Journal: Future Science OA

Article Title: Promising approach for targeting ROBO1 with CAR NK cells to combat ovarian cancer primary tumor cells and organoids

doi: 10.2144/fsoa-2023-0135

Figure Lengend Snippet: Strategy and materials in this study. (A) Diagram of the procedure in the study. The primary tumor cells and tumor organoids were generated from patients' tumor tissues and ROBO1-NK were constructed from patients' PBMCs. (B) IHC of ovarian tumor tissues. Negative control was used irrelevant IgG replacing primary antibody for incubating. Strong positive of ROBO1 expression on cell membrane was marked by red circles. (C) ROBO1 positively expressed in tumor tissues of the clinical ovarian cancer patient #01, #02, #03 and #04.

Article Snippet: In brief, PBMCs were isolated from the patient's peripheral blood using the MACSprepTM PBMC Isolation Kit (130-115-169, Miltenyi Biotec, Bergisch Gladbach, GER) employing the density gradient centrifugation method.

Techniques: Generated, Construct, Negative Control, Expressing, Membrane

Journal: Future Science OA

Article Title: Promising approach for targeting ROBO1 with CAR NK cells to combat ovarian cancer primary tumor cells and organoids

doi: 10.2144/fsoa-2023-0135

Figure Lengend Snippet: Characters of ROBO1-NK cells. (A) Morphology comparison of PBMCs (upper panel), PBMC-NK cells (mid panel) and ROBO1-NK cells (bottom panel) under bright field. (B) Biomarkers determination of ROBO1-NK cells by flow cytometry. For the biomarkers CD34, CD45, CD3, CD56 and CD16, negative controls were used irrelevant IgG replacing primary antibody for incubating, presented as red peaks. For the ROBO1-CAR detection, negative control was used PBMC-NK incubating with anti-CAR antibody, presented as red peak. Samples in detection were presented as blue peaks.

Article Snippet: In brief, PBMCs were isolated from the patient's peripheral blood using the MACSprepTM PBMC Isolation Kit (130-115-169, Miltenyi Biotec, Bergisch Gladbach, GER) employing the density gradient centrifugation method.

Techniques: Comparison, Flow Cytometry, Negative Control

Journal: Future Science OA

Article Title: Promising approach for targeting ROBO1 with CAR NK cells to combat ovarian cancer primary tumor cells and organoids

doi: 10.2144/fsoa-2023-0135

Figure Lengend Snippet: ROBO1-NK lyse ovarian cancer cell line and primary cancer cells. (A) Positive expression of ROBO1 on SKOV-3 cells. (B) Rate of SKOV-3 cell lysis with different E-T ratio. E: number of effective cells; T: number of target cells. (C) Time curve of different NK cells, including Mock-CAR-NK (blue), PBMC-NK (green), ROBO1-NK (red) and control (medium, black), lysing SKOV-3 cells. (D) Lysis rate of primary ovarian tumor cells from patient #01, #02, #03 and #04 by PBMC-NK and ROBO1-NK after 24 h, detected with CCK-8 assay. (E) Photograph of PBMC-NK and ROBO1-NK lysing efficacy on primary ovarian tumor cells after 48 h co-culturing. The primary cells were presented as spindle-like form while NK cells were spherical form with clusters.

Article Snippet: In brief, PBMCs were isolated from the patient's peripheral blood using the MACSprepTM PBMC Isolation Kit (130-115-169, Miltenyi Biotec, Bergisch Gladbach, GER) employing the density gradient centrifugation method.

Techniques: Expressing, Lysis, Control, CCK-8 Assay

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