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Integrative spatial analysis of cell morphologies and transcriptional states with MUSE
Bao F, Deng Y, Wan S, Shen SQ, Wang B, Dai Q, Altschuler SJ, Wu LF.. Nature biotechnology, 2022-03-28
Topic: Brain proteins, microglia and lab models
Spatial transcriptomics enables the simultaneous measurement of morphological features and transcriptional profiles of the same cells or regions in tissues. Here we present multi-modal structured embedding (MUSE), an approach to characterize cells and tissue regions by integrating morphological and spatially resolved transcriptional data. We demonstrate that MUSE can discover tissue subpopulations missed by either modality as well as compensate for modality-specific noise.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Bao, F., Deng, Y., Wan, S., Shen, S. Q., Wang, B., Dai, Q., et al. (2022). Integrative spatial analysis of cell morphologies and transcriptional states with MUSE. Nature biotechnology. https://doi.org/10.1038/s41587-022-01251-z
Vancouver
Bao F, Deng Y, Wan S, Shen SQ, Wang B, Dai Q, et al. Integrative spatial analysis of cell morphologies and transcriptional states with MUSE. Nature biotechnology. 2022. doi:10.1038/s41587-022-01251-z. PMID: 35347329.
BibTeX
@article{bao2022-35347329,
author = {Bao, F. and Deng, Y. and Wan, S. and Shen, S. Q. and Wang, B. and Dai, Q. and Altschuler, S. J. and Wu, L. F.},
title = {{Integrative spatial analysis of cell morphologies and transcriptional states with MUSE}},
journal = {Nature biotechnology},
year = {2022},
doi = {10.1038/s41587-022-01251-z},
pmid = {35347329},
url = {https://doi.org/10.1038/s41587-022-01251-z}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
Key findings
- Here we present multi-modal structured embedding (MUSE), an approach to characterize cells and tissue regions by integrating morphological and spatially resolved transcriptional data.
- We demonstrate that MUSE can discover tissue subpopulations missed by either modality as well as compensate for modality-specific noise.
- We apply MUSE to diverse datasets containing spatial transcriptomics (seqFISH+, STARmap or Visium) and imaging (hematoxylin and eosin or fluorescence microscopy) modalities.
- MUSE identified biologically meaningful tissue subpopulations and stereotyped spatial patterning in healthy brain cortex and intestinal tissues.
Why it matters
This paper presents MUSE, a computational method for integrating spatial transcriptomics and imaging data. It is a tool for studying tissues and does not test any treatment, cure or prevention for Alzheimer's disease.
New to this subject? Start with our plain-English guide: Genetics →
Study details
- Evidence strengthLow
How this study scored 48/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Nature biotechnology) |
| Journal's citation record | 4 / 4 | Nature Biotechnology: journal h-index 631 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2022 — within 6 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 122 times (OpenAlex, 2026-10-10) |
| Citations per year | 4 / 5 | 20.3 citations a year over 6 year(s) since 2022 |
| Impact for its field and year | 3 / 6 | Cited 6.7× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 3 / 4 | Cited 93 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Lani F. Wu: h-index 40 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of California, San Francisco: institution h-index 1,915 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 35347329) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 48 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41587-022-01251-z
- PubMed: 35347329
- Original source →
- Free full text: eScholarship (California Digital Library) →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Studies this one cites
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