Methods for single-cell multi-omics integration – scMOmix

Single-cell RNA sequencing (scRNAseq) is revolutionizing biology and medicine. The possibility to assess cellular heterogeneity at a previously inaccessible resolution, has profoundly impacted our understanding of development, of the immune system functioning and of many diseases. While scRNAseq is now mature, the single-cell technological development has shifted to other large-scale quantitative measurements, a.k.a. ‘omics’, and even spatial positioning. In addition, combined omics measurements profiled from the same single cell are becoming available.

Each single-cell omics presents intrinsic limitations and provides a different and complementary information on the same cell. Single-cell multi-omics integration, i.e. the simultaneous analysis of multiple single-cell omics, is thus expected to compensate for missing or unreliable information in any single omics and to provide tremendous power to untangle the complexity of human cells.
However, single-cell multi-omics integration is challenging. Different single-cell omics vary widely in signal range, in coverage depth and in the number and nature of the measured features. The challenge is thereby to extract biological signals shared across the multiple omics and masked by the wide across-omics variations. In addition, the huge number of profiled cells, billions in the near future, introduces all the computational and statistical challenges typical of “Big Data”. There is thus the imperative need for powerful and robust methodologies able to overcome such challenges and produce new biological knowledge through single-cell omics data integration.
scMOmix will contribute to this methodological breakthrough. Our aim is indeed to develop rigorous methods for multi-omics integration able to overcome the numerous intrinsic challenges of single-cell data and exploit their richness. In particular, we propose to develop dimensionality reduction (WP1) and network-based (WP2) approaches enabling the integration of multi-omics single-cell data and we will convert such methods to Open Source algorithms (WP3). By applying the developed approaches to real patient-derived data, scMOmix ultimately aims at improving our understanding of cancer heterogeneity and its underlying molecular mechanisms. In particular, the two applications in cancer research that we will consider are: (T1) assessing the heterogeneity of colorectal cancer subtypes and (T2) pinpointing markers and resistance mechanisms for a-PD1 treatment in metastatic melanoma.

The methodologies developed is scMOmix will impact interdisciplinary research fields. Potential users of scMOmix are computational biologists and sequencing/bioinformatics platforms in hospitals or research centers. Scientists in mathematics/ machine-learning could extend the methodological developments of scMOmix to the analysis of data unrelated to biology. Finally, the interest for scMOmix is not limited to the academic sphere, as we will also target end-users in pharmaceutical industry and AI companies. In the long run, the new tools proposed in scMOmix will benefit biologist and clinicians, thereby contributing to precision medicine and potentially resulting in new diagnostic tools or therapeutic targets. However, the impact of scMOmix will not be limited to cancer research or clinical applications, in fact it will have an effect also on a wide range of biological fields, having multi-omics profiles, going from immunology, neuroscience, environmental research and industrial biotechnology.

Finally, this ANR JCJC will strongly contribute to my evolution towards a team leader position. ScMOmix will boost my possibilities to favorably apply for competitive young PI grants (e.g. ERC Starting grant) and it will be a stepping stone to create my own group inside IBENS.