Artificial intelligence in single cell genomics: Two innovative technologies for biomedical research in highest resolution

Abstract

The individual cell represents the fundamental unit of life. Its manner of functioning has been the focus of biomedical research for centuries. In recent years, advances in high throughput so-called single cell sequencing techniques have made it possible to study individual cells and their genetic profile. This enables revolutionary new insights into tissue composition, cell–cell interactions and dynamic processes in health and disease. The resulting profile data, e.g. from single cell transcriptomics, however, provide analysts with new challenges: data sets are typically very large, noisy and highly interconnected with other annotation data, making them unsuitable for established procedures. The setting calls for the application of novel algorithms originating from the field of artificial intelligence, which are adapted to deal with this type of challenge. Together, single cell sequencing and artificial intelligence can be considered powerful tools for biomedical research, enabling insights at the highest resolution. This article provides a short overview of the recent developments in both technologies and gives examples for their impact on medical applications. Subsequently, it is demonstrated how methods from artificial intelligence can be successfully applied for the analysis of single cell transcriptomics data. Since the successful application of such methods still requires a detailed understanding of their requirements, an even stronger interaction between specialists of both disciplines may become necessary in the future. We therefore conclude this article with a comment on the use of new platforms for collaboration and knowledge exchange.