Multi-Omics in Poultry Meat: Proteins, Metabolites, Lipids
- Jun 18
- 4 min read
Wooden breast and spaghetti meat cost United States poultry producers somewhere between $200 million and $1 billion a year, and the molecular triggers stay murky. Transcript counts rarely predict the proteins and lipids that actually shape muscle texture. A 2025 Poultry Science Association symposium paper, which our Dalton Bioanalytics scientists co-authored, sets out how multi-omics built on mass spectrometry connects genomics, transcriptomics, proteomics, metabolomics, and lipidomics to meat quality traits. Austin Quach from our team wrote the section on MS-based proteomics, metabolomics, and lipidomics, drawing on the same single-injection workflow we run for clients.
How Multi-Omics Connects Genotype to Meat Phenotype
The paper pulls together five strands of work presented at the 2024 Poultry Science Association meeting in Louisville. Genomic annotation through the Functional Annotation of Animal Genomes project sits at one end, mapping promoters, enhancers, and insulators that govern complex traits. Mass spectrometry sits at the other, measuring proteins, metabolites, and lipids that mRNA levels often fail to predict. Between those poles, integrative omics, what Hasin and colleagues (2017) framed as combining several data types into one model, ties molecular readouts to phenotypes such as tenderness, color, and the breast myopathies now eating into producer margins.
Key Findings
One MS assay, thousands of molecules: A single LC-MS workflow profiled 2,593 molecules in chicken breast, including 1,903 proteins, 506 lipids, 181 compounds, and 3 electrolytes, with 632 features differing significantly between myopathic and normal meat.
Spaghetti meat carries a distinct protein and lipid signature: Calponin was the most upregulated protein at a fold change of 4.3, while NAD+ and steroid hormone biosynthesis dropped, separating spaghetti meat from healthy breast tissue.
Perivascular macrophage lipid handling marks wooden breast: Spatial transcriptomics localized altered lipid uptake and oxidative stress to perivascular macrophages, linking metabolic perturbation to early disease onset.
Regulatory-element maps that link variant to trait: ATAC-seq and ChIP-seq annotation predicted roughly 1.2 million enhancer-gene pairs, helping pinpoint causal variants behind growth and meat-quality phenotypes.
Figure 1. Overview of the MS-omics workflow used across the proteomics, metabolomics, and lipidomics analyses. Biofluids or tissue enter either protein digestion for proteomics or precipitation and extraction for metabolomics and lipidomics. Prepared samples undergo liquid chromatography separation, ionization, and mass spectrometry, with precursor ions and MS/MS fragment spectra feeding feature extraction, quantification, and identification. Adapted from Zhou et al. (2025), Poultry Science.
From Regulatory Elements to Protein Readouts
Genomic selection has pushed modern broilers to grow several times faster than birds from the 1950s, yet most trait-linked variants fall outside coding regions. That gap is why the paper pairs cis-regulatory annotation with direct molecular measurement. High-throughput assays scored chromatin accessibility and histone marks to classify active and inactive promoters and enhancers, then connected those elements to genes under selection. Mass spectrometry picks up where sequence leaves off, quantifying the proteins and small molecules that carry out the phenotype. Our team has catalogued this kind of cross-scale work among the studies listed in Dalton's publications.
Mapping Myopathies With Integrated Omics
Spaghetti meat and wooden breast share histology but diverge at the molecular level, and that distinction only surfaces once proteins, lipids, and polar metabolites are read together. A combined LC-MS assay flagged 503 differential proteins, 76 lipids, and 50 metabolites in spaghetti meat versus normal tissue. Carnitine, NAD+, and lactic acid fell, while triglycerides and taurine rose. Spatial transcriptomics added location, placing disrupted lipid metabolism inside perivascular macrophages during early wooden breast. The cohorts here are modest, so these molecular targets need replication before anyone builds a breeding or feed intervention around them.
One Sample, Every Omic Layer
Running proteins, metabolites, and lipids off a single muscle extract is exactly what makes the spaghetti meat result credible, because one LC-MS assay measured 2,593 molecules without the batch-effect tradeoffs that dog stitched-together datasets. That single-injection design is how our multi-omics services at Dalton keep protein, lipid, and metabolite readouts on the same quantitative footing, which matters the moment a panel scales into multi-omics biomarker discovery. The logic that ranks poultry myopathies by molecular signature is the same one we apply to human translational cohorts.
Frequently Asked Questions
What is multi-omics in meat science? Multi-omics combines two or more molecular layers, such as proteomics, metabolomics, and lipidomics, into a single analysis. In poultry research it links genotype and molecular readouts to traits like tenderness, color, and muscle myopathies. The aim is a mechanistic picture that no single assay delivers on its own.
What did multi-omics reveal about spaghetti meat and wooden breast? The integrated analysis showed spaghetti meat and wooden breast share many altered pathways yet keep distinct signatures. Calponin rose sharply in spaghetti meat, while perivascular macrophage lipid metabolism was disrupted early in wooden breast. These molecular differences point toward separate intervention strategies.
Can one mass spectrometry assay run proteomics, metabolomics, and lipidomics together? Yes. A single LC-MS workflow can profile proteins, lipids, and polar metabolites from the same sample, which is how the study measured 2,593 molecules at once. Running everything in one injection cuts sample volume and removes the batch effects that complicate combining separate runs.
Conclusion
It is now believable that MS-based omics can separate poultry breast myopathies by molecular signature and tie regulatory variants to production traits. What isn't settled is causation; most of these associations come from small cohorts and need replication before they guide breeding or feed decisions. For an R&D team, the practical lesson is that pulling several omic layers from one sample buys interpretive power you cannot get by chaining single-platform studies after the fact.
Related Reading
See how we run these analyses in one lab: Dalton's multi-omics CRO services.
Citation
Zhou, H., Quach, A., Nair, M., Abasht, B., Kong, B., & Bowker, B. (2025). Omics based technology application in poultry meat research. Poultry Science, 104, 104643. https://doi.org/10.1016/j.psj.2024.104643
Note
This blog post summarizes findings from the above-cited research. Figures are adapted from the original publication. For full details, please refer to the source article.
By Seungjun Yeo, CEO at Dalton Bioanalytics. Specializing in multi-omics mass spectrometry for drug discovery and biomarker research.