The Rise of Single-Cell Sequencing: A Window Into Cellular Diversity

THE RISE OF SINGLE-CELL SEQUENCING A Window Into Cellular Diversity Written by Mariana Gil PhD | Designed by Luiza Augusto Multicellular organisms develop specialized tissues built from diverse cell types. The functional and morphological characteristics of each cell type arise from intricate interactions among the genome, epigenome, transcriptome and proteome. As such, understanding cellular heterogeneity is crucial to deciphering complex biological systems. Yet, traditional bulk cell analysis only provides averaged data, obscuring the variability between individual cells. Over the last decade, single-cell sequencing technologies have emerged, allowing researchers to isolate and profile individual cells with unprecedented precision. This transformative approach is reshaping biomedical research by revealing cellular diversity, rare cell types and dynamic cellular processes. This infographic explores various single-cell sequencing methods and highlights key applications that are advancing our understanding of health and disease. Single-cell sequencing technologies have advanced rapidly since the first single-cell mRNA sequencing method was introduced in 2009.1 Today, researchers can perform unbiased, high-throughput and highresolution analyses of the genome, epigenome, transcriptome and proteome of individual cells within a population.2,3 Numerous platforms have been developed for single-cell sequencing, each generally adhering to a common set of workflow steps: Single-cell sequencing technologies are empowering scientists from numerous fields to explore cellular diversity. From identifying rare cell populations to paving the way for personalized medicine, this technology is driving breakthroughs in biology and transforming how we understand and treat diseases.4 As single-cell sequencing technologies continue to evolve, the field is rapidly shifting toward integrative, highresolution and clinically applicable platforms. In the coming years, emerging technological developments are set to drive improvements in multiomic integration, spatially resolved single-cell analysis and the translational impact of such data. As these technologies mature, they promise to revolutionize our understanding of complex biological systems while driving the next generation of precision diagnostics, disease monitoring and personalized therapies. Single cell isolation Each cell is encapsulated or indexed individually to preserve its identity using different methods: -FACS (fluorescence-activated cell sorting) -Microfluidics -Microwell arrays -Droplet-based systems SINGLE-CELL SEQ TECHNOLOGIES HOW DOES IT WORK? KEY APPLICATIONS FUTURE DIRECTIONS DNA DATA ANALYSIS SEQUENCING LIBRARY PREPARATION SAMPLE PREPARATION SINGLE-CELL MULTIOMICS Integrates two or more of the above for a holistic approach. RNA PROTEIN Single-Cell DNA Sequencing (scDNA-seq) Measures genomic variations such as mutations and CNVs. Genome + Epigenome e.g., scCOOL-seq Genome + Transcriptome e.g., DR-seq, G&T-seq Epigenome+ Transcriptome e.g., scM&T-seq, scNMT-seq Genome + Epigenome + Transcriptome e.g., scTrio-seq, scMT-seq Transcriptome + Proteome e.g., CITE-seq, REAP-seq Single-Cell Epigenomic Sequencing Measures chromatin accessibility (scATAC-seq) and DNA methylation (scDNA-MET-seq). Single-Cell RNA Sequencing (scRNA-seq) Measures gene expression at the transcript level. Single-Cell NGS-Based Proteomics (scAb-seq) Measures protein abundance. Labelling with DNA-barcoded antibodies Extraction and barcoding of genetic material scDNA-seq Mutations, CNVs Next-generation sequencing scDNA-MET-seq Methylation profile scATAC-seq Chromatin accessibility scRNA-Seq Gene expression scAb-seq Protein abundance Conversion of unmethylated cytosines DNA Amplified DNA DNA Amplified DNA Converted DNA Chromatin Amplified DNA fragments Single cell profiles Tagged DNA fragments RNA Amplified cDNA cDNA DNA-barcoded antibody Amplified DNA tags DNA tags Tagmentation: Tn5 transposase cuts and tags open chromatin Bioinformatics tools to cluster and map cell types Reverse transcription (RT) PCR or WGA PCR PCR PCR or IVT PCR Cell atlases construction Tumor immunology Pathways identification Neurological disease Developmental biology Infectious disease Novel targets discovery Cardiovascular disease Map the progeny of individual cells to investigate cellular differentiation trajectories.5 Identify host-pathogen dynamics, immune responses and design advanced therapeutic strategies.7 The Human Cell Atlas, compiling single-cell molecular profiles from healthy and diseased cells, can help uncover mechanisms underlying diseases. Explore cancer treatment strategies; for example, how T cells react during immunotherapy.6 Identify cellular signaling pathways to elucidate the mechanisms underlying pathological processes, including cancer.11 Understand brain complexity and advance the treatment of neuropsychiatric conditions.8 • Alzheimer’s disease • Autism • Schizophrenia • Ischemia Study regulatory relationships between molecules to unveil novel drug targets.10 Help predict disease, therapeutic target discovery and stratify patients.9 SINGLE-CELL SEQUENCING TECHNOLOGIES Improved multiomics integration Live-cell sequencing Increased throughput and resolution Advanced data analysis Spatially resolved analysis Enhanced clinical applications Long-read sequencing Greater accessibility Improved resolution and correlation across molecular layers. Methods to profile cells without killing them, allowing for functional studies and the study of dynamic cellular processes.14 Scalable platforms for profiling millions of cells. AI-driven algorithms and cloud-based platforms to improve the analysis, integration and handling of large-scale data. Techniques that map cells in their spatial context within tissues and track their behavior over time.12 Integration into routine clinical workflows and personalized medicine (patient-specific therapeutic strategies based on cellular profiles). Characterization of full-length transcripts and alternative splicing events, providing a more comprehensive view of cellular transcriptomes.13 Democratization of technology for wider research adoption (e.g., reduced costs, user-friendly, portable devices). 1. Tang F, Barbacioru C, Wang Y, et al. mRNA-seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6(5):377–382. doi:10.1038/nmeth.1315 2. Hu Y, An Q, Sheu K, Trejo B, Fan S, Guo Y. Single cell multi-omics technology: methodology and application. Front Cell Dev Biol. 2018;6:28. doi:10.3389/ fcell.2018.00028 3. Vaga S. Understanding single-cell sequencing, how it works and its applications. Technology Networks. https://www.technologynetworks.com/genomics/articles/ understanding-single-cell-sequencing-how-it-works-and-its-applications-357578. Published January 20, 2022. Updated February 26, 2024. 4. Wu X, Yang X, Dai Y, et al. Single-cell sequencing to multi-omics: technologies and applications. Biomark Res. 2024;12(1):110. doi:10.1186/s40364-024-00643-4 5. Weng C, Yu F, Yang D, et al. Deciphering cell states and genealogies of human haematopoiesis. Nature. 2024;627(8003):389–398. doi:10.1038/s41586-024-07066-z 6. Pai JA, Hellmann MD, Sauter JL, et al. Lineage tracing reveals clonal progenitors and longterm persistence of tumor-specific T cells during immune checkpoint blockade. Cancer Cell. 2023;41(4):776–790.e7. doi:10.1016/j.ccell.2023.03.009 7. Guo Y, Zhang G, Yang Q, et al. Discovery and characterization of potent pan-variant SARSCoV- 2 neutralizing antibodies from individuals with Omicron breakthrough infection. Nat Commun. 2023;14(1):3537. doi:10.1038/s41467-023-39267-x 8. Lopez-Lee C, Torres ERS, Carling G, Gan L. Mechanisms of sex differences in Alzheimer's disease. Neuron. 2024;112(8):1208–1221. doi:10.1016/j.neuron.2024.01.024 9. Miranda AMA, Janbandhu V, Maatz H, et al. Single-cell transcriptomics for the assessment of cardiac disease. Nat Rev Cardiol. 2023;20(5):289–308. doi:10.1038/s41569-022- 00805-7 10. Olatoke T, Wagner A, Astrinidis A, et al. Single-cell multiomic analysis identifies a HOXPBX gene network regulating the survival of lymphangioleiomyomatosis cells. Sci Adv. 2023;9(19):eadf8549. doi:10.1126/sciadv.adf8549 11. Fan J, Lu F, Qin T, et al. Multiomic analysis of cervical squamous cell carcinoma identifies cellular ecosystems with biological and clinical relevance. Nat Genet. 2023;55(12):2175– 2188. doi:10.1038/s41588-023-01570-0 12. Marx V. Method of the year: spatially resolved transcriptomics. Nat Methods. 2021;18(2):219. doi:10.1038/s41592-021-01065-y 13. Gupta P, O'Neill H, Wolvetang EJ, Chatterjee A, Gupta I. Advances in single-cell long-read sequencing technologies. NAR Genom Bioinform. 2024;6(2):lqae047. doi:10.1093/ nargab/lqae047 14. Chen W, Guillaume-Gentil O, Rainer PY, et al. Live-seq enables temporal transcriptomic recording of single cells. Nature. 2022;608(7924):733–740. doi:10.1038/s41586-022- 05046-9 References Solid tissue Dissociation Single-cell isolation Protein A Protein B • Cell lineage tracking • Embryonic development • Myocardial infarction • Atherosclerosis Hub Gene Sponsored by