Cytometry Part A最新文献

{"title":"Cytoflow: User-Friendly Python Software for Computational Flow Cytometry","authors":"Leah Teague","doi":"10.1002/cyto.a.70058","DOIUrl":"10.1002/cyto.a.70058","url":null,"abstract":"<p>Modern flow cytometry experiments routinely measure 18 or more fluorescent markers across many samples, patients, and tissues. As these experiments' complexity increases, manual gating becomes unacceptably inefficient and can introduce operator-to-operator variation. Computational cytometry algorithms such as unsupervised clustering, automated gating, and dimensionality reduction can increase the speed and reliability of these analyses, but using them requires time and expertise that many biomedical scientists lack. Cytoflow was built to bridge this gap. Cytoflow is open source, user-friendly point-and-click software written in Python that allows non-programmers to apply modern computational flow cytometry methods to their data sets. Additionally, Cytoflow's modules can be used directly in a Python script or a JupyterLab notebook. Finally, extending Cytoflow with new modules that support future applications is straightforward, and feedback from an active user community continues to guide ongoing development. As a result, Cytoflow can save an experimenter time and improve the reliability and reproducibility of their analysis. Cytoflow is available to download at https://cytoflow.github.io. Source code is hosted at https://github.com/cytoflow/cytoflow, and documentation is available at https://cytoflow.readthedocs.io.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 9","pages":"625-634"},"PeriodicalIF":2.9,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.70058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148759918","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":"Benchmarking of Fluorescent Proteins in Trichoderma reesei Using Flow Cytometry.","authors":"Métivier Killian, Bidard Frédérique, Jourdier Etienne, Mathis Hugues","doi":"10.1002/cyto.a.70071","DOIUrl":"https://doi.org/10.1002/cyto.a.70071","url":null,"abstract":"<p><p>Fungi are the subject of extensive research due to their significant impact on human life. However, the development of tools based on fluorescent proteins has been slow in these species. Trichoderma reesei, a filamentous fungus known for its high secretion capability, shows great potential for producing heterologous proteins. However, the limited range of available fluorescent proteins for T. reesei hinders progress in its biological study and industrial applications. The goal of this study is to expand the panel of fluorescent markers for T. reesei by testing six new fluorescent proteins. To enable accurate comparisons, the method previously designed for eGFP was optimized to insert coding genes as a single copy by replacing the native pTEF promoter with the Neurospora crassa equivalent. The benchmarking assay identified one yellow-fluorescent protein and two green-fluorescent proteins that outperformed eGFP for transformant screening applications and a red fluorescent protein whose screening performance is comparable to that of eGFP, which would be valuable for colabeling purposes. These results could not have been predicted from the values reported in the Fluorescent Protein Database and the relative excitation of each protein at 488 nm accounts for only part of the difference. Additionally, the data obtained on fluorescent proteins and the approach used may be beneficial for investigating other types of fungi.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149180073","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":"Volume 109A, Number 9, September 2026 Cover Image","authors":"","doi":"10.1002/cyto.a.70072","DOIUrl":"https://doi.org/10.1002/cyto.a.70072","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 9","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.70072","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149177025","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":"FlowCytoMonitoR: An R-Shiny Program for Advanced Monitoring Flow Cytometer Performance","authors":"Britt Kolen,&nbsp;G. IJsbrand Reyneveld,&nbsp;Tom B. Kuipers,&nbsp;Marjolijn Hameetman,&nbsp;Hailiang Mei,&nbsp;Tamar Tak","doi":"10.1002/cyto.a.70065","DOIUrl":"10.1002/cyto.a.70065","url":null,"abstract":"<p>Flow cytometers are highly sensitive scientific instruments that need to be in optimal condition to provide accurate and reliable data. To ensure consistent performance, cytometer performance is regularly assessed by measuring standardized reference particles during a quality control (QC) measurement. Cytometer manufacturers have developed dedicated modules in their operating software to monitor cytometer performance and award a pass or a fail for each QC to indicate if the instrument is performing to specifications. However, these are usually limited in their ease of use and data visualization options. For example, usually only a single parameter can be visualized and visualization over time is limited to 1 year or to the last baseline. Additionally, a shared interface to simultaneously monitor multiple cytometers from multiple manufacturers is not available. Furthermore, all instruments generate data that are not taken into account during QC, while these data can actually provide more insight in the performance of a flow cytometer and can allow early identification of problems. To facilitate more comprehensive analysis of instrument performance we have developed FlowCytoMonitoR, an interactive R-shiny program that does not require coding experience and allows users to analyze QC data in full detail for multiple instruments. The program is currently compatible with instruments from three different vendors (BD Biosciences, Beckman Coulter, and Cytek Biosciences) and allows visualization of all QC metrics. FlowCytoMonitoR has proved invaluable to our Shared Resource Laboratory (SRL) in identifying and potentially predicting problems, thereby increasing flow cytometer uptime and service quality.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 9","pages":"674-684"},"PeriodicalIF":2.9,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.70065","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149059050","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":"CYTO 2026—Concluding Thoughts on the Next Wave of Cytometry From the Conference Chairs","authors":"Silas Leavesley,&nbsp;Jingjing Zhao","doi":"10.1002/cyto.a.70064","DOIUrl":"10.1002/cyto.a.70064","url":null,"abstract":"&lt;p&gt;CYTO 2026, held June 6–10, recently concluded in sunny West Palm Beach, FL. The congress brought together some of the best minds and top talent across the diverse range of fields that comprise cytometry. The CYTO 2026 theme was “The Next Wave,” with a goal of highlighting novel and cutting-edge flow and imaging technologies. Talks and content spanned a range of topics, including new imaging and flow systems, probe development, autofluorescence characterization, single-molecule localization, microfluidics, whole-organism flow cytometry, and more.&lt;/p&gt;&lt;p&gt;Opening day (Sunday) featured an especially strong emphasis on new technologies. The State of the Art session highlighted key areas of ongoing innovation, with invited talks by ISAC President Dr. Jessica Houston, who described new technologies for fluorescence lifetime cytometry; Dr. Stephanie Xie, who detailed her work in identifying roles of epigenetic memory and inflammation in hematopoiesis; and Prof. Keisuke Goda, who presented novel systems for whole-organism flow cytometry (aka zoometry). A special plenary session was also added to the opening day to focus on autofluorescence and label-free cytometry, and included talks by Dr. Melissa Skala on autofluorescence lifetime flow cytometry, Dr. Pietro Ferraro on 3D label-free tomographic flow, and Prof. Stephen Evans on Raman spectroscopy and deformation cytometry. The Hooke Lecture was delivered by Prof. Sir David Klenerman, who discussed technological breakthroughs in single-molecule imaging and DNA sequencing, as well as insights into amyloid beta formation and neurodegenerative disease.&lt;/p&gt;&lt;p&gt;A prominent feature of the meeting was the CYTO Innovation and Technology Showcase, organized by the CYTO Innovation Committee on the evening preceding the official opening. During the Showcase, three finalists, Dr. Justin Hodgkiss of Advemto, Dr. Rebecca Turk MacLeod of CellsBin Inc., and Dr. Lei Chen of Rarity Bioscience AB, presented their technologies to a panel of expert judges. Rarity Bioscience was selected as the winner for its superRCA (super Rolling Circle Amplification) technology, which enables ultra-sensitive diagnostics on standard cytometers. The inaugural Tech Pioneer Lecture was delivered by Prof. Dino Di Carlo (UCLA), who discussed his work on “Cell-Cell Cytometry Using Lab on a Particle Technologies.” An Innovator Panel Discussion followed, featuring Dr. David Novo, Dr. John Nolan, Dr. Samson Rogers, and Prof. Sir David Klenerman. The Showcase celebrated emerging innovations and reinforced the Committee's commitment to translating cutting-edge scientific advances into real-world impact.&lt;/p&gt;&lt;p&gt;CYTO 2026 emphasized several technological themes in strategic areas of flow and imaging cytometry—specifically autofluorescence and label-free cytometry, validation and standardization, and the confluence of flow and imaging technologies. These topics formed the basis for multiple parallel sessions, invited talks, tutorials, and workshops. The e","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 9","pages":"623-624"},"PeriodicalIF":2.9,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.70064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148825974","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":"Velociraptor Machine Learning Quantifies Similarity to Known Cell Types and Matches Cells Across Flow and Imaging Cytometry Platforms","authors":"Claire E. Cross,&nbsp;Asa A. Brockman,&nbsp;Rebecca A. Ihrie,&nbsp;Jonathan M. Irish","doi":"10.1002/cyto.a.70066","DOIUrl":"10.1002/cyto.a.70066","url":null,"abstract":"<p>Suspension flow cytometry enables high-throughput cellular profiling at the single cell level, but these data lack positional information. Conversely, tissue-based 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-cell 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-platform cytometry analysis. Velociraptor begins with a graph-based implementation of MEM to calculate per-cell 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-measure of 0.81). Between SFC and CyTOF, a comparison of two fundamentally different probe types—fluorophores and metal tags—in 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⁺, and validated as spatially segregated to the tumor core. In both cycIHC (8-dimensional imaging) and IMC imaging (40-dimensional 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 https://github.com/cytolab.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 9","pages":"649-663"},"PeriodicalIF":2.9,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.70066","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148918832","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":"A Robust Computational Workflow Utilizing Generalized Least Squares (GLS) and Generalized Estimating Equations (GEE-GLM) for Longitudinal Flow Cytometry Maturation Data Analysis","authors":"Sixuan Joanna Wang,&nbsp;Ruben Deneer,&nbsp;Tom Schoenmakers,&nbsp;Stefan G. C. Mestrum,&nbsp;Yvonne M. C. Henskens,&nbsp;Otto Bekers,&nbsp;Anton H. N. Hopman,&nbsp;Frans C. S. Ramaekers,&nbsp;Mathie P. G. Leers","doi":"10.1002/cyto.a.70061","DOIUrl":"10.1002/cyto.a.70061","url":null,"abstract":"<p>Routinely used statistical hypothesis tests are often inadequate for longitudinal flow cytometry data, necessitating more sophisticated modeling approaches. Analyzing dynamic biomarker expression across hematopoietic maturation presents several analytical challenges, notably nonlinearity, serial correlation between subsequent stages, and varying total cell counts. This study addresses these issues by comparing traditional stage-by-stage hypothesis tests with two marginal modeling approaches: Generalized least squares (GLS) and a generalized linear model fitted with generalized estimating equations (GEE-GLM). We analyzed cell fractions expressing the dynamic biomarkers Bcl-2 (anti-apoptotic marker) and Ki-67 (proliferation marker), as well as the Bcl-2:Ki-67 ratio, across 20 stages of erythropoietic maturation in bone marrow aspirates from 25 patients with myelodysplastic syndromes (MDS), 25 patients with acute myeloid leukemia (AML), and 50 nonmalignant controls. The GLS modeling approach accounts for nonlinearity and correlation between subsequent maturation stages, while GEE-GLM additionally accounts for varying total cell counts by assigning greater weight to observations with larger cell counts. Instead of repeated stage-by-stage testing, GLS and GEE-GLM model the average maturation profile across different sub-populations, enabling visualization and robust population-level statistical inference. We demonstrate that variations in the total number of cells influence the estimated population mean profiles and their confidence intervals. This effect is particularly pronounced during early maturation stages, where low Ki-67+ cell counts can lead to substantially different estimates and conclusions when using the GEE-GLM approach. Conversely, the GLS approach offers the flexibility to model the unbounded Bcl-2:Ki-67 ratio, revealing differences between AML and nonmalignant patients across all 20 stages. The computational workflow and scripts are provided to facilitate reproducibility and application to similar time-dependent flow cytometry datasets.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 9","pages":"664-673"},"PeriodicalIF":2.9,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.70061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148789344","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":"High-Dimensional Spectral Flow Cytometry Panel for Immunophenotypic Characterization of Fixed Whole Blood Samples","authors":"Vaishnavi Parthasarathy,&nbsp;Alexander T. Heubeck,&nbsp;Tyanna Stuckey,&nbsp;Veronica Hernandez,&nbsp;Blessing Musgrove,&nbsp;Maia M. Bennett-Boehm,&nbsp;Troy Torgerson,&nbsp;Peter Skene,&nbsp;Julian Reading","doi":"10.1002/cyto.a.70059","DOIUrl":"10.1002/cyto.a.70059","url":null,"abstract":"<p>Peripheral blood mononuclear cells (PBMCs) are commonly used for immune profiling in clinical research, but their isolation and cryopreservation present logistical challenges, particularly in settings with limited access to research facilities. To address this, we implemented a whole blood fixation protocol to preserve surface protein epitopes and enable high-dimensional flow cytometry without the need for immediate sample processing. A 36-marker lymphoid and 35-marker myeloid spectral flow cytometry panels were developed and optimized to characterize diverse immune populations, including T cell subsets, B cells, NK cells, monocytes, dendritic cells, and granulocytes. Panel design focused on antibody clone compatibility with fixed antigens, minimizing non-specific binding, and reliably detecting chemokine receptors. Performance was evaluated by comparing fresh and fixed whole blood, revealing consistent identification of major immune lineages. Long-term stability was demonstrated by using cryopreserved fixed blood from a healthy donor over two and half years, with immune profiles remaining stable. These findings support the use of fixed whole blood for high-dimensional spectral cytometry and its utility in longitudinal clinical immune monitoring.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 9","pages":"635-648"},"PeriodicalIF":2.9,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.70059","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148789383","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":"Proposing November 12th as International Flow Cytometry Day: Celebrating History, Inspiring Society","authors":"Jordi Petriz,&nbsp;Marc Sorigue,&nbsp;Alberto Álvarez-Barrientos,&nbsp;José Enrique O'Connor","doi":"10.1002/cyto.a.70067","DOIUrl":"10.1002/cyto.a.70067","url":null,"abstract":"&lt;p&gt;True innovation in biology and technology often begins where separate disciplines meet. On November 12, 1965, more than 60 years ago, Mack Fulwyler published a landmark paper in &lt;i&gt;Science&lt;/i&gt; that fundamentally changed the course of biomedical research [&lt;span&gt;1&lt;/span&gt;]. By adapting inkjet droplet-deflection technology for biological use, he achieved electrostatic cell sorting based on electronic volume measurements [&lt;span&gt;2&lt;/span&gt;]. That breakthrough brought together precision fluidics, instrumentation engineering, and single-cell biology, creating a new foundation for modern flow cytometry and cell sorting [&lt;span&gt;3-5&lt;/span&gt;]. To honor this legacy and recognize the remarkable growth of the global cytometry community, we propose formally establishing November 12 as International Flow Cytometry Day.&lt;/p&gt;&lt;p&gt;Establishing an annual point of reference would allow the global scientific community to reflect on our shared history while driving future innovation. Central to this initiative could be an international, synchronized symposium tracing the field's evolution—from Mack Fulwyler's early fluidics concepts and the landmark historical developments documented across &lt;i&gt;Cytometry Part A&lt;/i&gt; [&lt;span&gt;4, 5&lt;/span&gt;] to state-of-the-art advances in high-dimensional multiomics, spectral cytometry, mass cytometry, and label-free imaging. Beyond celebrating technical milestones, this annual event would serve as a vital platform to advocate for standardized data formats, robust quality control, and enhanced reproducibility across clinical and academic settings. With endorsement from scientific societies, academic institutions, and industry partners, International Flow Cytometry Day can mature into an enduring framework for field-wide education and international collaboration.&lt;/p&gt;&lt;p&gt;Equally important, this initiative seeks to recognize the diverse pioneers who transformed an engineering milestone into an indispensable scientific platform. While Fulwyler pioneered droplet deflection making electrostatic cell sorting possible, the discipline truly blossomed through the combined efforts of researchers who developed novel fluorophores, monoclonal antibody applications, advanced computational algorithms, and shared resource facilities that brought flow cytometry into everyday biomedical research.&lt;/p&gt;&lt;p&gt;Women scientists, in particular, have played a foundational role in this journey, from the early days of flow cytometry cell sorting, hybridoma technology, optical biophysics, and pioneering software algorithms for list-mode data acquisition, automated gating, and data standardization (FCS), to contemporary leadership in high-dimensional computational cytomics, clinical translation, and core facility management [&lt;span&gt;6-15&lt;/span&gt;]. By explicitly honoring their contributions through the literature, International Flow Cytometry Day will ensure that the history of cytometry is told not only through its defining technical breakthroughs but also through the community that t","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"109 9","pages":"621-622"},"PeriodicalIF":2.9,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.70067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149015935","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":"Single-Cell Reporter Dynamics During the First Two Rounds of Rhinovirus Plaque Growth.","authors":"Rahul Ramachandran, Huicheng Shi, John Yin","doi":"10.1002/cyto.a.70069","DOIUrl":"https://doi.org/10.1002/cyto.a.70069","url":null,"abstract":"<p><p>When infection spreads from an initially infected cell through a cell monolayer, the resulting plaque records successive rounds of virus-cell interaction. Fluorescent reporter viruses permit this process to be followed at single-cell resolution, but whether secondary infected cells retain detectable signatures associated with their plaque of origin remains unclear. Here, we tracked enhanced green fluorescent protein expression in 154 cells from eight rhinovirus plaques and systematically compared feature-based, clustering, dimensionality-reduction, and sequence-alignment methods. Primary and secondary infected cells were distinguished mainly by the time of first detectable fluorescence; no consistent differences were detected in reporter-trajectory kinetics or amplitude. Among secondary cells, most methods detected no plaque-associated structure beyond chance. Affinity propagation detected a small plaque-label association that persisted after removal of the first detection time, although its biological interpretation was limited by session-associated intensity differences. Thus, by the secondary round, cell-to-cell heterogeneity largely exceeded any plaque-associated signature detectable under the present conditions. This work provides a framework for comparing single-cell trajectories across early rounds of plaque growth and for designing studies to resolve subtle lineage-associated effects.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149105686","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}