Overcoming segmentation bottlenecks in brain tissue for spatial proteomic inference at population scale
You L, Durbin JL, Meng G, Stallaert W, Li G, Uttam S, Feng H, Glausier JR et al.. bioRxiv, 2026-10-24
Topic: Genetics and APOE
In spatial proteomics across large cohorts, downstream estimates from tens of thousands of images depend on accurate cell segmentation. Yet pretrained segmentation models struggle with morphologically complex cell bodies in human brain tissue, and how domain adaptation affects downstream discovery remains unclear. Here, we introduce SPInfer, a workflow for spatial proteomic inference developed using 5,832 manually annotated cell bodies from 120 immunofluorescence images of the human prefrontal cortex. After benchmarking eight segmentation methods spanning classical, convolutional, and foundation models, we adapted Cellpose-SAM, raising cell segmentation F1 from 0.37 to 0.84 and recovering cell compositions concordant with matched single-nucleus RNA-seq and spatial transcriptomics. Across 14,765 images from 69 postmortem donors, identical Alzheimer’s disease pathology models found no significant associations after false discovery rate correction using zero-shot masks, versus four using fine-tuned masks; three linked higher OLIG2-positive fractions to greater neurofibrillary tangle and plaque burden. Using the fine-tuned masks, a graph attention network over nuclear neighborhoods classified four cell types without marker information, and differential expression analysis linked cell morphology to gene expression. This provides a pipeline for fine-tuning foundation models to segment complex tissues, enabling biological discovery in spatial proteomics at the population scale.
Key findings
- Across 14,765 images from 69 postmortem donors, identical Alzheimer’s disease pathology models found no significant associations after false discovery rate correction using zero-shot masks, versus four using fine-tuned masks; three linked higher OLIG2-positive fractions to greater neurofibrillary tangle and plaque burden.
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Study details
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 21 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 times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 13 of the 62 points we could judge it on, which is 21 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (bioRxiv) |
| 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 | 1 / 5 | Rated “low” 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 | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| 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 | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 13 / 62 | Scored on 12 of 20 parts. We have no data for the rest, so they are left out: 13 of the 62 points we could judge it on is 21 out of 100. |
| Total | 21 / 100 | Capped to 16 because this is not a peer-reviewed article. |
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APA 7th
You, L., Durbin, J. L., Meng, G., Stallaert, W., Li, G., Uttam, S., et al. (2026). Overcoming segmentation bottlenecks in brain tissue for spatial proteomic inference at population scale. bioRxiv. https://doi.org/10.64898/2026.10.22.761199
Vancouver
You L, Durbin JL, Meng G, Stallaert W, Li G, Uttam S, et al. Overcoming segmentation bottlenecks in brain tissue for spatial proteomic inference at population scale. bioRxiv. 2026. doi:10.64898/2026.10.22.761199.
BibTeX
@article{you2026-647daca9,
author = {You, L. and Durbin, J. L. and Meng, G. and Stallaert, W. and Li, G. and Uttam, S. and Feng, H. and Glausier, JR. and others},
title = {{Overcoming segmentation bottlenecks in brain tissue for spatial proteomic inference at population scale}},
journal = {bioRxiv},
year = {2026},
doi = {10.64898/2026.10.22.761199},
url = {https://doi.org/10.64898/2026.10.22.761199}
}