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New Commercial Developments in Single-cell Sequencing

This feature on single-cell sequencing technology is adapted from parts of Chapters 1 & 2 of the 2023 Spatial and Single-cell Analysis Playbook.

Single-cell sequencing has become a mainstay for genomic experiments. Originally only focused on RNA-sequencing, the technological capacity to sequence many different types of omics is available at the single-cell level. In this feature, we will briefly look back over the historical development of single-cell sequencing before summarising the latest commercial and technological developments.

Historical recap of single-cell sequencing

Single-cell sequencing has become a powerful tool for understanding complex biological systems1-4. The story begins in 2009 with a publication in which scientists were manually isolating mouse blastomeres for sequencing, acquiring transcriptomic information from individual cells5. From here, there was a gradual but significant advance in the number of cells that could be profiled. In 2011, the advent of cell-specific barcodes allowed for multiplexing and pooling, which meant 100s of cells could be sequenced using a method called STRT6. Fluidic circuits improved this further, and 2013 saw the release of the Fluidigm C1, the first single-cell automated prep system7,8. MARS-seq9 in 2014 combined fluorescence activated cell sorting (FACS) and automatic liquid handling to substantially increase the throughput.

The introduction of droplet methods in 2015, inDrop10 & Drop-seq11, saw a leap in cell throughput into the 10s of 1000s. The release of 10x Genomics’ Chromium in 2016 was pivotal, utilising this technology to create a benchtop option for all scientists. 2017 saw a new methodology emerge, a combinatorial indexing strategy known as sci-RNA-seq12, currently in its third iteration13, which, alongside SPLiT-seq14, is pushing the heights of single-cell throughput into the 100,000s and 1,000,000s.

Figure 1. Development of single-cell RNA sequencing technology. The direction of progress has seen, (A) an increase in the number of analysed cells, (B) a radical reduction in cost per cell, and (C) an increase in the number of published papers. (D) The technological landmarks that have occurred to increase the number of cells and quality of information gathered from single cells. Image Credit: Jovic, et al. 1

This advancement in single-cell throughput is proceeding faster the Moore’s law15, and if it continues at this pace it could be possible that we could eventually see methods capable of sequencing the trillions of cells that make up one human body. Figures 1A and 2 show the trajectory of cell throughput capacity of single-cell technologies and the technologies that have driven this progression.

Alongside the advances in throughput, technologies such as Smart-seq16,17 and CEL-seq18,19 that were pioneered in 2012 have been improving the sensitivity of single-cell RNA-sequencing, allowing many more transcripts to be detected while sacrificing the high cell throughput. Both of these techniques have seen updates with the most recent – Smart-Seq3, released in 202020 – allowing full length transcript coverage combined with UMI counting strategy to offer the most sensitive single-cell RNA sequencing method.

The most recent single-cell transcriptomic approaches are shifting in two directions. One set of methods is moving away from cell isolation towards measuring full transcriptomes from cells in-situ with spatial location information retained. This will be covered in further detail in Part 2 of this feature. covering spatial technology.

The other direction still involves isolated cells, but is diversifying away from the transcriptome to enable multiomics assessments. This could involve sequencing of the genome, proteome, epigenome, metabolome or multiple of these together21,22.

Figure 2. Timeline of single-cell technological developments. Technologies are displayed by original publication date and the typical throughput. Colours/shapes indicate the type of mRNA that the technology sequences. Significant advances are indicated by dotted borders. Image Credit: Zhang, et al. 23

Overall, while single-cell sequencing is now almost 15 years old and is maturing into a lab staple, it is still undergoing substantial development each year.

New Commercial Developments in single-cell

As summarised above, single-cell has drastically improved over the last decade. Each passing year sees further expansion in the number of commercial instruments available and the capacities of these instruments.

Despite its 15-year history, developments in single-cell sequencing have not been static, and multiomics capacity has been expanded. Furthermore, potentially game-changing instrument-free approaches have also been developed that could make sequencing more accessible and highly scalable.

In the following sections we will highlight a selection of commercial advances that have happened in the past two years, providing an overview of the directions of progress. This list is not exhaustive and was compiled in August 2023.

New Single-cell Multiomics capabilities

Single-cell RNA sequencing now has a well-established set of multiomics options to provide analysis of two or more analytes in the same experiment. Below we list some significant developments in single-cell multiomics:

  • BioSkryb Genomics’ ResolveOME™ kit allows the near-complete summary of the genome and mRNA transcriptome at single-cell resolution. With their associated BaseJumper™ data analysis software, this setup creates a unified workflow for DNA and RNA interrogation.
  • Mission Bio released Tapestri® v3 in May 2023, with DNA as its primary analyte. It allows the analysis of the genotype of a cell plus a variety of other phenotypic data such as CNVs, SNVs or proteins. The new v3 allows up to four times more cells captured per sample, which increases the ability to detect rare cells.
  • Singleron’s PromoScope™ kit, released in November 2022, allows the simultaneous quantification of the whole transcriptome, as well as protein glycosylation at the single-cell level. Relying on their SCOPE-chip® technology, this is the first kit to quantify protein modifications alongside transcriptomics.

Single-cell sequencing improves in throughput and full-length transcriptomic options

As discussed above, improvements in cell throughput have been continuous over the last 15 years. However, even in the last year, speed, throughput, long-read capacity and consistency have all been improved for both established and brand new instruments, keeping the wheels of progress turning. Some examples include:

  • BD Biosciences released the BD Rhapsody™ HT Xpress System in February 2023, which makes million-cell studies possible with over 320,000 cells per cartridge and with up to an 80% capture rate. This allows cell capture and barcoding with extremely high throughput. This joins the FACSDiscover S8™ Cell Sorter, which was released in May 2023 and allows cell sorting based on imaging the cells, and the FACSDuet™, launched in July 2023, which automates flow cytometry to automate the entire sample preparation process.
  • For brand new instruments, Singular Genomics have recently released the G4 sequencer in 2022, one of the most powerful benchtop sequencers available. With the new Max Read™ kit released in February 2023, the instrument now has the power to sequence up to 3.2 billion reads a day.
  • Pacific Biosciences launched their MAS-Seq kit for single-cell expression analysis in October 2022. It leverages 10x Genomics’ single-cell technology with PacBio’s HIFI technology for long-read RNA sequencing, to allow researchers to assess novel isoforms and the additional value of long read sequencing in single cells.
  • Takara presented the SMART-Seq® Pro kit in October 2021, which can perform full-length transcriptome sequencing on single cells isolated with their ICELL8® single-cell system, providing an automated end-to-end solution for sensitive transcriptomic analysis.
  • Singleron released NEO and Python Junior instruments in June 2023, which are portable instruments for library processing and tissue dissociation, respectively. The instruments allow half a million cells in one run, and were designed with standardization and automation in mind.

Scalable and cost-effective instrument-free approaches

To make single-cell sequencing accessible to any lab is a lofty goal for the research world. In progress towards this goal, the cost of performing a single-cell experiment has rapidly fallen over the last few years, with companies increasing the throughput and capacity of their instruments (see above). However, the inability to initially invest in the instruments leaves many labs unable to participate in the single-cell revolution. This is the space that the following companies have innovated in by supplying instrument-free single-cell solutions.

  • Parse Biosciences released version 2 of Evercode™ back in August 2022 (following the release of version 1 in February 2021). This new version is more sensitive and robust, and by using the cell or nucleus as the reaction vessel, no hardware is needed, meaning an initial expensive hardware purchase is avoided. Kits can process up to 1 million cells and Evercode™TCR allows T cell receptor profiling too.
  • Fluent Biosciences also updated their PIPseq™ platform to version 4 in February 2023. With one of the highest cell captures (85%) and better sensitivity, the kit also offers a scalable and cost-effective sequencing solution, compatible with Illumina NGS sequencing instruments. The T100 kit is the first single tube solution with the capacity for 100,000 cells.
  • Scale Biosciences presents their own technology to escape the need for cell partitioning instruments in single-cell sequencing, through their Scale Bio™ RNA kit (December 2022). Their product is also scalable, affordable and allows deep profiling of cells. The single-cell ATAC kit sets Scale Biosciences apart as offering instrument-free epigenomic pre-indexing.
  • Honeycomb Biotechnologies released HIVE CLX in June 2023, which has 160,000 picowells in their distinctive array. This allows for integration of sample storage and single-cell profiling without needing specialized instrumentation. Cells are captured quickly and effectively, allowing rare cell type capture, and can be stored as you go – meaning samples can be collected across a week without batch effects.


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2.         Carangelo, G., Magi, A. & Semeraro, R. From multitude to singularity: An up-to-date overview of scRNA-seq data generation and analysis. Frontiers in Genetics 13(2022).

3.         Pan, Y., Cao, W., Mu, Y. & Zhu, Q. Microfluidics Facilitates the Development of Single-Cell RNA Sequencing. Biosensors 12, 450 (2022).

4.         Wang, S. et al. The Evolution of Single-Cell RNA Sequencing Technology and Application: Progress and Perspectives. Int J Mol Sci 24(2023).

5.         Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nature Methods 6, 377-382 (2009).

6.         Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome research 21, 1160-1167 (2011).

7.         DeLaughter, D.M. The use of the Fluidigm C1 for RNA expression analyses of single cells. Current protocols in molecular biology 122, e55 (2018).

8.         Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nature Methods 10, 1093-1095 (2013).

9.         Jaitin, D.A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776-9 (2014).

10.       Klein, A.M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187-1201 (2015).

11.       Macosko, E.Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202-1214 (2015).

12.       Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661-667 (2017).

13.       Domcke, S. et al. A human cell atlas of fetal chromatin accessibility. Science 370, eaba7612 (2020).

14.       Rosenberg, A.B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176-182 (2018).

15.       Kharchenko, P.V. The triumphs and limitations of computational methods for scRNA-seq. Nature Methods 18, 723-732 (2021).

16.       Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nature methods 10, 1096-1098 (2013).

17.       Ramsköld, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nature biotechnology 30, 777-782 (2012).

18.       Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome biology 17, 1-7 (2016).

19.       Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell reports 2, 666-673 (2012).

20.       Hagemann-Jensen, M. et al. Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nature Biotechnology 38, 708-714 (2020).

21.       Vandereyken, K., Sifrim, A., Thienpont, B. & Voet, T. Methods and applications for single-cell and spatial multi-omics. Nature Reviews Genetics, 1-22 (2023).

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23.       Zhang, Y., Huang, Y., Hu, L. & Cheng, T. New insights into Human Hematopoietic Stem and Progenitor Cells via Single-Cell Omics. Stem Cell Reviews and Reports 18, 1322-1336 (2022).