mouse hippocampus 10x visium hd data set  (10X Genomics)

Journal: bioRxiv

Article Title: Single-cell spatial multiomics identifies POSTN + CAFs mediating chemoradiotherapy resistance in rectal cancer

doi: 10.64898/2026.04.30.721803

Figure Lengend Snippet: a, Schematic illustration of the neoadjuvant chemoradiotherapy (CRT) schedule and timing of tumor sampling in the study cohort (N = 24). The CRT regimen consisted of capecitabine in combination with 50.4 Gy of radiation delivered in 28 fractions. “Pre” refers to the time point prior to CRT, “JustAfter” indicates within 2 weeks after the completion of CRT, and “Resection” represents three months post-CRT. Biopsy samples were collected from all 24 patients at the Pre time point and from 4 patients at the JustAfter time point, and surgical samples were obtained from all 24 patients at the Resection time point. Among the 24 patients, 8 achieved a pathological complete response (pCR), 8 achieved a major pathological response (MPR), and 8 were classified as no response (NR). b, Each sample obtained at the designated time points was serially sectioned into four parts (Sections 1–4). Section 1 was used for Xenium analysis, PhenoCycler analysis, and hematoxylin and eosin (H&E) staining. Section 2 was used for Xenium analysis and H&E staining. Section 3 was used for Visium HD spatial transcriptomics on adjacent serial sections. Section 4 was used for whole-exome sequencing. c, Summary matrix showing patient characteristics (age, sex, recurrence status) and the availability of each modality. The purple circles represent samples analyzed at both the Pre and Resection time points, whereas the dark circles indicate samples analyzed at all three time points: Pre, JustAfter, and Resection.

Article Snippet: In addition, we generated transcriptome-wide spatial gene expression profiles using Visium HD (10x Genomics) on adjacent serial sections as a complementary dataset for future integrative analyses; Visium HD data were not used for the primary analyses presented in this study.

Techniques: Sampling, Staining, Spatial Transcriptomics, Sequencing

Journal: bioRxiv

Article Title: Probabilistic coupling of cellular and microenvironmental heterogeneity by masked self-supervised learning

doi: 10.64898/2026.04.21.719876

Figure Lengend Snippet: (a) Spatial visualization comparing k-means clustering of niche representations from methods (Mievformer, NicheCompass, CellCharter, Banksy, GraphST, STAGATE, ENVI, Spatial, PCA). cell type annotations (left). (b) Heatmap showing the Pearson correlation between eight evaluation metrics (CAS, SCAS, NEA, DREC, NCB-GCS, NCB-CAS, NCB-NASW, NCB-MLAMI) and ground-truth Niche ARI across varying simulation parameters. (c) Horizontal bar graphs showing dual recovery (DREC) metrics across methods and five datasets: Xenium GBM, Xenium Lung, Xenium Pancreas, Mouse Brain Stereo-seq, and Mouse Brain Visium HD.

Article Snippet: The Visium HD Mouse Brain data were obtained from the 10x Genomics public dataset repository ( https://www.10xgenomics.com/datasets ).

Techniques: