Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.01.07.697982

CELLWHISPER: INFERENCE OF DIRECT CELL-CELL COMMUNICATION FROM SPATIAL TRANSCRIPTOMICS

Kumar, A.·Moctezuma, F. R.·Aggarwal, B.·Zhang, N.·Coskun, A. F.·Sinha, S.
View original source ↗Export metadata ↓

Abstract

AbstractDirect, contact-dependent cell-cell communication shapes tissue physiology and disease. Spatial transcriptomics offers a unique opportunity to infer such communication mechanisms, but the few existing tools for such inference are prone to high error rates due to spatial structure in distribution of cell types and gene expression. We present CellWHISPER, a statistical framework for inferring contact-mediated cell-cell communication, including gap junctions and ligand-receptor mechanisms, from single-cell resolution spatial data. CellWHISPER achieves strict error control via a specialized hypothesis-testing procedure that accounts for the confounding effects of cell- type-specific expression and spatial organization. Its use of an exact closed-form significance score obviates expensive permutation testing and enables scalable analysis of large tissues an extensive signaling compendia. To uncover salient communication patterns, CellWHISPER also includes a latent variable model that distills recurrent patterns into maps of mutual preferences among cell types and signaling genes. Applied to mouse brain STEREO-seq and Xenium datasets, CellWHISPER identifies cell type-specific gap-junction coupling and yields a comprehensive connexin-interaction map. We experimentally confirmed Connexin 43-mediated interfaces among astrocytes, microglia and endothelial cells, which were among the highest- ranked predictions; in contrast, existing approaches fail to prioritize these confirmed interactions. Differential analysis of an Alzheimers disease model versus wild-type identifies preserved astroglial coupling but increased microglia-associated gap-junction communication. In summary, CellWHISPER disentangles direct cell-cell communication from structural proximity, enabling statistically robust and computationally scalable inference from spatial transcriptomics, leading to the discovery of tissue- and disease-associated contact-mediated signaling programs.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kumar, A., Moctezuma, F. R., Aggarwal, B., Zhang, N., Coskun, A. F., Sinha, S.. 2026-01-08. CELLWHISPER: INFERENCE OF DIRECT CELL-CELL COMMUNICATION FROM SPATIAL TRANSCRIPTOMICS. https://doi.org/10.64898/2026.01.07.697982

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗