{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,8]],"date-time":"2026-11-08T02:14:11Z","timestamp":1794104051744,"version":"4.8.0"},"reference-count":27,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cytometry Part B Clinical"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Manual gating for plasma cell (PC) identification in multiparametric flow cytometry (MFC) is time\u2010consuming and operator\u2010dependent, especially when PCs are scarce. Artificial intelligence approaches such as unsupervised clustering (e.g., FlowSOM) map high\u2010dimensional data that still require expert interpretation. The primary aim of this work was to develop a lightweight, transparent, spreadsheet\u2010based algorithm for a classification model that integrates with automated clustering outputs for standardized B\u2010cell and PC identification in research flow cytometry datasets. Bone marrow aspirates were stained with a standard BD OneFlow\u2122 PC screening tube (CD38, CD56, \u03b22\u2010microglobulin, CD19, cyIg\u03ba, cyIg\u03bb, CD45, CD138) and acquired on a BD FACSLyric\u2122 flow cytometer. FCS files were exported to CellEngine cytometry software and singlet nucleated events underwent FlowSOM clustering (8 clusters\/sample). Cluster\u2010level median fluorescence intensities (MFIs) were exported to an Excel \u201cPC Trainer Classifier\u201d that (i) normalizes markers to an in\u2010sample B\u2010cell anchor, (ii) computes a PC\n                    <jats:sub>score<\/jats:sub>\n                    with CD138 as a hard gate and CD38 as a soft gate, plus secondary features (CD19\u2193, CD45\u2193, CD56\u2191), (iii) applies a forced core fallback (highest CD38\/CD138 core score) when strict criteria yield no PCs, and (iv) derives a NEO\n                    <jats:sub>score<\/jats:sub>\n                    (CD56\u2191, CD19\u2193, CD45\u2193) for neoplastic phenotype. The rule\u2010based classifier was trained on expert assigned PC and B\u2010cell clusters from 40 samples (30 clonal and 10 polyclonal). Validation was done on a new set of 52 samples, independent of the model. Elements of the Excel formula design and error\u2010proofing were co\u2010developed with ChatGPT (OpenAI); all outputs were verified by the authors. Across 52 validation cases (8 clusters\/case; 416 clusters total), B\u2010cell detection achieved: Sensitivity 0.902 (0.79\u20130.96), Specificity 0.984 (0.96\u20130.99), Precision 0.885 (0.77\u20130.94), Accuracy 0.973 (0.95\u20130.99) and F1 0.893. PC identification achieved: Sensitivity 0.651 (0.54\u20130.75), Specificity 0.828 (0.79\u20130.86), Precision 0.458 (0.37\u20130.55), Accuracy 0.796 (0.76\u20130.83) and F1 0.537. A transparent Excel\u2010based classifier integrated with FlowSOM clustering enables highly reproducible B\u2010cell identification and provides a structured approach to PC classification in research flow cytometry datasets. While the B\u2010cell classifier demonstrated excellent discriminatory performance, PC identification yielded moderate sensitivity and precision, likely reflecting underlying biological and phenotypic heterogeneity. Consequently, the PC classification component is best interpreted as a triage or augmented\u2010intelligence tool intended to support, rather than replace, expert assessment. This approach provides a structured and auditable framework that reduces operator dependency and improves inter\u2010case harmonization. Its interpretability, low cost and portability make it particularly suited to research laboratories operating in resource\u2010variable settings. Further optimisation and prospective validation may refine PC classification performance.\n                  <\/jats:p>","DOI":"10.1002\/cyto.b.70058","type":"journal-article","created":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T09:52:40Z","timestamp":1785750760000},"page":"427-439","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automated\n                    <scp>B<\/scp>\n                    \u2010cell and plasma cell identification using unsupervised clustering by\n                    <scp>FlowSOM<\/scp>\n                    and an excel\u2010based classification model"],"prefix":"10.1002","volume":"110","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0523-8494","authenticated-orcid":false,"given":"Ethan James","family":"Gantana","sequence":"first","affiliation":[{"name":"Department of Pathology Stellenbosch University  Cape Town South Africa"},{"name":"Department of Haematology National Health Laboratory Service  Cape Town South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8080-3820","authenticated-orcid":false,"given":"Ernest","family":"Musekwa","sequence":"additional","affiliation":[{"name":"Department of Pathology Stellenbosch University  Cape Town South Africa"},{"name":"Department of Haematology National Health Laboratory Service  Cape Town South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7932-3210","authenticated-orcid":false,"given":"Erica\u2010Mari","family":"Nell","sequence":"additional","affiliation":[{"name":"Department of Pathology Stellenbosch University  Cape Town South Africa"},{"name":"Department of Haematology National Health Laboratory Service  Cape Town South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5489-8392","authenticated-orcid":false,"given":"Zivanai Cuthbert","family":"Chapanduka","sequence":"additional","affiliation":[{"name":"Department of Pathology Stellenbosch University  Cape Town South Africa"},{"name":"Department of Haematology National Health Laboratory Service  Cape Town South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.b.22116"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.b.22177"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.b.22249"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108820"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(21)00673-5"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/leu.2017.29"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clml.2017.03.032"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.b.20523"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/leu.2012.122"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/ajcp\/aqad150.258"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.a.20333"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12026-014-8519-y"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btw191"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2024-194599"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2024-201439"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1111\/bjh.18833"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.3389\/frhem.2024.1331109"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1182\/blood-2024-210959"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers14102537"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1309\/AJCPQ5KTKAGSSCFN"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1182\/bloodadvances.2025016126"},{"key":"e_1_2_11_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.b.22053"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.b.21619"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0300-7"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.1038\/leu.2012.120"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/cyto.a.22625"},{"key":"e_1_2_11_28_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.l6927"}],"container-title":["Cytometry 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