Every baby deserves a healthy start to life. Yet, worldwide, “295 000 newborns die before 4 weeks of age due to congenital anomalies and associated complications” (WHO 2022).
A major hurdle in the management of congenital disease is the poor diagnosis rate linked to a lack of understanding of which genes are responsible for these defects. For instance, 1 baby out of 100 is born with a congenital heart defect (CHD). To date, less than 20% of children diagnosed with an inherited form of CHD received a genetic diagnosis. Strikingly, 80% of the aetiology of CHD remains unknown [1].
Our team has a long-standing interest in identifying the specific gene sets required for the formation of a healthy heart based on the principle that perturbations in these genes will impair normal development, resulting in anatomical cardiac defects [2]. Thousands of genes are expressed in the whole heart at any given time point during development, but which of these genes are critical for the formation the different parts of the heart? To address this, we used a battery of spatially resolved transcriptome-based technologies ranging from spatial RNA-sequencing, Tomo-seq and Visium Spatial Transcriptomics coupled with epigenomic information.
We generated the first spatially resolved map of the murine heart [3] and identified synexpression groups expressed in distinct regions of the heart, which revealed novel anatomical domains of co-ordinated expression. Here I will describe our spatial journey from surprising insights in benchmarking of emerging tools for spatial transcriptomics data analysis, discovery of novel non-coding regulatory elements controlling spatial boundaries in synexpression groups, to developing novel visualisation tools for spatial data in virtual reality.
Altogether, these bioinformatic pipelines allow to unbiasedly uncover novel genetic determinants composing developmental gene regulatory networks, generating novel avenues to identify novel biomarkers for the early detection of congenital diseases.