Lecturer
- About
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- Email Address
- michael.morgan@abdn.ac.uk
- School/Department
- School of Medicine, Medical Sciences and Nutrition
Biography
The vision for my lab is the life course engineering of the immune system to promote healthy ageing. I work at the intersection of computational and experimental biology to identify molecular and cellular mechanisms that are affected by ageing and genetics, including the regulation of cell state, response to stimulation and cell-cell communication. My lab develops computational algorithms that are applied to single-cell 'omics data which allowing a broader impact of our research.
I have trained in a mixture of wet-lab experimentation techniques and computational biology in Leeds, Oxford and Cambridge before starting my lab here in Aberdeen.
Qualifications
- BSc Hons Medical Genetics2010 - University of Huddersfield
- PhD Pharmacogenetics2013 - University of Leeds
Memberships and Affiliations
- Internal Memberships
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Institute of Medical Sciences Equality, Diversity and Inclusion committee
Aberdeen Computational Biology Forum - founder & organiser
British Society of Immunology - Aberdeen Immunology Group committee
IMS ECR Seminar Series - founder & organiser
- External Memberships
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Genetics Society
British Society for Immunology
International Society for Computational Biology
Latest Publications
Milo2.0 unlocks population genetic analyses of cell state abundance using a count-based mixed model
Working Papers: Preprint Papers- [ONLINE] DOI: https://doi.org/10.1101/2023.11.08.566176
Origin and segregation of the human germline
Life Science Alliance, vol. 6, no. 8, e202201706Contributions to Journals: ArticlesSequential enhancer state remodelling defines human germline competence and specification
Nature Cell Biology, vol. 24, no. 4, pp. 448-460Contributions to Journals: ArticlesCoagulation factor V is a T-cell inhibitor expressed by leukocytes in COVID-19
iScience, vol. 25, no. 3, pp. 103971Contributions to Journals: ArticlesPBMC isolation
protocols.io.Other Contributions: Other Contributions
- Research
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Research Overview
My lab studies how ageing and genetics regulate immune cell states and cell-cell interactions, and how this impacts on immune-mediated diseases, notably autoimmunity. By developing state of the art computational algorithms, we model variation in cell states and cell-cell interactions using single cell 'omics data modalities. This involves combining graph theory and statistical models to identify which cell types or interactions are perturbed by ageing and genetics. The impact of our research is broadened by the computational algorithms that we develop, and their application through collaborations with research groups that study cancer and immunology both nationally and internationally.
Potential PhD student candidates should contact me to discuss supervision opportunities.
Research Areas
Accepting PhDs
I am currently accepting PhDs in Biomedical Sciences.
Please get in touch if you would like to discuss your research ideas further.
Biomedical Sciences
Accepting PhDsResearch Specialisms
- Bioinformatics
- Human Genetics
- Genomics
- Immunology
Our research specialisms are based on the Higher Education Classification of Subjects (HECoS) which is HESA open data, published under the Creative Commons Attribution 4.0 International licence.
Current Research
Single-cell omic profiling has revealed a bewildering diversity of cell types and states defined at the mRNA and protein levels. We are now in the position to scale up these experiments to profile biological systems across large cohorts of volunteers and patients to understand how these cell states are affected by our genetic make-up and the environments that we live in. Moreover, by profiling complex compositions of cells using dissociated single-cell RNA-sequencing and spatial transcriptomic/proteomic approaches we can resolve how interactions between cells are regulated at the genetic level, how this manifests as cell-to-cell variability, and how this predisposes people to immune-mediated diseases.
Computational research
In my previous appointment I developed a computational algorithm, called Milo, to identify perturbed cell states from single-cell experiments (Dann et al. Nature Biotech 2022). Milo uses a blend of graph theory (nearest-neighbour graphs) and statistical modelling (generalized linear models) to identify which cell states are enriched or depleted in an experiment. My lab continues to develop this framework to incorporate mixed effect models and employ graph-theory to improve the computational speed. Such advancements open the door to statistical genetic analyses of single cell data. My lab uses these computational advances to solve biomedical problems from basic research to disease - which broadens the impact of our research.
For further reading see Dann et al., Nature Biotech 2022 and Kluzer et al., bioRxiv 2023
Experimental research
I have recently developed an in vitro system to co-culture and stimulate peripheral immune system cells as a model of immune response and T cell:antigen presenting cell interactions. The motivation is to use this experimental system to probe how ageing and genetic variation shapes the way that immune cells communicate with each other to shape and direct proper immune responses. Using statistical genetics, we can then identify which genetic variants alter immune cell activation and cell-to-cell communication, and how this predisposes to a range of immune-related diseases, such as autoimmunity and cancer.
Past Research
My research to-date has spanned quantitative genetics, immunology and computational biology to understand the genetic and genomic regulation of cell-to-cell variability in protein and mRNA levels, altered cell fate decision-making in the ageing thymus, and the immune-related changes in blood of COVID19 patients at single-cell resolution. In tandem, I have developed the computational algorithms to integrate single-cell 'omic data and identify altered cell state abundance in single-cell experiments.
Select publications
Differential abundance testing using nearest-neighbour graphs
A neighbourhood graph derived from single thymic epithelial cells across mouse ageing. Points represent graph neighbourhoods and are coloured by the change in abundance over 52 weeks of the mouse lifecourse.
Milo performs differential abundance testing by modelling variation in cell content of nearest-neighbour graph neighbourhoods between experimental conditions, such as ageing, gene knock or comparing healthy controls vs. disease. For details see Dann et al. Nature Biotechnology (2022)
Collaborations
Collaborations
Georg Holländer - University of Basel & Oxford University - Progeny-progenitor relationships in the thymic epithelium
Doug Winton - Cancer Research UK-Cambridge Institute, University of Cambridge - Genetic and genomic regulation of intestinal stem cell dynamics
Alejandra Bruna - Institute of Cancer Research, London - Evolution of chemotherapeutic resistance in neuroblastoma
Tim Halim - Cancer Research UK-Cambridge Institute, University of Cambridge - Single-cell profiling of innate-like lymphoid cells in pancreatic cancer
Maike de la Roche - Cancer Research UK-Cambridge Institute, University of Cambridge - The role of Gli1 and hedgehog signalling in thymocyte development
Patrick Cao - Institute of Medical Sciences, University of Aberdeen - Developing Lecto-seq for combined single-cell profling of glycan-lectin interactions and transcriptomes
Funding and Grants
Royal Society Ageing and autoimmunity - the Ying and Yang of CTLA-4 and T cell function: (December 2023 - November 2024) - £69,557.25
Friends of ANCHOR Pushing the right buttons: a physiologically relevant, controllable, system for T cell activation in cancer research: (August 2023 - July 2024) - £11,827.01
Lab startup funds - £10,000
PhD studentship - £78,000
- Publications
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Page 2 of 3 Results 11 to 20 of 21
Ageing compromises mouse thymus function and remodels epithelial cell differentiation
eLife, vol. 9, e56221Contributions to Journals: ArticlesQuantitative genetic analysis deciphers the impact of cis and trans regulation on cell-to-cell variability in protein expression levels
PLoS Genetics, vol. 16, no. 3, e1008686.Contributions to Journals: ArticlesChallenges in measuring and understanding biological noise
Nature Reviews Genetics, vol. 20, no. 9, pp. 536-548Contributions to Journals: Articles- [ONLINE] DOI: https://doi.org/10.1038/s41576-019-0130-6
Maturing human CD127+ CCR7+ PDL1+ dendritic cells express AIRE in the absence of tissue restricted antigens
Frontiers in Immunology, vol. 10, no. JAN, 2902Contributions to Journals: Articles- [ONLINE] DOI: https://doi.org/10.3389/fimmu.2018.02902
- [ONLINE] View publication in Scopus
Genome-wide study of hair colour in UK Biobank explains most of the SNP heritability
Nature Communications, vol. 9, 5271Contributions to Journals: ArticlesCpG island composition differences are a source of gene expression noise indicative of promoter responsiveness
Genome Biology, vol. 19, 18Contributions to Journals: ArticlesBatch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors
Nature Biotechnology, vol. 36, pp. 421-427Contributions to Journals: Articles- [ONLINE] DOI: https://doi.org/10.1038/nbt.4091
High-fat diet disrupts metabolism in two generations of rats in a parent-of-origin specific manner
Scientific Reports, vol. 6, 31857Contributions to Journals: Articles- [ONLINE] DOI: https://doi.org/10.1038/srep31857
MTHFR functional genetic variation and methotrexate treatment response in rheumatoid arthritis: a meta-analysis
Pharmacogenomics, vol. 15, no. 4Contributions to Journals: Articles- [ONLINE] DOI: https://doi.org/10.2217/pgs.13.235
Allele-Dose Association of the C5orf30 rs26232 Variant With Joint Damage in Rheumatoid Arthritis
Arthritis and Rheumatism, vol. 65, no. 10, pp. 2555-2561Contributions to Journals: Articles- [ONLINE] DOI: https://doi.org/10.1002/art.38064