# Top–Down Proteomics of Skinned Human Muscle Fibers Reveals Proteoform‐Resolved Fiber‐to‐Fiber Variability

## TL;DR

This study uses a new top-down proteomics method to analyze individual human muscle fibers, revealing significant variability in protein forms between and within muscle fibers.

## Contribution

A high-sensitivity top-down proteomics method for single human muscle fibers is developed and applied to reveal proteoform-level variability.

## Key findings

- Extensive inter-donor and intra-donor fiber-to-fiber variability in isoform expression was identified.
- The method enables proteoform-level coverage of key sarcomeric proteins in individual fibers.
- The approach provides a foundation for studying skeletal muscle biology and muscle-related diseases.

## Abstract

Human skeletal muscle is composed of highly heterogeneous single muscle fibers (multinucleated single cells) that are commonly classified as fast or slow fiber types, yet proteoform‐resolved characterization of individual human muscle fibers remains lacking. Herein, we establish a high sensitivity top–down proteomics method for the analysis of single human muscle fibers (hSMFs). Specifically, we have optimized the surfactant‐free extraction protocol for analysis of chemically permeabilized (“skinned”) hSMFs, a common preparation used to isolate the sarcomere prior to contractile measurements. This approach enables robust and reproducible proteoform‐level coverage of key sarcomeric proteins from individual fibers using top–down LC–MS/MS. With this method, we identified extensive inter‐ and intra‐donor fiber‐to‐fiber variability in isoform expression and proteoform abundance in hSMFs extracted from the heterogeneous vastus lateralis muscles. Together, these results demonstrate the capability of single‐fiber top–down proteomics to resolve proteoform‐level heterogeneity in human skeletal muscle and establish a methodological foundation for future studies towards elucidating skeletal muscle biology and understanding muscle‐related diseases. Source data for this manuscript is available via the MassIVE repository at massive.ucsd.edu with identifier: MSV000100493.