Bioscience Methods 2025, Vol.16, No.2, 83-99 http://bioscipublisher.com/index.php/bm 96 The second is the expansion of multimodal omics. Current research focuses on transcriptomes, DNA methylation, chromatin accessibility, etc. In the future, single-cell data such as proteomes, epitranscriptomes (such as m6A maps), and metabolomes can be further introduced. In particular, single-cell proteomics and spatial omics are two cutting-edge directions. Single-cell proteomics technology is developing. Once mature, we can directly measure the protein expression profile of a single muscle cell, which is critical for verifying the results of transcriptional regulation and revealing post-translational regulation (Qiu et al., 2018). Spatial omics can obtain molecular information at different locations while maintaining tissue structure, which is extremely useful for organically connected tissues such as muscles. For example, spatial transcriptomes can tell us whether there are different expression programs in cells in the center and edges of muscle bundles, and whether there are metabolic differences between muscle fibers around capillaries and those in the middle. This information could only be inferred through histology in the past, but now it is expected to be quantitatively obtained through omics methods. Cross-species comparison is also a direction for expansion. Single-cell omics data of livestock can not only be compared with model animals to find commonalities and specificities; different livestock species can also be compared with each other to learn from each other. For example, goats and sheep are evolutionarily close and have similar production uses. If the differences in their muscle development omics characteristics can be compared, it may explain why the meat quality and fat deposition patterns of sheep are different from those of goats. This comparative study can also help transfer knowledge. For example, a key lncRNA regulatory mechanism found in sheep may also exist in goats, but the gene sequence is slightly different, and the corresponding sequence function needs to be located. 7.2 Converting research results into genetic improvement How to truly apply cutting-edge research results to genetic improvement is an important issue facing scientists and breeding experts. There is a conversion path from single-cell multi-omics to breeding practice, which requires overcoming some obstacles and adopting innovative strategies. First, it is necessary to screen key markers for stable inheritance. Many regulatory mechanisms discovered by single-cell omics are epigenetic or environmental related, and not all are directly determined by DNA sequences. Therefore, to be used in breeding, DNA markers (SNPs, InDels, CNVs, etc.) associated with these mechanisms must be found. For example, as mentioned above, the regulatory effect of TCF7L2 on intramuscular fat is clear, so the next step is to study the allelic variation of the TCF7L2 gene in the goat population to see if there are any mutations that affect its function or expression. If so, use it as a selection marker; if there is no significant variation, the regulation of TCF7L2 may be more at the level of regulatory regions (such as enhancers), and then genetic variations in upstream regulatory genes of TCF7L2 (such as other components of the Wnt pathway) can be sought. For example, miR-665 promotes myogenesis. We can check the sequence differences of the miR-665 gene or its target site BCL2L11 gene in different varieties to see if there are any mutations associated with muscle mass traits (Feng et al., 2025). This conversion from mechanism to marker is very labor-intensive, but it is a necessary step. Fortunately, the mechanistic knowledge provided by single-cell omics can significantly narrow the range of genes we focus on, making association analysis more targeted. Secondly, we can consider the application of new technologies such as gene editing in breeding. Traditional breeding accumulates favorable alleles through multiple generations of selection, while gene editing can introduce target mutations in one step. For example, if a DNA methylation hotspot region (an enhancer that regulates a certain inhibitory factor) is confirmed to have a negative impact on muscle growth, we can try to edit the region through CRISPR/Cas9 to weaken its activity and achieve a similar effect to transgenics, but in fact, it is only editing the regulatory elements rather than adding exogenous genes. This type of operation may be easier than introducing exogenous genes in terms of legal and social acceptance. Chinese scientists have bred sheep with a knockout of the myogenin gene, which significantly increased skeletal muscle mass (Wang et al., 2017). Third, computer breeding and simulation will become more important. As more and more data is obtained, computational models can be developed to predict how muscle development will change if certain gene expressions are changed. Such models can be trained with single-cell multi-omics data. Once the model is reliable,
RkJQdWJsaXNoZXIy MjQ4ODYzNA==