Approaches to overcome flow cytometry limitations in the analysis of cells from veterinary relevant species

Comparative proteomic profiling of the ovine and human PBMC inflammatory response

Understanding the cellular and molecular mechanisms of inflammation requires robust animal models. Sheep are commonly used in immune-related studies, yet the validity of sheep as animal models for immune and inflammatory diseases remains to be established. This cross-species comparative study analyzed the in vitro inflammatory response of ovine (oPBMCs) and human PBMCs (hPBMCs) using mass spectrometry, profiling the proteome of the secretome and whole cell lysate. Of the entire cell lysate proteome (oPBMCs: 4217, hPBMCs: 4574 proteins) 47.8% and in the secretome proteome (oPBMCs: 1913, hPBMCs: 1375 proteins) 32.8% were orthologous between species, among them 32 orthologous CD antigens, indicating the presence of six immune cell subsets. Following inflammatory stimulation, 71 proteins in oPBMCs and 176 in hPBMCs showed differential abundance, with only 7 overlapping. Network and Gene Ontology analyses identified 16 shared inflammatory-related terms and 17 canonical pathways with similar activation/inhibition patterns in both species, demonstrating significant conservation in specific immune and inflammatory responses. However, ovine PMBCs also contained a unique WC1+γδ T-cell subset, not detected in hPBMCs. Furthermore, differences in the activation/inhibition trends of seven canonical pathways and the sets of DAPs between sheep and humans, emphasize the need to consider interspecies differences in translational studies and inflammation research.

Raman Flow Cytometry and Its Biomedical Applications

Raman flow cytometry (RFC) uniquely integrates the "label-free" capability of Raman spectroscopy with the "high-throughput" attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.

Application of Multiparametric Flow Cytometry Panels to Study Lymphocyte Subpopulations in Tuberculin-Positive Cattle

Flow cytometry (FC) is widely used in microbiology, immunology, hematology, and oncology. In the veterinary field, FC enabled the study of the immune response in cattle infected with different pathogens, as well as vaccine testing. However, few fluorochrome-conjugated antibodies recognize bovine antigens, limiting the possible benefits of FC and the implementation of multiparametric analysis for more complex studies. Two cytometry panels with five colors each were designed and implemented for the study and identification of populations and subpopulations of T cells derived from the peripheral blood mononuclear cells of dairy heifers. Both panels detected differences in T cell subpopulations between heifers positively and negatively tested for tuberculin; they detected overexpression of CD25+ and CD45RO+ in tuberculin-positive heifers after stimulation with a culture filtrate protein extract (CFPE) from Mycobacterium bovis (M. bovis). We identified subpopulations of T cells from peripheral blood mononuclear cells using two multicolor panels. These panels could be used to analyze total bovine blood in immunopathogenic studies and vaccine development. The same strategy could be implemented in other species of veterinary interest.

IgG from Adult Atopic Dermatitis (AD) Patients Induces Nonatopic Neonatal Thymic Gamma-Delta T Cells (γδT) to Acquire IL-22/IL-17 Secretion Profile with Skin-Homing Properties and Epigenetic Implications Mediated by miRNA

γδT cells mature in the human thymus, and mainly produce IL-17A or IFN-γ, but can also produce IL-22 and modulate a variety of immune responses. Here, we aimed to evaluate whether IgG from AD patients (AD IgG) can functionally modulate thymic nonatopic γδT cells. Thymic tissues were obtained from 12 infants who had not had an atopic history. Thymocytes were cultured in mock condition, or in the presence of either AD IgG or therapeutic intravenous IgG (IVIg). Following these treatments, intracellular cytokine production, phenotype, and microRNA expression profiles were investigated. AD IgG could downregulate α4β7, upregulate CLA, and induce the production of IFN-γ, IL-17, and IL-22 in γδT cells. Although both AD IgG and IVIg could directly interact with γδT cell membranes, AD IgG could reduce γδT cell apoptosis. AD IgG could upregulate nine miRNAs compared to IVIg, and six when compared to the mock condition. In parallel, some miRNAs were downregulated. Target gene prediction and functional analysis indicated that some target genes were enriched in the negative regulation of cellular transcription. This study shows that AD IgG influences the production of IL-17 and IL-22 by intrathymic nonatopic γδT cells, and demonstrates epigenetic implications mediated by miRNAs.

High-Throughput, Living Single-Cell, Multiple Secreted Biomarker Profiling Using Microfluidic Chip and Machine Learning for Tumor Cell Classification

Secreted proteins provide abundant functional information on living cells and can be used as important tumor diagnostic markers, of which profiling at the single-cell level is helpful for accurate tumor cell classification. Currently, achieving living single-cell multi-index, high-sensitivity, and quantitative secretion biomarker profiling remains a great challenge. Here, a high-throughput living single-cell multi-index secreted biomarker profiling platform is proposed, combined with machine learning, to achieve accurate tumor cell classification. A single-cell culture microfluidic chip with self-assembled graphene oxide quantum dots (GOQDs) enables high-activity single-cell culture, ensuring normal secretion of biomarkers and high-throughput single-cell separation, providing sufficient statistical data for machine learning. At the same time, the antibody barcode chip with self-assembled GOQDs performs multi-index, highly sensitive, and quantitative detection of secreted biomarkers, in which each cell culture chamber covers a whole barcode array. Importantly, by combining the K-means strategy with machine learning, thousands of single tumor cell secretion data are analyzed, enabling tumor cell classification with a recognition accuracy of 95.0%. In addition, further profiling of the grouping results reveals the unique secretion characteristics of subgroups. This work provides an intelligent platform for high-throughput living single-cell multiple secretion biomarker profiling, which has broad implications for cancer investigation and biomedical research.

Betamethasone induces potent immunosuppression and reduces HIV infection in a PBMC in vitro model

Genital inflammation is an established risk factor for increased HIV acquisition risk. Certain HIV-exposed seronegative populations, who are naturally resistant to HIV infection, have an immune quiescent phenotype defined by reduced immune activation and inflammatory cytokines at the genital tract. Therefore, the aim of this study was to create an immune quiescent environment using immunomodulatory drugs to mitigate HIV infection. Using an in vitro peripheral blood mononuclear cell (PBMC) model, we found that inflammation was induced using phytohemagglutinin and Toll-like receptor (TLR) agonists Pam3CSK4 (TLR1/2), lipopolysaccharide (LPS) (TLR4) and R848 (TLR7/8). After treatment with anti-inflammatory drugs, ibuprofen (IBF) and betamethasone (BMS), PBMCs were exposed to HIV NL4-3 AD8. Multiplexed ELISA was used to measure 28 cytokines to assess inflammation. Flow cytometry was used to measure immune activation (CD38, HLA-DR and CCR5) and HIV infection (p24 production) of CD4+ T cells. BMS potently suppressed inflammation (soluble cytokines, p<0.05) and immune activation (CD4+ T cells, p<0.05). BMS significantly reduced HIV infection of CD4+ T cells only in the LPS (0.98%) and unstimulated (1.7%) conditions (p<0.02). In contrast, IBF had minimal anti-inflammatory and immunosuppressive but no anti-HIV effects. BMS demonstrated potent anti-inflammatory effects, regardless of stimulation condition. Despite uniform immunosuppression, BMS differentially affected HIV infection according to the stimulation conditions, highlighting the complex nature of these interactions. Together, these data underscore the importance of interrogating inflammatory signaling pathways to identify novel drug targets to mitigate HIV infection.