Alexander Monell

Spatial transcriptomics measures gene expression in tissue sections. However, interpretation requires anatomical maps that link gene expression and cellular composition to tissue structure. Annotating entire sections often requires extensive manual labor. We developed SpatialTRACE (Tissue Region and Axis Coordinate Estimation), consisting of graph- and image-based models, to extend annotations of a few structures into tissue-wide maps of anatomical axes and regions. SpatialTRACE-Graph combines gene-expression profiles with spatial neighborhoods to predict anatomical coordinates or region membership throughout spatial transcriptomic datasets. In held-out mouse small-intestine sections, it predicted crypt-villus and epithelial-distance axis coordinates using as few as 10 annotated training villi and identified Peyer’s patches from region annotations. SpatialTRACE-Image predicts the same anatomical axis coordinates and regions across entire tissue images from DAPI alone. This multiscale vision transformer learns from coordinate and region predictions generated by SpatialTRACE-Graph. We applied SpatialTRACE-Image to immunofluorescence images to map the anatomical distribution of antigen-specific P14 CD8 T cells responding to acute systemic infection with lymphocytic choriomeningitis virus (LCMV) Armstrong in the small intestine. Compared with the vehicle-treated section, a section treated with a retinoic acid receptor inhibitor contained fewer P14 CD8 T cells overall, with a smaller fraction in the upper-villus lamina propria and a relative enrichment in the muscularis. Overall, the SpatialTRACE models reduce repeated manual annotation and provide a route to extend anatomical maps learned from spatial transcriptomics to DAPI-containing microscopy data.

Summary: Hybridization-based spatial transcriptomics technologies have advanced our ability to map cellular and subcellular organization in complex tissues. However, existing methods remain constrained in gene coverage, multimodal compatibility, and scalability. Here, we present MERFISH+, an enhanced version of Multiplexed Error-Robust Fluorescence in Situ Hybridization (MERFISH), which integrates chemical probe anchoring in protective hydrogels with high-throughput microfluidics and microscopy. This optimized design supports robust and repeated hybridization cycles across an entire centimeter-scale tissue sample. MERFISH+ allowed to simultaneously quantify over 1,800 genes and resolve the 3D organization of chromatin loci and their associated epigenomic marks in developing human hearts. Using a generative integration framework for spatial multimodal data (Spateo-VI), we harmonized these MERFISH+ transcriptomic and chromatin data to reconstruct a 3D spatially-resolved multi-omic atlas of the developing human heart at subcellular resolution capturing 3.1 million cells across 34 distinct populations. This 3D atlas provides a holistic view of an entire organ enabling the characterization of 3D cellular neighborhoods and transcriptional gradients of substructures such as the descending arteries. Thus, MERFISH+ offers a robust, large-format platform for spatial multi-omics that enables high resolution mapping of gene expression at subcellular resolution and the characterization of cellular organization within 3D organs. One Sentence Summary: MERFISH+ is an spatial multi-omics platform that integrates hydrogel-based probe anchoring, automated high-throughput microfluidics, and large-format multimodal data production to enable comprehensive, subcellular resolution mapping of gene expression and chromatin organization across millions of cells within complex developing human organs. Highlights: MERFISH+ expands MERFISH capabilities to measure >1,800 genes and at whole-organ 3D imaging scale Combines chemical probe anchoring with high-throughput volumetric microscopy and microfluidics Generates a 3D molecular atlas of a developing human heart with > 3.1 million cells at subcellular resolution Introduces Spateo-VI, a novel generative framework integrating 3D multimodal datasets

Tissue-resident memory T cells (TRM) provide frontline protection against pathogens and emerging malignancies. Tumor-infiltrating lymphocytes (TIL) with TRM features are associated with improved clinical outcomes. However, the cellular interactions that program TRM differentiation and function are not well understood. Using murine genetic models and targeted spatial transcriptomics, we found that the CD8+ T cell–derived chemokine XCL1 is critical for TRM formation and conventional DC1 (cDC1) supported the positioning of intestinal CD8+ T cells during acute viral infection. In tumors, enforced Xcl1 expression by antigen-specific CD8+ T cells promoted intratumoral cDC1 accumulation and T cell persistence, leading to improved overall survival. Notably, analysis of human TIL and TRM revealed conserved expression of XCL1 and XCL2. Thus, we have shown that the XCL1–XCR1 axis plays a non-cell autonomous role in guiding intestinal CD8+ TRM spatial differentiation and tumor control.

Tissue-resident memory CD8 T (T_RM) cells provide protection from infection at barrier sites. In the small intestine, T_RM cells are found in at least two distinct subpopulations: one with higher expression of effector molecules and another with greater memory potential¹. However, the origins of this diversity remain unknown. Here we proposed that distinct tissue niches drive the phenotypic heterogeneity of T_RM cells. To test this, we leveraged spatial transcriptomics of human samples, a mouse model of acute systemic viral infection and a newly established strategy for pooled optically encoded gene perturbations to profile the locations, interactions and transcriptomes of pathogen-specific T_RM cell differentiation at single-transcript resolution. We developed computational approaches to capture cellular locations along three anatomical axes of the small intestine and to visualize the spatiotemporal distribution of cell types and gene expression. Our study reveals that the regionalized signalling of the intestinal architecture supports two distinct T_RM cell states: differentiated T_RM cells and progenitor-like T_RM cells, located in the upper villus and lower villus, respectively. This diversity is mediated by distinct ligand–receptor activities, cytokine gradients and specialized cellular contacts. Blocking TGFβ or CXCL9 and CXCL10 sensing by antigen-specific CD8 T cells revealed a model consistent with anatomically delineated, early fate specification. Ultimately, our framework for the study of tissue immune networks reveals that T cell location and functional state are fundamentally intertwined.

Plants lack specialized and mobile immune cells. Consequently, any cell type that encounters pathogens must mount immune responses and communicate with surrounding cells for successful defence. However, the diversity, spatial organization and function of cellular immune states in pathogen-infected plants are poorly understood¹. Here we infect Arabidopsis thaliana leaves with bacterial pathogens that trigger or supress immune responses and integrate time-resolved single-cell transcriptomic, epigenomic and spatial transcriptomic data to identify cell states. We describe cell-state-specific gene-regulatory logic that involves transcription factors, putative cis-regulatory elements and target genes associated with disease and immunity. We show that a rare cell population emerges at the nexus of immune-active hotspots, which we designate as primary immune responder (PRIMER) cells. PRIMER cells have non-canonical immune signatures, exemplified by the expression and genome accessibility of a previously uncharacterized transcription factor, GT-3A, which contributes to plant immunity against bacterial pathogens. PRIMER cells are surrounded by another cell state (bystander) that activates genes for long-distance cell-to-cell immune signalling. Together, our findings suggest that interactions between these cell states propagate immune responses across the leaf. Our molecularly defined single-cell spatiotemporal atlas provides functional and regulatory insights into immune cell states in plants.

The heart, which is the first organ to develop, is highly dependent on its form to function^1,2. However, how diverse cardiac cell types spatially coordinate to create the complex morphological structures that are crucial for heart function remains unclear. Here we integrated single-cell RNA-sequencing with high-resolution multiplexed error-robust fluorescence in situ hybridization to resolve the identity of the cardiac cell types that develop the human heart. This approach also provided a spatial mapping of individual cells that enables illumination of their organization into cellular communities that form distinct cardiac structures. We discovered that many of these cardiac cell types further specified into subpopulations exclusive to specific communities, which support their specialization according to the cellular ecosystem and anatomical region. In particular, ventricular cardiomyocyte subpopulations displayed an unexpected complex laminar organization across the ventricular wall and formed, with other cell subpopulations, several cellular communities. Interrogating cell–cell interactions within these communities using in vivo conditional genetic mouse models and in vitro human pluripotent stem cell systems revealed multicellular signalling pathways that orchestrate the spatial organization of cardiac cell subpopulations during ventricular wall morphogenesis. These detailed findings into the cellular social interactions and specialization of cardiac cell types constructing and remodelling the human heart offer new insights into structural heart diseases and the engineering of complex multicellular tissues for human heart repair.