{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T08:50:24Z","timestamp":1791881424202,"version":"4.3.4"},"reference-count":41,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":9,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["NS118580"],"award-info":[{"award-number":["NS118580"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["CA226833"],"award-info":[{"award-number":["CA226833"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["AI125056"],"award-info":[{"award-number":["AI125056"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["CA217450"],"award-info":[{"award-number":["CA217450"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["T32GM137793"],"award-info":[{"award-number":["T32GM137793"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008222","name":"Ben and Catherine Ivy Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008222","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cytometry Pt A"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Suspension flow cytometry enables high\u2010throughput cellular profiling at the single cell level, but these data lack positional information. Conversely, tissue\u2010based imaging cytometry techniques reveal a cell's location within the tissue architecture and can provide insight into cell biology. It would be especially valuable if data analysis tools could incorporate data from imaging and flow cytometry platforms to gain complementary strengths when quantifying features of cells and populations. We hypothesized that per\u2010cell Marker Enrichment Modeling (MEM) might provide a way to register cells between flow and imaging cytometry analysis. Here, we developed the Velociraptor machine learning workflow for cross\u2010platform cytometry analysis. Velociraptor begins with a graph\u2010based implementation of MEM to calculate per\u2010cell quantitative phenotype labels. With this information, Velociraptor can then quickly calculate similarity between each cell's phenotype and search terms describing established cell types, cells of interest, or cells observed in other samples. Velociraptor was effective in registering cells within and between cytometry platforms. Integrated identification of cell populations was tested in several challenges, including comparisons of high dimensional datasets from cancer and immunology. Tested instrument types included imaging mass cytometry (IMC), cyclic immunohistochemistry (cycIHC), suspension mass cytometry (CyTOF), and suspension spectral flow cytometry (SFC). Between IMC and CyTOF, a comparison across imaging and flow cytometry platforms that use the same mass tag probes, Velociraptor accurately identified and registered immune cell types (median F1\u2010measure of 0.81). Between SFC and CyTOF, a comparison of two fundamentally different probe types\u2014fluorophores and metal tags\u2014in suspension flow cytometry, Velociraptor was even more accurate at identifying and registering cells (concordance correlation coefficient of 0.99). Velociraptor was especially useful in heterogeneous samples where individual cells diverged in phenotype from the bulk population. In IMC imaging of human breast cancer, a previously unappreciated tumor cell subset was revealed by Velociraptor, characterized as CD15\n                    <jats:sup>+<\/jats:sup>\n                    , and validated as spatially segregated to the tumor core. In both cycIHC (8\u2010dimensional imaging) and IMC imaging (40\u2010dimensional imaging), Velociraptor accurately identified macrophages using a single search label as input. Notably, Velociraptor worked effectively with both extremely rare and highly abundant cell types and with cell search labels calculated from data and theoretical labels based on literature and expertise. The Velociraptor algorithm is freely available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/cytolab\">https:\/\/github.com\/cytolab<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1002\/cyto.a.70066","type":"journal-article","created":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T11:45:38Z","timestamp":1789040738000},"page":"649-663","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Velociraptor Machine Learning Quantifies Similarity to Known Cell Types and Matches Cells Across Flow and Imaging Cytometry Platforms"],"prefix":"10.1002","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5534-9491","authenticated-orcid":false,"given":"Claire E.","family":"Cross","sequence":"first","affiliation":[{"name":"Department of Pediatrics University of Colorado Anschutz Medical Campus  Aurora Colorado USA"},{"name":"Department of Cell and Developmental Biology Vanderbilt University  Nashville Tennessee USA"},{"name":"Department of Pathology, Microbiology and Immunology Vanderbilt University Medical Center  Nashville Tennessee USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0059-0392","authenticated-orcid":false,"given":"Asa A.","family":"Brockman","sequence":"additional","affiliation":[{"name":"Department of Pediatrics University of Colorado Anschutz Medical Campus  Aurora Colorado USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0439-0141","authenticated-orcid":false,"given":"Rebecca A.","family":"Ihrie","sequence":"additional","affiliation":[{"name":"Department of Pediatrics University of Colorado Anschutz Medical Campus  Aurora Colorado USA"},{"name":"Department of Cell and Developmental Biology Vanderbilt University  Nashville Tennessee USA"},{"name":"Vanderbilt\u2010Ingram Cancer Center Vanderbilt University Medical Center  Nashville Tennessee USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9428-8866","authenticated-orcid":false,"given":"Jonathan M.","family":"Irish","sequence":"additional","affiliation":[{"name":"Department of Pediatrics University of Colorado Anschutz Medical Campus  Aurora Colorado USA"},{"name":"Department of Cell and Developmental Biology Vanderbilt University  Nashville Tennessee USA"},{"name":"Department of Pathology, Microbiology and Immunology Vanderbilt University Medical Center  Nashville Tennessee USA"},{"name":"Vanderbilt\u2010Ingram Cancer Center Vanderbilt University Medical Center  Nashville Tennessee USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-61737-950-5_1"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/0471142956.cy1004s00"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.2594"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.1991"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2016.03.085"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2016.04.019"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.4314"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.a.22625"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.64653"},{"key":"e_1_2_10_11_1","article-title":"Continuous Visualization of Differences Between Biological Conditions in Single Cell Data","author":"Burns T. 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