stereo-seq mouse brain dataset  (Dropbox Inc)

Journal: bioRxiv

Article Title: STCS: A Platform-Agnostic Framework for Cell-Level Reconstruction in Sequencing-Based Spatial Transcriptomics

doi: 10.64898/2026.02.26.708370

Figure Lengend Snippet: Performance of STCS on high-resolution Stereo-seq mouse brain data. A) Tissue images and segmentations. Left: original H&E image; Center: nuclei segmentation mask; Right: Single-cell Segmented slide by STCS (colored by cell class, defined as higher-level groupings of CellTypist-predicted cell types. B) Spatial continuity metrics. Left: Cell-class coherence across neighborhood size. computed as the proportion of spatially nearest neighbors sharing the same cell-class label at each neighborhood size. Higher values indicate greater spatial consistency of cell-class assignments within local tissue contexts. Middle: CHAOS scores computed at the Leiden cluster level. Right: CHAOS scores computed at the cell-class level. Lower CHAOS scores indicate better spatial organization. The center line represents the median CHAOS score and the box spans the interquartile range. The annotated text are µ ± σ . C) Gene Level metrics. Left: Cell-class label-transfer accuracy to a scRNA-seq reference across varying numbers of highly variable genes (HVGs), assessing how reliably reconstructed spatial cells recover reference-defined transcriptional identities and the robustness of predictions based on different selected features. Right: Distribution of gene-expression cosine similarity between reconstructed cells and scRNA-seq reference profiles across cell classes, quantifying transcriptomic agreement. Cosine similarity was computed between each reconstructed cell’s expression profile and the mean expression profile of its assigned cell class in the scRNA-seq reference. Center lines indicate medians and boxes span the interquartile range. The annotated text are µ ± σ . D) Cell-type composition accuracy. Left: Cell-type richness between methods and a single-cell reference data. Right: Absolute error in estimated cellclass proportions compared to the reference. Lower values indicate closer agreement with the cellular composition of the reference data. The annotated text are µ ± σ and center lines indicate median.

Article Snippet: The Stereo-seq whole mouse brain dataset is available from STOmics ( https://en.stomics.tech/col1241/index.html ).

Techniques: Single Cell, Gene Expression, Expressing

Journal: bioRxiv

Article Title: Library size confounds biology in spatial transcriptomics data

doi: 10.1101/2023.03.15.532733

Figure Lengend Snippet: a-d) Detection density per bin/spot plot for Visium dorsolateral prefrontal cortex (DLPFC), Xenium mouse brain, STOmics mouse brain and CosMx non-small cell lung cancer (NSCLC), reveal tissue structure. e-h) Regions annotated for each bin/spot using the Allen Brain Atlas for the mouse brain and manual annotation based on immunofluorescence markers of CosMx NSCLC. i-l) Number of cells plot against the total detections/library sizes per bin/spot, coloured by the tissue region, showing the region-specific relationship between cells and detections/counts. m-p) Average detections/library sizes per cell for each region, computed as the sum of detections divided by the number of cells for each region, showing that related regions exhibit similar total detections/library sizes per cell.

Article Snippet: With the Visium brain dataset , we could clearly identify the layering of the cortex ( ) while with the Xenium and STOmics mouse brain datasets , we could visually identify the cortex (darker greens in ), white matter (pinks in ) and hippocampus (brighter greens in ).

Techniques: Immunofluorescence