DOI: 10.5061/dryad.p5hqbzm0z
title: MERFISH measurements of the mouse gastrointestinal tract in the presence and absence of the microbiome

description: The mammalian gastrointestinal tract is comprised of a diverse set of cell
 types, each responsive to a diversity of diet-, host-, and microbe-derived
 small molecules. These small molecules are sensed by a massive diversity
 of receptors and differential regional, spatial, and cellular expression
 of these receptors is a critical mode by which sensation is mediated. In
 parallel, the gut microbiome plays critical roles in modulating the
 homeostatic cellular, molecular, and spatial structure of the mammalian
 gut, including potential roles in shaping the receptors that are expressed
 and, thus, the molecular sensing capabilities of the gut. Here we provide
 a spatially resolved, single-cell atlas of gene expression across four
 regions of the murine gut in the presence and absence of the microbiome.
 These measurements provide a rich resource for understanding cell-type
 specific sensation capabilities, the way in which cells fine tune these
 capabilities across gut regions, and, on a smaller scale, how these
 capabilities are fine tuned along based on cellular location within the
 gut mucosa. 
description: # MERFISH measurements of the mouse gastrointestinal tract in the presence
 and absence of a microbiome Rosalind J. Xu, Jeffrey R. Moffitt Boston
 Children's Hospital, 2025 ## Description This repository contains a
 variety of data from a MERFISH study of the molecular and cellular
 organization of the murine gastrointestinal tract in the presence of a
 specific-pathogen-free (SPF) microbiota or no microbiota (germ-free [GF]).
 The respository includes metadata associated with the identity and
 location of all imaged RNAs, a model used for cell segmentation, gene
 expression and metadata associated with identified cells, and other
 downstream analysis. These data are organized in the following folders. ##
 transcripts This folder contains files that describe metadata associated
 with all RNAs identified with MERFISH. These metadata were derived either
 during the decoding of RNA barcodes or in the segmentation process. ####
 transcript\_metadata\_raw\.csv This file contains the location, identity,
 and associated metadata of all measured RNAs prior to cell segmentation.
 Each entry is an RNA with properties provided by the following fields: *
 dataset_ID: ID of dataset * dataset_name: name of dataset * slice_ID: ID
 of slice within the dataset * slice_full_name: unique name of all slices
 in all datasets * gene: gene identify of measured RNA * fov_ID: ID of FOV
 in MERFISH measurement * x [μm]: x position of RNA in μm * y [μm]: y
 position of RNA in μm * z [μm]: z position of RNA in μm * area [pixel]:
 area of RNA in number of pixels * brightness: average pixel brightness of
 RNA * weighted_distance: brightness-weighted average distance between
 pixels in RNA and barcode * qc_score: quality control score of RNA ####
 transcript\_metadata\_segmented.csv This file contains the metadata for
 all measured RNAs as described above but includes metadata added during
 the segmentation process. Note that all RNAs associated with a blank
 barcode (false-positive controls) and the Neat1 RNA were removed during
 segmentation and are, thus, not included. In addition to the above fields,
 each RNA contains the following additional information: * cell_ID: unique
 ID for all cells in dataset (format: datasetID_sliceID_cellID) from Baysor
 segmentation * cellpose_prior_ID: unique ID associated with cells
 determined via cellpose. * baysor_assignment_confidence: Baysor assignment
 probability of RNA to cell ## cellpose\_model This folder contains a file
 used in the initial cell segmentation process with cellpose. ####
 membrane\_IF\_model.cellpose In-house trained Cellpose model for
 Na+/K+-ATPase membrane immunofluorescence in murine small and large
 intestine. ## cell\_by\_gene This folder contains the final single-cell
 analysis file generated with the scanpy pipeline. ####
 cell\_by\_gene\_with\_metadata.h5ad Cell-by-gene matrix with associated
 metadata for all datasets, stored in the Scanpy anndata (adata.h5ad)
 format. This object contains only the cells and genes that passed all
 quality thresholds. It contains the following objects: ##### adata.X This
 numpy matrix contains the normalized and log-transformed cell-by-gene
 matrices. ##### adata.layers\['raw\_counts'] This numpy matrix
 contains the total counts for each RNA in each cell. ##### adata.obs This
 pandas dataframe contains a series of metadata associated with each cell.
 This includes the following fields: * index: cell_ID (format:
 datasetID_sliceID_cellID) * cell_ID: the id for the cell within each
 dataset * cell_name: unique name for all cells in all datasets (format:
 dataset_slice_cell) * dataset_ID: ID of dataset * dataset_name: name of
 dataset * slice_ID: ID of slice within the dataset * slice_full_name:
 unique name associated with the slice in which the cell was imaged *
 fov_ID: ID of FOV in MERFISH measurement * region: gut region (ile =
 ileum, ce = cecum, pcol = proximal colon, dcol = distal colon) *
 microbiome: microbiome condition (WT = specific-pathogen-free, GF =
 germ-free) * condition: condition of measurement (format:
 region_microbiome) * x [μm]: x position of cell centroid in μm * y [μm]: y
 position of cell centroid in μm * z [μm]: z position of cell centroid in
 μm * area [μm²]: area of cell in μm² * n_counts: number of transcripts in
 cell * n_genes: number of unique genes in cell * doublet_score [Scrublet]:
 Scrublet doublet score for cell * cell_class: major cell class identifier
 based on first tier clustering * cell_type: cell type identifier based on
 multi-tier clustering * anatomical_layer: Gut anatomical layer based on
 spatial neighborhood analysis * mucosal_pseudospace: mucosal pseudospace
 position (0 = crypt base, 1 = top). Cells not in the mucosa have a value
 of -1. * umap_coords_x [all]: UMAP coordinates for all-cell embedding, x *
 umap_coords_y [all]: UMAP coordinates for all-cell embedding, y *
 umap_coords_x [cell_class]: UMAP coordinates for cell class-specific
 embedding, x * umap_coords_y [cell_class]: UMAP coordinates for cell
 class-specific embedding, y ##### adata.var This dataframe contains
 information on each gene. * index: gene name * n_cells: number of cells
 expressing this gene * description: description of gene * \[receptor
 category]: boolean value indicating whether the gene belongs to the
 receptor category listed in the column name ##### Additional Information
 The adata structure also contains a series of additional information
 useful in interpreting the measurements. These include: *
 adata.uns['blank_names']: ordered names of blank barcodes *
 adata.obsm['blank_counts']: cell-by-gene matrix for blank
 barcode counts in cells * adata.uns['sequential_names']: ordered
 names of sequential smFISH genes *
 adata.obsm['sequential_intensities']: cell-by-gene matrix for
 average sequential smFISH intensities in cells ##
 downstream\_analysis/microbiota\_metabolites This folder and sub-folder
 contain results of the analysis of microbial metabolites that might
 activate specific cells based on their receptor expression. ####
 microbiota\_metabolite\_receptor\_interactions\_input\_dict.pkl A python
 pickle file that contains the input dictionary to the drug2cell pipeline
 encoding microbiota metabolite - receptor interactions. ####
 microbiota\_metabolite\_cell\_interactions.h5ad The microbiota metabolite
 - cell interaction scores (from drug2cell pipeline), stored in Scanpy
 anndata (adata.h5ad) format. * adata.X: microbiota metabolite - cell
 interaction scores (drug2cell) * adata.obs: same as adata.obs in
 cell_by_gene_with_metadata.h5ad (see above) * adata.var: * index:
 microbiota metabolite names * genes: target receptor gene names of the
 given microbiota metabolite (in MERFISH library) * all_genes: target gene
 names of the given microbiota metabolite (all target genes) * superclass,
 class, subclass, parent: chemical taxonomical information of the
 microbiota metabolite ## downstream\_analysis/receptor\_ligand This folder
 and sub-folder contain results of the analysis of a spatially informed
 receptor-ligand analysis. ####
 spatially\_informed\_receptor\_ligand\_interactions.csv The results of a
 spatially informed receptor ligand interaction analysis in csv format. The
 file contains the following fields: * region: gut region (ile: ileum, ce:
 cecum, pcol: prox. colon, dcol: dist.colon) in which the analysis was
 performed * microbiome: microbiome state (SPF: specific-pathogen-free, GF:
 germ-free) in which the analysis was performed * spatial_neighborhood:
 anatomical region in which the analysis was performed * ligand_cell_type:
 cell type expressing the ligand * receptor_cell_type: cell type expressing
 the receptor * ligand: ligand gene (translated to human gene name) *
 receptor: receptor gene (translated to human gene name) * mean
 [CellPhoneDB]: CellPhoneDB interaction score * p_value [CellPhoneDB]:
 p-value of the interaction * FDR: indicates whether the p-value is below
 0.05 after FDR correction