPO 60XW, Olympus Inc, Japan) and a tube lens (Lens 2, f = 175 mm). The sample beam was combined with the reference beam by a beam splitter and filtered by a linear polarizer. The resultant off-axis hologram was then recorded by a CMOS camera (FL3-U3-13Y3M-C, FLIR Systems, Inc, USA) synchronized with the DMD to record 49 holograms of the sample illuminated at different angles. Using a phase-retrieval algorithm, the amplitude and phase images of the 1:1 conjugate between a CART19 and K562-CD19 cell were retrieved from the measured holograms (Figure 1b). Based on the Fourier diffraction theorem with Rytov approximation (Wolf, 1969; Devaney, 1981), the 3D RI tomogram of the sample was reconstructed from the retrieved amplitude and phase images (Figure 1c). To fill the uncollected side scattering signals due to the limited numerical apertures of objective lenses, a regularization algorithm based on the non-negative constraint was used (Lim et al., 2015). The maximum theoretical resolutions of the ODT system were respectively 125 nm laterally and 471 nm axially, according to the Lauer criterion defined as the Nyquist sampling period (Lauer, 2002; Park et al., 2018b). Note that the empirical spatial resolution of ODT has been known to be between the Nyquist sampling period and the Abbe criterion (Simon et al., 2017), so the Nyquist sampling period was used to set the lowest bound of the resolution. Finally, we approximated the protein densities from the reconstructed RI values using a RI increment per protein concentration, α = dRI/dc = 0.185 mL/g25. We assumed this RI increment to be constant over cells for two reasons: (1) as in Barer et al., 1953, most protein molecules have narrow ranges of RI incremental values from 0.179 to 0.195 mL/g, (2) lymphocytes contain lipid-rich environment localized mostly on a 4-nm-thick membrane site, whose size is beyond optical resolution and thus the relatively voluminous adjacent cytoplasm would contribute the reconstructed RI tomogram rather than sub-resolved membrane layers. The average illumination intensity was 11.46 mW/cm2, at which the samples did not suffer from phototoxicity or signal losses during long-term assessment (Figure 1—video 1).

Figure 1 with 1 supplement see all
Data acquisition in optical diffraction tomography (ODT).

(a) The experimental setup for ODT is based on a digital micro-mirror device (DMD) for high-speed illumination scanning. (b) Forty-nine holograms of 1:1 conjugate between a CART19 and a K562-CD19 cell were recorded at various illumination angles, and their amplitude and phase delay distributions were retrieved. (c) A reconstructed refractive index (RI) map.

DeepIS establishment for automated assessment of CART19 IS dynamics

The IS tracking analysis is preceded by segmentation, which involves dividing volumetric sections for background, cell domains, and IS in the ODT RI map. However, iteration is required for parameter tuning of the manual segmentation method to obtain a single well-segmented label, which is prohibitive to obtain a dynamic dataset. Therefore, we established DeepIS framework based on the DCNN supervised learning method to enable general, high-throughput, and automated segmentation for 3D RI tomograms (Figure 2). The framework was developed in the following order: (i) Dataset preparation, (ii) Training stage, and (iii) Inference stage. These are detailed next.

Figure 2 with 4 supplements see all
Data flowchart in DeepIS framework.

(a) Dataset preparation; raw RI tomograms were annotated with manual parameter selections and curated by three experts. (b) Training stage; the prepared dataset was employed to iteratively train the DCNN model that regressed the distance map of CART19 and K562-CD19 cells with the opposite signs. (c) Inference stage; a raw RI tomogram was converted into an output distance map by the trained DCNN model. After post-processing, 3D masks of CART19, K562-CD19, and IS were reconstructed.

Dataset preparation

The preliminary step of supervised learning of DCNN is to prepare an annotated dataset (Figure 2a). To annotate the 3D masks of the CART19 and K562-CD19 cells, we applied a combination of image processing and the watershed algorithm to a raw RI tomogram according to the following steps (Figure 2—figure supplement 1, also see the Codes). First, we annotated the cell masks from a raw RI tomogram using manual selections of four hyper-parameters: (i) initial seed locations of each cell to obtain a 3D distance-transform map, (ii) RI threshold for defining cell boundaries, (iii) voxel dilation sizes for merging over-segmented grains into one discrete region, and (iv) standard deviation of the Gaussian smoothing mask. The processed data were then multiplied to the 3D distance-transform map of the cell regions and segmented by the watershed algorithm. Finally, after iterative adjustment of the parameters from over 2000 data points, three experts in cellular biology quantitatively validated consistent annotation performances via simultaneous truth and performance level estimation (Figure 2—figure supplement 2), and heuristically curated the 236 pairs of well-annotated 3D tomograms with consensus (Warfield et al., 2004). The curated data uniformly reflected the various stages of the IS dynamics, which ensured providing information about the immunological response in both the early and late stages.

Training stage

The segmentation tasks were challenged by the lack of distinct boundaries between CART19/K562-CD19 conjugates in RI distributions, diverse morphology of cells, and the demand for precise segmentation at high resolution. As a consequence, although typical segmentation tasks of DCNN were assigned to infer voxel-wise label classification, various types of failure occurred, such as fragmented labels and unnatural IS.

To overcome these limitations and improve the segmentation accuracy and robustness, we designed DCNN to predict the distance map (ypred), which was adapted from a previous study (Wang, 2018; Figure 2b, also see Materials and methods and Figure 2—figure supplement 3). We first conducted pre-processing of the 3D annotated masks of CART19 and K562-CD19 cells using the Euclidean distance transform to obtain a true distance map (ytrue). A primary difference from the prior study (Wang, 2018) is that the CART19 and K562-CD19 cells were distinguished by the signs of the distance maps (i.e. positive/negative for CART19/K562-CD19 cells). Presently, we set the background to zero.

With the pre-processed data, the DCNN was optimized using the Adam optimizer (Initial learning rate = 0.001, decay rate for the first moment estimate β1 = 0.5, decay rate for the second moment estimate β2 = 0.99, learning decay rate = 0.5 per 50 epochs) during the training stage to predict the signed 3D distance map of CART19 and K562-CD19 cells. The boundary-weighted L1 function was used as a loss function. To prevent overfitting that might arise from a relatively small number of the training data compared with a large number of parameters, early stopping and data augmentation were used. For early stopping, the obtained annotated dataset was into two disjoint subsets. In one subset, 198 tomogram data pairs were used for the optimization of model parameters. In the other subset, the remaining 36 tomogram data pairs were used for internal validation. Training of the model parameters was stopped if the performance of the model on the validation set had not improved for five consecutive epochs. For data augmentation, a larger annotated dataset was simulated using random rotations, horizontal reflection, cropping, and elastic transformation to make the resulting model more robust to irrelevant sources of variability. The network was trained on four graphics processing units (GPUs; GEFORCE GTX 1080 Ti) for 400 epochs, which took approximately 6 hr. Selection of a model for inference among trained models was based on performance on the internal validation set.

Inference stage

In the inference stage, the trained network was used to regress the distance maps of the CART19 and K562-CD19 cells from unlabeled RI tomograms (Figure 2c). In the post-processing stage, the output distance map was post-processed to yield cell domains masks of each CART19 and K562-CD19 cell through simple thresholding using a value of 54.5 nm, which is approximately half of a voxel pitch. The IS of CART19/K562-CD19 conjugate was defined by dilating the CART19 and K562-CD19 masks by a comparable length to the axial resolution of ODT (two voxels, 437 nm) and finding an overlapping region. The cell surface and the cell interior were obtained using binary image erosion by one voxel (218 nm) and defined as the boundary of the 3D cell mask and the remaining interior mask respectively (Figure 2—figure supplement 4).

Evaluation of DeepIS segmentation performance

Because the label-free segmentation approach aims to distinguish the boundaries between two attached cells at a sub-micrometer spatial resolution, it was essential to validate whether DeepIS provides sufficient segmentation accuracy for our purpose. This was first addressed by comparing the segmentation performances between DeepIS and manual segmentation (Figure 3, also see Figure 3—video 1 and Datasets). In the training dataset, this comparison showed that the model could define IS boundaries. Notably, DeepIS generally displayed better segmentation performance than the manual segmentation in the untrained dataset, without notable segmentation problems such as fragmentations and discontinuous boundaries. This observation indicated that DeepIS was well trained and could be exploited to predict the IS boundaries.

Figure 3 with 1 supplement see all
Representative segmentation results.

(Top row) Masks annotated by manual parameter selections. (Bottom row) Segmentation results using DeepIS. Blue shade indicates the curated data in the training stage. Red shade indicates the data which were poorly segmented by parameter-based annotation. White arrows illustrate poorly segmented regions. Lower right graph: rendering colormaps determined by the ranges of RI and RI gradient norm.

We then examined whether DeepIS could be applied for high-throughput analysis of IS morphology. Wide-area segmentation of CART19 and K562-CD19 cells was carried out over a lateral field-of-view exceeding 1 mm2 (Figure 4a). When cells were located based on RI contrast, DeepIS allowed rapid, automated, and on-site semantic segmentation (Figure 4b). Interestingly, DeepIS successfully labeled adjoining CART19/K562-CD19 cell conjugates as well as individual CART19 and K562-CD19 cells (Figure 4c), which validated the high-throughput, general segmentation performance of DeepIS.

High-throughput semantic segmentation using DeepIS.

(a) RI tomogram over 0.98 × 1.05 × 0.04 mm3 obtained by stitching method. (b) Segmentation using DeepIS. (c) Representative CART19, K562-CD19, and CART19/K562-CD19 cell conjugates are magnified.

The segmentation performance of DeepIS was further evaluated by quantifying the segmentation accuracy using manually delineated 3D labels obtained from correlative fluorescence microscopy (Figure 5). To prepare 3D manual labels, we mixed T cells and K562 cells expressing CAR-G4S-mCherry and hCD19-G4S-Zsgreen fusion proteins, respectively, and imaged fixed cell conjugates using correlative RI and fluorescence microscopy to delineate manual labels (Figure 5a, Materials and methods). We found that RI and fluorescence images overlapped well with the plasma-membrane fluorescence images, confirming that correlative RI and CAR/CD19 fluorescence images could sufficiently resolve the 3D cell topologies (Figure 5—figure supplement 1). Furthermore, multi-color 3D confocal microscopy showed with higher spatial resolution that CAR and CD19 were spread throughout the plasma membranes of CART19 and K562-CD19 cells respectively and coalesced into the IS, validating that CAR/CD19 fluorescence images were sufficient for defining the IS (Figure 5—figure supplement 2). We then compared the manually drawn labels with the segmentation masks obtained from DeepIS (Figure 5b). When 3D Pearson correlation coefficients for volumetric masks were quantified, we obtained the mean ± standard deviation (SD) values of 87.9 ± 7.7% and 92.0 ± 4.3% for CART19 and K562-CD19 cells, respectively, implying a greater than 80% good overlap between the manual labels and automatically segmented labels using DeepIS. In addition, the mean ± SD value for boundary displacement error was 2.82 ± 1.74 voxels, which corresponded to a sub-micrometer displacement error of 615.5 ± 379.8 nm. Considering that 3D segmentation is more challenging than 2D segmentation, these results indicated significantly small boundary errors (Wang, 2018; Lalaoui and Mohamadi, 2013).

Figure 5 with 2 supplements see all
Quantitative analysis of segmentation performance using correlative fluorescence microscopy and ODT.

(a) Representative 3d rendering images, the xy- and the xz- cross-sections of RI (first column), fluorescence (second column), and their overlapped images (third column). CART19 and K562-CD19 labels were manually delineated based on the correlative images (fourth column) and compared with the labels obtained from DeepIS (fifth column). (b) Quantifications of DeepIS segmentation performance (n = 17). Pearson correlation coefficient was measured for CART19 (87.9 ± 7.7%) and K562-CD19 (92.0 ± 4.3%). Boundary displacement error was measured for IS (2.82 ± 1.74 voxels; 615.5 ± 379.8 nm). Each boxplot indicates the median, upper, and lower quartiles of each population.

For a conclusive validation of the segmentation performance, we lastly compared the IS defined by DeepIS with the IS imaged by high-resolution fluorescence microscopy methods. For this purpose, we integrated the DeepIS framework with multicolor 3D structured illumination microscopy (3D-SIM; see Materials and methods) (Mobahi et al., 2011; Gustafsson et al., 2008; Demmerle et al., 2017; Kim et al., 2017). The integrated setup enabled us to image the protein compositions at CAR IS at sub-200-nm lateral and nearly sub-400-nm axial spatial resolution defined as full width at half-maximum (Figure 6—figure supplement 1).

Using 3D-SIM, we first assessed the 3D location of the synaptic cleft (Figure 6). We used the negative stain method by adding the FITC-labeled dextran solution with two different hydrodynamic diameters (4 and 54 nm) into the chemically fixed conjugates of K562-CD19 cells and T cells expressing CAR-G4S-mCherry (see Materials and methods). The resultant 3D-SIM images showed that the synaptic cleft was visible only when we used the smaller dextran (4 nm), suggesting that, similar with the IS of NK cells, CAR IS excluded dextran molecules above a size threshold (Cartwright et al., 2014; Figure 6a, also see Figure 6—figure supplement 2). We then compared the imaged synaptic clefts, the CAR fluorescence, and the IS drawn by DeepIS for validation (Figure 6b). The 3D overlaps of the three images validated the 3D segmentation accuracy of our proposed method (Figure 6c). We laterally plotted the signals across the IS to quantitatively assess the segmentation accuracy (Figure 6d). The displacements of the IS from the peak intensities of the CAR and the synaptic cleft were within 200 and 600 nm, respectively. The result suggested that the IS drawn by DeepIS reflected the IS boundary closer to CAR-T cells, whose accuracy was comparable to the resolution limit of ODT. Moreover, consistent with the previous study using confocal fluorescence microscopy (Cartwright et al., 2014), the lateral full-width half-maximum of the synaptic cleft was measured to be 894 nm, which may imply the presence of a spatial gap larger than tens of nanometers across the IS (McCann et al., 2003).

Figure 6 with 2 supplements see all
High-resolution validation of 3D IS using a negative stain method.

(a) The XY- and XZ-slice images of fluorescein-labeled dextran. White dashed lines: slice regions. (b) Corresponding XY- and XZ- images of SIM and ODT. (c) Comparison of dextran fluorescence, CAR fluorescence, and the IS defined by DeepIS. (d) Mean lateral line profiles for lines drawn across the IS.

Additionally, we validated whether the proposed method could be used to detect organelles at CAR IS. We specifically investigated the distributions of lytic granules, CAR and CD19 proteins, by imaging T cells expressing CD19-specific CAR-G4S-mCherry stained with lysotracker dyes and K562 cells expressing hCD19-G4S-Zsgreen. To verify successful staining of lytic granules before 3D-SIM, we employed widefield deconvolution fluorescence microscopy and confirmed the rapid dynamics of lytic granules coalescing into the IS (Figure 7—figure supplement 1, also see Figure 7—video 1). We also confirmed the RI detection sensitivity of the lytic granules using a histogram analysis, which showed that, compared to cell bodies, the mean RI value was lower for CAR IS and higher for lytic granules (Figure 7—figure supplement 2). We then chemically fixed stable CART19/K562-CD19 conjugates and imaged them using 3D-SIM and ODT for high-resolution multi-protein composition analysis (see Materials and methods).

As a result, our DeepIS framework demarcated the interface regions in close proximity to the overlapping areas of CAR and CD19 proteins (Figure 7a). Importantly, the demarcated region by DeepIS was consistent with the 3D-SIM images, which provided clearer 3D features of the IS between CART19 and K562-CD19 cells than widefield fluorescence microscopy. Along the IS demarcated by DeepIS, we quantitatively analyzed the en face images of CAR, CD19 and lytic granules imaged by 3D-SIM (Figure 7b). In agreement with the previous report (Davenport et al., 2018), the protein compositions of CAR exhibited asymmetric and granular distributions along the CAR IS. We analyzed the correlations between the total protein concentration distribution and the imaged proteins at CAR IS. The correlative line and surface profiles of the protein signals indicated the highest correlations of CAR with lytic granules, as well as colocalizations of CD19 proteins with the CAR clusters (Figure 7c). Interestingly, the total surficial protein densities approximated by ODT exhibited both correlated and uncorrelated clustered regions with the dense multi-protein clusters. Since ODT quantitatively estimates the total protein concentration, the uncorrelated signals are highly likely to imply the presence of clusters of other dominant proteins such as F-actin, Lck, and supramolecular attack particles (Xiong et al., 2018; Bálint et al., 2020).

Figure 7 with 3 supplements see all
High-resolution analysis of 3D IS compositions using 3D-SIM and DeepIS.

(a) XY- and XZ-slice images of representative CART19/K562-CD19 conjugates. White-dashed lines: slice regions. White bold lines in SIM + ODT: 3D IS areas defined by DeepIS. (b) En face projected images of the defined IS areas in a. (c) Surface (left each) and line (right each) profiles along the dashed lines of normalized fluorescence intensities and surficial protein densities. Arrows indicate representative colocalized signals.

Taken together, these results suggest that our DeepIS method based on ODT can be used to define IS area with high accuracy, and also provides collective information about the distribution of total proteins within the IS which may not be easily measured by using conventional high-resolution fluorescence microscopy.

Quantitative kinetic analysis of CART19 IS formation using DeepIS

The successfully established DeepIS was implemented in the detailed kinetic analysis of the IS formation between CART19 and K562-CD19 cells using morphological and biochemical parameters (Figure 8). Specifically, we analyzed 27 sets of IS dynamic datasets measured over 300 s to 10 min at time intervals of 3 to 8 s to determine the kinetics of synapse area, membrane protein density, and intracellular protein density.

Figure 8 with 4 supplements see all
Quantification of initial IS formation kinetics of CART19 cells.

(a) Representative snapshots of a video of CART19 cells responding to K562 (top row) and K562-CD19 (bottom row) cells. Purple: K562, Blue: CART19. 0 s: the initial contact point of the effector cell and target cells. White arrow: the blebbing point. (b) Temporal changes in the synapse areas of CART19 cells responding to K562 cells (n = 5) and K562-CD19 cells (n = 22). Black line: fitting curve with A(t)=Amaxt/(t + τ1/2), where Amax = 106.16 μm2 and τ1/2 = 39.63 seconds, Pearson correlation coefficient (ρ)=0.93. (c) Maximum en face projection of membrane protein density of IS (top row) and non-IS T surface (bottom row) for CART19/K562-CD19 conjugate. The direction of the en face projection and the projection plane: the green arrow and a dashed line in (a). (d) Temporal changes in the mean membrane protein density of CART19 immunological synapses (circles) and non-IS T surfaces (crosses) responding to K562-CD19 cells. (e) Maximum z-axis projection of intracellular protein density distributions of the CART19/K562-CD19 conjugate. The white contours: the boundaries of the CART19 mask. (f) Temporal changes in the displacements of the center-of-masses of CART19 and K562-CD19 cells from their initial locations. Black lines: Δd(t) = Δdmax t/(t + τ1/2), where Δdmax = 7.48 and 1.74 μm, τ1/2 = 37.84 and 14.56 s, ρ = 0.91 and 0.58 for CART19 and K562-CD19 respectively. (g) The average changes for 10 s in the early (0 s) and late (100 s) stages are marked by colored arrows, and statistically compared by two-tail paired Wilcoxon tests for CART19 and K562-CD19 cells, respectively. CART19: Δd/Δt10s = 818.5 ± 788.5 nm/s, and 26.6 ± 223.6 nm/s in the early and late stages. K562-CD19: Δd/Δt10s = 194.3 ± 246.5 nm/s, and 63.7 ± 125.6 nm/s in the early and late stages. *** indicates p<0.001. Each shaded error bar indicates the mean and standard deviation of line plots. Each boxplot indicates the median, upper, and lower quartiles of each population. The central points and shades in Figures 7b, d and f indicate the mean and standard deviations respectively. The numbers of analyzed cells per each experiment are listed in Table 1.

Table 1
Numbers of analyzed cells per each experiment.
DataExperiment12345678910Total
# of dataStatistics
(Figure 5)
710--------17
dynamics
(Figure 7)
933133112127
4-1BB
(Figure 8)
201925-------64
CD28
(Figure 8)
11110923-----54
Statistics
(Figure 5—figure supplement 1)
822--------30
10 kDa
(Figure 6—figure supplement 2)
37--------10
2000 kDa
(Figure 6—figure supplement 2)
63--------9
4-1BB
(Figure 8—figure supplement 1)
1121------5
CD28
(Figure 8—figure supplement 1)
21--------3

First, we examined the temporal changes of synapse areas depending on the expression of the target antigen, CD19, on K562 cells (Figure 8a). As expected, CART19 cells could not form a stable synapse with K562 cells (CD19-negative) in five independent experimental trials (Figure 8a, also see Figure 8—video 1). By contrast, CART19 cells formed a stable IS with K562-CD19 cells (CD19-positive), and induced apoptotic blebbing on the target cells about 9 min after the initial contact (Figure 8—video 2). For the statistical analysis of the initial IS area changes, DeepIS was applied to both dynamic datasets and successfully segmented CART19 cells, target cells, and IS boundaries. The individual temporal plots of IS showed that each data exhibited smooth IS increase and indicated no undulation problems due to ill segmentations in most cases (Figure 8—figure supplement 1). The temporal graphs of the mean synapse area showed that, whereas the IS for K562 cells was not stably formed for 300 s, the IS for K562-CD19 expanded to the half of the maximum synapse area (Amax = 106.16 μm2) within 40 s and reached a steady-state within only 3 min (Figure 8b).

Next, we also assessed whether the membrane protein amount differs between the IS and non-IS areas of CART19 cells by comparing the 2D maximum projected snapshots of membrane protein densities in each area (Figure 8c). Within the IS surface, a dramatic increase in membrane protein density, as well as the synapse area, was observed. By contrast, the non-IS T surfaces of CART19 cells maintained the constant, lower membrane protein densities. In line with this observation, quantification of temporal changes in the mean membrane protein densities revealed that up to 27 ± 4 fg/μm2 at 300 s have been accumulated in the IS surface, which was higher than average protein density (19 ± 1 fg/μm2) in the non-IS surface (Figure 8d).

We further explored the cell mechanics of CART19 and K562-CD19 cells during their initial IS formation. As in Figure 8e, the time-lapse snapshots of maximum 2D projection of intracellular protein densities visualized the dynamic action of CART19 cells, which incorporated the polarization of the intracellular protein contents. Furthermore, CART19 cells exerted mechanical forces during the dynamic IS formation, which led to the subsequent translational displacement of the K562-CD19 cells (Figure 8—video 3). To quantify the cell translocation dynamics, we traced the temporal changes in the displacement of the center-of-mass (the average displacement weighted by the intracellular protein density) with respect to the initial location for each cell (Figure 8f). As observed, CART19 cells exhibited more dynamic motions (Δdmax = 7.48 μm) than K562-CD19 cells (Δdmax = 1.74 μm) during the IS formation. For both cells, the mechanical translocations were more dramatic in the earlier stage of the IS formation, as confirmed statistically by comparing the average change in the cell translocations in the early- and late-stage (Figure 8g).

Overall, the results presented here generally recapitulate previously observed phenomena in the synapse studies based on conventional microscopy techniques. The rapid increase in the membrane protein density in the synapse area likely reflects the influx of large amounts of IS protein components, including CARs, actins, and other adhesion molecules (Xiong et al., 2018). In addition, perhaps the intracellular protein density changes in CART19 cells, which indicates polarization of the intracellular organelles in CART19 cells until stabilization, can be explained by the centrosome polarization of cytotoxic T lymphocytes (Ritter et al., 2015). Lastly, the force exerted by the CART19 cells on target cells during the IS formation suggests that CART19 cells also integrate mechanical potentiation during target cell killing similar to TCR-based cytotoxic T cells (Basu et al., 2016).

Statistical analysis of IS parameters depending on the co-stimulatory domains of CAR

Kymriah and Yescarta are the only two CD19-targeting CAR-T cell therapies that have been approved by US-FDA to date. Since one of the major differences between the two 2nd generation CAR-T cells lies in the sequence of the costimulatory signaling domain used (4-1BB for Kymriah and CD28 for Yescarta), we next thought to apply our method to compare the IS characteristics of the CAR-T cells with different costimulatory signaling domains. Primary human T cells transduced with the lentiviral vectors encoding CD19-28z or CD19-BBz CAR showed comparable transduction efficiency as well as similar surface expression levels of CARs as determined by flow cytometry (Figure 9—figure supplement 1).

Exploiting our established method, we studied the effect of CD28 or 4-1BB co-stimulatory domains on CAR IS characteristics (see Materials and methods). We first observed the early IS dynamics between CD28 and 4-1BB based CAR-T cells in the presence of K562-CD19 target cells, and found the stable IS formation within five minutes without statistically significant kinetic differences (Figure 9—video 1, also see Figure 9—figure supplement 2). We then compared the statistics of other IS parameters between the CD19-28z and CD19-BBz CAR-T cells in a steady state. Specifically, we incubated each type of CAR-T cells with K562-CD19 cells for 15 min to allow sufficient time for stable IS formation, and fixed them with 4% paraformaldehyde solution. When we analyzed the images for conjugates (Figure 9a), we found no significant difference in IS areas between the two CAR-T cell types (Figure 9b). However, statistical analysis indicated significantly higher IS protein densities and total IS protein amounts for CD19-BBz CAR-T cells, approximately by 10% compared with CD19-28z CAR-T cells (Figure 9c–d). Collectively, our results indicate that the quantitative analysis of IS parameters using DeepIS, in conjunction with other analytical methods such as fluorescence-based microscopy and quantitative mass-spectrometry (Salter et al., 2018), may help to elucidate the mechanistic details underlying the functional differences observed for the CAR-T cells with different signaling domains (Lee and Kim, 2019; van der Stegen et al., 2015; Guedan et al., 2019).

Figure 9 with 3 supplements see all
Statistical analyses of synapse morphologies depending on the co-stimulatory domains.

(a) Representative images for maximum z-axis projection of intracellular protein density distributions (top row) and en face projection of surface protein density distributions of IS (bottom row). White boundaries indicate the annotated membranes of CART19 cells using DeepIS. (b–d) Scatterplots of (b) synapse areas, (c) mean surface protein density, and (d) total IS protein amount. Each boxplot indicates the median, upper, and lower quartiles of each population. The vertical lines indicate population ranges. Perpendicular shades indicate normalized population density distributions. Two-tail unpaired Wilcoxon tests were performed. mean ± SD data are presented. The numbers of analyzed cells per each experiment are listed in Table 1.

Discussion

The collective results demonstrate that DeepIS in combination with ODT and deep neural network enables the label-free, time-lapse 3D IS tracking in an automated manner. The successful segmentation performance of DeepIS could not be possible without careful validations in the initial development stage, including data curation, model training, and qualitative and quantitative tests for general segmentation capabilities. This platform was applied to define the IS parameters related to their morphological and biochemical traits, and to quantify the IS dynamics of CART19 cells. We anticipate that the proposed method can be generalized to study a broad range of IS that are mediated by different types of immune receptors such as TCR and B-cell receptor (BCR) as well as invariant receptors expressed on innate natural killer (NK) cells. In particular, the model will be a powerful way to test whether the IS morphology is affected by chemical treatment and genetic mutations (Tamzalit et al., 2019).

Previously, Xiong W et al. have evaluated the quality of the CAR IS formed by CD28 co-stimulatory domain including 2nd CAR (CD19-28z CAR) or CD28 + 4-1BB costimulatory domain including 3rd CAR (CD19-28BBz CAR) through confocal microscopic analysis using glass-supported planar lipid bilayers (Xiong et al., 2018). Although they showed that the 3rd generation CAR incorporates more IS-related proteins (F-actin, phospho-CD3z) than 2nd generation CAR, there were no direct comparative experiments on the properties of 2nd CAR IS depending on the types of co-stimulatory domain. Our present work fills the missing gaps, suggesting that CAR IS can also vary among the groups of 2nd generation CAR. In particular, we demonstrated that the surface protein density and total protein amount differ between CD28 and 4-1BB based CAR IS, which recalls the importance of quantitative biochemical analysis of CAR IS.

The present work is, to the best of our knowledge, the first application of ODT and deep learning to understand IS dynamics. Refinements are anticipated in an immediate follow-up study. For example, more rapid immune dynamics can be investigated at higher spatiotemporal resolution in a more sophisticated experimental setup, which may reveal dynamic diffusions of subcellular organelles (Kim et al., 2016b) during the immune responses. Also, more efforts in data curation and network design may allow robust time-lapse IS tracking. In order to do so, segmentation based on active contour methods, and new network models based on recurrent neural networks (Mikolov et al., 2010), Bayesian neural network (Snoek, 2015), and pyramid pooling (Bian, 2018) may be incorporated. Although we have demonstrated the label-free imaging and analyzing IS, one of the limitations of the current work is that the error of synaptic cleft determination is on the order of 500 nm. To overcome this, the negative staining with the smallest dextran or correlative electron micrographs may be used to provide a ground truth for improving the precision of synaptic cleft determination.

Our study focused on the platform capable of quantitatively tracking 3D IS dynamics in a completely label-free manner. The RI contrast used in the present study alone does not provide information about individual proteins that are transported during the IS formation of CART19 cells. To provide such biochemically relevant information, we expect that correlative imaging with fluorescence microscopy will circumvent the inherent lack of chemical specificities (Demmerle et al., 2017; Kim et al., 2017). It also remains unclear how much force was associated during the IS formation, and whether this force correlates with the cytotoxic intensity of CART19 cells. To test the hypothesis, the simultaneous use of microscopic force measurement techniques and ODT will be helpful. For instance, a combined technique of ODT and holographic optical tweezers (Kim et al., 2015; Kim and Park, 2017) or traction force microscopy can supplement our DeepIS framework for understanding the mechanical potentiation during the IS kinetics. We expect that these future studies may be exploited for label-free predictions of dynamic molecular transports occurring in the IS formation, and provide complementary information required to elucidate the IS formation mechanisms.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Cell line
(Homo sapiens)
K562OtherRRID:CVCL_0004Obtained from Dr. Travis S. Young at California Institute for Biomedical research. The original stock was purchased from ATCC
Cell line
(Homo sapiens)
K562-CD19OtherObtained from Dr. Travis S. Young at California Institute for Biomedical research. CD19-negative K562 cells were transduced wtih lentiviral vector encoding human CD19.
Cell line
(Homo sapiens)
Lenti-X 293TTakaraCat. #: 632180Lentivirus production
Recombinant
DNA reagent
pLV-ΔLNGFR-P2A-CD19-BBz CARThis paperSignal peptide: aa 1–28 hLNGFR (Uniprot TNR16_Human)
Extracellular domain: aa 29–250 hLNGFR (Uniprot TNR16_Human)
Transmembrane domain: aa 251–272 hLNGFR (Uniprot TNR16_Human)
Cytosolic sequence: aa 273–275 (Uniprot TNR16_Human)
ΔLNGFR was fused with P2A sequence (GGAAGCGGAGCTACTAACTTCAGCCTGCTGAAGCAGGCTGGCGACGTGGAGGAGAACCCTGGACCT) and followed by CAR sequence
Signal peptide: aa 2–21 hCD8 (Uniprot CD8A_Human)
Extracellular domain: anti-CD19 scFv (Clone: FMC 63); VL –G4S linker VH
Hinge/Transmembrane domain aa 138–206 hCD8 (Uniprot CD8A_Human)
Co-stimulatory domain sequence: aa 214–255 (Uniprot TNR9_Human)
Signaling domain: aa 52–164 (Uniprot CD3Z_Human)
Recombinant
DNA reagent
pLV- ΔLNGFR -P2A-CD19-28z CARThis paperSignal peptide: aa 1–28 (Uniprot TNR16_Human)
Extracellular domain: aa 29–250 (Uniprot TNR16_Human)
Transmembrane domain: aa 251–272 (Uniprot TNR16_Human)
Cytosolic sequence: aa 273–275 (Uniprot TNR16_Human)
ΔLNGFR was fused with P2A sequence (GGAAGCGGAGCTACTAACTTCAGCCTGCTGAAGCAGGCTGGCGACGTGGAGGAGAACCCTGGACCT) and followed by CAR sequence
Signal peptide: aa 2–21 hCD8 (Uniprot CD8A_Human)
Extracellular domain: anti-CD19 scFv (Clone: FMC 63); VL-G4S linker-VH
Hinge/Transmembrane domain: aa 138–206 (Uniprot CD8A_Human)
Co-stimulatory domain sequence: aa 180–220 (Uniprot CD28_Human)
Signaling domain: aa 52–164 (Uniprot CD3Z_Human)
Recombinant
DNA reagent
pLV-CD19-BBz CAR-G4S-mcherryThis paperSignal peptide: aa 1–21 (Uniprot CD8A_Human)
Extracellular domain: anti-CD19 scFv (Clone: FMC 63) VL –G4S linker VH
Hinge/Transmembrane domain: aa 138–206 (Uniprot CD8A_Human)
Co-stimulatory domain sequence: aa 214–255 (Uniprot TNR9_Human)
Signaling domain: aa 52–164 (Uniprot CD3Z_Human)
CAR and mcherry was fused with G4S linker mCherry: aa 2–236 (Uniprot X5DSL3_ANAMA)
Recombinant
DNA reagent
pLV-hCD19-G4S-ZsgreenThis paperSignal peptide: aa 1–19 (Uniprot CD19_Human)
Extracellular domain: aa 20–291 (Uniprot CD19_Human)
Cytoplasmic domain: aa314-556 (Uniprot CD19_Human)
CD19 and Zsgreen was fused with G4S linker
Zsgreen: aa 2–231 (Uniprot GFPL1_ZOASP)
Recombinant
DNA reagent
pMDLg/pRREotherRRID:Addgene122513rd Lentivirus packaging vector
Recombinant
DNA reagent
pRSV-RevotherRRID:Addgene122533rd Lentivirus packaging vector
Recombinant
DNA reagent
pMD2.GotherRRID:Addgene12259Lentivirus envelop vector
Recombinant
DNA reagent
pMACS LNGFRMiltenyi BiotecCat. #:130-091-890
Peptide,recombinant proteinrhIL-2BMI KOREA300IU/mL
Peptide,recombinant proteinrhCD19-FcACRO BiosystemsCat. #: CD9-H5259FACS
Peptide,recombinant proteinAF647-conjugated streptavidinBiolegendCat. #: 405237FACS
AntibodyInVivoMab anti-human CD3Bio X cellCat. #: BE0001-2-5MGOKT3 clone
4 μg/mL coated
AntibodyInVivoMab anti-human CD28Bio X cellCat. #: BE0291-5MGCD28.2 clone
2 μg/mL soluble
AntibodyhLNGFR-APC antibodyMiltenyi BiotecCat. #: 130-113-418FACS
AntibodyhCD19-APC antibodyBiolegendCat. #: 302212FACS
OtherDPBSWelgeneCat. #: LB001-02
OtherFetal Bovine Serum (FBS)GibcoCat. #: 26140–079
OtherDMEMGibcoCat. #: 11965–118
OtherRPMIGibcoCat. #: 21870–092
OtherHBSS with Ca2+ and Mg2+GibcoCat #: 14-025-092
OtherHEPESGibcoCat. #: 1530080
Other2-MercaptoethanolSigmaCat. #:
M6250-100mL
OtherLipofectamine 2000ThermoFisherCat. #: 11668019
OtherGlutamaxGibcoCat. #: 35050–061
OtherProtamine SulfateSigmaCat. #: P3369
OtherMEM Non-essential amino acidGibcoCat. #: 111400500
OtherSodium pyruvateThermoFisherCat. #: 11360070
Otherpenicillin/streptomycinGibcoCat. #: 15140–122
OtherTomodishTomocube
OtherParaformaldehyde Solution, 4% in PBSThermoFisherCat. #: AAJ19943K2Fixation
OtherCellBrite Fix 640 Membrane DyeBiotiumCat. #: 30089Plasma membrane staining
OtherVECTASHIELD Hardset w/DAPIVECTOR LaboratoryCat. #: H-1400Mounting Medium
OtherLysoTracker Deep RedThermoFisherCat #: L12492Dye for labeling and tracking lytic granules
OtherTetraSpeck Microspheres,0.1 µm, fluorescent blue/green/orange/dark redThermoFisherCat #: T7279Fiducial markers
OtherFITC-dextran 10 kDaTdB LabsCat #: 20682Fluorescein-labeled dextran, 10 kDa
OtherFITC-dextran 2000 kDaTdB LabsCat #: 20584Fluorescein-labeled dextran, 2000 kDa
Commercial assay or kitCD271 Microbeads kits, HumanMiltenyi BiotecCat. #: 130-099-023Isolation of CAR+ T cells
Commercial assay or kitSepMate PBMC Isolation tubeSTEMCELL86460PBMC Isolation
Software, algorithmFlowjoFlowjoVersion 10
Software, algorithmPrismGraphPadVersion 7
Software, algorithmImageJNIH

Primer list

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Primer nameSource or referenceIdentifiersAdditional information (5`>3`)
LNGFR FThis paperPCR primersGGGGATCCCCCCATCAGTCCGCAAAG
LNGFR R-P2AThis paperPCR primersAGGTCCAGGGTTCTCCTCCACGTCGCCAGCCTGCTTCAGCAGGCTGAAGTTAGTAGCTCCGCTTCCCCACCTCTTGAAGGCTATGTAGG
P2A-CD19-BBz FThis paperPCR primersGGAAGCGGAGCTACTAACTTCAGCCTGCTGAAGCAGGCTGGCGACGTGGAGGAGAACCCTGGACCTGCCTTACCAGTGACCGCCTTGCTC
CD19 BBz RThis paperPCR primersTGTCGACTTAGCGAGGGGGCAGGGCCTGC
CD28/CD3 FThis paperPCR primersGTTATCACCCTTTACTGCAGGAGTAAGAGGAGCAGGCTC
CD28/CD3 RThis paperPCR primersGTCGACTTAGCGAGGGGGCAGGG
CD8Hinge/TM FThis paperPCR primersCAGTCACCGTCTCCTCAAC
CD8Hinge/TM RThis paperPCR primersGAGCCTGCTCCTCTTACTCCTGCAGTAAAGGGTGATAACCAG
mCherry FThis paperPCR primersGTGAGCAAGGGCGAGGAGGAT
mCherry-Sal1 RThis paperPCR primersTTGTCGACCTACTTGTACAGCTCGTCCATGCCGCCGG
G4S-mCherry FThis paperPCR primersGCTCCGGTGGTGGTGGTTCTGTGAGCAAGGGCGAGGAGGATAAC
CD19 BBz CAR FThis paperPCR primersCCGGGGATCCATGGCCTTACCAGTGACCG
CD19 BBz CAR G4S RThis paperPCR primersAGAACCACCACCACCGGAGCCGCCGCCGCCAGAACCACCACCACCGCGAGGGGGCAGGGCCTGCATGTGA
hCD19 FThis paperPCR primersGTTGGATCCATGCCACCTCCTCGCCTCCT
hCD19-G4S RThis paperPCR primersAGAACCACCACCACCGGAGCCGCCGCCGCCAGAACCACCACCACCCCTGGTGCTCCAGGTGCCCAT
G4S-Zsgreen FThis paperPCR primersGGTGGTGGTGGTTCTGGCGGCGGCGGCTCCGGTGGTGGTGGTTCTGCCACAACCATGGCCCAGTCCAAGC
Zsgreen RThis paperPCR primersGATTACGCGTATTGCTAGCTCAGGGCAAGGCGGAG

Cell lines and culture

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K562 cells and CD19-positive K562 cells (K562-CD19; target cells) were kindly provided by Travis S. Young (California Institute for Biomedical Research). The original stock of K562 cells was purchased from American Type Culture Collection (ATCC). K562-CD19 cells were generated by transducing K562 cells (CD19-negative) with a lentivirus encoding human CD19. The cells were cultured in RPMI-1640 medium supplemented with 10% heat-inactivated fetal bovine serum (FBS), 2 mM L-glutamine, and 1% penicillin/streptomycin in a humidified incubator with a 5% CO2 atmosphere at 37°C. The Lenti-X 293 T cell line was purchased from Takara Bio (Japan). The cells were maintained in Dulbecco’s modified Eagle medium supplemented with 10% heat-inactivated FBS, 2 mM L-glutamine, 0.1 mM non-essential amino acids, 1 mM sodium pyruvate, and 1% penicillin/streptomycin. We authenticated the identity of K562 and K562-CD19 cell lines using STR profiling, offered by Korean Cell Line Bank (KCIB). We also confirmed that K562 cell line are free from mycoplasma, which was performed by Korea Research Institute of Bioscience and Biotechnology (KRIBB). We attached the document for authentication (Supplementary file 1).

Plasmid construction

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CD19-specific chimeric antigen receptor (CD19-BBz CAR) was synthesized. The construct is composed of anti-CD19 scFv (FMC63) connected to a CD8α spacer domain and CD8α transmembrane domain, 4-1BB (CD137) co-stimulatory domains, and the CD3ζ signaling domain (Rodgers et al., 2016). The cytoplasmic domain comprised of a truncated CD271 (ΔLNGFR) gene for the isolation of T cells expressing CAR was amplified from pMACS-ΔLNGFR (Miltenyi Biotec, Germany) and overlapped with the P2A oligonucleotide. The ΔLNGFR-P2A gene was overlapped with the CD19-BBz CAR gene and then inserted into the BamHI and SalI sites of pLV vectors to generate pLV- ΔLNGFR-P2A-CD19-BBz CAR. For the generation of CD19-CAR containing CD28 co-stimulatory domain (CD19-28z CAR), synthesized hCD28/CD3ζ fusion gene was overlapped with amplified CD8α spacer domain and CD8α transmembrane domain-containing PCR product. The final PCR product was then digested with SgrA1 and Sal1 and ligated into SgrA1 and Sal1-digested pLV- ΔLNGFR-P2A-CD19-BBz CAR vector.

To define the IS between CD19 expressing (K562-CD19) cells and CD19-specific CAR-T (CART19; effector) cells, we generated a mCherry-tagged CD19 BBz CAR and Zsgreen-tagged hCD19. The mCherry gene was amplified from pLV-EF1a-MCS-IRES-RFP-Puro (Biosettia, USA) and overlapped with synthetic oligonucleotides of a G4S linker. The CD19 BBz CAR gene was amplified with specific primer sets. The G4S-mcherry PCR product was overlapped with CD19 BBz CAR PCR product. The final PCR product was digested with BamH1 and Sal1 and then inserted into BamH1 and Sal1 digested pLV to generate pLV-CD19 BBz CAR-G4S mCherry. For the generation of Zsgreen-tagged hCD19 expressing vector, The Zsgreen gene was overlapped with synthetic oligonucleotides of a G4S linker. The G4S-Zsgreen PCR product was overlapped with a synthesized hCD19 (MN_001770.5) gene and inserted into the BamHI and MluI sites of pLV vectors to generate pLV- hCD19-G4S linker-Zsgreen.

Generation of CAR-transduced human T cells

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To generate a recombinant lentivirus supernatant, 6 × 105 Lenti-X 293 T cells were cultured in wells of a six-well plate for 24 hr and then transfected with the lentivirus packaging vectors (pMDL, pRev, pMDG.1) and the pLV vectors encoding ΔLNGFR-P2A-CD19-BBz CAR, ΔLNGFR-P2A-CD19-28z CAR, or mCherry-tagged CD19-BBz CAR using 10 μL of Lipofectamine2000 (Thermo Fisher Scientific, USA). Two days after transfection, the lentivirus containing supernatant was collected and stored at −80°C until used.

Peripheral blood mononuclear cells (PBMCs) were separated from whole blood samples of healthy donors using SepMate tubes (STEMCELL Technologies, Canada) following the manufacturer's instructions. The PBMCs were stimulated with 4 μg/mL of plate-bound anti-CD3 antibody (clone OKT3; Bio X cell), 2 μg/mL of soluble anti-CD28 antibody (clone CD28.2; Bio X cell), and 300 IU/mL human recombinant IL-2 (BMI KOREA, Republic of Korea).

Two days after stimulation, the activated T cells were mixed with the lentivirus supernatant, centrifuged at 1000 × g for 90 min, and incubated overnight at 37°C. CAR-transduced T cells were cultured at a density of 1 × 106 cells/mL in RPMI-1640 supplemented with 10% heat-inactivated FBS, 2 mM L-glutamine, 0.1 mM non-essential amino acid, 1 mM sodium pyruvate, and 55 μM β–mercaptoethanol in the presence of human recombinant interleukin (IL)−2 (300 IU/mL) until isolation of CAR-expressing T cells from bulk T cells.

The percentage of CAR and ΔLNGFR-positive T cells was assessed by biotin-conjugated rhCD19-Fc (Cat # CD9-H5259, ACRO Biosystems, USA) with AF647-conjugated streptavidin (Cat # 405237, Biolegend, USA), and fluorescein isothiocyanate (FITC)-conjugated LNGFR antibody (Cat# 130-112-605, Miltenyi Biotec, Germany).

Isolation of CAR-transduced T cells

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CAR- and ΔLNGFR-positive T cells were isolated using the human CD271 MicroBead kit (Cat# 130-099-023, Miltenyi Biotec) following the manufacturer's instructions. Sorted CART19 cells were expanded for six days with RPMI-1640 medium supplemented with 10% heat-inactivated FBS, 2 mM L-glutamine, 0.1 mM non-essential amino acids, 1 mM sodium pyruvate, and 55 μM β–mercaptoethanol in the presence of recombinant human rhIL-2 (300 IU/mL).

Lentiviral engineering of Zsgreen-tagged hCD19 expressing cell lines

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K562-CD19 cell lines stably expressing Zsgreen were generated by lentiviral transduction with supernatant containing the Zsgreen. hCD19-G4S-linker-Zsgreen overexpressing K562 (K562-CD19-G4S-Zsgreen) cell lines were produced by lentiviral transduction with supernatant containing the hCD19-G4S-linker-Zsgreen. Two days after transduction, transduced cells were stained using hCD19 specific antibody (Biolegend, Clone HIB19) and analyzed with flow cytometry. We confirmed that the percentage of CD19+Zsgreen+ cells was nearly 98%. (Figure 8—figure supplement 1).

Sample preparation for imaging

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The effector or target cells were diluted at 600 cells/μL, respectively. We used a petri dish compatible with our experimental setup (TomoDish, Tomocube Inc, Republic of Korea). 12 μL of each diluted cell was seeded on TomoDish, gently mixed, and covered with a square cover-slip glass (22 × 22 mm2, No. 1.5H, Deckglaser Inc). The side of the dish was sealed by 10 μL of mineral oil (M-8410, Sigma), which prevented introduction of exterior contaminants and drying of the medium.

Membrane staining

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To image cell membranes using fluorescence microscopy, effector and target cells were respectively stained with 1X CellBrite Fix 640 Membrane Dye (Biotium) for 15 min at 37℃, twice washed with DPBS, and then suspended using RPMI medium. 2 × 105 cells of each effector and target were seeded on a Tomodish and incubated for 15 min at 37℃. Then the medium was replaced with 4% paraformaldehyde (PFA) solution to fix them. After 10 min, 4% PFA was removed and twice washed with DPBS. For membrane staining, the cells were mounted with 25 μL VECTASHIELD Antifade mounting medium with DAPI (VECTOR laboratory, H-1200).

Dextran solution

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To image the synaptic cleft, we used FITC-labeled dextran of molecular weights 10 and 2000 kDa (TdB Labs), whose reported hydrodynamic diameters were 4 and 54 nm, respectively. We diluted the dextran in PBS and the final concentration was 50 μM for the 4 nm dextran and 2.5 μM for the 54 nm dextran.

Lysotracker staining

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To image the lytic granules of CART19 cells using fluorescence microscopy, CART19 cells were stained with 75 nM Lysotracker Deep Red (Thermofisher) solution in 1X Hanks’ Balanced Salt solution (HBSS; Thermofisher) containing Ca2+ and Mg2+ ions for 50 min at 37℃, washed with HBSS, and then suspended using complete T cell medium. Similar to the imaging protocol for membrane-stained cells, the stained CART19 cells were mixed with K562-CD19 cells on a Tomodish. To image the transport dynamics of the lytic granules using widefield deconvolution microscopy, the mixed cells were imaged in live states. To image the CAR IS using 3D-SIM, the mixed cells were incubated for 15 min at 37℃, and chemically fixed by replacing the medium with 4% paraformaldehyde (PFA). After 10 min, 4% PFA was removed, washed and replaced with complete T cell medium.

Correlative fluorescence microscopy

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To evaluate the segmentation performance quantitatively, the evaluation data were prepared using a custom-built setup for correlative imaging between wide-field fluorescence microscopy and ODT (Kim et al., 2017). CART19 (mCherry) and K562-CD19 (Zsgreen) cells, and their plasma membranes were imaged in different fluorescence channels, respectively. To excite mCherry and GFP proteins and the plasma membrane of the prepared cells, blue (wavelength = 488 nm, Cobolt, 06-MLD), green (wavelength = 561 nm, CNI Laser, MLL-FN-561), and red (wavelength = 639 nm, CNI Laser, MLL-FN-639) DPSS lasers were illuminated in wide-field epi-fluorescence geometry. 3D fluorescence image stacks were obtained by scanning the objective lens with an axial spacing of 300 nm and imaged with an sCMOS camera (Neo 5.5 sCMOS, Andor Technology). The obtained fluorescence images were deconvolved using the blind Lucy algorithm (a deconvblind function in MATLAB) for ten maximal iterations with theoretical 3D point spread functions as initial estimates. The deconvoled fluorescence images were registered with RI tomograms, and the ground-truth labels of the CART19 and K562-CD19 cells were derived from expert biologists who manually thresholded, delineated, and smoothed the cell volume by means of the overlapped RI and fluorescence images.

3D confocal fluorescence microscopy

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To resolve the details of the membrane topology of the IS between mCherry-tagged CD19-CAR-expressing T cells and Zsgreen-tagged CD19 expressing K562 cells, 3D confocal fluorescence microscopy was performed. The membrane topology of IS was analyzed with a commercial confocal microscope (Nikon Eclipse Ti) and a high-NA objective lens (Nikon Apo 60×, 1.4 NA) to obtain a high-resolution, multicolor, 3D fluorescence image.

Complementary 3D-SIM and ODT

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For complementary imaging of ODT with high-resolution fluorescence microscopy, 3D-SIM was integrated in a custom-built setup based on a digital micromirror device (Figure 6—figure supplement 1Shin et al., 2018). A polarizing beam splitter separated scanning plane waves for ODT and structured illumination patterns for 3D-SIM into the transmitted and epi-illumination mode, respectively. A dichroic mirror combined the ODT and 3D-SIM signals, and a 10:90 beam splitter divided the merged signals to an ODT camera (MQ042MG-CM, Ximea) and a 3D-SIM camera (Panda 4.2, PCO) respectively. The raw 3D-SIM image stacks were acquired with five pattern phases spaced by 2π/5, three pattern orientations spaced 60° apart, and axial translation of an objective lens equipped with a piezoelectric Z stage (P-721.CDQ, Physik Instrumente), at the interval of 45–50 nm for 100-nm-diameter Tetraspeck beads (ThermoFisher) and 180 nm for CART19/K562-CD19 conjugates respectively. The 3D-SIM images were then reconstructed using the custom MATLAB codes based on Wiener deconvolution and Richardson-Lucy deconvolution for the beads and the cells, respectively (Brown et al., 2011; Müller et al., 2016).

Design of DCNN architecture

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The DCNN architecture was designed on the basis of UNet architecture, which features excellent performance for various biomedical volumetric segmentation tasks such as multi-cell (Ronneberger et al., 2015), organ (Roth, 2017), and tumor segmentation tasks (Dong et al., 2017). Our model employed five contracting and expanding layers comprising 32, 64, 128, 256, and 512 filters, respectively. To improve the segmentation performance, several modifications of the architecture were added while maintaining the overall U-shaped feature map flow line. First, we employed a series of ResNet blocks from ResNet (He et al., 2016) in the contracting paths for extracting the feature more robustly. Also, to increase the receptive field, we employed the feature skip connection that passes through the global convolutional network layer (Peng et al., 2017) with k = 13, 13, 9, 7, and 5, respectively. The overall schematic figure of DCNN architecture is shown in Figure 2—figure supplement 3. Our network was implemented in Python using the PyTorch package (http://pytorch.org), and the processing steps were performed in MATLAB (MathWorks, Inc).

Statistical analysis

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MATLAB was used in order to compare the sample means by two-tail paired Wilcoxon tests in Figure 8g and two-tail unpaired Wilcoxon tests in Figure 9, Figure 2—figure supplement 2b, and Figure 6—figure supplement 2c respectively. All of the numbers following the ± sign in the text are standard deviations.

Major datasets and codes

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We have provided pre-processing and post-processing codes, and training and untrained datasets used in Figure 3—video 1 (https://osf.io/9w32p/). Also, the DeepIS code and the processing codes are available through a GNU General Public License at https://github.com/JinyeopSong/2020__DeepIS (Song and Lee, 2020).

Data availability

We have provided pre-processing and post-processing codes, and training and validation datasets used in Figure 3-Video 1 (https://osf.io/9w32p/). Also, the Unet architecture code is available in https://github.com/JinyeopSong/190124_CART-Segmentation-best (copy archived at https://archive.softwareheritage.org/swh:1:rev:eca787f7cc0b3aa423c54ce3ac53088e6049948b/).

The following data sets were generated
    1. Lee M
    (2019) Open Science Framework
    ID 9w32p. DeepIS: deep learning framework for three-dimensional label-free tracking of immunological synapses.

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    IEEE Conference on Computer Vision and Pattern Recognition (CVPR)). pp. 1743–1751.
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  5. Conference
    1. Ronneberger O
    2. Fischer P
    3. Brox T
    (2015)
    MICCAI-Net: convolutional networks for biomedical image segmentation
    Medical Image Computing and Computer-Assisted Intervention.
  6. Conference
    1. Snoek J
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    Scalable bayesian optimization using deep neural networks
    International Conference on Machine Learning. pp. 2171–2180.

Decision letter

  1. Satyajit Rath
    Senior Editor; Indian Institute of Science Education and Research (IISER), India
  2. Michael L Dustin
    Reviewing Editor; University of Oxford, United Kingdom
  3. Michael L Dustin
    Reviewer; University of Oxford, United Kingdom
  4. Christoph Wülfing
    Reviewer; University of Bristol, United Kingdom
  5. Klaus Ley
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

Your manuscript demonstrates the power of using label free 3D imaging, provided by optical diffraction tomography, for long term studying of intercellular dynamical process. The example of imaging and analyzing the immunological synapse (IS) is potentially impactful, as it offers new avenues in studying immune responses and their downstream signalling events. The addition of deep convolutional neural network for 3D segmentation and resolving cell to cell interactions is also important to enable real time observation of synaptic events. This work may stimulate more interesting and unexplored biomedical applications using optical diffraction tomography. You have also identified approaches to more fully capture the power of the deep learning by removing subjective elements of the training process.

Decision letter after peer review:

Thank you for submitting your article "DeepIS: deep learning framework for three-dimensional label-free tracking of immunological synapses" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Michael L Dustin as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Satyajit Rath as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Christoph Wülfing (Reviewer #2); Klaus Ley (Reviewer #3).

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

There is agreement among reviewers that the use of deep learning to segment cell pairs based on holographic Optical diffraction tomography (Tomocube) is potentially useful for analysis of immunological synapses and other situations where cell-cell interfaces with particular refractive index contrast are generated. The method needs to be better validated with an objective segmentation approach and a few other issues regarding utility of the data better elaborated.

Essential revisions

1) It remains somewhat unclear how (and whether) membrane topology at the cellular interface is determined by Lee et al. and this should be explained in more detail. Looking at representative ODT data, e.g. in Figure 1C and Figure 2A, the cellular interface seems sufficiently tight that the T cell and the APC can't be distinguished unambiguously in the ODT images. This is consistent with electron microscopy data showing membrane apposition below the resolution limit of light microscopy. Therefore, the interface topology had to be inferred. This was done by training neural networks with manually curated data on 236 T cell/APC couples. The authors should describe how the interface was manually identified. Which quantitative criteria were used? The authors should show a number of manually annotated cell couples as raw data and with the manually annotated interface. T cell/APC interfaces can be highly undulating. Were such undulations resolved? Based on the representative ODT data that seems unlikely. From Figure 2B it looks like the interface was estimated as the surface that minimises distances from the centres of each cell. Because of membrane undulations, this would likely be biologically incorrect. Without reliable determination of the cellular interface ODT data on cell couples will remain highly limited.

2) Two-colour fluorescence data are used to validate the segmentation of T cell and APC in Figure 5. T cells are identified with a CAR-mCherry fusion protein, the APCs with actin-GFP. The fusion proteins are well suited to identify the cell types. However, as the CAR-mCherry fusion protein seems predominantly vesicular, its fluorescence is ill-suited to resolve membrane topology as one of the most important potential types of data derived by ODT. A non-transferring plasma membrane localised protein or lipid should be used instead to demarcate the membrane of each cell. If the authors can then access 3D STED or a localization method like double helix this may be their best bet to be able to get enough information about membrane topology to trace the interface in an inambiguous manner and validate the limits of the method. The authors may also want to use a lysotracker dye to identify the dense core granules and an extracellular dye with confocal microscopy sectioning to define the synaptic cleft to determine the conditions under which the method can identify these features.

3) An alternative approach to validation would be correlative electron microscopy. The most readily correlated data might be obtained by an ion beam milling system as the context of the cell on a indexed surface could be recorded in the ODT imaging and then use to find the same cell pair and map the interface in 3D. If this was done on a few cells, the training data could be potentially enable the machine learning algorithm to exceed a human in assessing how information in the ODT data set relates to the actual interface. While revisions in eLife are typically restricted to 2 months, if arranging access to such a system required more time and Lee et al. want to undertake it, we could provide an extension.

4) What do the data in Figure 6 tell the reader about how CAR T cells function? As shown, the data simply state that CAR T cells need engagement of the CAR to form a cell couple, something a much more basic cell coupling assay can establish. Is there any biological information in the morphological and refractive index changes observed? The authors may want to consider using different T cell activation conditions that both lead to cell coupling, e.g. comparing two CARs with different signalling motifs, to see whether they can, at the least, detect significant differences in morphological and refractive index parameters or better relate such changes to cellular function.

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

Thank you for submitting your article "Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells" for consideration by eLife.

The Senior and Reviewing Editor have carefully evaluated your resubmission and while a number of issues are addressed, an essential issue related to using a higher resolution methods to validate the interface area and composition and/or improve the training based on this information was not addressed. Recent studies suggest that membrane infolding and release of granules into the interface are important events and it would be critical to determine if there is enough information in the tomograms to train the system to correctly identify these events. The use of the system as it stands will not provide sufficiently robust output to be of broad utility and will be better published in a more specialised journal. If this essential revision can be addressed you can resubmit using the link below, but if not you should submit the generally improved paper to another journal.

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

Thank you for submitting your article "Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Michael L Dustin as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Satyajit Rath as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Christoph Wülfing (Reviewer #2); Renjie Zhou (Reviewer #4).

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

As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

The reviewers all appreciate the work put into the revision. It was suggested that correlative electron microscopy was needed to establish ground truth for training and assessment of accuracy, but instead you provide 3D SIM data on various cellular molecules that unfortunately have their own complex localisations and may not really reflect the actual synaptic cleft between the cells. The reviewers can make one further suggestion requiring some experiments the could be used to test the accuracy of the trained DeepIS algorithm at 3D SIM resolution limit of ~125 nm.

Essential revisions:

1) In 2014 Dan Davis' lab developed a negative stain method using fluorescent dyes in the media to define the location of the immunological synapse using confocal microscopy. See: Cartwright AN, Griggs J, Davis DM. The immune synapse clears and excludes molecules above a size threshold. Nat Commun. 2014;5:5479. Epub 2014/11/20. doi: 10.1038/ncomms6479. PubMed PMID: 25407222; PMCID: PMC4248232. It should be possible to use this method with 3D SIM or perhaps Airyscan confocal microscopy to define the location of "synaptic cleft" between the Car-T and target with 125 nm resolution. Correlative ODT scored with Deep IS to define the synapse could then be quantitatively compared to the 3D SIM/confocal analysis of the synaptic cleft to objectively assess the accuracy of deep IS to a ground truth measurement of greater resolution. See below discussion for resolution that would make the 3D SIM a reasonable ground truth for the ODT data sets. The reviewers would accept this approach as an alternative to electron microscopy.

2) For the resolution definition in ODT, you appear to be using the Nyquist sampling period as the imaging resolution. To resolve structures, we need the sample to be separated by at least 2 Nyquist sampling periods. Therefore, the optical resolution should be 2x of the Nyquist resolution you have used in the manuscript. This definition is also consistent with the Abbe resolution definition. Therefore, the lateral resolution in ODT should be λ/2NA which is more than 200 nm not 125 nm. Similarly, the axial resolution should be corrected. If you insist on using Nyquist sampling period as resolution (which seems incorrect), you should clarify it and compare it with the conventionally used Abbe resolution limit. You should also see if you can obtain a 3D standard sample, like an Argolight slide, to directly determine the resolution of the ODT system in a more direct manner.

3) Aside from resolution, there is also a concerned about the refractive index (RI) sensitivity of ODT. When structures have very small (RI) contrast, it becomes a sensitivity issue more than a resolution issue, e.g., ODT is not able to detect many organelles or granules in the cells even when their diameters are larger than 200 nm (the resolution limit). Could the authors provide an estimation of RI contrast between the membranes and estimate whether they have the detection sensitivity for detecting granule release events. With regard to RI, you continue to interpret the RI in terms of proteins density and this this seems overly simplistic as area within the resolution limit of the interface will contain many types of structures. So this interpretation should be toned down to more of a hypothetical argument about average macromolecule density given convolved RI of lipids, proteins and carbohydrates in the "synapse".

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

Thank you for resubmitting your work entitled "Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells" for further consideration by eLife. Your revised article has been evaluated by Satyajit Rath (Senior Editor) and a Reviewing Editor.

The manuscript demonstrates the power of using label-free 3D imaging, provided by optical diffraction tomography, for long-term studying of intercellular dynamical process. The example of imaging and analyzing the immunological synapse (IS) is potentially impactful, as it offers new avenues in studying immune responses and their downstream signaling events. The addition of deep convolutional neural network for 3D segmentation and resolving cell to cell interactions is also important to enable real-time observation of synaptic events. This work may stimulate more interesting and unexplored biomedical applications using optical diffraction tomography. There are 3 remaining issues that need to be addressed before acceptance, as outlined below:

1) The Results still state that “lymphocytes contain lipid-rich environment localized mostly on a 4-nm-thick membrane site, whose size is beyond optical resolution and implies a negligibly small amount of lipid molecules compared with proteins.” The reviewers and editor agree that the membrane lipid and protein contributions to the refractive index in relation to ODT is likely to be small for the reason you state, with the major contribution to the pixel intensity being cytoplasmic protein and cortical cytoskeleton. Can you rewrite this passage to clarify the situation that the issue is not so much lipid vs protein, but sub resolved membrane vs the relatively voluminous adjacent cytoplasm.

2) The error of synaptic cleft determination is on the order of 0.5 µm and this is a serious limitation currently as it suggest the ability of an expert trainer to correctly identify the synaptic cleft from ODT images is poor. Can you discuss the idea training using the negative staining with the smallest dextran as a ground truth might further improve the power of the approach by eliminating the training error. Then if the ODT contains subtle information that the algorithm can can be trained to exploit, this information will be delivered with not bias other than the resolution limit. Then you would need much more correlative negative stain-ODT for training set and a distinct testing set to determine if there is an improvement. We are not asking you to do this, just to discuss it as a possible way to improve the method in the future.

3) The statement in the Abstract that you can't do long term 3D fluorescence microscopy has been made false in recent years by lattice light sheet microscopes. While these are not widely available, they will be in future years based on the new Zeiss LLSM7 release. Please rewrite the Abstract to acknowledge lattice light sheet microscopy as a competing technology and perhaps discuss how ODT with analysis trained to identify the synapse will offer complementary information to LLSM.

https://doi.org/10.7554/eLife.49023.sa1

Author response

Essential revisions

1) It remains somewhat unclear how (and whether) membrane topology at the cellular interface is determined by Lee et al. and this should be explained in more detail. Looking at representative ODT data, e.g. in Figure 1C and Figure 2A, the cellular interface seems sufficiently tight that the T cell and the APC can't be distinguished unambiguously in the ODT images. This is consistent with electron microscopy data showing membrane apposition below the resolution limit of light microscopy. Therefore, the interface topology had to be inferred. This was done by training neural networks with manually curated data on 236 T cell/APC couples. The authors should describe how the interface was manually identified. Which quantitative criteria were used? The authors should show a number of manually annotated cell couples as raw data and with the manually annotated interface.

We thank the reviewers for the valuable comment. To address this issue, the performance of our segmentation framework is supplemented in the revised manuscript. In addition, relevant datasets and processing codes are provided (https://osf.io/9w32p/).

Here we further explain the details on our annotation method with manual parameter selections (Figure 2—figure supplement 1). The watershed segmentation on a distance-transformed mask caused an incorrect boundary definition (Figure 2—figure supplement 1A). To correct the ill boundary definition, we applied the watershed segmentation to the Gaussian-smoothed RI tomogram after iterative parameter adjustment (Figure 2—figure supplement 1B). Because the RI tomogram distinguished cell boundaries, adjoining cells could be segmented successfully. Although this process produced over-segmented fragments due to the presence of multiple local maxima, we could combine them into effector and target masks after comparing them with the original RI tomogram. A number of annotated masks are shown in Figure 3—video 1.

Next, we verified that human raters (three experts in cell biology) who participated in the data curation performed consistent and good segmentation performance. This was achieved by measuring the segmentation sensitivity using simultaneous truth and performance level estimation (STAPLE), which quantified the inter-rater variability of annotation performance (10.1109/TMI.2004.828354). Specifically, we asked the experts to manually draw each cell mask from representative cross-section images of RI maps (10 from curated dataset and 10 from untrained dataset), and used STAPLE algorithm to estimate the truth masks and inter-rater sensitivity.

For training and untrained datasets, we compared the representative STAPLE truth estimates with the annotation masks (Figure 2—figure supplement 2A). As expected, the two masks were sufficiently similar for both the training and untrained datasets. The similarity between the annotation masks and STAPLE truth estimates was quantitatively compared by statistical analysis of Pearson correlation coefficients ( Figure 2—figure supplement 2B). The comparison showed higher mean and smaller standard deviation values of the coefficient in the training dataset, indicating that the quality of curated annotation masks reached the expert levels. For both training and untrained datasets, we also validated the consistent annotation performance of the experts from their high inter-rater sensitivity (> 97.5%) (Figure 2—figure supplement 2C). Based on the performance validation, the experts heuristically curated well-segmented data with consensus.

T cell/APC interfaces can be highly undulating. Were such undulations resolved? Based on the representative ODT data that seems unlikely. From Figure 2B it looks like the interface was estimated as the surface that minimises distances from the centres of each cell. Because of membrane undulations, this would likely be biologically incorrect. Without reliable determination of the cellular interface ODT data on cell couples will remain highly limited.

We show that DeepIS resolved undulation problems by displaying 22 individual temporal synapse area curves used in Figure 6B (Figure 8—figure supplement 1). The smooth change in overall data suggests that our method resolved undulation problems in most cases. Several spikes attest to the fact that the method suffers from ill segmentations in some cases. Although these outlier problems could be alleviated by post-processing such as data blocking, we used the raw data for analysis because the number of ill-segmented data points was extremely small (5 out of 1329 data points for 200 seconds; 0.38%). Our supplementary videos also corroborate these results. Overall, our approach provided sufficient fidelity for the quantitative analysis of IS dynamics.

2) Two-colour fluorescence data are used to validate the segmentation of T cell and APC in Figure 5. T cells are identified with a CAR-mCherry fusion protein, the APCs with actin-GFP. The fusion proteins are well suited to identify the cell types. However, as the CAR-mCherry fusion protein seems predominantly vesicular, its fluorescence is ill-suited to resolve membrane topology as one of the most important potential types of data derived by ODT. A non-transferring plasma membrane localised protein or lipid should be used instead to demarcate the membrane of each cell. If the authors can then access 3D STED or a localization method like double helix this may be their best bet to be able to get enough information about membrane topology to trace the interface in an inambiguous manner and validate the limits of the method. The authors may also want to use a lysotracker dye to identify the dense core granules and an extracellular dye with confocal microscopy sectioning to define the synaptic cleft to determine the conditions under which the method can identify these features.

We thank the reviewers for the valuable suggestion. The raised question concerns whether CAR-mCherry is suitable for resolving the membrane topology. This issue might arise due to the fluorescence images in Figure 5, where the CAR fluorescence intensity on the plasma membrane was not clearly visible. Thus, we performed extended experiments to supplement the results in Figure 5. The overall results validated that RI image and CAR-mCherry fusion protein can adequately resolve the membrane topology of CART19 cells and our manual labeling method in Figure 5 was valid.

First, we elucidated the distribution of CAR proteins with a higher spatial resolution and sensitivity than epi-fluorescence microscopy. Among many suggestions by the reviewers, we used a multicolor 3D confocal microscope (Nikon Eclipse Ti, Nikon Apo 60×, 1.4 NA) because it already provided sufficient 3D spatial resolution to image overall structures. The imaged cells were fixed conjugate pairs of CART19-CAR-G4S-mCherry and K562-CD19-G4S-Zsgreen cells. To visualize the plasma membranes and nuclei of the cells, we stained the cells with APC-conjugated plasma membrane staining dye and DAPI, respectively. The resultant fluorescence image showed that the cells were successfully stained (Figure 5—figure supplement 2A). In particular, CART19 and K562-CD19 cells expressed CAR and CD19 respectively mostly on the plasma membrane. This was also true when a CART19 cell was conjugate to a K562-CD19 cell (Figure 5—figure supplement 2B-D). The 3D view confirmed that CAR was coalesced around the immunological synapse and also spread throughout the plasma membrane. Therefore, fluorescence imaging of CAR was sufficient to resolve the membrane topology although it was vesicular.

We then performed an additional experiment to demonstrate that RI data provides essential information about the cellular topology. For 3D correlative RI and fluorescence imaging, we used a custom-built 3D correlative fluorescence microscopy setup which was equipped with an sCMOS camera (Neo 5.5 sCMOS, Andor Technology). As suggested by the reviewers, we performed correlative analysis of membraned-stained CART19/K562-CD19 conjugate pairs (Figure 5—figure supplement 1). The membrane fluorescence images overlapped well with the RI borders and the IS interfaces co-localized by CAR and CD19 proteins (Figure 5—figure supplement 1A). Based on the correlative images, CART19 and K562-CD19 cell labels were manually delineated and compared with the DeepIS results (Figure 5—figure supplement 1B). Both Pearson correlation coefficients and boundary displacement errors were improved with the enhanced image quality.

The confocal and 3D correlative images convinced us that RI and CAR-mCherry signals can sufficiently resolve the membrane topology without membrane staining under improved imaging conditions. To verify this, we finally performed additional correlative analysis without membrane staining (Figure 5A). The resulting images provided clear distinctions between CART19 and K562 cells. Also, the remaining membrane structures of CART19 cells could be manually delineated using the correlative images. The accuracy parameters were also comparable to those of membrane-stained cells (Figure 5B). In conclusion, our previous manual labeling procedure was sufficient to demarcate the membrane structures of CART19 cells. These contents have been added to the revised manuscript.

3) An alternative approach to validation would be correlative electron microscopy. The most readily correlated data might be obtained by an ion beam milling system as the context of the cell on a indexed surface could be recorded in the ODT imaging and then use to find the same cell pair and map the interface in 3D. If this was done on a few cells, the training data could be potentially enable the machine learning algorithm to exceed a human in assessing how information in the ODT data set relates to the actual interface. While revisions in eLife are typically restricted to 2 months, if arranging access to such a system required more time and Lee et al. want to undertake it, we could provide an extension.

Although the suggested correlative electron microscopy is a scintillating approach, we consider that these experiments are far beyond the scope of current work. Instead, we focus on other supplementary experiments to address the remaining relevant issues. We believe that this choice will make the manuscript deliver a coherent message and better quality. The suggested idea will be a very interesting topic for the following studies.

4) What do the data in Figure 6 tell the reader about how CAR T cells function? As shown, the data simply state that CAR T cells need engagement of the CAR to form a cell couple, something a much more basic cell coupling assay can establish. Is there any biological information in the morphological and refractive index changes observed? The authors may want to consider using different T cell activation conditions that both lead to cell coupling, e.g. comparing two CARs with different signalling motifs, to see whether they can, at the least, detect significant differences in morphological and refractive index parameters or better relate such changes to cellular function.

We sincerely appreciate the critical comment. The most important aspect of Figure 6 is that we have established early IS kinetics by CART19 cells quantitatively without fluorescence microscopy. Using 3D high-speed ODT and deep learning, we concluded that both IS areas and surface protein density of CAR T cells reached a steady-state within a few minutes. As described in the original manuscript, these observations connect previous studies based on various fluorescence microscopy techniques, which revealed the protein aggregation at the IS and mechanochemical potentiation and centrosome polarization of T cells during early IS dynamics.

We nonetheless agree that the question raised by the reviewers is also relevant to the future biological applications of our approach. Particularly interesting is to understand whether the types of co-stimulatory domains affect CAR IS characteristics, because these studies may provide insights into the efficacy of different therapeutic CAR agents. For example, Kymriah and Yescarta are the only two CD19-targeting CAR-T cell therapies that have been approved by US-FDA to date. Since one of the major differences between the two 2nd generation CAR-T cells lies in the sequence of the costimulatory signaling domain used (4-1BB for Kymriah and CD28 for Yescarta), we thought to apply our method to compare the IS characteristics of the CAR-T cells with different costimulatory signaling domains. Primary human T cells transduced with the lentiviral vectors encoding CD19-28z or CD19-BBz CAR showed comparable transduction efficiency as well as similar surface expression levels of CARs as determined by flow cytometry (Figure 7—figure supplement 1a).

Exploiting our established method, we studied the effect of CD28 or 4-1BB co-stimulatory domains on CAR IS characteristics. We first quantified the differences of early IS kinetics between CD28 and 4-1BB based CAR-T cells in the presence of K562-CD19 target cells and found stable IS formation within several minutes, regardless of the types of costimulatory domains (Figure 7—video 1). Quantitative analysis also indicated no statistically significant differences in early IS kinetics between CD19-28z and CD19-BBz CAR-T cells (Figure 9—figure supplement 2). We then compared the statistics of other IS parameters between the CD19-28z and CD19-BBz CAR-T cells in a steady state. Specifically, we incubated each type of CAR-T cells with K562-CD19 cells for 15 minutes to allow sufficient time for stable IS formation, and fixed them with 4% paraformaldehyde solution. When we analyzed the images for conjugates (Figure 9A), we found no significant difference in IS areas between the two CAR-T cell types (Figure R8b). However, interestingly, significantly higher IS protein densities and total IS protein amounts were observed for CD19-BBz CAR-T cells, by approximately 10% as determined by quantitative statistical analyses, compared with CD19-28z CAR-T cells (Figures 9C-D).

Collectively, our results indicate that the quantitative analysis of IS parameters using DeepIS, in conjunction with other analytical methods such as fluorescence-based microscopy and quantitative mass-spectrometry, may help to elucidate the mechanistic details underlying the functional differences observed for the CAR-T cells with different signaling domains. Because this finding is significant, we have added this result as the main result (Figure 7) in the revised manuscript.

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

The Senior and Reviewing Editor have carefully evaluated your resubmission and while a number of issues are addressed, an essential issue related to using a higher resolution methods to validate the interface area and composition and/or improve the training based on this information was not addressed. Recent studies suggest that membrane infolding and release of granules into the interface are important events and it would be critical to determine if there is enough information in the tomograms to train the system to correctly identify these events. The use of the system as it stands will not provide sufficiently robust output to be of broad utility and will be better published in a more specialised journal. If this essential revision can be addressed you can resubmit using the link below, but if not you should submit the generally improved paper to another journal.

We thank all the reviewer for the helpful suggestions. The important remaining issue is to compare the immunological synapse (IS) obtained by our method with a conventional high-resolution method for conclusive validation. To address the reviewer’s comment, we have integrated three-dimensional structured illumination microscopy (3D-SIM) for high-resolution fluorescence microscopy to the used ODT setup (Figure 6—figure supplement 1A). The resolution improvement was validated by reconstructing the SIM images of 100-nm-diameter Tetraspeck beads (Figure 6—figure supplement 1B-C). The enhanced resolutions of both multicolor 3D-SIM and ODT were laterally sub-200 nm and axially nearly sub-400 nm, respectively (Figure 6—figure supplement 1D).

To investigate the protein compositions of along CAR IS, we used CAR-G4S-mCherry-expressing CART19 cells stained with lysotracker dyes and hCD19-G4S-Zsgreen-expressing K562-CD19 cells. To verify the successful staining of lytic granules before 3D-SIM, we employed wide-field deconvolution fluorescence microscopy and confirmed the rapid dynamics of lytic granules coalescing into the IS (Figure 7—figure supplement 1 and see Figure 6—video 1). We then chemically fixed stable CART19/K562-CD19 conjugates and imaged them using 3D-SIM and ODT for high-resolution multi-protein composition analysis.

We first checked whether additional training is needed. 3D-SIM images provided clearer 3D features of the IS between CART19 and K562-CD19 cells than wide-field microscopy (Figure 7A). Importantly, our DeepIS framework demarcated the interface regions in close proximity to the overlapping areas of CAR and CD19 proteins. From this result, we conclude that the current framework is sufficient for defining the 3D IS areas without additional training with high-resolution data. Next, in order to validate the protein compositions in the CAR IS, we quantitatively analyzed the 3D-SIM images of CAR, CD19, and lytic granules along the IS demarcated by DeepIS (Figure 7B). Consistent with the previous report (https://doi.org/10.1073/pnas.1716266115), the protein compositions of CAR exhibited asymmetric and granular distributions along the CAR IS. From the line and surface profiles of the protein signals, we additionally observed the highest correlations of CAR with lytic granules, as well as colocalizations of CD19 proteins (Figure 7C). Interestingly, the total surficial protein densities obtained by ODT exhibited both correlated and uncorrelated clustered regions with the dense multi-protein clusters. Since ODT quantifies the total protein concentration, the uncorrelated signals are highly likely to imply the presence of clusters of other dominant proteins such as F-actin, Lck, and supramolecular attack particles. Taken together, these results suggest that our DeepIS method based on ODT can be used to define IS area with high accuracy, and also provides collective information about the distribution of total proteins within the IS which may not be easily measured by using conventional fluorescence microscopy.

In summary, we believe that the new experimental results address the remaining issues commented by the reviewers and suggest that our present method provides a complementary analytical modality to fluorescence microscopy for assessing CAR IS. We have added these to the main section of the second revised manuscript.

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

Essential revisions:

1) In 2014 Dan Davis' lab developed a negative stain method using fluorescent dyes in the media to define the location of the immunological synapse using confocal microscopy. See: Cartwright AN, Griggs J, Davis DM. The immune synapse clears and excludes molecules above a size threshold. Nat Commun. 2014;5:5479. Epub 2014/11/20. doi: 10.1038/ncomms6479. PubMed PMID: 25407222; PMCID: PMC4248232. It should be possible to use this method with 3D SIM or perhaps Airyscan confocal microscopy to define the location of "synaptic cleft" between the Car-T and target with 125 nm resolution. Correlative ODT scored with Deep IS to define the synapse could then be quantitatively compared to the 3D SIM/confocal analysis of the synaptic cleft to objectively assess the accuracy of deep IS to a ground truth measurement of greater resolution. See below discussion for resolution that would make the 3D SIM a reasonable ground truth for the ODT data sets. The reviewers would accept this approach as an alternative to electron microscopy.

Thank you for the comment. We also appreciate the suggestion, to which we fully agree. As suggested, we conducted an additional experiment based on a negative stain method with 3D-SIM (Figure 6—figure supplement 2). The results show that the 3D SIM data can be used as a reasonable ground truth for the ODT data sets.

We used the FITC-labelled dextran with two varying hydrodynamic diameters of 4 and 54 nm. We added dextran solutions to the chemically fixed conjugate of K562-CD19 and T cells expressing CAR-G4S-mCherry for a negative stain method.

The 3D-SIM images showed that the synaptic cleft in the vicinity of the CAR protein was only visible with the smaller dextran (Figure 6—figure supplement 2A). The fluorescence intensity of dextran relative to the background solution quantified the size-dependent access of dextran into the CAR IS (Figure 6—figure supplement 2B). A two-tail unpaired Wilcoxon test indicated that the fluorescence peak signals were significantly stronger for the smaller dextran (Figure 6—figure supplement 2C). The overall results suggest that the CAR IS excludes dextran molecules above a size threshold, which is consistent with the previous study by Davis’ lab.

To validate, we then compared the imaged synaptic clefts, the CAR fluorescence, and the IS drawn by DeepIS (Figure 6A-B). The 3D overlaps of the three images confirmed the segmentation accuracy of our method (Figure 6C). We plotted the signals across the IS to quantitatively assess the segmentation accuracy (Figure 6—figure supplement 2D). The deviations of the IS from the peak intensities of the CAR and the synaptic cleft were within 200 and 600 nm, respectively. The analysis suggested that the IS retrieved by DeepIS reflected the IS boundary closer to CAR T cells, whose accuracy was comparable to the resolution limit of ODT. Moreover, consistent with the previous report using confocal fluorescence microscopy, the lateral full-width half-maximum of the synaptic cleft was measured to be 894 nm, which may imply the presence of a spatial cavity larger than tens of nanometers across the IS.

Altogether, the additional experiment with a negative stain method validated our proposed method and provided more insights about the CAR IS.

2) For the resolution definition in ODT, you appear to be using the Nyquist sampling period as the imaging resolution. To resolve structures, we need the sample to be separated by at least 2 Nyquist sampling periods. Therefore, the optical resolution should be 2x of the Nyquist resolution you have used in the manuscript. This definition is also consistent with the Abbe resolution definition. Therefore, the lateral resolution in ODT should be λ/2NA which is more than 200 nm not 125 nm. Similarly, the axial resolution should be corrected. If you insist on using Nyquist sampling period as resolution (which seems incorrect), you should clarify it and compare it with the conventionally used Abbe resolution limit. You should also see if you can obtain a 3D standard sample, like an Argolight slide, to directly determine the resolution of the ODT system in a more direct manner.

The reviewers were correct in the definition of resolution in incoherent microscopy. However, unlike incoherent microscopy such as fluorescence microscopy, the definition of resolution in a coherent imaging system such as ODT is not straightforward and varies widely in the community (please also see: doi.org/10.1038/nphoton.2015.279).

The empirical spatial resolution of ODT has been known to be between the Nyquist sampling period and the Abbe criterion (doi.org/10.1364/OPTICA.4.000460), so the Nyquist sampling period is used to set the lowest bound of the resolution. This holds with our previous comparison of the theoretical and experimental resolutions in Figure 6 (also supplement Figure 1(e)) using a 100-nm-diameter Tetraspeck bead as a 3D standard sample. In the manuscript, we defined the experimental resolution as the full width at half maximum, and the experimentally determined values were between the Nyquist sampling period and the Abbe resolution, confirming our statement. To avoid confusion, we have included additional explanations regarding the spatial resolution of ODT.

3) Aside from resolution, there is also a concerned about the refractive index (RI) sensitivity of ODT. When structures have very small (RI) contrast, it becomes a sensitivity issue more than a resolution issue, e.g., ODT is not able to detect many organelles or granules in the cells even when their diameters are larger than 200 nm (the resolution limit). Could the authors provide an estimation of RI contrast between the membranes and estimate whether they have the detection sensitivity for detecting granule release events.

Thank you for raising an important point. To determine the RI sensitivity, we have additionally analyzed a RI tomogram in the lytic granule transport data (Figure 7—figure supplement 2). We compared the RI statistics of a background, cell volume, IS defined by DeepIS, and lytic granules (defined by the overlapping region between a RI cell volume and a fluorescence image with higher than 20% intensity) (Figure 7—figure supplement 2A). The histograms of the four corresponding regions indicated significant differences (Figure 7—figure supplement 2B).

The experimental signal-to-background ratios of the RI contrast were larger than 30. Among the signal groups, the IS and the lytic granules exhibited the significantly large RI differences. The low RI value of the IS was due to the presence of the synaptic cleft, and the higher RI value of the lytic granules was due to the protein clustering. The large RI variance of the lytic granules implies the low chemical specificity of the RI contrast, which could be overcome with correlative fluorescence imaging as previously discussed in the manuscript. The results suggested that a combination of ODT and fluorescence imaging could provide sufficient sensitivity and specificity for detecting granule release events at the IS.

With regard to RI, you continue to interpret the RI in terms of proteins density and this this seems overly simplistic as area within the resolution limit of the interface will contain many types of structures. So this interpretation should be toned down to more of a hypothetical argument about average macromolecule density given convolved RI of lipids, proteins and carbohydrates in the "synapse".

We agree with the viewpoint. We have accordingly toned down several arguments in the revised manuscript.

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

There are 3 remaining issues that need to be addressed before acceptance, as outlined below:

1) The Results still state that “lymphocytes contain lipid-rich environment localized mostly on a 4-nm-thick membrane site, whose size is beyond optical resolution and implies a negligibly small amount of lipid molecules compared with proteins.” The reviewers and editor agree that the membrane lipid and protein contributions to the refractive index in relation to ODT is likely to be small for the reason you state, with the major contribution to the pixel intensity being cytoplasmic protein and cortical cytoskeleton. Can you rewrite this passage to clarify the situation that the issue is not so much lipid vs protein, but sub resolved membrane vs the relatively voluminous adjacent cytoplasm.

Agree. We have clarified the sentence, as suggested.

2) The error of synaptic cleft determination is on the order of 0.5 µm and this is a serious limitation currently as it suggest the ability of an expert trainer to correctly identify the synaptic cleft from ODT images is poor. Can you discuss the idea training using the negative staining with the smallest dextran as a ground truth might further improve the power of the approach by eliminating the training error. Then if the ODT contains subtle information that the algorithm can can be trained to exploit, this information will be delivered with not bias other than the resolution limit. Then you would need much more correlative negative stain-ODT for training set and a distinct testing set to determine if there is an improvement. We are not asking you to do this, just to discuss it as a possible way to improve the method in the future.

Thank you for the suggestion. In the Discussion of the revised manuscript, we have elaborated possible ways to enhance the precision of synaptic cleft determination, including the idea training using the negative staining with the smallest dextran or using correlative electron micrographs as a ground truth.

3) The statement in the Abstract that you can't do long term 3D fluorescence microscopy has been made false in recent years by lattice light sheet microscopes. While these are not widely available, they will be in future years based on the new Zeiss LLSM7 release. Please rewrite the Abstract to acknowledge lattice light sheet microscopy as a competing technology and perhaps discuss how ODT with analysis trained to identify the synapse will offer complementary information to LLSM.

Agree. As suggested, we have mentioned lattice light sheet microscopes in the Introduction of the revised manuscript, due to the Abstract length limitation.

https://doi.org/10.7554/eLife.49023.sa2

Article and author information

Author details

  1. Moosung Lee

    1. Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
    2. KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Project administration
    Contributed equally with
    Young-Ho Lee and Jinyeop Song
    Competing interests
    Mr. Moosung Lee has financial interests in Tomocube Inc, a company that commercializes optical diffraction tomography and quantitative phase-imaging instruments, and is one of the sponsors of the work.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2826-5401
  2. Young-Ho Lee

    1. Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
    2. Curocell Inc, Daejeon, Republic of Korea
    Contribution
    Conceptualization, Resources, Software, Supervision, Funding acquisition, Validation, Investigation, Project administration
    Contributed equally with
    Moosung Lee and Jinyeop Song
    Competing interests
    Dr. Y.H. Lee is an employee of Curocell Inc
  3. Jinyeop Song

    1. Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
    2. KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Validation, Investigation, Methodology, Project administration
    Contributed equally with
    Moosung Lee and Young-Ho Lee
    Competing interests
    No competing interests declared
  4. Geon Kim

    1. Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
    2. KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
    Contribution
    Conceptualization, Resources, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Methodology, Project administration
    Competing interests
    No competing interests declared
  5. YoungJu Jo

    1. Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
    2. KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
    Contribution
    Conceptualization, Resources, Software, Supervision, Funding acquisition, Validation, Project administration
    Competing interests
    No competing interests declared
  6. HyunSeok Min

    Tomocube Inc, Daejeon, Republic of Korea
    Contribution
    Supervision, Validation, Methodology
    Competing interests
    No competing interests declared
  7. Chan Hyuk Kim

    Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
    Contribution
    Conceptualization, Supervision, Funding acquisition, Validation, Project administration
    For correspondence
    kimchanhyuk@kaist.ac.kr
    Competing interests
    Prof. C. H. K. is a co-founder and shareholder of Curocell inc
  8. YongKeun Park

    1. Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
    2. KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
    Contribution
    Conceptualization, Resources, Software, Supervision, Funding acquisition, Validation, Project administration
    For correspondence
    yk.park@kaist.ac.kr
    Competing interests
    Prof. Park has financial interests in Tomocube Inc, a company that commercializes optical diffraction tomography and quantitative phase-imaging instruments, and is one of the sponsors of the work
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0528-6661

Funding

National Research Foundation of Korea (2017M3C1A3013923)

  • Moosung Lee
  • Jinyeop Song
  • Geon Kim
  • YongKeun Park

National Research Foundation of Korea (2015R1A3A2066550)

  • Moosung Lee
  • Jinyeop Song
  • Geon Kim
  • YongKeun Park

National Research Foundation of Korea (2018K000396)

  • Moosung Lee
  • Jinyeop Song
  • Geon Kim
  • YongKeun Park

The Ministry of Science and ICT (2014M3A9D8032525)

  • Young-Ho Lee
  • Chan Hyuk Kim

The Ministry of Science and ICT (N11190028)

  • Young-Ho Lee
  • Chan Hyuk Kim

National Research Foundation of Korea (2019R1A2C1004129)

  • Young-Ho Lee
  • Chan Hyuk Kim

KAIST (Up prorgam)

  • YongKeun Park

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

Acknowledgements

This work was supported by KAIST Up program, Tomocube Inc, National Research Foundation of Korea (NRF) (2017M3C1A3013923, 2015R1A3A2066550, 2018K000396, NRF-2019R1A2C1004129), the Bio and Medical Technology Development Program of NRF funded by the Ministry of Science & ICT (2014M3A9D8032525), KAIST GCORE(Global Center for Open Research with Enterprise) grant funded by the Ministry of Science and ICT (N11190028).

Senior Editor

  1. Satyajit Rath, Indian Institute of Science Education and Research (IISER), India

Reviewing Editor

  1. Michael L Dustin, University of Oxford, United Kingdom

Reviewers

  1. Michael L Dustin, University of Oxford, United Kingdom
  2. Christoph Wülfing, University of Bristol, United Kingdom
  3. Klaus Ley

Publication history

  1. Received: June 4, 2019
  2. Accepted: December 16, 2020
  3. Accepted Manuscript published: December 17, 2020 (version 1)
  4. Version of Record published: January 20, 2021 (version 2)

Copyright

© 2020, Lee 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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