---
title: "SPInfer: Fine-tuning Cellpose-SAM for scalable spatial proteomics in human brain tissue"
id: "biorxiv-1-overcoming-segmentation-bottlenecks-in-brain-tissue-for-spatial-proteomic"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-1-overcoming-segmentation-bottlenecks-in-brain-tissue-for-spatial-proteomic"
content_type: "clinical_feed_article"
specialty: "Neurology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.22.761199v1?rss=1"
published_at: "2026-10-24T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# SPInfer: Fine-tuning Cellpose-SAM for scalable spatial proteomics in human brain tissue
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-1-overcoming-segmentation-bottlenecks-in-brain-tissue-for-spatial-proteomic
- **Specialty:** [Neurology](https://medichelpline.com/clinical-feed/neurology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.22.761199v1?rss=1)
- **Published At:** 2026-10-24T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors developed **SPInfer**, a workflow to enable population-scale **spatial proteomics** by improving cell segmentation in human prefrontal cortex immunofluorescence images. - Training data comprised 5,832 manually annotated cell bodies drawn from 120 images; this dataset formed the basis for model adaptation and benchmarking. - Eight segmentation approaches were benchmarked across classical, convolutional, and foundation-model categories; the team identified **Cellpose-SAM** as the basis for fine-tuning. - Fine-tuning raised segmentation F1 from 0.37 (zero-shot) to 0.84 (fine-tuned), according to the authors’ reported metrics. - The workflow was applied to 14,765 images from 69 postmortem donors to test how segmentation choice affects downstream association testing with Alzheimer's disease pathology. - Using zero-shot masks, investigators reported no significant associations after false discovery rate correction; using fine-tuned masks, four associations passed FDR, including three linking higher **OLIG2**-positive cell fractions to greater neurofibrillary tangle and plaque burden. - With fine-tuned masks, a graph attention network over nuclear neighborhoods classified four cell types without marker input, and differential expression analyses associated cell morphology with gene expression. - The study team has shared code and data resources via GitHub and a Hugging Face dataset, and the preprint has not been peer reviewed.
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Durbin, Guanqun Meng, Wayne Stallaert, Guang Li, Shikhar Uttam, Hao Feng, Jill R. Glausier, Jiebiao Wang doi: https://doi.org/10.64898/2026.10.22.761199 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. Liang You 1 University of Pittsburgh; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Liang%2BYou%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=You%20L&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ALiang%2BYou%2B) Jamie L. Durbin 1 University of Pittsburgh; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Jamie%2BL.%2BDurbin%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Durbin%20JL&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AJamie%2BL.%2BDurbin%2B) Guanqun Meng 2 University of Texas Health Science Center at Houston * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Guanqun%2BMeng%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Meng%20G&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AGuanqun%2BMeng%2B) Wayne Stallaert 1 University of Pittsburgh; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Wayne%2BStallaert%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Stallaert%20W&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AWayne%2BStallaert%2B) Guang Li 1 University of Pittsburgh; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Guang%2BLi%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Li%20G&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AGuang%2BLi%2B) Shikhar Uttam 1 University of Pittsburgh; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Shikhar%2BUttam%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Uttam%20S&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AShikhar%2BUttam%2B) Hao Feng 2 University of Texas Health Science Center at Houston * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Hao%2BFeng%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Feng%20H&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AHao%2BFeng%2B) Jill R. Glausier 1 University of Pittsburgh; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Jill%2BR.%2BGlausier%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Glausier%20JR&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AJill%2BR.%2BGlausier%2B) Jiebiao Wang 1 University of Pittsburgh; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Jiebiao%2BWang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Wang%20J&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AJiebiao%2BWang%2B) * For correspondence: jbwang@pitt.edu * [Abstract](https://www.biorxiv.org/content/10.64898/2026.10.22.761199v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5899740/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.10.22.761199v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5899740/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.10.22.761199v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5899740/1) * [Data/Code](https://www.biorxiv.org/content/10.64898/2026.10.22.761199v1.external-links)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_data_code/node:5899740/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.10.22.761199v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5899740/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract 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. ### Competing Interest Statement The authors have declared no competing interest. ## Footnotes * * ## Funder Information Declared National Institutes of Health, R01AG099232, R01AG080590 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY 4.0 International license](http://creativecommons.org/licenses/by/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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[ Download PDF](https://www.biorxiv.org/content/10.64898/2026.10.22.761199v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/10/24/2026.10.22.761199.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [ Data/Code](https://www.biorxiv.org/content/early/2026/10/24/2026.10.22.761199.external-links) [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) Overcoming segmentation bottlenecks in brain tissue for spatial proteomic inference at population scale Liang You, Jamie L. Durbin, Guanqun Meng, Wayne Stallaert, Guang Li, Shikhar Uttam, Hao Feng, Jill R. Glausier, Jiebiao Wang bioRxiv 2026.10.22.761199; doi: https://doi.org/10.64898/2026.10.22.761199 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. 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