The Vast Potential of Single-Cell Analysis

It is now four years since a group of scientists met in London to discuss how to create a human cell atlas (HCA) – a collection of maps that describes and defines the cellular basis of health and disease. Research based on this atlas has also helped researchers create more specific maps – such as the COVID-19 Cell Atlas, which could help us in the fight against SARS-CoV-2.

Cell atlases are powerful – but, to unlock insights that will enable us to help specific patients, we need reference datasets of hundreds to thousands of patients to complement population-scale genomics datasets. This vision of precision medicine is coming ever closer thanks to the technological advances – particularly in the field of data handling and analysis – and single-cell research.

Advances in single-cell genomic analysis provide the industry with greater insights from clinical trials – for example, by allowing scientists to look further into specific molecular responses to different therapies. Of the many single-cell genomic analysis methods, scRNA-seq is the most widely used. This approach involves labeling biomolecules that originate from individual cells, allowing high-throughput molecular analysis at the single-cell level. In 2013, scRNA-seq was Nature’s Method of the Year. It earned the accolade a second time in 2019 due to its ability to sequence DNA and RNA in individual cells (1), allowing extrapolation of the biological differences between cells.

Massively parallel single-cell genomics assays can now profile hundreds of thousands of cells, meaning that researchers can gain more insights than ever before on certain cell characteristics and behaviors. The uptick in spatial single-cell analysis puts a further onus on technology development to preserve the contextual information of imaging so that researchers can augment individual cellular responses with regional and sub-regional information.

Technologies to profile DNA and proteins in single cells – as well as combinations of DNA, RNA, and proteins in the same cell – provide important additional layers of information to accelerate precision medicine. The advent of single-cell nucleus RNA sequencing (snRNA-seq) has allowed the extension of single-cell transcriptomics analyses to human diseases in which live tissue is not obtainable (2).

Computational algorithms have also emerged (and continue to evolve) to determine cell types, states, transitions, and locations – allowing single-cell analysis to extract more targeted insights from specific biomarkers. But there are 300 different cell types known in the human body, which itself comprises 37 trillion cells. And precision medicine research relies not just on the number of cells (because cells from one patient cannot be biological replicates!), but on the number of patients. It’s clear that these data must be stored and processed at scale to be effective.

Single-cell analysis may help us uncover never-before-seen physiological interconnections between tissues. With the understanding that exosomes and even naked nucleic acids can be used for intercellular communication, the need to quickly profile responses at the cellular level are even greater. The ability to find gene expression fingerprints and distinct cell types that may look unrelated, but might be corresponding with each other, could transform the way we diagnose and treat disease. If the full potential of single-cell analysis is realized, we will be able to navigate the physiology of humans from the molecule up – an exciting future that now sits tantalizingly within our reach.