ReviewJournal of pathology and translational medicine2023
Single-cell and spatial sequencing application in pathology.
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.
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.
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.
Who cites it
10 citing papers in PubMed, 14 citations in OpenAlex.
- Multiplex hyperspectral IHC: A 12-plex assay for high-dimensional analysis of the tumor microenvironment.ImmunoHorizons · 2026Article
- Review
- Single-cell RNA sequencing in osteosarcoma: applications in diagnosis, prognosis, and treatment.Medical oncology (Northwood, London, England) · 2025Review
- Spatial transcriptomics meets diabetic kidney disease: Illuminating the path to precision medicine.World journal of diabetes · 2025Review
- Characterizing Stroke Clots Using Single-Cell Sequencing.Journal of the American Heart Association · 2025Article
- Review
- The crosstalk role of CDKN2A between tumor progression and cuproptosis resistance in colorectal cancer.Aging · 2024Article
- Potential Therapeutic Targets for Hypotension in Duchenne Muscular Dystrophy.Medical hypotheses · 2024Article
- Incorporating Novel Technologies in Precision Oncology for Colorectal Cancer: Advancing Personalized Medicine.Cancers · 2024Review
- Fibroblasts - the cellular choreographers of wound healing.Frontiers in immunology · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
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
Identifiers
What OpenQuestion holds
Registered trials
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.