Cytometry Part A最新文献

This 36-color flow cytometry panel is designed to characterize multiple lymphocyte compartments, with a focus on T cells, their memory subpopulations, and immune checkpoints in human whole blood samples. In clinical settings, the amount of blood available from patients for scientific research is often limited. This restriction may be further exacerbated when working with samples from small children or in resource-poor settings—both scenarios commonly encountered in malaria and infectious disease research. Accordingly, this panel is designed to maximize the information that can be obtained from as little as 200 μL whole blood using flow cytometry. This panel allows a phenotypic characterization of the main subpopulations within T cells, as well as B cells and NK cells. It includes markers for the analysis of memory subpopulations, regulatory T cell subsets, and T follicular helper cells. Furthermore, surface and intracellular markers for activation and differentiation, effector functions, exhaustion, and immune checkpoints are included, allowing detailed characterization of the main lymphocyte subsets, in particular T cells. This panel was optimized for the analysis of fresh human blood samples obtained from malaria patients, but it may be adapted to the analysis of isolated PBMC or tissue samples, as well as samples from patients with other infectious or inflammatory diseases.

T. Liechti, I. Lelios, A. Schroeder, et al., “Potential and Challenges of Clinical High-Dimensional Flow Cytometry: A Call to Action,” Cytometry 105, no. 11 (2024): 829–837, https://doi.org/10.1002/cyto.a.24902

In the originally published article, an incorrect version of Figure 3 should have been included in the article. The correct version is below:

We apologize for this error.

The recent increase in the dimensionality of cytometry data has led to the development of various computational analysis methods. FlowSOM is one of the best-performing clustering methods but has room for improvement in terms of the consistency and speed of the clustering process. Here, we introduce Batch Learning FlowSOM (BL-FlowSOM), which is a consistent and highly accelerated FlowSOM based on parallelized batch learning. The change of the learning algorithm from online learning to batch learning with principal component analysis initialization improves consistency and eliminates randomness in the clustering process. It also enables the parallelization of the learning process, leading to significant acceleration of the clustering process with clustering quality equivalent to that of FlowSOM. BL-FlowSOM is available on Sony's Spectral Flow Analysis (SFA)-Life sciences Cloud Platform (https://www.sonybiotechnology.com/us/instruments/sfa-cloud-platform/).

T cells are essential for preventing diseases and providing long-term protective immunity. The functional capacity of T cells and the quality of their responses to antigens can be measured by the cytokines they produce. We developed a conventional flow cytometry panel utilizing commercially available antibodies to measure antigen-specific T cell mediated immune responses in swine. The panel can simultaneously detect Th1 and Th17 cytokines (IFN-γ, TNF, and IL-17A) to characterize multifunctional αβ and γδ T cells. We also included CD40L (CD154) to identify cells that are activated upon antigen recall and that may contribute to B-cell help or activate antigen presenting cells. The assay can be applied to study T cell mediated immune responses to vaccines and diseases and can be used with cryopreserved or freshly isolated peripheral blood mononuclear cells.

T cell responses are rarely measured in large-scale human vaccine studies due to the sample volumes required, as well as the logistical, technical, and financial challenges associated with available assays. Fluorescent cell barcoding has been proposed in other contexts to allow for more high-throughput flow cytometry-based assays. Here, we aimed to expand on existing barcoding approaches to develop a reagent and sample-sparing assay for in-depth assessment of T cell responses to vaccine antigens. By using various concentrations of two fixable viability dyes in a matrix format, up to 25 samples that were pooled and acquired together could be successfully deconvoluted based on their unique fluorescent signature. This fluorescent cell barcoding approach was then combined with extracellular and intracellular staining to identify functional (i.e., producing at least one cytokine) and polyfunctional (i.e., producing multiple cytokines) T cells in response to vaccine antigen stimulation. As a proof-of-concept, we plated just 200,000 peripheral blood mononuclear cells (PBMC) per condition, and by staining and acquiring only two pooled samples, we were able to detect rare antigen-specific T cell responses in eight donors to four stimulants each. The frequencies of antigen-induced cytokine-positive cells detected in barcoded samples with 200,000 input PBMC were strongly correlated with those detected in non-barcoded samples from the same donors with 1 million input PBMC, demonstrating the validity of this approach. In conclusion, by reducing the number of PBMC needed by five-fold, and the volume of staining reagents needed by 25-fold, this assay has widespread potential applications to human vaccine studies.

Artificial intelligence (AI) surpasses human accuracy in identifying ordinary objects, but it is still challenging for AI to be competitive in pollen grain identification. One reason for this gap is the extensive trait variation in pollen grains. In classical textbooks, pollen size relies on only 25–50 pollen grains, mostly for one plant and site. Lack of variation in pollen databases can cause limited application of machine learning approaches to real-world samples. Therefore, our study aims to investigate sources of spatial and temporal pollen trait variation for pollen morphology and fluorescence. For this purpose, 64,001 pollen grains from the four herbaceous and insect-pollinated plant species Achillea millefolium L., Lamium album L., Lathyrus vernus (L.) Bernh., and Lotus corniculatus L. sampled across four years and seven locations across Central Germany were measured using multispectral imaging flow cytometry. Observed trait variations were very species-specific; however, for most species, significant differences in spatial as well as temporal variation were found for at least one pollen trait. We could also show that this variability and the identity of a particular sample influence the accuracy of AI classifications and that multiple measurements of different origins provide the most robust AI-based identifications.