Evidence map›Paper›PMID 40461389›Full record

ReviewTrends in genetics : TIG2025

Spatial omics enters the microscopic realm: opportunities and challenges.

Yichen Si, Joo Sang Lee, Goo Jun, Hyun Min Kang, Jun Hee Lee

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Trends in genetics : TIG, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

20 citing papers in PubMed.

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  16. Endothelial Heterogeneity in Pulmonary Hypertension.Arteriosclerosis, thrombosis, and vascular biology · 2026
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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

5 authors.

Yichen SiEric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Joo Sang LeeDepartment of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon, Republic of Korea; Department of Artificial Intelligence, Sungkyunkwan University, Suwon, Republic of Korea; Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Republic of Korea.
Goo JunDepartment of Epidemiology and Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA.
Hyun Min KangDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA. Electronic address: hmkang@umich.edu.
Jun Hee LeeDepartment of Molecular and Integrative Physiology and Institute of Gerontology, University of Michigan Medical School, Ann Arbor, MI, USA. Electronic address: leeju@umich.edu.

Funding

The NRF2-FBP1 crossregulatory loop and the control of healthy and diseased liver metabolismR01DK133448 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Michael Karin, Jun Hee Lee · 2022 to 2026
$3.6M
Diabetes Progression with Metabolomic Profiling in Starr County Mexican AmericansR01DK118631 · NIDDK · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI JUN, GOO · 2019 to 2023
$3.1M
Sestrins-mediated integration of leucine and exercise benefits for mitochondrial homeostasisR01AG079163 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Myungjin Kim, Jun Hee Lee · 2023 to 2026
$2.1M
Seq-Scope: Microscopic Examination of Spatial Single Cell Transcriptome in Cell and Tissue SenescenceUH3CA268091 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LEE, JUN HEE · 2023 to 2025
$1.7M
Leveraging long-range haplotypes in sequencing data to advance large scale genetic studiesR01HG011031 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ZOELLNER, SEBASTIAN · 2020 to 2023
$1.4M
NCI NIH HHS UH3 CA268091NHGRI NIH HHS R01 HG011031NIA NIH HHS R01 AG079163NIDDK NIH HHS R01 DK118631NIDDK NIH HHS R01 DK133448
6 · The paper itself

Abstract

Spatial transcriptomics (ST) enables systematic profiling of whole-transcriptome gene expression in tissues while preserving spatial context. Recent advances in sequencing- and imaging-based ST technologies have ushered in the era of microscopic-resolution ST (μST), allowing transcriptome mapping at cellular and even subcellular scales with unprecedented precision. Despite these advances, μST faces substantial challenges, including sparse transcript discovery per submicron (or micron)-sized spatial units and data fragmentation across platforms, hindering integration and analysis. There is also a growing demand for scalable, segmentation-free, and universally applicable analysis methods, as well as strategies for 3D mapping, multi-omics integration, and artificial intelligence (AI)-driven spatial analysis. In this review, we highlight recent breakthroughs, outline key challenges, and discuss emerging experimental and computational solutions shaping the future of μST.

Indexed as

Gene Expression ProfilingGenomicsTranscriptomeAnimalsArtificial IntelligenceComputational BiologyHumanscell segmentationcomputational biologymicroscopic resolutionsingle-cell analysisspatial omicsspatial transcriptomics

Identifiers

PMID40461389
PMCPMC12486146

What OpenQuestion holds

Textmetadata
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.