Collections / Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection [Spatial transcriptomics]

Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection [Spatial transcriptomics]

To prevent autoimmunity, thymocytes expressing self-reactive T cell receptors (TCRs) are negatively selected, however, divergence into tolerogenic, agonist-selected lineages represent an alternative fate. As thymocyte development, selection, and lineage choices are dependent on spatial context and cell-to-cell interactions, we have performed Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq) and spatial transcriptomics on paediatric human thymu​​s. Thymocytes expressing markers of strong TCR signalling diverged from the conventional developmental trajectory prior to CD4+ lineage commitment, while markers of different agonist cell populations (CD8αα(I), CD8αα(II), T(agonist), Treg(diff) and Treg) exhibited variable timing of induction. Expression profiles of chemokines and co-stimulatory molecules, together with spatial localization, supported that dendritic cells, B cells, and stromal cells contribute to agonist selection, with different subsets influencing thymocytes at specific developmental stages within distinct spatial niches. Understanding factors influencing agonist T cells is needed to benefit from their immunoregulatory effects in clinical use.

Datasets8
Size~383 MB
AssayVisium
Samplehuman · thymus
KindOther
Flag a data issue
PMID:36741401 ↗
Python · sddb client
from sddb import Catalog

cohort = Catalog().collection("geo-gse207205")
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/")
Python · lamindb

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-gse207205")
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()]
Shell
# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse207205 --dest data/

Licenses: 8 NCBI GEO policy

Open in Vitessce
Visium
Human thymus
GSM6281322
human thymus 45.9 MB 4,992 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuethymus
Size45.9 MB
Spots4,992
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human thymus
GSM6281324
human thymus 58.3 MB 4,992 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuethymus
Size58.3 MB
Spots4,992
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human thymus
GSM6281326
human thymus 37.2 MB 4,992 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuethymus
Size37.2 MB
Spots4,992
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human thymus
GSM6281323
human thymus 45.4 MB 4,992 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuethymus
Size45.4 MB
Spots4,992
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human thymus
GSM6281325
human thymus 49.9 MB 4,992 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuethymus
Size49.9 MB
Spots4,992
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human thymus
GSM6281320
human thymus 35.9 MB 4,992 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuethymus
Size35.9 MB
Spots4,992
Magnificationother · 3.593 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human thymus
GSM6281321
human thymus 54.4 MB 4,992 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuethymus
Size54.4 MB
Spots4,992
Magnificationother · 3.466 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human thymus
GSM6281327
human thymus 56.1 MB 4,992 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuethymus
Size56.1 MB
Spots4,992
Magnificationother · 3.485 µm/px
bronze H&E NCBI GEO policy Open in Vitessce

Cite this collection

doi.org/10.3389/fimmu.2022.1092028 ↗
BibTeX
@article{Heimli_2023,
  title = {Multimodal human thymic profiling reveals trajectories and cellular milieu for T agonist selection},
  volume = {13},
  ISSN = {1664-3224},
  url = {http://dx.doi.org/10.3389/fimmu.2022.1092028},
  DOI = {10.3389/fimmu.2022.1092028},
  journal = {Frontiers in Immunology},
  publisher = {Frontiers Media SA},
  author = {Heimli, Marte and Flåm, Siri Tennebø and Hjorthaug, Hanne Sagsveen and Trinh, Don and Frisk, Michael and Dumont, Karl-Andreas and Ribarska, Teodora and Tekpli, Xavier and Saare, Mario and Lie, Benedicte Alexandra},
  year = {2023},
  month = Jan
}