Name: Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography (Video) | JoVE Journal
Uploaded: 2025-08-12T13:15:00.000+00:00
Duration: 10 min 40 s
Description: Discover label-free 3D imaging of intestinal organoids using low-coherence holotomography and machine learning analysis. This computational method enables real-time visualization of structural dynamics and drug responses.

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5 minutes

This work was supported by the National Research Foundation of Korea grant funded by the Korean government (MSIT) (RS-2024-00442348, RS-2024-00440577, 2022M3H4A1A02074314), Korea Institute for Advancement of Technology (KIAT) through the International Cooperative R&#38;D program (P0028463), and the Korean Fund for Regenerative Medicine (KFRM) grant funded by the Korea government (the Ministry of Science and ICT and the Ministry of Health &#38; Welfare) (21A0101L1-12). Data acquisition was conducted by M.J.L. and J.H.L., with J.H.L. responsible for data processing and J.M.C. for data analysis. All authors contributed to the manuscript writing. We extend our thanks to ChulMin Oh and Chungha Lee for valuable technical discussions and assistance with figure preparation.

NOTE: Details related to all materials, instruments, and software used in this protocol are provided in the Table of Materials. Equivalent products from different manufacturers may be used as alternatives. A schematic process of the entire protocol is presented in Figure 1.
1. Sample preparation
<ol>
	<li>Preparation of sIO culture medium
	<ol>
		<li>Prepare the commercially available culture medium according to the manufacturer&#39;s instructions. Sterilize the culture medium by filtering it through a 0.22 &#181;m filter unit. 
		NOTE: If the medium is prepared entirely under sterile conditions in a cell culture hood and all components are pre-sterilized (e.g., provided in sterile bottles and vials), then filtration through a 0.22 &#181;m filter may not be strictly necessary.</li>
		<li>Aliquot 15 mL of prepared medium into sterile storage tubes. Warm the aliquoted medium to room temperature (15 - 25 &#176;C) before use in organoid culture.</li>
	</ol>
	</li>
	<li>Thawing sIO stock
	<ol>
		<li>Quickly thaw the frozen organoid stock within 2 min using a 37 &#176;C water bath. Transfer the thawed organoid suspension to a microcentrifuge tube and centrifuge at 150 x g for 3 min to pellet the organoids.</li>
		<li>Carefully remove the supernatant to wash out DMSO, minimizing potential cytotoxic effects.</li>
		<li>Add media and fresh extracellular matrix (ECM) at a 1:4 ratio and gently pipette to ensure uniform resuspension of the pellet. Dispense 15 &#181;L per dome in each well of a 48-well plate. Keep ECM on ice to prevent premature polymerization.</li>
		<li>Place the plate upside-down in a 37 &#176;C, 5% CO2 incubator for 1 h to allow ECM polymerization. After polymerization, carefully add 200 &#181;L of fresh media to each well. 
		NOTE: This protocol is compatible with various commercial imaging dishes and well plates. Adjust the media volume according to the well type to ensure that the ECM dome is fully submerged.</li>
	</ol>
	</li>
	<li>Passaging of sIOs 
	NOTE: Organoids tend to adhere to pipette tips. To minimize adherence, pre-wet pipette tips with the medium.
	<ol>
		<li>Aspirate spent medium. Add 200 &#181;L of cell recovery solution to each well and incubate at 4 &#176;C for 30 min to facilitate ECM degradation and organoid retrieval.</li>
		<li>Collect the organoid suspension by gentle pipetting and transfer it to a microcentrifuge tube. Centrifuge at 150 x g for 3 min, then carefully remove the supernatant.</li>
		<li>(Optional) To passage as single cells, perform one of the following dissociation methods:
		<ol>
			<li>Enzymatic method: Add 100 &#181;L of single-cell dissociation solution to the pellet and incubate at 37 &#176;C, 5% CO2 incubator for 1-2 min. Add 150 &#181;L of media to neutralize. Perform pipetting or gentle tapping. Centrifuge at 150 x g for 3 min and remove the supernatant, leaving only the pellet. 
			NOTE: Avoid over-incubating, as prolonged exposure can be highly detrimental to cells.</li>
			<li>Mechanical dissociation: Resuspend the pellet in 200 &#181;L of culture medium. Using a P200 pipette, pipette up and down 20x-30x to mechanically dissociate organoids into smaller fragments or single cells. Centrifuge at 150 x g for 3 min, then aspirate the supernatant and retain the pellet.</li>
		</ol>
		</li>
		<li>Add media and fresh ECM at a 1:4 ratio and gently pipette to ensure uniform resuspension of the pellet. Dispense 15 &#181;L per dome in each well of a 48-well plate.</li>
		<li>Place the plate upside down in a 37 &#176;C, 5% CO2 incubator for 1 h to allow ECM to polymerize.</li>
		<li>After polymerization, add 200 &#181;L of fresh culture medium to each well and fill the empty outer wells with PBS to prevent evaporation. Replace the culture medium every 2-3 days and passage the organoids weekly. Adjust the passaging interval according to the desired organoid size.</li>
	</ol>
	</li>
	<li>Drug treatment
	<ol>
		<li>Prepare a 10 mM stock solution of cisplatin by dissolving 3.00 mg of cisplatin powder (MW &#8776; 300.05 g/mol) in 1 mL of DMSO. Vortex briefly and protect the solution from light using amber tubes or by wrapping them with aluminum foil.</li>
		<li>To prepare the treatment medium, dilute the 10 mM cisplatin stock 1:1,000 in complete sIO culture medium to obtain a final concentration of 10&#8239;&#181;M cisplatin and 0.1% DMSO.</li>
		<li>For the vehicle control, prepare 0.1% DMSO in culture medium to match the final DMSO concentration without cisplatin.</li>
		<li>Remove the spent medium from sIO cultures and gently replace it with 10 &#181;M cisplatin-containing medium or DMSO-only control medium.</li>
		<li>After treatment, transfer the samples to the imaging system incubator.</li>
	</ol>
	</li>
</ol>
2. Low-coherence holotomography imaging using operating software
<ol>
	<li>Sample preparation for imaging
	<ol>
		<li>Dispense 15 &#181;L of organoid-ECM dome onto #1.5 coverslip-bottom imaging dish by following the procedure described in steps 1.3.1-1.3.4. Place each dome in the dish as close to the center as possible for optimal imaging.</li>
		<li>Prior to polymerization, incubate at room temperature for 1 min to allow the organoids to settle down to the bottom.</li>
		<li>Place the plate upside down in a 37 &#176;C, 5% CO2 incubator for 1 h to polymerize the ECM. Then, gently add enough culture medium to fully submerge the organoids. 
		NOTE: Adjust the medium volume to ensure that the organoids are fully submerged according to the size of the coverslip-bottomed imaging dish.</li>
		<li>At 5 days post-passage, wash the sample 2x-3x with PBS immediately before imaging to remove debris and reduce optical artifacts.</li>
	</ol>
	</li>
	<li>Imaging setup
	<ol>
		<li>Turn on the environmental controller to maintain sample viability. The chamber controller unit automatically adjusts the temperature to 37 &#176;C and the CO2 concentration to 5%.</li>
		<li>Press the Door&#160;button on the front of the instrument to open the door. Add water to form a thin layer inside the chamber well to prevent the culture media from drying out.</li>
		<li>Place the imaging dish in a proper vessel holder, insert it into the imaging chamber, and secure it using a pin to prevent movement. Make sure that the dish is firmly attached to the vessel holder without tilting.</li>
		<li>Close the door of the instrument using the Door button to block external light interference.</li>
		<li>Launch TomoStudio X software and log in. Click Start to open the main window.</li>
		<li>Click Add Project in the top-left corner and assign experimental parameters (e.g., project name, medium type, sample type, etc.). The specimen panel will be created automatically. Ensure the correct medium type is assigned for the use of the appropriate RI in the experiment.</li>
		<li>Click on the Desired well in the panel and click Create at the top to register the wells as specimens.</li>
		<li>Click ROI Setup in the top-right corner to define the region of interest (ROI) in the dish. Once all parameters are set, click Run Experiment in the bottom-right corner to open the image acquisition window.</li>
		<li>Click Load Vessel in the top-right corner. A brightfield image will appear. Adjust the Z-position using the +Z and -Z buttons to focus.</li>
		<li>In the Single imaging tab, adjust the ROI size. Capture organoids within a 160 &#181;m x 160 &#181;m field of view and acquire axial stacks up to 140 &#181;m in depth. For larger organoids, acquire multiple RI tomograms and stitch images by selecting the Tile Imaging checkbox at the bottom.</li>
		<li>Navigate to the Time Lapse Imaging tab to configure long-term imaging. Set the duration and interval time as needed. In this experiment, time-lapse imaging was performed at 10 min intervals over a 24 h period, and analysis was conducted at 2 h intervals throughout the same duration.</li>
		<li>Click the Scan icon to capture the current ROI location. Manually center the ECM dome using brightfield imaging. Click the BF button to adjust the intensity and exposure values for brightfield imaging.</li>
		<li>Select ROI by moving the ROI box that appears in the Preview panel. Once the desired ROI is found, click Add Point at the bottom. The imaging point list will appear.</li>
		<li>Click Acquire to start imaging. The raw image data will be acquired.</li>
	</ol>
	</li>
	<li>Image acquisition 
	NOTE: This process generates a Tomocube Common File (TCF) from raw image data. The raw image data must be processed into a TCF file, which contains multiple types of images, such as an RI tomogram and a maximum intensity projection. The RI tomogram represents the sample&#39;s 3D RI distribution, and the maximum intensity projection shows the highest RI value for each lateral position in the XY plane.
	<ol>
		<li>Drag and drop raw image files to the HTX processing server. Click Process to generate a TCF file from the raw image file.</li>
		<li>To view the RI tomograms generated in the TCF file, use TomoAnalysis Viewer, MATLAB, or&#160;Python. To view a TCF file in MATLAB, use functions like h5info and h5read. Likewise, in Python, use the h5py package for reading H5 files.</li>
	</ol>
	</li>
</ol>
3. Image analysis
<ol>
	<li>Machine learning-based image segmentation
	<ol>
		<li>Export TCF image file to HDF5 format (Supplementary Coding File 1) to ensure the data is in a multi-dimensional format compatible with ilastik.</li>
		<li>Open ilastik and navigate to File &#62; New Project. Select Pixel Classification under the Segmentation Workflows section. Save the project file in a designated directory.</li>
		<li>Load the HDF5 file by navigating to 1. Input Data tab. Click Add New File, select the Appropriate H5 Dataset, and ensure that the correct image channels are assigned.</li>
		<li>Navigate to 2. Feature Selection tab to select relevant features (Color/Intensity, Edge,Texture) to optimize segmentation. 
		NOTE: Feature selection may vary depending on the dataset and image characteristics. For the current analysis, the selected features included: Color/Intensity (&#963; = 1.60), Edge (&#963; = 3.50), and Texture (&#963; = 1.60). Optimization based on a heuristic approach may be required depending on image complexity. Alternatively, the Suggest Features &#62; Run Feature Selection function in ilastik can be used to identify an optimal set of parameters.</li>
		<li>In 3. Training tab, label organoid and non-organoid regions by applying brush strokes of distinct colors. To improve segmentation fidelity, carefully annotate regions around boundaries.</li>
		<li>Click Live Update to preview segmentation results and make refinements as necessary.</li>
		<li>Navigate to 4. Prediction Export. In the Source Selection, choose Simple Segmentation to export labeled predictions. Click Export All, ensuring the output format is set to .h5 or .tiff, depending on downstream analysis requirements.</li>
	</ol>
	</li>
	<li>Quantitative feature analysis 
	NOTE: This protocol step is executed in MATLAB, with each procedure detailed in Supplementary Coding File 2.
	<ol>
		<li>By combining the RI tomogram with the mask image, create a masked RI image that includes only the organoid region.</li>
		<li>Using the masked RI image, calculate quantitative parameters such as organoid volume, protein density, and protein content.
		<ol>
			<li>Volume: Compute the total volume of the organoid by multiplying the number of pixels in the segmented mask by the pixel resolution for the X, Y, and Z. Calculate the number of pixels by counting all voxel values in the organoid-labeled mask array, using the built-in MATLAB function Sum. 
			NOTE: The pixel resolution is determined by the imaging system and can be found under Data &#62; 3D attributes group within the HDF5 structure.</li>
			<li>Protein Density: Calculate the protein density&#160;by subtracting the medium RI value from the mean organoid RI and dividing it by the Protein Refractive Index Increment.
			<ol>
				<li>Medium RI: Measure the RI of the surrounding medium using a refractometer prior to analysis.</li>
				<li>Mean RI Value: Determine the mean RI of the organoid by averaging the RI values of voxels located within the organoid mask.</li>
				<li>Protein Refractive Index Increment (&#945;): Use a previously reported empirical constant derived from refractive index increment studies.</li>
			</ol>
			</li>
			<li>Protein Content: Calculate the total protein content by multiplying the volume determined in Step 3.2.2.1 with the protein density obtained in Step 3.2.2.2.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>

<ol><li>Kim, J., Koo, B. K., Knoblich, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Human+organoids%3A+model+systems+for+human+biology+and+medicine%5BTitle%5D)+AND+%22Nat+Rev+Mol+Cell+Biol%22%5BJournal%5D)">Human organoids: model systems for human biology and medicine.</a> Nat Rev Mol Cell Biol. 21 (10), 571-584 (2020).</li>
<li>Schwank, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Functional+repair+of+CFTR+by+CRISPR%2FCas9+in+intestinal+stem+cell+organoids+of+cystic+fibrosis+patients%5BTitle%5D)+AND+%22Cell+stem+cell%22%5BJournal%5D)">Functional repair of CFTR by CRISPR/Cas9 in intestinal stem cell organoids of cystic fibrosis patients.</a> Cell stem cell. 13 (6), 653-658 (2013).</li>
<li>Arjmand, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Advancement+of+organoid+technology+in+regenerative+medicine%5BTitle%5D)+AND+%22Regen+Eng+Transl+Med%22%5BJournal%5D)">Advancement of organoid technology in regenerative medicine.</a> Regen Eng Transl Med. 9 (1), 83-96 (2023).</li>
<li>Zhang, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Patch+grafting+of+organoids+of+stem%2Fprogenitors+into+solid+organs+can+correct+genetic-based+disease+states%5BTitle%5D)+AND+%22Biomaterials%22%5BJournal%5D)">Patch grafting of organoids of stem/progenitors into solid organs can correct genetic-based disease states.</a> Biomaterials. 288, 121647(2022).</li>
<li>Hofer, M., Lutolf, M. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Engineering+organoids%5BTitle%5D)+AND+%22Nat+Rev+Mater%22%5BJournal%5D)">Engineering organoids.</a> Nat Rev Mater. 6 (5), 402-420 (2021).</li>
<li>Park, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Artificial+intelligence-enabled+quantitative+phase+imaging+methods+for+life+sciences%5BTitle%5D)+AND+%22Nat+Meth%22%5BJournal%5D)">Artificial intelligence-enabled quantitative phase imaging methods for life sciences.</a> Nat Meth. 20 (11), 1645-1660 (2023).</li>
<li>Fei, K., Zhang, J., Yuan, J., Xiao, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Present+application+and+perspectives+of+organoid+imaging+technology%5BTitle%5D)+AND+%22Bioengineering%22%5BJournal%5D)">Present application and perspectives of organoid imaging technology.</a> Bioengineering. 9 (3), 121(2022).</li>
<li>Fujii, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+simple+method+for+histopathological+evaluation+of+organoids%5BTitle%5D)+AND+%22J+Toxicol+Pathol%22%5BJournal%5D)">A simple method for histopathological evaluation of organoids.</a> J Toxicol Pathol. 31 (1), 81-85 (2018).</li>
<li>Yoshimoto, S., Taguchi, M., Sumi, S., Oka, K., Okamura, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Establishment+of+a+novel+protocol+for+formalin-fixed+paraffin-embedded+organoids+and+spheroids%5BTitle%5D)+AND+%22Biol+Open%22%5BJournal%5D)">Establishment of a novel protocol for formalin-fixed paraffin-embedded organoids and spheroids.</a> Biol Open. 12 (5), bio059882(2023).</li>
<li>Badder, L. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((3D+imaging+of+colorectal+cancer+organoids+identifies+responses+to+Tankyrase+inhibitors%5BTitle%5D)+AND+%22PLoS+One%22%5BJournal%5D)">3D imaging of colorectal cancer organoids identifies responses to Tankyrase inhibitors.</a> PLoS One. 15 (8), e0235319(2020).</li>
<li>Lacin, M. E., Yildirim, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Applications+of+multiphoton+microscopy+in+imaging+cerebral+and+retinal+organoids%5BTitle%5D)+AND+%22Front+Neurosci%22%5BJournal%5D)">Applications of multiphoton microscopy in imaging cerebral and retinal organoids.</a> Front Neurosci. 18, 1360482(2024).</li>
<li>Brémond Martin, C., Simon Chane, C., Clouchoux, C., Histace, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Recent+trends+and+perspectives+in+cerebral+organoids+imaging+and+analysis%5BTitle%5D)+AND+%22Fronti+Neurosci%22%5BJournal%5D)">Recent trends and perspectives in cerebral organoids imaging and analysis.</a> Fronti Neurosci. 15, 629067(2021).</li>
<li>Smits, L. M., Schwamborn, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Midbrain+organoids%3A+a+new+tool+to+investigate+Parkinson%27s+disease%5BTitle%5D)+AND+%22Front+Cell+Dev+Biol%22%5BJournal%5D)">Midbrain organoids: a new tool to investigate Parkinson's disease.</a> Front Cell Dev Biol. 8, 359(2020).</li>
<li>Huisken, J., Swoger, J., Del Bene, F., Wittbrodt, J., Stelzer, E. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Optical+sectioning+deep+inside+live+embryos+by+selective+plane+illumination+microscopy%5BTitle%5D)+AND+%22Science%22%5BJournal%5D)">Optical sectioning deep inside live embryos by selective plane illumination microscopy.</a> Science. 305 (5686), 1007-1009 (2004).</li>
<li>Delgado-Rodriguez, P., Brooks, C. J., Vaquero, J. J., Munoz-Barrutia, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Innovations+in+ex+vivo+light+sheet+fluorescence+microscopy%5BTitle%5D)+AND+%22Prog+Biophys+Mol+Biol%22%5BJournal%5D)">Innovations in ex vivo light sheet fluorescence microscopy.</a> Prog Biophys Mol Biol. 168, 37-51 (2022).</li>
<li>Masson, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((High-resolution+in-depth+imaging+of+optically+cleared+thick+samples+using+an+adaptive+SPIM%5BTitle%5D)+AND+%22Sci+Rep%22%5BJournal%5D)">High-resolution in-depth imaging of optically cleared thick samples using an adaptive SPIM.</a> Sci Rep. 5 (1), 16898(2015).</li>
<li>Kopper, O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((An+organoid+platform+for+ovarian+cancer+captures+intra-and+interpatient+heterogeneity%5BTitle%5D)+AND+%22Nat+Med%22%5BJournal%5D)">An organoid platform for ovarian cancer captures intra-and interpatient heterogeneity.</a> Nat Med. 25 (5), 838-849 (2019).</li>
<li>Renner, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+fully+automated+high-throughput+workflow+for+3D-based+chemical+screening+in+human+midbrain+organoids%5BTitle%5D)+AND+%22elife%22%5BJournal%5D)">A fully automated high-throughput workflow for 3D-based chemical screening in human midbrain organoids.</a> elife. 9, e52904(2020).</li>
<li>Laperrousaz, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Direct+transfection+of+clonal+organoids+in+Matrigel+microbeads%3A+a+promising+approach+toward+organoid-based+genetic+screens%5BTitle%5D)+AND+%22Nucl+Acids+Res%22%5BJournal%5D)">Direct transfection of clonal organoids in Matrigel microbeads: a promising approach toward organoid-based genetic screens.</a> Nucl Acids Res. 46 (12), e70-e70 (2018).</li>
<li>Susaki, E., Takasato, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Perspective%3A+Extending+the+Utility+of+Three-Dimensional+Organoids+by+Tissue+Clearing+Technologies%5BTitle%5D)+AND+%22Front+Cell+Dev+Biol%22%5BJournal%5D)">Perspective: Extending the Utility of Three-Dimensional Organoids by Tissue Clearing Technologies.</a> Front Cell Dev Biol. 9, 1-12 (2021).</li>
<li>Park, Y., Depeursinge, C., Popescu, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Quantitative+phase+imaging+in+biomedicine%5BTitle%5D)+AND+%22Nat+Photon%22%5BJournal%5D)">Quantitative phase imaging in biomedicine.</a> Nat Photon. 12 (10), 578-589 (2018).</li>
<li>Kim, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Holotomography%5BTitle%5D)+AND+%22Nat+Rev+Meth+Primers%22%5BJournal%5D)">Holotomography.</a> Nat Rev Meth Primers. 4 (1), 51(2024).</li>
<li>Wolf, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Three-dimensional+structure+determination+of+semi-transparent+objects+from+holographic+data%5BTitle%5D)+AND+%22Optics+Comm%22%5BJournal%5D)">Three-dimensional structure determination of semi-transparent objects from holographic data.</a> Optics Comm. 1 (4), 153-156 (1969).</li>
<li>Kim, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Integrating+holotomography+and+deep+learning+for+rapid+detection+of+NPM1+mutations+in+AML%5BTitle%5D)+AND+%22Sci+Rep%22%5BJournal%5D)">Integrating holotomography and deep learning for rapid detection of NPM1 mutations in AML.</a> Sci Rep. 14 (1), 23780(2024).</li>
<li>Kim, S. Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Label-free+imaging+and+evaluation+of+characteristic+properties+of+asthma-derived+eosinophils+using+optical+diffraction+tomography%5BTitle%5D)+AND+%22Biochem+BiophysRes+Comm%22%5BJournal%5D)">Label-free imaging and evaluation of characteristic properties of asthma-derived eosinophils using optical diffraction tomography.</a> Biochem BiophysRes Comm. 587, 42-48 (2022).</li>
<li>Kim, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((RNA-mediated+demixing+transition+of+low-density+condensates%5BTitle%5D)+AND+%22Nat+Comm%22%5BJournal%5D)">RNA-mediated demixing transition of low-density condensates.</a> Nat Comm. 14 (1), 2425(2023).</li>
<li>Kim, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Rapid+species+identification+of+pathogenic+bacteria+from+a+minute+quantity+exploiting+three-dimensional+quantitative+phase+imaging+and+artificial+neural+network%5BTitle%5D)+AND+%22Light+Sci+Appl%22%5BJournal%5D)">Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network.</a> Light Sci Appl. 11 (1), 190(2022).</li>
<li>Hugonnet, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Multiscale+label-free+volumetric+holographic+histopathology+of+thick-tissue+slides+with+subcellular+resolution%5BTitle%5D)+AND+%22Adv+Photon%22%5BJournal%5D)">Multiscale label-free volumetric holographic histopathology of thick-tissue slides with subcellular resolution.</a> Adv Photon. 3 (2), 026004-026004 (2021).</li>
<li>Lee, M. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Long-term+three-dimensional+high-resolution+imaging+of+live+unlabeled+small+intestinal+organoids+via+low-coherence+holotomography%5BTitle%5D)+AND+%22Exp+Mol+Med%22%5BJournal%5D)">Long-term three-dimensional high-resolution imaging of live unlabeled small intestinal organoids via low-coherence holotomography.</a> Exp Mol Med. 56 (10), 2162-2170 (2024).</li>
<li>Wang, D., Lippard, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cellular+processing+of+platinum+anticancer+drugs%5BTitle%5D)+AND+%22Nat+Rev+Drug+Discov%22%5BJournal%5D)">Cellular processing of platinum anticancer drugs.</a> Nat Rev Drug Discov. 4 (4), 307-320 (2005).</li>
<li>Berg, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Ilastik%3A+interactive+machine+learning+for+%28bio%29+image+analysis%5BTitle%5D)+AND+%22Nat+Meth%22%5BJournal%5D)">Ilastik: interactive machine learning for (bio) image analysis.</a> Nat Meth. 16 (12), 1226-1232 (2019).</li>
<li>Sbrana, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Label-free+three-dimensional+imaging+and+quantitative+analysis+of+living+fibroblasts+and+myofibroblasts+by+holotomographic+microscopy%5BTitle%5D)+AND+%22Microscopy+Res+Tech%22%5BJournal%5D)">Label-free three-dimensional imaging and quantitative analysis of living fibroblasts and myofibroblasts by holotomographic microscopy.</a> Microscopy Res Tech. 87 (11), 2757-2773 (2024).</li>
<li>Zhao, H., Brown, P. H., Schuck, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((On+the+distribution+of+protein+refractive+index+increments%5BTitle%5D)+AND+%22Biophys+J%22%5BJournal%5D)">On the distribution of protein refractive index increments.</a> Biophys J. 100 (9), 2309-2317 (2011).</li>
<li>Wallaeys, C., Garcia-Gonzalez, N., Libert, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Paneth+cells+as+the+cornerstones+of+intestinal+and+organismal+health%3A+a+primer%5BTitle%5D)+AND+%22EMBO+Mol+Med%22%5BJournal%5D)">Paneth cells as the cornerstones of intestinal and organismal health: a primer.</a> EMBO Mol Med. 15 (2), e16427(2023).</li>
<li>Hooper, L. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Epithelial+cell+contributions+to+intestinal+immunity%5BTitle%5D)+AND+%22Adv+Immunol%22%5BJournal%5D)">Epithelial cell contributions to intestinal immunity.</a> Adv Immunol. 126, 129-172 (2015).</li>
<li>Atanga, R., Singh, V., In, J. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Intestinal+enteroendocrine+cells%3A+present+and+future+druggable+targets%5BTitle%5D)+AND+%22Int+J+Mol+Sci%22%5BJournal%5D)">Intestinal enteroendocrine cells: present and future druggable targets.</a> Int J Mol Sci. 24 (10), 8836(2023).</li>
<li>Dicarlo, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Quercetin+exposure+suppresses+the+inflammatory+pathway+in+intestinal+organoids+from+winnie+mice%5BTitle%5D)+AND+%22Int+J+Mol+Sci%22%5BJournal%5D)">Quercetin exposure suppresses the inflammatory pathway in intestinal organoids from winnie mice.</a> Int J Mol Sci. 20 (22), 5771(2019).</li>
<li>Miura, S., Suzuki, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Generation+of+mouse+and+human+organoid-forming+intestinal+progenitor+cells+by+direct+lineage+reprogramming%5BTitle%5D)+AND+%22Cell+Stem+Cell%22%5BJournal%5D)">Generation of mouse and human organoid-forming intestinal progenitor cells by direct lineage reprogramming.</a> Cell Stem Cell. 21 (4), 456-471.e5 (2017).</li>
<li>Barer, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Determination+of+dry+mass%2C+thickness%2C+solid+and+water+concentration+in+living+cells%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">Determination of dry mass, thickness, solid and water concentration in living cells.</a> Nature. 172 (4389), 1097-1098 (1953).</li>
<li>Popescu, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Optical+imaging+of+cell+mass+and+growth+dynamics%5BTitle%5D)+AND+%22Am+J+Physiol+Cell+Physiol%22%5BJournal%5D)">Optical imaging of cell mass and growth dynamics.</a> Am J Physiol Cell Physiol. 295 (2), C538-C544 (2008).</li>
<li>Shin, S., Kim, D., Kim, K., Park, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Super-resolution+three-dimensional+fluorescence+and+optical+diffraction+tomography+of+live+cells+using+structured+illumination+generated+by+a+digital+micromirror+device%5BTitle%5D)+AND+%22Sci+Rep%22%5BJournal%5D)">Super-resolution three-dimensional fluorescence and optical diffraction tomography of live cells using structured illumination generated by a digital micromirror device.</a> Sci Rep. 8 (1), 9183(2018).</li>
<li>Kim, Y. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Combining+three-dimensional+quantitative+phase+imaging+and+fluorescence+microscopy+for+the+study+of+cell+pathophysiology%5BTitle%5D)+AND+%22Yale+Biol+Med%22%5BJournal%5D)">Combining three-dimensional quantitative phase imaging and fluorescence microscopy for the study of cell pathophysiology.</a> Yale Biol Med. 91 (3), 267(2018).</li>
<li>Liu, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Simultaneous+dual-contrast+three-dimensional+imaging+in+live+cells+via+optical+diffraction+tomography+and+fluorescence%5BTitle%5D)+AND+%22Photonics+Res%22%5BJournal%5D)">Simultaneous dual-contrast three-dimensional imaging in live cells via optical diffraction tomography and fluorescence.</a> Photonics Res. 7 (9), 1042-1050 (2019).</li>
<li>Schlüßler, R., et al. Combined fluorescence, optical diffraction tomography and Brillouin microscopy. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aSchl%C3%BC%C3%9Fler%2C+R+%22bioRvix%22">bioRvix</a>. , (2020).</li>
<li>Chowdhury, S., Eldridge, W. J., Wax, A., Izatt, J. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Structured+illumination+multimodal+3D-resolved+quantitative+phase+and+fluorescence+sub-diffraction+microscopy%5BTitle%5D)+AND+%22Biomed+Optics+Exp%22%5BJournal%5D)">Structured illumination multimodal 3D-resolved quantitative phase and fluorescence sub-diffraction microscopy.</a> Biomed Optics Exp. 8 (5), 2496-2518 (2017).</li>
<li>Jo, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Label-free+multiplexed+microtomography+of+endogenous+subcellular+dynamics+using+generalizable+deep+learning%5BTitle%5D)+AND+%22Nat+Cell+Biol%22%5BJournal%5D)">Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning.</a> Nat Cell Biol. 23 (12), 1329-1337 (2021).</li>
<li>Oh, C., Hugonnet, H., Lee, M., Park, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Digital+aberration+correction+for+enhanced+thick+tissue+imaging+exploiting+aberration+matrix+and+tilt-tilt+correlation+from+the+optical+memory+effect%5BTitle%5D)+AND+%22Nat+Comm%22%5BJournal%5D)">Digital aberration correction for enhanced thick tissue imaging exploiting aberration matrix and tilt-tilt correlation from the optical memory effect.</a> Nat Comm. 16 (1), 1685(2025).</li>
<li>Yasuhiko, O., Takeuchi, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((In-silico+clearing+approach+for+deep+refractive+index+tomography+by+partial+reconstruction+and+wave-backpropagation%5BTitle%5D)+AND+%22Light+Sci+Appl%22%5BJournal%5D)">In-silico clearing approach for deep refractive index tomography by partial reconstruction and wave-backpropagation.</a> Light Sci Appl. 12 (1), 101(2023).</li>
</ol>

M.J.L., J.H.L., S.M.L., and Y.K.P. have financial interests in Tomocube Inc., a company that commercializes holotomography and quantitative phase imaging instruments.

Holotomography presents a powerful approach for label-free, high-resolution imaging of live organoids, overcoming many challenges associated with conventional imaging techniques. In this protocol, we describe a comprehensive methodology for applying low-coherence HT to sIO research, enabling real-time, quantitative visualization of organoid morphology, growth dynamics, and responses to drug treatment. The integration of machine learning-based segmentation and quantitative analysis further enhances the analytical capabili...

Organoids are three-dimensional (3D) miniaturized versions of organs grown from stem cells, capable of recapitulating key structural and functional features of real organs in vitro1. Researchers have developed organoid systems for a wide range of tissues - including the intestine, brain, lung, liver, and kidney - each demonstrating the self-organizing architecture and cell-type diversity reminiscent of its in vivo counterpart. By mirroring native organ architecture and multicellular complexity, organoids serve as powerful model systems for studying human development, tissue regeneration, and disease mechanisms1. They have become pivotal tools for disease modeling and drug discovery, enabling researchers to explore disease pathways and test new therapeutics on patient-specific 3D tissues in the lab2. Notably, organoids also hold promise in regenerative medicine3: organoid-derived tissues can be engrafted in vivo to repair damage, as demonstrated with intestinal and liver organoids, underscoring their potential for restoring organ function4. This versatility makes organoids indispensable in modern biomedical research and personalized medicine.
Given their multicellular composition and 3D structural complexity, capturing and analyzing organoid dynamics is crucial. However, conventional imaging techniques face significant limitations in resolving organoid structures and dynamics, often producing 2D snapshots that fail to represent their full physiological state5.
Brightfield microscopy is a widely used, non-invasive method for capturing organoids. It conveniently monitors growth and morphology but provides limited contrast in unstained 3D samples. Additionally, it lacks optical sectioning, resulting in poor resolution of internal structures. Organoids appear largely transparent, hindering the detection of fine features beneath the surface6. Phase contrast enhances visibility by converting phase shifts to intensity differences, revealing general architecture (e.g., a central lumen) without staining. Despite its utility for real-time observation, phase contrast suffers from halo artifacts, low resolution, and limited penetration depth, making it less effective for thicker organoids7.
Serial sectioning followed by hematoxylin and eosin (H&#38;E) staining and antibody-based immunostaining is widely used to analyze organoid microstructure and cellular composition8. Improved tissue processing techniques, such as formalin-fixed paraffin embedding (FFPE) and cryosectioning, enhance morphology preservation, with FFPE offering superior results9. Embedding organoids in hydrogels like agarose and centrifugation align multiple samples in a single section, improving analysis efficiency. H&#38;E-stained sections of patient-derived tumor organoids reflect original tumor histology, while immunofluorescence staining highlights markers like CK8 for cell identification10. However, these techniques cannot monitor live organoids and may introduce artifacts such as tissue wrinkling or deformation during sectioning.
Confocal fluorescence microscopy is widely used for organoid imaging, yielding high-resolution (sub-micrometer) optical sections that enable detailed visualization of organoid architecture and cellular composition11,12. However, confocal imaging of 3D organoids is typically limited to superficial layers (up to 100 &#181;m depth) and can be time-consuming and phototoxic due to point-scanning laser illumination13. Light-sheet fluorescence microscopy (LSFM) overcomes some of these limitations by illuminating only the imaging plane, achieving rapid volumetric imaging of entire organoids with minimal photobleaching and phototoxicity14,15; however, this technique often requires tissue clearing for optimal imaging of thick specimens. Additionally, LSFM imaging depth is restricted by light scattering and refractive index (RI) mismatches, which can be mitigated through adaptive optics, multi-view imaging, and optimized excitation wavelengths16. Sample mounting also introduces structural deformations, while large 3D datasets pose computational challenges in storage and analysis. Recent advancements, including improved genetic labeling techniques, novel clearing reagents, adaptive optics, and AI-driven image analysis, are addressing these limitations, making LSFM increasingly feasible for organoid research7.
Despite these advancements, 3D fluorescence imaging techniques pose several challenges for the long-term monitoring of live organoids. First, the use of fluorescent labeling introduces phototoxic effects and perturbs biological processes, posing challenges for prolonged live imaging studies17. Second, antibody-based labeling suffers from limited penetration due to diffusion barriers within dense organoid structures, requiring advanced permeabilization and tissue-clearing methods, which take longer than 10 days of incubation for the use of first and secondary antibodies18. Third, transfection and stable fluorescent protein expression in organoids remain inefficient, as conventional gene delivery approaches struggle with 3D environments19. Fourth, optical clearing techniques, essential for deep imaging, often lead to fluorescence signal loss and tissue distortion, necessitating optimized clearing protocols that balance transparency and preservation of fluorescence20.
To address these limitations, holotomography (HT) has been introduced as a technique for imaging unlabeled live organoids with high resolution over time. Also known as 3D quantitative phase imaging, HT is a label-free, high-resolution modality that enables real-time, quantitative visualization of organoids while preserving their native physiological state. The principle of HT is analogous to X-ray computed tomography; by capturing multiple 2D optical field images under various illumination conditions, the 3D RI distribution of an unlabeled sample is reconstructed through inverse wave equation solving21,22,23. HT has been widely applied across various biological fields, including hematology24,25, phase condensation in cell biology26, microbiology27, and histopathology28. More recently, its high-resolution, label-free 3D imaging capability has been increasingly utilized for organoid research21,22,23,29, offering a non-invasive approach to studying their structural and functional dynamics.
In this study, we present a detailed protocol for the use of low-coherence HT in long-term, label-free monitoring of mouse small intestinal organoids (sIOs). This method enables real-time visualization of organoid growth and drug responses over 24 h while providing quantitative measurements of organoid volume, protein density, and protein content, facilitating rigorous, data-driven analysis of organoid behavior. To investigate morphological alterations in organoids, we treated the samples with cisplatin, a platinum-based chemotherapeutic agent known to induce apoptosis by forming DNA crosslinks and inhibiting cellular replication30. While 3D RI imaging captures structural details, systematic analysis is essential for quantifying organoid dynamics. To achieve this, we integrate ilastik, a machine learning-based segmentation toolkit31 to differentiate organoid regions from the background. This protocol outlines the complete workflow, including organoid culture preparation, 3D HT acquisition, machine learning-based segmentation, and quantitative analysis from RI tomograms. By offering a scalable, reproducible, and non-invasive imaging framework, this approach establishes a new standard for organoid research with broad applications in disease modeling, regenerative medicine, and drug screening.

<table><tbody><tr><td>48-well plate&amp;nbsp;</td><td>Corning</td><td>3548</td><td>Culture dish</td></tr><tr><td>Cell recovery solution&amp;nbsp;</td><td>Corning</td><td>354253</td><td>Matrigel degradation&amp;nbsp;</td></tr><tr><td>Cisplatin</td><td>supelco</td><td>PHR1624</td><td>N/A</td></tr><tr><td>Dimethyl sulfoxide (DMSO)</td><td>Sigma-Aldrich</td><td>D2650</td><td>Vehicle</td></tr><tr><td>Eppendorf tube</td><td>Corning</td><td>MCT-175-C-S</td><td>N/A</td></tr><tr><td>HTX processing server</td><td>Tomocube Inc., Korea</td><td>N/A</td><td>Data processing server</td></tr><tr><td>HT-X1</td><td>Tomocube Inc., Korea</td><td>N/A</td><td>Operating Software</td></tr><tr><td>Ilastik</td><td>N/A</td><td>N/A</td><td>Machine learning-based segmentation toolkit</td></tr><tr><td>Imaging dish vessel holder&amp;nbsp;</td><td>Tomocube Inc., Korea</td><td>N/A</td><td>Imaging dish holder</td></tr><tr><td>IntestiCult Organoid Growth Medium (Mouse)</td><td>STEMCELL Technologies</td><td>#06005</td><td>Commercially available pre-made culture medium&amp;nbsp;</td></tr><tr><td>Matlab</td><td>MathWorks</td><td>Matlab 2024a</td><td>Software for image processing</td></tr><tr><td>Matrigel</td><td>Corning</td><td>356231</td><td>ECM for 3D organoids</td></tr><tr><td>Millipore&amp;nbsp;Steritop&amp;nbsp;Vacuum Bottle Top Filter</td><td>Merck</td><td>S2GPT05RE</td><td>0.22 &amp;mu;m filter unit for medium sterilization</td></tr><tr><td>Mouse Intestinal Organoids</td><td>STEMCELL Technologies</td><td>#70931</td><td>N/A</td></tr><tr><td>Refractometer</td><td>ATAGO</td><td>R-5000</td><td>Refractive index calculator</td></tr><tr><td>TomoDish</td><td>Tomocube Inc., Korea</td><td>901002-02</td><td>#1.5 coverslip-bottomed imaging dish</td></tr><tr><td>TomoStudio X</td><td>Tomocube Inc., Korea</td><td>N/A</td><td>Imaging software</td></tr><tr><td>TrypLE</td><td>Gibco</td><td>12604</td><td>Single cell dissociation</td></tr></tbody></table>

label-free, high-resolution 3d imaging and machine learning analysis of intestinal organoids via low-coherence holotomography

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

Accurate, label-free imaging of intestinal organoids is crucial for studying their morphology, growth dynamics, and responses to environmental stimuli. Holotomography (HT) provides high-resolution, three-dimensional (3D) visualization of live organoids without the need for fluorescent markers, thereby minimizing phototoxicity and preserving sample integrity. Real-time phase-based imaging allows continuous, label-free tracking of structural and functional changes. By using the refractive index as an intrinsic imaging contrast, this method enables quantification of biophysical properties such as volume, protein density, and protein content.&#160;The imaging data are further processed through machine learning-driven segmentation and feature extraction to support consistent, high-throughput analysis. This protocol details the complete experimental workflow for employing low-coherence HT in organoid research, covering organoid preparation, imaging acquisition, and machine learning-based data analysis. By integrating computational segmentation and quantitative assessments, this approach enables unbiased evaluation of key organoid properties, including viability, structural organization, and drug response. The ability to capture real-time morphological changes at subcellular resolution makes this protocol highly applicable to organoid-based studies in regenerative medicine, disease modeling, and pharmaceutical screening. The step-by-step methodology outlined here facilitates reproducibility and broad adaptation across different organoid systems.

Figure 1 illustrates the schematic workflow for 4D imaging and quantitative analysis of sIOs. This protocol enables continuous live imaging and comprehensive assessment of sIOs through a four-step process: Culture, 4D Imaging, Image Processing, and Quantitative Analysis (Figure 1D).
After a 5-day incubation period, mature sIOs were transferred to coverslip-bottomed imaging dishes for visualization using low-coherence holotomography (H...

We present a step-by-step protocol for high-resolution, label-free, and three-dimensional imaging of organoids using low-coherence holotomography. This protocol details organoid culture preparation, imaging acquisition, and computational image analysis, enabling real-time visualization of structural dynamics and drug responses in living organoids.

Accurate, label-free imaging of intestinal organoids is crucial for studying their morphology, growth dynamics, and responses to environmental stimuli. ...

label-free-high-resolution-3d-imaging-machine-learning-analysis

Research

JoVE Journal

Biology

Lensfree optical tomography is a three-dimensional microscopy technique that offers a spatial resolution of  &lt;1 μm × &lt;1 μm × &lt;3 μm in x, y and z dimensions, respectively, over a large imaging-volume of 15-100 mm3, which can be particularly useful for integration with lab-on-a-chip platforms.

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Optical coherence tomography (OCT), a three-dimensional imaging technology, was used to monitor and characterize the growth kinetics of multicellular tumor spheroids. Precise volumetric quantification of tumor spheroids using a voxel counting approach, and label-free dead tissue detection in the spheroids based on intrinsic optical attenuation contrast, were demonstrated.

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

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Single-Cell Resolution Three-Dimensional Imaging of Intact Organoids

We describe the preparation of ribbons of serial sections and their collection on large transfer support for use as Array Tomography samples, along with automated imaging procedures in a scanning electron microscope. The protocol allows screening, retrieval, and targeted imaging of local, rare events, and the acquisition of large data volumes.

Array Tomography Workflow for the Targeted Acquisition of Volume Information using Scanning Electron Microscopy

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Staining and High-Resolution Imaging of Three-Dimensional Organoid and Spheroid Models

This paper presents a fabrication protocol for a new type of culture substrate with hundreds of microcontainers per mm2, in which organoids can be cultured and observed using high-resolution microscopy. The cell seeding and immunostaining protocols are also detailed.

A High-Throughput Platform for Culture and 3D Imaging of Organoids

This protocol describes a semi-automated method for medium- to high-throughput organoid drug screenings and microscope-agnostic, automated image analysis software to quantify and visualize multiparametric, single-organoid drug responses to capture intratumor heterogeneity.

Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis

Here, we describe a procedure that enables organ-wide detection of pathogenic bacteria during infection and quantification of fluorescent reporter activities.

High&#45;Resolution Three&#45;Dimensional Whole&#45;Organ Tomography of Microbial Infections

This protocol combines optical clearing and immunolabeling for full-volume confocal imaging of whole-mount retinal organoids. It preserves 3D structure, enabling detailed visualization of key neuronal pathways, improving the study of retinal maturation, spatial organization, and its potential applications in disease modeling and personalized medicine.

Whole-mount Retinal Organoid Visualization with Cellular Resolution

A method based on confocal Raman microscopy is presented that affords label-free visualization of lignin in plant cell walls and comparison of lignification in different tissues, samples or species.

Label-free in situ Imaging of Lignification in Plant Cell Walls

Discover label-free 3D imaging of intestinal organoids using low-coherence holotomography and machine learning analysis. This computational method enables real-time visualization of structural dynamics and drug responses.

Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

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