What is Single-Cell Omics? Unlocking New Insights in Genomics, Transcriptomics, Proteomics, and Metabolomics

Single-cell omics is a transformative technology in biology and medicine, enabling comprehensive molecular analysis of individual cells. This innovative approach includes genomics, transcriptomics, proteomics, and metabolomics, offering deeper insights into cellular behaviors and functions (Figure 1). Unlike traditional bulk omics techniques, which aggregate data from populations of cells, single-cell omics reveals cellular heterogeneity—key for understanding complex biological processes. Before this technology, most biological experiments relied on multi-cell samples, masking the subtle variations between individual cells. Today, single-cell omics allows scientists to explore gene expression, protein functions, and metabolic activities at an unprecedented resolution, uncovering critical differences between cells.

Figure 1. Single-cell Multi-omics Technologies (Wang et al., 2025)

Why Single-Cell Approaches Matter for Advancing Biological Research?

Single-cell omics has opened a new frontier in research, providing powerful tools for understanding the behavior, mechanisms, and interrelationships of individual cells within a population. This approach enables more precise analysis in developmental biology, cancer diagnosis, monitoring, and personalized medicine. It is crucial for understanding early-stage cancer development, tracking disease progression, and developing targeted therapies. As Allon Klein of Harvard University stated, "Single-cell sequencing allows us to summarize decades of research on cell differentiation in early life stages in just one day."

The Evolution and Impact of Single-Cell Omics Technologies

Single-cell sequencing technology began with the publication of the first single-cell transcriptome paper in 2009. By 2013, platforms like 10X Genomics and Fluidigm had increased throughput from hundreds to millions of cells. Key bioinformatics tools like Seurat (2015) and Scanpy (2017) laid the foundation for data analysis. Building on transcriptomics, CyTOF introduced high-throughput single-cell protein analysis in 2014. In 2020, mass spectrometry technologies like SCoPE-MS and plexDIA pushed proteomics to true single-cell resolution, revealing cellular signaling network heterogeneity. Around the same time, SCENITH, single-cell LC-MS/MS, and mass spectrometry imaging completed single-cell metabolic flux and metabolite profiling. By 2020, the integration of spatial omics and multi-omics (CITE-seq, REAP-seq, scMEP) merged transcriptomics, proteomics, and metabolomics, ushering in the "panoramic single-cell" era, which moves beyond the traditional "population average."

Key Types of Single-Cell Omics: Genomics to Metabolomics

Single-cell omics reveals cellular heterogeneity by analyzing the molecular features of individual cells, overcoming the limitations of traditional bulk analysis. The main types of single-cell omics are shown in Table 1:

Single-Cell Genomics (scDNA-seq)

Single-cell genomics focuses on analyzing the DNA sequence of individual cells to identify genetic variations such as single nucleotide variants (SNVs), copy number variations (CNVs), and insertions/deletions (Indels). This technique provides a deeper understanding of genetic diversity within a population of cells, essential for studying cellular heterogeneity and disease mechanisms. Due to the limited DNA available in a single cell, single-cell genomics relies on low-input amplification methods like whole-genome amplification (WGA) to generate sufficient material for analysis. One widely used amplification technique is MALBAC (Multiple Annealing and Looping-Based Amplification Cycles), which can significantly increase genome coverage to over 90% while minimizing amplification bias. Alternatively, DOP-PCR (Degenerate Oligonucleotide Primed PCR) is employed for targeted amplification of specific genomic regions; however, its genome coverage is typically limited to below 10%.

Single-cell genomics is essential for various research applications. In cancer research, it helps to understand tumor heterogeneity by revealing genetic diversity within tumors, including the identification of subclonal mutations and resistance mechanisms. This is crucial for advancing personalized cancer therapies. In developmental biology, single-cell genomics is used to track genetic mutations during embryonic development, offering insights into how mutations accumulate and their role in both normal and abnormal developmental processes.

Single-Cell Transcriptomics (scRNA-seq)

Single-cell transcriptomics is a powerful technique used to analyze the mRNA expression profiles of individual cells, offering a detailed view of gene activity and cellular states. This approach uncovers the molecular signatures that define cell types, behaviors, and functions, providing deeper insights into complex biological processes. Key technological advancements in this field include molecular barcoding and microfluidics (Figure 2). Barcode labeling enables precise identification of cell origins, allowing for the parallel sequencing of mixed samples and facilitating high-throughput analysis (e.g., 10X Genomics). Additionally, Unique Molecular Identifiers (UMI) eliminate amplification bias, improving quantification accuracy and ensuring reliable data.

Figure 2. The technology of single-cell isolation (Hwang et al., 2018)

a, the limiting dilution method isolates individual cells, leveraging the statistical distribution of diluted cells. b, Micromanipulation involves collecting single cells using microscope-guided capillary pipettes. c, FACS isolates highly purified single cells by tagging cells with fluorescent marker proteins. d, Laser capture microdissection (LCM) utilizes a laser system aided by a computer system to isolate cells from solid samples. e, microfluidic technology for single-cell isolation requires nanoliter-sized volumes. An example of in-house microdroplet-based microfluidics (e.g., Drop-Seq).

A recent study, "Single-cell transcriptome reveals the reprogramming of immune microenvironment during the transition from MASH to HCC," published in Molecular Cancer in 2025, utilized single-cell transcriptomics to analyze the immune microenvironment during the transition from Metabolic Associated Steatosis Hepatocellular Carcinoma (MASH) to Hepatocellular Carcinoma (HCC). The application of single-cell RNA sequencing revealed significant changes in immune cell populations and gene expression profiles, shedding light on ApoE's role in MASH-driven HCC. This research suggests that ApoE may serve as a promising therapeutic target for this type of liver cancer (Figure 3).

Figure 3. Immunosuppressive and exhausted γδ T cells were induced in the liver during the transition from MASH to HCC (Huang et al., 2025).

Single-Cell Epigenomics

Single-cell epigenomics focuses on studying the regulatory mechanisms that control gene expression, including chromatin accessibility, DNA methylation, and histone modifications. These regulatory processes are crucial for understanding cell differentiation, disease progression, and cellular responses to environmental signals. Key technologies in this field include scATAC-seq, which analyzes chromatin accessibility to identify active regulatory elements, such as enhancers, and scBS-seq, which enables DNA methylation profiling at the single-cell level to reveal variations in DNA methylation patterns across cells. A landmark 2021 study leveraged single-nucleus ATAC-seq to map chromatin accessibility across more than 7,000 cells from human atherosclerotic lesions. This comprehensive approach refined the functional annotation of coronary artery disease (CAD) GWAS loci by linking chromatin accessibility with disease-relevant cell types. The researchers found that CAD-associated genetic variants were significantly enriched in endothelial and smooth muscle cell-specific open chromatin regions, providing valuable insights into the underlying epigenetic mechanisms driving cardiovascular disease (Örd et al., 2021).

Figure 4. Association of coronary artery disease (CAD)/myocardial infarction (MMI) variants with smooth muscle cell (SMC) phenotypes (Örd et al., 2021).

Single-Cell Proteomics

Single-cell proteomics focuses on analyzing protein abundance, modifications, and signaling pathways in individual cells. Key techniques include CyTOF, which uses metal-tagged antibodies to label proteins for mass spectrometry detection, avoiding fluorescence interference. CITE-seq and REAP-seq enable simultaneous detection of RNA and surface proteins, providing a more complete picture of cellular activity. These methods are applied to a variety of research areas, such as distinguishing microglial cells from CNS macrophage subpopulations, studying immune checkpoints like PD-1, and correlating proteins with gene expression. This enables a deeper understanding of cell functions in both normal biology and disease processes.

A notable study published on August 21, 2025, by Bo T. Porse’s team at the University of Copenhagen, demonstrated the power of single-cell proteomics in mapping human blood cell differentiation. The research, published in Science (Furtwängler et al., 2025), constructed a proteomic-transcriptomic atlas of 2,506 human CD34+ hematopoietic stem cells, shedding light on key processes such as stem cell dormancy and metabolic regulation—insights that traditional mRNA analysis alone could not provide.

Figure 5. A single-cell proteomics dataset of FACS-isolated human HSPCs (Furtwängler et al., 2025).

Single-Cell Metabolomics

Single-cell metabolomics focuses on profiling small-molecule metabolites—typically those with a molecular weight under 2 kDa, such as amino acids, ATP, lipids, and other metabolites involved in cellular processes. Unlike DNA or RNA, metabolites cannot be amplified, and their short half-lives present significant challenges for detection. This requires highly sensitive, high-resolution tools like mass spectrometry and advanced techniques like mass spectrometry microscopy to measure minute quantities of metabolites in individual cells.

A major advancement in this field came from Professor Bai Yu’s team at Peking University, who developed dynamic single-cell metabolomics to study the metabolic interactions between tumor cells and macrophages within the tumor microenvironment. The team utilized a combination of mass cytometry and stable isotope tracing, enabling them to track metabolic changes and fluxes in real time. Their innovative platform is high-throughput, label-free, and offers a deeper understanding of how cellular metabolism is regulated in cancer. This approach also allows researchers to explore the metabolic reprogramming that occurs in tumor cells and their interactions with immune cells, offering new perspectives on cancer progression and potential therapeutic targets.

Figure 6. Schematic workflow of the dynamic single-cell metabolomics system (Zhang et al., 2025).

Challenges and Innovations in Single-Cell Omics

Single-cell genomics is rapidly advancing from basic observation to deep, comprehensive analysis. With improved sequencing coverage, in-situ labeling, and AI algorithms, multi-omics—combining transcriptomics, epigenomics, proteomics, and metabolomics—are now integrated into spatial coordinates, enabling a four-dimensional map of DNA, RNA, proteins, and metabolites within a single cell. These advancements allow researchers to gain insights into cellular processes at an unprecedented level of detail. From a computational standpoint, deep learning techniques are helping to overcome challenges like batch effects, cross-modal alignment, and cell trajectory analysis, as well as identifying cellular interactions. Clinically, high-resolution single-cell maps are linking non-coding variants from GWAS to specific cell types and enhancers. This opens the door to personalized medicine, driving innovations in targeted gene editing, epigenetic therapies, and immunotherapy. In the next five years, as costs decrease, FFPE sample compatibility improves, and open-source workflows become more widespread, we can expect "single-cell + spatial + AI" tools to become standard in diagnosing and treating cancer, immune disorders, and rare diseases, ultimately closing the gap between molecular maps and precision interventions.

Figure 7. Future directions in single-cell and spatial genomics (Wang et al., 2025)

A. Increasing data coverage in single cells (NGS, next-generation sequencing; SMRT, single-molecule real-time sequencing). B. Enabling multi-modal detection in single cells. C. Integration of single-cell genomic data with spatial genomic and in situ sequencing data. D. Leveraging AI for single-cell and spatial genomics.

Despite the revolutionary progress of single-cell omics, several challenges remain. First, single-cell capture efficiency is not perfect, with some cells being damaged or lost during preparation, which can introduce bias and reduce the diversity of detected cell types. Additionally, amplification-based methods like scRNA-seq are prone to technical issues, such as amplification bias and "dropout" effects, leading to inaccurate gene detection and quantification. The technology also remains costly and has limited throughput, generating vast amounts of data that pose significant challenges for computational analysis and biological interpretation, particularly in batch effect correction and cell annotation. Furthermore, most single-cell techniques involve dissociating tissue into suspension cells, which destroys the spatial context crucial for understanding cellular functions and interactions within tissues. While spatial multi-omics technologies are emerging to address these issues, integrating multi-dimensional data in a cost-effective and reliable manner remains a key challenge.

Reference

1. Wang J, Ye F, Chai H, et al. Advances and applications in single-cell and spatial genomics. Sci China Life Sci. 2025;68(5):1226-1282. doi:10.1007/s11427-024-2770-x

2. Hwang B, Lee JH, Bang D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med. 2018;50(8):1-14. Published 2018 Aug 7. doi:10.1038/s12276-018-0071-8

3. Huang Y, Xie Y, Zhang Y, et al. Single-cell transcriptome reveals the reprogramming of immune microenvironment during the transition from MASH to HCC. Mol Cancer. 2025;24(1):177. Published 2025 Jun 11. doi:10.1186/s12943-025-02370-2

4. Örd T, Õunap K, Stolze LK, et al. Single-Cell Epigenomics and Functional Fine-Mapping of Atherosclerosis GWAS Loci. Circ Res. 2021;129(2):240-258. doi:10.1161/CIRCRESAHA.121.318971

5. Furtwängler B, Üresin N, Richter S, et al. Mapping early human blood cell differentiation using single-cell proteomics and transcriptomics. Science. 2025;390(6770):eadr8785. doi:10.1126/science.adr8785

6. Zhang Y, Shi M, Li M, Qin S, Miao D, Bai Y. Dynamic single-cell metabolomics reveals cell-cell interaction between tumor cells and macrophages. Nat Commun. 2025;16(1):4582. Published 2025 May 16. doi:10.1038/s41467-025-59878-w