Cytometry Part A最新文献

{"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":"<p>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 (<span>1</span>). However, the way we manage and interact with AF in our analysis is evolving at pace with technological advancements and our experimental demands.</p><p>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 (<span>2</span>), 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 (<span>2, 3</span>).</p><p>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.</p><p>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 (<span>4</span>) and even misclassification of cellular phenotypes when AF is incorrectly identified as fluorochrome signal (<span>5</span>). With these potential complications, it is essential to design panels for samples with heterogenous AF to minimize its impact on marker detection and resolution.</p><p>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 (<span>6</span>), marker resolution can be substantially improved","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"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}

{"title":"Noninvasive detection for bladder cancer: Quantitative interferometric imaging flow cytometry","authors":"Shubin Wei,&nbsp;Cheng Lei","doi":"10.1002/cyto.a.24887","DOIUrl":"10.1002/cyto.a.24887","url":null,"abstract":"<p>Noninvasive detection is crucial for achieving a convenient and painless diagnosis of bladder cancer. In a recent report published in <i>Cytometry Part A</i>, Matan Dudaie and coworkers have successfully employed a combination of quantitative interference imaging flow cytometry and machine learning to achieve a noninvasive, label-free approach for detecting bladder cancer cells in urine samples [<span>1</span>].</p><p>Noninvasive detection of bladder cancer based on urine samples has been a highly challenging problem. So far, the gold standard for clinical diagnosis still relies on invasive methods such as cystoscopy and tissue biopsy [<span>2</span>]. These approaches not only have high costs but also carry a certain risk of infection and other side effects after testing, greatly increasing the burden on patients. As the excreted substance of the bladder, urine is the most valuable detection medium [<span>3</span>]. In clinical practice, urine cytology is sometimes used to screen for bladder cancer cells, but the efficiency of this method is very low. To achieve a fast and noninvasive detection method, people have also tried to use flow cytometry to detect urine samples [<span>4</span>]. However, the scattering parameters of flow cytometry are insufficient to differentiate between bladder cancer and normal cells, and relying on fluorescence intensity poses a heightened risk of false positives. These reasons have hindered the effective development of noninvasive detection methods for bladder cancer. Therefore, the development of noninvasive methods for detecting bladder cancer is crucial for reducing the burden on patients.</p><p>Matan Dudaie and coworkers presented their efforts in developing a novel imaging flow cytometry method for noninvasive detection of bladder cancer in <i>Cytometry Part A</i>. By constructing a quantitative interferometric imaging flow cytometry system, they achieved label-free detection of bladder cancer. Their detection unit consists of microfluidic channels and a quantitative interferometric microscope, and the image processing unit is composed of a convolutional neural network (CNN). The key advantage lies in achieving noninvasive, label-free, highly accurate detection of bladder cancer cells simply by collecting urine samples.</p><p>Imaging flow cytometry, as a novel method for cell analysis, can be considered a fusion of optical microscopy and flow cytometry. It enables high-throughput and high-content cell imaging, thereby enhancing the efficiency of morphology-based cell analysis. Currently, the commonly used imaging flow cytometry technique collects images based on intensity imaging principles, where intensity often represents cell morphology but struggles to convey information about the cellular metabolic state.</p><p>The refractive index, as an intrinsic optical property of cells, can provide information about the cellular metabolic state [<span>5</span>]. Through quantitative phase imaging technology, refra","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"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.24887","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141579181","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":"Points2Regions: Fast, interactive clustering of imaging-based spatial transcriptomics data","authors":"Axel Andersson,&nbsp;Andrea Behanova,&nbsp;Christophe Avenel,&nbsp;Jonas Windhager,&nbsp;Filip Malmberg,&nbsp;Carolina Wählby","doi":"10.1002/cyto.a.24884","DOIUrl":"10.1002/cyto.a.24884","url":null,"abstract":"<p>Imaging-based spatial transcriptomics techniques generate data in the form of spatial points belonging to different mRNA classes. A crucial part of analyzing the data involves the identification of regions with similar composition of mRNA classes. These biologically interesting regions can manifest at different spatial scales. For example, the composition of mRNA classes on a cellular scale corresponds to cell types, whereas compositions on a millimeter scale correspond to tissue-level structures. Traditional techniques for identifying such regions often rely on complementary data, such as pre-segmented cells, or lengthy optimization. This limits their applicability to tasks on a particular scale, restricting their capabilities in exploratory analysis. This article introduces “Points2Regions,” a computational tool for identifying regions with similar mRNA compositions. The tool's novelty lies in its rapid feature extraction by rasterizing points (representing mRNAs) onto a pyramidal grid and its efficient clustering using a combination of hierarchical and <span></span><math>\u0000 <mrow>\u0000 <mi>k</mi>\u0000 </mrow></math>-means clustering. This enables fast and efficient region discovery across multiple scales without relying on additional data, making it a valuable resource for exploratory analysis. Points2Regions has demonstrated performance similar to state-of-the-art methods on two simulated datasets, without relying on segmented cells, while being several times faster. Experiments on real-world datasets show that regions identified by Points2Regions are similar to those identified in other studies, confirming that Points2Regions can be used to extract biologically relevant regions. The tool is shared as a Python package integrated into TissUUmaps and a Napari plugin, offering interactive clustering and visualization, significantly enhancing user experience in data exploration.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24884","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141491241","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 lifetime flow cytometry with time-correlated single photon counting","authors":"Kayvan Samimi,&nbsp;Ojaswi Pasachhe,&nbsp;Emmanuel Contreras Guzman,&nbsp;Jeremiah Riendeau,&nbsp;Amani A. Gillette,&nbsp;Dan L. Pham,&nbsp;Kasia J. Wiech,&nbsp;Darcie L. Moore,&nbsp;Melissa C. Skala","doi":"10.1002/cyto.a.24883","DOIUrl":"10.1002/cyto.a.24883","url":null,"abstract":"<p>Autofluorescence lifetime imaging microscopy (FLIM) is sensitive to metabolic changes in single cells based on changes in the protein-binding activities of the metabolic co-enzymes NAD(P)H. However, FLIM typically relies on time-correlated single-photon counting (TCSPC) detection electronics on laser-scanning microscopes, which are expensive, low-throughput, and require substantial post-processing time for cell segmentation and analysis. Here, we present a fluorescence lifetime-sensitive flow cytometer that offers the same TCSPC temporal resolution in a flow geometry, with low-cost single-photon excitation sources, a throughput of tens of cells per second, and real-time single-cell analysis. The system uses a 375 nm picosecond-pulsed diode laser operating at 50 MHz, alkali photomultiplier tubes, an FPGA-based time tagger, and can provide real-time phasor-based classification (i.e., gating) of flowing cells. A CMOS camera produces simultaneous brightfield images using far-red illumination. A second PMT provides two-color analysis. Cells are injected into the microfluidic channel using a syringe pump at 2–5 mm/s with nearly 5 ms integration time per cell, resulting in a light dose of 2.65 J/cm² that is well below damage thresholds (25 J/cm² at 375 nm). Our results show that cells remain viable after measurement, and the system is sensitive to autofluorescence lifetime changes in Jurkat T cells with metabolic perturbation (sodium cyanide), quiescent versus activated (CD3/CD28/CD2) primary human T cells, and quiescent versus activated primary adult mouse neural stem cells, consistent with prior studies using multiphoton FLIM. This TCSPC-based autofluorescence lifetime flow cytometer provides a valuable label-free method for real-time analysis of single-cell function and metabolism with higher throughput than laser-scanning microscopy systems.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24883","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141466829","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":"List of Peer-Reviewers for Cytometry Part A (2019 to 2024)","authors":"","doi":"10.1002/cyto.a.24882","DOIUrl":"https://doi.org/10.1002/cyto.a.24882","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326682","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 105A, Number 6, June 2024 Cover Image","authors":"","doi":"10.1002/cyto.a.24752","DOIUrl":"https://doi.org/10.1002/cyto.a.24752","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24752","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326710","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}