Cytometry Part A最新文献

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.

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.

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.

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.

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.

Circulating inflammatory cells in eyes have emerged as early indicators of numerous major diseases, yet the monitoring of these cells remains an underdeveloped field. In vivo flow cytometry (IVFC), a noninvasive technique, offers the promise of real-time, dynamic quantification of circulating cells. However, IVFC has not seen extensive applications in the detection of circulating cells in eyes, possibly due to the eye's unique physiological structure and fundus imaging limitations. This study reviews the current research progress in retinal flow cytometry and other fundus examination techniques, such as adaptive optics, ultra-widefield retinal imaging, multispectral imaging, and optical coherence tomography, to propose novel ideas for circulating cell monitoring.

Full spectrum flow cytometry is a powerful tool for immune monitoring on a single-cell level and with currently available machines, panels of 40 or more markers per sample are possible. However, with an increased panel size, spectral unmixing issues arise, and appropriate single stain reference controls are required for accurate experimental results and to avoid unmixing errors. In contrast to conventional flow cytometry, full spectrum flow cytometry takes into account even minor differences in spectral signatures and requires the full spectrum of each fluorochrome to be identical in the reference control and the fully stained sample to ensure accurate and reliable results. In general, using the cells of interest is considered optimal, but certain markers may not be expressed at sufficient levels to generate a reliable positive control. In this case, compensation beads show some significant advantages as they bind a consistent amount of antibody independent of its specificity. In this study, we evaluated two types of manufactured compensation beads for use as reference controls for 30 of the most commonly used and commercially available fluorochromes in full spectrum cytometry and compared them to human and murine primary leukocytes. While most fluorochromes show the same spectral profile on beads and cells, we demonstrate that specific fluorochromes show a significantly different spectral profile depending on which type of compensation beads is used, and some fluorochromes should be used on cells exclusively. Here, we provide a list of important considerations when selecting optimal reference controls for full spectrum flow cytometry.