Semi-automated image analysis: Detecting carbonylation in subcellular regions of skeletal muscle

Vratislav Kostal, Kiara Levar, Mark Swift, Erik Skillrud, Mark Chapman, Ladora V. Thompson, Edgar A. Arriaga

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


The level of carbonylation in skeletal muscle is a marker of oxidative damage associated with disease and aging. While immunofluorescence microscopy is an elegant method to identify carbonylation sites in muscle cross-sections, imaging analysis is manual, tedious, and time consuming, especially when the goal is to characterize carbonyl contents in subcellular regions. In this paper, we present a semi-automated method for the analysis of carbonylation in subcellular regions of skeletal muscle cross-sections visualized with dual fluorescent immunohistochemistry. Carbonyls were visualized by their reaction with 2,4-dinitrophenylhydrazine (DNPH) followed by immunolabeling with an Alexa488-tagged anti-DNP antibody. Mitochondria were probed with an anti-COXI primary antibody followed by the labeling with an Alexa568-tagged secondary antibody. After imaging, muscle fibers were individually analyzed using a custom-designed, lab-written, computer-aided procedure to measure carbonylation levels in subsarcolemmal and interfibrillar mitochondrial regions, and in the cytoplasmic and extracellular regions. Using this procedure, we were able to decrease the time necessary for the analysis of a single muscle fiber from 45 min to about 1 min. The procedure was tested by four independent analysts and found to be independent on inter-person and intra-person variations. This procedure will help increase highly needed throughput in muscle studies related to ageing, disease, physical performance, and inactivity that use carbonyl levels as markers of oxidative damage.

Original languageEnglish (US)
Pages (from-to)213-222
Number of pages10
JournalAnalytical and Bioanalytical Chemistry
Issue number1
StatePublished - Apr 2011

Bibliographical note

Funding Information:
Acknowledgements This work was supported by the National Institutes of Health grants R01-AG20866 (EA) and R01-AG17768 (LVT). KB and MS were supported by the University of Minnesota Undergraduate Research Opportunity Program. EA acknowledges support as a Fesler-Lampert Chair in Aging.


  • Aging
  • Carbonylation
  • Fluorescence microscopy
  • Image analysis
  • ImageJ
  • MatLab
  • Mitochondria
  • Muscle fiber


Dive into the research topics of 'Semi-automated image analysis: Detecting carbonylation in subcellular regions of skeletal muscle'. Together they form a unique fingerprint.

Cite this