Evidence map›Paper›PMID 39810754›Full record

ReviewBME frontiers2025

Spatially Resolved Multiomics: Data Analysis from Monoomics to Multiomics.

Changxiang Huan, Jinze Li, Yingxue Li, Shasha Zhao, Qi Yang, Zhiqi Zhang, Chuanyu Li, Shuli Li, Zhen Guo, Jia Yao and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BME frontiers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Article
  6. Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Review
  12. TSPAN4Frontiers in immunology · 2025
    Article
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

12 authors.

Changxiang HuanCAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.ORCID https://orcid.org/0009-0001-6260-362X
Jinze LiCAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
Yingxue LiCAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
Shasha ZhaoCAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
Qi YangCAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
Zhiqi ZhangCAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
Chuanyu LiCAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
Shuli LiCAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
Zhen GuoCAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
Jia YaoCAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
Wei ZhangCAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.ORCID https://orcid.org/0000-0003-2390-2675
Lianqun ZhouCAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.ORCID https://orcid.org/0000-0001-9250-7236

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spatial monoomics has been recognized as a powerful tool for exploring life sciences. Recently, spatial multiomics has advanced considerably, which could contribute to clarifying many biological issues. Spatial monoomics techniques in epigenomics, genomics, transcriptomics, proteomics, and metabolomics can enhance our understanding of biological functions and cellular identities by simultaneously measuring tissue structures and biomolecule levels. Spatial monoomics technology has evolved from monoomics to spatial multiomics. Moreover, the spatial resolution, high-throughput detection capability, capture efficiency, and compatibility with various sample types of omics technology have considerably advanced. Despite the technological advances in this field, data analysis frameworks have stagnated. Current challenges include incomplete spatial monoomics data analysis pipeline, overly complex data analysis tasks, and few established spatial multiomics data analysis strategies. In this review, we systematically summarize recent developments of various spatial monoomics techniques and improvements in related data analysis pipeline. On the basis of the spatial multiomics technology, we propose a data integration strategy with cross-platform, cross-slice, and cross-modality. We summarize the potential applications of spatial monoomics technology, aiming to provide researchers and clinicians with a better understanding of how such applications have advanced. Spatial multiomics technology is expected to substantially impact biology and precision medicine through measurements of cellular tissue structures and the extraction of biomolecular features.

Identifiers

PMID39810754
PMCPMC11725630

What Socratic 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 Socratic graph.