DOI: 10.5061/dryad.rjdfn2zh3
title: Spatial gene expression in the mouse colon during experimental colitis measured with MERFISH

description: Gut inflammation involves contributions from immune and non-immune cells,
 whose interactions are shaped by the spatial organization of the healthy
 gut and its remodeling during inflammation. The crosstalk between stromal
 and immune cells is an important axis in this process, but our
 understanding has been challenged by incomplete cell-type definition and
 biogeography. To address this challenge, we used MERFISH to profile the
 expression of 940 genes in 1.35 million cells in colon slices collected
 across the onset and recovery from a mouse colitis model. We identified a
 large diversity of cell populations; charted their spatial organization;
 and revealed their polarization or recruitment in inflammation. We found a
 staged progression of inflammation-associated tissue neighborhoods
 orchestrated, in part, by multiple inflammation-associated fibroblasts,
 with unique expression profiles, spatial localization, cell-cell
 interactions, and healthy fibroblast origins. Similar signatures in
 ulcerative colitis suggest conserved processes in humans. Broadly, we
 provide a resource for understanding inflammation-induced remodeling in
 the gut and other tissues. 
description: This dataset was collected using multiplexed error robust
 fluorescence in situ hybridization (MERFISH) as described in the methods
 of the supporting paper. The methods section of this paper also describes
 the approaches used to analyze the data, which include the use of publicly
 available pipelines for the analysis of raw MERFISH images
 (github.com/ZhuangLab/MERFISH_analysis), RNA assignment to cells
 (github.com/kharchenkolab/Baysor), and single-cell analysis
 (github.com/scverse/scanpy). The organization of the provided files is
 described in the included README.md. 
description: We have provided the output of our single-cell analysis in the
 AnnData format utilized by scanpy and saved in hdf5. Consult the scanpy
 documentation for instructions on loading this file. To facilitate
 additional access to these data, we have also provided the key elements
 included in the AnnData format in CSV format. All other information is
 provided in CSV format. We have also provided AnnData format for our
 reanalysis of published scRNA-seq data relevant to comparisons in our
 manuscript.
description: # Spatial gene expression in the mouse colon during experimental colitis
 measured with MERFISH Jeffrey R. Moffitt Paolo Cadinu Boston
 Children's Hospital, 2024 ## File organization ### Anndata structure
 for Day 0 - Day 21 MERFISH data #### adata.h5ad This file was generated
 with the scanpy pipeline and can be loaded in Python with the tools
 associated with this pipeline. The adata object contains a measure of the
 normalized counts observed in each cell for each of the genes measured
 with MERFISH. The values stored in the X matrix are normalized by the
 total counts per cell, scaled to a uniform value, and then converted to
 logarithmic space by adding a pseudo count and applying a log10 transform.
 The adata object contains an obs field that contains a data frame that
 describes the properties of each field. The fields of this data frame are
 defined as follows: * x: This field describes the x position of the
 centroid of the cell in microns * y: This field describes the y position
 of the centroid of the cell in microns * Mouse_ID: This field contains a
 unique name for the mouse from which the cell was measured *
 Technical_repeat_number: This field contains a unique name for each
 technical repeat (defined as a measurement of different slices but from
 the same mouse) * Sample_type: This field describes the disease stage at
 which the mouse was harvested. Healthy (Day 0), DSS3 (Day 3), DSS9 (Day
 9), and DSS21 (Day 21) * Slice_ID: This field contains the unique name for
 the slice from which the cell was measured * FOV: This field contains the
 ID associated with the original field-of-view in which the cell was imaged
 * cell_IDs: This field contains the unique ID associated with each cell *
 sample: This field contains a unique numeric ID for each sample * N_genes:
 This field contains the number of genes for which there was at least one
 counts * Tier1: This field contains the name of the class to which the
 cell was assigned * Tier2: This field contains the name of the tier 2
 cluster to which the cell was assigned * Tier3: This field contains the
 name of the final cluster to which the cell was assigned * Leiden_neigh:
 This field contains the name of the neighborhood to which the cell was
 assigned. 'others' indicates the cell was assigned to a
 neighborhood not explored * Neigh_umap_x(y): This field contains the x or
 y coordinate of the cell in the neighborhood UMAP * Tier1_umap_x(y): This
 field contains the x or y coordinate of the cell in the UMAP constructed
 during Tier 1 clustering. * Tier2_umap_x(y): This field contains the x or
 y coordinate of the cell in the UMAP constructed during Tier 2 clustering.
 There is a different UMAP for each cell class * Tier3_umap_x(y): This
 field contains the x or y coordinate of the cell in the UMAP constructed
 during Tier 3 clustering. There is a different UMAP for each cell type for
 which Tier 3 clustering was applied ### Anndata items for Day 0 - Day 21
 MERFISH data To facilitate access to the core elements of the anndata
 object without the need to load this format, we also provide the following
 items from the anndata object in CSV format. * X.csv - This file contains
 a matrix of expression values, normalized and transformed with a
 pseudocount and a log10 transformation. The rows represent different cells
 and the columns different genes. These are the same numeric values found
 in the X matrix described above. * X_raw.csv - This file contains a matrix
 of the counts for each gene in each cell. These data are normalized. *
 gene_names.csv - This file contains the gene names associated with the
 columns in X in the same order as the columns in X * cell_properties.csv -
 This file contains the same fields and information described in the pandas
 data frame above (the obs object). The rows of this file represent
 different cells and are in the same order as the rows in X. ### Anndata
 structure for Day 35 MERFISH data #### adata\_day35.h5ad This file was
 generated with the scanpy pipeline and can be loaded in Python with the
 tools associated with this pipeline. The adata object contains a measure
 of the normalized counts observed in each cell for each of the genes
 measured with MERFISH. The values stored in the X matrix are normalized by
 the total counts per cell, scaled to a uniform value, and then converted
 to logarithmic space by adding a pseudo count and applying a log10
 transform. The adata object contains an obs field that contains a data
 frame that describes the properties of each field. The fields of this data
 frame are defined as follows: * x: This field describes the x position of
 the centroid of the cell in microns * y: This field describes the y
 position of the centroid of the cell in microns * Mouse_ID: This field
 contains a unique name for the mouse from which the cell was measured *
 Technical_repeat_number: This field contains a unique name for each
 technical repeat (defined as a measurement of different slices but from
 the same mouse) * Sample_type: This field describes the disease stage at
 which the mouse was harvested. * Slice_ID: This field contains the unique
 name for the slice from which the cell was measured * FOV: This field
 contains the ID associated with the original field-of-view in which the
 cell was imaged * cell_IDs: This field contains the unique ID associated
 with each cell * Tier1: This field contains the name of the class to which
 the cell was assigned * Tier3: This field contains the name of the final
 cluster to which the cell was assigned * Leiden_neigh: This field contains
 the name of the neighborhood to which the cell was assigned.
 'others' indicates the cell was assigned to a neighborhood not
 explored ### Anndata items for Day 35 MERFISH data To facilitate access to
 the core elements of the anndata object without the need to load this
 format, we also provide the following items from the anndata object in CSV
 format. * X_day35.csv - This file contains a matrix of expression values,
 normalized and transformed with a pseudocount and a log10 transformation.
 The rows represent different cells and the columns different genes. These
 are the same numeric values found in the X matrix described above. *
 X_raw_day35.csv - This file contains a matrix of the counts for each gene
 in each cell. These data are normalized. * gene_names_day35.csv - This
 file contains the gene names associated with the columns in X in the same
 order as the columns in X * cell_properties_day35.csv - This file contains
 the same fields and information described in the pandas data frame above
 (the obs object). The rows of this file represent different cells and are
 in the same order as the rows in X. ### Individual RNA metadata files for
 all MERFISH data In addition, there is a CSV file associated with each
 slice measured with MERFISH that describes the properties associated with
 each RNA measured within that slice. Each CSV file is named by the
 specific name of the mouse and slice. An example of one of these files is
 100221_D9_m5_1_slice_3.csv. These CSV files have the following fields for
 each RNA * gene: This field contains the common name of the gene
 associated with the identified RNA * fov_id: This field contains the
 number of the specific field-of-view (fov) in the microscopy tile
 collected for that slice in which the cell was found * x: This field
 describes the x position of the centroid of the RNA in microns * y: This
 field describes the y position of the centroid of the RNA in microns * z:
 This field describes the z position (the depth within the tissue slice) of
 the centroid of the RNA in microns * total_magnitude: This field describes
 the sum of the normalized brightness associated with each pixel assigned
 to the given RNA * area: This field describes the number of pixels
 assigned to the given RNA * error_bit: This field contains the number of
 the bit at which error correction was applied. 0 indicates that no error
 correction was applied. * error_dir: This field describes the direction of
 the error (1 = 1-&gt;0 error; 0 = 0-&gt;1 error) * cell (optional): The
 numeric identity of the cell measured in the dataset from which the slice
 was taken. 0 indicates the RNA was not assigned to the cell * is_noise
 (optional): Whether or not Baysor determined the RNA should (FALSE) or
 should not (TRUE) be assigned to a cell * ncv_color (optional): The color
 value assigned to the RNA by Baysor based on the local composition of RNAs
 around it * cell_name (optional): A unique identifier associated with each
 cell in the dataset. These names are found within the adata.hdf5 file *
 slice_id (optional): A unique name associated with the specific slice ###
 Bulk RNAseq abundance #### bulk\_rnaseq\_FPKM.csv This CSV file contains
 the following fields: * FPKM: abundance expressed in FPKM * geneName: gene
 name ### Ligand receptor pairing ####
 ligand\_receptor\_pair\_masterlist.csv This CSV file contains information
 on ligand-receptor pairs as determined from KEGG. It contains the
 following fields: * Ligand: The name of the ligand * Receptor: The name of
 the receptor * Coreceptor_1: The name of a possible co-receptor *
 Coreceptor_2: The name of a second possible co-receptor * Category: The
 category of ligand * Sub-category: The sub-category of ligand ### Data
 associated with single-cell RNAseq datasets mined from previously
 published works: The raw data for the following papers can be downloaded
 from the links provided with the associated papers. These public data were
 downloaded and reanalyzed to produce annotations used in the analysis of
 our paper. Here we provide CSV files that contain the name associated with
 each cell and the final cluster label from the analysis of these data.
 These CSV files contain two fields. * cell_name: The unique ID associated
 with each cell as provided in the original publication * leiden_annotated:
 The label assigned to each cell The raw data for the cells in
 Ho_annotations.csv was downloaded from PMID 33862275 The raw data for the
 cells in Brugger_annotations.csv was downloaded from PMID 33306673 The raw
 data for the cells in Kinchen_M_DSS_annotations.csv and
 Kinchen_M_healthy_annotations.csv was downloaded from PMID 30270042 The
 raw data for the cells in Jasso_annotations.csv was downloaded from PMID
 35085231 The raw data for the cells in Xie_DSS_annotations.csv was
 downloaded from PMID 35569814