Analysis of the pattern of proteins or messengerRNAs (mRNAs) in histological tissue sections is a cornerstone in biomedical research and diagnostics. This typically involves the visualization of a few proteins or expressed genes at a time. We have devised a strategy, which we call "spatial transcriptomics," that allows visualization and quantitative analysis of the transcriptome with spatial resolution in individual tissue sections. By positioning histological sections on arrayed reverse transcription primers with unique positional barcodes, we demonstrate high-quality RNA-sequencing data with maintained two-dimensional positional information from the mouse brain and human breast cancer. Spatial transcriptomics provides quantitative gene expression data and visualization of the distribution of mRNAs within tissue sections and enables novel types of bioinformatics analyses, valuable in research and diagnostics.

Highlights d 51 cell subsets in colon mucosa of 18 ulcerative colitis and 12 healthy individuals d M-like cells, inflammatory monocytes and fibroblasts, and CD8 + IL-17 + T cells expand in disease d Oncostatin M circuit in inflammatory monocytes and fibroblasts may affect drug response d Co-expression of genes within cells allows inference of causal genes across risk loci

and PCT/EP2016/057355 applied for by Spatial Transcriptomics AB (10x Genomics) covering the described technology. M.R. is employed by Illumina Inc. A.R. is a founder and equity holder of Celsius Therapeutics and an SAB member of Syros Pharmaceuticals and Thermo Fisher Scientific.Reporting summary: Further information on research design is available in the Life Sciences Reporting Summary linked to this article. Data availability:The raw mouse data have been deposited to NCBI's GEO archive GSE130682. Raw files for the breast cancer sample are available through an MTA with Åke Borg

Intra-tumor heterogeneity is one of the biggest challenges in cancer treatment today. Here we investigate tissue-wide gene expression heterogeneity throughout a multifocal prostate cancer using the spatial transcriptomics (ST) technology. Utilizing a novel approach for deconvolution, we analyze the transcriptomes of nearly 6750 tissue regions and extract distinct expression profiles for the different tissue components, such as stroma, normal and PIN glands, immune cells and cancer. We distinguish healthy and diseased areas and thereby provide insight into gene expression changes during the progression of prostate cancer. Compared to pathologist annotations, we delineate the extent of cancer foci more accurately, interestingly without link to histological changes. We identify gene expression gradients in stroma adjacent to tumor regions that allow for re-stratification of the tumor microenvironment. The establishment of these profiles is the first step towards an unbiased view of prostate cancer and can serve as a dictionary for future studies.

Charting an organs’ biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehensively, but lose spatial information. Spatial transcriptomics allows for spatial measurements, but at lower resolution and with limited sensitivity. Targeted in situ technologies solve both issues, but are limited in gene throughput. To overcome these limitations we present Tangram, a method that aligns sc/snRNA-seq data to various forms of spatial data collected from the same region, including MERFISH, STARmap, smFISH, Spatial Transcriptomics (Visium) and histological images. Tangram can map any type of sc/snRNA-seq data, including multimodal data such as those from SHARE-seq, which we used to reveal spatial patterns of chromatin accessibility. We demonstrate Tangram on healthy mouse brain tissue, by reconstructing a genome-wide anatomically integrated spatial map at single-cell resolution of the visual and somatomotor areas.

Paralysis occurring in amyotrophic lateral sclerosis (ALS) results from denervation of skeletal muscle as a consequence of motor neuron degeneration. Interactions between motor neurons and glia contribute to motor neuron loss, but the spatiotemporal ordering of molecular events that drive these processes in intact spinal tissue remains poorly understood. Here, we use spatial transcriptomics to obtain gene expression measurements of mouse spinal cords over the course of disease, as well as of postmortem tissue from ALS patients, to characterize the underlying molecular mechanisms in ALS. We identify pathway dynamics, distinguish regional differences between microglia and astrocyte populations at early time points, and discern perturbations in several transcriptional pathways shared between murine models of ALS and human postmortem spinal cords.

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Understanding complex biological systems requires functional characterization of specialized tissue domains. However, existing strategies for generating and analysing high-throughput spatial expression profiles were developed for a limited range of organisms, primarily mammals. Here we present the first available approach to generate and study high-resolution, spatially resolved functional profiles in a broad range of model plant systems. Our process includes high-throughput spatial transcriptome profiling followed by spatial gene and pathway analyses. We first demonstrate the feasibility of the technique by generating spatial transcriptome profiles from model angiosperms and gymnosperms microsections. In Arabidopsis thaliana we use the spatial data to identify differences in expression levels of 141 genes and 189 pathways in eight inflorescence tissue domains. Our combined approach of spatial transcriptomics and functional profiling offers a powerful new strategy that can be applied to a broad range of plant species, and is an approach that will be pivotal to answering fundamental questions in developmental and evolutionary biology.