PQG Seminar

Mengjie Chen

Associate Professor of Medicine
Associate Professor of Human Genetics
University of Chicago

Beyond variability: a novel gene expression stability metric to unveil homeostasis and regulation

A homeostatic cell performs regular functions to maintain internal balance by continuously responding to both internal and external stimuli, a process that often involves transcriptional regulation. Most genes within such cells exhibit transcriptional stability, while a smaller subset may enter a regulatory or compensatory state in response to stimuli. Key candidates for this type of regulation include ‘first responder’ genes, interferons, and heat shock proteins, among others. When these responses accumulate to a certain threshold, they can lead to observable phenotypic changes and, in some cases, pathological outcomes. Therefore, identifying genes with precise regulation within homeostatic cells is crucial.

Existing statistical tools have mainly focused on cells with uniform behaviors, often overlooking the nuanced regulation of genes in specific cell subsets. In this presentation, I will discuss an unexpected journey that starts with modeling zero-inflation in single-cell data and progresses to the introduction of the Gene Homeostasis Z-index—a novel metric for gene expression stability. This index reveals genes undergoing precise regulation within specific cell subsets, offering insights into their roles in cellular adaptation. For example, we discover regulatory patterns for neuropeptides like insulin and somatostatin, which exhibit extreme values in a limited number of cells. These findings highlight the limitations of conventional mean-based approaches and demonstrate how our method provides a more refined understanding of gene expression stability.

Bo Wang

Lead Scientist of the Artificial Intelligence Team for Peter Munk Cardiac Centre at University Health Network
Assistant Professor, Departments of Computer Science and Laboratory Medicine & Pathobiology
University of Toronto

Building Foundation Models for Single-cell Omics and Imaging

This talk delves into the innovative utilization of generative AI in propelling biomedical research forward. By harnessing single-cell sequencing data, we developed scGPT, a foundational model that extracts biological insights from an extensive dataset of over 33 million cells. Analogous to how words form text, genes define cells, effectively bridging the technological and biological realms. The strategic application of scGPT via transfer learning significantly boosts its efficacy in diverse applications such as cell-type annotation, multi-batch integration, and gene network inference.Additionally, the talk will spotlight MedSAM, a state-of-the-art segmentation foundational model. Designed for universal application, MedSAM excels across various medical imaging tasks and modalities. It showcased unprecedented advancements in 30 segmentation tasks, outperforming existing models considerably. Notably, MedSAM possesses the unique ability for zero-shot and few-shot segmentation, enabling it to identify previously unseen tumor types and swiftly adapt to novel imaging modalities.Collectively, these breakthroughs emphasize the importance of developing versatile and efficient foundational models. These models are poised to address the expanding needs of imaging and omics data, thus driving continuous innovation in biomedical analysis.

Qing Nie
Distinguished Professor of Mathematics and Developmental & Cell Biology
UC Irvine

Systems Learning of Single Cells

Cells make fate decisions in response to dynamic environments, and multicellular structures emerge from multiscale interplays among cells and genes in space and time. While single-cell omics data provides an unprecedented opportunity to profile cellular heterogeneity, the technology requires fixing the cells, often leading to a loss of spatiotemporal and cell interaction information. How to reconstruct temporal dynamics from single or multiple snapshots of single-cell omics data? How to recover interactions among cells, for example, cell-cell communication from single-cell gene expression data? I will present a suite of our recently developed computational methods that learn the single-cell omics data as a spatiotemporal and interactive system. Those methods are built on a strong interplay among systems biology modeling, dynamical systems approaches, machine-learning methods, and optimal transport techniques. The tools are applied to various complex biological systems in development, regeneration, and diseases to show their discovery power. Finally, I will discuss the methodology challenges in systems learning of single-cell data.