Rodriques lab

Applied Biotechnology Laboratory

: Contextual transcriptomics

Introduction

Single cell RNA sequencing has revolutionized the field of genomics by allowing us to interrogate the diversity of cell types in tissues.

However, these approaches require the tissue to be dissociated, and thereby discard the spatial and temporal context associated with those cell types. Thus, although one gets a snapshot of gene expression in a given cell type, it is impossible to determine which genes a cell expressed in the past, or where in the tissue the cell was located.

We have recently invented two methods aimed at solving this challenge. In the spatial domain, we invented Slide-seq, a method for profiling the spatial distribution of RNA in tissue with single cell resolution. Essentially, this method enables us to take 20,000 images of a piece of tissue – one image for each gene – in a single biochemical assay.

Using new bioinformatic approaches, we can then discover novel spatial patterns of gene expression de novo, and link these patterns to disease states. In the temporal domain, we invented RNA Timestamps, a method for tracking when in the history of the cell a given RNA was produced, allowing us to determine how the gene expression in the cell changed over time in response to perturbations.

However, these methods are still severely lacking. Slide-seq is restricted to freshly frozen tissue, whereas most clinical samples are preserved by formalin fixation and paraffin embedding. Moreover, the bioinformatic approaches we have to combine gene expression with spatial expression data are still in their infancy.

On the other hand, RNA Timestamps currently only reveals the temporal aspects of a single gene, whereas an ideal tool would monitor all of the genes in the transcriptome simultaneously. Through further technology development, we aim to solve these and other problems, and thus to expand the utility and maturity of contextual transcriptomics.