Cytometry Part A最新文献

Senescence is an irreversible arrest of the cell cycle that can be characterized by markers of senescence such as p16, p21, and KI-67. The characterization of different senescence-associated phenotypes requires selection of the most relevant senescence markers to define reliable cytometric methodologies. Mass cytometry (a.k.a. Cytometry by time of flight, CyTOF) can monitor up to 40 different cell markers at the single-cell level and has the potential to integrate multiple senescence and other phenotypic markers to identify senescent cells within a complex tissue such as skeletal muscle, with greater accuracy and scalability than traditional bulk measurements and flow cytometry-based measurements. This article introduces an analysis framework for detecting putative senescent cells based on clustering, outlier detection, and Boolean logic for outliers. Results show that the pipeline can identify putative senescent cells in skeletal muscle with well-established markers such as p21 and potential markers such as GAPDH. It was also found that heterogeneity of putative senescent cells in skeletal muscle can partly be explained by their cell type. Additionally, autophagy-related proteins ATG4A, LRRK2, and GLB1 were identified as important proteins in predicting the putative senescent population, providing insights into the association between autophagy and senescence. It was observed that sex did not affect the proportion of putative senescent cells among total cells. However, age did have an effect, with a higher proportion observed in fibro/adipogenic progenitors (FAPs), satellite cells, M1 and M2 macrophages from old mice. Moreover, putative senescent cells from muscle of old and young mice show different expression levels of senescence-related proteins, with putative senescent cells of old mice having higher levels of p21 and GAPDH, whereas putative senescent cells of young mice had higher levels of IL-6. Overall, the analysis framework prioritizes multiple senescence-associated proteins to characterize putative senescent cells sourced from tissue made of different cell types.

Imaging-based spatial transcriptomics techniques generate data in the form of spatial points belonging to different mRNA classes. A crucial part of analyzing the data involves the identification of regions with similar composition of mRNA classes. These biologically interesting regions can manifest at different spatial scales. For example, the composition of mRNA classes on a cellular scale corresponds to cell types, whereas compositions on a millimeter scale correspond to tissue-level structures. Traditional techniques for identifying such regions often rely on complementary data, such as pre-segmented cells, or lengthy optimization. This limits their applicability to tasks on a particular scale, restricting their capabilities in exploratory analysis. This article introduces “Points2Regions,” a computational tool for identifying regions with similar mRNA compositions. The tool's novelty lies in its rapid feature extraction by rasterizing points (representing mRNAs) onto a pyramidal grid and its efficient clustering using a combination of hierarchical and $��k�$-means clustering. This enables fast and efficient region discovery across multiple scales without relying on additional data, making it a valuable resource for exploratory analysis. Points2Regions has demonstrated performance similar to state-of-the-art methods on two simulated datasets, without relying on segmented cells, while being several times faster. Experiments on real-world datasets show that regions identified by Points2Regions are similar to those identified in other studies, confirming that Points2Regions can be used to extract biologically relevant regions. The tool is shared as a Python package integrated into TissUUmaps and a Napari plugin, offering interactive clustering and visualization, significantly enhancing user experience in data exploration.

Autofluorescence lifetime imaging microscopy (FLIM) is sensitive to metabolic changes in single cells based on changes in the protein-binding activities of the metabolic co-enzymes NAD(P)H. However, FLIM typically relies on time-correlated single-photon counting (TCSPC) detection electronics on laser-scanning microscopes, which are expensive, low-throughput, and require substantial post-processing time for cell segmentation and analysis. Here, we present a fluorescence lifetime-sensitive flow cytometer that offers the same TCSPC temporal resolution in a flow geometry, with low-cost single-photon excitation sources, a throughput of tens of cells per second, and real-time single-cell analysis. The system uses a 375 nm picosecond-pulsed diode laser operating at 50 MHz, alkali photomultiplier tubes, an FPGA-based time tagger, and can provide real-time phasor-based classification (i.e., gating) of flowing cells. A CMOS camera produces simultaneous brightfield images using far-red illumination. A second PMT provides two-color analysis. Cells are injected into the microfluidic channel using a syringe pump at 2–5 mm/s with nearly 5 ms integration time per cell, resulting in a light dose of 2.65 J/cm² that is well below damage thresholds (25 J/cm² at 375 nm). Our results show that cells remain viable after measurement, and the system is sensitive to autofluorescence lifetime changes in Jurkat T cells with metabolic perturbation (sodium cyanide), quiescent versus activated (CD3/CD28/CD2) primary human T cells, and quiescent versus activated primary adult mouse neural stem cells, consistent with prior studies using multiphoton FLIM. This TCSPC-based autofluorescence lifetime flow cytometer provides a valuable label-free method for real-time analysis of single-cell function and metabolism with higher throughput than laser-scanning microscopy systems.

As I conclude my tenure as Editor-in-Chief of Cytometry Part A, I am pleased to announce that in July 2024, Professor Bartek Rajwa, a long-standing Associate Editor, will assume this role. His extensive experience and dedication make him an excellent successor, poised to lead the journal into an exciting new era. I am confident that under his stewardship, Cytometry Part A will continue to thrive as the premier publication in the field of quantitative single-cell science.

Reflecting on my journey, I am profoundly grateful for the support of my colleagues at ISAC. I especially want to thank the leadership, conference organizers, and speakers who honored me with a heartfelt farewell at the CYTO 2024 conference in Edinburgh, Scotland. Serving the cytometry community for the past 18 years has been both a privilege and a pleasure. I extend my deepest thanks to our authors and readers, whose innovative contributions and insights have been instrumental in advancing the journal.

A special note of gratitude goes to the Associate Editors, esteemed experts who have diligently upheld the highest scientific quality standards. Throughout my term, 46 colleagues have supported my efforts, some for the entire duration and others for several years. Additionally, I am indebted to the hundreds of anonymous reviewers whose dedication and critical evaluations have been crucial. The complete list of reviewers from the past 5 years can be found at the end of this issue of Cytometry Part A (add page number).

This month marks the 44th anniversary of the journal, a milestone that began with its first issue in July 1980, published by founding editor Brian H. Mayall [1]. During my tenure, we celebrated both the 30th [2] and 40th anniversaries [3] of the journal. As I pass the torch to Bartek Rajwa, I wish him a successful start. I am confident that Cytometry Part A, the Journal of Quantitative Single Cell Science, will continue to flourish and make significant contributions to the field for many more years.

Attila Tárnok: Writing – original draft.

With the recent discovery of their ability to produce neutrophil extracellular traps (NETs), neutrophils are increasingly appreciated as active participants in infection and inflammation. NETs are characterized as large, web-like networks of DNA and proteins extruded from neutrophils, and there is considerable interest in how these structures drive disease in humans. Advancing research in this field is contingent on developing novel tools for quantifying NETosis. To this end, we have developed a 7-marker flow cytometry panel for analyzing NETosis on human peripheral neutrophils following in vitro stimulation, and in fresh circulating neutrophils under inflammatory conditions. This panel was optimized on neutrophils isolated from whole blood and analyzed fresh or in vitro stimulated with phorbol 12-myristate 13-acetate (PMA) or ionomycin, two known NET-inducing agonists. Neutrophils were identified as SSC^highFSC^highCD15⁺CD66b⁺. Neutrophils positive for amine residues and 7-Aminoactinomycin D (7-AAD), our DNA dye of choice, were deemed necrotic (Zombie-NIR⁺7-AAD⁺) and were removed from downstream analysis. Exclusion of Zombie-NIR and positivity for 7-AAD (Zombie-NIR^dim7-AAD⁺) was used here as a marker of neutrophil-appendant DNA, a key feature of NETs. The presence of two NET-associated proteins – myeloperoxidase (MPO) and neutrophil elastase (NE) – were utilized to identify neutrophil-appendant NET events (SSC^highFSC^highCD15⁺CD66b⁺Zombie NIR^dim7-AAD⁺MPO⁺NE⁺). We also demonstrate that NETotic neutrophils express citrullinated histone H3 (H3cit), are concentration-dependently induced by in vitro PMA and ionomycin stimulation but are disassembled with DNase treatment, and are present in both chronic and acute inflammation. This 7-color flow cytometry panel provides a novel tool for examining NETosis in humans.

Autofluorescence is an intrinsic feature of cells, caused by the natural emission of light by photo-excitatory molecular content, which can complicate analysis of flow cytometry data. Different cell types have different autofluorescence spectra and, even within one cell type, heterogeneity of autofluorescence spectra can be present, for example, as a consequence of activation status or metabolic changes. By using full spectrum flow cytometry, the emission spectrum of a fluorochrome is captured by a set of photo detectors across a range of wavelengths, creating an unique signature for that fluorochrome. This signature is then used to identify, or unmix, that fluorochrome's unique spectrum from a multicolor sample containing different fluorescent molecules. Importantly, this means that this technology can also be used to identify intrinsic autofluorescence signal of an unstained sample, which can be used for unmixing purposes and to separate the autofluorescence signal from the fluorophore signals. However, this only works if the sample has a singular, relatively homogeneous and bright autofluorescence spectrum. To analyze samples with heterogeneous autofluorescence spectral profiles, we setup an unbiased workflow to more quickly identify differing autofluorescence spectra present in a sample to include as “autofluorescence signatures” during the unmixing of the full stained samples. First, clusters of cells with similar autofluorescence spectra are identified by unbiased dimensional reduction and clustering of unstained cells. Then, unique autofluorescence clusters are determined and are used to improve the unmixing accuracy of the full stained sample. Independent of the intensity of the autofluorescence and immunophenotyping of cell subsets, this unbiased method allows for the identification of most of the distinct autofluorescence spectra present in a sample, leading to less confounding autofluorescence spillover and spread into extrinsic phenotyping markers. Furthermore, this method is equally useful for spectral analysis of different biological samples, including tissue cell suspensions, peripheral blood mononuclear cells, and in vitro cultures of (primary) cells.