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). This data 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 organized into 2 parts. This is the second part associated with this dataset are from donors B009, B010, B011, B012.

This data is complimentary and can be linked to the single-cell processed dataframe already provided in another Dryad repository based on the donor name, folder name, and region number: https://doi.org/10.5061/dryad.pk0p2ngrf

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.

For a detailed description of each of the steps of protocols and processes to obtain this data see the detailed materials and methods in the associated manuscript. Briefly, intestine pieces from 8 different sites across the small intestine and colon were taken and frozen in OCT. These were assembled into an array of 4 tissues, cut into 7 um slices, and stained with a panel of 57 CODEX DNA-oligonucleotide barcoded antibodies. Tissues were imaged with a Keyence microscope at 20x objective and then processed using image stitching, drift compensation, deconvolution, and cycle concatenation. 

The overall structure of the datasets is organized into 2 parts. This second part associated with this dataset is from donors B009, B010, B011, and B012. Each donor has a total of 8 tissue imaging regions of the different areas of the intestine (except for B009, which includes 1 extra folder of 4 images that were used as "test" imaging data). Each donor was split into two folders of 4 regions each stained and imaged at the same time in a 4-tissue array. For B004 through B008, all small bowel (SB) or colon (CL) from the same donor were imaged together in the same array (hence the SB or CL designation in folder structure). For B009 through B012, each donor had 2 tissue regions from the small bowel and 2 tissue regions from the colon imaged together from the same array (hence the A or B designation in folder structure). These folder structure names will be useful to map back to the single cell CODEX dataframe (https://doi.org/10.5061/dryad.pk0p2ngrf) that connects each region back to its exact location within the small intestine or colon for each donor. 

One region was imaged per tissue region per donor for a 8 total image stacks per donor in 2 different zipped folders (with the exception of B008_SB which has a duplicate imaged area of one region because this tissue was large enough on the coverslip to allow imaging of two separate areas). These image stacks have been zipped with the channelnames.txt file which describes the order of the fluorescent image stacks. The structure of this file is such that the markers are first ordered by the number of cycles, and then ordered within cycles in the following channel order: DAPI, then alexa488, then Cy3, then Cy5. Thus this provides the labels for the image stacks that are provided as a stacked TIF file that has one layer as the channels and the other layer as the cycles. 

Most often we use FIJI (ImageJ) for visual analysis of the images and further image processing. However, QuPath and Python also offer functionality for opening large stacked tiff files.