Dr Matthew D Young

Principal Staff Scientist

Matthew is a principal staff scientist in cellular genetics. He directs the analytic work of the Behjati group and leads projects investigating how copy number changes impact gene expression in development and disease.

With a background in physics, bioinformatics, and transcriptomics, my research attempts to better understand the transcriptional properties of normal human development and how these processes are reflected in disease.

Recent technological advances have made it possible to measure the patterns of gene expression (the transcriptome) of thousands of individual cells.  Using these single cell transcriptomic technologies, we have been able to create high quality reference maps of human tissues in development and maturity, as part of the Human and  Developmental Cell Atlas projects.  We have then compared these reference maps with tumour cells of childhood cancers, yielding insights into these tumours normal cell correlates, transcriptional transformation, and developmental origins (e.g. Young et al., 2018).  Our ongoing efforts in this area continue to improve our understanding of a broad range of childhood cancers.

These efforts have provided convincing evidence of the developmental origins of childhood tumours, and provide the basis for further efforts to understand the relationship between development and disease.  In particular, phylogenetic techniques can demonstrate tumour’s cell of origin, and by precisely defining the differences between normality and disease we can understand the biologically relevant commonalities and differences within and between tumour types.

My current research focuses on integrating measurements of genomic transformations (such as copy number changes) with single cell transcrptomic methods to understand the precise transcriptomic consequences of these changes.  I study what effect these changes have in both normal tissues and in disease.

I also lead the development of computational tools for the analysis of single cell transcrptomic data and its integration with other data types.   This includes the development of SoupX, a tool to remove ambient RNA contamination from droplet based single cell data.

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