Bioscience Methods 2025, Vol.16, No.2, 83-99 http://bioscipublisher.com/index.php/bm 93 integration can reveal the association of intracellular events. For example, a cell may change the expression of a series of downstream genes due to an epigenetic mutation (such as abnormal chromatin opening), which is easy to be diluted or misunderstood in bulk data, but can be accurately captured in single-cell multi-omics. Of course, multi-omics methods also have limitations. The first is technical complexity and cost. The current single-cell platforms that can detect multiple omics simultaneously (for example, 10x Multiome can measure RNA and ATAC simultaneously) are expensive and have high sample requirements. For tissues of large animals (such as adult goats), it is challenging to prepare single cell nuclei freshly and keep different omics molecules intact. In addition, simultaneous multi-omics often results in a decrease in the data quality of each omics compared to single sequencing. For example, the number of genes and open regions that can be detected in the same cell are less than the level when they are sequenced separately. Secondly, data analysis is more difficult. It is necessary to deal with high-dimensional and high-sparse data, and design effective statistical tests to support the inference of causal associations between multiple omics. Some integrated conclusions may still be relevant and require additional experimental verification. Third, data standardization and control are a problem. Different omics have different dynamic ranges and noise characteristics, and it is difficult to interpret their changes at the same scale. Multi-omics research also requires interdisciplinary collaboration, and bioinformatics analysts and experimental biologists need to work closely together to interpret the data. 6 Case study: Application of Single-Cell Multi-Omics in Muscle Development Research 6.1 Representative studies in livestock muscle research Single-cell multi-omics has many successful cases in model organisms such as mice and humans, and has also begun to emerge in livestock muscle research. In 2024, Zhu et al. (2024) reported the first single-nuclear transcriptome map of goat skeletal muscle development, focusing on the regulation of adipogenesis in muscle. They selected goat longissimus dorsi muscles at different developmental stages for single-nuclear RNA sequencing, and obtained transcriptional expression profiles of up to thousands of nuclei. Through cluster analysis, the study identified several major subpopulations in goat skeletal muscle, including muscle satellite cells, myoblasts, fibroblast/adipocyte progenitor cells (FAPs), and immune cells. Of particular concern is that FAPs are subdivided into several subpopulations, one of which is enriched in adipogenic marker genes (such as PPARγ), indicating that it is on the path of differentiation into adipocytes. By constructing a ligand-receptor interaction network, the authors found that there are significant signal connections between FAPs and muscle satellite cells, such as the BMP pathway and the IGF pathway, which means that FAPs may affect satellite cells and myoblasts by secreting factors such as BMP and IGF, thereby regulating the balance between muscle and adipocytes. More importantly, the authors used single-cell gene regulatory network analysis to predict and verify the key role of TCF7L2 transcription factor in early intramuscular fat production. Single-cell data suggested that TCF7L2 may regulate a network of multiple fat-related genes. They then knocked down TCF7L2 in primary cells, and the result was that fat differentiation was inhibited, confirming this inference. This is a rare study in large animals that discovered and functionally verified transcription factors by single-cell means. This case fully reveals the relationship between myocytes and adipocyte precursors in goat muscle tissue, and finds out the key factors that affect meat quality (fat deposition), which has direct significance for animal husbandry. For example, TCF7L2 can be regarded as a large candidate gene that affects goat meat quality, and future breeding can focus on whether its allele variation is associated with meat quality traits. 6.2 Insights from epigenetic and transcriptional regulation Muscle-fat fate is determined by a complex signaling network, and single-cell technology has helped to dismantle this network. The traditional view is that muscle and fat originate from different cell lineages and do not interfere with each other. However, recent single-cell studies have broken this dualism and revealed that there is a close interaction and balance between myogenic cells and adipocyte precursors within muscle tissue. For example, studies in pigs and goats have pointed out that when signals that promote myogenesis are dominant (such as active pathways such as IGF and Ca2+), muscle growth is dominant; conversely, when factors that promote adipogenesis (such as PPAR signals) are enhanced, intramuscular fat deposition increases and muscle fiber growth is restricted (Qiu et al., 2020; Zhu et al., 2024). This balance is influenced by both genetics and the environment. At the
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