Abstract Holo-tomographic microscopy (HTM) is a label-free microscopy method reporting the fine changes of a cell’s refractive indices (RIs) in three dimensions at high spatial and temporal resolution. By combining HTM with epifluorescence, we demonstrate that mammalian cellular organelles such as lipid droplets (LDs) and mitochondria show specific RI 3D patterns. To go further, we developed a computer-vision strategy using FIJI, CellProfiler3 (CP3), and custom code that allows us to use the fine images obtained by HTM in quantitative approaches. We could observe the shape and dry mass dynamics of LDs, endocytic structures, and entire cells’ division that have so far, to the best of our knowledge, been out of reach. We finally took advantage of the capacity of HTM to capture the motion of many organelles at the same time to report a multiorganelle spinning phenomenon and study its dynamic properties using pattern matching and homography analysis. This work demonstrates that HTM gives access to an uncharted field of biological dynamics and describes a unique set of simple computer-vision strategies that can be broadly used to quantify HTM images.

Citation: Sandoz PA, Tremblay C, van der Goot FG, Frechin M (2019) Image-based analysis of living mammalian cells using label-free 3D refractive index maps reveals new organelle dynamics and dry mass flux. PLoS Biol 17(12): e3000553. https://doi.org/10.1371/journal.pbio.3000553 Academic Editor: Sandra L. Schmid, UT Southwestern Medical Center, UNITED STATES Received: September 16, 2019; Accepted: November 15, 2019; Published: December 19, 2019 Copyright: © 2019 Sandoz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the paper and its Supporting Information files. Funding: This work was supported by a grant from the Swiss National Science Foundation (SNSF) and the Swiss SystemsX.ch initiative evaluated by the Swiss National Science Foundation (LipidX). MF was funded by Nanolive SA. PS, CT, and FGvdG were funded by LipidX and SNSF. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: I have read the journal's policy, and the authors of this manuscript have the following competing interests: Dr. Mathieu Fréchin is an employee of Nanolive SA. Abbreviations: CARS, Coherent Anti-Stokes Raman Scattering; CMOS, complementary metal oxide semiconductor; CP3, CellProfiler 3; DIC, differential interference contrast; EPFL, École polytechnique fédérale de Lausanne; ER, endoplasmic reticulum; Fis1, fission protein 1; GFP, green fluorescent protein; HTM, holo-tomographic microscopy; LBPA, lysobisphosphatidic acid; LD, lipid droplet; MEF, mouse embryonic fibroblast; mESC, mouse embryonic stem cell; MO, Microscope Objective; NA, Numerical Aperture; NAGTI, N-acetylglucosaminyltransferase I; OA, oleic acid; PFA, paraformaldehyde; RANSAC, Random Sample Consensus; RI, refractive index; RPE1, human retinal pigment epithelial cell line; SIFT, scale-invariant features transform; SRS, Stimulated Raman Scattering; YFP, yellow fluorescent protein

Introduction Because of the transparent nature of a cell, microscopy techniques either use fluorescent markers or transform optical properties of the sample into an observable contrast (for example, phase contrast, differential interference contrast [DIC]). Each of these techniques comes with limitations. Photobleaching, phototoxicity, and interference of markers or ectopically expressed engineered proteins are major concerns. Classical label-free imaging techniques, while less perturbing, provide images with low information content because of poor contrast and resolution. In this context, holo-tomographic microscopy (HTM) [1] is of great interest because it can provide label-free, high-content images using a very low-power light source that generates no phototoxicity. The HTM device used in this study is based on quantitative phase microscopy [2–6], in which the object’s complex wave field is encoded into a hologram. A partially coherent light beam generated by a laser diode (520 nm) is split into two beams to create a Mach–Zehnder interferometer setup [7]; the first one, called the object beam, interacts with the sample before being collected by a 60× objective, while the second one is the reference beam. The two beams are later brought to interference, and the resulting hologram is recorded on a complementary metal oxide semiconductor (CMOS) camera [1–4]. Moreover, the device combines this classical holographic approach with rotational scanning of the specimen [5] using a rotating arm equipped with a mirror as described in Fig 1A. The synthesis of the rotational series of scattering spectra is achieved using complex field deconvolution [8–10] in order to reconstruct a full 3D refractive index (RI) tomogram of even live [11] samples. Importantly, dynamically adjustable mirrors allow the optics of the HTM device to self-adjust during an acquisition experiment in order to adapt to sample changes such as medium evaporation [12]. While RI distributions have begun to be used in life sciences studies [13–16] and specific RI signatures for cell structures [17,18] have been partially analyzed, HTM systems can suffer from coherent noise created by scattered light from the rotational scanning mechanism [19]. This coherent noise perturbs the quality of the generated holograms and impedes spatial resolution, which results in reduced RI sensitivity and perceived image quality [20]. Thanks to its rotational scanning mechanism made of a rotating mirror, the HTM setup used in this study overcomes this limitation and allows, to the best of our knowledge, a unique characterization of cellular and organelle details by RI in space and time. The next challenge was to go beyond a qualitative approach and to use computer-assisted image analysis in order to harness the full potential of HTM images. We developed computer-vision strategies using FIJI [21], CellProfiler3 (CP3) [22], and custom code in order to investigate the evolution of number, shape, and dry mass flow of lipid droplets (LDs) and endocytic structures as well as of full mouse embryonic stem cells (mESCs) over division. Thanks to the inherent multiplexing capacity of HTM, i.e., its capacity to capture multiple organelles and cellular structures all at once, we could finally observe a multiorganelle rotation within mammalian cells. In order to characterize this phenomenon and pave the road for more in-depth studies of its molecular origins and role for the cell, we developed a computer-vision strategy adapted to quantify the rotation of complex patterns using pattern-matching and homography [23,24]. PPT PowerPoint slide

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larger image TIFF original image Download: Fig 1. HTM of subcellular structures. (a) Scheme of the HTM setup coupled to epifluorescence used in this study. (b) 2D and 3D images of flat, unlabeled, and unperturbed HeLa cells taken with HTM demonstrate the necessity of a strategy to harness data. HTM, holo-tomographic microscopy; MO, Microscope Objective; NA, Numerical Aperture. https://doi.org/10.1371/journal.pbio.3000553.g001

Discussion In this work, we demonstrate that state-of-the-art HTM is suitable for the study of cellular organelles. Its label-free nature, combined with its excellent resolution and contrast and low phototoxicity, allows for imaging of organelles that have lipid membranes such as LDs, mitochondria, and pathologically lipid-loaded structures over long periods of time with great details and without sacrificing high temporal resolution. Importantly, HTM has the inherent capacity to capture multiple biological objects and phenomena simultaneously, a property also called multiplexing. This peculiar feature allows for observing complex cellular dynamics such as organellar rotations that implicate many subcellular structures. The true limitation of HTM lies, however, in its main strength: because it works label-free, the produced images are extremely rich and crowded with information that the researcher must sort out in an exploitable way. To this extent, we developed a set of image analysis approaches that are adapted for typical HTM label-free images and are easily reusable because they rely on existing and free image analysis software as well as on simple Python code. With such resources, we want to demonstrate that label-free holotomography is ready for integrating quantitative biology research strategies and that it can provide new insights in a broad range of biological dynamics. Our quantitative study of LDs identified insightful dynamics for understanding the process of early LD formation: we observe that newly forming LDs, all budding at the ER [30], are created at the expense of older LDs’ material; this observation implies that LDs must exchange material over relatively short timescales, and the ER offers the perfect media for that purpose [36]. We also observe bursts in dry mass fluxes within LDs, suggesting a synchronization of fluxes in at least a subset of them, which could also be greatly facilitated by a continuity in their membrane through the ER. These observations are in support of a fluid and continuous connectivity between LDs [34–36]. Perturbing the link between LDs and the ER [61] while doing similar time-lapse experiments and quantifications of LD growth would provide new details on the connectivity existing between LDs and nascent ones or between growing LDs. More generally, the combination of our approach with genetic and chemical perturbations of the various machineries [30,36] identified for controlling LD maintenance and biogenesis could greatly improve our understanding of LDs. We applied a similar approach to record fine dynamic features of the late endosomal accumulation of lipid and cholesterol. This phenomenon was triggered with the compound U18666A, which is classically used to emulate the Nieman–Pick type C phenotype [39]. Our investigation of dry mass fluxes in particular extends our knowledge of the effect of U18666A. We observe that this compound triggers an accumulation of material in late endosomal structures by shifting the equilibrium between recycling and uptake. We observe that recycling still occurs because the average dry mass flux per object oscillates over time and regularly takes a negative value, but not to such an extent that it could counterbalance accumulation. We demonstrate that our simple label-free approach combining HTM with proper image analysis gives access to unique features such as vesicle shape, size, number, and accumulation speed and dry mass amounts and fluxes. Importantly, this can be performed with minimal sample preparation, removing labeling- and phototoxicity-induced perturbations to which lipidic structures are very sensitive [62,63]. We believe that our work addresses a need for relevant, quantitative readouts that could accelerate the discovery of genes implicated in lysosomal storage diseases and the identification of drugs able to fight them [64]. We also developed a set of image analysis procedures that allow us to segment and follow entire cells and applied them to track mESCs before, during, and after mitosis. This task is a technical challenge [40,41] that requires specific devices and methods [42–45], which led to contradicting results regarding what happens to cell size during mitosis [42,44,45]. We demonstrate here that HTM images provide a sufficient level of details for performing very fine cellular segmentation and tracking using CP3, which opens up new possibilities for studying an entire cell’s dynamics. We finally observed striking organellar rotations within mESCs as well as in preadipocytes. Quantifying them was a challenge because, to the best of our knowledge, no preexisting tool in image analysis software would propose an integrated way to measure a coordinated multiobject rotation. We adapted a computer-vision strategy developed for the matching of different views of macro-objects [56] in order to detect and match rotating objects [57–59] in HTM time-lapse experiments. Such an approach has the advantage of working without prior object segmentation and is therefore well-adapted to the treatment of HTM images, which show complex textures composed of multiple biological objects. This simple strategy allowed us to contribute significantly to the understanding of the phenomenon of organellar rotation. Firstly, we could conclude that the entire nucleus rotates and not only the karyoplasm [50]. Furthermore, we could show that such rotation extends to the cellular space because we also observe a rotation of LDs, which could possibly be the consequence of a broader cytoplasmic streaming. Secondly, we characterized dynamic features of unperturbed rotations; observing those features such as speed, acceleration, rotation length, and time with specific perturbations [51–53] will allow a better understanding of the molecular origin of the phenomenon and of its role for the cell’s physiology, particularly in fundamental functions such as the dormant state of germ cells [60].

Methods Cell cultures and seeding Cells were cultured in MEM complemented with 10% FBS, 1% Pen/Strep, 1% L-glutamine, and 1% nonessential amino acids. Cells were seeded for 24 h at low concentration on glass bottom FluoroDishes of 25 mm and 0.17 mm thickness (World Precision Instruments Inc., Sarasota, FL, USA). For mESCs, FluoroDishes were first coated with Vitronectin following the manufacturer’s protocol. Transfection Cells were retrieved with trypsin from tissue culture dishes and seeded in FluoroDishes. After 24 h, the medium was changed, and the cells were transfected using Fugene (Promega, Madison, WI, USA) according to the manufacturer’s protocol. Cell fixation Fixation was performed with PFA or cold methanol. For PFA fixation, cells were first washed 3× with PBS. Then, 2 mL of PFA (3%) was added for 30 minutes at 37°C. Afterwards, dishes were washed 3× with PBS. Quenching of the preparations was performed with 50 mM NH 4 Cl in PBS at room temperature for 10 minutes before 3× PBS washes. Permeabilization was performed using 0.1% Triton X100 for 5 minutes at room temperature. In the case of methanol fixation, cells were washed 3× with PBS before adding precooled methanol at -20°C for 4 minutes. Cells were washed 3× with PBS after fixation. Immunofluorescence Cells were seeded in FluoroDishes for 48 h (if transfected, including transfection) prior to fixation and permeabilization. Overnight blocking was performed in PBS with 0.5% BSA. Primary and secondary antibodies were applied for 30 minutes at room temperature each with in between 3× washes of PBS-0.5% BSA for 5 min. Finally, the preparation was washed again 3 times with PBS-0.5% BSA and postfixed for 15 min at room temperature with 3% PFA, followed by 3× PBS washes. Hoechst (Invitrogen, Carlsbad, CA, USA) was used at 2 μg/mL for 30 minutes at room temperature, Bodipy (Invitrogen) at 1 μg/mL for 30 minutes in physiological conditions. Filipin (Sigma-Aldrich, St. Louis, MO, USA) was used at a dilution of 1:50 (from stock 50 μg/ml). Drug treatments U18666A was applied to the cells at a dilution of 1:2,000 (from a stock of 10 mg/ml). OA was applied at a dilution of 1:5,000 (from stock 1 mg/mL). Imaging HTM, in combination with epifluorescence, was performed on the 3D Cell-Explorer Fluo (Nanolive, Ecublens, Switzerland) using a 60× air objective (NA = 0.8) at a wavelength of λ = 520 nm (Class 1 low power laser, sample exposure 0.2 mW/mm2) and USB 3.0 CMOS Sony IMX174 sensor, with quantum efficiency (typical) 70% (at 545 nm), dark noise (typical) 6.6 e-, dynamic range (typical) 73.7 dB, field of view 90 × 90 × 30 μm, axial resolution 400 nm, and maximum temporal resolution 0.5 3D RI volume per second. The theoretical sensitivity is 2.71 × 10−4. The correlative acquisitions with brightfield, phase contrast, and DIC were done on an Axiovert 200M (Zeiss, Oberkochen, Germany) using a 63× objective (NA 1.4). Live cell imaging Physiological conditions for live cell imaging were reached with a top-stage incubator (Oko-lab, Pozzuoli, Italy). A constant temperature of 37°C and an air humidity saturation as well as a level of 5% CO 2 were achieved throughout the acquisitions. Image analysis of LD dynamics An export was performed within the software STEVE, which controls the HTM microscope, to transform RI volumes into .tiff format. By doing so, RI volumes can be read by the software FIJI. The exported 3D tiffs must be in float format to keep the explicit RI for each voxel value. The 3D RI volumes in .tiff format were then processed in batch within FIJI for performance purposes. 3D RI volumes were transformed into 2D RI maps using maximum intensity projections and were also saved as .tiff files. The resulting series of 2D frames could then be processed using CP3, which does not support full 3D data analysis yet. The CP3 pipeline was designed to load each 2D RI map, segment the contained objects using the primary objects detection module, and extract area, shape, and intensity features using the measurements modules. A critical point for proper object detection was to use a manual threshold value. While automatic threshold detection is suited for most fluorescent microscopy images, whose signal dynamics are related to a plethora of factors, RI images are quantitative and depend only on the nature of the biological object that is observed. Therefore, the threshold can be entered as a fixed value, in our case 1.354, to ensure relative stability of this first step of the detection procedure. The object size limits that we entered were designed to encompass the full spectrum of potential LD diameters from 1 to 7 pixels. The segmented objects were finally used to extract the area and the mean RI value of each LD in each frame of the time-lapse experiment. The data were exported as a .csv file into a Python environment, in which we used the extracted diameter of each LD to calculate its spherical volume; we could then calculate each LD dry mass content using a well-established linear calibration model [32]. The data were finally plotted with the Python library matplotlib. Image analysis of late endosomal accumulation of cholesterol and lipids An export was performed within the software STEVE, which controls the HTM microscope, to transform RI volumes into .tiff format. By doing so, RI volumes can be read by the software FIJI. The exported 3D tiffs must be in float format to keep the explicit RI for each voxel value. The 3D RI volumes in .tiff format were then processed in batch within FIJI for performance purposes. 3D RI volumes were transformed into 2D RI maps using maximum intensity projections and were saved also as .tiff files. The resulting series of 2D frames could then be processed using CP3, which does not support full 3D data analysis yet. The CellProfiler3 pipeline was designed to load each 2D RI map, segment the contained objects using the primary objects detection module, and extract area, shape, and intensity features using the measurements modules. A critical point for proper object detection was to use a manual threshold value. While automatic threshold detection is suited for most fluorescent microscopy images, whose signal dynamics are related to a plethora of factors, RI images are quantitative and depend only on the nature of the biological object that is observed. Therefore, the threshold can be entered as a fixed value, in our case 1.347, to ensure relative stability of this first step of the detection procedure. The object size limits were small and narrow from 1 to 3 pixels. The empirical process of finding the right values is fundamental to avoid detection of large clumps or the exclusion of unusually big structures. The segmented objects were finally used to extract the area and the mean RI value of each late endosomal structures in each frame of the time-lapse experiment. The data were exported as a .csv file into a Python environment, in which we used the extracted diameter to calculate its spherical volume; we could then calculate the dry mass content using a well-established linear calibration model [32]. The data were finally plotted with the Python library matplotlib. Image analysis of mESCs An export was performed within the software STEVE, which controls the HTM microscope, to transform RI volumes into .tiff format. By doing so, RI volumes can be read by the software FIJI. The exported 3D tiffs must be in float format to keep the explicit RI for each voxel value. The 3D RI volumes in .tiff format were then processed in batch within FIJI for performance purposes. 3D RI volumes were transformed into 2D RI maps using maximum intensity projections and were saved also as .tiff files. The resulting series of 2D frames could then be processed using CP3, which does not support full 3D data analysis yet. The CP3 pipeline was designed to load each 2D RI map and to rescale them such that intracellular details do not perturb the global cellular detection; to do so, a simple rescaling from 0 to 1 of RI values between 1.32 and 1.34 was required. The subsequent segmentations of the cell objects were done using the secondary objects detection module based on primary objects defined a priori using the manual Primary Object detection module. The area, shape, and intensity features were then extracted using the related measurements modules on the unmodified RI maps. We preferred using a manual threshold value within the secondary object detection module, together with the propagation algorithm, for the best cellular segmentation. While automatic threshold detection is suited for most fluorescent microscopy images, whose signal absolute values are related to a plethora of factors, RI images are quantitative and depend only on the nature of the biological object that is observed. Therefore, the threshold can be entered as a fixed value because images were rescaled between 0 and 1; the manual threshold value we used is 0.89. The object size limits were between 80 and 250 pixels. The empirical process of finding the right values is fundamental to avoid the detection of under- or oversegmented cells. The objects obtained from the 2D RI frames segmentation were then used in CP3 to measure the objects in each z-frame at each time point, excluding any voxel whose RI was out of the RI range of the objects’ RI defined at the previous step. The data were exported as a .csv file into a Python environment; the extracted area and RI values from each z-frame and time points were then reassembled to obtain 3D measurements, from which we could, for example, calculate total and mean dry mass using a well-established linear calibration model [32]. The data were finally plotted with the Python library matplotlib. Analysis of organellar rotations An export was performed within the software STEVE, which controls the HTM microscope, to transform RI volumes into .tiff format. By doing so, RI volumes can be read by the software FIJI. The exported 3D tiffs must be in float format to keep the explicit RI for each voxel value. The 3D RI volumes in .tiff format were then processed in batch within FIJI for performance purposes. 3D RI volumes were transformed into 2D RI maps using maximum intensity projections and were also saved as .tiff files. The .tiff files opened as a stack in FIJI are then cropped to keep only one cell presenting a rotating nucleus and organelles in the field of view. The resulting smaller 2D RI frames were then processed with a custom Python algorithm implementing the feature matching and homography procedures of the OpenCV library. The first step consists in detecting at least 10 SIFT descriptors [56] for each of the 2D RI frame-adjacent pairs based on Lowe’s ratio test. The identical features from T to T + 1 were then used to define a fitting homography matrix describing the transformation of T into T + 1. To do so, we used the RANSAC method [57–59]. The rotation angle was then extracted from the homography matrices and plotted as regular and rose plots using the matplotlib Python library. For the details and reuse of the procedure, please see our adaptable Python algorithm (S1 Text).

Acknowledgments We thank José Artacho from École polytechnique fédérale de Lausanne (EPFL) Imaging Platform for his great help with the phase contrast and DIC correlative acquisitions. We thank Sebastien Equis for sharing Fig 1A image material and helpful discussions and Lisa Polaro and Yann Cotte for helpful discussions. In addition, we thank Hubert Becker for hand-labeling of mitochondria and LDs, Aleksandra Mandic for providing mESCs, Kristina Shoonjans for the Hepa 1.6 cells, Pierre Gönczy for the U2OS cells, Sebastian Jessberger for KDEL-GFP, Anne-Laure Mahul for Mito-YFP, and Jean Grunberg for anti-LBPA.