Journal: NPJ Systems Biology and Applications

Article Title: Association of copy number alterations with the immune transcriptomic landscape in cancer

doi: 10.1038/s41540-026-00649-8

Figure Lengend Snippet: A Overview of the 294,159 bulk transcriptomic profiles collected from the three datasets: GPL570 , ARCHS4, and TCGA. Consensus independent component analysis (c-ICA) was applied to each dataset to disentangle the bulk transcriptomic profiles into statistically independent transcriptional components (TCs). The TCs were then classified as CNA-TCs if they captured the effect of copy number alterations (CNA) based on the transcriptional adaptation to CNA profiling (TACNA). Additionally, TCs that capture immune-related processes using gene set enrichment analysis (GSEA) were defined as immune-TCs. B Heatmaps showing CNA regions captured by the CNA-TCs. Each column corresponds to a CNA-TC, with genes arranged in genomic order. For each CNA-TC, regions where many genes have high gene weights—indicating a CNA effect as determined by TACNA—are marked in red (see inset example). Only the red-marked regions, which represent the specific CNA effect captured by the corresponding CNA-TC, are shown. The CNA-TCs are sorted based on the position of the CNA region they capture. C Heatmap showing the z-value of GSEA for each immune-TC across all immune-related gene sets from Gene Ontology—Biological Process and REACTOME.

Article Snippet: Spatial transcriptomic profiles were obtained from the 10x Genomics website ( https://www.10xgenomics.com ).

Techniques: Capture-C

Journal: NPJ Systems Biology and Applications

Article Title: Association of copy number alterations with the immune transcriptomic landscape in cancer

doi: 10.1038/s41540-026-00649-8

Figure Lengend Snippet: A Three examples of immune-TC activity across cell types are shown. Single-cell RNA sequencing included 114,253 cells from 181 patients with 13 different cancer types from the single-cell tumor immune atlas for precision oncology. The transcriptomic profile of each cell was projected onto the GPL570 immune-TCs. Cell annotation was based on the labels defined in the immune atlas. Box plot colors represent major cell type groups. The boxplot displays the median as the central line, with box hinges representing the second and third quartiles, whiskers extending by half the interquartile range, and outliers shown as individual dots. B Three examples of tumor spatial transcriptomic datasets from 10x Genomics Visium are shown. The transcriptomic profile of each spatial spot was projected onto the GPL570 CNA- and immune-TCs, and CNA burden was inferred. The spatial organization of the activity of three immune-TCs is shown for each tumor.

Article Snippet: Spatial transcriptomic profiles were obtained from the 10x Genomics website ( https://www.10xgenomics.com ).

Techniques: Activity Assay, Single Cell, RNA Sequencing

Journal: Nature Communications

Article Title: Isotope-encoded spatial biology identifies plaque-age-dependent maturation and synaptic loss in an Alzheimer’s disease mouse model

doi: 10.1038/s41467-025-63328-y

Figure Lengend Snippet: A PULSE-CHASE iSILK paradigm. Experimental Design 1: PULSE period ( 15 N diet) between 6-10 months of age. Experimental Design 2: PULSE period between 6-10 months of age, CHASE period ( 14 N diet) between 10–18 months of age. Resulting Aβ1-42 MALDI MS isotopologue pattern that is right-shifted (Δm) due to increasing 15 N incorporation. B Representative images of plaque load from GeoMx whole slide scans, repeated on four independent whole-brain slices at 18-months and three at 10-months. C MALDI MSI single ion image of Aβ1-42 in cortex section. D Schematic overview of the correlative hyperspectral imaging and MALDI MSI experiment. 15 N enrichment (nitrogen index) was calculated as the AUC ratio of the 4th to 3rd peak in the Aβ1-42 isotopologue pattern. Higher values indicate greater 15 N incorporation. E Schematic overview of the correlative spatial transcriptomics (GeoMx) and MALDI MSI experiment. Stable 15 N enrichments (nitrogen index) corresponding to plaque age was calculated by extracting the FWHM of the Aβ1-42 peak, where a broader peak indicates increased 15 N incorporation and higher age. F Schematic overview of the validation experiment. Plaque morphology was evaluated by LCO hyperspectral imaging. IHC of selected proteins were correlated with plaque age, as evaluated by hyperspectral imaging. G Representative spectra from MALDI MSI showing the 14 N and 15 N-enriched Aβ1-42 m/z peak. H Aβ1-42 mass analysis comparing the plaque center (Cen) vs. the periphery (Peri) in 10-month-old mice ( p = 0.00017), ( I ) in 18-month-old mice ( p = 0.00000077), and ( J ) differences between cortex and hippocampus ( p = 0.022). H , I Linear Mixed Model accounting for across animals and repeated measures, point color indicates animal, 15 replicates over n = 3 m mice and 22 replicates over n = 4 m mice, respectively. J Two-sided Paired t-test, 22 replicates over n = 4 m mice, data presented as mean ± SEM. K Representative MALDI MSI image of 15 N and 14 N enriched Aβ1-42 distribution in plaques in 18-month-old mice. Parts of the figure created in BioRender. Szadziewska, A. ( https://BioRender.com/4qpojxz ) Image in ( E ) provided by Bruker Spatial Biology. Significance levels: *** P < 0.001, ** P < 0.01; * P < 0.05. Source data are provided as a Source Data file. FWHM full width at half maximum, RP reflector mode, LP linear mode.

Article Snippet: The spatial transcriptomics platform GeoMx® Digital Spatial Profiler was selected over other sequencing techniques due to its ability to target plaque-specific gene expression changes, offering a more spatially resolved technique for AD pathology-associated alterations compared to the more commonly used RNA sequencing methods .

Techniques: Pulse Chase, Imaging, Biomarker Discovery

Journal: bioRxiv

Article Title: Imp1 acts as a dosage- and stage-dependent temporal rheostat orchestrating radial glial fate transitions and cortical morphogenesis

doi: 10.1101/2025.11.18.688993

Figure Lengend Snippet: (A,D,G) Representative images of control, continuous Imp1 overexpression, and T1 Imp1 conditions showing TEMPO reporters, Cux1/Ctip2 immunostaining and overlays. Boxed regions: Ctip2+ TEMPO neurons (dashed) or double-positive Cux1+/Ctip2+ TEMPO cells (solid). (B,E,H) High magnification images of boxed regions highlight CFP-/RFP-labeled neurons in layers V-VI colocalizing with Ctip2 (outlined arrowheads) or double-positive for both markers (solid arrowheads). (C,F,I) Quantification of marker expression in CFP+ and RFP+ neurons residing in layers V-VI. Following continuous or T1 Imp1 overexpression, neurons in deep layer maintain appropriate deep-layer molecular identities (predominantly Ctip2+), demonstrating that laminar distribution reflects bona fide fate specification changes rather than mislocalization. (C) In control conditions, CFP+: Ctip2 (78.31% ± 13.82%), Cux1 (2.93% ± 2.11%), double-negative (18.76% ± 11.73%). RFP+: Ctip2 (50.35% ± 17.01%), Cux1 (1.85% ± 1.85%), double-negative (46.76% ± 17.20%), double-positive (1.04% ± 1.04%). (F) Following continuous Imp1 overexpression, CFP+: Ctip2+ (71.18% ± 3.06%), Cux1+ (7.38% ± 4.93%), double-negative (16.06% ± 1.93%), double-positive (5.38% ± 1.80%). RFP+: Ctip2+ (58.92% ± 7.18%), Cux1+ (5.84% ± 3.01%), double-negative (5.10% ± 3.12%), double-positive (30.14% ± 11.98%). (I) In T1 Imp1 overexpression, CFP+: Ctip2+ (47.26% ± 4.88%), Cux1+ (14.63% ± 3.76%), double-negative (36.41% ± 5.64%), double-positive (1.68% ± 0.57%). RFP+: Ctip2+ (47.38% ± 13.91%), Cux1+ (9.17% ± 4.68%), double-negative (26.86% ± 5.03%), double-positive (16.59% ± 10.02%). Dashed lines: upper (II-IV), lower cortical layers (V-VI) and subplate zone (SPZ). Scale bars: (A,D,G) 100 µm and (B,E,H) 20 µm. Data show mean±SEM. Statistics: two-tailed unpaired Welch’s t-test (*P < 0.05, **P < 0.01, *** P < 0.001).

Article Snippet: Spatial transcriptomics profiling TEMPO+ populations at multiple developmental stages would reveal the full spectrum of molecular changes and identify specific transcript targets whose stabilization drives the observed phenotypes.

Techniques: Control, Over Expression, Immunostaining, Labeling, Marker, Expressing, Two Tailed Test

Journal: Journal of Translational Medicine

Article Title: Identification of matrix stiffness-related molecular subtypes in HCC via integrating multi-omics analysis and machine learning algorithms

doi: 10.1186/s12967-025-06733-7

Figure Lengend Snippet: The expression pattern and tissue localization of PPARG in tumor samples. ( A ) PPARG expression levels in tumor and normal samples of the TCGA dataset. ( B ) PPARG was correlated with pathological grades in the TCGA dataset. ( C ) Survival analysis of OS time between high and low-PPARG groups. ( D ) Survival analysis of DSS time between high and low-PPARG groups. ( E ) Correlation analyses between PPARG expression and tumor phenotypes. ( F , H , J ) PPARG expression in different cell types of spatial transcriptomics. F : LIHC1, H : LIHC2, J : LIHC3. ( G , I , K ) The comparisons of PPARG expression levels between malignant and normal samples. ( L ) The visualizations of the relationship between PPARG expression and various components of TME

Article Snippet: Spatial transcriptomics (ST) profiles were obtained from Mendeley Data (skrx2fz79n) [ ].

Techniques: Expressing