Journal: NAR Genomics and Bioinformatics

Article Title: SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics

doi: 10.1093/nargab/lqag039

Figure Lengend Snippet: SpNeigh reveals intermediate cell populations near boundaries in mouse brain Xenium data. ( a ) Spatial plots showing different annotation types. Left: Cells colored by clusters with overlaid boundaries of cluster 2. Middle: Manual cluster-level annotations based on brain anatomy. Right: Reference-based single-cell annotations, with selected subtypes merged. CGE: caudal ganglionic eminence; MGE: medial ganglionic eminence. ( b ) Neighborhood analysis of cluster 2. Top: Boundary and ring regions. Bottom: Cells within boundary and ring regions for region 1, with donut plots showing cluster proportions (labels shown for proportions \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\ge$\end{document} 5%). ( c ) Expression of Slc17a7 and Sox10 in cluster 2 cells inside boundaries and surrounding rings. Slc17a7, a marker of cortical excitatory neurons, shows elevated expression in outer cells near the boundary. Sox10 is broadly expressed in oligodendrocytes and remains consistent across both inner and outer cells in cluster 2. ( d ) Boundary 1 of cluster 2 split into discrete edges. ( e ) Spatial weights relative to edge 2 for cortical cells. Black line indicates edge 2. ( f ) Top spatially varying genes identified by RunSpatialDE using weights from edge 2. ( g ) Expression of Ccn2 and Cplx3 near edge 2. Cells include cortical layer 4/5/6 neurons, L6b neurons, astrocytes, and oligodendrocytes. L6b cells are localized along edge 2.

Article Snippet: Mouse brain tiny Xenium dataset: https://www.10xgenomics.com/datasets/fresh-frozen-mouse-brain-for-xenium-explorer-demo-1-standard .

Techniques: Single Cell, Expressing, Marker

Journal: NAR Genomics and Bioinformatics

Article Title: SpNeigh: spatial neighborhood and differential expression analysis for high-resolution spatial transcriptomics

doi: 10.1093/nargab/lqag039

Figure Lengend Snippet: Overview of the SpNeigh workflow. ( a ) Input includes a spatial coordinate data frame ( x, y , cell, cluster) and a normalized expression matrix. Data can originate from platforms such as Xenium, Visium HD, MERFISH, or others. ( b ) Spatial boundary detection and neighborhood extraction. Left: Cluster boundaries are identified after removing spatial outliers based on local k-nearest neighbor density. Right: Ring regions are constructed by buffering outward from the cluster boundaries. Black lines denote cluster boundaries; blue lines indicate outer ring boundaries. ( c ) Spatial weight computation. Cells are assigned weights based on their distance to either the boundary (left) or the centroid (right) of the cluster using inverse distance decay. Weights range from 0 (far) to 1 (close), reflecting proximity. ( d ) Neighborhood composition and interaction analysis. Top: Pie chart showing the proportion of neighboring cell types within the rings. Bottom: Heatmap of spatial interaction scores between focal and neighboring clusters. ( e ) Downstream analyses enabled by SpNeigh. Left: Differential expression analysis between cells of the same cluster in the inner region versus the ring. Middle: Spatial differential expression analysis using smooth functions of distance-based weights. Right: Spatial enrichment analysis quantifying expression bias relative to spatial proximity.

Article Snippet: Mouse brain Visium HD dataset: https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-mouse-brain-fresh-frozen .

Techniques: Expressing, Extraction, Construct, Quantitative Proteomics

Journal: bioRxiv

Article Title: MilliMap: interactive closed-loop analysis for spatial omics

doi: 10.64898/2026.05.01.722104

Figure Lengend Snippet: (a) sST: Visium HD mouse brain, grid expression over H&E. (b) iST: Xenium human breast cancer; DAPI/IF morphology (left) and cluster-colored centroids (right). (c) SP: CODEX human intestine with protein-defined clusters. (d) scRNA-seq: honey bee brain, 3D UMAP. (e, f) Lasso-defined inner (e) and large (f) Kenyon cell (KC) ROIs (left); linked embedding confirms molecular coherence (right). (g) Differential expression between inner and large KCs (left: Dop3 -colored spatial view; right: DEG heatmap). (h) Spatially varying gene CHIT1 expression: whole tissue (left), ROI1 (middle), ROI2 (right). (i) Same layout as h, CD83 . (j) Spatially resolved ROI1 cell-type clusters (left) and TAMs (cluster 11) sub-clusters (right). (k) Spatially resolved ROI2 cell-type clusters. (l) Cell type composition of ROI1 and ROI2. (m) Volcano of ROI1-core-specific TAMs (11.1) vs other TAMs (11.0 and 11.2).

Article Snippet: The Visium HD Mouse Brain dataset (FFPE; C57BL/6; Space Ranger v3.0.0) is available from 10x Genomics at https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-brain-he , licensed under CC BY 4.0.

Techniques: Expressing, Quantitative Proteomics

Journal: Briefings in Bioinformatics

Article Title: Cross-modality representation and multi-sample integration of spatially resolved omics data

doi: 10.1093/bib/bbag214

Figure Lengend Snippet: PRESENT facilitates accurate spatial domain identification in spatial RNA-ADT data. (a) spatial visualization of the 10x Genomics Visium RNA-protein human lymph node sample colored by ground truth domain labels. (b) Quantitative comparison of spatial domain identification performance between PRESENT and other baseline methods, shown as a bar plot for the human lymph node dataset. (c) Quantitative comparison between PRESENT utilizing both RNA and ADT data (RNA & ADT) and PRESENT using only RNA (RNA-only) or ADT data (ADT-only), shown as a radar plot in the human lymph node dataset. (d) UMAP visualization of latent embeddings from different methods, colored by ground truth domain labels in the human lymph node dataset. (e) UMAP visualization of latent embeddings, colored by cluster labels in the human lymph node dataset. (f) Spatial visualization of spots colored by cluster labels in the human lymph node dataset. The cluster labels in e and f were derived from latent embeddings of different methods using the Leiden algorithm. (g) Histology image of the SPOTS mouse spleen dataset and spatial visualization of spots colored by cluster labels in the SPOTS mouse spleen dataset. The cluster labels were derived from latent embeddings of PRESENT using Leiden algorithm. (h) Differentially expressed proteins of each spatial domain through one-versus-all Wilcoxon rank-sum test, shown as a dot plot. (i) DEGs of all the spatial domains through one-versus-all Wilcoxon rank-sum test, shown as a dot plot. (j) Volcano plot showing the DEGs of Mac1-enriched domain and Mac2-enriched domain through Mac1-versus-Mac2 Wilcoxon rank-sum test, where the x axis denotes the log(fold-change) (log(FC)), while the y axis denotes the significance measured by -log10(false discovery rate) (−log10(FDR)). The vertical dashed line represents the threshold for log(FC)= \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} $\pm$\end{document} 0.2, while the horizontal dashed line denotes the threshold for -log10(FDR) = 0.05. (k) Chord plot demonstrating the linkage of DEGs in the Mac1-enriched domain and the corresponding enriched pathways. The left semicircle represents DEGs while the right semicircle denotes the enriched biological processes. Bar plot is employed to demonstrate the significance of each pathway (x axis, −log10(FDR)). (l) The linkage of DEGs in the Mac2-enriched domain and corresponding pathways as well as the significance of each enriched pathway.

Article Snippet: The 10x Visium mouse brain datasets, including a sagittal anterior section and a sagittal posterior section, are accessible at the 10x Genomics websites https://www.10xgenomics.com/datasets/mouse-brain-serial-section-2-sagittal-anterior-1-standard and https://www.10xgenomics.com/datasets/mouse-brain-serial-section-2-sagittal-posterior-1-standard , respectively.

Techniques: Comparison, Derivative Assay

Journal: Briefings in Bioinformatics

Article Title: Cross-modality representation and multi-sample integration of spatially resolved omics data

doi: 10.1093/bib/bbag214

Figure Lengend Snippet: PRESENT integrates single-omics samples of multiple developmental stages or dissected areas. (a) The quantitative evaluation of different integration methods on the three spatial ATAC mouse embryo samples using 14 metrics divided into two categories, namely batch effect removal and biological variance conservation. The category scores of these two aspects were calculated by averaging the metrics within each category. An overall score for each integration method was computed using a 40/60 weighted mean of the category scores for batch effect removal and biological variance conservation. (b) The spatial visualization of spots across the three spatial ATAC mouse embryo samples colored by ground truth spatial domains. (c) The spatial visualization of spots across the three spatial ATAC mouse embryo samples colored by the spatial clusters identified based on different integration methods. The first, second and third row of b and c denotes the samples from E12.5, E13.5 and E15.5 stages, respectively. (d) The anatomic annotation of the sagittal region in P56 mouse brain provided by Allen Reference Atlas . (e) The joint spatial clustering results based on the latent embeddings obtained by STAligner on the two horizontal mouse brain sagittal samples generated by the 10x Genomics Visium platform. (f) The joint spatial clustering results based on the latent embeddings obtained by GraphST on the two horizontal mouse brain sagittal samples generated by the 10x Genomics Visium platform. (g) The joint spatial clustering results based on the latent embeddings obtained by PRESENT on the two horizontal mouse brain sagittal samples generated by the 10x Genomics Visium platform.

Article Snippet: The 10x Visium mouse brain datasets, including a sagittal anterior section and a sagittal posterior section, are accessible at the 10x Genomics websites https://www.10xgenomics.com/datasets/mouse-brain-serial-section-2-sagittal-anterior-1-standard and https://www.10xgenomics.com/datasets/mouse-brain-serial-section-2-sagittal-posterior-1-standard , respectively.

Techniques: Generated

Journal: Cell Genomics

Article Title: Robust integration and annotation of single-cell and spatial omics data using interpretable gene programs

doi: 10.1016/j.xgen.2025.101105

Figure Lengend Snippet: Spatial deconvolution and annotation performance of SSpMosaic across different tissue types (A) Anatomical structure of MOB layers from inner to outer: GCL, MCL, GL, and ONL. (B) Main cell-type inference by six deconvolution methods at each spot in MOB10 dataset. (C) Performance comparison of six deconvolution methods across 12 MOB slices using Accuracy, F1 scores, and Recall metrics. (D) Schematic illustration of mouse hippocampal organization and its three main cell types. (E) Metaprogram scores of SSpMosaic for three major hippocampal cell types in Visium HD mouse brain sections. (F) Expression levels of cell-type-specific markers (CA1: Wfs1 ; CA3: Chgb ; DG: Nedd4l ) for three main hippocampal cell types in Visium HD mouse brain data. (G) Main cell-type inference by six deconvolution methods at each spot in the hippocampal region of Visium HD mouse brain data. (H) Boxplots showing Pearson correlations between inference scores and corresponding marker expression levels for the three main hippocampal cell types across different methods in Visium HD mouse brain slices. (I) Cell-type inference by SSpMosaic in CosMx human NSCLC data. (J) Spatial expression patterns of markers for four major cell types in NSCLC data. (K) Spatial domains identified through clustering based on SSpMosaic-inferred cell types in NSCLC data. (L) Stacked bar plot showing the proportion of different cell types within each spatial domain in NSCLC data. (M) Right: spatial domain annotations; left: intercellular communication networks between different spatial regions in NSCLC data. Line thickness represents communication strength. (N) Bubble plot showing signaling pathway communication probabilities between different spatial regions.

Article Snippet: Mouse coronal brain spatial dataset , 10X Genomics , https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-mouse-brain-he.

Techniques: Comparison, Expressing, Marker