Daniel MacDonald, a Research Fellow in the Gibson Lab, is a 2024 recipient of the Banting Postdoctoral Fellowship.
The Banting Postdoctoral Fellowship program provides funding to the very best postdoctoral applicants, both nationally and internationally, who will positively contribute to the country’s economic, social and research-based growth. The award is designed for Canadian citizens, permanent residents of Canada and foreign citizens of Canada.
Dan is a second year fellow researching machine learning for the gut microbiome. Researchers typically study the microbiome by counting the different species of microbes in a stool sample—there may be hundreds to thousands of species and trillions of individual microbes. Stool samples are a good representation of the microbes found in the colon, but they underrepresent species in the small intestine and species that stick to the intestinal walls, which may play a critical role in maintaining the host’s bodily functions. Previously, researchers in the lab developed uncertainty-aware machine learning (ML) models of the gut microbiome that can predict how the hundreds-to-thousands of microbial species grow and die in response to changes in diet. These ML models were trained using stool samples, which underrepresent species upstream from the colon, and aren’t designed to accommodate other types of samples, such as microbe samples from tissue in the small intestine. In this research, Dan and other lab members are developing new uncertainty-aware ML models that will be trained not only on stool samples, but also using microbe measurements throughout the intestinal tract. This will provide insight into the as-yet unknown microbial interactions of the entire intestinal tract. They are designing this model to flexibly incorporate new measurement modalities, such as advanced imaging techniques, which will allow them to quickly adapt this model for new experiments in the ever-growing microbiome field, shedding light on the hidden inhabitants of our bodies.
I’m grateful to be a recipient of a 2024 Banting Postdoctoral Fellowship. This is shared achievement, as it was only possible with the support and countless opportunities provided by Dr. Travis Gibson and our team in the Division of Computational Pathology at BWH. I’d also like to express gratitude to my PhD supervisor, Prof. David Steinman, who laid the foundations for my academic pursuits and shaped my approach to research. It is truly an honour to receive this fellowship from NSERC, and I look forward to making meaningful scientific advancements through the completion of this research. –Dan MacDonald
BWH Computational Pathology Special Seminar
Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross sections that can poorly represent the tissue due to sampling bias. To holistically characterize 3D histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by the complex and time-consuming requirements for manual evaluation, as well as the current lack of computational platforms to distill clinical insights from these large, high-resolution datasets. We present a deep learning model for processing tissue volumes and predicting patient outcomes with weak supervision. Recurrence risk-stratification models were trained with archived prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D block-based prognostication achieves superior performance to traditional 2D slice-based approaches, including existing clinical/histopathological baselines. Incorporating larger tissue volumes is shown to improve prognostic accuracy. This framework offers a promising direction for clinical decision support and 3D biomarker discovery, with the potential to further catalyze the growth of 3D spatial biology techniques for clinical applications.
Cell identity drives cell-cell communication and tissue architecture and is in return regulated by cell-extrinsic cues. Cell identity is determined by the combination of intrinsic developmentally established transcription factor use (TF) and constitutive as well as cell communication-dependent TF activities. Presented work shows two probabilistic models that we developed to advance the understanding of these processes using single-cell and spatial genomic data.
The human gut microbiome, and its metabolic activity in particular, have been implicated in a wide range of disease states, including metabolic disorders, inflammatory bowel diseases, and colorectal cancer. This growing appreciation for the impact of the gut microbiome’s metabolism on human health has given rise to studies that generate both microbiome and metabolome high-throughput data from human gut microbiome samples. Truly integrated analysis of both omic datasets, however, remains a challenging task. My research aims to develop new frameworks for analyzing and integrating these datasets using a combination of machine learning, metabolic modeling, and network analysis. I’ll specifically present two projects: the first evaluates the robustness of microbiome-metabolome associations using machine learning and meta-analysis models; the second aims to identify “multi-omic modules” that capture both cross-omic associations and associations with disease simultaneously. Taken together, these frameworks can enhance our understanding of microbiome-metabolome connections and equip the microbiome research community with novel methods for such integrated data analysis.
Fibrosis is a pathology of excessive scarring which causes morbidity and mortality worldwide. Fibrosis is a complex process involving thousands of factors, therefore, to better understand fibrosis and develop new therapeutic approaches, it is necessary to simplify and clarify the underlying concepts. In this talk, I will introduce a mathematical model we recently developed for a cell circuit between myofibroblasts and macrophages – the two cell types that produce and remodel the scar. The mathematical framework predicts two types of fibrosis – hot fibrosis with abundant macrophages and myofibroblasts, and cold fibrosis dominated by myofibroblasts alone. Moreover, we use the model to predict that the autocrine signal for myofibroblast division is a potential therapeutic target to reduce fibrosis. Finally, I will discuss how we use myocardial infarction (MI), a widely studied in-vivo injury model for cardiac fibrosis, to test these theoretical concepts and intervention strategies experimentally.