Journal: Cell reports

Article Title: ΔNp63 drives dysplastic alveolar remodeling and restricts epithelial plasticity upon severe lung injury

doi: 10.1016/j.celrep.2022.111805

Figure Lengend Snippet:

Article Snippet: Longitudinal single-cell transcriptomics analysis of mouse lung upon bleomycin-induced injury , Herbert Schiller Lab (Helmholtz Zentrum München) , GEO: GSE141259.

Techniques: Virus, Recombinant, Lysis, Magnetic Beads, Migration, Single-cell Transcriptomics, Software

Journal: Journal of Translational Medicine

Article Title: Genome-wide association, single-cell, and spatial transcriptomics analyses reveal the role of the STK24-expressing positive cells in LUAD progression and the tumor microenvironment, identifying STK24 as a potential therapeutic target

doi: 10.1186/s12967-025-07111-z

Figure Lengend Snippet: STK24 is elevated in LUAD epithelial cells. A UMAP showing cell types after batch correction and dimensionality reduction clustering. B Bubble plot showing STK24 expression levels across various cell types. C Violin plot showing STK24 expression in normal and tumor cells across various cell types. D , E STK24 expression levels and regional variation analysis in spatial transcriptomics. F Violin plot showing STK24 expression in normal and tumor samples in the TCGA-LUAD cohort. G Immunohistochemistry results showing STK24 staining in LUAD and normal tissue samples from the HPA database. H Independent prognostic analysis to evaluate whether the association between STK24 and tumor survival is independent of traditional clinical variables. **** P < 0.0001, *** P < 0.001, ** P < 0.01, * P < 0.05, ns P > 0.05

Article Snippet: E – H Spatial transcriptomics heterotypic cell network analysis shows colocalization of STK24posEpi and fibroblasts.

Techniques: Expressing, Immunohistochemistry, Staining

Journal: Journal of Translational Medicine

Article Title: Genome-wide association, single-cell, and spatial transcriptomics analyses reveal the role of the STK24-expressing positive cells in LUAD progression and the tumor microenvironment, identifying STK24 as a potential therapeutic target

doi: 10.1186/s12967-025-07111-z

Figure Lengend Snippet: Exploring the origins of STK24 Group cells through spatial transcriptomics (ST). A Schematic diagram of RCTD deconvolution and spatial trajectory analysis of spatial transcriptomics data. B – D Cell types after ST deconvolution. E , F Cell developmental trajectory and trajectory tree in ST ERS17014180. G , H Cell developmental trajectory and trajectory tree in ST ERS17014184. I , J Cell developmental trajectory and trajectory tree in ST ERS17014196. (K-M) Scatter plots showing the correlation between STK24 gene expression and developmental trajectory genes

Article Snippet: E – H Spatial transcriptomics heterotypic cell network analysis shows colocalization of STK24posEpi and fibroblasts.

Techniques: Gene Expression

Journal: Journal of Translational Medicine

Article Title: Genome-wide association, single-cell, and spatial transcriptomics analyses reveal the role of the STK24-expressing positive cells in LUAD progression and the tumor microenvironment, identifying STK24 as a potential therapeutic target

doi: 10.1186/s12967-025-07111-z

Figure Lengend Snippet: Interactions between STK24-positive tumor epithelial cells (STK24posEpi) and fibroblasts. A Analysis of interaction strength between STK24posEpi and various cell types. B Activated pathways in various cell communications. C Analysis of activated ligand-receptor pairs. D Schematic diagram of Heterotypic cellular network analysis and cell co-localization analysis of spatial tran-scriptomics data. E – H Spatial transcriptomics heterotypic cell network analysis shows colocalization of STK24posEpi and fibroblasts. I Heatmap displaying cell–cell dependency analysis in the colocated, neighboring, and extended neighboring (15-point) regions of the spatial transcriptomics data

Article Snippet: E – H Spatial transcriptomics heterotypic cell network analysis shows colocalization of STK24posEpi and fibroblasts.

Techniques:

Journal: Journal of Translational Medicine

Article Title: Genome-wide association, single-cell, and spatial transcriptomics analyses reveal the role of the STK24-expressing positive cells in LUAD progression and the tumor microenvironment, identifying STK24 as a potential therapeutic target

doi: 10.1186/s12967-025-07111-z

Figure Lengend Snippet: Communication and signal flow changes between STK24posEpi and fibroblasts in spatial transcriptomics (ST). A Schematic diagram of Cell–cell communication analysis and signal flow direction analysis of spatial transcriptomics data. B Analysis of communication intensity between STK24posEpi and fibroblasts by integrating multiple spatial transcriptomics samples. C , D Communication between STK24posEpi and fibroblasts in the PDGF signaling pathway across different spatial transcriptomics samples. E Importance of Sender, Receiver, Mediator, and Influencer in different cell types in the PDGF signaling pathway. F , G Expression and co-expression of ligand-receptor pairs related to the PDGF signaling pathway in various spatial transcriptomics samples. H Importance of Sender, Receiver, Mediator, and Influencer in different cell types in the VEGF signaling pathway. I , J Communication between STK24posEpi and fibroblasts in the VEGF signaling pathway across different spatial transcriptomics samples. K Importance of Sender, Receiver, Mediator, and Influencer in different cell types in the MIF signaling pathway. L , M Communication between STK24posEpi and fibroblasts in the MIF signaling pathway across different spatial transcriptomics samples. N , O COMMOT analysis showing the direction of MIF signal flow and expression of Senders and Receivers in various spatial transcriptomics samples

Article Snippet: E – H Spatial transcriptomics heterotypic cell network analysis shows colocalization of STK24posEpi and fibroblasts.

Techniques: Expressing

Journal: Journal of Translational Medicine

Article Title: Genome-wide association, single-cell, and spatial transcriptomics analyses reveal the role of the STK24-expressing positive cells in LUAD progression and the tumor microenvironment, identifying STK24 as a potential therapeutic target

doi: 10.1186/s12967-025-07111-z

Figure Lengend Snippet: Exploration of apoptosis and STK24posEpi-related pathways in spatial transcriptomics (ST). A Schematic diagram of Pathway dependency analysis of spatial transcriptomics data. B Enrichment results for the ST apoptosis pathway and comparison of differences between regions. C Heatmap displaying apoptosis-dependent cell pathways within regions in the spatial context. D , F Network diagrams showing apoptosis-dependent cell pathways in intra ( D ), juxta_5 ( E ), and para_15 ( F ) regions. G Enrichment results for the ST cell proliferation pathway and comparison of differences between the STK24 Group. H Enrichment results for the ST cell damage pathway and comparison of differences between the STK24 Group. I Comparison of ST cell cycle and DNA repair pathways between the STK24 Groups. J , K Heatmaps showing cell pathway dependency analysis for different cell types within the intra ( J ) and para_15 ( K ) regions in the spatial context. **** P < 0.0001, *** P < 0.001, ** P < 0.01, * P < 0.05, ns P > 0.05

Article Snippet: E – H Spatial transcriptomics heterotypic cell network analysis shows colocalization of STK24posEpi and fibroblasts.

Techniques: Comparison

Journal: Journal of Translational Medicine

Article Title: Genome-wide association, single-cell, and spatial transcriptomics analyses reveal the role of the STK24-expressing positive cells in LUAD progression and the tumor microenvironment, identifying STK24 as a potential therapeutic target

doi: 10.1186/s12967-025-07111-z

Figure Lengend Snippet: Clinical significance of STK24posEpi. A Schematic diagram of Homotypic cellular network analysis of spatial transcriptomics data. B Homotypic cell network analysis of STK24posEpi in spatial transcriptomics. C Survival analysis of STK24posEpi across multiple bulk transcriptome cohorts after Bayesian deconvolution. D Comparison of tumor-infiltrating lymphocyte scores between STK24posEpi Groups in the TCGA-LUAD cohort. E Histological slides showing differences in tumor-infiltrating lymphocytes between STK24posEpi Groups in the TCGA-LUAD cohort. F Correlation analysis of STK24posEpi and B cells in multiple bulk transcriptomes. G Differential expression of BCR signaling pathway-related genes between STK24posEpi Groups in the TCGA-LUAD cohort. H Differential expression of antigen processing and presentation pathway-related genes between STK24posEpi Groups in the TCGA-LUAD cohort. I Comparison of clinical factors between STK24posEpi Groups in the TCGA-LUAD cohort. **** P < 0.0001, *** P < 0.001, ** P < 0.01, * P < 0.05, ns P > 0.05

Article Snippet: E – H Spatial transcriptomics heterotypic cell network analysis shows colocalization of STK24posEpi and fibroblasts.

Techniques: Comparison, Quantitative Proteomics

Journal: Advanced Science

Article Title: Multiscale Cell–Cell Interactive Spatial Transcriptomics Analysis

doi: 10.1002/advs.202508358

Figure Lengend Snippet: Overview of MCIST workflow. Gene expression data are treated as a point cloud of cells, from which we construct a sequence of multiscale cell‐cell interaction graphs based on an affinity measure between expression profiles and k‐nearest neighbors (kNNs). These graphs give rise to an ensemble of low‐dimensional multiscale topological PCA representations of the gene expression data, each characterizing a specific combination of cell–cell connectivities. A latent space representation of the spatially resolved gene expression data is also constructed from a deep learning model to pair with the multiscale topological representation. These representations are then aligned for downstream ensemble clustering‐enabling spatial domain detection, residue‐similarity index (RSI)‐optimized trajectory inference, and differential gene expression analysis.

Article Snippet: In this study, we present the MultiScale Cell‐Cell Interactive Spatial Transcriptomics Analysis method, which unites the strengths of spatially resolved deep learning techniques with a topological representation of multi‐scale cell‐cell similarity relations.

Techniques: Gene Expression, Construct, Sequencing, Expressing, Residue