Background: Pancreatic ductal adenocarcinoma (PDAC) has a poor survival rate largely due to late detection of disease. PDAC arises from precursor microscopic lesions, termed pancreatic intraepithelial neoplasia (PanIN), that develop at least a decade before overt disease progression––this provides an opportunity to intercept PanIN–to–PDAC progression. However, immune interception strategies require a full understanding of PanIN and PDAC cellular architecture. Methods: Surgical specimens containing PanIN and PDAC lesions from a unique cohort of treatment-naïve patients with PDAC were surveyed using spatial-omics (proteomic and transcriptomic). Results: We uncovered, for the first time, the organization of lymphoid cells into tertiary lymphoid structures (TLSs) adjacent to PanIN lesions. These TLSs lack CD21+CD23+ B cells in comparison to more mature TLSs. Mature TLSs were found near the invasive PDAC border, despite patients being treatment-naïve. PanINs harbored mostly CD4+ T cells with fewer Tregs and exhausted T cells than PDAC tumors. Peri-tumoral space was enriched with naïve CD4+ and central memory T cells. Conclusions: These data show that early PanIN lesions recruit effector lymphoid populations capable of organizing into immature TLSs with few immune suppressive T cells relative to PDAC-associated TLS. These results implicate the opportunity to modulate the immune microenvironment in pre–malignant PanINs before immune exclusion and immunosuppression emerge during progression into PDACs.
from sddb import Catalog
cohort = Catalog().collection("geo-gse294669")
cohort.members # the 13 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-gse294669")
coll.ordered_artifacts.to_dataframe() # the 13 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-gse294669 --dest data/Licenses: 13 NCBI GEO policy
@article{Lyman_2025,
title = {Spatial proteomics and transcriptomics reveal early immune cell organization in pancreatic intraepithelial neoplasia},
volume = {10},
ISSN = {2379-3708},
url = {http://dx.doi.org/10.1172/jci.insight.191595},
DOI = {10.1172/jci.insight.191595},
number = {15},
journal = {JCI Insight},
publisher = {American Society for Clinical Investigation},
author = {Lyman, Melissa R. and Mitchell, Jacob T. and Raghavan, Sidharth and Kagohara, Luciane T. and Huff, Amanda L. and Haldar, Saurav D. and Shin, Sarah M. and Guinn, Samantha and Barrett, Benjamin and Longway, Gabriella and Hernandez, Alexei and Coyne, Erin M. and Yuan, Xuan and Andaloori, Lalitya and Lai, Jiaying and Liu, Yun Zhou and Karchin, Rachel and Gupta, Anuj and Kiemen, Ashley L. and Forjaz, André and Wirtz, Denis and Wu, Pei-Hsun and Deshpande, Atul and Lee, Jae W. and Armstrong, Todd D. and Azad, Nilofer S. and Zimmerman, Jacquelyn W. and Wood, Laura D. and Anders, Robert A. and Thompson, Elizabeth D. and Jaffee, Elizabeth M. and Fertig, Elana J. and Ho, Won Jin and Zaidi, Neeha},
year = {2025},
month = June
}