Cytometry Part A最新文献

{"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":"https://doi.org/10.1002/cyto.a.24928","url":null,"abstract":"<p><p>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/.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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, David J Dowling","doi":"10.1002/cyto.a.24926","DOIUrl":"https://doi.org/10.1002/cyto.a.24926","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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":"https://doi.org/10.1002/cyto.a.24927","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Volume 107A, Number 1, January 2025 Cover Image","authors":"","doi":"10.1002/cyto.a.24857","DOIUrl":"https://doi.org/10.1002/cyto.a.24857","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"107 1","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24857","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602814","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-Calibration Cell Size Measurement With Flow Cytometry.","authors":"Philip Davies, Massimo Cavallaro, Daniel Hebenstreit","doi":"10.1002/cyto.a.24924","DOIUrl":"https://doi.org/10.1002/cyto.a.24924","url":null,"abstract":"<p><p>Measuring the size of individual cells in high-throughput experiments is often important in biomedical research and applications. Nevertheless, popular tools for high-throughput single-cell biology, such as flow cytometers, only offer proxies of a cell's size, typically reported in arbitrary scales and often subject to changes in the instrument's settings as selected by multiple users. In this paper, we demonstrate that it is possible to calibrate flowcytometry laser scatter signals with accurate measures of cell diameter from separate devices and that the calibration can be conserved upon changes in the laser settings. We demonstrate our approach based on flow cytometric sorting of cells of a mammalian cell line according to a selection of scatter parameters, followed by cell size determination with a Coulter counter. A straightforward procedure is presented that relates the flow cytometric scatter parameters to the absolute size measurements using linear models, along with a linear transformation that converts between different instrument settings on the flow cytometer. Our method makes it possible to record on a flow cytometer a cell's size in absolute units and correlate it with other features that are recorded in parallel in the fluorescence detection channels.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143604291","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":"Enhancing Bacterial Phenotype Classification Through the Integration of Autogating and Automated Machine Learning in Flow Cytometric Analysis.","authors":"In Jae Jeong, Jin-Kyung Hong, Young Jun Bae, Tea Kwon Lee","doi":"10.1002/cyto.a.24923","DOIUrl":"https://doi.org/10.1002/cyto.a.24923","url":null,"abstract":"<p><p>Although flow cytometry produces reliable results, the data processing from gating to fingerprinting is prone to subjective bias. Here, we integrated autogating with Automated Machine Learning in flow cytometry to enhance the classification of bacterial phenotypes. We analyzed six bacterial strains prevalent in the soil and groundwater-Bacillus subtilis, Burkholderia thailandensis, Corynebacterium glutamicum, Escherichia coli, Pseudomonas putida, and Pseudomonas stutzeri. Using the H2O-AutoML framework, we applied gradient-boosting machine (GBM) models to classify bacteria across different metabolic phases. Our results demonstrated an overall classification accuracy of 82.34% for GBM. Notably, accuracy varied across metabolic phases, with the highest observed during the late log (88.06%), lag (88.43%), and early log phases (89.37%), whereas the stationary phase showed a slightly lower accuracy of 80.73%. P. stutzeri exhibited consistently high sensitivity and specificity across all the phases, which indicated that it was the most distinctly identifiable strain. In contrast, E. coli showed low sensitivity, particularly in the stationary phase, which indicated challenges in its classification. Overall, this study with incorporating autogating and the AutoML framework, substantially reduces subjective biases and enhances the reproducibility and accuracy of microbial classification. Our methodology offers a robust framework for microbial classification in flow cytometric analysis, paving the way for more precise and comprehensive analyses of microbial ecology.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584916","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":"EGFR-HER2 Transactivation Viewed in Space and Time Through the Versatile Spectacles of Imaging Cytometry-Implications for Targeted Therapy.","authors":"László Ujlaky-Nagy, János Szöllősi, György Vereb","doi":"10.1002/cyto.a.24922","DOIUrl":"https://doi.org/10.1002/cyto.a.24922","url":null,"abstract":"<p><p>Ligand-induced formation of signaling platforms composed of homo- and/or heterodimers of receptor tyrosine kinases is considered essential for their activation and consequential contribution to the progression of many cancers. Epidermal Growth Factor Receptor (EGFR) acts as a signal receiver upon EGF binding and produces mitogenic input for many cells also through receptor-heterodimerization with its ligandless partner, Human Epidermal growth factor Receptor 2 (HER2). Ligand-driven transactivation is a key step leading to changes in the cell surface pattern of EGFR and HER2; their interaction plays a key role in various malignancies, especially when HER2 molecules are overexpressed. Our clinically relevant model system is the SK-BR-3 breast tumor cell line, overexpressing HER2 and moderately expressing EGFR. This cell line shows significant dependency on EGF-driven HER2 signaling. We studied changes in the interaction between EGFR and HER2 in the cell membrane upon EGF binding, applying various biophysical approaches with different time scales. Changes in molecular proximity were characterized by fluorescence lifetime imaging microscopy (FLIM) techniques assessing Förster resonance energy transfer (FRET), which confirmed the ligand-enhanced interaction of EGFR and HER2, followed by an increase in HER2 homoassociation. EGF binding and transactivation were reflected in the phosphorylation of both receptor types as well. At the same time, superresolution Airyscan microscopy and fluorescence correlation and cross-correlation spectroscopy (FCS/FCCS), sensitive to changes in the size of stationary and diffusing aggregates, respectively, have revealed cyclic increases in the aggregation and stable co-diffusion of membrane-localized HER2, possibly caused by internalization and recycling, eventually leading to a new equilibrium. Such dynamic fluctuation of receptor interaction may open a window for the binding of therapeutic antibodies that are aimed at inhibiting heterodimerization, such as pertuzumab. The complementary array of state-of-the-art imaging cytometry approaches thus demonstrates a spatiotemporal pattern of spontaneous and induced receptor aggregation states that could provide mechanistic insights into the potential success of targeted therapies directed at the HER family of receptor tyrosine kinases.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572474","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":"Cytometry at the Intersection of Metabolism and Epigenetics in Lymphocyte Dynamics.","authors":"Nicole Vaughn","doi":"10.1002/cyto.a.24919","DOIUrl":"https://doi.org/10.1002/cyto.a.24919","url":null,"abstract":"<p><p>Landmark studies at the turn of the century revealed metabolic reprogramming as a driving force for lymphocyte differentiation and function. In addition to metabolic changes, differentiating lymphocytes must remodel their epigenetic landscape to properly rewire their gene expression. Recent discoveries have shown that metabolic shifts can shape the fate of lymphocytes by altering their epigenetic state, bringing together these two areas of inquiry. The ongoing evolution of high-dimensional cytometry has enabled increasingly comprehensive analyses of metabolic and epigenetic landscapes in lymphocytes that transcend the technical limitations of the past. Here, we review recent insights into the interplay between metabolism and epigenetics in lymphocytes and how its dysregulation can lead to immunological dysfunction and disease. We also discuss the latest technical advances in cytometry that have enabled these discoveries and that we anticipate will advance future work in this area.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572471","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":"Visual Quality Control With CytoMDS, a Bioconductor Package for Low Dimensional Representation of Cytometry Sample Distances.","authors":"Philippe Hauchamps, Simon Delandre, Stéphane T Temmerman, Dan Lin, Laurent Gatto","doi":"10.1002/cyto.a.24921","DOIUrl":"https://doi.org/10.1002/cyto.a.24921","url":null,"abstract":"<p><p>Quality Control (QC) of samples is an essential preliminary step in cytometry data analysis. Notably, the identification of potential batch effects and outlying samples is paramount to avoid mistaking these effects for true biological signals in downstream analyses. However, this task can prove to be delicate and tedious, especially for datasets with dozens of samples. Here, we present CytoMDS, a Bioconductor package implementing a dedicated method for low-dimensional representation of cytometry samples composed of marker expressions for up to millions of single cells. This method allows a global representation of all samples of a study, with one single point per sample, in such a way that projected distances can be visually interpreted. CytoMDS uses Earth Mover's Distance for assessing dissimilarities between multi-dimensional distributions of marker expression and Multi-Dimensional Scaling for low-dimensional projection of distances. Some additional visualization tools, both for projection quality diagnosis and for user interpretation of the projection coordinates, are also provided in the package. We demonstrate the strengths and advantages of CytoMDS for QC of cytometry data on three real biological datasets, revealing the presence of low-quality samples, batch effects, and biological signal between sample groups.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143540135","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":"Flow Cytometry in Microbiology: A Review of the Current State in Microbiome Research, Probiotics, and Industrial Manufacturing.","authors":"Joanna Śliwa-Dominiak, Kamila Czechowska, Alfonso Blanco, Katarzyna Sielatycka, Martyna Radaczyńska, Karolina Skonieczna-Żydecka, Wojciech Marlicz, Igor Łoniewski","doi":"10.1002/cyto.a.24920","DOIUrl":"https://doi.org/10.1002/cyto.a.24920","url":null,"abstract":"<p><p>Flow cytometry (FC) is a versatile and powerful tool in microbiology, enabling precise analysis of single cells for a variety of applications, including the detection and quantification of bacteria, viruses, fungi, as well as algae, phytoplankton, and parasites. Its utility in assessing cell viability, metabolic activity, immune responses, and pathogen-host interactions makes it indispensable in both research and diagnostics. The analysis of microbiota (community of microorganisms) and microbiome (collective genomes of the microorganisms) has become essential for understanding the intricate role of microbial communities in health, disease, and physiological functions. FC offers a promising complement, providing rapid, cost-effective, and dynamic profiling of microbial communities, with the added ability to isolate and sort bacterial populations for further analysis. In the probiotic industry, FC facilitates fast, affordable, and versatile analyses, helping assess both probiotics and postbiotics. It also supports the study of bacterial viability under stress conditions, including gastric acid and bile, improving insight into probiotic survival and adhesion to the intestinal mucosa. Additionally, the integration of Machine Learning in microbiology research has transformative potential, improving data analysis and supporting advances in personalized medicine and probiotic formulations. Despite the need for further standardization, FC continues to evolve as a key tool in modern microbiology and clinical diagnostics.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":" ","pages":""},"PeriodicalIF":2.5,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143540511","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}