Journal: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

Article Title: Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis

doi:

Figure Lengend Snippet: A) Data collection protocol for Visium spatial transcriptomics slide. B) Training protocol for spatial omics-driven crossmodal contrastive model; two views are generated per modality, per patch; each view is passed through the corresponding branch of the crossmodal model; embeddings are transformed using a shared projection head; unimodal and crossmodal contrastive losses are applied to output embeddings.

Article Snippet: The collected datasets and the downstream tasks they were used on, are summarized below: Visium spatial transcriptomics slides (n=4; 20,000 spots/patches; Co-Registered Spatial Transcriptomics, H&E WSI): to pretrain contrastive crossmodal model Dartmouth Hitchcock Medical Center (n=708 H&E WSI): used for histological stage prediction and clustering analysis TCGA Cohort (n=350 H&E WSI): used for lymph node metastasis prediction, survival prognostication, and tumor infiltrating lymphocyte (TIL) alignment analysis All analyses were conducted on a machine using a single Nvidia Tesla v100 GPU with 32 gigabytes of VRAM, and 100 gigabytes of RAM.

Techniques: Generated, Transformation Assay

Journal: Frontiers in Oncology

Article Title: Spatial genomics reveals cholesterol metabolism as a key factor in colorectal cancer immunotherapy resistance

doi: 10.3389/fonc.2025.1549237

Figure Lengend Snippet: Spatial transcriptomic map of ICI-treated MC38 tumors. (A) Spatial distributions of unique molecular identifiers (UMIs) per spot with the base Visium protocol (top) or optimized protocol with collagenase and dispase permeabilization step (bottom). (B) Gene expression capture metrics of the base and optimized spatial transcriptomics protocols. (C) UMAP of unsupervised clusters from 48,636 ST spots across 21 tumor sections from 16 MC38 tumors. Clusters represent tumor and stroma subsets, named based on differentially expressed genes ( Supplementary Table S2 ). (D) Spatial distribution of unsupervised clusters from (C) for a single tissue section (right) and its corresponding hematoxylin & eosin (H&E) stain (left). (E) Dot plot of differentially expressed biomarkers for each unsupervised cluster in the MC38 spatial atlas. Clusters are colored to match the legend above in (C) . Dot size is proportional to the number of spots that express the gene, and color matches the z-scaled gene expression. (F) Treatment and response group composition based on unsupervised clusters present within each tumor. Proportions are first averaged across replicate sections, if applicable, and then treatment group.

Article Snippet: Spatial transcriptomics libraries were generated with Visium for FFPE Gene Expression mouse reagents (10X Genomics #1000337) according to the manufacturer’s protocol (10x Genomics, CG000407|Rev D) with an added permeabilization step during RNA Digestion.

Techniques: Gene Expression, Staining