Journal: PLoS pathogens

Article Title: TGF-β1 down-regulation of NKG2D/DAP10 and 2B4/SAP expression on human NK cells contributes to HBV persistence.

doi: 10.1371/journal.ppat.1002594

Figure Lengend Snippet: Figure 2. Deficient NK cell function in IT patients. (A) Deficient NK cell cytotoxicity from IT patients. NK cell cytotoxicity was assessed in primary NK cells from IT, IA, and IN patients and healthy controls using a 51Cr-release assay with K562 target cells. (B) Results show the mean 6 SEM values in 19 patients and 5 controls. Values for IT patients were less than controls (P= 0.0384). (C) Positive correlation between NK cell cytotoxicity and the percentage of NK cells expressing NKG2D and 2B4. (D) Fresh PBMCs were stimulated with/without IL-12 as described in the Materials and Methods. After 16 h, IFN-c production was determined using flow cytometry by gating on CD32CD56+ NK cells. A representative dot plot displaying intracellular IFNc staining in NK cells with the subsets indicated is shown. (E) Cumulative data are shown. (F, G) Fresh PBMCs from patients before treatment (black) or after treatment (white) were stimulated with IL-12 for 16 h. IFNc (F) and CD107a (G) production was determined using flow cytometry by gating on CD32CD56+ NK cells. (H) Induction of ex vivo NK cell cytotoxic activity after in vivo administration of antiviral treatment with nucleoside analogues until ALT reached normal levels. NK cytotoxicity was determined ex vivo by measuring the lysis of 51Cr-labelled target cells before treatment (red) or after treatment (green). Cumulative data are shown. doi:10.1371/journal.ppat.1002594.g002

Article Snippet: Flow cytometric analysis Fresh human peripheral blood mononuclear cells (PBMCs) were isolated from peripheral blood by Ficoll-Isopaque (Solarbio, China) gradient centrifugation.

Techniques: Cell Function Assay, Release Assay, Expressing, Flow Cytometry, Staining, Ex Vivo, Activity Assay, In Vivo, Analogues, Lysis

Journal: bioRxiv

Article Title: Systems-level longitudinal immune profiling reveals individualized immunotypes and genetic associations

doi: 10.64898/2026.03.21.713378

Figure Lengend Snippet: (a) The S3WP cohort comprises 101 clinically healthy individuals characterized by longitudinal multi-omics profiling. Repeated measurements from the same individual enabled assessments of intra-and inter-individual immune variation and cross-omic integration. (b) The participants were followed longitudinally across six visits over two years. At each visit, PBMCs and plasma samples were collected for CyTOF, RNA-seq, and Olink proteome profiling; WGS was performed at baseline. (c) Sex distribution of the participants. (d) Age distribution at enrollment. (e) Frequency distributions of 18 major immune cell populations derived from CyTOF analysis across all samples. (f) UMAP clustering of PBMC transcriptomics profiles from the S3WP cohort alongside immune-cell transcriptomics from the Human Protein Atlas. Colors indicate immune cell populations.

Article Snippet: To assess the quality of the PBMC transcriptomics data, we compared the PBMC transcriptomic profiles from the S3WP study with the PBMC and 18 flow-sorted immune cell transcriptomes from the Human Protein Atlas (HPA) ; as expected, the PBMC samples from the S3WP project were clustered together with PBMC samples from the HPA ( ).

Techniques: Biomarker Discovery, Clinical Proteomics, RNA Sequencing, Derivative Assay, Transcriptomics

Journal: bioRxiv

Article Title: Systems-level longitudinal immune profiling reveals individualized immunotypes and genetic associations

doi: 10.64898/2026.03.21.713378

Figure Lengend Snippet: (a-d) t-SNE performed on (a) PBMC immune frequencies, (b) PBMC gene expression, (c) immune frequencies and gene expression combined, and (d) plasma proteomics. (e) Clustering of transcriptomic data from our study and the Human Protein Atlas. (f) Distribution of Euclidean distance in transcriptomic profiles between collected samples (i) from the same participant within the first year (visit 1-4), (ii) from the same participant between year 1 and year 2 (visit 5-6), and (iii) from different participants. Wilcoxon tests were used for statistical analysis (ns, not significant; ****, P < 0.0001). (g) Euclidean distance between each transcriptomic profile from the last visit and the sample collected during the first year, divided into (i) pairs of samples from the same individual (light blue) and (ii) pairs of samples from different participants (red). (h) Distribution of intraclass correlation coefficient of immune cell profiling, transcriptomic and plasma proteomics. (i) Intraclass correlation coefficient of all 53 immune populations. (j) Intra- and inter-individual coefficients of variation of gene expression. (k) Examples of genes, immune populations and proteins showing individual expression profiles. Samples are colored according to their expression levels at visit 1 (orange: 0-25%, green: 25–50%, blue: 50–75%, purple: 75–100%). The highlighted dots indicate the median level of the corresponding group at that visit.

Article Snippet: To assess the quality of the PBMC transcriptomics data, we compared the PBMC transcriptomic profiles from the S3WP study with the PBMC and 18 flow-sorted immune cell transcriptomes from the Human Protein Atlas (HPA) ; as expected, the PBMC samples from the S3WP project were clustered together with PBMC samples from the HPA ( ).

Techniques: Gene Expression, Clinical Proteomics, Expressing

Journal: bioRxiv

Article Title: Systems-level longitudinal immune profiling reveals individualized immunotypes and genetic associations

doi: 10.64898/2026.03.21.713378

Figure Lengend Snippet: (a) t-SNE sample clustering based on transcriptomic profiles of genes in the PBMC association network. (b) Immune frequencies patterns across the three sample clusters. (c) Distribution of (top) innate immune cell frequencies and (bottom) CD4:CD8 ratio across the three clusters. (d) Immune frequencies of the modules in the PBMC network, divided by sample clusters. (e) Number of up- and down-regulated genes in each module (FDR<0.05) obtained from differential expression analysis between each cluster and the remaining two. (f-h) t-SNE clustering colored based on CD4:CD8+ T cells ratio, and immune frequencies of the B cells, cytotoxic and myeloid modules. (j) (Top) CRP levels of participant P3920. (Bottom) Sample clusters of participants across visits; highlighted in black are the samples from participant P3920. (k) Pattern of clinical variables across the three clusters. (l) Patterns of the 30 proteins with the most significant up-regulation in any of the three clusters. SBP, systolic blood pressure; DBP, diastolic blood pressure; TNT, troponin T; CRP, C-reactive protein; HDL, high density lipoprotein; ALAT, alanine aminotransferase; GGT, gamma-glutamyl transferase. P-values are calculated by Wilcoxon tests in c-d; ns, not significant; * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001.

Article Snippet: To assess the quality of the PBMC transcriptomics data, we compared the PBMC transcriptomic profiles from the S3WP study with the PBMC and 18 flow-sorted immune cell transcriptomes from the Human Protein Atlas (HPA) ; as expected, the PBMC samples from the S3WP project were clustered together with PBMC samples from the HPA ( ).

Techniques: Quantitative Proteomics

Journal: iScience

Article Title: A combined AI and cell biology approach surfaces targets and mechanistically distinct Inflammasome inhibitors

doi: 10.1016/j.isci.2024.111404

Figure Lengend Snippet:

Article Snippet: Isolated cells from male and female donors aged 45–55 were acquired commercially from iXCells Biotechnology (10HU-003-CR10M, 10HU-003-CR100M).

Techniques: Recombinant, Software