Unbiased single-cell spatial transcriptomics

• Applicant: Dr. Nikolaos Karaiskos
• Subject Area: Bioinformatics and Theoretical Biology
• Term: from 2018 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 411678873

RNA molecules are now regarded as excellent indicators of health states and can be used to guide treatment. Recent high-throughput technological advances have made possible to study the full transcriptome of a tissue at the single-cell level, but they all possess a major drawback: the information about the cell's spatial origin is not preserved. State of the art experimental and computational methods are severely limited by the number of genes quantified, or by requiring the generation of additional information. The objective of the proposed project is to develop computational methodologies that will allow the investigation of single-cell spatial transcriptomics in a straightforward and unbiased manner. Two major goals are hereby identified: (i) the design of computational tools to reconstruct spatial information from single-cell RNA sequencing datasets without and prior information and (ii) the analysis of single-cell RNA datasets stemming from 3D-seq, an innovative experimental technique which naturally retains the spatial component.Current computational approaches to reconstruct the spatial information from single-cell RNA sequencing data rely on the existence of gene expression atlases, which are used as guides. Here we propose a different approach. Our main hypothesis is that gene expression changes in a continuous way, which is reasonable for developing embryos, or other symmetric tissues. Under this hypothesis, it is conceivable that spatial gene expression can be reconstructed solely from sequencing data. To tackle this objective, we will develop computational methods by using single-cell RNA sequencing datasets from tissues of both known and unknown geometry. Our preliminary work supports our hypothesis and the feasibility of this de novo spatial reconstruction. In the second objective of the proposed research project we will develop a flexible, universal scheme for the analysis of single-cell RNA datasets stemming from 3D-seq. 3D-seq is a novel technique which combines existing single-cell RNA sequencing methodologies with the application of a physical grid, therefore naturally preserving the spatial information. We will develop a gold-standard computational analysis scheme for 3D-seq datasets, including the design of the grid's geometry and the cell barcodes that are required for the protocol. 3D-seq is performed on cryosections of a tissue. We will develop the necessary computational tools that will allow us to align these sections and obtain the full three-dimensional structure of the tissue under investigation. Our developed methods will enable the study of cell-cell interactions in a given tissue. All developed methodologies and tools will be freely provided as stand-alone, easy-to-use software packages, will be valuable to the wider community of single-cell biology, and in particular of critical importance for the Human Cell Atlas consortium.

• DFG Programme: Research Grants