Publications

Cell cycle control is a key aspect of numerous physiological and pathological processes. The contribution of biophysical cues, such as stiffness or elasticity of the underlying extracellular matrix (ECM), is critically important in regulating cell cycle progression and proliferation. Indeed, increased ECM stiffness causes aberrant cell cycle progression and proliferation. However, the molecular mechanisms that control these stiffness-mediated cellular responses remain unclear. Here, we address this gap and show good evidence that lamellipodin, previously known as a critical regulator of cell migration, stimulates ECM stiffness-mediated cyclin expression and intracellular stiffening. We observed that increased ECM stiffness upregulates lamellipodin expression. This is mediated by an integrin-dependent FAK-Cas-Rac signaling module and supports stiffness-mediated lamellipodin induction. Mechanistically, we find that lamellipodin overexpression increased and lamellipodin knockdown reduced stiffness-induced cell cyclin expression and cell proliferation, and intracellular stiffness. Overall, these results suggest that lamellipodin levels may be critical for regulating cell proliferation.

Cells respond heterogeneously to molecular and environmental perturbations. Phenotypic heterogeneity, wherein multiple phenotypes coexist in the same conditions, presents challenges when interpreting the observed heterogeneity. Advances in live cell microscopy allow researchers to acquire an unprecedented amount of live cell image data at high spatiotemporal resolutions. Phenotyping cellular dynamics, however, is a nontrivial task and requires machine learning (ML) approaches to discern phenotypic heterogeneity from live cell images. In recent years, ML has proven instrumental in biomedical research, allowing scientists to implement sophisticated computation in which computers learn and effectively perform specific analyses with minimal human instruction or intervention. In this review, we discuss how ML has been recently employed in the study of cell motility and morphodynamics to identify phenotypes from computer vision analysis. We focus on new approaches to extract and learn meaningful spatiotemporal features from complex live cell images for cellular and subcellular phenotyping.

To accurately segment cell edges and quantify cellular morphodynamics from live-cell imaging data, we developed a deep learning-based pipeline termed MARS-Net (multiple-microscopy-type-based accurate and robust segmentation network). MARS-Net utilizes transfer learning and data from multiple types of microscopy to localize cell edges with high accuracy. For effective training on distinct types of live-cell microscopy, MARS-Net comprises a pretrained VGG19 encoder with U-Net decoder and dropout layers. We trained MARS-Net on movies from phase-contrast, spinning-disk confocal, and total internal reflection fluorescence microscopes. MARS-Net produced more accurate edge localization than the neural network models trained with single-microscopy-type datasets. We expect that MARS-Net can accelerate the studies of cellular morphodynamics by providing accurate pixel-level segmentation of complex live-cell datasets. *Co-first authors: J. Jang and C. Wang

Machine learning approaches have shown great promise in biology and medicine discovering hidden information to further understand complex biological and pathological processes. In this study, we developed a deep learning-based machine learning algorithm to meaningfully process image data and facilitate studies in vascular biology and pathology. Vascular injury and atherosclerosis are characterized by neointima formation caused by the aberrant accumulation and proliferation of vascular smooth muscle cells (VSMCs) within the vessel wall. Understanding how to control VSMC behaviors would promote the development of therapeutic targets to treat vascular diseases. However, the response to drug treatments among VSMCs with the same diseased vascular condition is often heterogeneous. Here, to identify the heterogeneous responses of drug treatments, we created an in vitro experimental model system using VSMC spheroids and developed a machine learning-based computational method called HETEROID (heterogeneous spheroid). First, we established a VSMC spheroid model that mimics neointima-like formation and the structure of arteries. Then, to identify the morphological subpopulations of drug-treated VSMC spheroids, we used a machine learning framework that combines deep learning-based spheroid segmentation and morphological clustering analysis. Our machine learning approach successfully showed that FAK, Rac, Rho, and Cdc42 inhibitors differentially affect spheroid morphology, suggesting that multiple drug responses of VSMC spheroid formation exist. Overall, our HETEROID pipeline enables detailed quantitative drug characterization of morphological changes in neointima formation, that occurs in vivo, by single-spheroid analysis. *Co-first authors: K. Vaidyanathan and C. Wang. **Co-corresponding authors: Y. Bae and K. Lee

Cell protrusion is morphodynamically heterogeneous at the subcellular level. However, the mechanism of cell protrusion has been understood based on the ensemble average of actin regulator dynamics. Here, we establish a computational framework called HACKS (deconvolution of heterogeneous activity in coordination of cytoskeleton at the subcellular level) to deconvolve the subcellular heterogeneity of lamellipodial protrusion from live cell imaging. HACKS identifies distinct subcellular protrusion phenotypes based on machine-learning algorithms and reveals their underlying actin regulator dynamics at the leading edge. Using our method, we discover "accelerating protrusion", which is driven by the temporally ordered coordination of Arp2/3 and VASP activities. We validate our finding by pharmacological perturbations and further identify the fine regulation of Arp2/3 and VASP recruitment associated with accelerating protrusion. Our study suggests HACKS can identify specific subcellular protrusion phenotypes susceptible to pharmacological perturbation and reveal how actin regulator dynamics are changed by the perturbation. *Co-first authors: C. Wang and H. J. Choi

Lens-free digital in-line holography (LDIH) is a promising microscopic tool that overcomes several drawbacks (e.g., limited field of view) of traditional lens-based microcopy. However, extensive computation is required to reconstruct object images from the complex diffraction patterns produced by LDIH. This limits LDIH utility for point-of-care applications, particularly in resource limited settings. We describe a deep transfer learning (DTL) based approach to process LDIH images in the context of cellular analyses. Specifically, we captured holograms of cells labeled with molecular-specific microbeads and trained neural networks to classify these holograms without reconstruction. Using raw holograms as input, the trained networks were able to classify individual cells according to the number of cell-bound microbeads. The DTL-based approach including a VGG19 pretrained network showed robust performance with experimental data. Combined with the developed DTL approach, LDIH could be realized as a low-cost, portable tool for point-of-care diagnostics. *Co-first authors: S. Kim and C. Wang. **Co-corresponding authors: K. Lee and H. Lee

Highly redundant pathways often contain components whose functions are difficult to decipher from the responses induced by genetic or molecular perturbations. Here, we present a statistical approach that samples and registers events observed in images of intrinsic fluctuations in unperturbed cells to establish the functional hierarchy of events in systems with redundant pathways. We apply this approach to study the recruitment of actin assembly factors involved in the protrusion of the cell membrane. We find that the formin mDia1, along with nascent adhesion components, is recruited to the leading edge of the cell before protrusion onset, initiating linear growth of the lamellipodial network. Recruitment of Arp2/3, VASP, cofilin, and the formin mDia2 then promotes sustained exponential growth of the network. Experiments changing membrane tension suggest that Arp2/3 recruitment is mechano-responsive. These results indicate that cells adjust the overlapping contributions of multiple factors to actin filament assembly during protrusion on a ten-second timescale and in response to mechanical cues.

Filopodia are finger-like protrusive structures, containing actin bundles. By incubating frog egg extracts with supported lipid bilayers containing phosphatidylinositol 4,5 bisphosphate, we have reconstituted the assembly of filopodia-like structures (FLSs). The actin assembles into parallel bundles, and known filopodial components localize to the tip and shaft. The filopodia tip complexes self-organize—they are not templated by preexisting membrane microdomains. The F-BAR domain protein toca-1 recruits N-WASP, followed by the Arp2/3 complex and actin. Elongation proteins, Diaphanous-related formin, VASP, and fascin are recruited subsequently. Although the Arp2/3 complex is required for FLS initiation, it is not essential for elongation, which involves formins. We propose that filopodia form via clustering of Arp2/3 complex activators, self-assembly of filopodial tip complexes on the membrane, and outgrowth of actin bundles.

The analysis of DNA microarry data is one of the most important things for functional genomics research. The matrix representation of microarray data and its successive optimal incisional hyperplanes is a useful platform for developing optimization algorithms to determine the optimal partitioning of pairwise proximity matrix representing completely connected and weighted graph. We developed Deterministic Annealing (DA) approach to determine the successive optimal binary partitioning. DA algorithm demonstrated good performance with the ability to find the globally optimal binary partitions. In addition, the objects that have not been clustered at small non-zero temperature, are considered to be very sensitive to even small randomness, and can be used to estimate the reliability of the clustering.

We present a statistical physics model to describe the stochastic behaviorof ion transport and channel transitions under an applied membrane voltage.To get pertinent ideas we apply our general theoretical scheme to ananalytically tractable model of the channel with a deep binding site whichinteracts with the permeant ions electrostatically. It is found that theinteraction is modulated by the average ionic occupancy in the bindingsite, which is enhanced by the membrane voltage increases. Above acritical voltage, the interaction gives rise to a emergence of a newconducting state along with shift of S4 charge residues in the channel.This exploratory study calls for further investigations to correlate thecomplex transition behaviors with a variety of ion channels, withparameters in the model, potential energy parameters, voltage, and ionic concentration.