Evidence map›Paper›PMID 39833687›Full record

ReviewBMC genomics2025

A practical guide for choosing an optimal spatial transcriptomics technology from seven major commercially available options.

Hyun Ju Lim, Ye Wang, Anton Buzdin, Xinmin Li

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
64citing papers in PubMed, 1 pooled it
–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

64 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 more citing papers are in PubMed but not listed here.

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.

Hyun Ju Lim *UCLA Technology Center for Genomics & Bioinformatics, Department of Pathology & Laboratory Medicine, 650 Charles E Young Dr. South, Los Angeles, CA, 90095, USA.
Ye Wang *Clinical Laboratory, The Affiliated Qingdao Central Hospital of Medical College of Qingdao University, Qingdao, 266042, China.
Anton BuzdinWorld-Class Research Center "Digital biodesign and personalized healthcare", I.M. Sechenov First Moscow State Medical University, Moscow, 119991, Russia.
Xinmin LiUCLA Technology Center for Genomics & Bioinformatics, Department of Pathology & Laboratory Medicine, 650 Charles E Young Dr. South, Los Angeles, CA, 90095, USA. xinminli@mednet.ucla.edu.

Funding

Women's CancersP30CA016042 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Samuel Wheeler French · 1985 to 2026
$134.5M
Targeting Super-Enhancers Suppresses Cancer Stemness and Invasion of HNSCCR01DE029173 · NIDCR · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI WANG, CUN-YU · 2020 to 2024
$2.0M
Ministry of Science and Higher Education of the Russian Federation 075-15-2022-304NCI NIH HHS P30 CA016042NIDCR NIH HHS R01 DE029173NIH HHS R01DE29173
6 · The paper itself

Abstract

Spatial transcriptomics technology enables the mapping of gene expression within tissues, allowing researchers to visualize the spatial distribution of RNA molecules and gain insights into cellular organization, interactions, and functions in their native environments. A variety of spatial technologies are now commercially available, each offering distinct technical parameters such as cellular resolution, detection sensitivity, gene coverage, and throughput. This wide range of options can make it challenges or create confusion for researchers to select the most appropriate platform for their specific research objectives. In this paper, we will analyze and compare seven major commercially available spatial platforms to guide researchers in choosing the most suitable option for their needs.

Indexed as

Gene Expression ProfilingTranscriptomeHumans10X Visium10X visium HDCosMx SMIGeoMx DSPMerscopeRNA sequencingSpatial transcriptomicsStereoseqXenium

Identifiers

PMID39833687
PMCPMC11744898

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.