Cytometry Part A最新文献

Cationic lipids are widely used for gene delivery. Here, we report the transient formation of nuclear actin filaments in mammalian cells transfected with commercially available transfection reagents regardless of the proteins transfected. Readily detectable with phalloidin, nuclear actin ranges from short filaments to a fully developed network. Nuclear actin filaments persist for hours, peak 20 h after transfection, and may be involved in DNA damage repair.

The water channel aquaporin 4 (AQP4) contributes to water flow and waste removal across the blood–brain barrier and its levels, organization and localization are perturbed in various neurological diseases, including Alzheimer's Disease. This renders AQP4 a potentially valuable therapeutic target. However, most functional assays aimed at identifying modulators of AQP4 function are performed with primary rodent cells and do not consider inter-cellular variations in AQP4 abundance and presentation. To address this, we have established and applied a robust live cell microscopy assay that captures the contribution of AQP4 in the osmotically driven (de-)quenching of the vital dye Calcein-AM with single-cell resolution. Using human astrocytoma cells, we found that performing measurements on cellular regions instead of whole fields of view yielded a more sensitive readout of the osmotic response, which correlated with AQP4 abundance. Stable co-expression of the two major AQP4 isoforms, but not of the individual isoforms, provoked a faster adaptation to osmotic changes, while siRNA-mediated knockdown of AQP4 had the opposite effect. Post-hoc correlation with the canonical membrane marker CD44 revealed that the speed of the osmotic response scaled with AQP4 membrane enrichment. Coating the substrate with laminin promoted AQP4 membrane enrichment, while cell confinement with fixed-size micropatterns further increased the speed of osmoregulation, underscoring the influence of extracellular factors. The osmotic response of primary fetal astrocytes and human iPSC-derived astrocyte models was comparable to AQP4-deficient astrocytoma cells, in line with their low AQP4 levels and indicative of their immature state. In conclusion, a correlative single-cell approach based on the quantification of Calcein-AM quenching capacity, AQP4 abundance and AQP4 membrane enrichment, allows resolving osmoregulation in a more sensitive manner and reveals heterogeneity between and within human astrocyte (–like) cultures, which could prove crucial for future screens aimed at identifying AQP4 modulators.

Liquid biopsies developed into a range of sensitive technologies aiming to analyze for example, circulating tumor cells (CTCs) in peripheral blood, which significantly deepens understanding of the metastatic process. Nevertheless, examination of CTCs is mostly limited to their enumeration and usually only 2–3 markers-based phenotyping, not offering yet sufficient insight into their biology. In contrast, quantitative analysis of their morphological details might extend our knowledge about dissemination and even improve CTC isolation or label-free identification methods dependent on their physical features such as size, and deformability. Current study was conducted to describe CTCs' and their size, shape, presence of protrusions, and micronuclei across various types of cancers (lung, n = 29; ovarian, n = 24, breast, n = 54; and prostate, n = 33). Epithelial (pan-keratins), mesenchymal (vimentin), and two exclusion markers were used to identify CTCs and classify them into four epithelial and epithelial-mesenchymal transition-related phenotypes using standardized and throughput method, imaging flow cytometry. The morphological characteristics of CTCs, including their nuclei, such as circularity, the maximum, and minimum diagonal values were determined using an open-source software QuPath. On average, detected CTCs (n = 1156) were larger, and more irregular in shape compared to leukocytes/endothelial cells (n = 400). Epithelial and mesenchymal CTCs had the largest (median = 18.2 μm) and the smallest diameter (median = 10.4 μm), respectively. In terms of cancer-specific variations, the largest CTCs were identified in lung cancer, whereas the smallest—in prostate and breast cancers. Epithelial CTCs and those negative for both epithelial and mesenchymal markers exhibited the highest degree of elongation, whereas mesenchymal CTCs were the most irregular in shape. Protrusions and micronuclei were observed extremely rarely within CTCs of breast and prostate cancer (0.6%–0.8% of CTCs). Micronuclei were observed only in epithelial and epithelial-mesenchymal CTCs. This study underscores the significant variability in the morphological features of CTCs in relation to their phenotypic classification or even the particular organ of origin, potentially influencing for example, size-dependent CTC isolation methods. It demonstrates for the first time the morphological measurements of CTCs undergoing epithelial-mesenchymal transition, and some specific morphological details (i.e., protrusions, micronuclei) within CTCs in general.

Mass cytometry enables deep profiling of biological samples at single-cell resolution. This technology is more than relevant in cancer research due to high cellular heterogeneity and complexity. Downstream analysis of high-dimensional datasets increasingly relies on machine learning (ML) to extract clinically relevant information, including supervised algorithms for classification and regression purposes. In cancer research, they are used to develop predictive models that will guide clinical decision making. However, the development of supervised algorithms faces major challenges, such as sufficient validation, before being translated into the clinics. In this work, we provide a framework for the analysis of mass cytometry data with a specific focus on supervised algorithms and practical examples of their applications. We also raise awareness on key issues regarding good practices for researchers curious to implement supervised ML on their mass cytometry data. Finally, we discuss the challenges of supervised ML application to cancer research.

The need for more in-depth exploration of the human immune system has moved the flow cytometry field forward with advances in instrumentation, reagent development and availability, and user-friendly implementation of data analysis methods. We developed a high-quality human 45-color panel, for comprehensive characterization of major cell lineages present in circulation including T cells, γδ T cells, NKT-like cells, B cells, NK cells, monocytes, basophils, dendritic cells, and ILCs. Assay optimization steps are described in detail to ensure that each marker in the panel was optimally resolved. In addition, we highlight the outstanding discernment of cell activation, exhaustion, memory, and differentiation states of CD4+ and CD8+ T cells using this 45-color panel. The panel enabled an in-depth description of very distinct phenotypes associated with the complexity of the T cell memory response. Furthermore, we present how this panel can be effectively used for cell sorting on instruments with a similar optical layout to achieve the same level of resolution. Functional evaluation of sorted specific rare cell subsets demonstrated significantly different patterns of immunological responses to stimulation, supporting functional and phenotypic differences within the T cell memory subsets. In summary, the combination of full spectrum profiling technology and careful assay design and optimization results in a high resolution multiparametric 45-color assay. This panel offers the opportunity to fully characterize immunological profiles present in peripheral blood in the context of infectious diseases, autoimmunity, neurodegeneration, immunotherapy, and biomarker discovery.

The fixation and permeabilization of cells are essential for labeling intracellular biomarkers in flow cytometry. However, these chemical treatments often alter fragile targets, such as cell surface and fluorescent proteins (FPs), and can destroy chemically-sensitive fluorescent labels. This reduces measurement accuracy and introduces compromises into sample workflows, leading to losses in data quality. Here, we demonstrate a novel multi-pass flow cytometry approach to address this long-standing problem. Our technique utilizes individual cell barcoding with laser particles, enabling sequential analysis of the same cells with single-cell resolution maintained. Chemically-fragile protein markers and their fluorochrome conjugates are measured prior to destructive sample processing and adjoined to subsequent measurements of intracellular markers after fixation and permeabilization. We demonstrate the effectiveness of our technique in accurately measuring intracellular FPs and methanol-sensitive antigens and fluorophores, along with various surface and intracellular markers. This approach significantly enhances assay flexibility, enabling accurate and comprehensive cellular analysis without the constraints of conventional one-time measurement flow cytometry. This innovation paves new avenues in flow cytometry for a wide range of applications in immuno-oncology, stem cell research, and cell biology.

Clinical biomarker strategies increasingly integrate translational research to gain new insights into disease mechanisms or to define better biomarkers in clinical trials. High-dimensional flow cytometry (HDFCM) holds the promise to enhance the exploratory potential beyond traditional, targeted biomarker strategies. However, the increased complexity of HDFCM poses several challenges, which need to be addressed in order to fully leverage its potential and to align with current regulatory requirements in clinical flow cytometry. These challenges include among others extended timelines for assay development and validation, the necessity for extensive knowledge in HDFCM, and sophisticated data analysis strategies. However, no guidelines exist on how to manage such challenges in adopting clinical HDFCM. Our CYTO 2024 workshop “Potential and challenges of clinical high-dimensional flow cytometry” aimed to find consensus across the pharmaceutical industry and broader scientific community on the overall benefits and most urgent challenges of HDFCM in clinical trials. Here, we summarize the insights we gained from our workshop. While this report does not provide a blueprint, it is a first step in defining and summarizing the most pressing challenges in implementing HDFCM in clinical trials. Furthermore, we compile current efforts with the goal to overcome some of these challenges. As such we bring the scientific community and health authorities together to build solutions, which will accelerate and simplify the full adoption of HDFCM in clinical trials.

Imaging flow cytometry (IFC) provides single-cell imaging data at a high acquisition rate. It is increasingly used in image-based profiling experiments consisting of hundreds of thousands of multi-channel images of cells. Currently available software solutions for processing microscopy data can provide good results in downstream analysis, but are limited in efficiency and scalability, and often ill-adapted to IFC data. In this work, we propose Scalable Cytometry Image Processing (SCIP), a Python software that efficiently processes images from IFC and standard microscopy datasets. We also propose a file format for efficiently storing IFC data. We showcase our contributions on two large-scale microscopy and one IFC datasets, all of which are publicly available. Our results show that SCIP can extract the same kind of information as other tools, in a much shorter time and in a more scalable manner.