Single-Cell Multi-Omics in Livestock: Transcriptomic, Epigenomic, and Proteomic Resolution of Development, Immunity, and Production Biology

Abstract

Single- cell multi- omics technologies — encompassing single- cell RNA sequencing (scRNA- seq), single- cell ATAC sequencing (scATAC- seq), single- cell DNA methylation sequencing, spatial transcriptomics, and their combinatorial implementations (CITE seq, 10x Multitone, SNARE- seq) — have revolutionised our ability to dissect the transcriptomic, epigenomic, and proteomic heterogeneity of complex tissues at cellular resolution. Following their initial development and deployment in biomedical model organisms, single- cell technologies are now being applied with increasing sophistication to livestock species, generating reference cell atlases for key production tissues (mammary gland, rumen epithelium, liver, skeletal muscle) and advancing our understanding of fundamental biological processes including early embryo development, gametogenesis, placentation, lactation, immune ontogeny, and the cellular basis of disease susceptibility. This review comprehensively examines the major single- cell and spatial omics platforms, their technical requirements and limitations for livestock applications, the growing catalogue of single- cell datasets generated in cattle, pigs, sheep, goats, and poultry, and the computational methods — including trajectory analysis, RNA velocity, cell communication inference, and mosaic integration with bulk genomic data — used to extract biological insight from these datasets. The transformative potential of single- cell data for livestock science is illustrated through detailed examination of three application domains: bovine early embryo lineage specification and its implications for SCNT and iPSC reprogramming; mammary gland cell type heterogeneity and its relationship to milk composition variation; and immune cell atlas construction and its relevance for vaccine development and disease resistance genetics. The review concludes with a prospectus on the integration of single- cell data with genomic selection models and genome editing target identification.