Cytometry Part A最新文献

Since its inception in the 1970s flow cytometry has been a valuable tool to study the characteristics of cells. Technical advancements lead to a rapid increase in the number of lasers and detectors, enabling assessment of an expanding number of parameters. As every flow cytometry user has experienced the frustration of not being able to fit all markers of interest into their panel, the prevailing mindset seemingly became the bigger the better. So, when spectral flow cytometers became commercially available, their ability to combine spectrally similar fluorophores, measure cellular autofluorescence and utilize the full light spectrum more effectively was received with enthusiasm. These attributes make spectral cytometry particularly applicable for limited or precious (clinical) material.

The recent paper from Dott et al. describes the development of a standardized protocol for sample handling, staining and acquisition, for the application of two large spectral panels in a cohort study using the ID7000™ Spectral Cell Analyzer (Sony Biotechnology).¹ The authors specifically address the repeatability and reproducibility of staining over time, which is relevant in the context of cohort studies. By combining a 34- and 35-color spectral panel, the authors were able to quantify and identify 182 cell phenotypes in whole blood samples.

Our expanding immunological knowledge of rare and unique subsets through deep profiling has necessitated the increasing number of cellular parameters required in a single panel for accurate cell identification. However, a new set of challenges arise with increasing panel size. Although the principle of panel design and analysis remain similar, assessing data quality, defining cell populations and translating phenotypic changes into biological insight become increasingly difficult. While many papers have addressed one of these challenges, Dott et al. touch upon all three of these concerns.¹

Paula Niewold: Conceptualization; writing – original draft.

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.

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.

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.

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.