MERFISH data from the murine hypothalamus in health and under induced sickness states
Data files
Dec 24, 2025 version files 94.75 GB
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merfish_adata.h5ad
4.01 GB
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molecules.zip
90.74 GB
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README.md
2.74 KB
Abstract
In natural environments, animals encounter diverse pathogens that engage specific peripheral host defense programs and elicit sickness behavior – a set of stereotyped physiological and behavioral changes thought to promote host fitness. So far, most studies have relied on one or few mouse models of infection, limiting insights into pathogen-specific neuroimmune interactions that generate sickness.
We hypothesized that different pathogens may elicit distinct sickness states and engage various brain circuits. Using different models of infection and inflammation representing bacterial, viral, allergic, parasitic and colitis conditions, we assessed sickness across scales: organismal – behavior and physiology; cellular – brain-wide neural activity; and molecular – single-cell in situ transcriptomics in the hypothalamus, associated with social and homeostatic functions affected during sickness. Remarkably, immune challenges each elicited unique repertoires of changes across all scales. Our findings reveal specific pathogen-specific sickness states encoded by the brain across scales, thereby broadening our understanding of how infections make us sick.
This repository contains all MERFISH-related data associated with these studies.
Bertrand J. Wong, Jeffrey R. Moffitt
Boston Children's Hospital, 2025
File Organization
molecules.zip
This folder contains csv files that describe the properties of all identified RNAs with MERFISH. Each csv file is named 'processed_barcodes_name.csv', where name represents the unique name assigned to each animal. The name describes the sickness condition (PBS: control; DSS, PIC, PLA, LPS, or STAG) and the mouse number (m#). Each csv file has the following fields:
- gene_name: The name of the gene from which the RNA was transcribed
- fov: A unique ID for the field-of-view in which the RNA was imaged
- section: A unique ID for the section in which the RNA was imaged
- cell: A unique ID for the cell to which the RNA was assigned
- cell_detailed: A name for each cell that is unique across the entire dataset
- x: The x position of the RNA in microns
- y: The y position of the RNA in microns
- z: The z position of the RNA in microns
- xf: The x position of the RNA within the fov in microns
- yf: The y position of the RNA within the fov in microns
- zi: The index of the z-slice in which the RNA was imaged
- brightness: The normalized brightness of the RNA
- area: The number of pixels associated with the RNA
- weighted_distance: The distance between the measured intensity profile for the RNA and that predicted for the matching barcode
- xgb_raw: The normalized quality score of the RNA (distinguishing it from a blank barcode)
- mir: The misidentification rate of the RNA
- concordance: Is the RNA correctly within a region where both libraries are overlapping
- isBlank: Is the RNA associated with a blank barcode
- library: The name of the library in which the RNA was identified
merfish_adata.h5ad
This file was created from the scanpy package and can be loaded with tools from that package. It contains the following items:
- X: The matrix of expression for all cells and all genes. This matrix was divided by total counts per cell then log1p-transformed.
- obs: A dataframe with the following properties for all cells
The index of each row in this data frame is a unique name for each cell- batch: A unique name for each MERFISH dataset
- x: The centroid of that cell in x, in microns
- y: The centroid of that cell in y, in microns
- z: The average position of that cell in z-stacks, in microns
- cellType: The cell type label associated with that cell
- condition: The treatment condition for the mouse in which the cell was imaged
- section: An index for the section in which the cell was imaged
- ignoreCell: A flag that indicates that the cell was not included in subsequent analysis
