Martin Hirst

Associate Professor

Research Classification

Cellular Differentiation
Molecular Genetics

Research Interests


Relevant Degree Programs


Research Methodology

molecular biology
Computational Biology
DNA Sequencing


Master's students
Doctoral students
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Any time / year round
I support experiential learning experiences, such as internships and work placements, for my graduate students and Postdocs.
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Great Supervisor Week Mentions

Each year graduate students are encouraged to give kudos to their supervisors through social media and our website as part of #GreatSupervisorWeek. Below are students who mentioned this supervisor since the initiative was started in 2017.


I’m extremely grateful to have such a brilliant, caring, and enthusiastic supervisor. He has been supportive and motivating at every step of the PhD process. He is not only a role model as a scientist but also as a leader. He motivates his students and directs them on a path to better themselves.

Anonymous (2017)


Graduate Student Supervision

Master's Student Supervision (2010 - 2018)
Epigenetic heterogeneity revealed through single-cell DNA methylation sequencing (2018)

Increasing evidence of functional and transcriptional heterogeneity in phenotypically similar single-cells has prompted interest in protocols for obtaining parallel methylome data. Despite appreciable advancements in experimental protocols for single-cell DNA methylation measurements, methods for analyzing the resulting data are still immature. To address the challenge of stochastic data loss associated with single cell measurements, current strategies average methylation in windows or region sets. However previous studies have demonstrated that single CpGs are functional and our analysis of single cell methylation measurements revealed a rapid decay in concordance neighbouring CpG states beyond 1kb. To leverage the information content of individual CpGs in the context of single cell methylation measurements we developed an analytical strategy for deriving single-cell DNA methylation states using individual CpGs, which we term PDclust. We validated PDclust on existing datasets and on data we generated from single index-sorted murine and human hematopoietic stem cells (HSCs) that are highly enriched in functionally defined stem cells. Using PDClust, we identified epigenetically distinct subpopulations within these HSC populations. Strikingly, human cord blood derived HSC populations were separable by donor specific methylation states whereas more differentiated hematopoietic cells separated solely by cell type. Interestingly, removal of methylation sites near genetic variants did not impact this separation, suggesting that these epigenetic states may be a consequence of environmental differences. Finally, through protocol optimization and deep sequencing we generated one of the most comprehensive sets of single cell methylome profiles (20% of CpGs on average) and from these were able to generate genomewide profiles from as little as 6 epigenetically related HSCs to derive subtype-specific regulatory states.

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Current Students & Alumni

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