Journal: iScience

Article Title: Peripheral immune landscape in pancreatic ductal adenocarcinoma reveals expansion of effector states with disease progression

doi: 10.1016/j.isci.2026.115034

Figure Lengend Snippet: Dynamics of the peripheral immune population across PDAC progression Linear regression plots (left) and violin plots (right) illustrate the relationship between immune population abundance and PDAC progression. The selected immune populations include PD-1 + CD4 + T cells (A), central memory CD4 + T cells (B), early-like effector CD4 + T cells (C), CD45RA-CCR7- CD4 + T cells (D), T follicular helper cells (Tfh) (E), memory regulatory T (Treg) cells (F), and mature natural killer (NK) cells (G). In each linear regression plot (left), the x axis represents PDAC disease stages (1 = stage 1 PDAC patients, 2 = stage 2 PDAC patients, 3 = stage 3 PDAC patients, 4 = stage 4 PDAC patients), and the y axis represents the min-max normalized (MMN) percentages of a specific immune population. The blue regression line indicates the trend across disease stages. The coefficient of determination (R 2 ) and p value are displayed in red at the top of each plot. In each violin plot (right), the x axis compares healthy individuals (purple) and PDAC patients (red), and the y axis represents the original immune population percentage in total CD45 + cells or the absolute cell counts per mL of the whole blood. Each dot represents an individual sample, and the data are represented as mean ± SEM. The t test p value, indicating statistical significance, is displayed at the top of each violin plot. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001.

Article Snippet: PerCP Anti-Human CD45 (Clone: 2D1) , Cytek Biosciences , Cat# 67-9459-T100; RRID: AB_2621921.

Techniques:

Journal: iScience

Article Title: Peripheral immune landscape in pancreatic ductal adenocarcinoma reveals expansion of effector states with disease progression

doi: 10.1016/j.isci.2026.115034

Figure Lengend Snippet: Machine learning classification identifies predictive immune markers distinguishing PDAC from healthy individuals (A) Schematic overview of the machine learning workflow used to identify predictive immune markers from high-dimensional spectral flow cytometry data. Peripheral blood samples from 38 healthy individuals and 39 treatment-naive PDAC patients (stages I–IV) were analyzed. Mean fluorescence intensity (MFI) values of surface markers were extracted from CD45 + cells, asinh-normalized, and aggregated at the individual level. The dataset was split into 70% training and 30% testing sets for model development and evaluation. (B–D) Top 10 predictive markers ranked by feature importance scores across three independent classifiers: random forest (RF; B), gradient boosting (GB; C), and Bayesian additive regression tree (BART; D). Markers are ranked by their Gini importance scores. (E and F) Receiver operating characteristic (ROC) curves showing model performance in classifying healthy versus PDAC samples (E) and in predicting PDAC stages (F). Curves represent random forest (orange), GB (green), and BART (blue) classifiers, with area under the curve (AUC) values indicating model accuracy. (G and H) Overall expression levels of CD95 and CD45RA, and (H) the representative expression patterns of CD95 in PD1+ CD4 + T cells, CD45RA+ terminal effector (TE) CD8 + T cell, CD11c+ dendritic cells (DCs), and non-classical monocytes (Mo) of healthy individuals and PDAC patients. Each bar represents Z score normalized mean fluorescence intensity (MFI) across individuals. The data are represented as mean ± SEM, and the t test p value, indicating statistical significance, is displayed at the top of each violin and boxplot. ∗∗∗ p < 0.001; NS, non-significance.

Article Snippet: PerCP Anti-Human CD45 (Clone: 2D1) , Cytek Biosciences , Cat# 67-9459-T100; RRID: AB_2621921.

Techniques: Flow Cytometry, Fluorescence, Biomarker Discovery, Expressing

Journal: JCI insight

Article Title: IRF5 genetic risk variants drive myeloid-specific IRF5 hyperactivation and presymptomatic SLE.

doi: 10.1172/jci.insight.124020

Figure Lengend Snippet: Figure 2. Plasma cells are elevated in the circulation of IRF5 homozygous risk donors. (A–C) Freshly isolated PBMCs were surface-stained and plasma cells gated as CD45+CD19+IgD–CD38+ (PB, plasmablasts). (A) Representa- tive dot plots from flow cytometry are shown from a single round of blood draws from independent homozygous risk (n = 5) and nonrisk (n = 5) donors. A is pregated for CD45 and CD19. (B) The number of circulating PBs from n = 12 risk and n = 11 nonrisk donors is shown as a percentage of the CD19+ gate (unpaired 2-tailed t test). (C) Similar to B except percentage of PBs from homozygous nonrisk, risk, and representative mixed haplotypes from Figure 1A are plotted together (1-way ANOVA with Bonferroni’s multiple-comparisons test using risk [E/E] as control group and excluding groups A/E B/C and E/J because of insufficient sample size. (D) Isolated naive B cells from homozy- gous nonrisk and risk donors were in vitro cultured for 7 days with 150 ng/mL CD40L alone or with 100 ng/mL IL-21, 10 μg/mL anti-IgM antibody, and 2.5 μg/mL CpG-B to drive PB differentiation. Representative dot plots from flow cytometry analysis of a single matched nonrisk and risk donor after 7 days of culture. (E) Summarized data from D of n = 4 risk and nonrisk donors are shown (unpaired 2-tailed t test). Experiments were repeated 4 or more times (A and B), were performed once (C), or were repeated twice (D and E). Single data points represent individual donors. Data are presented as mean ± SEM. ; *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001.

Article Snippet: PBMCs were washed and blocked in PBS supplemented with Fc blocker (422302, BioLegend) for 15 minutes and then stained with antibodies against surface makers for 1 hour (BioLegend: CD303-BV421 354212, CD123-BV510 306022, CD14-PE 301806, CD16-APC Cy7 360710, CD40-PE 334308, CD45-PerCP 304026, CD38-BV711 303528, IgD-APC 348222, CD27-BV421 356418, CD19BV510 302242, CD20-BV711 302342, PE secondary 406421; BD Biosciences: CD24-PerCP-Cy5.5 561647; Abcam: cit-H3 ab5103; Novus: MPO-PerCP NB100-64803PCP; eBioscience: CD66b-APC 1706662; 1 5insight.jci.org https://doi.org/10.1172/jci.insight.124020 Molecular Probes, Thermo Fisher Scientific: LIVE/DEAD fixable green stain L23101).

Techniques: Clinical Proteomics, Isolation, Staining, Flow Cytometry, Control, In Vitro, Cell Culture

Journal: JCI insight

Article Title: IRF5 genetic risk variants drive myeloid-specific IRF5 hyperactivation and presymptomatic SLE.

doi: 10.1172/jci.insight.124020

Figure Lengend Snippet: Figure 4. IRF5 homozygous risk donors have elevated numbers of circulating pDCs and spontaneous NETosis. (A and B) Similar to Figure 2, A and B, except freshly isolated PBMCs were surface-stained and pDCs gated as CD45+CD123+BDCA2+. (A) Representative dot plots from flow cytometry are shown from a single round of blood draws. A is pregated for CD45. (B) The number of circulating pDCs from n = 10 risk and n = 12 nonrisk donors is shown as a percentage of CD45+ gate (unpaired 2-tailed t test). (C) Representative dot plots from flow cytometry analysis of myeloperoxidase-positive, citrullinated histone H3–positive (MPO+cit-H3+) NETs in donor samples (n = 5 risk and nonrisk). C is pregated on CD66b+ cells. (D) Quantification of NETs from n = 12 risk and n = 14 nonrisk donors is shown as a percentage from CD66b+ cells (1-way ANOVA with Tukey’s multiple-comparisons test). (E) The presence of NETs was visualized by plating equal numbers of freshly isolated neutrophils from homozygous nonrisk, risk, and patients with SLE on poly-l-lysine–coated coverslips for 4 hours. Representative images are from stain- ing with Sytox green and DAPI or MPO, Cit-H3, and DAPI. PMA was used as a positive control for NET induction on nonrisk neutrophils (original magnification ×20). Experiments were repeated 3 times (A–E). Single data points represent individual donors. Data are presented as mean ± SEM. *P ≤ 0.05.

Article Snippet: PBMCs were washed and blocked in PBS supplemented with Fc blocker (422302, BioLegend) for 15 minutes and then stained with antibodies against surface makers for 1 hour (BioLegend: CD303-BV421 354212, CD123-BV510 306022, CD14-PE 301806, CD16-APC Cy7 360710, CD40-PE 334308, CD45-PerCP 304026, CD38-BV711 303528, IgD-APC 348222, CD27-BV421 356418, CD19BV510 302242, CD20-BV711 302342, PE secondary 406421; BD Biosciences: CD24-PerCP-Cy5.5 561647; Abcam: cit-H3 ab5103; Novus: MPO-PerCP NB100-64803PCP; eBioscience: CD66b-APC 1706662; 1 5insight.jci.org https://doi.org/10.1172/jci.insight.124020 Molecular Probes, Thermo Fisher Scientific: LIVE/DEAD fixable green stain L23101).

Techniques: Isolation, Staining, Flow Cytometry, Positive Control