{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:56:48Z","timestamp":1753891008445,"version":"3.41.2"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100001006","name":"Breast Cancer Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:p>Cellular composition and structural organization of cells in the tissue determine effective antitumor response and can predict patient outcome and therapy response. Here we present Seg-SOM, a method for dimensionality reduction of cell morphology in H&amp;amp;E-stained tissue images. Seg-SOM resolves cellular tissue heterogeneity and reveals complex tissue architecture. We leverage a self-organizing map (SOM) artificial neural network to group cells based on morphological features like shape and size. Seg-SOM allows for cell segmentation, systematic classification, and <jats:italic>in silico<\/jats:italic> cell labeling. We apply the Seg-SOM to a dataset of breast cancer progression images and find that clustering of SOM classes reveals groups of cells corresponding to fibroblasts, epithelial cells, and lymphocytes. We show that labeling the Lymphocyte SOM class on the breast tissue images accurately estimates lymphocytic infiltration. We further demonstrate how to use Seq-SOM in combination with non-negative matrix factorization to statistically describe the interaction of cell subtypes and use the interaction information as highly interpretable features for a histological classifier. Our work provides a framework for use of SOM in human pathology to resolve cellular composition of complex human tissues. We provide a python implementation and an easy-to-use docker deployment, enabling researchers to effortlessly featurize digitalized H&amp;amp;E-stained tissue.<\/jats:p>","DOI":"10.3389\/fimmu.2021.765923","type":"journal-article","created":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T14:23:57Z","timestamp":1635517437000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Self-Organizing Maps for Cellular In Silico Staining and Cell Substate Classification"],"prefix":"10.3389","volume":"12","author":[{"given":"Edwin","family":"Yuan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Magdalena","family":"Matusiak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Korsuk","family":"Sirinukunwattana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sushama","family":"Varma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u0141ukasz","family":"Kidzi\u0144ski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"West","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2021,10,29]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1876-x","article-title":"The Single-Cell Pathology Landscape of Breast Cancer","volume":"578","author":"Jackson","year":"2020","journal-title":"Nature"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1341","DOI":"10.1016\/j.cell.2020.07.005","article-title":"Coordinated Cellular Neighborhoods Orchestrate Antitumoral Immunity at the Colorectal Cancer Invasive Front","volume":"182","author":"Sch\u00fcrch","year":"2020","journal-title":"Cell"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1101\/2020.12.06.20244913","article-title":"Immune Cell Topography Predicts Response to PD-1 Blockade in Cutaneous T Cell Lymphoma","author":"Phillips","year":"2020","journal-title":"bioRxiv"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0014-x","article-title":"Understanding the Tumor Immune Microenvironment (TIME) for Effective Therapy","volume":"24","author":"Binnewies","year":"2018","journal-title":"Nat Med"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1016\/j.cell.2018.08.039","article-title":"A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging","volume":"174","author":"Keren","year":"2018","journal-title":"Cell"},{"key":"B6","article-title":"Automatic Detection of Invasive Ductal Carcinoma in Whole Slide Images With Convolutional Neural Networks","volume-title":"Medical Imaging 2014: Digital Pathology","author":"Cruz-Roa","year":"2014"},{"key":"B7","article-title":"Automated Grading of Gliomas Using Deep Learning in Digital Pathology Images: A Modular Approach With Ensemble of Convolutional Neural Networks","volume":"2015","author":"Ertosun","year":"2015","journal-title":"AMIA Annu Symp Proc"},{"key":"B8","doi-asserted-by":"publisher","first-page":"26286","DOI":"10.1038\/srep26286","article-title":"Deep Learning as a Tool for Increased Accuracy and Efficiency of Histopathological Diagnosis","volume":"6","author":"Litjens","year":"2016","journal-title":"Sci Rep"},{"key":"B9","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1016\/j.cell.2018.03.040","article-title":"In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images","volume":"173","author":"Christiansen","year":"2018","journal-title":"Cell"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1136\/medethics-2020-106820","article-title":"Who Is Afraid of Black Box Algorithms? 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