Journal: Patterns

Article Title: MUSTANG: Multi-sample spatial transcriptomics data analysis with cross-sample transcriptional similarity guidance

doi: 10.1016/j.patter.2024.100986

Figure Lengend Snippet: Analysis of four anterior and posterior sections of mouse brain tissue on sagittal plane with MUSTANG (A) Paired anterior-posterior slices placed on the 10X Visium gene expression slides. (B) Spot-based spatial pie charts of MUSTANG-inferred brain region proportions for all four mouse brain tissue sections. (C) Left: MUSTANG-inferred cell numbers for brain region 5 matching the spatial pattern of the cortex anatomical brain region. Middle: spot-level expression visualization of the known cortex layer marker gene Tbr1. Right: the ISH images of this marker gene from the Allen Brain Atlas. (D) Left: MUSTANG-inferred cell numbers for brain region 3 matching the spatial pattern of the hypothalamus anatomical brain region. Middle: spot-level expression visualization of the known hypothalamus layer marker gene Zcchc12. Right: the ISH images of this marker gene from the Allen Brain Atlas.

Article Snippet: We have evaluated our MUSTANG for analysis of multi-sample ST data from semi-synthetic ST data as well as three real-world ST datasets generated by the 10X Genomics Visium platform.

Techniques: Expressing, Marker

Journal: GEN biotechnology

Article Title: Influence of Alzheimer’s disease related neuropathology on local microenvironment gene expression in the human inferior temporal cortex

doi: 10.1089/genbio.2023.0019

Figure Lengend Snippet: (A) Schematic of experimental design using Visium Spatial Proteogenomics (Visium-SPG) to investigate the impact of Aβ and pTau aggregates on the local microenvironment transcriptome in the post-mortem human brain. Human ITC blocks were acquired from 3 donors with AD and 1 age-matched neurotypical control. Tissue blocks were cryosectioned at 10μm to obtain 2–3 replicates per donor and sections were collected onto individual capture arrays of a Visium spatial gene expression slide, yielding a total of 3 gene expression experiments. The entire slide (4 tissue sections) was stained and scanned using multispectral imaging methods to detect Aβ and pTau immunofluorescence (IF) signals as well as autofluorescence. Following imaging, tissue sections were permeabilized and subjected to on-slide cDNA synthesis after which libraries were generated and sequenced. Transcriptomic data was aligned with the respective IF image data to generate gene expression maps of the local transcriptome with respect to Aβ plaques and pTau elements, including neurofibrillary tangles. (B) High magnification images show Aβ plaques (white triangles) and various neurofibrillary elements such as tangles (white arrowheads), neuropil threads (red arrowheads), and neuritic tau plaques (yellow arrowheads). Lipofuscin (cyan) was identified through spectral unmixing and pixels confounded with this autofluorescent signal were excluded from analysis, scale bar, 20μm. (C) ITC tissue block from Br3880 (left) and corresponding spotplots (right) from the Visium data show gene expression of MOBP and SNAP25, which demarcates the border between gray matter (GM) and white matter (WM), scale bar, 1mm. Color scale indicates spot-level gene expression in logcounts. (D) Image processing and quantification of Aβ and pTau per Visium spot. Aβ and pTau signals were thresholded in their single IF channels for segmentation against autofluorescence background, including lipofuscin. Thresholded Aβ and pTau signals were aligned to the gene expression map of the same tissue section from Br3880 and quantified as the proportion of number of pixels per Visium spot, which is visualized in a spotplot, scale bar, 1mm.

Article Snippet: To better understand molecular signaling in the tissue environment local to pathology in the human brain during late-stage AD, we utilized spatial profiling coupled with multiplex immunofluorescence using the 10x Genomics Visium Spatial Proteogenomics platform (Visium-SPG) to generate a proteomic-based, spatially-resolved, transcriptome-scale map of the human inferior temporal cortex (ITC), a region which displays reduced cortical thickness in AD.

Techniques: Control, Gene Expression, Staining, Imaging, Immunofluorescence, cDNA Synthesis, Generated, Blocking Assay

Journal: GEN biotechnology

Article Title: Influence of Alzheimer’s disease related neuropathology on local microenvironment gene expression in the human inferior temporal cortex

doi: 10.1089/genbio.2023.0019

Figure Lengend Snippet: (A) Flowchart of experimental design and data analysis. Human ITC tissues from 3 original AD donors plus additional male AD donor (Br8549) were subjected to multiplexed staining using RNAscope smFISH combined with immunofluorescence (FISH-IF) to detect genes of interest (GOIs) and Aβ plaques. Images were analyzed with HALO image analysis software to assess spatial relationships between Aβ and cells expressing GOI. The FISH-IF module of HALO was used for image segmentation and quantification of Aβ and GOIs. The proximity analysis module was used to determine a distance between Aβ and cells expressing or not expressing GOIs. The outputs of the two modules were integrated to measure the gene expression of GOIs within a predefined proximity of Aβ at cellular resolution. (B) Schematic describing proximity analysis. An Aβ-associated microenvironment was demarcated by approximating the Visium spot grid-line system in which the center of a single Visium spot is 127.5μm away from its neighboring spot. This distance was further subdivided into 6 evenly spaced intervals, resulting in a total of 7 bins to finely resolve the spatial gene expression gradients of GOIs. The proximity between Aβ and nearby cells expressing and not expressing GOIs was measured and used to classify into the 7 bins for quantifying the average GOI gene expression. (C) RNA-protein co-detection of Aβ and IDI1, C3, NINJ1, PPP3CA reveals the spatial distribution patterns of Aβ (cyan) and GOIs (magenta) at lower (Top, scale bar: 50μm) and higher magnifications (Bottom, scale bar: 12.5μm). Proximity lines indicate the distance between Aβ and nearby cells expressing GOIs (max: 127.5μm). (D) Bar plots show quantification of gene expression levels for GOIs in Figure 3C across 7 consecutive bins representing increased distance from Aβ, as modeled in Figure 3B. Gene expression levels were determined with log2 (X+1) transformation where X represents the counts of puncta in a single cell for a given GOI. Data are mean ± SEM. The first bin was compared to all the rest by default for statistical tests (Kruskal-Wallis test, *p<0.05, &p<0.005, and #p<0.0001). The bracket denotes statistical testing between two specified bins. Violin plots are provided in Figure S18C describing the cellular distribution and numbers counted for each bin.

Article Snippet: To better understand molecular signaling in the tissue environment local to pathology in the human brain during late-stage AD, we utilized spatial profiling coupled with multiplex immunofluorescence using the 10x Genomics Visium Spatial Proteogenomics platform (Visium-SPG) to generate a proteomic-based, spatially-resolved, transcriptome-scale map of the human inferior temporal cortex (ITC), a region which displays reduced cortical thickness in AD.

Techniques: Staining, RNAscope, Immunofluorescence, Software, Expressing, Gene Expression, Transformation Assay

Journal: bioRxiv

Article Title: NicheAgent: LLM-Guided Zero-Shot Niche Identification for Spatial Transcriptomics

doi: 10.64898/2025.12.09.693287

Figure Lengend Snippet: Each panel reports the distribution of scores (mean ± SD) for all evaluated methods on NMI (left), HOM (middle), and COM (right), aggregated over STARmap, Visium, and MERFISH datasets. Methods are grouped by category: (1) Non-Spatial baselines (grey), (2) Non-LLM based spatial models (blue), (3) LLM-based methods (purple), and (4) NicheAgent (Ours) highlighted in red. Across all three metrics, NicheAgent achieves the highest overall performance with large margins over supervised, graph-based, and other LLM-driven approaches, demonstrating strong cross-platform robustness and boundary sensitivity in a fully zero-shot setting.

Article Snippet: Spatial transcriptomics (ST) technologiesincluding MERFISH [ ], STARmap [ ], and 10x Visium enable in situ measurement of gene expression while preserving the native spatial organization of tissues.

Techniques:

Journal: Nature

Article Title: Cells of the adult human heart

doi: 10.1038/s41586-020-2797-4

Figure Lengend Snippet: a – d , Spatial expression (log 2 FC) of CDH5 (pan-EC marker), SEMA3G and GJA5 (arterial EC markers) ( a ), ACKR1 and PLVAP (venous EC markers) ( b ), MYH11 and ACTA2 (pan-SMC markers) ( c ), and JAG1 and NOTCH2 ( d ) on publicly available 10X Visium section of human left ventricle. JAG1 and NOTCH2 are the predicted interaction partners for arterial ECs and SMCs, respectively.

Article Snippet: Data are available in Supplementary Table . e , Spatial mapping of the CD74 – MIF interaction between LYVE1 + MP and FB4 on a publicly available 10X Genomics Visium dataset for left ventricular myocardium.

Techniques: Expressing, Marker

Journal: Nature

Article Title: Cells of the adult human heart

doi: 10.1038/s41586-020-2797-4

Figure Lengend Snippet: a , Visualization of transcriptional signatures from published studies. The score values represent the likelihood of the external transcriptional signature to be present when comparing it against the transcriptional background of a cardiac immune population. Bajpai_2018 = CCR2 - MERTK + tissue-resident macrophages from ref. . Dick_2019 = self-renewing tissue macrophages from ref. . Bian_2020 = yolk sac-derived macrophages from ref. . The complete signature can be found in Supplementary Table . b , Expression (log 2 FC) of LYVE1 , FOLR2 and TIMD4 characteristic of the self-renewing tissue-resident murine macrophages previously described , as well as MERTK as previously described and the TREM2 expression associated to lipid-associated macrophages (LAM) previously described . Complete signatures can be found in Supplementary Table . c , Scaled expression (log 2 FC) of genes differentiating DOCK4 + MP1 from DOCK4 + MP2: IL4R , ITGAM , STAT3 , DOCK1 , HIF1A and RASA2 . d , Predicted cell–cell interactions calculated for 69,295 cardiomyocytes, fibroblasts and myeloid cells from 14 donors ( n = 14) and enriched for ‘extracellular matrix organization’. Mean of combined gene expression of interacting pairs (log 2 FC). Data are available in Supplementary Table . e , Spatial mapping of the CD74 – MIF interaction between LYVE1 + MP and FB4 on a publicly available 10X Genomics Visium dataset for left ventricular myocardium. We identified four spots where we observe co-expression of FN1 , LYVE1 , CD74 and MIF , as predicted from the cell–cell interactions. The bar represents the log 2 FC. f , Confusion matrix for the logistic regression model trained on cardiac immune cells. This model reached an accuracy score of 0.6862, showing a stronger accuracy with lymphoid cells, compared with the myeloid ones.

Article Snippet: Data are available in Supplementary Table . e , Spatial mapping of the CD74 – MIF interaction between LYVE1 + MP and FB4 on a publicly available 10X Genomics Visium dataset for left ventricular myocardium.

Techniques: Derivative Assay, Expressing

Journal: bioRxiv

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

doi: 10.1101/2025.11.07.687304

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: Spatial transcriptomics technologies such as Xenium, MERFISH, and Visium HD enable high-resolution profiling of gene expression while preserving tissue architecture.

Techniques: Expressing, Extraction, Construct, Quantitative Proteomics