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Liam Hodgson

Doctorat - McGill
Superviseur⋅e principal⋅e
Danilo Bzdok
Sujets de recherche
Apprentissage automatique médical
Biologie computationnelle
Découverte de médicaments

Publications

Mitochondria‐nucleus crosstalk characterizes Alzheimer's disease across 1,5 million brain cells
Chloé Savignac
Liam Hodgson
Jack Stanley
Anwesha Bhattacharya
Danilo Bzdok
Emerging insight from stem cell research reinforces Alzheimer's disease (AD) to affect mitochondrial protein expression. Compelling new evid… (voir plus)ence points to mitochondrial reactive oxygen species (ROS) as potential driving player in Aβ toxicity, mediated through glial cells and ultimately impacting neuronal health. A comprehensive understanding of how oxidative phosphorylation variations relate to cell function remains largely unexplored, especially through a cell type lens. Leveraging today's largest single‐nucleus RNA sequencing dataset of AD, we unveil how cell‐type‐specific mitochondrial alterations reverberate in the nuclear transcriptome, in 424 AD patients and healthy controls from ROSMAP. By adopting a supervised latent factor modelling approach, we identified distinct gene modules capturing unique aspects of the mitochondrial crosstalk in 6 major brain cell types across 5,427 nuclear and 13 mitochondrial genes. We found that nuclear‐mitochondrial crosstalk varies distinctly with cell identity, reflecting metabolic demands and functional specialization. In neurons and oligodendrocytes, ATP synthase (complex V) takes a central role, whereas type 1 NADH dehydrogenase (complex I) is more prominent in astrocytes, microglia, and OPCs. Screening across >1 million gene expression profiles from ∼20,000 drug perturbations identified mitochondrial‐nuclear signatures that resemble those activated by parthenolide and niclosamide—two chemical compounds previously associated with oxidative stress and cytotoxicity via ubiquitination—as most predictive of AD. Microglia and OPCs achieved the highest overall classification accuracy, with stronger predictive performance observed in males than in females. Mapping gene module expressions to the Allen Human Brain Atlas revealed shared whole‐brain patterns highlighting the precuneus, which we implicated in ubiquitin‐cascade‐enriched modules. Clinical phenotyping revealed that males with higher AD risk, as indicated by their mitochondrial‐nuclear scores on glial gene modules, exhibited a greater pathological burden, including higher amyloid load, Parkinson's‐like symptoms, and neuroticism‐related traits. Finally, by comparing our findings with 2.5 million CRISPRi‐based perturbations, we identified neural signatures associated with female‐biased transcription factors and fatty acid biosynthesis, while glial signatures were linked to DNA damage and oxidative stress. By integrating multiple layers of biological data from established reference atlases, our analysis of mitochondria‐nuclear crosstalk revealed distinct transcriptional signatures associated with AD risk in glial and neural cells, with these associations exhibiting sex‐biased patterns.
2025-11-30
Alzheimer's & Dementia (publié)
doi.org
Recovering undersampled single-cell transcriptomes with HyperCell
Liam Hodgson
Jack Stanley
Chloé Savignac
Anwesha Bhattacharya
Badr Ait Hammou
Emmanuel Noutahi
Alisandra K. Denton
Joseph E. Powell
Guy Wolf
Smita Krishnaswamy
Danilo Bzdok
Abstract

Single-cell transcriptomic technology has now matured, allowing quantification of mRNA transcripts corres… (voir plus)ponding to tens of thousands of genes within a cell. However, still only a small fraction of these mRNA is captured and measured by today’s single-cell assays. There are likely hundreds of thousands of mRNA copies present within a typical human cell, yet these assays omit a majority of the transcripts that are actually present. This introduces technical noise, especially non-biological variability and excessive sparsity, which frustrates downstream analysis and potentially skews biological conclusions. To overcome these challenges, we here develop HyperCell, a probabilistic deep learning approach that explicitly models this undersampling to produce estimates of each cell’s original gene transcript abundances across the whole transcriptome. We demonstrate that our framework offers benefits in various mRNA modeling settings, by i) correctly differentiating between spurious sampling-induced and real biological zeros, outperforming existing approaches, ii) estimating the total mRNA content of cells across states to reduce contamination due to background transcripts, iii) reducing contamination due to background transcripts, and iv) helping to counteract biases that may appear during typical differential gene expression analyses using widespread normalization approaches. Our approach to correcting for the technical noise introduced by the single-cell experimental process brings us closer to studying biology, starting from the true transcriptome of cells.

2025-04-22
Research Square (prépublication)
doi.org
Cell type transcriptomics reveal shared genetic mechanisms in Alzheimer’s and Parkinson’s disease
Anwesha Bhattacharya
Edward A. Fon
Alain Dagher
Yasser Iturria-Medina
Jo Anne Stratton
Chloé Savignac
Jack Stanley
Liam Hodgson
Badr Ait Hammou
David A Bennett
Danilo Bzdok
Historically, Alzheimer’s disease (AD) and Parkinson’s disease (PD) have been investigated as two distinct disorders of the brain. Howev… (voir plus)er, a few similarities in neuropathology and clinical symptoms have been documented over the years. Traditional single gene-centric genetic studies, including GWAS and differential gene expression analyses, have struggled to unravel the molecular links between AD and PD. To address this, we tailor a pattern-learning framework to analyze synchronous gene co-expression at sub-cell-type resolution. Utilizing recently published single-nucleus AD (70,634 nuclei) and PD (340,902 nuclei) datasets from postmortem human brains, we systematically extract and juxtapose disease-critical gene modules. Our findings reveal extensive molecular similarities between AD and PD gene cliques. In neurons, disrupted cytoskeletal dynamics and mitochondrial stress highlight convergence in key processes; glial modules share roles in T-cell activation, myelin synthesis, and synapse pruning. This multi-module sub-cell-type approach offers insights into the molecular basis of shared neuropathology in AD and PD.
2025-02-18
bioRxiv : the preprint server for biology (publié)
doi.org
Cell type transcriptomics reveal shared genetic mechanisms in Alzheimer’s and Parkinson’s disease
Anwesha Bhattacharya
Edward A. Fon
Alain Dagher
Yasser Iturria-Medina
Jo Anne Stratton
Chloé Savignac
Jack Stanley
Liam Hodgson
Badr Ait Hammou
David A Bennett
Danilo Bzdok
Historically, Alzheimer’s disease (AD) and Parkinson’s disease (PD) have been investigated as two distinct disorders of the brain. Howev… (voir plus)er, a few similarities in neuropathology and clinical symptoms have been documented over the years. Traditional single gene-centric genetic studies, including GWAS and differential gene expression analyses, have struggled to unravel the molecular links between AD and PD. To address this, we tailor a pattern-learning framework to analyze synchronous gene co-expression at sub-cell-type resolution. Utilizing recently published single-nucleus AD (70,634 nuclei) and PD (340,902 nuclei) datasets from postmortem human brains, we systematically extract and juxtapose disease-critical gene modules. Our findings reveal extensive molecular similarities between AD and PD gene cliques. In neurons, disrupted cytoskeletal dynamics and mitochondrial stress highlight convergence in key processes; glial modules share roles in T-cell activation, myelin synthesis, and synapse pruning. This multi-module sub-cell-type approach offers insights into the molecular basis of shared neuropathology in AD and PD.
2025-02-18
bioRxiv (prépublication)
doi.org
Estimating Unknown Population Sizes Using the Hypergeometric Distribution
Liam Hodgson
Danilo Bzdok
The multivariate hypergeometric distribution describes sampling without replacement from a discrete population of elements divided into mult… (voir plus)iple categories. Addressing a gap in the literature, we tackle the challenge of estimating discrete distributions when both the total population size and the sizes of its constituent categories are unknown. Here, we propose a novel solution using the hypergeometric likelihood to solve this estimation challenge, even in the presence of severe under-sampling. We develop our approach to account for a data generating process where the ground-truth is a mixture of distributions conditional on a continuous latent variable, such as with collaborative filtering, using the variational autoencoder framework. Empirical data simulation demonstrates that our method outperforms other likelihood functions used to model count data, both in terms of accuracy of population size estimate and in its ability to learn an informative latent space. We demonstrate our method's versatility through applications in NLP, by inferring and estimating the complexity of latent vocabularies in text excerpts, and in biology, by accurately recovering the true number of gene transcripts from sparse single-cell genomics data.
2024-07-07
Proceedings of the 41st International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press
Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression
Liam Hodgson
Yue Li
Yasser Iturria-Medina
Jo Anne Stratton
Guy Wolf
Smita Krishnaswamy
David A. Bennett
Danilo Bzdok
Late onset Alzheimer’s disease (AD) is a progressive neurodegenerative disease, with brain changes beginning years before symptoms surface… (voir plus). AD is characterized by neuronal loss, the classic feature of the disease that underlies brain atrophy. However, GWAS reports and recent single-nucleus RNA sequencing (snRNA-seq) efforts have highlighted that glial cells, particularly microglia, claim a central role in AD pathophysiology. Here, we tailor pattern-learning algorithms to explore distinct gene programs by integrating the entire transcriptome, yielding distributed AD-predictive modules within the brain’s major cell-types. We show that these learned modules are biologically meaningful through the identification of new and relevant enriched signaling cascades. The predictive nature of our modules, especially in microglia, allows us to infer each subject’s progression along a disease pseudo-trajectory, confirmed by post-mortem pathological brain tissue markers. Additionally, we quantify the interplay between pairs of cell-type modules in the AD brain, and localized known AD risk genes to enriched module gene programs. Our collective findings advocate for a transition from cell-type-specificity to gene modules specificity to unlock the potential of unique gene programs, recasting the roles of recently reported genome-wide AD risk loci. Designing a supervised latent factor framework for snRNA-seq human brain, the authors find distinct Alzheimer’s-predictive gene modules across celltypes, suggesting subcelltype disease progression trajectories.
2024-05-16
Communications Biology (publié)
doi.org