Targeted Quantitative Analysis of Specific Proteins in Cytosolic, Mitochondrial, and Nuclear Fractions Using PRM

Mitochondria play a central role in liver physiology by regulating key metabolic processes. Consequently, mitochondrial dysfunction is a hallmark of multiple liver diseases, including steatosis, steatohepatitis, and liver failure following hepatectomy. Subcellular fractionation is widely used to isolate mitochondria from liver cells or tissue; however, the enrichment and purity of isolated fractions are critical to ensure reliable downstream functional and proteomic analyses. Conventional validation methods, such as immunoblotting of organelle-specific markers, are limited by low throughput, restricted sensitivity, and variability. In this study, we present a targeted proteomics strategy based on parallel reaction monitoring (PRM) to quantitatively assess the enrichment of cytosolic, mitochondrial, and nuclear fractions obtained from liver samples using commercial isolation kits. PRM analyses demonstrated robust and compartment-specific enrichment in both PLC/PRF/5 cells and mouse liver tissue. In PLC/PRF/5 cells, high nuclear/cytosolic enrichment was observed for Prelamin A/C, while mitochondrial markers such as ATPase showed strong mitochondrial/cytosolic ratios. Cytosolic markers consistently displayed enrichment in the cytosolic fraction. Similar trends were observed in mouse liver tissue, confirming applicability across biological systems. Overall, these results highlight PRM as a sensitive, reproducible, and cost-effective alternative to immunodetection approaches for evaluating subcellular fraction purity, supporting high-quality mitochondrial preparations for translational hepatology studies.

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Publication Details

Journal
Journal of Proteome Research
Published
2026-02-25
DOI
https://doi.org/10.1021/acs.jproteome.5c00697
Primary Topic
Machine Learning in Bioinformatics
Type
article
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article

Targeted Quantitative Analysis of Specific Proteins in Cytosolic, Mitochondrial, and Nuclear Fractions Using PRM

Fernando José Corrales, Alberto Paradela, Alejandra Delgado-Sequera
Journal of Proteome Research
Machine Learning in Bioinformatics
article

Targeted Quantitative Analysis of Specific Proteins in Cytosolic, Mitochondrial, and Nuclear Fractions Using PRM

Fernando José Corrales, Alberto Paradela, Alejandra Delgado-Sequera
article en

Abstract

Mitochondria play a central role in liver physiology by regulating key metabolic processes. Consequently, mitochondrial dysfunction is a hallmark of multiple liver diseases, including steatosis, steatohepatitis, and liver failure following hepatectomy. Subcellular fractionation is widely used to isolate mitochondria from liver cells or tissue; however, the enrichment and purity of isolated fractions are critical to ensure reliable downstream functional and proteomic analyses. Conventional validation methods, such as immunoblotting of organelle-specific markers, are limited by low throughput, restricted sensitivity, and variability. In this study, we present a targeted proteomics strategy based on parallel reaction monitoring (PRM) to quantitatively assess the enrichment of cytosolic, mitochondrial, and nuclear fractions obtained from liver samples using commercial isolation kits. PRM analyses demonstrated robust and compartment-specific enrichment in both PLC/PRF/5 cells and mouse liver tissue. In PLC/PRF/5 cells, high nuclear/cytosolic enrichment was observed for Prelamin A/C, while mitochondrial markers such as ATPase showed strong mitochondrial/cytosolic ratios. Cytosolic markers consistently displayed enrichment in the cytosolic fraction. Similar trends were observed in mouse liver tissue, confirming applicability across biological systems. Overall, these results highlight PRM as a sensitive, reproducible, and cost-effective alternative to immunodetection approaches for evaluating subcellular fraction purity, supporting high-quality mitochondrial preparations for translational hepatology studies.

Journal of Proteome Research
Instituto de Salud Carlos III (ES), Centro Nacional de Biotecnología (ES)
Zero hunger
Openalex Percentile: Top 43%
Machine Learning in Bioinformatics
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