Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session B04: Physics of Cell Fate Transitions IFocus Recordings Available
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Sponsoring Units: DBIO Chair: Jianhua Xing, University of Pittsburgh Room: McCormick Place W-176C |
Monday, March 14, 2022 11:30AM - 12:06PM |
B04.00001: Geometry and Genetics Invited Speaker: Eric D Siggia The application of quantitative methods to biological problems faces the choice of how much detail to include and the generality of the conclusions. Both routine data analysis and airy pronouncements that have nothing to say about everything are to be avoided. The middle ground entails some use phenomenology, a well-used approach in both high and low energy physics. A sampling of examples will be presented from my work in the area of developmental biology, to give a flavor of what is possible. They include experiments in synthetic embryology where human stem cells are coaxed into making patterns and structures similar to real embyos, use of modern (ie post 1960) mathematics to enumerate categories of dynamical behaviors, and a bit of computational evolution to address the question of what pattern forming systems can be evolved by incremental changes. |
Monday, March 14, 2022 12:06PM - 12:18PM |
B04.00002: Inferring the molecular mechanisms that guide developmental bifurcations Simon L Freedman, Addison Howe, Sidhartha Goyal, Madhav Mani Embryonic and tissue development is often viewed as a sequence of discrete state transitions through a low-dimensional cell-fate landscape, but it is unclear how this view relates to the high-dimensional molecular data collected in experiments. We have recently shown that cell-fate transitions which stem from their underlying dynamical systems bifurcating can be pin-pointed directly from transcriptomic trajectories. Moreover, the direction of the bifurcation, corresponding to the soft mode of the dynamical system’s Jacobian, is analytically extractable, providing critical clues toward the correlative mechanisms that drive the transition. Here, we investigate how the dynamics of the Jacobian’s soft mode can be used to infer asymmetric gene relationships for the design of predictive molecular network models. We demonstrate our analysis on in-silico gene-regulatory networks, and use it to elucidate transcriptomic trajectories in the mouse endoderm and neural tube. We also examine live-imaging data from the formation of the compound fly-eye, and show how bifurcations can elucidate the mechanical connectivity that enables morphogenesis. Our work demonstrates how dynamical systems theory enables inferring molecular-scale mechanisms from cell-scale state changes. |
Monday, March 14, 2022 12:18PM - 12:30PM |
B04.00003: Cell fate specification and transitions in multicellular systems Matthew Smart, Anton Zilman In complex organisms, large numbers of cells must precisely coordinate their phenotypes for proper tissue self-organization during development, homeostasis, and the immune response. This coordination is mediated by a combination of intracellular gene regulation and cell-cell signaling, which leads to phenotypic transitions in single cells. However, as each cell shifts its phenotype, it may in turn contribute new signals to the tissue microenvironment, leading to complex collective behavior. We have developed a generalized model of multicellular gene expression which accounts for intracellular and intercellular gene regulation. The model is a type of spin glass where each spin describes the expression state of a gene in a particular cell. Intracellular gene interactions are defined using a Hopfield network, which enforces the stability of certain cell types in the absence of signaling. We represent multicellular tissue by a graph of Hopfield networks interacting through cell-cell signaling. Local minima of the collective gene expression landscape correspond to different stable tissue configurations. We characterize how the space of stable gene expression patterns evolves as the strength of signaling is tuned. Counterintuitively, we find that random signaling networks tend to stabilize tissue states which are spatially and compositionally simple [1]. These results provide new perspectives on cell fate within a collective tissue context. |
Monday, March 14, 2022 12:30PM - 12:42PM |
B04.00004: Sensing and making sense of fluctuating cellular states Felix J Meigel, Lina Hellwig, Philipp Mergenthaler, Steffen Rulands The self-organisation of cells into complex tissue relies on the tight regulation of cellular behavior. Typically, the regulation of cell decisions is attributed to pathways controlling the concentration of molecular species in response to intrinsic or extrinsic signals, such as in gene regulatory networks. Here, by contrast, we show in the paradigmatic example of cell death that cells manipulate how fluctuations propagate across spatial scales to regulate cellular behavior. Specifically, we find that the feedback between molecular and mesoscopic organelle fluctuations gives rise to a quasi-particle degree of freedom whose intriguing kinetic properties construct a kinetic low-pass filter of time-dependent concentrations of signaling molecules. This allows cells to distinguish between fast fluctuations and slow, biologically relevant, changes in environmental signals. We demonstrate an order of magnitude effect of this phenomenon on the quality of the cell death decision and validate our predictions experimentally by dynamically perturbing the intrinsic apoptosis pathway. Our work reveals a new mechanism of cell fate decision making. |
Monday, March 14, 2022 12:42PM - 12:54PM |
B04.00005: To Biofilm or Not To Biofilm: A biophysical threshold for biofilm formation Sujit Datta, Jenna A Ott, Selena Chiu, Daniel Amchin, Tapomoy Bhattacharjee Bacteria are ubiquitous in our daily lives, either as motile planktonic cells or as surface-attached biofilms. These different states have considerable functional implications for processes in agriculture, environment, industry, and medicine; hence, it is critically important to understand the conditions that regulate the onset of biofilm formation. Unfortunately, the transition from the planktonic to biofilm state depends on a dizzyingly complex array of cellular and environmental factors. To address this issue, here, we develop a generally-applicable biophysical model that captures essential features of the interplay between motility-mediated dispersal and biofilm formation. Using this model, we establish a universal rule predicting how the onset and extent of biofilm formation depend collectively on cell concentration and motility, nutrient diffusion and consumption, chemotactic sensing, and autoinducer secretion. Our work thus provides a key first step toward quantitatively predicting and controlling biofilm formation in diverse and complex settings. |
Monday, March 14, 2022 12:54PM - 1:06PM |
B04.00006: Stochastic dynamics of cell shape during cell fate transitions Wolfram Pönisch, Iskra Yanakieva, Aki S Stubb, Guillaume Salbreux, Ewa K Paluch The development of an organism is characterized by a series of cellular fate transitions where cells become increasingly specialized. For many animal cells, fate transitions are accompanied by shape changes and there are strong indications of coupling between cell shape and fate. Here, we present a pipeline to quantify and analyse cell shapes as cells undergo the epithelial-to-mesenchymal transition (EMT). We then apply our analysis pipeline to investigate the coupling between cell shape and fate during the EMT of MDCK cells. We find that cell morphology is closely associated with their state: While epithelial cells display spherical shapes, mesenchymal cells undergo spreading. After defining the distinct cellular shapes corresponding to cell states, we study how exactly the morphological features of a cell evolve during EMT. To this aim, we investigate cell trajectories of morphological features in a low-dimensional space and describe the evolution of cellular features as a stochastic process. By integrating morphometric analysis into studies of cell fate transitions, we aim to better understand the crosstalk between cell fate and shape. |
Monday, March 14, 2022 1:06PM - 1:18PM |
B04.00007: Designing and Decoding Transcription Factor Screening Experiments Forrest C Sheldon Steering cell fate decisions by controlling the expression of particular transcription factors is the central idea of cell programming. While several cell types can now be produced from pluripotent stem cells, identifying sets of transcription factors capable of driving programming has remained a fundamental hurdle in producing new cell types. Searching the large space of transcription factor combinations and their outcomes is captured by the following question: Given a set of noisy measurements, both of a cell’s fate and the transcription factors it contains, when can a programming set be inferred? We map this to a communication problem in which a cell acts as a noisy parity bit informing us about the presence of a programming set. This allows us to leverage group testing and inference methods towards cell programming experiments. Using these, we derive simple guidelines for experimental design, such as signal-to-noise ratios and optimal parameter regimes. To decode experiments, we investigate constraint relaxations that render the combinatorial search computationally feasible. Analyzing this apparently simple experimental problem uncovers a rich array of mathematical puzzles with the potential to accelerate our ability to discover new cell type programs. |
Monday, March 14, 2022 1:18PM - 1:30PM |
B04.00008: Label-free Cell Tracking Enables Collective Motion Phenotyping in Epithelial Monolayers Shuyao Gu, Rachel Lee, Zackery A Benson, Chenyi Ling, Michele Vitolo, Stuart Martin, Joe Chalfoun, Wolfgang Losert Collective cell migration is an umbrella term for a rich variety of cell behaviors, whose distinct character is essential for biological function, notably for cancer metastasis. One essential feature of collective behavior is the motion of cells relative to their immediate neighbors. We introduce an AI-based pipeline to segment and track cell nuclei from phase-contrast images. Nuclei segmentation is based on a U‐Net convolutional neural network trained on images with nucleus staining. Tracking, based on the Crocker-Grier algorithm, quantifies nuclei movement and allows for robust downstream analysis of collective motion. Since the AI algorithm required no new training data, our approach promises to be applicable to and yield new insights for vast libraries of existing collective motion images. In a systematic analysis of a cell line panel with oncogenic mutations, we find that the collective rearrangement metric, D2min, which reflects non-affine motion, shows promise as an indicator of metastatic potential. |
Monday, March 14, 2022 1:30PM - 1:42PM |
B04.00009: Systems-level interdependence in organelle biogenesis Kiandokht Panjtan Amiri, Deepthi Kailash, Shankar Mukherji Eukaryotic cells contain hundreds of subcellular structures that serve different functions to maintain cellular homeostasis. A hallmark of Eukaryotic cells is its compartmentalization into membrane-bound organelles. While the function of individual organelles and their role in cellular homeostasis is well studied, less is known about the cell's coordinated control over their synthesis. Recent discoveries have unveiled the mechanisms by which cells regulate the size and abundance of individual membranous organelles. However, we know little about the autonomy or dependence of growth amongst different types of organelles. Here we have characterized the systems-level patterns of interdependence in organelle biogenesis using Saccharomyces cerevisiae as a model system. We have engineered budding yeast cells to fluorescently label six of their membranous organelles simultaneously and imaged them using confocal hyperspectral microscopy. By perturbing genetic factors involved in the biogenesis of each individual organelle, we measured the response in the growth of other organelles. Our statistical analyses revealed a correlation between the growth of mitochondria and the endoplasmic reticulum (ER), the ER and peroxisomes, and mitochondria and peroxisomes. We will incorporate these correlations into our mathematical model of organelle biogenesis control as a step toward capturing the principles by which the cell allocates its finite resources during growth and homeostasis. |
Monday, March 14, 2022 1:42PM - 1:54PM |
B04.00010: Multigenerational memory in cell size homeostasis Motasem ElGamel, Harsh Vashistha, Hanna Salman, Andrew Mugler Cells need to maintain a stable size as they grow and divide to survive. Most experiments suggest that fluctuations around the stable size last for only a generation or two. However, recent evidence suggests that after controlling for environmental effects, fluctuations can persist for many generations. Here we develop a minimal model that takes into account the competition between environmental effects and phenotypic inheritance to explain these results. Our model suggests that the role of the environment is to set the homeostasis parameters over long timescales; different environments have different parameters. Thus, multigenerational memory is revealed in constant environments but obscured when averaging over many different environments. Inferring the parameters of our model from cell size data in microfluidic experiments, we recapitulate the observed statistics of homeostasis and phenotypic inheritance. Our work provides new insights into the impact of the environment on cell homeostasis. |
Monday, March 14, 2022 1:54PM - 2:06PM |
B04.00011: Volume segregation programming in a nematode's early embryogenesis Guoye Guan Nematode species are well-known for their invariant cell lineage pattern during development. Combining knowledge about the fate specification induced by asymmetric division and the anti-correlation between cell cycle length and cell volume in Caenorhabditis elegans, we propose a minimal model to simulate lineage initiation by altering cell volume segregation ratio in each division, and quantify the derived pattern's performance in proliferation speed, fate diversity, and space robustness. The stereotypic pattern in C. elegans embryo is found to be one of the most optimal solutions taking minimum time to achieve the cell number before gastrulation, by programming asymmetric divisions as a strategy. |
Monday, March 14, 2022 2:06PM - 2:18PM |
B04.00012: ANDOR and beyond: dynamically switchable logic gates as modules for flexible information processing Carl D Modes, Mohammadreza Bahadorian Understanding how complex (bio-)chemical pathways and regulatory networks may be capable of processing information in efficient, flexible, and robust ways is a key question with implications touching biology, synthetic biology, and dynamical systems theory. While considerable effort has been focused on identification and characterization of structural motifs and their dynamics involved in biological information processing, a framework for studying context-dependency and flexibility of the motifs is lacking. We here propose a small set of effective modules that are capable of performing different logical operations based on the basin of attraction in which the system resides or is steered to. These dynamically switchable logic gates require fewer components than their traditional analogs where static, separate gates are used for each desired function. The multi-stability enabling this multi-functionality arises from interactions among the components making the switchability an emergent behavior. We demonstrate the applicability and limits of these circuits by determining a robust range of parameters over which they correctly operate and then characterize the resilience of their function against intrinsic noise of the constituent reactions using the theory of large deviations. |
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