Cytometry Part A最新文献

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 3","pages":"210-229"},"PeriodicalIF":2.1,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148071436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 3","pages":"165-166"},"PeriodicalIF":2.1,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148063435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 3","pages":"192-209"},"PeriodicalIF":2.1,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148085177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Gating of CD34\u0000 + Cells in Cord Blood: Performance Evaluation of a Machine Learning-Based ISHAGE Protocol","authors":"Carl Simard,&nbsp;Diane Fournier,&nbsp;Patrick Trépanier","doi":"10.1002/cyto.a.70017","DOIUrl":"10.1002/cyto.a.70017","url":null,"abstract":"<div>\u0000 \u0000 <p>Precise quantification of cellular subsets is fundamental for qualifying grafts and supporting emerging therapies. CD34⁺ enumeration in cord blood using the ISHAGE protocol exemplifies the operator variability inherent to manual gating. We evaluated whether a machine-learning approach could provide standardized automated enumeration and reduce variability. A machine-learning–based automatic gating algorithm was trained on 29 manually gated FCS files and applied to raw flow cytometry data. Performance was compared with manual gating from nine laboratories from a previously published multicenter study using <i>Z</i>-scores, rank positioning, absolute deviation, correlations, Bland–Altman analysis, and intraclass correlation coefficients. Across 12 samples, AI1 remained within ± 2 SD of the human consensus in all cases, whereas AI2 exceeded this threshold in two. AI1 consistently ranked closer to the human median and showed narrower deviations. Both models correlated strongly with manual gating (AI1: <i>r</i> = 0.991; AI2: <i>r</i> = 0.968). Bland–Altman analysis showed minimal bias and narrow limits of agreement for AI1 versus its human reference, while AI2 and human–human comparisons displayed greater variability. ICCs indicated high reliability across all comparisons, with the strongest agreement observed for AI1 versus Lab1 (ICC = 0.995). A machine learning–based automatic gating approach can reproduce expert CD34⁺ enumeration with high fidelity. By reducing operator-dependent variability, this method may strengthen cytometry standardization across cord blood banking and broader cellular therapy workflows.</p>\u0000 </div>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 2","pages":"108-114"},"PeriodicalIF":2.1,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146257580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imaging Flow Cytometry Detection of Cytogenetic Abnormalities in Circulating CD34+ Cells Predicts Leukemic Transformation in Myelofibrosis","authors":"Ruby M. Hamilton,&nbsp;Ryan J. Collinson,&nbsp;Henry Y. Hui,&nbsp;Zi Yun Ng,&nbsp;Hun S. Chuah,&nbsp;Malcolm Webb,&nbsp;Belinda B. Guo,&nbsp;Wendy N. Erber,&nbsp;Kathy A. Fuller","doi":"10.1002/cytoa.70012","DOIUrl":"10.1002/cytoa.70012","url":null,"abstract":"<p>Myelofibrosis is a myeloproliferative neoplasm with potential to transform to acute myeloid leukemia. This evolution is unpredictable and current assays lack the sensitivity and applicability needed to predict this transformation. While population-level data utilizing comprehensive genomic profiling can identify subgroups at higher risk of progression, they do not provide individualized information on the likelihood or timing of leukemia. Cytogenetic alterations are typically present in secondary leukemia. We aimed to determine whether these changes could be detected at an early stage. To achieve this we established and tested a single-cell imaging flow cytometric method for chromosomal aberrations using fluorescence in situ hybridization (FISH) probes to analyze circulating CD34/CD45-positive cells. Peripheral blood samples from 14 patients, collected at up to eight timepoints over a 34-month period, were analyzed for defects involving chromosomes 1, 5, and 17. Following cell immunophenotyping and FISH probe hybridization, a mean of 174,216 mononuclear cells was assessed per sample. Chromosomal abnormalities including gain(1q), del(5q), idic (5), monosomy 17, and/or del(17p) were identified in eight patients, at frequencies down to 0.2% of mononuclear cells. Serial analyses revealed emergence of new chromosomal lesions, clonal evolution, dominance, and multi-hit abnormalities. In three patients, acquired chromosome 17 abnormalities preceded progression to secondary leukemia by up to 7 months. This pilot study demonstrates that imaging flow cytometry-based FISH of circulating CD34/CD45-positive cells enables real-time, blood-based surveillance for cytogenetic evolution in myelofibrosis. The ability to dynamically track clone size and hierarchy highlights its potential as an early predictor of leukemic transformation in myelofibrosis.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 2","pages":"98-107"},"PeriodicalIF":2.1,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cytoa.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146112421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Label-Free Holographic Imaging Flow Cytometry With Deep-Learning-Based Detection and Classification of Thousands of Cells Per Second","authors":"Dana Yagoda-Aharoni,&nbsp;Eden Dotan,&nbsp;Matan Dudaie,&nbsp;Natan T. Shaked","doi":"10.1002/cytoa.70008","DOIUrl":"10.1002/cytoa.70008","url":null,"abstract":"<p>We present a new end-to-end neural network approach for real-time biological cell detection and classification via label-free quantitative imaging flow cytometry based on digital holography, offering a comprehensive representation of cellular structures without the need for chemical cell staining. In contrast to previous studies, our method is the first to obtain classification and detection of cells, imaged during flow using large-magnification microscopy, in 0.44 msec, allowing real-time label-free imaging flow cytometry, with more than 10× speedup compared to YOLOv8n. The custom-made two-stage neural network consists of fixed convolution layers using image processing filters to detect a single location per object, followed by two convolutional layers that classify each detected cell. This approach enables reducing computational complexity and offers high-throughput, label-free imaging-based analysis suitable for real-time imaging flow cytometry. We validate the method on two cell datasets, T-cells at different activation stages and cancer cells of different metastatic potentials, demonstrating the method's adaptability. Our results show the ability to image, detect, and classify thousands of cells per second during flow, highlighting the potential of label-free imaging flow cytometry for real-time cell monitoring, early disease detection, and high-speed diagnostics.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 2","pages":"89-97"},"PeriodicalIF":2.1,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cytoa.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146050817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GeoMx and RNAscope: A Comparative Assessment of Their Utility for Spatial mRNA Expression Profiling in Formalin-Fixed Breast Cancer Tissue","authors":"Christian Thomsen,&nbsp;Birgit Truumees,&nbsp;Søren Nielsen,&nbsp;Boye Schnack Nielsen,&nbsp;Sara Rose Newell Jensen,&nbsp;Kasper Thorsen,&nbsp;Rasmus Røge","doi":"10.1002/cyto.a.70020","DOIUrl":"10.1002/cyto.a.70020","url":null,"abstract":"<p>mRNA expression analysis in formalin-fixed tissue is essential for many biomarker and tumor microenvironment studies. NanoString GeoMx Digital Spatial Profiler is a recent technique that offers high-plex spatial transcriptomics (up to 18,000 genes), while RNAscope (1–4 plex) is a well-established mRNA in situ hybridization method. This study compares quantitative expression estimates obtained by GeoMX and RNAscope. Serial sections from two TMAs containing mammary cancers, including triple-negative (<i>n</i> = 48) and varying ER/HER2 status (<i>n</i> = 45), were analyzed using GeoMx Cancer Transcriptomics Atlas (CTA) covering approximately 1,800 genes and RNAscope probes for GATA3, SOX10, and PD-L1 mRNAs. Expression was quantified as counts/cell (GeoMx, geometric mean of five probes per gene) and average dots/cell (RNAscope, QuPath). Positivity was defined as above the limit of quantification (GeoMx) or &gt; 0.1 dots/cell (RNAscope). High correlation was observed for GATA3 (<i>R</i> = 0.87) and SOX10 (<i>R</i> = 0.77) between methods. PD-L1 expression was high in only one core, precluding correlation analysis. RNAscope demonstrated higher sensitivity and a broader dynamic range than GeoMx. In conclusion, GeoMx CTA and RNAscope exhibit a strong correlation. GeoMx enabled highly multiplexed gene expression analysis, whereas RNAscope provided better sensitivity and single-cell resolution. The choice of method should be guided by study objectives.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 2","pages":"147-154"},"PeriodicalIF":2.1,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147376455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"eFluor 450 Enables Superior Detection of Intracellular Staphylococcus aureus in High-Autofluorescence Triple-Negative Breast Cancer Cells","authors":"Sima Kianpour Rad,&nbsp;Runhao Li,&nbsp;Kenny K. L. Yeo,&nbsp;Yoko Tomita,&nbsp;Timothy J. Price,&nbsp;Wendy V. Ingman,&nbsp;Amanda R. Townsend,&nbsp;Eric Smith","doi":"10.1002/cyto.a.70019","DOIUrl":"10.1002/cyto.a.70019","url":null,"abstract":"<p>Accurate discrimination of extracellular from intracellular bacteria is critical for assessing microbial invasiveness in host cells. Flow cytometry offers high-throughput, quantitative analysis but can be confounded by high autofluorescence in certain tumor-derived cell types. This study compared the performance of two fluorescent dyes, carboxyfluorescein succinimidyl ester (CFSE), a widely used cell-permeant dye, and eFluor 450, a fixable amine-reactive dye, for labeling \u0000 <i>Staphylococcus aureus</i>\u0000 during intracellular infection of triple-negative breast cancer cell lines. Both dyes produced multiplicity-of-infection–dependent increases in fluorescence; however, eFluor 450 consistently achieved superior population separation and higher estimated fluorochrome abundance following spectral unmixing, even in highly autofluorescent backgrounds. These differences reflect more favorable spectral separation from cellular autofluorescence rather than generic signal enhancement. Operationally, eFluor 450 required shorter labeling time and lower reagent volume than CFSE without compromising bacterial viability. The advantages of eFluor 450 were most pronounced at low bacterial loads and in applications requiring precise population discrimination. To our knowledge, this is the first demonstration of eFluor 450 for intracellular bacterial detection by spectral flow cytometry in cancer models. Together, these findings establish eFluor 450 as an efficient alternative to conventional dyes and highlight the importance of fluorochrome spectral properties for unmixing-based host–pathogen analyses.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 2","pages":"135-146"},"PeriodicalIF":2.1,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.70019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147303319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Volume 109A, Number 2, February 2026 Cover Image","authors":"","doi":"10.1002/cyto.a.70022","DOIUrl":"https://doi.org/10.1002/cyto.a.70022","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 2","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Volume 109A, Number 2, February 2026 Cover Image","authors":"","doi":"10.1002/cyto.a.70022","DOIUrl":"https://doi.org/10.1002/cyto.a.70022","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 2","pages":""},"PeriodicalIF":2.1,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}