Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session N10: Cell Fate TransitionsFocus
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Sponsoring Units: DBIO Chair: Jianhua Xing, University of Pittsburgh; Sahand Rahi, Ecole Polytechnique Federale de Lausanne Room: Room 202 |
Wednesday, March 8, 2023 11:30AM - 12:06PM |
N10.00001: 3D genome organization in the epithelial-mesenchymal transition spectrum Invited Speaker: Ruby Huang The plasticity along the epithelial-mesenchymal transition (EMT) spectrum has been shown to be regulated by various epigenetic repertoires. Emerging evidence of local chromatin conformation changes suggests that regulation of EMT may occur at a higher order of three-dimensional genome level. |
Wednesday, March 8, 2023 12:06PM - 12:18PM |
N10.00002: Inferring single-cell transcriptomic dynamics with structured dynamical representations of RNA velocity Spencer G Farrell, Madhav Mani, Sidhartha Goyal RNA velocity provides directional information for trajectory inference from single-cell RNA-sequencing data by combining measurements of spliced and unspliced RNA with a dynamical model of transcription and RNA splicing. Traditional approaches to computing RNA velocity rely on strict assumptions about the equations describing the dynamics of transcription and splicing. This results in issues when these assumptions are violated, such as multiple distinct lineages or time-dependent kinetic rates. We have developed "LatentVelo" to generalize RNA velocity with deep learning. Our approach embeds cells into a lower-dimensional latent space , and describes more general differentiation dynamics on this latent space, while still incorporating the causal structure of the transcription and splicing dynamics. These more general dynamics enable accurate trajectory inference, and the latent space approach enables the generation of dynamics-based embeddings of cell states and batch correction of cell states and of RNA velocity. The flexible structure of the model enables modelling a variety of regulatory structures and multi-omic data, or incorporating additional information such as cell-type annotations or experimental metadata to improve the embedding. LatentVelo infers latent trajectories of dynamics, describing the inferred developmental or reprogramming path for individual cells. We demonstrate the capabilities of LatentVelo on both developmental and reprogramming datasets. |
Wednesday, March 8, 2023 12:18PM - 12:30PM |
N10.00003: Bursty RNA Velocity of Gene Programs for Trajectory Inference Frank Gao, Suriyanarayanan Vaikuntanathan, Samantha Riesenfeld
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Wednesday, March 8, 2023 12:30PM - 12:42PM |
N10.00004: Cancer cell states in a perturbed epigenetic landscape Andreas Kraemer Using a simple model we explore how transitions to cancer cell states could arise through external perturbations of the somatic epigenetic landscape. Here, the epigenetic landscape is described using a Hopfield model originating from cooperative feedback loops with master regulator genes [1]. Perturbations, for instance caused by the effect of mutations, are modeled by random fields targeted to certain groups of genes that can be thought to be “selected” during the evolutionary process of cancer progression. The model is applied to gene expression data in the context of hematopoiesis and acute myeloid leukemia. |
Wednesday, March 8, 2023 12:42PM - 12:54PM |
N10.00005: Bifurcation and multistability in three-gene-driven network models Rebecca J Rousseau, Rob Phillips Control of transcription presides over a vast array of biological processes. This control typically manifests through a web of regulatory circuits with different genes interacting under a range of feedback architectures, often exhibiting multistability as a result. Our work uses a geometric approach grounded in bifurcation theory to study the stability profile of a mutually repressing three-gene network across different regions of an unconstrained parameter space. The symmetric network exhibits a distinct dynamic topology as the relative repressive strengths among the genes change, with greater complexity as the genes become more similar in their regulatory activity. We also observe transitions across topologies in the bifurcation plane, and the parameter thresholds under which they occur. These boundaries broaden in parameter space as coupling sensitivity rises via the Hill coefficient and as a higher growth rate implies more available energy. We then show that the model extends to higher-order dynamic networks in which one or more genes from the core three-gene motif drive each downstream gene, and contrast our frameworks with Ising forms. The work highlights dynamic relationships between a system's parametrization and the resulting gene expression phenotypes (i.e., fixed point profiles) that may improve understanding of the mechanisms through which complex networks evolve in nature. |
Wednesday, March 8, 2023 12:54PM - 1:06PM |
N10.00006: Single cell dynamics of the Lac operon induction Louis B Cortes, Guillaume Lambert For decades, the lac operon has been used as a model system to study gene expression. Seminal studies on this paradigmatic operon have uncovered the key role of transcription factors and complex regulatory networks in controlling gene expression. More recent work, benefitting from development on single cell imaging techniques, made use of the lac operon to characterize the dynamics of transcription factors and the importance of stochastic processes in gene networks. |
Wednesday, March 8, 2023 1:06PM - 1:18PM |
N10.00007: Coupling between epithelial-to-mesenchymal transition and cell cycle progression Jianhua Xing Epithelial to mesenchymal transition, EMT, is involved in numerous biological processes such as wound healing, tissue fibrosis, and cancer metastasis. Existing literatures have been debated on whether the transition proceeds through a single or multiple paths, and how cell cycle couples to EMT. To address the questions, we first generated scRNA-seq dataset, where mammary epithelial MCF10A cells were treated with different doses of TGFβ, an EMT inducer. Then we analyzed the data with dynamo, a machine-learning based analytical framework we developed to reconstruct single cell dynamical equations (Qiu et al. Cell, 2022, 185: 690-711). From the obtained vector fields, we applied the transition path analyses, which are originally developed in studying chemical reactions, on simulated single cell trajectories. The analyses reveal two unique types of transition paths, corresponding to either an arrest in the G1/S or G2/M phase, when cells undergo EMT. The existence of two paths agrees with our previous live cell imaging studies (Wang et al., Sci Adv 2020, 6:eaba9309; eLife 2022, 11:e74866), but not pseudotime analyses reported in the literature. Our analyses also reveal a surprising backward cell cycle propagation of cells arrested in G2/M to a G1/S attractor through mitotic skipping. We obtained simialr results with a number of other EMT scRNA-seq data sets, then confirmed with live cell imaging using a A549-Vim/RFP-PCNA-EGFP cell line. |
Wednesday, March 8, 2023 1:18PM - 1:30PM |
N10.00008: A novel FRET-based reporter for real time interrogation of p38-mediated stress response in human cells Thomas E Kuhlman, Michael Worcester Stress signaling in human cells is accomplished through three major mitogen activated protein kinase (MAPK) pathways: c-Jun N-terminal kinase (JNK), extracellular signal-regulated kinase (ERK), and p38. The p38 stress signaling pathway is the least well-understood of the three, and its activity and misregulation are associated with a variety of human diseases, including Alzheimer’s Disease and HIV-Associated Neurocognitive Disorder (HAND). p38 is activated as a result of phosphorylation by upstream kinases in response to a variety of applied stresses. Activated p38 results in differential activation and expression of transcription factors appropriate to the specific applied stress that regulate differentiation, apoptosis, inflammatory response, etc. How appropriate differential responses result from the single input of phosphorylation state of p38 remains unclear. Here, we demonstrate a novel FRET-based p38 reporter that allows us to simultaneously visualize p38 activation state and intracellular localization. We demonstrate that a variety of stresses lead to unique patterns of activation and localization, and our goal is to elucidate how information about the incident stress is encoded in these variables and their dynamics. |
Wednesday, March 8, 2023 1:30PM - 1:42PM |
N10.00009: scTOP: physics-inspired order parameters for cell fate classification and visualization of single cells Maria Yampolskaya, Pankaj Mehta Recent advances in single-cell RNA-sequencing (scRNA-seq) and lineage tracing techniques provide an unprecedented window into the biology of cellular identity. The wealth of data calls for new theoretical and computational frameworks for understanding cell fate specification, accurately classifying cell fates from expression data, and integrating knowledge from cell atlases. Here, we present single-cell Type Order Parameters (scTOP): a statistical-physics-inspired approach for constructing "order parameters" for cell fate given a reference basis of cell types. scTOP achieves near state-of-the-art performance for cell identification at the resolution of single cells and yields interpretable visualizations of developmental trajectories, such as bifurcations between closely related cell fates. Importantly, scTOP does this without using feature selection, statistical fitting, or dimensional reduction (e.g. UMAP, t-SNE, PCA, SPRING). We illustrate the power of scTOP on a wide variety of human and mouse datasets (both in vivo and in vitro), including existing tissue atlases and lineage tracing data. We also provide an easy-to-use Python package implementation of scTOP. Our results suggest that physics-inspired order parameters can serve as an important tool for understanding and analyzing developmental landscapes and cellular identity across biological contexts and organisms. |
Wednesday, March 8, 2023 1:42PM - 1:54PM |
N10.00010: Phenotypic consequences of gene expression driven by positive and negative feedback gene circuits. Rafal Krzyszton, Joshua Azukas, Yiming Wan, Helmut H Strey, Gabor Balazsi Cellular processes such as metastasis or differentiation are governed by the network of dynamically interacting transcription factors (TFs) and their effectors. Prolonged or shortened expression of TFs can lead to aberrant network activity and in consequence affect the phenotype and phenotypic plasticity of living cells. Here, we investigate the influence of common network motifs, such as negative or positive feedback regulation, on the ectopic expression of the metastasis-related TF, Bach1. We use a site-specific integration strategy to introduce BACH1-controlling synthetic gene circuits into the genome of the MDA-MB-231 cell line. Constructs consisting of Bach1 co-expressed with eGFP and driven by repressor- or activator-dependent promoters generate negative and positive feedback, respectively. Using live cell imaging on single-cell arrays, we perform correlation analysis of Bach1 expression and relate it with the invasiveness of both engineered cell lines. |
Wednesday, March 8, 2023 1:54PM - 2:06PM Author not Attending |
N10.00011: Critical growth of cerebral tissue in organoids: theory and experiments Egor I Kiselev, Arndt von Haeseler, Florian Pflug We develop a Fokker-Planck theory of tissue growth with three types of cells (symmetrically |
Wednesday, March 8, 2023 2:06PM - 2:18PM |
N10.00012: Models of branching morphogenesis of dendrites in fly sensory neurons Xiaoyi Ouyang Over approximately one week of larval development in the fruit fly, class IV sensory neurons form an approximately planar and highly branched meshwork of dendrites just under the cuticle. The mesh serves to detect localized noxious stimuli such as penetration by the barbs of parasitic wasps. The tips of the dendrites are highly dynamic: they undergo transitions between growing, shrinking and paused states on the minute timescale (much faster than development), they form lateral branches, and they retract upon contact with other dendrites. To test models of morphogenesis, we have developed a mean-field, continuum model of dendrite growth in which polar branch segments grow at one end (the tip), stochastically nucleate new segments, and disappear randomly at a rate proportional to branch density (to simulate contact-triggered retraction). The model generalizes the Dogterom-Leibler model of microtubule dynamic instability. We solved the model analytically in the central region of the dendrite where the density is spatially uniform and unchanging in time. The analytic results were verified by numerical simulations. The model predictions, when using experimentally measured microscopic parameters, predicted many of the observed mean-field properties of the dendrites including: the mean and exponential distribution of the branch lengths, the mean tip density, and the surprising parabolic relationship between branch and tip densities. The agreement between models and experiment demonstrates that a slow, large-scale and complex morphogenetic process can be understood in terms of the rapid, microscopic properties of the constituent elements, the dendrite tips. |
Wednesday, March 8, 2023 2:18PM - 2:30PM |
N10.00013: Minimal model for emergent spatiotemporal patterning of synthetic Notch expression in vivo Jonathan E Dawson, Paul Langridge, Abdul N Malmi-Kakkada Contact-mediated cell-cell communication can coordinate and pattern the growth of developing multicellular tissues and other cell collectives. Synthetic forms of this communication have the advantage of generating customizable signals which have the potential to shape 3D tissues for regenerative medicine and tissue engineering. However, the mechanisms that regulate the dynamics of a activated synthetic signal in a growing tissue is not well understood. Towards this goal, we present a vertex-based model of spatio-temporal synthetic Notch (synNotch) activation in the epithelium of the Drosophila wing imaginal disc. Through a combination of experiments and modeling, we show that a minimal model assuming uniform cell growth and contact dependent synthetic Notch signaling can largely account for the pattern of signal output observed in clonal populations of synNotch receiving cells in vivo. Our analysis indicates that the extent of synNotch output is dependent on the number of synNotch cells, and the shape of the synNotch cell population. The model highlights growth and output synthesis and degradation rates as the most useful parameters in predicting the extent of synNotch activation within a tissue and also sets the ground-work for predicting outputs of more complex synthetic circuits in vivo. |
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