Cytometry Part A最新文献

{"title":"OMIP‐069 version 2: Update to the 40‐color full Spectrum flow cytometry panel for deep immunophenotyping of major cell subsets in human peripheral blood","authors":"Lily M. Park, Joanne Lannigan, Quentin Low, Maria C. Jaimes, Diana L. Bonilla","doi":"10.1002/cyto.a.24898","DOIUrl":"https://doi.org/10.1002/cyto.a.24898","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249534","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-107: 8-color whole blood immunophenotyping panel for the characterization and quantification of lymphocyte subsets and monocytes in swine","authors":"Riccardo Arrigucci,&nbsp;Abby Patterson,&nbsp;Peter Dube","doi":"10.1002/cyto.a.24897","DOIUrl":"10.1002/cyto.a.24897","url":null,"abstract":"<p>We developed this whole blood immunophenotyping panel with the aim to monitor and quantify major lymphocyte subsets (CD4⁺, CD8⁺, CD4⁺CD8⁺ αβ T cells, γδ-T cells, B and NK cells) and monocytes in pigs. The panel involved the use of commercially available reagents, avoiding secondary antibody staining or in-house antibody conjugations, with the aim to make the assay accessible and reproducible across laboratories. The assay is accurate, robust and represents a useful tool for immune monitoring of swine in the pharmacology and toxicology fields, or to monitor the immune status in response to vaccination and diseases.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24897","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142249535","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":"An AI-based imaging flow cytometry approach to study erythrophagocytosis","authors":"S. Neri,&nbsp;E. T. Brandsma,&nbsp;F. P. J. Mul,&nbsp;T. W. Kuijpers,&nbsp;H. L. Matlung,&nbsp;R. van Bruggen","doi":"10.1002/cyto.a.24894","DOIUrl":"10.1002/cyto.a.24894","url":null,"abstract":"<p>Erythrophagocytosis is a process consisting of recognition, engulfment and digestion by phagocytes of antibody-coated or damaged erythrocytes. Understanding the dynamics that are behind erythrophagocytosis is fundamental to comprehend this cellular process under specific circumstances. Several techniques have been used to study phagocytosis. Among these, an interesting approach is the use of Imaging Flow Cytometry (IFC) to distinguish internalization and binding of cells or particles. However, this method requires laborious analysis. Here, we introduce a novel approach to analyze the phagocytosis process by combining Artificial Intelligence (AI) with IFC. Our study demonstrates that this approach is highly suitable to study erythrophagocytosis, categorizing internalized, bound and non-bound erythrocytes. Validation experiments showed that our pipeline performs with high accuracy and reproducibility.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142153395","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":"Size and fluorescence calibrated imaging flow cytometry: From arbitrary to standard units","authors":"Wouter W. Woud,&nbsp;Haley R. Pugsley,&nbsp;Britta A. Bettin,&nbsp;Zoltán Varga,&nbsp;Edwin van der Pol","doi":"10.1002/cyto.a.24895","DOIUrl":"10.1002/cyto.a.24895","url":null,"abstract":"<p>Imaging flow cytometry (IFCM) is a technique that can detect, size, and phenotype extracellular vesicles (EVs) at high throughput (thousands/minute) in complex biofluids without prior EV isolation. However, the generated signals are expressed in arbitrary units, which hinders data interpretation and comparison of measurement results between instruments and institutes. While fluorescence calibration can be readily achieved, calibration of side scatter (SSC) signals presents an ongoing challenge for IFCM. Here, we present an approach to relate the SSC signals to particle size for IFCM, and perform a comparability study between three different IFCMs using a plasma EV test sample (PEVTES). SSC signals for different sizes of polystyrene (PS) and hollow organosilica beads (HOBs) were acquired with a 405 nm 120 mW laser without a notch filter before detection. Mie theory was applied to relate scatter signals to particle size. Fluorescence calibration was accomplished with 2 μm phycoerythrin (PE) and allophycocyanin (APC) MESF beads. Size and fluorescence calibration was performed for three IFCMs in two laboratories. CD235a-PE and CD61-APC stained PEVTES were used as EV-containing samples. EV concentrations were compared between instruments within a size range of 100–1000 nm and a fluorescence intensity range of 3–10,000 MESF. 81 nm PS beads could be readily discerned from background based on their SSC signals. Fitting of the obtained PS bead SSC signals with Mie theory resulted in a coefficient of determination &gt;0.99 between theory and data for all three IFCMs. 216 nm HOBs were detected with all instruments, and confirmed the sensitivity to detect EVs by SSC. The lower limit of detection regarding EV-size for this study was determined to be ~100 nm for all instruments. Size and fluorescence calibration of IFCM data increased cross-instrument data comparability with the coefficient of variation decreasing from 33% to 21%. Here we demonstrate – for the first time – scatter calibration of an IFCM using the 405 nm laser. The quality of the scatter-to-diameter relation and scatter sensitivity of the IFCMs are similar to the most sensitive commercially available flow cytometers. This development will support the reliability of EV research with IFCM by providing robust standardization and reproducibility, which are pre-requisites for understanding the biological significance of EVs.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24895","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142139573","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":"OMIP-106: A 30-color panel for analysis of check-point inhibitory networks in the bone marrow of acute myeloid leukemia patients","authors":"Jan Musil,&nbsp;Antonin Ptacek,&nbsp;Sarka Vanikova","doi":"10.1002/cyto.a.24892","DOIUrl":"10.1002/cyto.a.24892","url":null,"abstract":"<p>Acute myeloid leukemia (AML) is the most common form of acute leukemia diagnosed in adults. Despite advances in medical care, the treatment of AML still faces many challenges, such as treatment-related toxicities, that limit the use of high-intensity chemotherapy, especially in elderly patients. Currently, various immunotherapeutic approaches, that is, CAR-T cells, BiTEs, and immune checkpoint inhibitors, are being tested in clinical trials to prolong remission and improve the overall survival of AML patients. However, early reports show only limited benefits of these interventions and only in a subset of patients, showing the need for better patient stratification based on immunological markers. We have therefore developed and optimized a 30-color panel for evaluation of effector immune cell (NK cells, γδ T cells, NKT-like T cells, and classical T cells) infiltration into the bone marrow and analysis of their phenotype with regard to their differentiation, expression of inhibitory (PD-1, TIGIT, Tim3, NKG2A) and activating receptors (DNAM-1, NKG2D). We also evaluate the immune evasive phenotype of CD33⁺ myeloid cells, CD34⁺CD38⁻, and CD34⁺CD38⁺ hematopoietic stem and progenitor cells by analyzing the expression of inhibitory ligands such as PD-L1, CD112, CD155, and CD200. Our panel can be a valuable tool for patient stratification in clinical trials and can also be used to broaden our understanding of check-point inhibitory networks in AML.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24892","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142079625","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-throughput screen to identify and optimize NOT gate receptors for cell therapy","authors":"S. Martire,&nbsp;X. Wang,&nbsp;M. McElvain,&nbsp;V. Suryawanshi,&nbsp;T. Gill,&nbsp;B. DiAndreth,&nbsp;W. Lee,&nbsp;T. P. Riley,&nbsp;H. Xu,&nbsp;C. Netirojjanakul,&nbsp;A. Kamb","doi":"10.1002/cyto.a.24893","DOIUrl":"10.1002/cyto.a.24893","url":null,"abstract":"<p>Logic-gated engineered cells are an emerging therapeutic modality that can take advantage of molecular profiles to focus medical interventions on specific tissues in the body. However, the increased complexity of these engineered systems may pose a challenge for prediction and optimization of their behavior. Here we describe the design and testing of a flow cytometry-based screening system to rapidly select functional inhibitory receptors from a pooled library of candidate constructs. In proof-of-concept experiments, this approach identifies inhibitory receptors that can operate as NOT gates when paired with activating receptors. The method may be used to generate large datasets to train machine learning models to better predict and optimize the function of logic-gated cell therapeutics.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24893","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141995517","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 105A, Number 8, August 2024 Cover Image","authors":"","doi":"10.1002/cyto.a.24756","DOIUrl":"https://doi.org/10.1002/cyto.a.24756","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24756","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141973620","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":"Evaluation of single-cell sorting accuracy using antibody-derived tag-based qPCR","authors":"Xiaoshan Shi,&nbsp;Woodrow E. Lomas III,&nbsp;Aaron Middlebrook,&nbsp;Wei Fan,&nbsp;Louise M. D'Cruz,&nbsp;Vishnu Ramani,&nbsp;Stephanie J. Widmann,&nbsp;Aaron J. Tyznik","doi":"10.1002/cyto.a.24888","DOIUrl":"10.1002/cyto.a.24888","url":null,"abstract":"<p>Single-cell sorting (index sorting) is a widely used method to isolate one cell at a time using fluorescence-activated cell sorting (FACS) for downstream applications such as single-cell sequencing or single-cell expansion. Despite widespread use, few assays are available to evaluate the proteomic features of the sorted single cell and further confirm the accuracy of single-cell sorting. With this caveat, we developed a novel assay to confirm the protein expression of sorted single cells by co-staining cells with the same marker using both antibody-derived tags (ADTs) and fluorescent antibodies. After single-cell sorting, we amplified the oligo of the ADT reagent as a surrogate signal for the protein expression using multiplex TaqMan™ qPCR on sorted cells. This assay is not only useful for confirming the identity of a sorted single cell but also an efficient method to profile proteomic features at the single-cell level. Finally, we applied this assay to characterize protein expression on whole cell lysate. Because of the sensitivity of the TaqMan™ qPCR, we can detect protein expression from a small number of cells. In summary, the ADT-based qPCR assay developed here can be utilized to confirm single-cell sorting accuracy and characterizing protein expression on both single cells and whole cell lysate.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24888","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141916345","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":"OMIP-105: A 30-color full-spectrum flow cytometry panel to characterize the immune cell landscape in spleen and tumor within a syngeneic MC-38 murine colon carcinoma model","authors":"Gabriel DeNiro,&nbsp;Kathryn Que,&nbsp;Trevor Fujimoto,&nbsp;Soo Min Koo,&nbsp;Bridget Schneider,&nbsp;Anandaroop Mukhopadhyay,&nbsp;Jeong Kim,&nbsp;Anandi Sawant,&nbsp;Tuan Andrew Nguyen","doi":"10.1002/cyto.a.24886","DOIUrl":"10.1002/cyto.a.24886","url":null,"abstract":"<p>This panel was designed to characterize the immune cell landscape in the mouse tumor microenvironment as well as mouse lymphoid tissues (e.g., spleen). As an example, using the MC-38 mouse syngeneic tumor model, we demonstrated that we could measure the frequency and characterize the functional status of CD4 T cells, CD8 T cells, regulatory T cells, NK cells, B cells, macrophages, granulocytes, monocytes, and dendritic cells. This panel is especially useful for understanding the immune landscape in “cold” preclinical tumor models with very low immune cell infiltration and for investigating how therapeutic treatments may modulate the immune landscape.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24886","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141897043","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-detector multiplex imaging flow cytometry for cancer cell classification with deep learning","authors":"Zhiwen Wang,&nbsp;Qiao Liu,&nbsp;Jie Zhou,&nbsp;Xuantao Su","doi":"10.1002/cyto.a.24890","DOIUrl":"10.1002/cyto.a.24890","url":null,"abstract":"<p>Imaging flow cytometry, which combines the advantages of flow cytometry and microscopy, has emerged as a powerful tool for cell analysis in various biomedical fields such as cancer detection. In this study, we develop multiplex imaging flow cytometry (mIFC) by employing a spatial wavelength division multiplexing technique. Our mIFC can simultaneously obtain brightfield and multi-color fluorescence images of individual cells in flow, which are excited by a metal halide lamp and measured by a single detector. Statistical analysis results of multiplex imaging experiments with resolution test lens, magnification test lens, and fluorescent microspheres validate the operation of the mIFC with good imaging channel consistency and micron-scale differentiation capabilities. A deep learning method is designed for multiplex image processing that consists of three deep learning networks (U-net, very deep super resolution, and visual geometry group 19). It is demonstrated that the cluster of differentiation 24 (CD24) imaging channel is more sensitive than the brightfield, nucleus, or cancer antigen 125 (CA125) imaging channel in classifying the three types of ovarian cell lines (IOSE80 normal cell, A2780, and OVCAR3 cancer cells). An average accuracy rate of 97.1% is achieved for the classification of these three types of cells by deep learning analysis when all four imaging channels are considered. Our single-detector mIFC is promising for the development of future imaging flow cytometers and for the automatic single-cell analysis with deep learning in various biomedical fields.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141888754","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}