Expanding Horizons in Individual Cell Gene Analysis Through Recent Industry Advancements
In the ever-evolving landscape of scientific research, single-cell multiomics technologies have emerged as a game-changer, enabling simultaneous profiling of genomes, transcriptomes, epigenomes, proteomes, and metabolomes at the single-cell level. This revolution has shed new light on cellular heterogeneity, dynamic cell states, and spatial context within tissues.
The journey of single-cell multiomics began in 2009 with the manual isolation of mouse blastomeres for sequencing. Since then, significant advancements have been made, transforming the field.
One of the key developments has been the integration of multiple molecular layers within a single cell. Platforms like RNA, chromatin accessibility (epigenome), proteins, and metabolites, provide comprehensive cellular characterization in one assay. Techniques such as MERFISH (multiplexed error-robust fluorescence in situ hybridization) enable mapping molecular features in situ at near single-cell or subcellular resolution, linking molecular profiles with tissue architecture and microenvironment interactions.
The integration of spatial omics is another significant stride. Techniques such as 10x Visium and MERFISH allow for the mapping of molecular features within tissues, providing valuable insights into cellular interactions and tissue architecture.
Automation and scalability have also been key to the advancement of single-cell multiomics. Fully automated workstations, such as those by MGI, and integrated platforms like Mission Bio’s Tapestri, facilitate high-throughput and clinically relevant single-cell multiomics workflows, supporting applications from cancer biomarker discovery to clinical trials.
Cost reduction and accessibility have also been a focus. Innovations such as Illumina’s NovaSeq X Series and Single Cell 3’ RNA Prep kits have significantly lowered costs, enabling more widespread adoption of high-throughput single-cell multiomics experiments.
Computational advances have also played a crucial role. The emerging integration of artificial intelligence and deep learning with multiomics data enhances data interpretation, enabling more precise dissection of cellular dynamics and identifying mechanisms underlying diseases.
Recent applications of single-cell multiomics focus on understanding tumor heterogeneity, immune microenvironments, developmental biology, and aging processes by dissecting genome, transcriptome, epigenome, proteome, and metabolome layers at single-cell resolution integrated with spatial context.
In summary, single-cell multiomics technologies have progressed to allow simultaneous high-resolution multi-parameter measurements at reduced cost and increased throughput, integrated with spatial profiling and advanced computational tools, thereby powering precision medicine and biological discovery in unprecedented detail.
Key terms:
- scRNA-seq: single-cell RNA sequencing to profile transcriptomes
- scATAC-seq: single-cell assay for transposase-accessible chromatin, profiling epigenomes
- MERFISH: multiplexed error-robust fluorescence in situ hybridization, spatial transcriptomics
- Tapestri platform: multiplexed single-cell DNA and protein profiling system used for clinical biomarker analysis
This reflects the state as of mid-2025 based on recent literature and industry updates.
Notable advancements include the use of MARS-seq in 2014, which combined fluorescence activated cell sorting (FACS) and automatic liquid handling to substantially increase the throughput of single-cell sequencing. In 2011, cell-specific barcodes allowed for multiplexing and pooling, which enabled the sequencing of hundreds of cells using a method called STRT.
Single-cell sequencing technology has become a mainstay for genomic experiments. With the progression of single-cell throughput surpassing Moore's law, it is possible that methods could eventually sequence the trillions of cells that make up one human body. The most recent single-cell transcriptomic approaches are shifting towards measuring full transcriptomes from cells in-situ with spatial location information retained.
The technology was originally focused on RNA-sequencing but now sequences many different types of omics at the single-cell level. The use of the Fluidigm C1 for RNA expression analyses of single cells was discussed in Current protocols in molecular biology in 2018. The introduction of droplet methods in 2015, such as inDrop & Drop-seq, saw a leap in cell throughput into the 10s of 1000s. The release of 10x Genomics' Chromium in 2016 utilised droplet technology to create a benchtop option for all scientists.
References:
[1] Vandereyken, K., Sifrim, A., Thienpont, B. & Voet, T. (2023). Methods and applications for single-cell and spatial multi-omics. Nature Reviews Genetics.
[2] Baysoy, A., Bai, Z., Satija, R. & Fan, R. T (2023). The technological landscape and applications of single-cell multi-omics. Nature Reviews Molecular Cell Biology.
[3] Carangelo, G., Magi, A. & Semeraro, R. (2022). From multitude to singularity: An up-to-date overview of scRNA-seq data generation and analysis. Frontiers in Genetics.
[4] Zhang, Y., Huang, Y., Hu, L. & Cheng, T. (2022). New insights into Human Hematopoietic Stem and Progenitor Cells via Single-Cell Omics. Stem Cell Reviews and Reports.
[5] Pan, Y., Cao, W., Mu, Y. & Zhu, Q. (2022). Microfluidics Facilitates the Development of Single-Cell RNA Sequencing. Biosensors.
- The simultaneous profiling of genomes, transcriptomes, epigenomes, proteomes, and metabolomes at the single-cell level, propelled by single-cell multiomics technologies, has opened new avenues for understanding cellular heterogeneity and dynamic cell states.
- The use of manual isolation of mouse blastomeres for sequencing in 2009 marked the beginning of the single-cell multiomics journey, followed by significant advancements in the field.
- Platforms like RNA, chromatin accessibility (epigenome), proteins, and metabolites, integrated within a single assay, offer comprehensive cellular characterization.
- Techniques such as MERFISH enable mapping molecular features in situ at near single-cell or subcellular resolution, connecting molecular profiles with tissue architecture and microenvironment interactions.
- Integration of spatial omics has been another significant stride, with techniques like 10x Visium and MERFISH providing insights into cellular interactions and tissue architecture.
- Automation and scalability have been instrumental in the advancement of single-cell multiomics, facilitating high-throughput and clinically relevant single-cell multiomics workflows.
- Cost reduction and accessibility have also been a focus, with innovations like Illumina’s NovaSeq X Series and Single Cell 3’ RNA Prep kits lowering costs, thereby enabling more widespread adoption of high-throughput single-cell multiomics experiments.
- The emergence of artificial intelligence and deep learning with multiomics data is enhancing data interpretation, enabling more precise dissection of cellular dynamics and identifying mechanisms underlying diseases.
- Applications of single-cell multiomics focus on understanding tumor heterogeneity, immune microenvironments, developmental biology, and aging processes, by dissecting genome, transcriptome, epigenome, proteome, and metabolome layers at single-cell resolution integrated with spatial context, thereby contributing to precision medicine and biological discovery.
