Evidence map›Paper›PMID 36623813›Full record

ReviewJournal of pathology and translational medicine2023

Single-cell and spatial sequencing application in pathology.

Yoon-Seob Kim, Jinyong Choi, Sug Hyung Lee

Open access · goldAbstract readReview
In one paragraph

Review in Journal of pathology and translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.2field-weighted citation impact, top 13% of its field
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

10 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Characterizing Stroke Clots Using Single-Cell Sequencing.Journal of the American Heart Association · 2025
    Article
  6. Review
  7. Article
  8. Article
  9. Review
  10. 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

3 authors at 1 institution in 1 country.

Yoon-Seob KimDepartment of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Jinyong ChoiDepartment of Microbiology, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Sug Hyung LeeBiomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Catholic University of Korea · KR

Funding

National Research Foundation of Korea 2019R1A5A2027588National Research Foundation of Korea 2020R1A2C2005031
6 · The paper itself

Abstract

Traditionally, diagnostic pathology uses histology representing structural alterations in a disease's cells and tissues. In many cases, however, it is supplemented by other morphology-based methods such as immunohistochemistry and fluorescent in situ hybridization. Single-cell RNA sequencing (scRNA-seq) is one of the strategies that may help tackle the heterogeneous cells in a disease, but it does not usually provide histologic information. Spatial sequencing is designed to assign cell types, subtypes, or states according to the mRNA expression on a histological section by RNA sequencing. It can provide mRNA expressions not only of diseased cells, such as cancer cells but also of stromal cells, such as immune cells, fibroblasts, and vascular cells. In this review, we studied current methods of spatial transcriptome sequencing based on their technical backgrounds, tissue preparation, and analytic procedures. With the pathology examples, useful recommendations for pathologists who are just getting started to use spatial sequencing analysis in research are provided here. In addition, leveraging spatial sequencing by integration with scRNA-seq is reviewed. With the advantages of simultaneous histologic and single-cell information, spatial sequencing may give a molecular basis for pathological diagnosis, improve our understanding of diseases, and have potential clinical applications in prognostics and diagnostic pathology.

Indexed as

DiseasesHistologyPathologySingle-cell sequencingSpatial sequencingTranscriptome

Identifiers

PMID36623813
PMCPMC9846004
OpenAlexW4315436248

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.