Processed single cell data from CODEX multiplexed imaging of the human intestine

We performed CODEX (co-detection by indexing) multiplexed imaging on 64
sections of the human intestine (~16 mm2) from 8 donors (B004, B005, B006,
B008, B009, B010, B011, and B012) using a panel of 57
oligonucleotide-barcoded antibodies. Subsequently, images underwent
standard CODEX image processing (tile stitching, drift compensation, cycle
concatenation, background subtraction, deconvolution, and determination of
best focal plane), single cell segmentation, and column marker
z-normalization by tissue. The outputs of this process were data frames of
2.6 million cells with 57 antibody fluorescence values quantified from
each marker. Each cell has its cell type, cellular neighborhood, community
of neighborhooods, and tissue unit defined with x, y coordinates
representing pixel location in the original image. This is from a total of
25 cell types, 20 multicellular neighborhoods, 10 communities of
neighborhoods, and 3 tissue segments that could be used to understand the
cellular interactions, composition, and structure of the human intestine
from the duodenum to the sigmoid colon and understand differences between
different areas of the intestine. This data could be used as a healthy
baseline to compare other single-cell datasets of the human intestine,
particularly multiplexed imaging ones.  The overall structure of
the datasets is individual cells segmented out in each row.
Columns MUC2 through CD161 are the
markers used for clustering the cell types. These are the columns that are
the values of the antibody staining the target protein within the tissue
quantified at the single-cell level. This value is the per cell/area
averaged fluorescent intensity that has subsequently been z normalized
along each column as described
above. OLFM4 through MUC6 were captured in the
quantification but not used within the clustering of cell types. Other
columns are explained in the table in the Usage Notes section below. Along
with this main data table, there is also a donor metadata table that links
the donor ids to clinical metadata such as: age, sex, race, BMI, history
of diabetes, history of cancer, history of hypertension, and history of
gastorintestinal disease. The raw imaging data can be found at
(https://portal.hubmapconsortium.org/). We have created a landing page
with links to all the raw dataset IDs and the HuBMAP ID for this
Collection is HBM692.JRZB.356 and the DOI is:10.35079/HBM692.JRZB.356.
This can be used to also pair it with the matched snRNAseq and snATACseq
for each section of tissue.