Oliveira et al. 2024 - Human Colorectal Cancer study: 11 spatial samples across Visium HD, Visium CytAssist, and Xenium (patients P1/P2/P5 tumor, P3/P5 normal-adjacent colon). Source: 10x Genomics human-CRC dataset family.
from sddb import Catalog
cohort = Catalog().collection("oliveira-2024-colorectal")
cohort.members # the 11 datasets as a table
print(cohort.licenses()) # check the terms before you download
sdatas = cohort.to_dict() # {uid: SpatialData}, cached locally
# or keep the .zarr files in a folder of your choice:
# sdatas = cohort.to_dict(dest="data/")Needs pip install lamindb and network access to the Lamin database, which is limited during the alpha. If it fails, use the Client tab.
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
coll = db.Collection.get(key="oliveira-2024-colorectal")
coll.ordered_artifacts.to_dataframe() # the 11 members, ordered
# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]# sddb client: download every member .zarr into data/
sddb-cli download --collection oliveira-2024-colorectal --dest data/Licenses: 11 CC BY 4.0
Colorectal cancer (CRC) is the second-deadliest cancer in the world, yet a deeper understanding of spatial patterns of gene expression in the tumor microenvironment (TME) remains elusive. Here, we introduce the Visium HD platform (10x Genomics) and use it to investigate human CRC and normal adjacent mucosal tissues from formalin fixed paraffin embedded (FFPE) samples. The first assay available on Visium HD is a probe-based spatial transcriptomics workflow that was developed to enable whole transcriptome single cell scale analysis. We demonstrate highly refined unsupervised spatial clustering in Visium HD data that aligns with the hallmarks of colon tissue morphology and is notably improved over earlier Visium assays. Using serial sections from the same FFPE blocks we generate a single cell atlas of our samples, then we integrate the data to comprehensively characterize the immune cell types present in the TME, specifically at the tumor periphery. We observed enrichment of two pro-tumor macrophage subpopulations with differential gene expression profiles that were localized within distinct tumor regions. Further characterization of the T cells present in one of the samples revealed a clonal expansion that we were able to localize in the tissue using in situ gene expression analysis. In situ analysis also allowed us to perform in-depth characterization of the microenvironment of the clonally expanded T cell population and we identified a third macrophage subpopulation with gene expression profiles consistent with an anti-tumor response. Our study provides a comprehensive map of the cellular composition of the CRC TME and identifies phenotypically and spatially distinct immune cell populations within it. We show that the single cell-scale resolution afforded by Visium HD and the whole transcriptome nature of the assay allows investigations into cellular function and interaction at the tumor periphery in FFPE tissues, which has not been previously possible.
@article{Oliveira_2024,
title = {Characterization of immune cell populations in the tumor microenvironment of colorectal cancer using high definition spatial profiling},
url = {http://dx.doi.org/10.1101/2024.06.04.597233},
DOI = {10.1101/2024.06.04.597233},
publisher = {openRxiv},
author = {Oliveira, Michelli F. and Romero, Juan P. and Chung, Meii and Williams, Stephen and Gottscho, Andrew D. and Gupta, Anushka and Pilipauskas, Susan E. and Mohabbat, Syrus and Raman, Nandhini and Sukovich, David and Patterson, David and Taylor, Sarah E. B.},
year = {2024},
month = June
}