Funding for this project was provided in part by the Defense Health Program (HU0001-16-2-0006 and HU0001-16-2-00014) to the Uniformed Services University for the Gynecologic Cancer Center of Excellence. The sponsors had no role in the design, execution, interpretation, or writing of the study. Disclaimer: The views expressed herein are those of the authors and do not reflect the official policy of the Department of Army/Navy/Air Force, Department of Defense, or U.S. Government.

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	<li>Motohara, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+evolving+story+of+the+metastatic+voyage+of+ovarian+cancer+cells:+cellular+and+molecular+orchestration+of+the+adipose-rich+metastatic+microenvironment.">An evolving story of the metastatic voyage of ovarian cancer cells: cellular and molecular orchestration of the adipose-rich metastatic microenvironment.</a> Oncogene. 38 (16), 2885-2898 (2019).</li><li>Aran, D., Sirota, M., Butte, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+pan-cancer+analysis+of+tumour+purity.">Systematic pan-cancer analysis of tumour purity.</a> Nature Communications. 6, 8971 (2015).</li><li>Hunt, A. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Extensive+three-dimensional+intratumor+proteomic+heterogeneity+revealed+by+multiregion+sampling+in+high-grade+serous+ovarian+tumor+specimens.">Extensive three-dimensional intratumor proteomic heterogeneity revealed by multiregion sampling in high-grade serous ovarian tumor specimens.</a> iScience. 24 (7), 102757 (2021).</li><li>Dou, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proteogenomic+characterization+of+endometrial+carcinoma.">Proteogenomic characterization of endometrial carcinoma.</a> Cell. 180 (4), 729-748 (2020).</li><li>Zhang, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrated+proteogenomic+characterization+of+human+high-grade+serous+ovarian+cancer.">Integrated proteogenomic characterization of human high-grade serous ovarian cancer.</a> Cell. 166 (3), 755-765 (2016).</li><li>Gillette, M. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proteogenomic+characterization+reveals+therapeutic+vulnerabilities+in+lung+adenocarcinoma.">Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma.</a> Cell. 182 (1), 200-225 (2020).</li><li>Silvestri, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+pathway+biomarker+analysis+of+human+cancer+reveals+requirement+for+upfront+cellular-enrichment+processing.">Protein pathway biomarker analysis of human cancer reveals requirement for upfront cellular-enrichment processing.</a> Laboratory Investigation. 90 (5), 787-796 (2010).</li><li>Echle, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+learning+in+cancer+pathology:+a+new+generation+of+clinical+biomarkers.">Deep learning in cancer pathology: a new generation of clinical biomarkers.</a> British Journal of Cancer. 124 (4), 686-696 (2021).</li><li>Lee, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+analysis+of+clinically+defined+subsets+of+high-grade+serous+ovarian+cancer.">Molecular analysis of clinically defined subsets of high-grade serous ovarian cancer.</a> Cell Reports. 31 (2), 107502 (2020).</li><li>Xuan, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Standardization+and+harmonization+of+distributed+multi-center+proteotype+analysis+supporting+precision+medicine+studies.">Standardization and harmonization of distributed multi-center proteotype analysis supporting precision medicine studies.</a> Nature Communications. 11 (1), 5248 (2020).</li><li>Hughes, C. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+profiling+of+single+formalin+fixed+tumour+sections:+proteomics+for+translational+research.">Quantitative profiling of single formalin fixed tumour sections: proteomics for translational research.</a> Scientific Reports. 6 (1), 34949 (2016).</li><li>Espina, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+portrait+of+tissue+phosphoprotein+stability+in+the+clinical+tissue+procurement+process.">A portrait of tissue phosphoprotein stability in the clinical tissue procurement process.</a> Molecular &amp; Cellular Proteomics. 7 (10), 1998-2018 (2008).</li><li>Espina, V., Heiby, M., Pierobon, M., Liotta, L. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Laser+capture+microdissection+technology.">Laser capture microdissection technology.</a> Expert Review of Molecular Diagnostics. 7 (5), 647-657 (2007).</li><li>Havnar, C. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+dissection+protocol+for+tumor+enrichment+in+low+tumor+content+tissues.">Automated dissection protocol for tumor enrichment in low tumor content tissues.</a> Journal of Visualized Experiments. (169), e62394 (2021).</li><li>Mueller, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=One-step+preservation+of+phosphoproteins+and+tissue+morphology+at+room+temperature+for+diagnostic+and+research+specimens.">One-step preservation of phosphoproteins and tissue morphology at room temperature for diagnostic and research specimens.</a> PLoS One. 6 (8), (2011).</li></ol>

T.P.C. is a ThermoFisher Scientific, Inc SAB member and receives research funding from AbbVie.

While there have been multiple study precedents aimed at developing and/or improving workflows for enrichment of target cellular subpopulations from FFPE and/or fresh-frozen tissues and methodologies for maintaining sample quality during processing9,12,13,14,15, a substantial need exists to develop automated strategies for preparing clinical tissue specimens for molecular analyses to decrease variability and increase reproducibility. This workflow describes a standardized, semiautomated protocol that integrates existing image analysis software tools (see the Table of Materials) for histology-resolved harvest of discrete cell populations by LMD from clinical tissue specimens.
Spatially-resolved LMD enrichment of ROIs capturing discrete cell populations represents a next-generation tissue processing step prior to multiomic analyses to improve molecular characterization and identification and facilitate cell-selective biomarker discovery. This protocol improves upon existing methodologies by reducing the often-long exposure of tissue sections to the ambient environment that is associated with manual segmentation of ROI by a histologist (which can take &#62;1-2 h prior to the LMD collection). This workflow instead allows ROI to be preidentified by AI-guided classification and segmentation. Limiting tissue dwell time will decrease spurious variations in the assessments of highly labile molecular targets, such as phosphopeptides and mRNA, or for antibody-based analytical techniques that rely on a target protein being in its native conformation for detection.
Cutting neat calibrator fiducials onto the PEN membrane slide that are clearly visible in the scanned slide image is one of the key components enabling integration of the image analysis software (see the Table of Materials) with the LMD workflow. Ensuring that the calibrators have a precise (&#34;clean&#34;) point at the bottom of the &#34;V&#34; shape allows for selection of a precise point in the image analysis software for the calibrator lines to be drawn from, as described in steps 5.1.6 and 5.2.13. Alignment of these points during import into the LMD software is critical for properly overlaying the annotations (facilitated through the generation of a compatible .xml file using the &#34;Malleator&#34; and/or &#34;Dap&#7885;&#34; algorithms) onto the relevant tissue ROI on the physical LMD slide. It is necessary to highlight all shapes and collectively &#34;drag and drop&#34; into place even when the alignment is precise upon import into the LMD software to register the vertical (z-plane) position of the slide stage on the laser microscope. Minor adjustments to the positioning of the annotations over the tissue ROI can also be made during this step, if needed.
A limitation of the current version of the Malleator algorithm is that it is not compatible with the predefined annotation shape tools provided by the image analysis software (see the Table of Materials), although future updates/versions of the algorithm will aim to improve this compatibility. The .annotation file for shapes drawn using these tools contains only two sets of paired x and y coordinates for each annotation, without the complete spatial orientation around those points. Current use of these tools results in the annotations being converted to straight lines defined by only two points during the import process. Manual definition of tissue ROI segments is required for successful conversion to XML format and LMD import. This can either be performed by manually defining each ROI with individual free-hand polygonal annotations specific to the target area or by applying an approximated circular or rectangular annotation across all tissue ROI segments, if desired, and will be compatible with this workflow.
While the workflow presented here was demonstrated for proteomic analysis of fresh-frozen human cancer tissue specimens, this AI-driven LMD workflow can be equivalently used with FFPE tissues, non-cancerous tissue types, and those from non-human sources. It can also support other downstream molecular profiling workflows, including transcriptomic, genomic, or phosphoproteomic analyses. This workflow can also leverage other uses of the image analysis software (see the Table of Materials), including with capabilities associated with cell counting or other analytical modules, including the &#34;Multiplex IHC&#34; module or the &#34;Tissue Microarray (TMA) Add-on&#34;. Future applications of this workflow may also benefit from predefining the number of cells per ROI segment, thereby ensuring equivalent cellular inputs across multiple collections, or by using alternative methods to define cellular ROIs of interest, such as by immunohistochemistry or cell sociology.

The TME represents a complex ecosystem populated by a highly diverse array of cell types, such as tumor cells, stromal cells, immune cells, endothelial cells, other mesenchymal cell types, and adipocytes, along with a complex extracellular matrix1. This cellular ecosystem varies within and across different disease organ sites, resulting in complex tumor heterogeneity2,3. Recent studies have shown that heterogeneous tumors and tumors with low tumor cellularity (low purity) often correlate with poor disease prognosis2,3.
To understand the molecular interplay between tumor and non-tumor cell populations within the TME at scale, standardized and high-throughput strategies are necessary to selectively harvest distinct cellular populations of interest for downstream multiomic analysis. Quantitative proteomics represents a rapidly evolving and increasingly important technique to further the understanding of cancer biology. To date, the preponderance of studies employing proteomics have done so with proteins extracted from whole tumor tissue preparations (e.g., cryopulverized), leading to a paucity in the understanding of proteome-level heterogeneity in the TME4,5,6.
The development of sample collection strategies that seamlessly integrate with and harness information from clinical pathology workflows will enable a new generation of histology-resolved proteomics that are highly complementary to gold-standard, diagnostic pathology workflows. LMD enables direct and selective collection of cellular subpopulations or regions of interest (ROIs) through microscopic inspection of histologically stained tissue thin sections7. Recent major advances in digital pathology and AI-enabled analysis have demonstrated the ability to identify unique compositional features and ROIs within the TME in an automated fashion, many of which correlate with molecular alterations and clinical disease features, such as resistance to therapy and disease prognosis8.
The workflow described in the protocol presented here leverages commercial software solutions to selectively annotate tumor ROIs within digital histopathology images, and utilizes software tools developed in-house to transfer these tumor ROIs to laser microscopes for automated collection of discrete cellular populations of interest that seamlessly integrates with downstream multiomic analysis workflows. This integrated strategy significantly diminishes LMD operator time and minimizes the duration for which tissues are required to be at ambient temperature. The integration of automated feature selection and LMD harvest with high-throughput quantitative proteomics is demonstrated through a differential analysis of the TME from two representative epithelial ovarian cancer histologic subtypes, high-grade serous ovarian cancer (HGSOC) and ovarian clear cell carcinoma (OCCC).

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>1260 Infinity II System</td>
			<td>Agilent Technologies Inc</td>
			<td></td>
			<td>Offline LC system</td>
		</tr>
		<tr>
			<td>96 MicroCaps (150uL) in bulk</td>
			<td>Pressure Biosciences Inc</td>
			<td>MC150-96</td>
			<td></td>
		</tr>
		<tr>
			<td>96 MicroPestles in bulk</td>
			<td>Pressure Biosciences Inc</td>
			<td>MP-96</td>
			<td></td>
		</tr>
		<tr>
			<td>96 MicroTubes in bulk (no caps)</td>
			<td>Pressure Biosciences Inc</td>
			<td>MT-96</td>
			<td></td>
		</tr>
		<tr>
			<td>9mm MS Certified Clear Screw Thread Kits</td>
			<td>Fisher Scientific</td>
			<td>03-060-058</td>
			<td>Sample vial for offline LC frationation and mass spectrometry</td>
		</tr>
		<tr>
			<td>Acetonitrile, Optima LC/MS Grade</td>
			<td>Fisher Chemical</td>
			<td>A995-4</td>
			<td>Mobile phase solvent</td>
		</tr>
		<tr>
			<td>Aperio AT2</td>
			<td>Leica Microsystems</td>
			<td>23AT2100</td>
			<td>Slide scanner</td>
		</tr>
		<tr>
			<td>Axygen&#160;PCR Tubes with 0.5 mL Flat Cap</td>
			<td>Fisher Scientific</td>
			<td>14-222-292</td>
			<td>Sample tubes; size fits PCT tubes and thermocycler</td>
		</tr>
		<tr>
			<td>Barocycler 2320EXT</td>
			<td>Pressure Biosciences Inc</td>
			<td>2320-EXT</td>
			<td>Barocycler</td>
		</tr>
		<tr>
			<td>BCA Protein Assay Kit</td>
			<td>Fisher Scientific</td>
			<td>P123225</td>
			<td></td>
		</tr>
		<tr>
			<td>cOmplete, Mini, EDTA-free Protease Inhibitor Cocktail</td>
			<td>Roche</td>
			<td>11836170001</td>
			<td></td>
		</tr>
		<tr>
			<td>Easy-nLC 1200</td>
			<td>Thermo Fisher Scientific</td>
			<td></td>
			<td>Liquid Chromatography&#160;</td>
		</tr>
		<tr>
			<td>EasyPep Maxi Sample Prep Kit</td>
			<td>Thermo Fisher Scientific</td>
			<td>NCI5734</td>
			<td>Post-label sample clean up column</td>
		</tr>
		<tr>
			<td>EASY-SPRAY C18 2UM 50CM X 75</td>
			<td>Fisher Scientific</td>
			<td>ES903</td>
			<td>Analytical column</td>
		</tr>
		<tr>
			<td>Eosin Y Solution Aqueous</td>
			<td>Sigma Aldrich</td>
			<td>HT110216</td>
			<td></td>
		</tr>
		<tr>
			<td>Formic Acid, 99+ %</td>
			<td>Thermo Fisher Scientific</td>
			<td>28905</td>
			<td>Mobile phase additive</td>
		</tr>
		<tr>
			<td>ggplot2 version 3.3.5</td>
			<td>CRAN</td>
			<td>https://cran.r-project.org/web/packages/ggplot2/</td>
			<td></td>
		</tr>
		<tr>
			<td>HALO</td>
			<td>Indica Labs</td>
			<td></td>
			<td>Image analysis software</td>
		</tr>
		<tr>
			<td>IDLE&#160;(Integrated Development and Learning Environment)</td>
			<td>Python Software Foundation</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>iheatmapr version 0.5.1</td>
			<td>CRAN</td>
			<td>https://cran.r-project.org/web/packages/iheatmapr/</td>
			<td></td>
		</tr>
		<tr>
			<td>iRT Kit</td>
			<td>Biognosys</td>
			<td>Ki-3002-1</td>
			<td>LC-MS QAQC Standard</td>
		</tr>
		<tr>
			<td>limma version 3.42.2</td>
			<td>Bioconductor</td>
			<td>https://bioconductor.org/packages/release/bioc/html/limma.html</td>
			<td></td>
		</tr>
		<tr>
			<td>LMD Scanning stage Ultra LMT350</td>
			<td>Leica Microsystems</td>
			<td>11888453</td>
			<td>LMD stage model outfitted with PCT tube holder</td>
		</tr>
		<tr>
			<td>LMD7 (software version 8.2.3.7603)</td>
			<td>Leica Microsystems</td>
			<td></td>
			<td>LMD apparatus (microscope, laser, camera, PC, tablet)</td>
		</tr>
		<tr>
			<td>Mascot Server</td>
			<td>Matrix Science</td>
			<td></td>
			<td>Data analysis software</td>
		</tr>
		<tr>
			<td>Mass Spec-Compatible Human Protein Extract, Digest</td>
			<td>Promega</td>
			<td>V6951</td>
			<td>LC-MS QAQC Standard</td>
		</tr>
		<tr>
			<td>Mayer&#8217;s Hematoxylin Solution</td>
			<td>Sigma Aldrich</td>
			<td>MHS32</td>
			<td></td>
		</tr>
		<tr>
			<td>PEN Membrane Glass Slides</td>
			<td>Leica Microsystems</td>
			<td>11532918</td>
			<td></td>
		</tr>
		<tr>
			<td>Peptide Retention Time Calibration Mixture</td>
			<td>Thermo Fisher Scientific</td>
			<td>88321</td>
			<td>LC-MS QAQC Standard</td>
		</tr>
		<tr>
			<td>Phosphatase Inhibitor Cocktail 2</td>
			<td>Sigma Aldrich</td>
			<td>P5726</td>
			<td></td>
		</tr>
		<tr>
			<td>Phosphatase Inhibitor Cocktail 3</td>
			<td>Sigma Aldrich</td>
			<td>P0044</td>
			<td></td>
		</tr>
		<tr>
			<td>Pierce LTQ Velos ESI Positive Ion Calibration Solution</td>
			<td>Thermo Fisher Scientific</td>
			<td>88323</td>
			<td>Instrument calibration solution</td>
		</tr>
		<tr>
			<td>PM100 C18 3UM 75UMX20MM NV 2PK</td>
			<td>Fisher Scientific</td>
			<td>164535</td>
			<td>Pre-column</td>
		</tr>
		<tr>
			<td>Proteome Discoverer</td>
			<td>Thermo Fisher Scientific</td>
			<td>OPTON-31040</td>
			<td>Data analysis software</td>
		</tr>
		<tr>
			<td>Python</td>
			<td>Python Software Foundation</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Q Exactive HF-X</td>
			<td>Thermo Fisher Scientific</td>
			<td></td>
			<td>Mass spectrometer</td>
		</tr>
		<tr>
			<td>R version 3.6.0</td>
			<td>CRAN</td>
			<td>https://cran-archive.r-project.org/bin/windows/base/old/2.6.2/</td>
			<td></td>
		</tr>
		<tr>
			<td>RColorBrewer version 1.1-2</td>
			<td>CRAN</td>
			<td>https://cran.r-project.org/web/packages/RColorBrewer/</td>
			<td></td>
		</tr>
		<tr>
			<td>Soluble Smart Digest Kit</td>
			<td>Thermo Fisher Scientific</td>
			<td>3251711</td>
			<td>Digestion reagent</td>
		</tr>
		<tr>
			<td>TMTpro 16plex Label Reagent Set</td>
			<td>Thermo Fisher Scientific</td>
			<td>A44520</td>
			<td>isobaric TMT labeling reagents</td>
		</tr>
		<tr>
			<td>Veriti 60 well thermal cycler</td>
			<td>Applied Biosystems</td>
			<td>4384638</td>
			<td>Thermocycler</td>
		</tr>
		<tr>
			<td>Water, Optima LC/MS Grade</td>
			<td>Fisher Chemical</td>
			<td>W6-4</td>
			<td>Mobile phase solvent</td>
		</tr>
		<tr>
			<td>ZORBAX Extend 300 C18, 2.1 x 12.5 mm, 5 &#181;m, guard cartridge (ZGC)</td>
			<td>Agilent Technologies Inc</td>
			<td>821125-932</td>
			<td>Offline LC trap column</td>
		</tr>
		<tr>
			<td>ZORBAX Extend 300 C18, 2.1 x 150 mm, 3.5 &#181;m</td>
			<td>Agilent Technologies Inc</td>
			<td>763750-902</td>
			<td>Offline LC analytical column</td>
		</tr>
	</tbody>
</table>

industrialized, artificial intelligence-guided laser microdissection for microscaled proteomic analysis of the tumor microenvironment

The tumor microenvironment (TME) represents a complex ecosystem comprised of dozens of distinct cell types, including tumor, stroma, and immune cell populations. To characterize proteome-level variation and tumor heterogeneity at scale, high-throughput methods are needed to selectively isolate discrete cellular populations in solid tumor malignancies. This protocol describes a high-throughput workflow, enabled by artificial intelligence (AI), that segments images of hematoxylin and eosin (H&#38;E)-stained, thin tissue sections into pathology-confirmed regions of interest for selective harvest of histology-resolved cell populations using laser microdissection (LMD). This strategy includes a novel algorithm enabling the transfer of regions denoting cell populations of interest, annotated using digital image software, directly to laser microscopes, thus enabling more facile collections. Successful implementation of this workflow was performed, demonstrating the utility of this harmonized method to selectively harvest tumor cell populations from the TME for quantitative, multiplexed proteomic analysis by high-resolution mass spectrometry. This strategy fully integrates with routine histopathology review, leveraging digital image analysis to support enrichment of cellular populations of interest and is fully generalizable, enabling harmonized harvests of cell populations from the TME for multiomic analyses.

Fresh-frozen tissue thin sections from two HGSOC and two OCCC patients were analyzed using this integrated AI-driven tissue ROI identification, segmentation, LMD, and quantitative proteomic analysis workflow (Figure 1). Representative H&#38;E-stained tissue sections for each tumor were reviewed by a board-certified pathologist; tumor cellularity ranged from 70% to 99%. Tissues were thin-sectioned onto PEN membrane slides (Supplemental File 2) and precut with calibrator fiducials (Supplemental File 1), enabling integration of positional orientation data from annotations generated in the image analysis software (see the Table of Materials) with Cartesian coordinate orientation in the LMD software. Following H&#38;E staining, high-resolution images (20x) of the PEN slides containing the tissue plus calibrators were captured.
Tumor and stromal cell populations in the micrographs were segmented using image analysis software (see the Table of Materials) for selective harvest by LMD, along with harvests representing the entire tissue thin section (e.g., whole tumor tissue) (Figure 1). Non-discriminate annotations for whole tumor tissue collections were generated by partitioning the entire tissue section with tiles of 500 &#181;m2, leaving a 40 &#181;m gap between tiles to maintain PEN membrane integrity and prevent the membrane from curling during LMD. On slides for histology-resolved LMD enrichment, the AI classifier in the image analysis software (see the Table of Materials) was trained to discriminate between tumor and stromal cells, along with the blank glass slide background. Representative tumor, stroma, and blank glass regions were manually highlighted, and the classifier tool was used to segment these ROIs throughout the entire tissue section. The segmented layers representing whole tissue, tumor epithelium, and stroma were saved separately as individual .annotation files (Supplemental File 3 and Supplemental File 6). In a separate copy of the image file (without the partitioned ROI annotations), a short line from the centermost tip of each of the three fiducial calibrators was annotated and saved as a .annotation file using the same file name as each of the LMD annotation layer files but appended with the suffix &#34;_calib&#34; (Supplemental File 4). These lines were used to co-register the position of the PEN membrane calibrators with the annotation shape list data drawn in the image analysis software.
The present study provides two algorithms, &#34;Malleator&#34; and &#34;Dap&#7885;&#34; in Python to support this AI-driven LMD workflow, which are available at https://github.com/GYNCOE/Mitchell.et.al.2022. The Malleator algorithm extracts the specific Cartesian coordinates for all individual annotations (tissue ROI and calibrators) from the paired .annotation files and merges these into a single Extensible Markup Language (XML) import file (Supplemental File 5). Specifically, the Malleator algorithm uses the directory name from a parent folder as input to search all subdirectory folders and generates .xml files for any subfolders that do not already have a .xml merged file. The Malleator algorithm merges all annotation layers in the image analysis software (see the Table of Materials) into a single layer and converts the AI-generated shape list data, which is saved as proprietary .annotation file type, into .xml format compatible with the LMD software. After merging the annotation and calibrator files, the algorithm-generated .xml file is saved and imported into the LMD software. Slight adjustments are necessary to manually adjust the alignment of annotations, which also serves to register the vertical (z-plane) position of the slide stage on the laser microscope. The Dap&#7885; algorithm is used specifically for LMD-enriched collections. Partitioned tiles are automatically assigned to individual annotation layers by the image analysis software. The Dap&#7885; algorithm merges all partitioned tiles into a single annotation layer prior to use of the Classifier tool, thereby reducing the Classifier analysis run time for LMD enriched collections.
The whole tumor and LMD-enriched tissue samples were digested, labeled with TMT reagents, multiplexed, fractionated offline, and analyzed via quantitative MS-based proteomics as previously described9. The mean peptide yield (43-60 &#181;g) and recovery (0.46-0.59 &#181;g/mm2) for samples harvested using this AI-driven workflow were comparable with previous reports9,10. A total of 5,971 proteins were co-quantified across all samples (Supplemental Table S1). Unsupervised hierarchical clustering using the 100 most variable proteins resulted in segregation of the HGSOC and OCCC histotypes from the LMD-enriched and whole tumor samples (Figure 2A), similar to that previously described11. By contrast, the LMD-enriched stroma samples from both HGSOC and OCCC clustered together and independently from the LMD-enriched tumor and whole tumor samples. Among the 5,971 quantified proteins, 215 were significantly altered (LIMMA adj. p &#60; 0.05) between whole tumor collections from HGSOC and OCCC specimens (Supplemental Table S2). These altered proteins were compared with those identified to differentiate HGSOC and OCCC tumor tissue by Hughes et al.11. Of the 76 signature proteins quantified by Hughes et al., 57 were co-quantified in this dataset and were highly correlated (Spearman Rho = 0.644, p &#60; 0.001) (Figure 2B).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/64171/64171fig01.jpg" /> 
Figure 1: Summary of the integrated workflow for automated tissue region of interest selection for laser microdissection for downstream quantitative proteomics. Calibration fiducials are cut onto PEN membrane slides to co-register positional orientation data from AI-derived segments of tissue ROI in the image analysis software, HALO, with horizontal positioning on the LMD microscope. The Malleator algorithm is used to merge the annotated segmentation data across all annotation layers for a slide with the _calib reference file, and to convert it to a .xml file compatible with the LMD software. LMD-harvested tissue for proteomic analysis is digested and analyzed by high-throughput quantitative proteomics as previously described9. Abbreviations: LMD = laser microdissection; ROI = region of interest; TMT = tandem mass tag; Quant. = quantification; Ident. = identification; LC-MS/MS = liquid chromatography-tandem mass spectrometry. <a href="https://www.jove.com/files/ftp_upload/64171/64171fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/64171/64171fig02.jpg" /> 
Figure 2: Analysis of the proteins in LMD-enriched and whole tumor samples. (A) Unsupervised hierarchical cluster analysis of the 100 most variably abundant proteins in HGSOC and OCCC LMD enriched and whole tumor samples. (B) Correlation of log2 fold-change protein abundances between HGSOC and OCCC whole tumor harvests in the present study (Mitchell et al., x-axis) and a similar study by Hughes et al. (y-axis)11. Abbreviations: LMD = laser microdissection; HGSOC = high-grade serous ovarian cancer; OCCC = ovarian clear cell carcinoma; log2FC = log2-transformed proteomic abundance. <a href="https://www.jove.com/files/ftp_upload/64171/64171fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental Table S1: Abundances of 5,971 proteins co-quantified across all LMD enriched and whole tumor samples from HGSOC and OCCC tissue specimens. Abbreviations: LMD = laser microdissection; HGSOC = high-grade serous ovarian cancer; OCCC = ovarian clear cell carcinoma. <a href="https://www.jove.com/files/ftp_upload/64171/JoVE64171_Supplemental Table 1 JoVE Review.zip" target="_blank">Please click here to download this Table.</a>
Supplemental Table S2: Differentially expressed proteins (215) in whole tumor collections from HGSOC vs OCCC (LIMMA adj. p &#60; 0.05). Abbreviations: HGSOC = high-grade serous ovarian cancer; OCCC = ovarian clear cell carcinoma. <a href="https://www.jove.com/files/ftp_upload/64171/JoVE_Supplemental Table 2_4 May 22 (1).zip" target="_blank">Please click here to download this Table.</a>
Supplemental File 1: Representative shape list data (.sld) file containing standard calibrator fiducials for four slide positions. The file can be imported into the LMD software. <a href="https://www.jove.com/files/ftp_upload/64171/Supplemental File 1_12 Apr 22.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 2: Representative .svs image file of a H&#38;E-stained high-resolution (20x) tissue section. The file can be opened and viewed using image analysis software or LMD software. Abbreviation: H&#38;E = hematoxylin and eosin; LMD = laser microdissection. <a href="https://www.jove.com/files/ftp_upload/64171/Supplemental JoVE64171_Supplemental File 2.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 3: Representative .annotation file of partitioned whole tumor segments. The file can be imported into image analysis software. <a href="https://www.jove.com/files/ftp_upload/64171/Supplemental File 3_12 Apr 22.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 4: Representative _calib.annotation file of calibrator fiducial segments. Coordinate information represents oriental positioning of the short calibrator lines drawn from each arrowhead fiducial. The file can be imported into image analysis software. <a href="https://www.jove.com/files/ftp_upload/64171/Supplemental File 4_12 Apr 22.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 5: Representative extensible markup language (.xml) file generated by the Malleator algorithm. The file can be imported into the laser microdissection software. <a href="https://www.jove.com/files/ftp_upload/64171/Supplemental File 5_12 Apr 22.zip" target="_blank">Please click here to download this File.</a>
Supplemental File 6: Representative .annotation file of partitioned AI-classified segments for LMD-enriched collections. The file can be imported into image analysis software. Abbreviations: AI = artificial intelligence; LMD = laser microdissection. <a href="https://www.jove.com/files/ftp_upload/64171/Supplemental File 6_12 Apr 22.zip" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment at JoVE.com

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

This protocol describes a high-throughput workflow for artificial intelligence-driven segmentation of pathology-confirmed regions of interest from stained, thin tissue section images for enrichment of histology-resolved cell populations using laser microdissection. This strategy includes a novel algorithm enabling the transfer of demarcations denoting cell populations of interest directly to laser microscopes.

The tumor microenvironment (TME) represents a complex ecosystem comprised of dozens of distinct cell types, including tumor, stroma, and immune cell ...

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Cancer Research

3.4K Views.  Uniformed Services University and Walter Reed National Military Medical Center. This protocol describes a high-throughput workflow for artificial intelligence-driven segmentation of pathology-confirmed regions of interest from stained, thin tissue section images for enrichment of histology-resolved cell populations using laser microdissection. This strategy includes a novel algorithm enabling the transfer of demarcations denoting cell populations of interest directly to laser microscopes.

Video: Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

A Mimic of the Tumor Microenvironment: A Simple Method for Generating Enriched Cell Populations and Investigating Intercellular Communication

Understanding the early heterotypic interactions between cancer cells and the surrounding non-cancerous stroma is important in elucidating the events leading to stromal activation and establishment of the tumor microenvironment (TME). Several in vitro and in vivo models of the TME have been developed; however, in general these models do not readily permit isolation of individual cell populations, under non-perturbing conditions, for further study. To circumvent this difficulty, we have employed an in vitro TME model using a cell growth substrate consisting of a permeable microporous membrane insert that permits simple generation of highly enriched cell populations grown intimately, yet separately, on either side of the insert&#39;s membrane for extended co-culture times. Through use of this model, we are capable of generating greatly enriched cancer-associated fibroblast (CAF) populations from normal diploid human fibroblasts following co-culture (120 hr) with highly metastatic human breast carcinoma cells, without the use of fluorescent tagging and/or cell sorting. Additionally, by modulating the pore-size of the insert, we can control for the mode of intercellular communication (e.g., gap-junction communication, secreted factors) between the two heterotypic cell populations, which permits investigation of the mechanisms underlying the development of the TME, including the role of gap-junction permeability. This model serves as a valuable tool in enhancing our understanding of the initial events leading to cancer-stroma initiation, the early evolution of the TME, and the modulating effect of the stroma on the responses of cancer cells to therapeutic agents.

We adapted a permeable microporous membrane insert to mimic the tumor microenvironment (TME). The model consists of a mixed cell culture, allows simplified generation of highly enriched individual cell populations without using fluorescent tagging or cell sorting, and permits studying intercellular communication within the TME under normal or stress conditions.

Understanding the early heterotypic interactions between cancer cells and the surrounding non-cancerous stroma is important in elucidating the events ...

Simple and Rapid Method to Obtain High-quality Tumor DNA from Clinical-pathological Specimens Using Touch Imprint Cytology

It is critical to determine the mutational status in cancer before administration and treatment of specific molecular targeted drugs for cancer patients. In the clinical setting, formalin-fixed paraffin-embedded (FFPE) tissues are widely used for genetic testing. However, FFPE DNA is generally damaged and fragmented during the fixation process with formalin. Therefore, FFPE DNA is sometimes not adequate for genetic testing because of low quality and quantity of DNA. Here we present a method of touch imprint cytology (TIC) to obtain genomic DNA from cancer cells, which can be observed under a microscope. Cell morphology and cancer cell numbers can be evaluated using TIC specimens. Furthermore, the extraction of genomic DNA from TIC samples can be completed within two days. The total amount and quality of TIC DNA obtained using this method was higher than that of FFPE DNA. This rapid and simple method allows researchers to obtain high-quality DNA for genetic testing (e.g., next generation sequencing analysis, digital PCR, and quantitative real time PCR) and to shorten the turnaround time for reporting results.

Obtaining high-quality genomic DNA from tumor tissues is an essential first step for analyzing genetic alterations using next generation sequencing. In this article, we present a simple and rapid method to enrich tumor cells and obtain intact DNA from touch imprint cytology specimens.

It is critical to determine the mutational status in cancer before administration and treatment of specific molecular targeted drugs for cancer patients. ...

Digital Polymerase Chain Reaction Assay for the Genetic Variation in a Sporadic Familial Adenomatous Polyposis Patient Using the Chip-in-a-tube Format

The quantitative analysis of human genetic variation is crucial for understanding the molecular characteristics of serious medical conditions, such as tumors. Because digital polymerase chain reactions (PCR) enable the precise quantification of DNA copy number variants, they are becoming an essential tool for detecting rare genetic variations, such as drug-resistant mutations. It is expected that molecular diagnoses using digital PCR (dPCR) will be available in clinical practice in the near future; thus, how to efficiently conduct dPCR with human genetic material is a hot topic. Here, we introduce a method to detect Adenomatous polyposis coli (APC) somatic mosaicism using dPCR with the chip-in-a-tube format, which allows eight dPCR reactions to be simultaneously conducted. Care should be taken when filling and sealing the reaction mixture on the chips. This article demonstrates how to avoid the over- and underestimation of positive partitions. Furthermore, we present a simple procedure for collecting the dPCR product from the partitions on the chips, which can then be used to confirm the specific amplification. We hope that this methods report will help promote the dPCR with the chip-in-a-tube method in genetic research.

Digital polymerase chain reaction (PCR) is a useful tool for the high-sensitivity detection of single nucleotide variants and DNA copy number variants. Here, we demonstrate key considerations for measuring rare variants in the human genome using digital PCR with the chip-in-a-tube format.

The quantitative analysis of human genetic variation is crucial for understanding the molecular characteristics of serious medical conditions, such as ...

Measuring Bone Remodeling and Recreating the Tumor-Bone Microenvironment Using Calvaria Co-culture and Histomorphometry

Bone is a connective tissue constituted of osteoblasts, osteocytes, and osteoclasts and a mineralized extracellular matrix, which gives it its strength and flexibility and allows it to fulfill its functions. Bone is continuously exposed to a variety of stimuli, which in pathological conditions can deregulate bone remodeling. To study bone biology and diseases and evaluate potential therapeutic agents, it has been necessary to develop in vitro and in vivo models.
This manuscript describes the dissection process and culture conditions of calvarias isolated from neonatal mice to study bone formation and the bone tumor microenvironment. In contrast to in vitro and in vivo models, this ex vivo model allows preservation of the three-dimensional environment of the tissue as well as the cellular diversity of the bone while culturing under defined conditions to simulate the desired microenvironment. Therefore, it is possible to investigate bone remodeling and its mechanisms, as well as the interactions with other cell types, such as the interactions between cancer cells and bone.
The assays reported here use calvarias from 5-7 day old BALB/C mice. The hemi-calvarias obtained are cultured in the presence of insulin, breast cancer cells (MDA-MB-231), or conditioned medium from breast cancer cell cultures. After analysis, it was established that insulin induced new bone formation, while cancer cells and their conditioned medium induced bone resorption. The calvarial model has been successfully used in basic and applied research to study bone development and cancer-induced bone diseases. Overall, it is an excellent option for an easy, informative, and low-cost assay.

The ex vivo culture of bone explants can be a valuable tool for the study of bone physiology and the potential evaluation of drugs in bone remodeling and bone diseases. The presented protocol describes the preparation and culture of calvarias isolated from newborn mice skulls, as well as its applications.

Bone is a connective tissue constituted of osteoblasts, osteocytes, and osteoclasts and a mineralized extracellular matrix, which gives it its strength ...

Analysis of Side Population in Solid Tumor Cell Lines

Cancer stem cells (CSCs) are an important cause of tumor growth, metastasis, and recurrence. Isolation and identification of CSCs are of great significance for tumor research. Currently, several techniques are used for the identification and purification of CSCs from tumor tissues and tumor cell lines. Separation and analysis of side population (SP) cells are two of the commonly used methods. The methods rely on the ability of CSCs to rapidly expel fluorescent dyes, such as Hoechst 33342. The efflux of the dye is associated with the ATP-binding cassette (ABC) transporters and can be inhibited by ABC transporter inhibitors. Methods for staining cultured tumor cells with Hoechst 33342 and analyzing the proportion of their SP cells by flow cytometry are described. This assay is convenient, fast, and cost-effective. Data generated in this assay can contribute to a better understanding of the effect of genes or other extracellular and intracellular signals on the stemness properties of tumor cells.

A convenient, fast, and cost-effective method to measure the proportion of side population cells in solid tumor cell lines is presented.

Cancer stem cells (CSCs) are an important cause of tumor growth, metastasis, and recurrence. Isolation and identification of CSCs are of great ...

Isolation of Proximal Fluids to Investigate the Tumor Microenvironment of Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death, and soon to become the second. There is an urgent need of variables associated to specific pancreatic pathologies to help preoperative differential diagnosis and patient profiling. Pancreatic juice is a relatively unexplored body fluid, which, due to its close proximity to the tumor site, reflects changes in the surrounding tissue. Here we describe in detail the intraoperative collection procedure. Unfortunately, translating pancreatic juice collection to murine models of PDAC, to perform mechanistic studies, is technically very challenging. Tumor interstitial fluid (TIF) is the extracellular fluid, outside blood and plasma, which bathes tumor and stromal cells. Similarly to pancreatic juice, for its property to collect and concentrate molecules that are found diluted in plasma, TIF can be exploited as an indicator of microenvironmental alterations and as a valuable source of disease-associated biomarkers. Since TIF is not readily accessible, various techniques have been proposed for its isolation. We describe here two simple and technically undemanding methods for its isolation: tissue centrifugation and tissue elution.

Pancreatic juice is a precious source of biomarkers for human pancreatic cancer. We describe here a method for intraoperative collection procedure. To overcome the challenge of adopting this procedure in murine models, we suggest an alternative sample, tumor interstitial fluid, and describe here two protocols for its isolation.

Pancreatic adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death, and soon to become the second. There is an urgent need of variables ...

Generation of Zebrafish Larval Xenografts and Tumor Behavior Analysis

Zebrafish larval xenografts are being widely used for cancer research to perform in vivo and real-time studies of human cancer. The possibility of rapidly visualizing the response to anti-cancer therapies (chemo, radiotherapy, and biologicals), angiogenesis and metastasis with single cell resolution, places the zebrafish xenograft model as a top choice to develop preclinical studies.

The zebrafish larval xenograft assay presents several experimental advantages compared to other models, but probably the most striking is the reduction of size scale and consequently time. This reduction of scale allows single cell imaging, the use of a relatively low number of human cells (compatible with biopsies), medium-high-throughput drug screenings, but most importantly enables a significant reduction of the time of the assay. All these advantages make the zebrafish xenograft assay extremely attractive for future personalized medicine applications.

Many zebrafish xenograft protocols have been developed with a wide diversity of human tumors; however, a general and standardized protocol to efficiently generate zebrafish larval xenografts is still lacking. Here we provide a step-by-step protocol, with tips to generate xenografts and guidelines for tumor behavior analysis, whole-mount immunofluorescence, and confocal imaging quantification.

Here, we provide a step-by-step protocol, with tips to generate xenografts and guidelines for tumor behavior analysis, whole-mount immunofluorescence, and confocal imaging quantification.

Zebrafish larval xenografts are being widely used for cancer research to perform in vivo and real-time studies of human cancer. The possibility of rapidly ...

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

The ability to visualize complex and dynamic physiological interactions between numerous cell types and the extracellular matrix (ECM) within a live tumor microenvironment is an important step toward understanding mechanisms that regulate tumor progression. While this can be accomplished through current intravital imaging techniques, it remains challenging due to the heterogeneous nature of tissues and the need for spatial context within the experimental observation. To this end, we have developed an intravital imaging workflow that pairs collagen second harmonic generation imaging, endogenous fluorescence from the metabolic co-factor NAD(P)H, and fluorescence lifetime imaging microscopy (FLIM) as a means to non-invasively compartmentalize the tumor microenvironment into basic domains of the tumor nest, the surrounding stroma or ECM, and the vasculature. This non-invasive protocol details the step-by-step process ranging from the acquisition of time-lapse images of mammary tumor models to post-processing analysis and image segmentation. The primary advantage of this workflow is that it exploits metabolic signatures to contextualize the dynamically changing live tumor microenvironment without the use of exogenous fluorescent labels, making it advantageous for human patient-derived xenograft (PDX) models and future clinical use where extrinsic fluorophores are not readily applicable.

The intravital imaging method described here utilizes collagen second harmonic generation and endogenous fluorescence from the metabolic co-factor NAD(P)H to non-invasively segment an unlabeled tumor microenvironment into tumor, stromal, and vascular compartments for in-depth analysis of 4D intravital images.

The ability to visualize complex and dynamic physiological interactions between numerous cell types and the extracellular matrix (ECM) within a live tumor ...

Rapid Optical Clearing for Semi-High-Throughput Analysis of Tumor Spheroids

Tumor spheroids are fast becoming commonplace in basic cancer research and drug development. Obtaining data regarding protein expression within the spheroid at the cellular level is important for analysis, yet existing techniques are often expensive, laborious, use non-standard equipment, cause significant size distortion, or are limited to relatively small spheroids. This protocol presents a new method of mounting and clearing spheroids that address these issues while allowing for confocal analysis of the inner structure of spheroids. In contrast to existing approaches, this protocol provides for rapid mounting and clearing of a large number of spheroids using standard equipment and laboratory supplies. Mounting spheroids in a pH-neutral agarose-PBS gel solution before introducing a refractive-index-matched clearing solution minimizes size distortion common to other similar techniques. This allows for detailed quantitative and statistical analysis where the accuracy of size measurements is paramount. Furthermore, compared to liquid clearing solutions, the agarose gel technique keeps spheroids fixed in place, allowing for the collection of three-dimensional (3D) confocal images. The present article elaborates how the method yields high-quality two- and 3D images that provide information about inter-cell variability and inner spheroid structure.

Tumor spheroids are becoming increasingly utilized to assess tumor cell-microenvironment interactions and therapy response. The present protocol describes a robust but simple method for semi-high-throughput imaging of 3D tumor spheroids using rapid optical clearing.

Tumor spheroids are fast becoming commonplace in basic cancer research and drug development. Obtaining data regarding protein expression within the ...

Zebrafish Larvae as a Model to Evaluate Potential Radiosensitizers or Protectors

Zebrafish are extensively used in several kinds of research because they are one of the easily maintained vertebrate models and exhibit several features of a unique and convenient model system. As highly proliferative cells are more susceptible to radiation-induced DNA damage, zebrafish embryos are a front-line in vivo model in radiation research. In addition, this model projects the effect of radiation and different drugs within a short time, along with major biological events and associated responses. Several cancer studies have used zebrafish, and this protocol is based on the use of radiation modifiers in the context of radiotherapy and cancer. This method can be readily used to validate the effects of different drugs on irradiated and control (non-irradiated) embryos, thus identifying drugs as radio sensitizing or protective drugs. Although this methodology is used in most drug screening experiments, the details of the experiment and the toxicity assessment with the background of X-ray radiation exposure are limited or only briefly addressed, making it difficult to perform. This protocol addresses this issue and discusses the procedure and toxicity evaluation with a detailed illustration. The procedure describes a simple approach for using zebrafish embryos for radiation studies and radiation-based drug screening with much reliability and reproducibility.

The zebrafish has recently been exploited as a model to validate potential radiation modifiers. The present protocol describes the detailed steps to use zebrafish embryos for radiation-based screening experiments and some observational approaches to evaluate the effect of different treatments and radiation.

Zebrafish are extensively used in several kinds of research because they are one of the easily maintained vertebrate models and exhibit several features ...