While regulatory T (Treg) cells are traditionally viewed as professional suppressors of antigen presenting and effector T cells in both autoimmunity and cancer, recent findings of distinct Treg functions in tissue maintenance suggest that their regulatory purview extends to a wider range of cells and is broader than previously assumed. To elucidate tumoral Treg “connectivity” to diverse tumor-supporting accessory cell types, we explored immediate early changes in their single cell transcriptomes upon punctual Treg depletion in experimental lung cancer and injury-induced inflammation. Prior to any notable T cell activation and inflammation, fibroblasts, endothelial and myeloid cells exhibited pronounced changes in their gene expression in both cancer and injury settings. Factor analysis revealed shared Treg-dependent gene programs, foremost, prominent upregulation of VEGF signaling-related genes upon Treg deprivation in either setting, as well as in Treg-poor vs Treg-rich human lung adenocarcinomas. Accordingly, punctual Treg depletion combined with short-term VEGF blockade showed markedly improved control of PD-1 blockade-resistant lung adenocarcinoma progression in mice compared to the corresponding monotherapies, highlighting a promising factor-based querying approach to elucidating novel rational combination treatments of solid organ cancers.
from sddb import Catalog
cohort = Catalog().collection("geo-gse215361")
cohort.members # the 8 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="geo-gse215361")
coll.ordered_artifacts.to_dataframe() # the 8 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 geo-gse215361 --dest data/Licenses: 8 NCBI GEO policy
@article{Glasner_2023,
title = {Conserved transcriptional connectivity of regulatory T cells in the tumor microenvironment informs new combination cancer therapy strategies},
volume = {24},
ISSN = {1529-2916},
url = {http://dx.doi.org/10.1038/s41590-023-01504-2},
DOI = {10.1038/s41590-023-01504-2},
number = {6},
journal = {Nature Immunology},
publisher = {Springer Science and Business Media LLC},
author = {Glasner, Ariella and Rose, Samuel A. and Sharma, Roshan and Gudjonson, Herman and Chu, Tinyi and Green, Jesse A. and Rampersaud, Sham and Valdez, Izabella K. and Andretta, Emma S. and Dhillon, Bahawar S. and Schizas, Michail and Dikiy, Stanislav and Mendoza, Alejandra and Hu, Wei and Wang, Zhong-Min and Chaudhary, Ojasvi and Xu, Tianhao and Mazutis, Linas and Rizzuto, Gabrielle and Quintanal-Villalonga, Alvaro and Manoj, Parvathy and de Stanchina, Elisa and Rudin, Charles M. and Pe’er, Dana and Rudensky, Alexander Y.},
year = {2023},
month = May,
pages = {1020–1035}
}