Jungsik Noh

Mesenchymal cell migration starts with the protrusion of the cell’s leading edge, enabled by the branching growth of the actin network. Two crucial molecular players in protrusion are actin filaments and the Arp2/3 complex, the branching agent. Traditionally, protrusion models are intuited from several perturbative experiments. Recent multiplex microscopy imaging data promise to change this paradigm by providing measurements of naturally fluctuating densities of key proteins at the leading edge of unperturbed cells. We report an attempt to reconstruct the mechanistic protrusion model from such data. We analyze fluctuations of densities of actin filaments and Arp2/3 complex and cell edge velocity using phase space and regression analysis to reconstruct linearized dynamics of the coupled actin-Arp2/3-velocity system. We then build a nonlinear model of these dynamics based on previous knowledge of this system and parsimony. The resulting models recover the rates of reactions and mechanical and transport processes in the lamellipodium from a single nonperturbative experiment. The models posit that the dominant nonlinear term in the lamellipodial dynamics corresponds to the retrograde flow of the actin network in the cell frame. The models suggest that the protrusion is dominated by a damped oscillatory cycle resulting from a combination of positive feedback from the Arp2/3 complex to protrusion velocity and negative feedback from actin density to the velocity.

For over three decades, we have known that oncogenic RAS alters the actin cytoskeleton organization and cell surface morphology. RAS activates the GTPase RAC1, which triggers the growth of branched actin networks to promote cell membrane protrusions. In melanoma, the hyperactive RAC1 mutant, Rac1P29S, was recently shown to drive extended lamellipodia, which then empower cell proliferation through sequestration and localized inhibition of the merlin tumor suppressor. This discovery illustrates cell morphological programs not only as outputs but also as regulators of human oncogenic signals. Hence, we wondered whether the pronounced branched actin-driven membrane protrusions (BAMPs) downstream of oncogenic RAS are not mere outputs of RAS signaling but rather an active component in mediating the oncogenic penetrance of RAS mutants. We used volumetric light sheet microscopy and biochemical approaches to investigate the role of BAMPs in regulating the molecular signaling of oncogenic KRAS in pancreatic and lung cancer models. We found that elevated BAMP formation regulated the interaction of oncogenic KRAS with downstream effectors, specifically with the RAC1 GEF TIAM1. This implies that BAMPs amplify their own upstream regulators in a positive feedback. This meritorious cycle upregulates cyclin D1 expression by inactivating the merlin tumor suppressor, independently of the mitogen activated protein kinase pathway (MAPK). In the absence of BAMPs, cells carrying oncogenic KRAS mutations are unable to attain their full penetrance in proliferation. Overall, this work unveils the long-overlooked role of branched actin-driven cell morphology in the functionalization of KRAS mutants as potent oncogenes.

Mesenchymal cell migration starts with the protrusion of the cell’s leading edge, enabled by the branching growth of the actin network. Two crucial molecular players in protrusion are actin filaments and the Arp2/3 protein complex, the branching agent. Traditionally, protrusion models are intuited from several perturbative experiments. Recent multiplex microscopy imaging data promise to drastically change this paradigm by providing measurements of naturally fluctuating densities of key proteins at the leading edge of unperturbed cells. We report the first attempt to reconstruct the mechanistic protrusion model from such data. We analyze fluctuations of F-actin and Arp2/3 densities and cell edge velocity using phase space and regression analysis to reconstruct linearized stochastic actin-Arp2/3-velocity coupled dynamics. We then build a nonlinear partial differential equation model of these dynamics based on previous knowledge of this system and parsimony. The resulting model recovers the rates of reactions and mechanical and transport processes in the lamellipodium from a single non-perturbative experiment. The model posits that the only essential nonlinearities in the lamellipodial dynamics stem from the retrograde flow of F-actin. The model suggests that the protrusion is dominated by a damped oscillatory cycle resulting from a combination of positive feedback from Arp2/3 to protrusion velocity and negative feedback from F-actin to the velocity. Significance statement: Traditionally, models of intracellular mechanochemistry are intuited from perturbative experiments. Multiplex microscopy imaging changes this by providing measurements of naturally fluctuating densities of key proteins in unperturbed cells. We use such data to reconstruct a mechanistic model of cell leading edge. We build equations for actin system and edge velocity using data, previous knowledge and parsimony. The resulting model recovers the rates of reactions and mechanical and transport processes at the leading edge. The model suggests that the protrusion is dominated by a damped oscillatory cycle resulting from a combination of positive feedback from Arp2/3 to protrusion velocity and negative feedback from F-actin to the velocity.

The extension of lamellipodia, which are thin, fanlike projections at the cell periphery, requires the assembly of branched actin networks under the control of the small GTPase Rac1. In melanoma, a hyperactive P29S Rac1 mutant is associated with resistance to inhibitors that target the kinases BRAF and MAPK and with more aggressive disease because it sequesters and inactivates the tumor suppressor merlin (encoded by NF2 ) inside abnormally large lamellipodia. Here, we investigated how these merlin-inactivating lamellipodia are maintained using quantitative, live cell imaging of cell morphology and signaling dynamics. We showed that Rac1 and merlin activity were regulated in spatially confined regions or microdomains within the lamellipodium. The role of merlin as a proliferation-limiting tumor suppressor required its ability to inhibit lamellipodial extension and to locally inhibit Rac1 signaling. Conversely, local inactivation of merlin in lamellipodia released these restraints on morphology and signaling, leading to enhanced proliferation. Merlin and Rac1 are thus in a morphologically and dynamically regulated double-negative feedback loop, a signaling motif that can amplify and stabilize modest stimuli of lamellipodia extensions that enable melanoma to sustain mitogenic signaling under growth challenge. This represents an example of how acute oncogenicity is promoted by collaborations between cell morphological programs and biochemical signaling.

Calcium fluorescence imaging enables us to investigate how individual neurons of live animals encode sensory input or drive specific behaviors. Extracting and interpreting large-scale neuronal activity from imaging data are crucial steps in harnessing this information. A significant challenge arises from uncorrectable tissue deformation, which disrupts the effectiveness of existing neuron segmentation methods. Here, we propose an open-source software, DynamicNeuronTracker (DyNT), which generates dynamic neuron masks for deforming and/or incompletely registered 3D calcium imaging data using patch-matching iterations. We demonstrate that DyNT accurately tracks densely populated neurons, whereas a widely used static segmentation method often produces erroneous masks. DyNT also includes automated statistical analyses for interpreting neuronal responses to multiple sequential stimuli. We applied DyNT to analyze the responses of pheromone-sensing neurons in mice to controlled stimulation. We found that four bile acids and four sulfated steroids activated 15 subpopulations of sensory neurons with distinct combinatorial response profiles, revealing a strong bias toward detecting sulfated estrogen and pregnanolone. Teaser DynamicNeuronTracker extracts neuronal activity from deforming 3D calcium imaging datasets using patch-matching iterations.

Cellular quiescence is a nonproliferative state required for cell survival under stress and during development. In most quiescent cells, proliferation is stopped in a reversible state of low Cdk1 kinase activity; in many organisms, however, quiescent states with high-Cdk1 activity can also be established through still uncharacterized stress or developmental mechanisms. Here, we used a microfluidics approach coupled to phenotypic classification by machine learning to identify stress pathways associated with starvation-triggered high-Cdk1 quiescent states in Saccharomyces cerevisiae. We found that low- and high-Cdk1 quiescent states shared a core of stress-associated processes, such as autophagy, protein aggregation, and mitochondrial up-regulation, but differed in the nuclear accumulation of the stress transcription factors Xbp1, Gln3, and Sfp1. The decision between low- or high-Cdk1 quiescence was controlled by cell cycle–independent accumulation of Xbp1, which acted as a time-delayed integrator of the duration of stress stimuli. Our results show how cell cycle–independent stress-activated factors promote cellular quiescence outside G1/G0.

Since the beginning of the coronavirus disease 2019 (COVID-19) pandemic, daily counts of confirmed cases and deaths have been publicly reported in real-time to control the virus spread. However, substantial undocumented infections have obscured the true size of the currently infected population, which is arguably the most critical number for public health policy decisions. We developed a machine learning framework to estimate time courses of actual new COVID-19 cases and current infections in all 50 U.S. states and the 50 most infected countries from reported test results and deaths. Using published epidemiological parameters, our algorithm optimized slowly varying daily ascertainment rates and a time course of currently infected cases each day. Severe under-ascertainment of COVID-19 cases was found to be universal across U.S. states and countries worldwide. In 25 out of the 50 countries, actual cumulative cases were estimated to be 5–20 times greater than the confirmed cases. Our estimates of cumulative incidence were in line with the existing seroprevalence rates in 46 U.S. states. Our framework projected for countries like Belgium, Brazil, and the U.S. that ~10% of the population has been infected once. In the U.S. states like Louisiana, Georgia, and Florida, more than 4% of the population was estimated to be currently infected, as of September 3, 2020, while in New York this fraction is 0.12%. The estimation of the actual fraction of currently infected people is crucial for any definition of public health policies, which up to this point may have been misguided by the reliance on confirmed cases.

Since the beginning of the COVID-19 pandemic, daily counts of confirmed cases and deaths have been publicly reported in real-time to control the virus spread. However, substantial undocumented infections have obscured the true prevalence of the virus. A machine learning framework was developed to estimate time courses of actual new COVID-19 cases and current infections in 50 countries and 50 U.S. states from reported test results and deaths, as well as published epidemiological parameters. Severe under-reporting of cases was found to be universal. Our framework projects for countries like Belgium, Brazil, and the U.S. ∼10% of the population has been once infected. In the U.S. states like Louisiana, Georgia, and Florida, more than 4% of the population is estimated to be currently infected, as of September 3, 2020, while in New York the fraction is 0.12%. The estimation of the actual fraction of currently infected people is crucial for any definition of public health policies, which up to this point may have been misguided by the reliance on confirmed cases.

The coronavirus disease 2019 (COVID-19) pandemic is causing public health emergency and economic crisis all over the globe. Being widely spread, the virus can make any place in the world a new epicenter of the possible second wave of outbreaks. To control the pandemic progression, monitoring of the virus spreading is imperative. This paper proposes a simple and robust approach to monitor the COVID-19 pandemic progression in many countries or regions. This data science pipeline can provide actionable insights via straightforward COVID-19 data visualization for many regions at a glance, which informs of relative time delay of the pandemic progression, projected numbers of confirmed cases in the near future, and the sizes of infections.