Clinical and Medical Research
Powerful, Large-scale Analytics Brings Single-cell Omics into Clinical Reality Abstract / Summary Recent advances in sample preparation, biochemistry, and informatics tools for single-cell analysis have enabled the rapid adoption of single-cell omics in both biomedical research and, more recently, in clinical practice. While empowering the development of better therapeutics and diagnostic tools, the ongoing evolution of such methods, including single-cell DNA and RNA sequencing and proteomics, has resulted in vast, ever-growing datasets that require powerful data management and computational capabilities to capture clinical value. Here, we highlight the challenges imposed by current single-cell computational methods in handling vast datasets from disparate sources, and what is needed from an analytics platform for robust and reliable scientific data modelling, storage, and large-scale computation. We present the results of a recent study demonstrating the utility of such a platform, which enables the rapid profiling of key genes involved in COVID-19 infection. Growth of Single-cell Analysis Developments in single-cell analysis have substantially improved our understanding of disease mechanisms in recent years. The single-cell analysis market is projected to reach USD 5.6 billion by 2025 from USD 2.1 billion in 2019, with its growth attributed to technological advancements in single-cell analysis products, increasing government funding for cell-based research, growing biotechnology and biopharmaceutical industries, wide applications of single-cell analysis in cancer research, growing focus on precision medicine, and the increasing incidence and prevalence of chronic and infectious diseases.1 Single-cell analysis has therefore become a major focus for translational and pharmaceutical research, enabling multi-omics analysis at the singlecell level and allowing the identification of minor subpopulations of cells that may play a crucial role in various biological processes. 34 INTERNATIONAL PHARMACEUTICAL INDUSTRY
As a highly-sensitive tool, single-cell analysis can clarify specific molecular mechanisms and pathways, and even reveal the nature of cell heterogeneity. With this advancing technology, researchers and clinicians can look for insights into the transition from ‘healthy’ to ‘disease’ states, study potential biomarkers, and assess response to drug targets or available therapeutic regimens. Market Shift The notion of precision medicine – designing healthcare strategies according to a person's genes, lifestyle, and environment – is not a new one. Over the last two decades, significant advances in genomic, proteomic, transcriptomic, and epigenomic sciences, in conjunction with the growing availability of vast patient data repositories, have gradually facilitated a landscape of data-driven clinical decision-making. As well as predicting personal risk factors for particular diseases and how individual responses to various treatments might differ, this precision medicine methodology is slowly extending into the drug discovery paradigm. In 2018, a record number of 25 new molecular entities (NMEs) approved by the U.S. Food and Drug Administration (FDA)’s Center for Drug Evaluation and Research (CDER) were categorised as personalised medicines (42% of all 2018 new drug approvals).2 In addition, governments around the world are recognising the considerable potential of precision medicine to transform patient care. Former US President Barack Obama launched the Precision Medicine Initiative in 2015, which has since evolved into the National Institute for Health (NIH) All of Us Research Program that aims to gather health data from more than a million US volunteer-citizens to enable individualised treatment and healthcare.3 The ‘Cells-patients-data’ Relationship Despite the potential of single-cell omics to bring precision medicine approaches into routine clinical practice, the lack of analytics solutions available to cope with large-scale single-cell datasets poses a significant barrier. Single-cell DNA and RNA sequencing produce vast amounts of
data. Information from tens of thousands of cells per patient is available and while this provides clear opportunities in terms of increasing the statistical power of growing datasets, the technical and interpretative challenges associated with such ‘Big Data’ are currently limiting accessibility to biological insights. To unlock the value of recent advances in single-cell technology, life scientists will need to tackle the variety of omics layers (genomes, epigenomes, transcriptomes, and proteomes), along with reference maps like the Human Cell Atlas (HCA), at unprecedented levels of resolution, specificity, and volume. As the scale of single-cell datasets continues to increase, there is an unmet technological need to develop database platforms that can evaluate key biological hypotheses by querying atlases of single-cell data. However, current single-cell data are generated from a small number of individuals, and statistical significance relies on the number of patients studied, rather than the number total of cells. This is because cells from the same patient are ‘siblings’ and not true biological replicates, so datasets with 100,000s of patients/ treatment conditions will necessitate technology to manage billions of cells. For example, the Immune Cell Survey in the HCA – an initiative aiming to map the numerous cell types and states comprising a human being – currently contains 780,000 cells from only 16 individuals. The HCA itself has fewer than 400 patients, with very few patients donating cells from more than one organ system. This ‘cells-patients-data’ relationship is further compounded by a lack of scalability of single-cell software, as well as temporal factors, as researchers study the evolution of cell (sub) populations and the effects of treatments over time. Overcoming the Data Dilemma As well as moving beyond the small number of patients currently used in the available datasets, the pharmaceutical industry must take several steps in order to overcome the data management and analysis roadblocks in single-cell workflows. Firstly, there is a need for a single unified repository to store an organisation’s entire single-cell data – both raw and normalised – that is Winter 2020 Volume 12 Issue 4
