A semi-automated pipeline for morphological analysis of myonuclei along single muscle fibers.

Esben T Schroeder, Helia G Megowan, Madeline Luu, Adam Shuaib, Adam C Fries, Jake Searcy, Hans C Dreyer

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Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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7 authors.

Esben T SchroederDepartment of Human Physiology, University of Oregon, Eugene, OR 97403, USA.

Helia G MegowanDepartment of Human Physiology, University of Oregon, Eugene, OR 97403, USA.

Madeline LuuDepartment of Computer Science, University of Oregon, Eugene, OR 97403, USA.

Adam ShuaibDepartment of Neuroscience, University of Oregon, Eugene, OR 97403, USA.

Adam C FriesGenomics & Cell Characterization Core Facility, University of Oregon, Eugene, OR 97403, USA.

Jake SearcyDepartment of Data Science, University of Oregon, Eugene, OR 97403, USA.

Hans C DreyerDepartment of Human Physiology, University of Oregon, Eugene, OR 97403, USA.

Funding

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6 · The paper itself

Abstract

Manual quantitation of skeletal muscle myonuclear number, spatial orientation, and morphology is time-consuming and subject to error and bias. To overcome these limitations, we developed and validated a semi-automated, quantitative, and reproducible image-analysis pipeline. The workflow combines FIJI-based preprocessing with custom Python scripts to process immunohistological images of individual muscle fibers, enabling high resolution and scalable quantification of nuclei. The analyses incorporate morphometric parameters including nuclear position, shape, and three-dimensional orientation, as well as centroid-to-skeleton distance and nearest-neighbor relationships to capture spatial patterns of myonuclear organization along the fiber. Outputs include per-fiber and biopsy-level summaries integrated with IMARIS metrics. This semi-automated approach provides a robust and efficient platform for high-throughput analysis of myonuclear number and structural features across large single fiber datasets.

Indexed as

automationImageJmusclepipeline

Identifiers

PMID42571222

DOI10.1016/j.isci.2026.116961

PMCPMC13451813

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LicenceCC BY

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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.