transcriptomic profiling  (Hua Da Inc)

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: Moreover, we downloaded the transcriptomic profiles available from the Human Protein Atlas ( https://www.proteinatlas.org/humanproteome/blood ) and used them to verify the expression patterns of key genes across cell types.

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: Moreover, we downloaded the transcriptomic profiles available from the Human Protein Atlas ( https://www.proteinatlas.org/humanproteome/blood ) and used them to verify the expression patterns of key genes across cell types.

Techniques: Quantitative Proteomics