Evidence map›Paper›PMID 35069263›Full record

ReviewFrontiers in physiology2021

Principles of Spatial Transcriptomics Analysis: A Practical Walk-Through in Kidney Tissue.

Teia Noel, Qingbo S Wang, Anna Greka, Jamie L Marshall

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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  8. A Review of the Application of Spatial Transcriptomics in Neuroscience.Interdisciplinary sciences, computational life sciences · 2024
    Review
  9. Article
  10. The dawn of spatial omics.Science (New York, N.Y.) · 2023
    Review
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  17. Computational solutions for spatial transcriptomics.Computational and structural biotechnology journal · 2022
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Teia NoelKidney Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, United States.
Qingbo S WangProgram in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, United States.
Anna GrekaKidney Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, United States.
Jamie L MarshallKidney Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spatial transcriptomic technologies capture genome-wide readouts across biological tissue space. Moreover, recent advances in this technology, including Slide-seqV2, have achieved spatial transcriptomic data collection at a near-single cell resolution. To-date, a repertoire of computational tools has been developed to discern cell type classes given the transcriptomic profiles of tissue coordinates. Upon applying these tools, we can explore the spatial patterns of distinct cell types and characterize how genes are spatially expressed within different cell type contexts. The kidney is one organ whose function relies upon spatially defined structures consisting of distinct cellular makeup. Thus, the application of Slide-seqV2 to kidney tissue has enabled us to elucidate spatially characteristic cellular and genetic profiles at a scale that remains largely unexplored. Here, we review spatial transcriptomic technologies, as well as computational approaches for cell type mapping and spatial cell type and transcriptomic characterizations. We take kidney tissue as an example to demonstrate how the technologies are applied, while considering the nuances of this architecturally complex tissue.

Indexed as

kidney spatial transcriptomicskidney transcriptomicsslide-seqslide-seqV2spatial transcriptomics

Identifiers

PMID35069263
PMCPMC8770822

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.