Tools & Techniques Diagnostics & prognostics

Waiting in the Wings

The accurate identification of cellular phenotype, intercellular signaling networks, and the spatial arrangement of cells within organs is critical to providing a deeper understanding of physiological processes and the disruptions that cause disease. Cellular heterogeneity is a common feature of many malignancies – and so, techniques that allow us to detect and characterize oncologic cellular heterogeneity can help advance cancer diagnostics and therapies.

One promising technology for characterizing cellular heterogeneity is single-cell sequencing. Conventional bulk sequencing methods use many cells, but lose cell heterogeneity information after the signals are summarized and averaged. Conversely, single-cell techniques use next-generation sequencing to analyze the genetic content of individual cells, providing valuable insights into their unique functional characteristics.

Single-cell technologies have rapidly grown in scope and scale in recent years, and I believe they are poised to yield significant discoveries in the study of human disease. Molecular pathology is leading the charge with approaches, such as single-cell RNA sequencing, single-cell ATAC-seq (1,2), and single-cell DNA sequencing (3,4). Since single-cell sequencing was recognized as Nature’s Method of the Year in 2013, use of the technique has dramatically increased; for example, single-cell multi-omics, which was little known in 2013, has grown in prominence, accounting for over 800 publications on PubMed. And yet, though there have been some early successes in using single-cell sequencing for diagnosing and treating cancer (5,6,7,8,9), there are still several challenges to address before it can be used in clinics. The most pressing issues include a lack of skilled lab professionals in single-cell sequencing, pre-analytical variation caused by differences in laboratory workflow, and the need for more standardized workflow and quality control methods.

Lack of skilled lab professionals

Unlike bulk sequencing approaches, which can be easily automated and performed on a relatively large scale, single-cell sequencing is still a rapidly developing field with varied techniques and methods. In the past few years, several research groups have systematically compared and contrasted available single-cell technologies (10,11,12), all with variations in chemistry, hardware, and software requirements. Such techniques require highly-qualified professionals who understand the technology and its mechanisms; put another way, accurate and reproducible results are highly dependent on skilled lab professionals. Indeed, the lack of trained and knowledgeable personnel has been one of the key barriers to moving single-cell sequencing from bench to bedside.

One possible solution? Increase the availability of training and education programs in single-cell sequencing technologies. There are single-cell service providers attempting to fill this gap but, because each cell’s informative gene expression patterns can change under external stressors, they struggle to maintain sample quality and integrity during material transfer.

Another possible solution is laboratory automation. Several companies offer instruments and reagents that are not yet fully automated, but help to simplify steps and reduce hands-on time. The development of fully automated platforms that take in fresh tissue samples and perform sample processing, library preparation, sequencing, and bioinformatics analysis in a closed system would be the ideal situation; however, we are still a few years away from such systems.

The need for a standardized workflow

Though technology is rapidly progressing and commercial products are being developed to facilitate single-cell workflows, there is no standardization as to how labs conduct sample processing, library preparation, sequencing, and analysis. When asking what type of workflow and controls should be used, the usual response is, “it depends!” This lack of quality control has resulted in confounding results and a lack of reproducibility across laboratories. Variation can also be attributed to differences in lab practices, equipment, and reagents used, possible operator errors, and data analysis pipelines.

Research communities and commercial entities have been a part of many ongoing efforts to develop more standardized and reproducible methods for single-cell sequencing – a challenge that can be separated into bench and in silico portions. The bench portion includes all steps from sample acquisition to library preparation and sequencing, whereas the in silico portion covers all the bioinformatics steps of data processing, normalization, and statistical analysis. Each portion needs to be tackled individually with specialized expertise, but with a common goal of achieving standardized workflows. Furthermore, more research is needed to determine how to improve pre-analytical variations and workflow stability, as well as develop standardized quality control methods for single-cell sequencing.

Pre-analytical variation in workflow

There is a diverse range of methods for conducting single-cell sequencing experiments. The pre-analytical phase (all steps from cell isolation to library construction) is particularly variable because it often requires different specialized techniques that are not sufficiently standardized. There are several critical steps in this phase; for example, pathologists need to take care to ensure high-yield and viability of dissociated single cells from tissues, and protocol consistency with controls is required to minimize variability throughout the workflow. Cells often experience environmental stress when they are removed from their native state, so we must minimize the time between tissue collection and processing of a single cell suspension for sequencing.

The starting materials must be of the highest possible quality in all cases, which, of course, is highly dependent on the source. DNA and RNA from fresh tissue samples tend to be high quality, whereas nucleic acids extracted from frozen tissue or formalin-fixed tissues are typically much lower quality. Single-cell analysis requires a sufficient amount of high-quality cells; however, there is currently no single-cell stabilization method able to achieve the cell numbers and quality gained from fresh cells. This unsolved problem equates to long hours in the lab for scientists who conduct single-cell experiments and also limits wider adoption.

Over the years, scientists have developed their own techniques to address the problem, such as fixing single cells with methanol or cryopreservation, and there has been some progress; however, one solution for a single scenario often cannot be replicated in another lab or for a different type of cell or tissue. A standardized protocol for cell collection, stabilization, and processing is urgently needed to allow for wider and easier adoption of single-cell technology in the clinic.

Where do we go from here?

With further technological developments and improved quality control standards, we may see single-cell sequencing becoming an invaluable part of disease diagnosis and treatment – and not too far in the future. Single-cell genomics, transcriptomes, and proteomes may be used to construct a comprehensive molecular map of a tumor’s cell types, helping researchers understand cancer progression, metastasis, and drug resistance. The ultimate objective is to gain new insight into the physiological mechanisms and pathological processes of diseases at the single-cell level. Emerging studies may lead to the development of new diagnostic markers or therapeutic targets, but only if we overcome the obstacles in our path.

As a first step, we must increase the availability of training and education programs in single-cell sequencing technologies. More research is also needed on improving consistency and stability in the workflow, particularly with sample preparation, collection, and stabilization. Finally, it is crucial to develop standardized quality control methods for single-cell sequencing that can be used across different labs, technologies, and cell types. By taking measures to overcome these key barriers, we become one step closer to the realization of single-cell sequencing for clinical use.

Receive content, products, events as well as relevant industry updates from The Translational Scientist and its sponsors.

When you click “Subscribe” we will email you a link, which you must click to verify the email address above and activate your subscription. If you do not receive this email, please contact us at [email protected].
If you wish to unsubscribe, you can update your preferences at any point.

  1. S Taavitsainen et al., “Single-Cell Atac and RNA sequencing reveal pre-existing and persistent subpopulations of cells associated with relapse of prostate cancer,” Nat Commun, 12, 5307 (2021). PMID: 34489465.
  2. C Chen et al., “Single-cell multiomics reveals increased plasticity, resistant populations and stem-cell-like blasts in KMT2A-rearranged leukemia,” Blood, 139, 2198 (2022). PMID: 34864916.
  3. A Nikolic et al., “Copy-scat: Deconvoluting single-cell chromatin accessibility of genetic subclones in cancer,” Sci Adv, 7, eabg6045 (2021). PMID: 34644115.
  4. Y Hu et al., “Single Cell Multi-omics technology: Methodology and application,” Front Cell Dev Biol, 6, 28 (2018). PMID: 29732369.
  5. Y Wang et al., “Clonal Evolution in breast cancer revealed by single nucleus genome sequencing,” Nature, 512, 155 (2014). PMID: 25079324.
  6. C Gawad et al., “Dissecting the clonal origins of Childhood Acute lymphoblastic leukemia by single-cell genomics,” Proc Natl Acad Sci U S A, 111, 17947 (2014). PMID: 25425670.
  7. L Zhang et al., “Lineage tracking reveals dynamic relationships of T cells in colorectal cancer,” Nature, 564, 268 (2018). PMID: 30479382.
  8. AS Venteicher et al., “Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq,” Science, 355, eaai8478 (2017). PMID: 28360267.
  9. RP Owen et al., “Single cell RNA-seq reveals profound transcriptional similarity between Barrett’s oesophagus and oesophageal submucosal glands,” Nat Commun, 9, 4261 (2018). PMID: 30323168.
  10. J Ding et al., “Systematic comparison of single-cell and single-nucleus RNA-sequencing methods,” Nat Biotechnol, 38, 737 (2020). PMID: 32341560.
  11. G Chen et al., “Single-cell RNA-seq technologies and related computational data analysis,” Front Genet, 10, 317 (2019). PMID: 31024627.
  12. KN Natarajan et al., “Comparative analysis of sequencing technologies for single-cell transcriptomics,” Genome Biol, 20, 70 (2019). PMID: 30961669.
About the Author
Tian Yu

Chief Scientific Officer of Truckee Applied Genomics, Nevada, USA. She is a Molecular Biologist experienced with life science and healthcare startups, currently focusing on leading research and development in Truckee Applied Genomics and developing tools to solve the “Garbage In, Garbage out” problems in molecular pathology.

Register to The Translational Scientist

Register to access our FREE online portfolio, request the magazine in print and manage your preferences.

You will benefit from:

  • Unlimited access to ALL articles
  • News, interviews & opinions from leading industry experts