Computational Molecular Biology 2025, Vol.15, No.2, 91-101 http://bioscipublisher.com/index.php/cmb 92 Single-cell omics refers to the acquisition and analysis of multi-level information at the single-cell level, including genomic, transcriptomic, epigenomic and even proteomic information. Its rise stems from the urgent need for research on cellular heterogeneity and rare cell types. Traditional high-throughput sequencing requires a large number of cells to be mixed, thereby only providing the average population information and masking the significant differences between different cell states. The development of single-cell sequencing technology enables scientists to "disassemble" tissues and redefine cell classification and state from bottom to top (Wu et al., 2024). Researchers achieved single-cell transcriptome sequencing for the first time, conducting mRNA sequencing analysis on a single embryonic cell and demonstrating the feasibility of whole-transcriptome analysis at the single-cell level. Since then, with the combination of microfluidic technology and high-throughput sequencing methods, single-cell RNA-seq methods have evolved from low-throughput (such as the SMART seq series based on cell capture) to high-throughput (such as the 10x Genomics platform based on oil droplet microdroplet) (Cao et al., 2022; Pan et al., 2022). At present, each single-cell RNA-seq experiment can simultaneously analyze thousands to tens of thousands of cells, making it possible to construct single-cell maps of complex tissues. For instance, the Cell Atlas Project of Human tissues identified numerous previously undescribed cell types using single-cell RNA sequencing, updating the understanding of tissue cell composition in anatomy textbooks (Hanamsagar et al., 2019; David et al., 2020). The integrated analysis of single-cell RNA-seq and scATAC-seq data can achieve the comparison of information at the transcriptional level and the epigenetic level, thereby mapping the gene regulatory network at the single-cell scale. This multi-dimensional correlation is the key to understanding the mechanism of cell fate determination. The research of Ma et al. (2020) discovered the so-called "regulated chromatin regions (DORCs)"-a group of open chromatin regions closely related to gene expression-by simultaneously obtaining the gene expression and chromatin accessibility data of each cell. These DORCs often enrich super enhancers, and the changes in their open states precede the expression changes of the corresponding genes, which has been proposed as an indicator of "chromatin potential" for evaluating cell fate transitions. This indicates that the integration of scRNA-seq and scATAC-seq can capture the sequence and causal relationship of epigenetic regulation and transcriptional response during cell differentiation (Lee et al., 2023). The analysis of integrated data can also identify the core regulatory factors that determine cell fate. On the one hand, scATAC-seq data can provide information on potentially active cis-regulatory elements (such as enhancers). Combined with the scRNA-seq expression data of genes near these elements, it is possible to infer whether a specific enhancer-gene pair may have a regulatory relationship. On the other hand, by calculating the number of transcription factor binding sites (motifs) abundant in the open regions of chromatin in each cell, the possible active transcription factors in each cell can be inferred. Comparing this with the gene expression profile of the same cell can verify whether these inferred transcription factors do indeed play a role at the transcriptional level. For instance, in the integrated analysis of hematopoietic stem cell differentiation, researchers analyzed the scATAC-seq motif enrichment through the chromVAR algorithm and found that the activity changes of key transcription factors specific to lineages (such as GATA1, TAL1, etc.) were consistent with the expression changes of the corresponding genes in scRNA-seq. Such results directly correlate the epigenetic level of factor activity with the transcriptional level of functional output, enhancing the credibility of the role of regulatory factors. 2 Principles and Applications of Single-Cell Omics Technology 2.1 Principles and applications of scRNA-seq technology The basic principle of single-cell RNA sequencing (scRNA-seq) technology is to reverse transcribe, amplify and sequence intracellular mRNA at the single-cell level, thereby obtaining the gene expression profile of the cell (Khan et al., 2023). To achieve this, it is necessary to address the challenge of having an extremely small initial amount of single-cell RNA (only picked-level RNA), so full-length cDNA amplification or tag sequence amplification methods are usually adopted. Early RNA-SEq methods, such as the Domer strand displacement amplification were capable of capturing full-length transcripts, but their throughput was relatively low. Later
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