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GSE171406

GEO first seen 2021

Integration of spatial and single cell transcriptomics localizes epithelial-immune cross-talk in kidney injury

Organism
Homo sapiens; Mus musculus
Samples
4
Type
Expression profiling by high...
Submitted
2021-04-02

Single cell sequencing studies have characterized the transcriptomic signature of cell types within the kidney. However, the spatial distribution of acute kidney injury (AKI) is regional and affects cells heterogeneously. We first optimized coordination of spatial transcriptomics and single nuclear sequencing datasets, mapping 30 dominant cell types to a human nephrectomy. The predicted cell type spots corresponded with the underlying histopathology. To study the implications of AKI on transcrip...

Provenance — who produced it, who reused it

Linked to 3 papers in the literature. Roles are inferred factual signals (who deposited the data vs who reused it), with counts — never a judgement about any author.

Deposited / produced by
Eadon MichaelRicardo Melo FerreiraYing-Hua Cheng
Reused by

2 further papers cite this accession but reuse could not be confirmed.

Deep data QC

metadata only · no data-level QC for this type

Standardized, field-standard QC computed by touching the data — every metric states how it was obtained

No quantitative QC rubric exists for this data type yet, so it is deliberately left unscored — this is an honest "not applicable", not a poor rating.

QC cost 23 s compute

measured = computed from the data · extrapolated/reported = derived or from the repository · dq-1.0 · provisional — verify independently