Cytometry Part A最新文献_第2页

Highly Efficient Calibration-Free Color Compensation Algorithm for Imaging Flow Cytometry 用于成像流式细胞术的高效免校准颜色补偿算法。

IF 2.5 4区生物学

Cytometry Part A Pub Date : 2025-04-09 DOI: 10.1002/cyto.a.24931

Ziqi Zhou, Zhaoyu Lai, Rui Tang, Xinyu Chen, Yunjia Qu, Lin Xia, Micayla George, Adonary Munoz, Minhong Zhou, Yu-Chen Tai, Yingxiao Wang, Hu Cang, Yu-Hwa Lo

{"title":"Highly Efficient Calibration-Free Color Compensation Algorithm for Imaging Flow Cytometry","authors":"Ziqi Zhou, Zhaoyu Lai, Rui Tang, Xinyu Chen, Yunjia Qu, Lin Xia, Micayla George, Adonary Munoz, Minhong Zhou, Yu-Chen Tai, Yingxiao Wang, Hu Cang, Yu-Hwa Lo","doi":"10.1002/cyto.a.24931","DOIUrl":"10.1002/cyto.a.24931","url":null,"abstract":"<div>\u0000 \u0000 As an emerging platform gaining significant attention from the biomedical community, multiplexed fluorescent imaging from imaging flow cytometry enables simultaneous detection of numerous biological targets within a single cell. Due to the spectral overlap, signals from one fluorophore can bleed into other detection channels, leading to spillover artifacts, which cause erroneous results and false discoveries. Existing color compensation algorithms use special samples to calibrate the fluorophores individually, a time-consuming and laborious process that is cumbersome and hard to scale. While recent developments in calibration-free algorithms produce promising results in multi-color microscope images, these algorithms, when applied to single-cell images with all the fluorophores within a small and constrained area, tend to cause overcorrection by treating real signals as crosstalk and triggering stability problems during the iterative computation process. Here we demonstrate a simple and intuitive algorithm that greatly reduces overcorrection and is computationally efficient. While designed for imaging flow cytometers, our calibration-free crosstalk removal algorithm can be readily applied to microscopy as well. We have validated its effectiveness on various datasets, including simulated cell images, 2D and 3D imaging flow cytometry images, and microscopic images. Our algorithm offers an effective solution for multi-parameter single-cell images where channels are often both spectrally and spatially overlapped within the limited area of a single cell.\u0000 </div>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 5","pages":"309-320"},"PeriodicalIF":2.5,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143810736","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}

引用次数: 0

Multispectral Imaging Flow Cytometry for Spatio-Temporal Pollen Trait Variation Measurements of Insect-Pollinated Plants 昆虫传粉植物花粉性状时空变异的多光谱成像流式细胞术研究。

IF 2.5 4区生物学

Cytometry Part A Pub Date : 2025-04-08 DOI: 10.1002/cyto.a.24932

Franziska Walther, Martin Hofmann, Demetra Rakosy, Carolin Plos, Till J. Deilmann, Annalena Lenk, Christine Römermann, W. Stanley Harpole, Thomas Hornick, Susanne Dunker

{"title":"Multispectral Imaging Flow Cytometry for Spatio-Temporal Pollen Trait Variation Measurements of Insect-Pollinated Plants","authors":"Franziska Walther, Martin Hofmann, Demetra Rakosy, Carolin Plos, Till J. Deilmann, Annalena Lenk, Christine Römermann, W. Stanley Harpole, Thomas Hornick, Susanne Dunker","doi":"10.1002/cyto.a.24932","DOIUrl":"10.1002/cyto.a.24932","url":null,"abstract":"Artificial intelligence (AI) surpasses human accuracy in identifying ordinary objects, but it is still challenging for AI to be competitive in pollen grain identification. One reason for this gap is the extensive trait variation in pollen grains. In classical textbooks, pollen size relies on only 25–50 pollen grains, mostly for one plant and site. Lack of variation in pollen databases can cause limited application of machine learning approaches to real-world samples. Therefore, our study aims to investigate sources of spatial and temporal pollen trait variation for pollen morphology and fluorescence. For this purpose, 64,001 pollen grains from the four herbaceous and insect-pollinated plant species Achillea millefolium L., Lamium album L., Lathyrus vernus (L.) Bernh., and Lotus corniculatus L. sampled across four years and seven locations across Central Germany were measured using multispectral imaging flow cytometry. Observed trait variations were very species-specific; however, for most species, significant differences in spatial as well as temporal variation were found for at least one pollen trait. We could also show that this variability and the identity of a particular sample influence the accuracy of AI classifications and that multiple measurements of different origins provide the most robust AI-based identifications.","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 5","pages":"293-308"},"PeriodicalIF":2.5,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24932","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143810737","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}

引用次数: 0

Evaluation of Blood Cytotoxicity Against Tumor Cells Using a Live-Cell Imaging Platform 利用活细胞成像平台评价血液细胞对肿瘤细胞的毒性。

IF 2.5 4区生物学

Cytometry Part A Pub Date : 2025-04-08 DOI: 10.1002/cyto.a.24930

Roser Salvia, Laura G. Rico, Teresa Morán, Michael W. Olszowy, Michael D. Ward, Jordi Petriz

引用次数: 0

Diving Deep: Profiling Exhausted T Cells in the Tumor Microenvironment Using Spectral Flow Cytometry 深入研究：利用光谱流式细胞术分析肿瘤微环境中耗尽的T细胞。

IF 2.5 4区生物学

Cytometry Part A Pub Date : 2025-04-03 DOI: 10.1002/cyto.a.24929

Karen Wei Weng Teng, Weng Hua Khoo, Nicholas Ching Wei Ho, S. Jasemine Yang, Douglas C. Wilson, Edmond Chua, Shu Wen Samantha Ho

{"title":"Diving Deep: Profiling Exhausted T Cells in the Tumor Microenvironment Using Spectral Flow Cytometry","authors":"Karen Wei Weng Teng, Weng Hua Khoo, Nicholas Ching Wei Ho, S. Jasemine Yang, Douglas C. Wilson, Edmond Chua, Shu Wen Samantha Ho","doi":"10.1002/cyto.a.24929","DOIUrl":"10.1002/cyto.a.24929","url":null,"abstract":"<div>\u0000 \u0000 Fresh tumor cytometric profiling is essential for interrogating the tumor microenvironment (TME) and identifying potential therapeutic targets to enhance antitumor immunity. Challenges arise due to the limited number of cells in clinical biopsies and inter-patient variability. To maximize data derived from a single biopsy, spectral cytometry was leveraged, enabling extensive profiling with significantly fewer cells than mass cytometry. Furthermore, the utilization of multiple markers within one tube can potentially reveal novel and extensive dynamic immune characteristics in cancer, thereby aiding treatment strategies and enhancing patient outcomes. Here, we introduce a customized 39-color panel for in-depth phenotyping of exhausted T cells (TEX), which are dysfunctional T-cell subsets that arise during cancer progression. This study aims to investigate profiles of CD4 T, CD8 T, regulatory T (Treg), and γδ2 cells while exploring the heterogeneity of CD8+ TEX subsets. Given the rarity and heterogeneity of tumor biopsies, we evaluated the effects of tissue dissociation enzymes on staining protocols using cryopreserved peripheral blood mononuclear cells (PBMCs). This is vital for the development of high-dimensional cytometry panels, especially since collagenases may cleave markers in dissociated tumor cells (DTCs). Our protocol also optimizes intracellular marker staining, enhancing insights into TEX function and biology, ultimately identifying potential therapeutic targets.\u0000 </div>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 4","pages":"271-280"},"PeriodicalIF":2.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143771827","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}

引用次数: 0

Volume 107A, Number 2, February 2025 Cover Image 107A卷，第2号，2025年2月封面图片

IF 2.5 4区生物学

Cytometry Part A Pub Date : 2025-03-27 DOI: 10.1002/cyto.a.24859

引用次数: 0

Uncertainty Quantification in Flow Cytometry Using a Cell Sorter 流式细胞术中使用细胞分选器的不确定度定量。

IF 2.5 4区生物学

Cytometry Part A Pub Date : 2025-03-26 DOI: 10.1002/cyto.a.24925

Amudhan Krishnaswamy-Usha, Gregory A. Cooksey, Paul N. Patrone

{"title":"Uncertainty Quantification in Flow Cytometry Using a Cell Sorter","authors":"Amudhan Krishnaswamy-Usha, Gregory A. Cooksey, Paul N. Patrone","doi":"10.1002/cyto.a.24925","DOIUrl":"10.1002/cyto.a.24925","url":null,"abstract":"<div>\u0000 \u0000 In cytometry, it is difficult to disentangle the contributions of population variance and instrument noise toward total measured variation. Fundamentally, this is due to the fact that one cannot measure the same particle multiple times. We propose a simple experiment that uses a cell sorter to distinguish instrument-specific variation. For a population of beads whose intensities are distributed around a single peak, the sorter is used to collect beads whose measured intensities lie below some threshold. This subset of particles is then remeasured. If the variation in the measured values is only due to the sample, the second set of measurements should also lie entirely below our threshold. Any “spillover” is therefore due to instrument-specific effects—we demonstrate how the distribution of the post-sort measurements is sufficient to extract an estimate of the cumulative variability induced by the instrument. A distinguishing feature of our work is that we do not make any assumptions about the sources of said noise. We then show how “local affine transformations” let us transfer these estimates to cytometers not equipped with a sorter. We use our analysis to estimate noise for a set of three instruments and two bead types, across a range of sample flow rates. Lastly, we discuss the implications of instrument noise on optimal classification, as well as other applications.\u0000 </div>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 4","pages":"248-262"},"PeriodicalIF":2.5,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143709090","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}

引用次数: 0

TimeFlow: A Density-Driven Pseudotime Method for Flow Cytometry Data Analysis TimeFlow：用于流式细胞术数据分析的密度驱动伪时间方法。

IF 2.5 4区生物学

Cytometry Part A Pub Date : 2025-03-20 DOI: 10.1002/cyto.a.24928

Margarita Liarou, Thomas Matthes, Stéphane Marchand-Maillet

{"title":"TimeFlow: A Density-Driven Pseudotime Method for Flow Cytometry Data Analysis","authors":"Margarita Liarou, Thomas Matthes, Stéphane Marchand-Maillet","doi":"10.1002/cyto.a.24928","DOIUrl":"10.1002/cyto.a.24928","url":null,"abstract":"Pseudotime methods order cells undergoing differentiation from the least to the most differentiated. We developed TimeFlow, a new method for computing pseudotime in multi-dimensional flow cytometry datasets. TimeFlow tracks the differentiation path of each cell on a graph by following smooth changes in the cell population density. To compute the probability density function of the cells, it uses a normalizing flow model. We profiled bone marrow samples from three healthy patients using a 20-color antibody panel for flow cytometry and prepared datasets that ranged from 5,000 to 600,000 cells and included monocytes, neutrophils, erythrocytes, and B-cells at various maturation stages. TimeFlow computed fine-grained pseudotime for all the datasets, and the cell orderings were consistent with prior knowledge of human hematopoiesis. Experiments showed its potential in generalizing across patients and unseen cell states. We compared our method to 11 other pseudotime methods using in-house and public datasets and found very good performance for both linear and branching trajectories. TimeFlow's pseudotemporal orderings are useful for modeling the dynamics of cell surface proteins along linear trajectories. The biologically meaningful results in branching trajectories suggest the possibility of future applications with automated cell lineage detection. Code is available at https://github.com/MargaritaLiarou1/TimeFlow and data at https://osf.io/ykue7/.","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 4","pages":"233-247"},"PeriodicalIF":2.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24928","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143662838","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}

引用次数: 0

OMIP-111: Immune-Profiling of T Helper 1 (Th1), Th2, and Th17 Signatures in Murine Splenocytes by Targeting Intracellular Cytokines OMIP-111：靶向细胞内细胞因子的小鼠脾细胞中辅助性T细胞1 （Th1）、Th2和Th17特征的免疫分析

IF 2.5 4区生物学

Cytometry Part A Pub Date : 2025-03-17 DOI: 10.1002/cyto.a.24926

Soumik Barman, Aisling Kelly, Danica Dong, Arsh Patel, Michael J. Buonopane, Jake Gonzales, Ben Janoschek, Andrew Draghi II, David J. Dowling

{"title":"OMIP-111: Immune-Profiling of T Helper 1 (Th1), Th2, and Th17 Signatures in Murine Splenocytes by Targeting Intracellular Cytokines","authors":"Soumik Barman, Aisling Kelly, Danica Dong, Arsh Patel, Michael J. Buonopane, Jake Gonzales, Ben Janoschek, Andrew Draghi II, David J. Dowling","doi":"10.1002/cyto.a.24926","DOIUrl":"10.1002/cyto.a.24926","url":null,"abstract":"Functional cytokines shape both innate and adaptive immune responses in the host after infection or immunization. Deep immunophenotyping of the key functional cytokine signatures associated with T cells in murine lymphoid tissue, especially in the spleen, is challenging. Using spectral flow cytometry, we developed a 17-parameter panel to profile major immune cell subsets along with T cells, memory phenotypes, and functional cytokines in murine splenocytes in steady state as well as in stimulated conditions. This panel dissects the memory T cell compartment via CD62L and CD44 expression after mitogen stimulation. To profile T helper (Th) cell distribution after mitogen stimulation, established Th1 markers IFNγ, TNF, and IL-2; Th2 markers IL-4/5; and the Th17 marker, IL-17, are included. This optimized multicolor spectral flow panel allows a detailed immune-profiling of functional cytokines in the murine T cell compartment and might be useful for exploratory analysis of how these functional cytokines shape host immunity after infection or vaccination. Our panel could be easily modified if researchers wish to tailor the panel to their specific needs.","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 4","pages":"221-225"},"PeriodicalIF":2.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24926","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647399","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}

引用次数: 0

OMIP-112: 42-Parameter (40-Color) Spectral Flow Cytometry Panel for Comprehensive Immunophenotyping of Human Peripheral Blood Leukocytes OMIP-112: 42参数（40色）光谱流式细胞仪面板，用于人外周血白细胞的综合免疫表型。

IF 2.5 4区生物学

Cytometry Part A Pub Date : 2025-03-17 DOI: 10.1002/cyto.a.24927

Laurien A. Waaijer, Bram van Cranenbroek, Hans J. P. M. Koenen

{"title":"OMIP-112: 42-Parameter (40-Color) Spectral Flow Cytometry Panel for Comprehensive Immunophenotyping of Human Peripheral Blood Leukocytes","authors":"Laurien A. Waaijer, Bram van Cranenbroek, Hans J. P. M. Koenen","doi":"10.1002/cyto.a.24927","DOIUrl":"10.1002/cyto.a.24927","url":null,"abstract":"Profiling the human immune system is essential to understanding its role in disease, but it requires advanced and novel technologies. Spectral flow cytometry (SFM) enables deep profiling at the single-cell level. It is able to detect many fluorescent parameters within one measurement; therefore, it is vastly useful when patient material is limited. However, designing and analyzing these high-dimensional datasets remains complex. We optimized a 42-parameter panel (40 commercially available fluorochromes, one stacked fluorochrome and an autofluorescent (AF) parameter) that enables the identification of innate and adaptive immune cell composition. It is the first 42-parameter panel that is optimized on peripheral whole blood, and it outperforms other published OMIPs of 40 colors in terms of complexity. With this panel, we are able to identify neutrophils, basophils, eosinophils, monocytes, dendritic cells, CD4 T cells, CD8 T cells, regulatory T cells, mucosal-associated invariant T (MAIT) cells, γδ T cells, B cells, NK cells, dendritic cells, and innate lymphoid cells (ILCs). Furthermore, with the utilization of co-stimulatory, checkpoint, activation, homing, and maturation markers, this panel enables deeper phenotyping. Within one measurement, more than 80 distinct immune cell subsets were identified by FlowSOM and annotated manually. In conclusion, with this high-dimensional SFM panel, we aim to generate immune profiles to understand disease and monitor therapy response.","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 4","pages":"226-232"},"PeriodicalIF":2.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24927","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647609","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}

引用次数: 0

Volume 107A, Number 1, January 2025 Cover Image 107A卷，第1期，2025年1月封面图片

IF 2.5 4区生物学

Cytometry Part A Pub Date : 2025-03-12 DOI: 10.1002/cyto.a.24857

引用次数: 0