Cytometry Part A最新文献

{"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":"105 10","pages":"741-751"},"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":"105 8","pages":""},"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":"105 10","pages":"772-785"},"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":"105 9","pages":"659-665"},"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":"105 9","pages":"666-676"},"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}

{"title":"Best practices for user consultation in flow cytometry shared resource laboratories","authors":"Kewal Asosingh,&nbsp;Alice Bayiyana,&nbsp;Michele C. Black,&nbsp;Uttara Chakraborty,&nbsp;Michael J. Clemente,&nbsp;Amy C. Graham,&nbsp;Michael D. Gregory,&nbsp;Karen G. Hogg,&nbsp;Gert Van Isterdael,&nbsp;ChunChun Liu,&nbsp;Lola Martínez,&nbsp;Charlotte C. Petersen,&nbsp;Ziv Porat,&nbsp;Kylie M. Price,&nbsp;Laura B. Prickett,&nbsp;Aja M. Rieger,&nbsp;Caroline E. Roe,&nbsp;Erica Smit","doi":"10.1002/cyto.a.24891","DOIUrl":"10.1002/cyto.a.24891","url":null,"abstract":"<p>This “Best Practices in User Consultation” article is the result of a 2022 International Society for the Advancement of Cytometry (ISAC) membership survey that collected valuable insights from the shared research laboratory (SRL) community and of a group discussion at the CYTO 2022 workshop of the same name. One key takeaway is the importance of initiating a consultation at the outset of a flow cytometry project, particularly for trainees. This approach enables the improvement and standardization of every step, from planning experiments to interpreting data. This proactive approach effectively mitigates experimental bias and avoids superfluous trial and error, thereby conserving valuable time and resources. In addition to guidelines, the optimal approaches for user consultation specify communication channels, methods, and critical information, thereby establishing a structure for productive correspondence between SRL and users. This framework functions as an exemplar for establishing robust and autonomous collaborative relationships. User consultation adds value by providing researchers with the necessary information to conduct reproducible flow cytometry experiments that adhere to scientific rigor. By following the steps, instructions, and strategies outlined in these best practices, an SRL can readily tailor them to its own setting, establishing a personalized workflow and formalizing user consultation services. This article provides a pragmatic guide for improving the caliber and efficacy of flow cytometry research and aggregates the flow cytometry SRL community's collective knowledge regarding user consultation.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"105 9","pages":"704-712"},"PeriodicalIF":2.5,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24891","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141878525","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":"AutoMitoNetwork: Software for analyzing mitochondrial networks in autofluorescence images to enable label-free cell classification","authors":"Shannon Handley,&nbsp;Ayad G. Anwer,&nbsp;Aline Knab,&nbsp;Akanksha Bhargava,&nbsp;Ewa M. Goldys","doi":"10.1002/cyto.a.24889","DOIUrl":"10.1002/cyto.a.24889","url":null,"abstract":"<p>High-resolution mitochondria imaging in combination with image analysis tools have significantly advanced our understanding of cellular function in health and disease. However, most image analysis tools for mitochondrial studies have been designed to work with fluorescently labeled images only. Additionally, efforts to integrate features describing mitochondrial networks with machine learning techniques for the differentiation of cell types have been limited. Herein, we present AutoMitoNetwork software for image-based assessment of mitochondrial networks in label-free autofluorescence images using a range of interpretable morphological, intensity, and textural features. To demonstrate its utility, we characterized unstained mitochondrial networks in healthy retinal cells and in retinal cells exposed to two types of treatments: rotenone, which directly inhibited mitochondrial respiration and ATP production, and iodoacetic acid, which had a milder impact on mitochondrial networks via the inhibition of anaerobic glycolysis. For both cases, our multi-dimensional feature analysis combined with a support vector machine classifier distinguished between healthy cells and those treated with rotenone or iodoacetic acid. Subtle changes in morphological features were measured including increased fragmentation in the treated retinal cells, pointing to an association with metabolic mechanisms. AutoMitoNetwork opens new options for image-based machine learning in label-free imaging, diagnostics, and mitochondrial disease drug development.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"105 9","pages":"688-703"},"PeriodicalIF":2.5,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24889","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141792142","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 7, July 2024 Cover Image","authors":"","doi":"10.1002/cyto.a.24754","DOIUrl":"https://doi.org/10.1002/cyto.a.24754","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"105 7","pages":""},"PeriodicalIF":2.5,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24754","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730063","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":"Multiparametric identification of putative senescent cells in skeletal muscle via mass cytometry","authors":"Yijia Li,&nbsp;Nameera Baig,&nbsp;Daniel Roncancio,&nbsp;Kris Elbein,&nbsp;Dawn Lowe,&nbsp;Michael Kyba,&nbsp;Edgar A. Arriaga","doi":"10.1002/cyto.a.24853","DOIUrl":"10.1002/cyto.a.24853","url":null,"abstract":"<p>Senescence is an irreversible arrest of the cell cycle that can be characterized by markers of senescence such as p16, p21, and KI-67. The characterization of different senescence-associated phenotypes requires selection of the most relevant senescence markers to define reliable cytometric methodologies. Mass cytometry (a.k.a. Cytometry by time of flight, CyTOF) can monitor up to 40 different cell markers at the single-cell level and has the potential to integrate multiple senescence and other phenotypic markers to identify senescent cells within a complex tissue such as skeletal muscle, with greater accuracy and scalability than traditional bulk measurements and flow cytometry-based measurements. This article introduces an analysis framework for detecting putative senescent cells based on clustering, outlier detection, and Boolean logic for outliers. Results show that the pipeline can identify putative senescent cells in skeletal muscle with well-established markers such as p21 and potential markers such as GAPDH. It was also found that heterogeneity of putative senescent cells in skeletal muscle can partly be explained by their cell type. Additionally, autophagy-related proteins ATG4A, LRRK2, and GLB1 were identified as important proteins in predicting the putative senescent population, providing insights into the association between autophagy and senescence. It was observed that sex did not affect the proportion of putative senescent cells among total cells. However, age did have an effect, with a higher proportion observed in fibro/adipogenic progenitors (FAPs), satellite cells, M1 and M2 macrophages from old mice. Moreover, putative senescent cells from muscle of old and young mice show different expression levels of senescence-related proteins, with putative senescent cells of old mice having higher levels of p21 and GAPDH, whereas putative senescent cells of young mice had higher levels of IL-6. Overall, the analysis framework prioritizes multiple senescence-associated proteins to characterize putative senescent cells sourced from tissue made of different cell types.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"105 8","pages":"580-594"},"PeriodicalIF":2.5,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24853","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141589897","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":"Autofluorescence: From burden to benefit","authors":"Katherine R. Pilkington","doi":"10.1002/cyto.a.24885","DOIUrl":"10.1002/cyto.a.24885","url":null,"abstract":"&lt;p&gt;With the progression from conventional flow cytometry to full spectrum flow cytometry moving as fast as manufacturers create new reagents to expand our fluorochrome palette, a certain factor of flow cytometric analysis continues to appear as a major challenge in data analysis: cellular autofluorescence (AF). More specifically, heterogeneity of cellular AF. The idea of AF in our cytometry assays is not new, one must only search the term “autofluorescence” in this journal to see nearly 1000 publications associated with the subject dating back to the earliest days of publication (&lt;span&gt;1&lt;/span&gt;). However, the way we manage and interact with AF in our analysis is evolving at pace with technological advancements and our experimental demands.&lt;/p&gt;&lt;p&gt;AF is any light emitted from cells by endogenous cellular components that fluoresce. Components like collagen, elastin, tryptophan, NADH, and flavins to name just a few (&lt;span&gt;2&lt;/span&gt;), the emission of these components falls predominantly between 400 and 600 nm in mammalian cells. These components, and many others, contribute to the variety of cellular AF found within samples. Cell type, size, granularity, and metabolic state all contribute to variations in AF (&lt;span&gt;2, 3&lt;/span&gt;).&lt;/p&gt;&lt;p&gt;Historically, when encountering a sample with high AF, such as that from an enzymatically digested tissue, one would simply choose red and far-red emitting fluorochromes, thus avoiding the shorter wavelengths most impacted by autofluorescence. In addition, voltages of detectors were often decreased to lower the visual impact of the AF, but this method also dampens the sensitivity of the detector with respect to the intended fluorochrome for analysis. With conventional cytometers and 6–8 parameter assays, this strategy was somewhat effective, but very limiting. The increasing demand to analyze more parameters from each sample means researchers need to embrace new analysis strategies.&lt;/p&gt;&lt;p&gt;The burden that AF complexity contributes to our assays is easily recognized within our data, but what benefits can we reap if we take the time to optimize our analysis strategies? Without proper care and consideration, data with incorrectly managed heterogeneous AF can result in masking of poorly expressed tertiary markers (&lt;span&gt;4&lt;/span&gt;) and even misclassification of cellular phenotypes when AF is incorrectly identified as fluorochrome signal (&lt;span&gt;5&lt;/span&gt;). With these potential complications, it is essential to design panels for samples with heterogenous AF to minimize its impact on marker detection and resolution.&lt;/p&gt;&lt;p&gt;With a spectral flow cytometer, the unique AF properties of different samples can be characterized and leveraged when designing new panels. By thinking of the spectral signature of the AF as just another fluorochrome and implementing good panel design practices with respect to antigen coexpression, fluorochrome brightness, and fluorochrome similarity (&lt;span&gt;6&lt;/span&gt;), marker resolution can be substantially improved","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":"105 8","pages":"563-567"},"PeriodicalIF":2.5,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24885","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141562914","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}