Cytometry Part A最新文献

{"title":"Simple, fast, cost-efficient, reliable, and highly automated DNA content analysis of cells in adherent cultures","authors":"Jana Čížková,&nbsp;Alžběta Filipová,&nbsp;Anna Carrillo,&nbsp;Marie Ehrlichová,&nbsp;Alžběta Spálenková,&nbsp;Alžbeta Magdolenová,&nbsp;Miroslav Hájek,&nbsp;Pavel Horák,&nbsp;Aneta Erbenova,&nbsp;Zuzana Šinkorová","doi":"10.1002/cyto.a.24840","DOIUrl":"10.1002/cyto.a.24840","url":null,"abstract":"<p>The most commonly used flow cytometric (FCM) analysis of cellular DNA content relies on ethanol fixation followed by RNA digestion and propidium iodide (PI) intercalation into double-stranded DNA. This is a laborious and time-consuming procedure that is subject to systematic errors due to centrifugation and washing steps associated with sample preparation. It can adversely affect the reliability of the results. Here, we present a modified concept of DNA quantification in adherent cell lines by FCM that involves neither ethanol fixation nor any washing and cell transferring steps. Our high throughput assay of adherent cell lines reduces sample-processing time, requires minimal workload, provides a possibility for automation, and, if needed, also allows a significant reduction in the size of individual samples. Working with a well-proven commercial tool—The BD Cycletest™ Plus DNA Reagent Kit—primarily designed for cell cycle analysis and aneuploidy determination in experimental and clinical samples, we suggest a novel, very efficient, and robust approach for DNA research in adherent cell cultures.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140840366","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":"Impact of different tissue dissociation protocols on endothelial cell recovery from developing mouse lungs","authors":"Francesco Palumbo,&nbsp;Miša Gunjak,&nbsp;Patty J. Lee,&nbsp;Stefan Günther,&nbsp;Anne Hilgendorff,&nbsp;István Vadász,&nbsp;Susanne Herold,&nbsp;Werner Seeger,&nbsp;Christian Mühlfeld,&nbsp;Rory E. Morty","doi":"10.1002/cyto.a.24843","DOIUrl":"10.1002/cyto.a.24843","url":null,"abstract":"<p>Flow cytometry and fluorescence-activated cell sorting are widely used to study endothelial cells, for which the generation of viable single-cell suspensions is an essential first step. Two enzymatic approaches, collagenase A and dispase, are widely employed for endothelial cell isolation. In this study, the utility of both enzymatic approaches, alone and in combination, for endothelial cell isolation from juvenile and adult mouse lungs was assessed, considering the number, viability, and subtype composition of recovered endothelial cell pools. Collagenase A yielded an 8-12-fold superior recovery of viable endothelial cells from lung tissue from developing mouse pups, compared to dispase, although dispase proved superior in efficiency for epithelial cell recovery. Single-cell RNA-Seq revealed that the collagenase A approach yielded a diverse endothelial cell subtype composition of recovered endothelial cell pools, with broad representation of arterial, capillary, venous, and lymphatic lung endothelial cells; while the dispase approach yielded a recovered endothelial cell pool highly enriched for one subset of general capillary endothelial cells, but poor representation of other endothelial cells subtypes. These data indicate that tissue dissociation markedly influences the recovery of endothelial cells, and the endothelial subtype composition of recovered endothelial cell pools, as assessed by single-cell RNA-Seq.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24843","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140805305","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":"Detection of bladder cancer cells using quantitative interferometric label-free imaging flow cytometry","authors":"Matan Dudaie,&nbsp;Eden Dotan,&nbsp;Itay Barnea,&nbsp;Miki Haifler,&nbsp;Natan T. Shaked","doi":"10.1002/cyto.a.24846","DOIUrl":"10.1002/cyto.a.24846","url":null,"abstract":"<p>Bladder cancer is one of the most common cancers with a high recurrence rate. Patients undergo mandatory yearly scrutinies, including cystoscopies, which makes bladder cancer highly distressing and costly. Here, we aim to develop a non-invasive, label-free method for the detection of bladder cancer cells in urine samples, which is based on interferometric imaging flow cytometry. Eight urothelial carcinoma and one normal urothelial cell lines, along with red and white blood cells, imaged quantitatively without staining by an interferometric phase microscopy module while flowing in a microfluidic chip, and classified by two machine-learning algorithms, based on deep-learning semantic segmentation convolutional neural network and extreme gradient boosting. Furthermore, urine samples obtained from bladder-cancer patients and healthy volunteers were imaged, and classified by the system. We achieved accuracy and area under the curve (AUC) of 99% and 97% for the cell lines on both machine-learning algorithms. For the real urine samples, the accuracy and AUC were 96% and 96% for the deep-learning algorithm and 95% and 93% for the gradient-boosting algorithm, respectively. By combining label-free interferometric imaging flow cytometry with high-end classification algorithms, we achieved high-performance differentiation between healthy and malignant cells. The proposed technique has the potential to supplant cystoscopy in the bladder cancer surveillance and diagnosis space.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24846","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140805237","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-104: A 30-color spectral flow cytometry panel for comprehensive analysis of immune cell composition and macrophage subsets in mouse metabolic organs","authors":"Joost M. Lambooij,&nbsp;Tamar Tak,&nbsp;Arnaud Zaldumbide,&nbsp;Bruno Guigas","doi":"10.1002/cyto.a.24845","DOIUrl":"10.1002/cyto.a.24845","url":null,"abstract":"<p>Obesity-induced chronic low-grade inflammation, also known as metaflammation, results from alterations of the immune response in metabolic organs and contributes to the development of fatty liver diseases and type 2 diabetes. The diversity of tissue-resident leukocytes involved in these metabolic dysfunctions warrants an in-depth immunophenotyping in order to elucidate disease etiology. Here, we present a 30-color, full spectrum flow cytometry panel, designed to (i) identify the major innate and adaptive immune cell subsets in murine liver and white adipose tissues and (ii) discriminate various tissue-specific myeloid subsets known to contribute to the development of metabolic dysfunctions. This panel notably allows for distinguishing embryonically-derived liver-resident Kupffer cells from newly recruited monocyte-derived macrophages and KCs. Furthermore, several adipose tissue macrophage (ATM) subsets, including perivascular macrophages, lipid-associated macrophages, and pro-inflammatory CD11c⁺ ATMs, can also be identified. Finally, the panel includes cell-surface markers that have been associated with metabolic activation of different macrophage and dendritic cell subsets. Altogether, our spectral flow cytometry panel allows for an extensive immunophenotyping of murine metabolic tissues, with a particular focus on metabolically-relevant myeloid cell subsets, and can easily be adjusted to include various new markers if needed.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24845","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140805394","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-102: 50-color phenotyping of the human immune system with in-depth assessment of T cells and dendritic cells","authors":"Andrew J. Konecny,&nbsp;Peter L. Mage,&nbsp;Aaron J. Tyznik,&nbsp;Martin Prlic,&nbsp;Florian Mair","doi":"10.1002/cyto.a.24841","DOIUrl":"10.1002/cyto.a.24841","url":null,"abstract":"<p>We report the development of an optimized 50-color spectral flow cytometry panel designed for the in-depth analysis of the immune system in human blood and tissues, with the goal of maximizing the amount of information that can be collected using currently available flow cytometry platforms. We established and tested this panel using peripheral blood mononuclear cells (PBMCs), but included CD45 to enable its future use for the analysis of human tissue samples. The panel contains lineage markers for all major immune cell subsets, and an extensive set of phenotyping markers focused on the activation and differentiation status of the T cell and dendritic cell (DC) compartment. We outline the biological insight that can be gained from the simultaneous measurement of such a large number of proteins and propose that this approach provides a unique opportunity for the comprehensive exploration of the immune status in human samples with a limited number of cells. Of note, we tested the panel to be compatible with cell sorting for further downstream applications. Furthermore, to facilitate the wide-spread implementation of such a panel across different cohorts and samples, we established a trimmed-down 45-color version which can be used with different spectral cytometry platforms. Finally, to generate this panel, we utilized not only existing panel design guidelines, but also developed new metrics to systematically identify the optimal combination of 50 fluorochromes and evaluate fluorochrome-specific resolution in the context of a 50-color unmixing matrix.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24841","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627307","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-precision screening and sorting of double emulsion droplets","authors":"Siyuan Zhuang,&nbsp;Lucie Semenec,&nbsp;Stephanie S. Nagy,&nbsp;Amy K. Cain,&nbsp;David W. Inglis","doi":"10.1002/cyto.a.24842","DOIUrl":"10.1002/cyto.a.24842","url":null,"abstract":"<p>Mounting evidence suggests that cell populations are extremely heterogeneous, with individual cells fulfilling different roles within the population. Flow cytometry (FC) is a high-throughput tool for single-cell analysis that works at high optical resolution. Sub-populations with unique properties can be screened, isolated and sorted through fluorescence-activated cell sorting (FACS), using intracellular fluorescent products or surface-tagged fluorescent products of interest. However, traditional FC and FACS methods cannot identify or isolate cells that secrete extracellular products of interest. Double emulsion (DE) droplets are an innovative approach to retaining these extracellular products so cells producing them can be identified and isolated with FC and FACS. The water-in-oil-in-water structure makes DE droplets compatible with the sheath flow of flow cytometry. Single cells can be encapsulated with other reagents into DEs, which act as pico-reactors. These droplets allow biological activities to take place while allowing for cell cultivation monitoring, rare mutant identification, and cellular events characterization. However, using DEs in FACS presents technical challenges, including rupture of DEs, poor accuracy and low sorting efficiency. This study presents high-performance sorting using fluorescent beads (as simulants for cells). This study aims to guide researchers in the use of DE-based flow cytometry, offering insights into how to resolve the technical difficulties associated with DE-based screening and sorting using FC.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24842","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627244","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":"Setting the gold standard: Commentary on designing and optimizing high-parameter flow cytometry panels","authors":"Stephen C. De Rosa,&nbsp;Yolanda D. Mahnke","doi":"10.1002/cyto.a.24844","DOIUrl":"10.1002/cyto.a.24844","url":null,"abstract":"<p>Technological advances in flow cytometry have greatly expanded its capabilities. These have occurred gradually over time, but there have also been several key advances that have more markedly affected the technology and how it is used. A prime feature of flow cytometry is the ability to characterize cell marker expression at the single-cell level as high-throughput and high volume. The number of cell markers that could be measured simultaneously was initially very low but has been increasing almost exponentially over time. There are many reasons for this increase including advances in hardware for the instrumentation, introduction of new types of fluorescent dyes beyond those found in nature, and also advances in analysis tools that not only enable efficient data analysis but have elegantly allowed for new insights into fluorescent dye/detector interactions that provide theoretical bases for optimal staining panel design in high dimensions.</p><p>OMIP-102 [<span>1</span>] published here represents a milestone in flow cytometry technology and makes use of and expands upon all of those cumulative advances. Simply the demonstration of a “50-color” staining panel is remarkable, but in addition, the approach to the design and the careful and methodical description of the design process both in the main text and the online material provide a definitive syllabus for staining panel design integrating all of the best practices to date.</p><p>Because of the wide breadth of information building upon so many of these major advances, it is worthwhile to break down some of those advances into digestible pieces to highlight the significance of this achievement. One key advance in hardware has been the optimization of the instrument optics to enable the relatively weak fluorescent signal to be subdivided most efficiently across large arrays of detectors. Instrument manufacturers have developed their own methods to achieve this goal and the results have been successful with current routine capabilities to detect separate signals from up to 28 fluorescent dyes, and this is expanding. There are multiple Optimized Multicolor Immunofluorescence Panel (OMIP) publications demonstrating successful staining panels at this scale. Likely the most significant advance representing a new paradigm is spectral optics. This is a brilliant concept that in retrospect seems so obvious as the likely best approach. While full spectrum cytometry was first demonstrated in 2004 [<span>2, 3</span>], it required further hardware and software advances to enable routine implementation by a wide user base.</p><p>The potential of exploiting the light spectrum more completely for interrogating fluorescently labeled biological specimens directly called for the development of new fluorescent dyes in order to make high-parameter flow cytometry a reality. As the discovery of natural fluorochromes with the appropriate brightness and spectral characteristics was limiting, luckily, custom-designed","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24844","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140627191","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 4, April 2024 Cover Image","authors":"","doi":"10.1002/cyto.a.24748","DOIUrl":"https://doi.org/10.1002/cyto.a.24748","url":null,"abstract":"","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24748","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606360","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":"A flow cytometry-based assay to measure neutralizing antibodies against SARS-CoV-2 virus","authors":"Veridiane M. Pscheidt,&nbsp;Priscila Oliveira de Souza,&nbsp;Tiago Fazolo,&nbsp;José Luiz Proença Modena,&nbsp;Camila Simeoni,&nbsp;Daniel Teixeira,&nbsp;Natália Brunetti Silva,&nbsp;Karina Bispo dos Santos,&nbsp;Luiz Rodrigues Júnior,&nbsp;Cristina Bonorino","doi":"10.1002/cyto.a.24838","DOIUrl":"10.1002/cyto.a.24838","url":null,"abstract":"<p>The COVID-19 pandemic caused by the SARS-CoV-2 virus has highlighted the need for serological assays that can accurately evaluate the neutralizing efficiency of antibodies produced during infection or induced by vaccines. However, conventional assays often require the manipulation of live viruses on a level-three biosafety (BSL3) facility, which presents practical and safety challenges. Here, we present a novel, alternative assay that measures neutralizing antibodies (NAbs) against SARS-CoV-2 in plasma using flow cytometry. This assay is based on antibody binding to the S protein and has demonstrated precision in both intra- and inter-assay measurements at a dilution of 1:50. The cut-off was determined using Receiver Operating Characteristic (ROC) analysis and the value of 36.01% has shown high sensitivity and specificity in distinguishing between pre-pandemic sera, COVID-19 patients, and vaccinated individuals. The efficiency significantly correlates with the gold standard test, PRNT. Our new assay offers a safe and efficient alternative to conventional assays for evaluating NAbs against SARS-CoV-2.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140595431","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":"Comprehensive data analysis of white blood cells with classification and segmentation by using deep learning approaches","authors":"Şeyma Nur Özcan,&nbsp;Tansel Uyar,&nbsp;Gökay Karayeğen","doi":"10.1002/cyto.a.24839","DOIUrl":"10.1002/cyto.a.24839","url":null,"abstract":"<p>Deep learning approaches have frequently been used in the classification and segmentation of human peripheral blood cells. The common feature of previous studies was that they used more than one dataset, but used them separately. No study has been found that combines more than two datasets to use together. In classification, five types of white blood cells were identified by using a mixture of four different datasets. In segmentation, four types of white blood cells were determined, and three different neural networks, including CNN (Convolutional Neural Network), UNet and SegNet, were applied. The classification results of the presented study were compared with those of related studies. The balanced accuracy was 98.03%, and the test accuracy of the train-independent dataset was determined to be 97.27%. For segmentation, accuracy rates of 98.9% for train-dependent dataset and 92.82% for train-independent dataset for the proposed CNN were obtained in both nucleus and cytoplasm detection. In the presented study, the proposed method showed that it could detect white blood cells from a train-independent dataset with high accuracy. Additionally, it is promising as a diagnostic tool that can be used in the clinical field, with successful results in classification and segmentation.</p>","PeriodicalId":11068,"journal":{"name":"Cytometry Part A","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cyto.a.24839","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140335113","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}