Martin Hirst

Associate Professor

Research Classification

Carcinogenesis
Leukemia
Cellular Differentiation
Bioinformatics
Genomics
Molecular Genetics

Research Interests

Epigenomics

Relevant Degree Programs

 
 

Research Methodology

molecular biology
Computational Biology
DNA Sequencing

Recruitment

Master's students
Doctoral students
Postdoctoral Fellows
Any time / year round
I support experiential learning experiences, such as internships and work placements, for my graduate students and Postdocs.
I am interested in hiring Co-op students for research placements.

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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

Doctoral Student Supervision (Jan 2008 - Nov 2019)
Epigenetic regulation in normal hematopoiesis and its dysfunction in leukemia (2020)

Epigenetic modifications including reversible and non-uniform chemical marks to chromatin support activation and silencing of gene transcription. Alterations in normal epigenetic states are associated with transformation across a wide range of cancer types including myeloid malignancies. To understand the role of epigenetic regulation of normal human hematopoietic progenitors and its dysfunction in myeloid transformation, I developed a low-cell input chromatin immunoprecipitation method that, when combined with an analytical framework enables a simultaneous assessment of chromatin accessibility and histone modification state. This method enabled a comparative analysis of the epigenomic states of normal and malignant human blood cell compartments. Application of this methodology to highly purified, phenotypically defined subsets of primitive and terminally differentiating normal human cord blood cells showed that multiple human hematopoietic progenitor phenotypes display a common H3K27me3 signature. This signature includes many large organized H3K27me3 domains co-marked by H3K9me3 also found in the mature lymphoid cells in cord blood (CB) but not in co-isolated monocytes or erythroblasts. These results indicate a marked difference in the epigenomic changes primitive human neonatal hematopoietic cells undergo when they initiate terminal differentiation of the lymphoid and myeloid lineages. Further evidence that this differential H3K27me3 contraction directly impacts hematopoietic differentiation was obtained by manipulating H3K27me3 regulators in cell line models of inducible neutrophil differentiation in vitro.These methodologies were then used to explore epigenomic dysfunction found in the leukemic cells obtained from patients presenting with acute myeloid leukemia (AML) whose blasts differed in their content of neomorphic isocitrate dehydrogenase (IDH) mutations. Comparison of the methylation landscape in the AML cells with and without IDH mutations revealed a higher fractional DNA methylation level at active enhancers in the IDH mutant cells. However, there was no significant difference in global occupancy of histone modifications between the leukemic cells from the two patient groups.Collectively, these findings reveal previously unknown relationships of epigenetic modifications in normal and malignant human blood cells.

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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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Publications

Current Students & Alumni

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