CYTOMETRY PART A细胞计量学A辑
CYTOMETRY PART A(英文缩写 CYTOM PART A),ISSN 1552-4922,eISSN 1552-4930,中文译名:细胞计量学A辑 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 4.714 | Q2 |
| 2022 | 3.700 | Q2 |
| 2023 | 2.500 | Q3 |
| 2024 | 2.100 | Q3 |
| 2025 | 2.900 | Q2 |
CYTOMETRY PART A 最新收录文献
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1. Single-Cell Reporter Dynamics During the First Two Rounds of Rhinovirus Plaque Growth.
1. 鼻病毒空斑生长前两轮中的单细胞报告基因动态PMID:日期:2026-10-01When 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.
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2. FlowCytoMonitoR: An R-Shiny Program for Advanced Monitoring Flow Cytometer Performance.
2. FlowCytoMonitoR：一款用于高级监测流式细胞仪性能的R-Shiny程序PMID:日期:2026-09-26Flow 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.
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4. Velociraptor Machine Learning Quantifies Similarity to Known Cell Types and Matches Cells Across Flow and Imaging Cytometry Platforms.
4. Velociraptor机器学习量化与已知细胞类型的相似性，并实现跨流式细胞术和成像细胞术平台的细胞匹配PMID:日期:2026-09-10Suspension 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.
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6. High-Dimensional Spectral Flow Cytometry Panel for Immunophenotypic Characterization of Fixed Whole Blood Samples.
6. 用于固定全血样本免疫表型表征的高维光谱流式细胞术检测panelPMID:日期:2026-08-21[中文摘要] 外周血单个核细胞（PBMCs）常用于临床研究中的免疫分析，但其分离和冷冻保存存在后勤方面的挑战，尤其是在研究设施可及性有限的环境中。为解决这一问题，我们实施了一种全血固定方案，以保存表面蛋白表位，并使高维流式细胞术无需即时样本处理即可进行。我们开发并优化了36标志物淋系和35标志物髓系光谱流式细胞术panel，用于表征多种免疫细胞群体，包括T细胞亚群、B细胞、NK细胞、单核细胞、树突状细胞和粒细胞。Panel设计重点关注抗体克隆与固定抗原的兼容性、尽量减少非特异性结合以及可靠检测趋化因子受体。通过比较新鲜与固定全血评估性能，显示主要免疫谱系的鉴定结果一致。通过使用来自健康供者的冷冻保存固定血液历时两年半，证明了长期稳定性，免疫图谱保持稳定。这些发现支持将固定全血用于高维光谱流式细胞术及其在纵向临床免疫监测中的实用性。
[英文摘要] 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.
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7. A Robust Computational Workflow Utilizing Generalized Least Squares (GLS) and Generalized Estimating Equations (GEE-GLM) for Longitudinal Flow Cytometry Maturation Data Analysis.
PMID:日期:2026-08-20Routinely 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.
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8. Decoding Immune Cell Doublets in Humans: From Artifacts to Insights.
8. 解码人类免疫细胞双重体：从人工制品到洞察力PMID:日期:2026-08-14Although routinely detected in human blood and tissue samples, immune cell doublets are typically discarded during data acquisition or analysis, often dismissed as technical artifacts arising from ex vivo cell handling. However, emerging evidence suggests that these immune cell doublets reflect biologically meaningful interactions, offering critical insights into immune communication during health and disease. In this review, we evaluate the current evidence supporting the existence of biological immune cell doublets across various human tissues, highlighting their diverse phenotypes and functions that distinguish them from technical noise. We explore how existing single-cell datasets and analytical frameworks can be repurposed to study doublets, and we examine the primary challenge in the field, which is the lack of definitive markers to distinguish biological doublets from artifacts. Finally, we examine how novel technologies, namely high-throughput image-enabled sorting, are facilitating the next generation of research on this topic.
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9. Cytoflow: User-Friendly Python Software for Computational Flow Cytometry.
9. Cytoflow：用于计算流式细胞术的用户友好型Python软件PMID:日期:2026-08-13[中文摘要] 现代流式细胞术实验通常要在众多样本、患者和组织中检测18种或更多的荧光标记。随着这些实验复杂性的增加，人工设门（manual gating）变得效率低下到令人难以接受的程度，并且可能引入操作者之间的差异。计算流式细胞术算法，如无监督聚类、自动设门和降维，可以提高这些分析的速度和可靠性，但使用这些算法需要许多生物医学科学家所缺乏的时间和专业知识。Cytoflow正是为弥合这一差距而构建的。Cytoflow是用Python编写的开源、用户友好的点击式软件，使非程序员能够将现代计算流式细胞术方法应用于他们的数据集。此外，Cytoflow的模块可以直接在Python脚本或JupyterLab笔记本中使用。最后，用支持未来应用的新模块扩展Cytoflow是简单直接的，而活跃用户社区的反馈持续指导着正在进行的开发。因此，Cytoflow可以为实验者节省时间，并提高其分析的可靠性和可重复性。Cytoflow可从https://cytoflow.github.io下载。源代码托管于https://github.com/cytoflow/cytoflow，文档可在https://cytoflow.readthedocs.io获取。
[英文摘要] 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.
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10. Spectral, Semi-Spectral, and Bandwidth Flow Cytometric Implementation of Multiplex Cytokine Bead Assays.
10. 多细胞因子微珠检测的光谱、半光谱和带宽流式细胞术实现PMID:日期:2026-07-30Multiplex cytokine bead assays (MCBA) have previously been carried out on conventional (aka classical, or bandwidth) flow cytometers, and issues involving unmixing distortions were encountered when using the manufacturer's set-up beads for unmixing full-spectrum data. After resolving such issues, we introduce and characterize the first full-spectrum flow cytometry implementation of MCBAs. In more detail, the set-up beads were originally produced for conventional cytometers, and a temporary work-around was issued for the semi-spectral mode only: spectral acquisition but bandwidth data display and analysis. We proceeded to make our own reference fluorophore controls (RFCs), starting with polystyrene beads derivatized with carboxyl functional groups that were first conjugated with streptavidin and then titrated with PE-biotin. The autofluorescence profile of the in-house conjugated PE beads matched that of the kit beads, and proper use of PE and "APC" RFCs enabled normal spectral unmixing without any distortions. Comparison of the three modes, namely spectral, semi-spectral, and bandwidth, illustrated the advantages of the spectral mode, including precision and resolution. In addition to enabling proper spectral implementation of the MCBA, a full-spectrum approach opens the door for various types of more complex multi-analyte bead assays yielding richer information, which have previously remained out of reach in conventional cytometry.