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Population-Scale Integration of Spatial Omics Networks for Clinical Prediction and Biological Discovery by SPIN
Liu, T.; Ko, E.; Yang, Y.; Wu, H.; Unjitwattana, T.; Wang, S.; Kang, J.; Li et al.. medRxiv (preprint), 2026-09-09
Topic: Brain proteins, microglia and lab models
Spatial omics enables the integration of high-dimensional molecular organization with clinical outcomes, yet incorporating spatial single-cell information into predictive models at the population scale remains challenging. Here, we introduce SPIN (Spatial Predictive Integration Network), which integrates subject-specific, spatially informed co-expression or co-abundance networks for population-scale clinical prediction. Current implementation of SPIN includes three key modules: network construction, molecular-to-clinical prediction by Bayesian scalar-on-network regression with manifold learning (BSNMani), and visualization & interpretation module. In the SEA-AD MERFISH transcriptomics cohort, SPIN framework achieved an accuracy of 0.81 for dementia-status prediction and revealed gene-gene co-expression subnetworks with clear biological relevance, such as glutamatergic synapses- and neurogenesis-related subnetworks. SPIN also achieved robust survival prediction in a breast cancer single proteomics cohort (C-index=0.78), identifying two survival-associated spatial proteomic subnetworks. SPIN can further use cell-type-specific spatial omics data to enhance the granularity. In summary, SPIN is a valuable tool that uses high-dimensional spatial omics data for clinical outcome prediction at the population scale across diverse disease settings, revealing biological insights while maintaining interpretation. SPIN package is available at: https://github.com/lanagarmire/SPIN
Key findings
- In the SEA-AD MERFISH transcriptomics cohort, SPIN framework achieved an accuracy of 0.81 for dementia-status prediction and revealed gene-gene co-expression subnetworks with clear biological relevance, such as glutamatergic synapses- and neurogenesis-related subnetworks.
Why it matters
This study introduced a tool called SPIN that uses spatial omics data to predict dementia status and find related gene networks. It is a technical method for research and prediction, not a treatment, cure, or prevention for Alzheimer's disease.
New to this subject? Start with our plain-English guide: Alzheimer's in numbers →
Study details
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 26 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on impact for its field and year, so that was left out: it earned 24 of the 94 points we could judge it on, which is 26 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv (preprint)) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 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 | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.5 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 1 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Lana X. Garmire: h-index 19 (OpenAlex, whole career) |
| Institution's track record | 3 / 3 | Harvard University: institution h-index 3,235 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 6 institution(s) in 1 country |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 24 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 24 of the 94 points we could judge it on is 26 out of 100. |
| Total | 26 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
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's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Liu, T., Ko, E., Yang, Y., Wu, H., Unjitwattana, T., Wang, S., et al. (2026). Population-Scale Integration of Spatial Omics Networks for Clinical Prediction and Biological Discovery by SPIN. medRxiv (preprint). https://doi.org/10.1101/2025.08.09.25333297
Vancouver
Liu T, Ko E, Yang Y, Wu H, Unjitwattana T, Wang S, et al. Population-Scale Integration of Spatial Omics Networks for Clinical Prediction and Biological Discovery by SPIN. medRxiv (preprint). 2026. doi:10.1101/2025.08.09.25333297.
BibTeX
@article{liu2026-47e80dff,
author = {Liu, T. and Ko, E. and Yang, Y. and Wu, H. and Unjitwattana, T. and Wang, S. and Kang, J. and Li and others},
title = {{Population-Scale Integration of Spatial Omics Networks for Clinical Prediction and Biological Discovery by SPIN}},
journal = {medRxiv (preprint)},
year = {2026},
doi = {10.1101/2025.08.09.25333297},
url = {https://doi.org/10.1101/2025.08.09.25333297}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.
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