Cytometry Part A最新文献

{"title":"Volume 107A, Number 8, August 2025 Cover Image","authors":"","doi":"10.1002/cyto.a.24871","DOIUrl":"https://doi.org/10.1002/cyto.a.24871","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 8","pages":""},"PeriodicalIF":2.1,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24871","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145012912","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":"Per-Event Uncertainty Quantification for Flow Cytometry Using Calibration Beads","authors":"Prajakta Bedekar,&nbsp;Megan A. Catterton,&nbsp;Matthew DiSalvo,&nbsp;Gregory A. Cooksey,&nbsp;Anthony J. Kearsley,&nbsp;Paul N. Patrone","doi":"10.1002/cyto.a.24954","DOIUrl":"10.1002/cyto.a.24954","url":null,"abstract":"<div>\u0000 \u0000 <p>Flow cytometry measurements are widely used in diagnostics and medical decision making. Incomplete understanding of sources of measurement uncertainty can make it difficult to distinguish autofluorescence and background sources from signals of interest. Moreover, established methods for modeling uncertainty overlook the fact that the apparent distribution of measurements is a convolution of the inherent population variability (e.g., associated with calibration beads or cells) and instrument-induced effects. Such issues make it difficult, for example, to identify signals from small objects such as extracellular vesicles. To overcome such limitations, we formulate an explicit probabilistic measurement model that accounts for volume and labeling variation, background signals, and fluorescence shot noise. Using raw data from routine per-event calibration measurements, we use this model to separate the aforementioned sources of uncertainty and demonstrate how such information can be used to facilitate decision making and instrument characterization.</p>\u0000 </div>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 9","pages":"587-596"},"PeriodicalIF":2.1,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144946169","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":"CytoNormPy Enables a Fast and Scalable Removal of Batch Effects in Cytometry Datasets","authors":"Tarik Exner,&nbsp;Nicolaj Hackert,&nbsp;Luca Leomazzi,&nbsp;Sofie Van Gassen,&nbsp;Yvan Saeys,&nbsp;Hanns-Martin Lorenz,&nbsp;Ricardo Grieshaber-Bouyer","doi":"10.1002/cyto.a.24953","DOIUrl":"10.1002/cyto.a.24953","url":null,"abstract":"<p>Cytometry has evolved as a crucial technique in clinical diagnostics, clinical studies, and research. However, batch effects due to technical variation complicate the analysis of cytometry data in clinical and fundamental research settings and have to be accounted for. Here, we present a Python implementation of the widely used CytoNorm algorithm for the removal of batch effects, implementing the complete feature set of the recently published CytoNorm 2.0. Our implementation ran up to 85% faster than its R counterpart while being fully compatible with common single-cell data structures and frameworks of Python. We extend the previous functionality by adding common clustering algorithms and provide key visualizations of the algorithm and its evaluation. The CytoNormPy implementation is freely available on GitHub: https://github.com/TarikExner/CytoNormPy.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 9","pages":"629-635"},"PeriodicalIF":2.1,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24953","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144946173","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":"Reducing Spreading: Removing the Impact of Irrelevant Dyes Improves Unmixed Flow Cytometry Data","authors":"Ryan Kmet,&nbsp;David Novo","doi":"10.1002/cyto.a.24957","DOIUrl":"10.1002/cyto.a.24957","url":null,"abstract":"<div>\u0000 \u0000 <p>Staining panels have become increasingly complex in recent years, with 40 to 50 dye panels regularly reported, particularly on “spectral” flow cytometers. It is universal practice to include all dyes in the mixing matrix when unmixing the data, even though it is well-known that individual events within the sample only stain with a subset of dyes. Adding dyes to the mixing matrix increases the variance of the unmixed abundance distributions, even if those dyes are not present on particular events. This manuscript introduces a novel unmixing method called TRU-OLS. TRU-OLS utilizes a priori biological knowledge (i.e., unstained controls) to unmix each event with only the dyes present on that event. We show that TRU-OLS decreases the variance of the unmixed abundances both statistically and visually in simple (4–6 color) and complex (40 color) staining panels.</p>\u0000 </div>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 9","pages":"573-586"},"PeriodicalIF":2.1,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144946090","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":"Uncertainty Quantification of Fluorescence Signals in Flow Cytometry Part I: An Analytical Perspective Beyond Q and B","authors":"Paul N. Patrone,&nbsp;Anthony J. Kearsley,&nbsp;Megan A. Catterton,&nbsp;Gregory A. Cooksey","doi":"10.1002/cyto.a.24955","DOIUrl":"10.1002/cyto.a.24955","url":null,"abstract":"<div>\u0000 \u0000 <p>This manuscript is the first in a series that develops and realizes core ideas from metrology and uncertainty quantification (UQ) as applied to flow cytometry. The work herein is motivated by the problem of estimating the detection efficiency (<i>Q</i>) and background (<i>B</i>) of cytometers. Despite more than 30 years of study, canonical solutions to this problem make approximations that both ignore and amplify various sources of noise, thereby leading to unstable estimators of <span></span><math>\u0000 \u0000 <semantics>\u0000 \u0000 <mrow>\u0000 \u0000 <mi>Q</mi>\u0000 </mrow>\u0000 </semantics>\u0000 </math> and negative values of <span></span><math>\u0000 \u0000 <semantics>\u0000 \u0000 <mrow>\u0000 \u0000 <mi>B</mi>\u0000 </mrow>\u0000 </semantics>\u0000 </math>. Moreover, it is not always clear how to compare instruments on the basis of such properties. To address these issues, we propose a global data analysis strategy that combines measurements taken with different gains while simultaneously accounting for gain-independent background effects, which are typically ignored but often dominant. Of note, this technique yields stable estimates of <span></span><math>\u0000 \u0000 <semantics>\u0000 \u0000 <mrow>\u0000 \u0000 <mi>Q</mi>\u0000 </mrow>\u0000 </semantics>\u0000 </math> and <span></span><math>\u0000 \u0000 <semantics>\u0000 \u0000 <mrow>\u0000 \u0000 <mi>B</mi>\u0000 </mrow>\u0000 </semantics>\u0000 </math> while also quantifying the relative impacts of other noise sources. Conceptually, our analysis also unifies and explains the shortcomings of existing data analysis methods. Most importantly, however, this work allows us to rigorously define concepts such as <i>limits of detection and quantification associated with instrument performance alone</i> and in a way that removes effects associated with sample preparation, operator effects, and so forth. Importantly, this allows for direct comparison of cytometers on the basis of sample-independent uncertainty metrics and yields information for optimizing cytometer performance in terms of instrument-induced uncertainties. Results are experimentally verified using both commercial instruments and a NIST-developed serial cytometer, with extensions considered in companion manuscripts of this series.</p>\u0000 </div>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 8","pages":"508-523"},"PeriodicalIF":2.1,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144946179","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":"MuSARCyto: Multi-Head Self-Attention-Based Representation Learning for Unsupervised Clustering of Cytometry Data","authors":"Anubha Gupta,&nbsp;Ritika Hooda,&nbsp;Sachin Motwani,&nbsp;Dikshant Sagar,&nbsp;Priya Aggarwal,&nbsp;Vinayak Abrol,&nbsp;Ritu Gupta","doi":"10.1002/cyto.a.24956","DOIUrl":"10.1002/cyto.a.24956","url":null,"abstract":"<div>\u0000 \u0000 <p>Cytometry enables simultaneous assessment of individual cellular characteristics, offering vital insights for diagnosis, prognosis, and monitoring various human diseases. Despite its significance, the process of manual cell clustering, or gating, remains labor-intensive, tedious, and highly subjective, which restricts its broader application in both research and clinical settings. Although automated clustering solutions have been developed, manual gating continues to be the clinical gold standard, possibly due to the suboptimal performance of automated solutions. We hypothesize that their performance can be improved via an appropriate representation of data from the clustering point of view. To this end, this work presents a novel unsupervised deep learning (DL) architecture wherein an efficient cytometry data representation is learned that helps discover cluster assignments. Specifically, we propose <i>MuSARCyto</i>, a multi-head self-attention-based representation learning network (RN) for the unsupervised clustering of cytometry data, utilizing a fully-connected representation network backbone. To benchmark <i>MuSARCyto</i> against the state-of-the-art cytometry clustering methods, we propose a cluster evaluation metric adjudicator score (<span></span><math>\u0000 \u0000 <semantics>\u0000 \u0000 <mrow>\u0000 \u0000 <msub>\u0000 \u0000 <mi>Ad</mi>\u0000 \u0000 <mi>n</mi>\u0000 </msub>\u0000 </mrow>\u0000 </semantics>\u0000 </math>), which is an ensemble of prevalent cluster evaluation metrics. Extensive experimentation demonstrates the superior performance of <i>MuSARCyto</i> against the existing state-of-the-art cytometry clustering methods across six publicly available mass and flow cytometry datasets. The proposed DL achitectures are small and easily deployable for clinical settings. This work further suggests using DL methods for identifying meaningful clusters, particularly in the context of critical immunology applications.</p>\u0000 </div>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 8","pages":"551-567"},"PeriodicalIF":2.1,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144815971","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":"Uncertainty Quantification of Fluorescence Signals for Cytometry Part II: Comparison of Serial and Traditional Flow Cytometers","authors":"Megan A. Catterton,&nbsp;Matthew DiSalvo,&nbsp;Paul N. Patrone,&nbsp;Gregory A. Cooksey","doi":"10.1002/cyto.a.24952","DOIUrl":"10.1002/cyto.a.24952","url":null,"abstract":"<div>\u0000 \u0000 <p>Flow cytometers are powerful tools for bioanalytical applications, yet new systems that promise better measurements are continuously being introduced as sensors and other technologies advance. One such advancement by NIST was the recently demonstrated a serial microcytometer that enables unique capabilities for uncertainty quantification on a per-object basis. In an effort to benchmark and improve the measurement capabilities of the serial microcytometer, we found limitations to the quantitative comparison of instruments using conventional metrics and methods. To address these shortcomings, we recently developed an improved model that builds upon conventional models to improve comparability (Patrone et al. “Uncertainty Quantification of Fluorescence Signals in Flow Cytometry Part I: An Analytical Perspective Beyond Q and B” submitted in conjunction with this manuscript). In Part I, and continued here, our aim was to develop metrics that enable comparisons based on upper limit of linearity, limit of background, limit of detection, noise-to-signal ratio, and uncertainty decomposition thereof. We found that the NIST serial microcytometer has similar performance capabilities to a conventional analytical flow cytometer. This manuscript continues the development of uncertainty quantification (UQ) for flow cytometry by demonstrating how a serial microcytometer facilitates separation of the instrument-and population-dependent contributions to UQ. Component-level contributions to UQ can also be analyzed. Ultimately, these methods establish robust metrics for instrument performance and introduce per-object uncertainty as a mechanism facilitating better classification and utilization of cytometry data in research and clinical use.</p>\u0000 </div>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 8","pages":"524-537"},"PeriodicalIF":2.1,"publicationDate":"2025-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144803858","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":"OMIP-116: A 39-Color Full Spectrum Flow Cytometric Panel to Deeply Characterize Human Thymopoiesis","authors":"Simone Balin,&nbsp;Paolo Marzano,&nbsp;Daniele Manganaro,&nbsp;Anna Villa,&nbsp;Paolo Andrea Zucali,&nbsp;Domenico Mavilio,&nbsp;Silvia Della Bella","doi":"10.1002/cyto.a.24951","DOIUrl":"10.1002/cyto.a.24951","url":null,"abstract":"<p>We report the development of a 39-color (43-parameter) full spectrum flow cytometry panel designed and optimized to deeply characterize the intrathymic development of human conventional and unconventional T cells. The panel was designed using strategies dictated by best practices for full spectrum and multiparametric flow cytometry, and was validated using appropriate negative and positive controls. By including several markers that are variably expressed during T cell development, this panel allows the definition of T cell maturation stages and the investigation of possible deviation from normal thymopoiesis at unprecedented resolution, thus representing a valuable tool for understanding immune dysregulation associated with altered thymopoiesis, as occurring in immune deficiencies, thymic lesions, and immunosenescence. Notably, because most of the molecules targeted in this panel are also commonly used as activation markers or immune checkpoints on mature T cells, this 39-color panel can also be applied for a comprehensive profiling of peripheral T cells, particularly in those peripheral tissues where unconventional T cells, including Vδ1, Vδ2, and Vδ3 T cell subsets and MAIT cells, interact with αβ T cells to shape the local microenvironment.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 8","pages":"501-507"},"PeriodicalIF":2.1,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24951","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144689464","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 107A, Number 6, June 2025 Cover Image","authors":"","doi":"10.1002/cyto.a.24867","DOIUrl":"https://doi.org/10.1002/cyto.a.24867","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 6","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24867","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144615089","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":"Critical Pitfalls in the Flow Cytometric Analysis of Mast Cells in Patients With Systemic Mastocytosis","authors":"Abdulrazzaq Alheraky,&nbsp;Kees Meijer,&nbsp;Marije T. Nijk,&nbsp;Saskia K. Klein,&nbsp;Hanneke N. G. Oude Elberink,&nbsp;Ido P. Kema,&nbsp;André B. Mulder","doi":"10.1002/cyto.a.24950","DOIUrl":"10.1002/cyto.a.24950","url":null,"abstract":"<p>Systemic mastocytosis (SM) is a neoplastic disease characterized by abnormal mast cell (MC) activation and proliferation. Accurate diagnosis often relies on flow cytometry to detect aberrant CD25, CD2, and CD30 expression on MCs in bone marrow (BM). However, the frequently low abundance of MCs in BM, lack of completely specific antigens, and strong and highly variable autofluorescence can cause misinterpretation and lead to diagnostic misclassifications. We investigated the potentially interfering cell populations in flow cytometric analysis of MCs based on literature and expert insights, focusing on CD117, CD45, CD203c, and FcεR1. Additionally, we determined the most appropriate approach to quantify aberrant CD25, CD2, and CD30 expression. Apoptotic granulocytes frequently cause misinterpretation by mimicking strong CD117 and aberrant CD25, CD2, and CD30 expression, and must be distinguished from MCs with a viability dye like DRAQ7. CD117-positive myeloblasts and promyelocytes overlap with CD117-reduced immature MCs in advanced SM disease and can be differentiated using CD203c. Quantifying CD25, CD2, and CD30 expression is skewed on log-transformed scales due to the strong and highly heterogeneous autofluorescence of MCs. Linear calculation of net expression levels of CD25, CD2, and CD30 yields the highest accuracies in predicting SM with a Youden index of 0.96, 0.93, and 0.88, respectively. Incorporating a viability dye like DRAQ7 and CD203c into the flow cytometric analysis for MC identification, along with the linear quantification of aberrant expression, significantly enhances the correct identification of MCs and increases the diagnostic accuracy of aberrant CD25, CD2, and CD30 expression for SM.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 8","pages":"538-550"},"PeriodicalIF":2.1,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24950","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144599707","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}