Biology subjects

Choi, H. J.

Publications and source records attributed to Choi, H. J..

5 recordsLinked to original sources

bioRxiv2018

Subcellular Time Series Modeling of Heterogeneous Cell Protrusion

In this paper, a new biological modeling approach is proposed for predicting complex heterogeneous subcellular behaviors. Cell protrusion which initiates cell migration has a significant amount of subcellular heterogeneity in micrometer length and minute time scales. It is driven by actin polymerization, e.g., pushing the plasma membrane forward, and then regulated by a multitude of actin regulators. While mathematical modeling is central to system-level understandings of cell protrusion, most of the modeling is based on the ensemble average of actin regulator dynamics at the cellular or population levels, preventing from capturing the heterogeneous cellular activities. With these in mind, a systematic modeling framework is proposed in this paper for predicting velocities of heterogeneous protrusion of migrating cells driven by multiple molecular mechanisms. The modeling framework is developed through the integration of the multiple AutoRegressive eXogenous (ARX) models employing probability density input variables. Unlike conventional ARX models, it provides an effective framework for modeling heterogeneous subcellular behaviors with complex nonlinearities and uncertainties of dynamic systems. To train and validate the proposed model, numerous subcellular time series are extracted from time-lapse movies of migrating PtK1 cells using spinning disk confocal microscope: The current edge velocities and fluorescent intensities of mDia1, actin at the leading edge are used as the input while the future cell edge velocities are selected as an output. It is demonstrated that the proposed approach is highly effective in predicting the future trends of heterogeneous cell protrusion. In particular, by capturing the various multiple activities from the dataset, it is expected that it would improve the understanding of the molecular mechanism underlying cellular and subcellular heterogeneity.

systems biology↗

bioRxiv2018

The Effect of Aldehyde Dehydrogenase Activator, Alda-1(R), on the Ethanol-induced Brain Damage in a Rat of Binge Ethanol Intoxication.

AimsThis study aimed to investigate whether an aldehyde dehydrogenase (ALDH) activator (Alda-1(R)) reduces neuronal damage in a rat model of binge ethanol exposure.\n\nMethodsThirty-six adolescent male rats (130-150 g) were randomly assigned into three groups: sham, ethanol-only group (25% ethanol intragastrically thrice daily for four days, approximately 10 g/kg/day) and ethanol with Alda-1(R) group (10 mg/kg thrice daily for four days). The ALDH activity at baseline and 90 min after the last infusion in each group was measured. Brain damage was investigated using Luxol fast blue-Cresyl violet staining in the hippocampus, CA1 and CA2/3. The activation of astrocytes and microglia was examined using immunohistochemistry for antiglial fibrillary acidic protein (GFAP) and anti-ionized calcium-binding adapter molecule 1 (Iba-1).\n\nResultsAfter a four-day binge, the ALDH activity level was doubled in the ethanol with Alda-1(R) group (mean: 7.87, SD: 0.67), whereas the levels in the sham group (mean: 4.07, SD: 0.53) and ethanol-only group (mean: 3.77, SD: 0.36) were slightly decreased. More significant neuronal shrinkage, fewer neurons, and loss of Nissl in the hippocampus were observed in the ethanol-only group compared to the ethanol with Alda-1(R) group. Astrocytosis and microgliosis of the hippocampus also showed increased activation in the ethanol only group compared with the ethanol with Alda-1(R) group.\n\nConclusionAlda-1(R) administration reduces cytotoxic damage to the hippocampus in adolescent rats with binge ethanol exposure.

neuroscience↗

bioRxiv2018

Co-option of neurotransmitter signaling for inter-organismal communication in C. elegans

Biogenic amine neurotransmitters play a central role in metazoan biology, and both their chemical structures and cognate receptors are evolutionarily conserved. Their primary roles are in intra-organismal signaling, whereas biogenic amines are not normally recruited for communication between separate individuals. Here, we show that in C. elegans, a neurotransmitter-sensing G protein-coupled receptor, TYRA-2, is required for avoidance responses to osas#9, an ascaroside pheromone that incorporates the neurotransmitter octopamine. Neuronal ablation, cell-specific genetic rescue, and calcium imaging show that tyra-2 expression in the nociceptive neuron ASH is necessary and sufficient to induce osas#9 avoidance. Ectopic expression in the AWA neuron, which is generally associated with attractive responses, reverses the response to osas#9, resulting in attraction instead of avoidance behavior, confirming that TYRA-2 partakes in sensing osas#9. The TYRA-2/osas#9 signaling system thus represents an inter-organismal communication channel that evolved via co-option of a neurotransmitter and its cognate receptor.

neuroscience↗

bioRxiv2017

Edge Detection of Cryptic Lamellipodia Assisted by Deep Learning

Cell protrusion plays important roles in cell migration by pushing plasma membrane forward. Cryptic lamellipodia induce the protrusion of submarginal cells in collective cell migration where cells are attached and move together. Although computational image analysis of cell protrusion has been done extensively, the study on protrusion activities of cryptic lamellipodia is limited due to difficulties in image segmentation. This study seeks to aid in the computational analysis of submarginal cell protrusion in collective cell migration by using deep learning to detect the cryptic lamellipodial edges from fluorescence time-lapse movies. Due to the noisy features within overlapping cells, the conventional image analysis algorithms such as Canny edge detector and intensity thresholding are limited. By combining Canny edge detector, Convolutional Neural Networks (CNNs), and local intensity thresholding, we were able to detect cryptic lamellipodial edges of submarginal cells with high accuracy from the fluorescence time-lapse movies of PtK1 cells stained with a plasma membrane marker. We used relatively small effort to prepare the training set to train the CNN to detect the cryptical lamellipodial edges in fluorescence time-lapse movies. This work demonstrates that deep learning can be combined with the conventional image analysis algorithms to facilitate the computational analysis of highly complex time-lapse movies of collective cell migration.

bioinformatics↗

bioRxiv2017

Deconvolution Of Subcellular Protrusion Heterogeneity And The Underlying Actin Regulator Dynamics From Live Cell Imaging

Cell protrusion is morphodynamically heterogeneous at the subcellular level. However, the mechanistic understanding of protrusion activities is usually based on the ensemble average of actin regulator dynamics at the cellular or population levels. Here, we establish a machine learning-based computational framework called HACKS (deconvolution of Heterogeneous Activity Coordination in cytosKeleton at a Subcellular level) to deconvolve the subcellular heterogeneity of lamellipodial protrusion in migrating cells. HACKS quantitatively identifies distinct subcellular protrusion phenotypes from highly heterogeneous protrusion activities and reveals their underlying actin regulator dynamics at the leading edge. Furthermore, it can identify specific subcellular protrusion phenotypes susceptible to pharmacological perturbation and reveal how actin regulator dynamics are changed by the perturbation. Using our method, we discovered accelerating protrusion phenotype in addition to fluctuating and periodic protrusions. Intriguingly, the accelerating protrusion was driven by the temporally coordinated actions between Arp2/3 and VASP: initiated by Arp2/3-mediated actin nucleation, and then accelerated by VASP-mediated actin elongation. We were able to confirm it by pharmacological perturbations using CK666 and Cytochalasin D, which specifically reduced strong accelerating protrusion activities. Taken together, we have demonstrated that HACKS allows us to discover the fine differential coordination of molecular dynamics underlying subcellular protrusion heterogeneity via a machine learning analysis of live cell imaging data.

cell biology↗