Cytometry Part A最新文献

Pseudotime methods order cells undergoing differentiation from the least to the most differentiated. We developed TimeFlow, a new method for computing pseudotime in multi-dimensional flow cytometry datasets. TimeFlow tracks the differentiation path of each cell on a graph by following smooth changes in the cell population density. To compute the probability density function of the cells, it uses a normalizing flow model. We profiled bone marrow samples from three healthy patients using a 20-color antibody panel for flow cytometry and prepared datasets that ranged from 5,000 to 600,000 cells and included monocytes, neutrophils, erythrocytes, and B-cells at various maturation stages. TimeFlow computed fine-grained pseudotime for all the datasets, and the cell orderings were consistent with prior knowledge of human hematopoiesis. Experiments showed its potential in generalizing across patients and unseen cell states. We compared our method to 11 other pseudotime methods using in-house and public datasets and found very good performance for both linear and branching trajectories. TimeFlow's pseudotemporal orderings are useful for modeling the dynamics of cell surface proteins along linear trajectories. The biologically meaningful results in branching trajectories suggest the possibility of future applications with automated cell lineage detection. Code is available at https://github.com/MargaritaLiarou1/TimeFlow and data at https://osf.io/ykue7/.

Functional cytokines shape both innate and adaptive immune responses in the host after infection or immunization. Deep immunophenotyping of the key functional cytokine signatures associated with T cells in murine lymphoid tissue, especially in the spleen, is challenging. Using spectral flow cytometry, we developed a 17-parameter panel to profile major immune cell subsets along with T cells, memory phenotypes, and functional cytokines in murine splenocytes in steady state as well as in stimulated conditions. This panel dissects the memory T cell compartment via CD62L and CD44 expression after mitogen stimulation. To profile T helper (Th) cell distribution after mitogen stimulation, established Th1 markers IFNγ, TNF, and IL-2; Th2 markers IL-4/5; and the Th17 marker, IL-17, are included. This optimized multicolor spectral flow panel allows a detailed immune-profiling of functional cytokines in the murine T cell compartment and might be useful for exploratory analysis of how these functional cytokines shape host immunity after infection or vaccination. Our panel could be easily modified if researchers wish to tailor the panel to their specific needs.

Profiling the human immune system is essential to understanding its role in disease, but it requires advanced and novel technologies. Spectral flow cytometry (SFM) enables deep profiling at the single-cell level. It is able to detect many fluorescent parameters within one measurement; therefore, it is vastly useful when patient material is limited. However, designing and analyzing these high-dimensional datasets remains complex. We optimized a 42-parameter panel (40 commercially available fluorochromes, one stacked fluorochrome and an autofluorescent (AF) parameter) that enables the identification of innate and adaptive immune cell composition. It is the first 42-parameter panel that is optimized on peripheral whole blood, and it outperforms other published OMIPs of 40 colors in terms of complexity. With this panel, we are able to identify neutrophils, basophils, eosinophils, monocytes, dendritic cells, CD4 T cells, CD8 T cells, regulatory T cells, mucosal-associated invariant T (MAIT) cells, γδ T cells, B cells, NK cells, dendritic cells, and innate lymphoid cells (ILCs). Furthermore, with the utilization of co-stimulatory, checkpoint, activation, homing, and maturation markers, this panel enables deeper phenotyping. Within one measurement, more than 80 distinct immune cell subsets were identified by FlowSOM and annotated manually. In conclusion, with this high-dimensional SFM panel, we aim to generate immune profiles to understand disease and monitor therapy response.

Measuring the size of individual cells in high-throughput experiments is often important in biomedical research and applications. Nevertheless, popular tools for high-throughput single-cell biology, such as flow cytometers, only offer proxies of a cell's size, typically reported in arbitrary scales and often subject to changes in the instrument's settings as selected by multiple users. In this paper, we demonstrate that it is possible to calibrate flowcytometry laser scatter signals with accurate measures of cell diameter from separate devices and that the calibration can be conserved upon changes in the laser settings. We demonstrate our approach based on flow cytometric sorting of cells of a mammalian cell line according to a selection of scatter parameters, followed by cell size determination with a Coulter counter. A straightforward procedure is presented that relates the flow cytometric scatter parameters to the absolute size measurements using linear models, along with a linear transformation that converts between different instrument settings on the flow cytometer. Our method makes it possible to record on a flow cytometer a cell's size in absolute units and correlate it with other features that are recorded in parallel in the fluorescence detection channels.

Although flow cytometry produces reliable results, the data processing from gating to fingerprinting is prone to subjective bias. Here, we integrated autogating with Automated Machine Learning in flow cytometry to enhance the classification of bacterial phenotypes. We analyzed six bacterial strains prevalent in the soil and groundwater— Bacillus subtilis , Burkholderia thailandensis , Corynebacterium glutamicum , Escherichia coli , Pseudomonas putida , and Pseudomonas stutzeri . Using the H2O-AutoML framework, we applied gradient-boosting machine (GBM) models to classify bacteria across different metabolic phases. Our results demonstrated an overall classification accuracy of 82.34% for GBM. Notably, accuracy varied across metabolic phases, with the highest observed during the late log (88.06%), lag (88.43%), and early log phases (89.37%), whereas the stationary phase showed a slightly lower accuracy of 80.73%. P. stutzeri exhibited consistently high sensitivity and specificity across all the phases, which indicated that it was the most distinctly identifiable strain. In contrast, E. coli showed low sensitivity, particularly in the stationary phase, which indicated challenges in its classification. Overall, this study with incorporating autogating and the AutoML framework, substantially reduces subjective biases and enhances the reproducibility and accuracy of microbial classification. Our methodology offers a robust framework for microbial classification in flow cytometric analysis, paving the way for more precise and comprehensive analyses of microbial ecology.