Dataset folders from "TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses". If using the processed data or TISSUE algorithm, please cite: https://doi.org/10.1101/2023.04.25.538326.

The directory of datasets are compressed in tar gzip format. The top level contains folders with dataset names and within each of those folders, there are the relevant data files which include:

- Spatial_count.txt --- a tab-delimited file containing spatial transcriptomics counts matrix

- scRNA_count.txt --- a tab-delimited file containing RNAseq counts matrix

- Locations.txt --- a tab-delimited file containing the (x,y) spatial coordinates of cells in the spatial transcriptomics data

- Metadata.txt --- for some datasets, this is a comma-separated file containing the metadata table for the spatial transcriptomics data

These files are formatted and organized to be read into AnnData objects using the native loading functions in the TISSUE package (https://github.com/sunericd/TISSUE). Some folders will also have additional accessory files such as gene lists corresponding to some experiments present in our manuscript and/or adjacency matrix objects.

Also included are the two simulated spatial transcriptomics datasets that we generated using SRTsim.

The SVZ folders contain our processed MERFISH spatial transcriptomics dataset on the adult mouse subventricular zone. Refer to the SVZFullFinal folder for the full dataset with TISSUE-informed cell labels. All other folders are processed data accessed from publicly available sources. The identity of numbered folders can be found in the Data Availability statement of the benchmarking paper from which they were retrieved: https://doi.org/10.1038/s41592-022-01480-9

"svz_merfish_data.zip" includes the raw MERFISH dataset on the adult mouse subventricular zone.

This dataset contains Spatial Transcriptomics (ST) data matching with Matrix Assisted Laser Desorption/Ionization - Mass Spetrometry Imaging (MALDI-MSI). This data is complementary to data contained in the same project. FIles with the same identifiers in the two datasets originated from the very same tissue section and can be combined in a multimodal ST-MSI object. For more information about the dataset please see our manuscript posted on BioRxiv (doi: https://doi.org/10.1101/2023.01.26.525195). This dataset includes ST data from 19 tissue sections, including human post-mortem and mouse samples. The spatial transcriptomics data was generated using the Visium protocol (10x Genomics). The murine tissue sections come from three different mice unilaterally injected with 6-OHDA. 6-OHDA is a neurotoxin that when injected in the brain can selectively destroy dopaminergic neurons. We used this mouse model to show the applicability of the technology that we developed, named Spatial Multimodal Analysis (SMA). Using our technology on these mouse brain tissue sections we were able to detect both dopamine with MALDI-MSI and the corresponding gene expression with ST. This dataset includes also one human post-mortem striatum sample that was placed on one Visium slide across the four capture areas. This sample was analyzed with a different ST protocol named RRST (Mirzazadeh, R., Andrusivova, Z., Larsson, L. et al. Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples. Nat Commun 14, 509 (2023). https://doi.org/10.1038/s41467-023-36071-5), where probes capturing the whole transcriptome are first hybridized in the tissue section and then spatially detected. Each tissue section contained in the dataset has been given a unique identifier that is composed of the Visium array ID and capture area ID of the Visium slide that the tissue section was placed on. This unique identifier is included in the file names of all the files relative to the same tissue section, including the MALDI-MSI files published in the other dataset included in this project. In this dataset you will find the following files for each tissue section: - raw files: these are the read one fastq files (containing the pattern *R1*fastq.gz in the file name), read two fastq files (containing the pattern *R1*fastq.gz in the file name) and the raw microscope images (containing the pattern Spot.jpg in the file name). These are the only files needed to run the Space Ranger pipeline, which is freely available for any user (please see the 10x Genomics website for information on how to install and run Space Ranger); - processed data files: we provide processed data files of two types: a) Space Ranger outputs that were used to produce the figures in our publication; b) manual annotation tables in csv format produced using Loupe Browser 6 (csv tables with file names ending _RegionLoupe.csv, _filter.csv, _dopamine.csv, _lesion.csv, _region.csv patterns); c) json files that we used as input for Space Ranger in the cases where the automatic tissue detection included in the pipeline failed to recognize the tissue or the fiducials. Using these processed files the user can reproduce the figures of our publication without having to restart from the raw data files. The MALDI-MSI analyses preceding ST was performed with different matrices in different tissue section. We used 1) 9-aminoacridine (9-AA) for detection of metabolites in negative ionization mode, 2) 2,5-dihydroxybenzoic acid (DHB) for detection of metabolites in positive ionization mode, 3) 4-(anthracen-9-yl)-2-fluoro-1-ethylpyridin-1-ium iodide (FMP-10), which charge-tags molecules with phenolic hydroxyls and/or primary amines, including neurotransmitters. The information about which matrix was sprayed on the tissue sections and other information about the samples is included in the metadata table. We also used three types of control samples: - standard Visium: samples processed with standard Visium (i.e. no matrix spraying, no MALDI-MSI, protocol as recommended by 10x Gemomics with no exeptions) - internal controls (iCTRL): samples not sprayed with any matrix, neither processed with MALDI-MSI, but located on the same Visium slide were other samples were processed with MALDI-MSI - FMP-10-iCTRL: sample sprayed with FMP-10, and then processed as an iCTRL. This and other information is provided in the metadata table.

We acquired 10x Visium spatial transcriptomics (ST) data from 9 patients with invasive adenocarcinomas [1–5] to explore the role of the tumour microenvironment (TME) on intratumor heterogeneity (ITH) and drug response in breast cancer. By leveraging a new version of Beyondcell [6] (cnio-bu/beyondcell), a tool for identifying tumour cell subpopulations with distinct drug response patterns, we predicted sensitivity to over 1,200 drugs while accounting for the spatial context and interaction between the tumour and TME compartments. Moreover, we also used Beyondcell to compute spot-wise functional enrichment scores and identify niche-specific biological functions.

Here, you can find:

In signatures folder:

• SSc breast: Collection of gene signatures used to predict sensitivity to > 1,200 drugs derived from breast cancer cell lines.
• Functional signatures: Collection of gene signatures used to compute enrichment in different biological pathways.

In visium folder:

• Visium objects: Processed ST Seurat objects with deconvoluted spots, SCTransform-normalised counts, and clonal composition predicted with SCEVAN [7]. These objects, together with the signatures, were used to compute the Beyondcell objects.

In single-cell folder:

• Single-cell objects: Raw and filtered merged single-cell RNA-seq (scRNA-seq) Seurat objects with unnormalised counts used as a reference for spot deconvolution.

In beyondcell folder:

• Beyondcell sensitivity objects with prediction scores for all drug response signatures in SSc breast.
• Beyondcell functional objects with enrichment scores for all functional signatures.

Visium spatial transcriptomics (10X Genomics) performed on 4 CCA samples. Each sample has two paired-end sequencing runs: the first (I1 & I2) are a pair reading indexes; the second (R1 & R2) are a pair reading inserts, with R1 additionally reading 10X barcodes. For histology images, please contact authors.

The samples in the dataset are connected to a study focusing on studying breast cancer intratumoral heterogeneity using spatial transcriptomic data and computational pathology. The dataset contains 14 samples from 3 patients (one triple negative breast cancer and two HER2-positive breast cancer). Multiple regions of the tumor were collected for analysis. Each sample is one tumor region from one of the patients.

Libraries for spatial transcriptomics were prepared using Visium spatial gene expression kits (10x genomics). Sequencing was performed using the Illumina NovaSeq 6000 platform at the National Genomics Infrastructure, SciLifeLab in Solna, Sweden.

The dataset contains 28 fastq files, compressed with GNUzip (gzip), from paired-end RNA sequencing (10X Visium spatial transcriptomics). The meta data is described in SND_metadata.xlsx file. The md5sum.txt file is provided for validation of data integrity. The total size of the dataset is approximately 300 GB.

U-2 OS MERFISH data set prepared by the Han lab at UIUC based off of procedures developed in Moffitt et al. Proc. Natl. Acad. Sci. USA 113 (39), 11046–11051. Data is comprised of ~2 million spots from 130 genes with x,y,z location, cell assignment, and correction status.

Multiscale data integration of spatial transcriptomics and scRNA-seq data.

Dataset created in the study "A Spatial Transcriptomics Atlas of the Malaria-infected Liver Indicates a Crucial Role for Lipid Metabolism and Hotspots of Inflammatory Cell Infiltration"

Structure

ST_berghei_liver

contains data generated during stpipeline analysis and imaging on 2k arrays Spatial Transcriptomics platform as well as data necessary for and from hepaquery analysis. These samples include 38 sections in total of which 8 are from mice (n=4) infected with sporozoites for 12h, 5 sections from control mice (n=3) at 12h, 7 sections from mice (n=4) infected with sporozoites for 24h and 4 sections from control mice (n=3) for 24 as well as 8 samples of mice (n=2) infected with sporozoites for 38h and control mice (n =2) for 38h.

• count contains gene expression matrix output from stpipeline in .tsv format
• spotfiles contains coordinate files for count matrices
• images contains scaled H&E, Fluorescence (FL) and annotated H&E images (from FL annotations) scaled to 10% of the original image size.
• masks contains image masks for hepaquery analysis
• distances contains distance measurements from original section sorted by timepoint as well as combined across timepoints
• cluster contains clustering information across spatial positions used in spatial enrichment analysis

STUtiility_mus_pb_ST.RDS describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in ST_berghei_liver

visium_berghei_liver

contains data generated with the spaceranger pipeline and imaging using the Visium spatial transcriptomics platform. These samples include 8 sections in total, of which 1 was infected with sporozoites for 12h, 1 control section at 12h, 1 section infected with sporozoites for 24h and 1 control section at 24 as well as 2 sporozoite infected sections, and 2 control sections at 38h.

• V10S29-135_A1 contains spaceranger output for section 1 for infected and control sections at 38h post-infection

• V10S29-135_B1 contains spaceranger output for section 1 for infected and control sections at 12h post-infection

• V10S29-135_C1 contains spaceranger output for section 1 for infected and control sections at 24h post-infection

• V10S29-135_D1 contains spaceranger output for section 2 for infected and control sections at 38h post-infection

se_visium.RDS describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in visium_berghei_liver

snSeq_berghei_liver

contains data generated with the cellranger pipeline and imaging using the Visium spatial transcriptomics platform. These samples include single nuclei of 2 infected and control mice after 12h, 2 infected and control mice after 24h, 2 infected and control mice after 38h, and 2 uninfected mice prior to a challenge.

• cellranger_cnt_out contains feature count matrix information from cell ranger output

final_merged_curated_annotations_270623.RDS describes seurat object generated using the STUtility package using ST data of the 38 liver sections of which the data is stored in snSeq_berghei_liver.tar.gz

raw images.zip contains raw images for supplementary figures 20-22

adjusted images.zip contains brightness and contrast adjusted images for supplementary figures 20-22

Raw sequencing data (fastq and BAM files) of stage III and IV HNSCC samples.

4 Visium Spatial Transcriptomics datasets downloaded 10X Genomics data site ,and organized in the way to be used for Cellxgene VIP input.

10X_demo_data_Breast_Cancer_Block_A_Section_1 10X_demo_data_Breast_Cancer_Block_A_Section_2 10X_demo_data_Human_Heart 10X_demo_data_Human_Lymph_Node

Recent breakthroughs in spatial transcriptomics technologies have enhanced our understanding of diverse cellular identities, spatial organizations, and functions. Yet existing spatial transcriptomics tools are still limited in either transcriptomic coverage or spatial resolution, hindering unbiased, hypothesis-free transcriptomic analyses at high spatial resolution. Here we develop Reverse-padlock Amplicon Encoding FISH (RAEFISH), an image-based spatial transcriptomics method with whole-genome coverage and single-molecule resolution in intact tissues. We demonstrate spatial profiling of 23,000 human or 22,000 mouse transcripts in single cells and tissue sections. Our analyses reveal transcript-specific subcellular localization, cell-type-specific and cell-type-invariant zonation-dependent transcriptomes, and gene programs underlying preferential cell-cell interactions. Finally, we further develop our technology for direct spatial readout of gRNAs in an image-based high-content CRISPR screen. Overall, these developments provide the research community with a broadly applicable technology that enables high-coverage, high-resolution spatial profiling of both long and short, native and engineered RNA species in many biomedical contexts.

The data for training the GAN (Inversion) model and reproduce the results reported in the paper

This folder contains the fastq-files that are generated during the Grand Challenge project using 10X Genomics Visium on head&neck squamous cell carcinoma samples. It contains 4 fastq-files (R1 and R2 for each of the two sequencing lanes) per patient (for each patient, 2 samples (biopsy and resection) were collected, and the two samples of 1 patient (HNI40020) was analyzed twice).

Visium (10x Genomics) spatially resolved transcriptomics data generated from normal and Idiopathic Pulmonary Fibrosis (IPF) lung parenchyma tissues collected from human donors. The fresh-frozen tissues that were analyzed were from four healthy control (HC) subjects and from four IPF patients. For each IPF patient, three different tissues were selected representing areas of mild (“B1”), moderate (“B2") or severe (“B3”) fibrosis within the same donor, as determined by histological inspection of Hematoxylin and Eosin (H&E)-stained samples. Data from a total of 25 tissue sections, from 16 unique lung tissue blocks. The lung tissues were collected post-mortem (HC donors) or during lung transplant/resection (IPF patients) after obtaining informed consent. The study protocols were approved by the local human research ethics committee (HC: Lund, permit number Dnr 2016/317; IPF: Gothenburg, permit number 1026-15) and the samples are anonymized and cannot/should not be traced back to individual donors. Data included in this repository: - Visium data in the format of selected Space Ranger output files ("filtered_feature_bc_matrix.h5", "raw_feature_bc_matrix.h5", "web_summary.html", and the "spatial/" folder) for each individual section analysed. Zipped into one folder: "hs_visium_spaceranger_output.zip" - Sample metadata containing information for each sample with linked subject information: "hs_visium_metadata.tsv" - R object produced using STUtility and contains the processed data used for downstream analyses, most importantly all spot metadata with assigned data and deconvolution results (NMF, cell2location): "hs_visium_stutility_obj.rds" - Cell2location output files ("*_spot_cell_abundances_5pc.csv"), zipped into one folder: "cell2location_habermann2020.zip" - Full resolution H&E images ("*.jpg") of each tissue section that was used as input for spaceranger together with alignment json and sequencing fastq files. Zipped into one folder: "he_fullres_jpgs.zip" - Spot alignment files ("*.json") created in Loupe Browser using the corresponding full resolution H&E image in which spots under the tissue was identified. Zipped into one folder: "loupe_alignment_jsons.zip" Space Ranger output found within the zipped files in folders named "V*****-***-*1". To generate these files, raw FastQ files from the NovaSeq sequencing were processed with the Space Ranger pipeline (v. 1.2.2, 10x Genomics), where the reads were mapped to the GRCh38 reference genome. Manual spot alignment was performed in the Loupe Browser (v. 6, 10x Genomics) software. Cell type mapping results were obtained using the cell2location (v. 0.1) method, integrating the Space Ranger output data with annotated single cell RNA-seq data produced from human IPF lung, published by Habermann et al., 2020 (DOI: 10.1126/sciadv.aba1972, GEO accession: GSE135893). Seurat/STUtility object was generated from the Space Ranger output files, using the R packages STUtility (v. 1.1.1) and Seurat (v. 4.1.1) in R (v. 4.0.5) . All R scripts used for the data analyses can be found at https://github.com/lfranzen/spatial-lung-fibrosis. The deposited data is presented in the article "Mapping spatially resolved transcriptomes in human and mouse pulmonary fibrosis" by Franzén L & Olsson Lindvall M, et al. (preprint: "Translational mapping of spatially resolved transcriptomes in human and mouse pulmonary fibrosis", bioRxiv, https://doi.org/10.1101/2023.12.21.572330).

Additional file 5: RMarkdown on how to use the RegionNeighbours function.

Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cell types and their heterogeneity within the human liver, but the spatial organization at single-cell resolution has not yet been described. Here we apply multiplexed error robust fluorescent in situ hybridization (MERFISH) to map the zonal distribution of hepatocytes, resolve subsets of macrophage and mesenchymal populations, and investigate the relationship between hepatocyte ploidy and gene expression within the healthy human liver. We next integrated spatial information from MERFISH with the more complete transcriptome produced by single- nucleus RNA sequencing (snRNA-seq), revealing zonally enriched receptor-ligand interactions. Finally, analysis of fibrotic liver samples identified two hepatocyte populations that are not restricted to zonal distribution and expand with injury. Together these spatial maps of the healthy and fibrotic liver provide a deeper understanding of the cellular and spatial remodeling t..., Two measurement modalities were used to generate these data, including multiplexed error robust fluorescence in situ hybridization (MERFISH) and single-nucleus RNA sequencing (snRNAseq)., , # MERFISH and snRNAseq data from Watson, Paul et al

This README file contains information on the data deposited for the manuscript "Spatial transcriptomics of healthy and fibrotic human liver at single-cell resolution" by Watson, Paul and colleagues.

Anndata Structures

Multiple anndata structures are provide as h5ad files for different datasets. These anndata structures were generated with the scanpy pipeline (v1.8.1) and can be loaded in python with the associated tools. These include: (1) adata_healthy_merfish.h5ad (2) adata_healthy_diseased_merfish.h5ad (3) adata_healthy_merfish_nucseq.h5ad (4) adata_healthy_nucseq.h5ad

Each anndata frame contains distinctive values for the respective data set as follows:

(1) adata_healthy_merfish.h5ad This structure contains data from healthy patient samples which were imaged with MERFISH. Raw data is stored in the adata.raw.X while adata.X is normalized by the total counts per cell, scaled to a uniform value, and then converted to logarithm...

Spatial Transcriptomics Platforms Market Outlook

As per our latest research, the global Spatial Transcriptomics Platforms market size reached USD 410 million in 2024, reflecting robust momentum driven by the growing demand for high-resolution spatial gene expression analysis across biomedical research and clinical settings. The market is set to expand at a compelling CAGR of 13.7% from 2025 to 2033, with the market forecasted to reach an impressive USD 1,247 million by 2033. The primary growth factor behind this surge is the increasing adoption of spatial transcriptomics technologies in oncology, neuroscience, and drug discovery, as researchers and clinicians seek more precise insights into tissue heterogeneity and disease mechanisms.

One of the most significant growth drivers for the spatial transcriptomics platforms market is the rapid advancements in sequencing and imaging technologies. These technological improvements have enabled the generation of high-throughput, spatially resolved transcriptomic data, which was previously unattainable with conventional bulk or single-cell RNA sequencing methods. As a result, academic and research institutions are increasingly integrating spatial transcriptomics into their workflows to dissect complex tissue architecture, understand cellular interactions, and map gene expression within the context of intact tissue sections. The surge in funding for genomics and spatial biology research globally has further accelerated the adoption of these platforms, fostering innovation and expanding the range of available products and services in the market.

Another pivotal factor fueling the growth of the spatial transcriptomics platforms market is the expanding applications in personalized medicine and targeted drug development. The ability to visualize and quantify gene expression at single-cell resolution within tissue samples has become indispensable for oncology research, especially for understanding tumor microenvironment heterogeneity and immune cell infiltration patterns. Pharmaceutical and biotechnology companies are leveraging spatial transcriptomics data to identify novel biomarkers, stratify patient populations, and optimize therapeutic interventions. This growing industry collaboration is not only driving the demand for advanced instruments and consumables but also boosting the need for sophisticated software solutions capable of managing and interpreting complex spatial datasets.

Furthermore, the market is witnessing a surge in strategic partnerships, mergers, and acquisitions among key players, aimed at expanding product portfolios and accelerating the commercialization of innovative spatial transcriptomics solutions. The influx of venture capital and increased government support for precision medicine initiatives have also played a crucial role in propelling market growth. In addition, the integration of artificial intelligence and machine learning algorithms with spatial transcriptomics platforms is enhancing data analysis capabilities, enabling researchers to extract deeper biological insights and uncover novel therapeutic targets. These collaborative efforts and technological synergies are expected to sustain the market's upward trajectory throughout the forecast period.

From a regional perspective, North America currently dominates the spatial transcriptomics platforms market, owing to its advanced healthcare infrastructure, significant research funding, and the presence of leading biotechnology firms. Europe follows closely, supported by strong academic research activities and government initiatives promoting genomics and spatial biology. The Asia Pacific region is anticipated to exhibit the fastest growth over the next decade, driven by increasing investments in life sciences research, expanding healthcare expenditure, and a rapidly growing biotechnology sector. As adoption continues to rise across emerging markets, the global landscape of spatial transcriptomics is poised for dynamic transformation, with opportunities for growth and innovation extending well beyond traditional geographies.

This study consists of the spatial transcriptomics data generated by : Visium CytAssist Spatial Gene Expression for FFPE (10x) for steatotic liver disease-associated hepatocellular carcinoma (SLD-HCC) (n=7) and non-SLD-HCC (n=5)
The goal of this data is to compare SLD-HCC vs non-SLD-HCC as well as response to immunotherapy

Spatial Transcriptomics Software Market Outlook

According to our latest research, the global spatial transcriptomics software market size in 2024 stands at USD 375 million, reflecting a robust expansion driven by the increasing adoption of spatial omics technologies in biomedical research. The market is anticipated to grow at a CAGR of 15.2% from 2025 to 2033, reaching a forecasted value of USD 1.23 billion by 2033. This remarkable growth trajectory is primarily attributed to the rising demand for high-throughput spatial gene expression analysis, advancements in imaging technologies, and the integration of artificial intelligence with bioinformatics platforms across research and clinical settings.

One of the primary growth factors propelling the spatial transcriptomics software market is the surging need for spatially resolved transcriptomic data in understanding complex biological processes, particularly in oncology and neuroscience. Researchers are increasingly recognizing the limitations of bulk RNA sequencing, which fails to capture the spatial context of gene expression within tissues. The ability of spatial transcriptomics software to map gene activity at a cellular level within intact tissue sections is revolutionizing research in tumor microenvironments, neurodegenerative diseases, and developmental biology. As a result, both academic and commercial entities are investing heavily in spatial transcriptomics platforms and software, further fueling market expansion.

Another significant driver is the rapid technological evolution in imaging and sequencing techniques, which has led to the generation of massive spatial omics datasets. This surge in data volume necessitates advanced computational tools for efficient analysis, visualization, and interpretation. Spatial transcriptomics software solutions are being enhanced with machine learning algorithms, scalable cloud-based architectures, and user-friendly interfaces to accommodate the growing complexity and size of datasets. These innovations are enabling researchers to extract actionable insights from spatial transcriptomics experiments, driving adoption across pharmaceutical, biotechnology, and diagnostic sectors.

Furthermore, the increasing collaboration between software developers, instrument manufacturers, and research institutions is accelerating the development of integrated spatial omics solutions. Strategic partnerships are resulting in the creation of comprehensive platforms that combine hardware, reagents, and software, streamlining the workflow from sample preparation to data analysis. This integrated approach not only improves efficiency and reproducibility but also lowers the barrier to entry for new users. The proliferation of open-source spatial transcriptomics software and the establishment of data-sharing consortia are also fostering innovation and standardization across the industry, contributing to sustained market growth.

From a regional perspective, North America currently dominates the spatial transcriptomics software market, owing to its strong presence of leading research institutions, well-established biotechnology and pharmaceutical industries, and high adoption of advanced omics technologies. Europe follows closely, supported by robust funding for life sciences research and a growing focus on precision medicine. The Asia Pacific region is rapidly emerging as a key growth area, driven by expanding investments in genomics infrastructure and increasing awareness of spatial omics applications. Meanwhile, Latin America and the Middle East & Africa are witnessing gradual adoption, propelled by improvements in healthcare infrastructure and rising research activities. The global landscape is poised for dynamic growth, with regional markets contributing uniquely to the evolution of spatial transcriptomics software.

Product Type Analysis

The spatial transcriptomics software market is segmented by product type into standalone software and integrated software suites. Standalone software solutions are designed to perform specific analytical tasks such as image processing, spatial mapping, or gene expression quantification. These tools are favored by advanced users and specialized research groups who require customized workflows and the flexibility to integrate with other bioinformatics platforms. Standalone products often feature modular architectures, allowing users to select and deploy functionalities that align precisely with their experimental requirements. This segment is witnessing steady deman

Spatial transcriptomics seeks to integrate single-cell transcriptomic data within the 3-dimensional space of multicellular biology. Current methods use glass substrates pre-seeded with matrices of barcodes or fluorescence hybridization of a limited number of probes. We developed an alternative approach, called ‘ZipSeq’, that uses patterned illumination and photocaged oligonucleotides to serially print barcodes (Zipcodes) onto live cells within intact tissues, in real-time and with on-the-fly selection of patterns. Using ZipSeq, we mapped gene expression in three settings: in-vitro wound healing, live lymph node sections and in a live tumor microenvironment (TME). In all cases, we discovered new gene expression patterns associated with histological structures. In the TME, this demonstrated a trajectory of myeloid and T cell differentiation, from periphery inward. A combinatorial variation of ZipSeq efficiently scales in number of regions defined, providing a pathway for complete mapping ...