The 2020 virtual q-bio Conference

Powerful new ways to image the internal structures and complex dynamics of cells are revolutionizing cell biology and bio-medical research. In this talk, I will focus on how emerging fluorescent technologies are increasing spatio-temporal resolution dramatically, permitting simultaneous multispectral imaging of multiple cellular components. In addition, results will be discussed from whole cell milling using Focused Ion Beam Electron Microscopy (FIB-SEM), which reconstructs the entire cell volume at 4 voxel resolution. Using these tools, it is now possible to begin constructing an “organelle interactome”, describing the interrelationships of different cellular organelles as they carry out critical functions. The same tools are also revealing new properties of organelles and their trafficking pathways, and how disruptions of their normal functions due to genetic mutations may contribute to important diseases.

The consortium of microbes living in and on our bodies is intimately connected with human biology and deeply influenced by physical forces. Despite incredible gains in describing this community, and emerging knowledge of the mechanisms linking it to human health, understanding the basic physical properties and responses of this ecosystem has been comparatively neglected. Most diseases have significant physical effects on the gut; diarrhea alters osmolality, fever and cancer increase temperature, and bowel diseases affect pH. Furthermore, the gut itself is comprised of localized niches that differ significantly in their physical environment, and are inhabited by different commensal microbes. Understanding the impact of common physical factors is necessary for engineering robust microbiota members and communities; however, our knowledge of how they affect the gut ecosystem is poor. We are investigating how changes in osmolality affect the host and the microbial community and lead to mechanical shifts in the cellular environment. Osmotic perturbation is extremely prevalent in humans, caused by the use of laxatives, lactose intolerance, or celiac disease. In our studies we monitored osmotic shock to the microbiota using a comprehensive and novel approach, which combined in vivo experiments to imaging, physical measurements, computational analysis and highly controlled microfluidic experiments. By bridging several disciplines, we developed a mechanistic understanding of the processes involved in osmotic diarrhea, linking single-cell biophysical changes to large-scale community dynamics. Our results indicate that physical perturbations can profoundly and permanently change the competitive and ecological landscape of the gut, and affect the cell wall of bacteria differentially, depending on their mechanical characteristics.

Single-cell measurements of mRNA copy numbers inform our understanding of stochastic gene expression, but these measurements coarse-grain over the individual copies of the gene, where transcription and its regulation take place stochastically. We recently combined single-molecule quantification of mRNA and gene loci to measure the transcriptional activity of an endogenous gene in individual Escherichia coli bacteria. When interpreted using a theoretical model for mRNA dynamics, the single-cell data allowed us to obtain the probabilistic rates of promoter switching, transcription initiation and elongation, mRNA release and degradation. Unexpectedly, we found that gene activity can be strongly coupled to the transcriptional state of another copy of the same gene present in the cell, and to the event of gene replication during the bacterial cell cycle. These gene-copy and cell-cycle correlations demonstrate the limits of mapping whole-cell mRNA numbers to the underlying stochastic gene activity and highlight the contribution of previously hidden variables to the observed population heterogeneity.

We have developed a robotic platform allowing us to monitor cytokines dynamics (including IL-2, IFN-g, TNF, IL-6) of immune cells in vitro, with unprecedented resolution. To understand the complex emerging dynamics, we use interpretable machine learning techniques to build a generative model of cytokine response. We discover that, surprisingly, immune activity is encoded into one global parameter, encoding ligand antigenic properties and to a less extent ligand quantity. Based on this we build a simple interpretable model which can fully explain the broad variability of cytokines dynamics. We validate our approach using different lines of cells and different ligands. Two processes are identified, connected to timing and intensity of cytokine response, which we successfully modulate using drugs or by changing conditions such as initial T cell numbers. Our work reveals a simple "cytokine code", which can be used to better understand immune response in different contexts including immunotherapy. More generally, it reveals how robotic platforms and machine learning can be leveraged to build and validate systems biology models.

The Ca2+ modulated pulsatile secretions of glucagon and insulin by pancreatic α and β cells play a key role in glucose metabolism and homeostasis. However, how different types of cells in the islet couple and coordinate to give rise to various Ca2+ oscillation patterns and how these patterns are being tuned by paracrine regulation are still elusive. Here we developed a microfluidic device to facilitate long-term recording of islet Ca2+ activity at single cell level and found that islets show heterogeneous but intrinsic oscillation patterns. The α and β cells in an islet oscillate in antiphase and are globally phase locked to display a variety of oscillation modes. A mathematical model of islet oscillation maps out the dependence of the oscillation modes on the paracrine interactions between α and β cells. Our study reveals the origin of the islet oscillation patterns and highlights the role of paracrine regulation in tuning them.