Cytometry Part A最新文献

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.

We developed this whole blood immunophenotyping panel with the aim to monitor and quantify major lymphocyte subsets (CD4⁺, CD8⁺, CD4⁺CD8⁺ αβ T cells, γδ-T cells, B and NK cells) and monocytes in pigs. The panel involved the use of commercially available reagents, avoiding secondary antibody staining or in-house antibody conjugations, with the aim to make the assay accessible and reproducible across laboratories. The assay is accurate, robust and represents a useful tool for immune monitoring of swine in the pharmacology and toxicology fields, or to monitor the immune status in response to vaccination and diseases.

Erythrophagocytosis is a process consisting of recognition, engulfment and digestion by phagocytes of antibody-coated or damaged erythrocytes. Understanding the dynamics that are behind erythrophagocytosis is fundamental to comprehend this cellular process under specific circumstances. Several techniques have been used to study phagocytosis. Among these, an interesting approach is the use of Imaging Flow Cytometry (IFC) to distinguish internalization and binding of cells or particles. However, this method requires laborious analysis. Here, we introduce a novel approach to analyze the phagocytosis process by combining Artificial Intelligence (AI) with IFC. Our study demonstrates that this approach is highly suitable to study erythrophagocytosis, categorizing internalized, bound and non-bound erythrocytes. Validation experiments showed that our pipeline performs with high accuracy and reproducibility.