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Results for “spatial transcriptomics data”

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PubMed2004

A gene atlas of the mouse and human protein-encoding transcriptomes.

The tissue-specific pattern of mRNA expression can indicate important clues about gene function. High-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a genome scale. Toward this end, we have designed custom arrays that interrogate the expression of the vast majority of protein-encoding human and mouse genes and have used them to profile a panel of 79 human and 61 mouse tissues. The resulting data set provides the expression patterns for thousands of predicted genes, as well as known and poorly characterized genes, from mice and humans. We have explored this data set for global trends in gene expression, evaluated commonly used lines of evidence in gene prediction methodologies, and investigated patterns indicative of chromosomal organization of transcription. We describe hundreds of regions of correlated transcription and show that some are subject to both tissue and parental allele-specific expression, suggesting a link between spatial expression and imprinting.

Animals↗

PubMed2026

ARCADIA reveals spatially dependent transcriptional programs through integration of scRNA-seq and spatial proteomics.

MOTIVATION: Cellular states are strongly influenced by spatial context, but single-cell RNA sequencing (scRNA-seq) loses information about local tissue organization, while spatial proteomic assays capture limited marker panels that constrain transcriptomic inference. Integrating these modalities can elucidate how spatial niches shape transcriptional programs, yet existing approaches depend on either feature-level correspondence such as gene-protein linkage or cell-level barcode pairing, which is often unavailable. RESULTS: We present ARCADIA (ARchetype-based Clustering and Alignment with Dual Integrative Autoencoders), a generative framework for cross-modal integration that operates without cell barcode pairing and does not assume direct feature-to-feature correspondence. ARCADIA identifies modality-specific archetypes, that is, convex combinations of cells representing extreme phenotypic states, and aligns these anchors across modalities by minimizing the discrepancy between their cell-type composition profiles. The aligned archetypes define a shared coordinate system that anchors dual variational autoencoders (VAEs) trained with cross-modal geometric regularization, preserving archetype structure and spatial neighborhood information while enabling bidirectional translation between modalities. On semi-synthetic CITE-seq data, ARCADIA outperforms existing weak-linkage methods. Applied to independent human tonsil scRNA-seq and CODEX data, ARCADIA reconstructs known tissue architecture and reveals spatially dependent transcriptional programs linking B-cell maturation and T-cell activation or exhaustion to microenvironmental niches. AVAILABILITY AND IMPLEMENTATION: Source code is accessible at https://github.com/azizilab/ARCADIA_public. Reproducibility scripts and data are available at https://github.com/azizilab/arcadia_reproducibility.

Proteomics↗

PubMed2026

Reimagining research papers as interactive and reliable AI agents.

Here we introduce Paper2Agent, an automated framework that converts research papers into artificial intelligence (AI) agents. Paper2Agent transforms research output from passive artefacts into active systems that accelerate use and discovery. Conventional research papers require readers to understand and adapt the paper's code, data and methods to their work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by converting a paper into an AI agent that functions as a virtual corresponding author, exposing its manuscript, supplementary materials, datasets, code and workflows as active, agent-native knowledge rather than static text. It analyses the paper and codebase using multiple agents to construct a model context protocol (MCP) server, then generates and runs tests to refine and increase robustness of the MCP. These paper MCPs can be connected to a chat agent (such as Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the paper. We demonstrate Paper2Agent's effectiveness through case studies. Paper2Agent created an agent that leveraged AlphaGenome1 to interpret genomic variants and agents based on Scanpy2 and TISSUE (transcript imputation with spatial single-cell uncertainty estimation)3 to conduct single-cell and spatial transcriptomics analyses. We validate that these agents reproduce the results of the original papers and carry out novel user queries. Paper2Agent created multiple agents that collaborate to prioritize a causal gene for psoriasis. By turning static papers into interactive AI agents, Paper2Agent introduces a paradigm for knowledge dissemination and a collaborative ecosystem of AI co-scientists.

Journal Article↗

PubMed2005

Antiquity of microRNAs and their targets in land plants.

MicroRNAs (miRNAs) affect the morphology of flowering plants by the posttranscriptional regulation of genes involved in critical developmental events. Understanding the spatial and temporal dynamics of miRNA activity during development is therefore central for understanding miRNA functions. We describe a microarray suitable for detection of plant miRNAs. Profiling of Arabidopsis thaliana miRNAs during normal development extends previous expression analyses, highlighting differential expression of miRNA families within specific organs and tissue types. Comparison of our miRNA expression data with existing mRNA microarray data provided a global intersection of plant miRNA and mRNA expression profiles and revealed that tissues in which a given miRNA is highly expressed are unlikely to also show high expression of the corresponding targets. Expression profiling was also used in a phylogenetic survey to test the depth of plant miRNA conservation. Of the 23 families of miRNAs tested, expression of 11 was detected in a gymnosperm and eight in a fern, directly demonstrating that many plant miRNAs have remained essentially unchanged since before the emergence of flowering plants. We also describe an empirical strategy for detecting miRNA target genes from unsequenced transcriptomes and show that targets in nonflowering plants as deeply branching as ferns and mosses are homologous to the targets in Arabidopsis. Therefore, several individual miRNA regulatory circuits have ancient origins and have remained intact throughout the evolution and diversification of plants.

Arabidopsis↗

PubMed2026

Integrated analysis of amide proton transfer weighted MRI and proteomics uncovers altered protein dynamics in glioblastoma.

PURPOSE: Elevated amide proton transfer-weighted (APTw) MRI signals in glioblastoma (GBM) are often linked to increased intracellular mobile proteins, but the associated molecular patterns in human tissue remain unclear. We examined the relationship between regional APTw features and cellular protein composition and profiled proteomic differences between tumor and peritumoral tissue. METHODS: In this single-center prospective study, preoperative MRI data were integrated with intraoperative neuronavigation for 12 image-guided tissue samples (8 tumor and 4 peritumoral). Total, cytoplasmic, and nuclear proteins were quantified using bicinchoninic acid (BCA) assay. Data-independent acquisition (DIA) proteomics identified exploratory differentially expressed proteins (DEPs), followed by functional enrichment and protein-protein interaction (PPI) network analyses. Transcript-level expression patterns and survival associations were queried in The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets to provide indirect external clinical context. RESULTS: Tumor regions showed higher APTw signals than peritumoral regions (p < 0.001) and increased cytoplasmic protein concentration (p < 0.05), without a corresponding increase in total or nuclear protein levels. DIA identified 654 DEPs. Further analysis highlighted 36 higher-significance DEPs, and prioritized 12 hub proteins in the PPI network. In public transcriptomic datasets, ERBB2, RUNX1, and SHC1 showed higher expression in GBM and were associated with poorer overall survival. CONCLUSION: These findings suggest that elevated APTw signal in GBM may be associated with increased cytoplasmic protein content and distinct proteomic alterations. This imaging-proteomic framework provides exploratory regional context for future mechanistic and follow-up studies, but larger, spatially matched and independently validated cohorts are required to confirm the molecular contributors to APTw contrast.

Humans↗

PubMed2004

Transcriptome profiling in root nodules and arbuscular mycorrhiza identifies a collection of novel genes induced during Medicago truncatula root endosymbioses.

Transcriptome profiling based on cDNA array hybridizations and in silico screening was used to identify Medicago truncatula genes induced in both root nodules and arbuscular mycorrhiza (AM). By array hybridizations, we detected several hundred genes that were upregulated in the root nodule and the AM symbiosis, respectively, with a total of 75 genes being induced during both interactions. The second approach based on in silico data mining yielded several hundred additional candidate genes with a predicted symbiosis-enhanced expression. A subset of the genes identified by either expression profiling tool was subjected to quantitative real-time reverse-transcription polymerase chain reaction for a verification of their symbiosis-induced expression. That way, induction in root nodules and AM was confirmed for 26 genes, most of them being reported as symbiosis-induced for the first time. In addition to delivering a number of novel symbiosis-induced genes, our approach identified several genes that were induced in only one of the two root endosymbioses. The spatial expression patterns of two symbiosis-induced genes encoding an annexin and a beta-tubulin were characterized in transgenic roots using promoter-reporter gene fusions.

Annexins↗

PubMed2026

TFPI-high myofibroblast states and a meta-program-related five-gene prognostic signature in breast cancer.

Intratumoral heterogeneity and tumor-microenvironment interactions limit prognostic stratification in breast cancer, but the prognostic relevance and cellular context of recurrent transcriptional meta-programs remain unclear. We aimed to derive a meta-program-related prognostic signature and characterize its component transcripts at single-cell resolution. Six paired institutional tumors and adjacent non-tumor tissues served as a proof-of-concept comparison. Univariable Cox screening and least absolute shrinkage and selection operator Cox regression were used to derive a five-gene score from a prespecified meta-program-related candidate set in The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) training cohort; the score was tested internally and assessed in GSE20685 using fixed coefficients and cohort-specific median cutoffs. GSE161529 single-cell transcriptomic data were used to map signature transcripts across 136,526 quality-controlled cells, while donor-aware pseudobulk analysis compared upper- and lower-quartile TFPI expression states in annotated myofibroblasts. The score comprised TCN1, FOXJ1, PIGR, SLAIN1, and TFPI and was associated with overall survival in the training, testing, and external cohorts, with concordance indices of 0.782, 0.756, and 0.721, respectively. TFPI transcripts were detected across endothelial, fibroblast, and myofibroblast compartments. TFPI-high myofibroblasts showed transcriptional enrichment of extracellular matrix and collagen fibril organization, transforming growth factor beta signaling, epithelial-mesenchymal transition, and myogenesis, together with lower oxidative phosphorylation and fatty acid metabolism programs. In bulk TCGA-BRCA tissue, TFPI expression correlated positively with stromal (r = 0.48), immune (r = 0.25), and composite microenvironment scores (r = 0.40; all p < 0.001). These findings identify a hypothesis-generating five-gene bulk-tissue prognostic signature and an expression-associated TFPI-high myofibroblast state but do not establish a discrete lineage, the cellular source of bulk TFPI, a TFPI-dependent mechanism, or clinical utility. Independent prospective cohorts, spatial and protein-level validation, and functional perturbation studies are required.

Journal Article↗

PubMed2024

Spatiotemporal single-cell roadmap of human skin wound healing.

Wound healing is vital for human health, yet the details of cellular dynamics and coordination in human wound repair remain largely unexplored. To address this, we conducted single-cell multi-omics analyses on human skin wound tissues through inflammation, proliferation, and remodeling phases of wound repair from the same individuals, monitoring the cellular and molecular dynamics of human skin wound healing at an unprecedented spatiotemporal resolution. This singular roadmap reveals the cellular architecture of the wound margin and identifies FOSL1 as a critical driver of re-epithelialization. It shows that pro-inflammatory macrophages and fibroblasts sequentially support keratinocyte migration like a relay race across different healing stages. Comparison with single-cell data from venous and diabetic foot ulcers uncovers a link between failed keratinocyte migration and impaired inflammatory response in chronic wounds. Additionally, comparing human and mouse acute wound transcriptomes underscores the indispensable value of this roadmap in bridging basic research with clinical innovations.

Humans↗

PubMed2026

Shared genetic architecture of smoking dependence and Crohn's disease: A cross-trait analysis of GWAS summary statistics.

INTRODUCTION: Smoking dependence (SD) and Crohn's disease (CD) are epidemiologically associated, but whether this relationship reflects shared genetic susceptibility remains unclear. METHODS: We conducted a cross-trait genetic analysis of SD and CD using publicly available genome-wide association study (GWAS) summary statistics from European-ancestry populations. Genome-wide genetic correlation was estimated using linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL). Pleiotropic variants were identified using PLACO and mapped to genomic loci using FUMA. Regional signal sharing was assessed by Bayesian colocalization. Functional analyses included stratified LDSC, Multi-marker Analysis of GenoMic Annotation (MAGMA), GTEx tissue analysis, and Metascape. Expression-linked candidate genes were prioritized using expression quantitative trait locus (eQTL)-based summary-data-based Mendelian randomization (SMR) with heterogeneity in dependent instruments (HEIDI) testing. Genetically informed spatial mapping of cells for complex traits (gsMap) was used for spatial mapping. RESULTS: SD and CD showed positive genetic correlation by LDSC (rg=0.2090, p=0.0008) and HDL (rg=0.3817, p=0.00106). PLACO identified 81 genome-wide significant pleiotropic SNPs, which were mapped by FUMA to three loci at 1p31.3, 5p13.1, and 12q12, represented by rs11209031, rs1395152, and rs17467116, respectively. MAGMA identified 22 FDR-significant genes, four of which remained Bonferroni significant: LRRK2, TNFRSF6B, ZGPAT, and RP4-583P15.15. Cross-trait tissue analysis showed significant enrichment of the shared genetic signal in whole blood and small intestine, while gene-set analysis highlighted inflammatory response (pbon=1.86×10-5) and T-helper 17 cell differentiation (pbon=7.37×10-4). SMR/HEIDI analysis further prioritized RPS6KB1 as a shared expression-linked candidate. Spatial mapping revealed a prominent signal in the embryonic gastrointestinal tract and gene-specific regional patterns involving LRRK2 and SLC2A13 in the adult mouse brain. CONCLUSIONS: SD and CD showed measurable shared genetic susceptibility, with convergent evidence from pleiotropic loci, immune-inflammatory pathway enrichment, tissue-level associations, and spatial transcriptomic mapping.

Crohn's disease↗

PubMed2025

Integrative genomics elucidates the evolutionary, temporal, and developmental origins of a hydrocephalus risk gene.

INTRODUCTION: A prior integrative, multi-omics human genetics and functional genomics study identified maelstrom (MAEL), a gene involved in regulation of DNA transposon activity and genome structure, as a transcriptome-wide predictor of hydrocephalus (HC) in the brain cortex. Here we expand on this discovery and further characterize the evolutionary origin and expression of MAEL across developmental timescales and cell-lineages in the neonatal human brain towards a mechanistic understanding how variation in MAEL expression may cause HC. OBJECTIVE: To characterize the evolutionary, temporal, developmental, and lineages of MAEL expression in HC and the developing human brain. METHODS: Ensembl was used to delineate the evolution and taxonomy of MAEL across species. Analysis of single-cell RNA sequencing (scRNA-seq) of 49 brain regions across pre- and post-natal timescales from the Developing Human Brain Atlas (Allen Institute) identified temporal and spatial MAEL expression patterns. We quantified MAEL expression in primary cortical brain tissue obtained during the surgical treatment of HC. RESULTS: We performed taxonomic gene-mapping to define the evolutionary origin of MAEL to assess suitability for mechanistic characterization in vitro and in vivo across species. We find that MAEL is among the top 0.01% human-specific genes and < 50% sequence homology among commonly used model organisms with highly divergent functions, necessitating mechanistic validation in human tissue. scRNA-seq of the non-disease prenatal human brain identified MAEL expression enriched in cortical excitatory neurons, which was recapitulated in primary HC brain tissue obtained during surgery. Finally, using scRNA-seq of primary HC brain tissue, we functionally validated reduced MAEL expression, consistent with a prior human TWAS analysis. CONCLUSIONS: We identify the evolutionary, temporal, and developmental expression pattern of MAEL in the neonatal human brain. We also provide direct evidence for reduced MAEL expression in human HC brain tissue. These data, at least in part, implicate reduced MAEL expression underlying human HC across etiologies.

Journal Article↗

PubMed2003

A novel family in Medicago truncatula consisting of more than 300 nodule-specific genes coding for small, secreted polypeptides with conserved cysteine motifs.

Transcriptome analysis of Medicago truncatula nodules has led to the discovery of a gene family named NCR (nodule-specific cysteine rich) with more than 300 members. The encoded polypeptides were short (60-90 amino acids), carried a conserved signal peptide, and, except for a conserved cysteine motif, displayed otherwise extensive sequence divergence. Family members were found in pea (Pisum sativum), broad bean (Vicia faba), white clover (Trifolium repens), and Galega orientalis but not in other plants, including other legumes, suggesting that the family might be specific for galegoid legumes forming indeterminate nodules. Gene expression of all family members was restricted to nodules except for two, also expressed in mycorrhizal roots. NCR genes exhibited distinct temporal and spatial expression patterns in nodules and, thus, were coupled to different stages of development. The signal peptide targeted the polypeptides in the secretory pathway, as shown by green fluorescent protein fusions expressed in onion (Allium cepa) epidermal cells. Coregulation of certain NCR genes with genes coding for a potentially secreted calmodulin-like protein and for a signal peptide peptidase suggests a concerted action in nodule development. Potential functions of the NCR polypeptides in cell-to-cell signaling and creation of a defense system are discussed.

Amino Acid Sequence↗

PubMed2026

Spatially Contextualized Integrative Genomics Highlights Neuronal and Glial Regulatory Programs in Low Back Pain.

PURPOSE: Low back pain (LBP) is a heterogeneous pain condition with a measurable genetic contribution, but the genes, brain cell types, and spatial tissue contexts through which inherited risk is expressed remain unclear. We aimed to define cell-type-specific and spatially contextualized genetic mechanisms underlying LBP. METHODS: FinnGen R12 LBP GWAS summary statistics (42,521 cases and 353,224 controls) were integrated with brain single-nuclei eQTL data across eight major brain cell classes. We evaluated genome-wide polygenic signal using LDSC, prioritized genes using MAGMA and PoPS, and performed brain cell-type-specific eQTL-anchored Mendelian randomization, primarily based on single-instrument Wald ratio estimates, followed by Bayesian colocalization. Spatial genetic mapping was conducted using gsMap in an E16.5 murine embryonic atlas and two adult human lumbar spinal cord Visium sections. Selected candidates were assessed by RT-qPCR in neuronal-like and astroglial-like inflammatory cell models. RESULTS: LDSC supported interpretable polygenic signal for LBP. MAGMA and PoPS showed partial gene-level convergence, with TCF4 and TMEFF2 supported by both approaches. Across 1641 tested gene-cell type exposures, significant eQTL-anchored MR associations were concentrated in excitatory neurons, oligodendrocytes, inhibitory neurons, and astrocytes. Integrated eQTL-anchored MR, colocalization, and gene-prioritization evidence highlighted CLEC18A, QPRT, and GMPPB as higher-priority non-MHC candidates with moderate, but not strong, colocalization support. gsMap localized LBP-associated enrichment to neuroaxis-related embryonic regions, including brain, spinal cord, sympathetic nerve, and dorsal root ganglion, and to neuronal-like niches in adult lumbar spinal cord. RT-qPCR showed model-dependent expression changes, with QPRT and LGI4 preferentially responsive in neuronal-like SH-SY5Y cells and GMPPB and DPYSL5 responsive in astroglial-like U251 cells. CONCLUSION: These findings support neuronal and glial regulatory programs as plausible contributors to LBP genetic susceptibility and highlight CLEC18A, QPRT, and GMPPB as higher-priority non-MHC candidates with moderate colocalization support. The results provide a spatially contextualized framework for candidate prioritization in LBP, while emphasizing the need for larger cell-type-specific eQTL resources and functional validation before therapeutic or mechanistic conclusions can be drawn.

Mendelian randomization↗