A Multi-omic Approach for the Study of Heart Regeneration Using Zebrafish as a Model Organism

Importance of the Topic

The ability of zebrafish to regenerate heart tissue offers a powerful model for understanding mechanisms of cardiac repair. Multi-omic profiling of plasma provides a systemic view of molecular changes during regeneration, which may reveal biomarkers and pathways relevant to regenerative medicine and translational research.

Objectives and Study Overview

This study applied a label-free, data-independent acquisition strategy combining lipidomics and proteomics to plasma samples from zebrafish subjected to heart tissue amputation or sham operation. The goal was to capture qualitative and quantitative changes over a three-day post-amputation period to elucidate key pathways involved in heart regeneration.

Methodology and Instrumentation

Sample preparation included tryptic digestion for proteomics and protein precipitation for lipidomics. Analyses were performed using LC-HDMSE workflows. Instrumentation:

• Waters ACQUITY UPLC M-Class for proteomics
• Waters ACQUITY UPLC I-Class for lipidomics
• Waters SYNAPT G2-Si mass spectrometer
• Progenesis QI and Progenesis QI for Proteomics software

Chromatographic and MS parameters were optimized for high sensitivity, with gradient elution and ion mobility separation to increase peak capacity.

Key Results and Discussion

Unsupervised PCA of proteomic and lipidomic data demonstrated clear separation between operated and sham groups. Over 30 000 lipid features and 440 proteins were detected, with 18% of proteins and numerous lipid classes (PC, TG, LPC, ceramides, DG, PS, PE) showing significant regulation (fold change >2, ANOVA p≤0.05). Time-course profiling revealed dynamic lipid abundance changes, with normalization by day 21. Pathway analysis highlighted HDL transport, platelet activation, and signaling cascades, implicating ABCA1-mediated lipid efflux and apolipoprotein interactions in the regenerative response.

Benefits and Practical Applications

• High sensitivity workflow suitable for limited-volume plasma samples
• Comprehensive coverage of proteome and lipidome in a single acquisition
• Seamless data processing to pathway interrogation for systems-level insights
• Identification of potential biomarkers and therapeutic targets for cardiac repair

Future Trends and Possibilities

Integration with transcriptomic and metabolomic data may enable deeper mechanistic understanding. Advances in spatial omics and single-cell proteomics could localize regenerative signals. Machine learning on multi-omic datasets will enhance biomarker discovery and aid in translating zebrafish findings to mammalian models.

Conclusion

This label-free, multi-omic approach successfully profiled plasma changes during zebrafish heart regeneration, revealing key proteins, lipids, and pathways. The methodology offers a powerful platform for regenerative biology studies and biomarker identification.

Reference

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