Novel tools for data analysis and clinical interpretation
The clinical translation of single cell analysis approaches into routine practice represents an exciting opportunity to further improve the diagnosis, prognosis and treatment of patients. These approaches are also becoming indispensable tools in drug development; for instance, for discovery of new targets and biomarkers, as well as for mechanism studies.
As such, technologies such as high-throughput single cell sequencing are rapidly gaining recognition among clinicians and clinical researchers as a key enabler of precision medicine.
Numerous improvements in single cell sequencing data analysis and interpretation have been made in recent years to advance the power of this technology in clinical research and precision medicine.
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What this webinar will cover
This webinar will address two recent improvements on data analysis, using practical case studies to explore new solutions that confront and alleviate major existing bottlenecks experienced by single cell practitioners – particularly those interested in clinical translation.
IMPROVEMENT 1: Exploration of new computational approaches for the unbiased identification of molecular signatures from single cell sequencing.
IMPROVEMENT 2: The development of a comprehensive new single cell database for translational research and drug discovery.
Talks and Speakers
TALK 1: Computational Approaches for the Unbiased Identification of Molecular Signatures from Single-Cell Sequencing
SPEAKER: Antonio Rausell, Group Leader, Clinical Bioinformatics, Imagine Institute
ABSTRACT: The exhaustive exploration of human cell heterogeneity requires the unbiased identification of molecular signatures that can serve as unique identity cards for every cell, tissue and organ in the body. However, because of the stochasticity associated with high-throughput single-cell sequencing, current methods for exploring cell-type diversity rely on clustering-based computational approaches, in which heterogeneity is characterised at cell-subpopulation level, rather than at full single-cell resolution.
In this talk Dr. Rausell will first present the ‘Cell-ID’ approach, a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell sequencing data. Cell-ID signatures have been shown to be reproducible across different donors, tissues of origin, species and single-cell omics technologies, and can be used for automatic cell-type annotation and cell matching across datasets.
Secondly, Dr Rausell will present the Sample-ID method, which allows users to derive unique molecular signatures at the sample level. Sample-ID signatures enable global biological interpretation of the cell heterogeneity found within a sample and allows users to perform sample matching across reference datasets and disease cohorts.
Finally, the use of unbiased molecular signatures derived from single-cell sequencing will be illustrated, using a case study outlining the comprehensive characterisation of cell type diversity from reference human kidney samples.
TALK 2: SynEcoSys: A comprehensive single cell database for translational research and drug discovery
SPEAKER: Jue Fan, Senior Vice President and Head of Bioinformatics and Data Science, Singleron Biotechnologies
ABSTRACT: Single cell RNA sequencing provides an unbiased view of the gene expression profiles of thousands of individual cells simultaneously. As the technology has become more popular, thousands of datasets have become publicly available, which provide a unique opportunity for researchers to acquire cell type specific signatures under normal and disease conditions. However, inconsistent data labelling and a lack of bioinformatic skills among some users prevents efficient utilization of these data.
Here Dr. Fan will introduce a standardized database – SynEcoSys – that allows meta-analysis across hundreds of high-quality single-cell datasets interactively. Reference datasets and cell markers in SynEcoSys could also enable interactive analysis of user uploaded datasets. Demo case studies will then be presented, including data mining strategies for applications such as drug target discovery and patient stratification.
Q&A with all speakers.
Reference: A portion of the work presented in this webinar has recently been published in the following paper:
Cortal A, Martignetti L, Six E, Rausell A. Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID. Nat Biotechnol. 2021 Sep;39(9):1095-1102. doi: 10.1038/s41587-021-00896-6. Epub 2021 Apr 29. PMID: 33927417.
This webinar has been produced with the kind support of Singleron Biotechnologies. You can find out more about the technologies outlined in this webinar by visiting the Singleron Biotechnologies website.