Journal: bioRxiv

Article Title: PLXNB1 and other signaling drives a pathologic astrocyte state contributing to cognitive decline in Alzheimer’s Disease

doi: 10.1101/2025.02.24.639868

Figure Lengend Snippet: (A) Overview of our systematic approach to identify microglial and/or astrocytic cell-cell signals regulating Astrocyte 10 (Ast10). (1) NicheNet prioritizes ligand-receptor pairs based on their expression and how well their downstream signaling activities recapitulating the Ast10 transcriptional signature. (2) Partial Least Squares Regression (PLSR) models predict Ast10 frequency per donor using expression patterns of prioritized ligands or receptors. (3) Validation includes replication in independent datasets, spatial transcriptomics to confirm ligand-Ast10 colocalization, immunohistochemistry for coexpression of an Ast10 marker with a top receptor, and genetic depletion of the top receptor in iPSC-derived and murine astrocytes, followed by scRNA-seq. (B) Ligand activity z-scores from NicheNet for the top 100 sender-ligand-receptor interactions. A high z-score indicates that a ligand’s predicted target genes are enriched for Ast10 signature genes. A positive z-score reflects above-average activity relative to all other ligands analyzed. (C) Differential expression of the top ligands across all analyzed astrocytic and microglial sender states. Color indicates log fold-change (logFC) in ligand expression relative to other sender populations; circle size represents the percentage of cells expressing each ligand. (D) Differential expression of the receptors for top-ranked ligands from (B). Color denotes logFC of receptor expression in Ast10 compared to other astrocytic and microglial subsets.

Article Snippet: Fresh-frozen dorsolateral prefrontal cortex (DLPFC) samples from ROSMAP participants were processed using the Visium Spatial Transcriptomics (ST) platform, coupled with immunofluorescence.

Techniques: Expressing, Biomarker Discovery, Immunohistochemistry, Marker, Derivative Assay, Activity Assay, Quantitative Proteomics

Journal: The Journal of Pathology

Article Title: Assessing spatial sequencing and imaging approaches to capture the molecular and pathological heterogeneity of archived cancer tissues

doi: 10.1002/path.6383

Figure Lengend Snippet: Developing and assessing protocols to perform spatial transcriptomics to capture thousands of genes in FFPE cancer tissue. (A) Poly(A)‐capture required the optimisation of tissue permeabilisation step. Probe‐capture required a tissue adherence test. (B) Optimisation for tissue multiplexing and sectioning thickness. (C) Tissue stainning was processed in different conditions in two protocols. (D) Decrosslinking was performed in the same way. (E) In permeabilisation, the mRNA molecules or hybridised probes were released from cells and bound to the spatial oligos on the glass slide. Reverse transcription produced cDNA products in poly(A)‐capture protocol or extended probes in probe‐capture protocol. (F) Eluting captured molecules/probes and preparing the library for long/short cDNA sequencing.

Article Snippet: The Visium FFPE ST platform (10x Genomics) is a technology capable of measuring ~18,000 genes representing the whole transcriptome while generating histological‐grade H&E images, allowing for pathological annotation.

Techniques: Multiplexing, Staining, Reverse Transcription, Produced, Sequencing

Journal: The Journal of Pathology

Article Title: Assessing spatial sequencing and imaging approaches to capture the molecular and pathological heterogeneity of archived cancer tissues

doi: 10.1002/path.6383

Figure Lengend Snippet: Visium probe‐capture for melanoma FFPE samples stored at different periods of time. (A, D, and G) Pathologist annotation as coloured circles. (B, E, and H) Corresponding clustering results from tissues in A, D, and G, respectively. (C, F, and I) Heatmaps of gene marker expression for each cluster in B, E, and H, respectively.

Article Snippet: The Visium FFPE ST platform (10x Genomics) is a technology capable of measuring ~18,000 genes representing the whole transcriptome while generating histological‐grade H&E images, allowing for pathological annotation.

Techniques: Marker, Expressing

Journal: bioRxiv

Article Title: Spatial transcriptomics exploration of the primary neuroblastoma microenvironment unveils novel paracrine interactions

doi: 10.1101/2024.12.21.629891

Figure Lengend Snippet: ( A ) The Visium spatial transcriptomics platform was used to profile 3 tumors (2 sections each) from 2 NB patients ( NB1 and NB2 ). Both patients received prior chemotherapy ( NB1Post and NB2Post ) and for NB1 we also profiled pretherapy tumor materials ( NB1Pre) . Created in BioRender. ( B ) Hematoxylin and eosin (H&E) staining of the 6 tumor sections that were used in this study. ( C ) Clustering and annotation of 7 main spatial clusters across the 6 samples. Cluster annotations were based on the most representative cell type, as predicted from marker gene expression, enrichment analyses and similarities to single cell data. See - for details. ( D ) Dot plots showing relative expression (colors) and proportional expression in the spots (sizes) of the top 5 representative genes for each cluster. Genes derived from the leading edges from the GSEA shown in . ( E ) UMAP plots showing the main clusters corresponding to each tumor (left), the CNV score, which is representative for the overall copy number variability (middle) and the cell state (adrenergic or mesenchymal as indicated by color key; right). NE, neuroendocrine cells; CAF, cancer associated fibroblasts; Schwann, Schwann cells; Macro, macrophages; Endo, endothelial cells; Plasma, plasma cells; AC-like, adrenocortical-like; ADRN, adrenergic; MES, mesenchymal.

Article Snippet: Two sections were profiled from each tumor using the 10X Genomics Visium spatial transcriptomics (ST) platform, resulting in the analyses of 6 sections obtained from 3 different tumors ( ).

Techniques: Staining, Marker, Expressing, Derivative Assay

Journal: bioRxiv

Article Title: Spatial transcriptomics exploration of the primary neuroblastoma microenvironment unveils novel paracrine interactions

doi: 10.1101/2024.12.21.629891

Figure Lengend Snippet: Adrenocortical signatures were analyzed in independent human transcriptomics studies , ( A ) Dot plot comparing GSEA results of selected Reactome gene sets in our study with 2 scRNA-Seq studies. Dot sizes and colors correspond to normalized enrichment scores (NES) and P values, as indicated by color key. See table S2 for complete GSEA results. ( B ) Heatmaps showing UCell scores of fetal and postnatal adrenocortical cell type signatures on the clusters that were described by both studies. AP, adrenal primordium; FZ, fetal zone; DZ, definitive zone; ZG, zona glomerulosa; ZF, zona fasciculata; ZR, zona reticularis. ( C ) Scatter plots showing the correlation between expression of ALK , ALKAL2 and the AC-like expression signature as function of time during human adrenal gland development. Linear regression line and Pearson’s correlation coefficient and P value indicated. Data derived from Del Valle et al., 2022 .

Article Snippet: Two sections were profiled from each tumor using the 10X Genomics Visium spatial transcriptomics (ST) platform, resulting in the analyses of 6 sections obtained from 3 different tumors ( ).

Techniques: Expressing, Derivative Assay