Biology subjects

Cho, H.

Publications and source records attributed to Cho, H..

101 records · Page 6Linked to original sources

bioRxiv2019

Mathematical modeling with single-cell sequencing data

Single-cell sequencing technologies have revolutionized molecular and cellular biology and stimulated the development of computational tools to analyze the data generated from these technology platforms. However, despite the recent explosion of computational analysis tools, relatively few mathematical models have been developed to utilize these data. Here we compare and contrast two approaches for building mathematical models of cell state-transitions with single-cell RNA-sequencing data with hematopoeisis as a model system; by solving partial differential equations on a graph representing discrete cell state relationships, and by solving the equations on a continuous cell state-space. We demonstrate how to calibrate model parameters from single or multiple time-point single-cell sequencing data, and examine the effects of data processing algorithms on the model calibration and predictions. As an application of our approach, we demonstrate how the calibrated models may be used to mathematically perturb normal hematopoeisis to simulate, predict, and study the emergence of novel cell types during the pathogenesis of acute myeloid leukemia. The mathematical modeling framework we present is general and can be applied to study cell state-transitions in any single-cell genome sequencing dataset.\n\nAuthor summaryHere we compare and contrast graph- and continuum-based approaches for constructing mathematical models of cell state-transitions using single-cell RNA-sequencing data. Using two publicly available datasets, we demonstrate how to calibrate mathematical models of hematopoeisis and how to use the models to predict dynamics of acute myeloid leukemia pathogenesis by mathematically perturbing the process of cellular proliferation and differentiation. We apply these modeling approaches to study the effects of perturbing individual or sets of genes in subsets of cells, or by modeling the dynamics of cell state-transitions directly in a reduced dimensional space. We examine the effects of different graph abstraction and trajectory inference algorithms on calibrating the models and the subsequent model predictions. We conclude that both the graph- and continuum-based modeling approaches can be equally well calibrated to data and discuss situations in which one method may be preferable over the other. This work presents a general mathematical modeling framework, applicable to any single-cell sequencing dataset where cell state-transitions are of interest.

cell biology↗

bioRxiv2019

Dynamic healing and remodeling of mandibular ramus in segmental mobility and cortical overlap after intraoral vertical osteotomy

The ramus immediately after intraoral vertical ramus osteotomy (IVRO) shows segmental mobility, cortical overlap, and projection, which yield smooth-surfaced continuity and stability after one year. This study aimed to elucidate the three-dimensional (3D) morphological changes driven by regional healing and remodeling 12 months postoperatively (Post) and their relationships with the immediate postoperative (Imm) segmental overlapping pattern. We performed a retrospective study of the morphological change of mandibular ramus in terms of time and amount of cortical overlap after IVRO. 3D computed tomography data of 108 hemi-mandibles were analyzed by independent superimposition of Imm proximal and distal segment to Post mandible. Analysis showed extensive regional bone resorption and apposition resulting in a relatively flat-surfaced and morphologically intact Post ramus. The middle ramal remodeling was significantly associated with Imm overlapping patterns and Post mandibular movement, new bony projections of various shapes being observed at the posterior border of the Post ramus. The ramus after IVRO underwent dynamic morphological changes during healing/remodeling which were partly associated with Imm overlap. Moreover, the Post functional and smooth-surfaced ramus seems to have been facilitated by the close surgical approximation of segments and the postoperative mandibular functional rehabilitation, even with segmental positional changes.

pathology↗

bioRxiv2019

Kv4.1, a key ion channel for low frequency firing of dentate granule cells, is crucial for pattern separation

The dentate gyrus (DG) in the hippocampus may play key roles in remembering distinct episodes through pattern separation, which may be subserved by the sparse firing properties of granule cells (GCs) in the DG. Low intrinsic excitability is characteristic of mature GCs, but ion channel mechanisms are not fully understood. Here, we investigated ionic channel mechanisms for firing frequency regulation in GCs of the rat hippocampus, and identified Kv4.1 as a key player. Immunofluorescence analysis showed that Kv4.1 was preferentially expressed in the DG, and its expression level was higher at 8-week than 3-week-old mice, suggesting a developmental regulation of Kv4.1 expression. With respect to firing frequency, GCs are categorized into two distinctive groups: low frequency (LF) and high frequency (HF) firing GCs. Input resistance (Rin) of most LF-GCs is lower than 200 M{Omega}, suggesting that LF-GCs are fully mature GCs. Kv4.1 channel inhibition by intracellular perfusion of Kv4.1 antibody increased firing rates and gain of the input-output relationship selectively in LF-GCs with no significant effect on resting membrane potential and Rin, but had no effect in HF-GCs. Importantly, mature GCs from mice depleted of Kv4.1 transcripts in the DG showed increased firing frequency, and these mice showed an impairment in contextual discrimination task. Our findings suggest that Kv4.1 expression occurring at late stage of GC maturation is essential for low excitability of DG networks and thereby contributes to pattern separation.

neuroscience↗

bioRxiv2019

Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape

Large-scale single-cell RNA-sequencing (scRNA-seq) studies that profile hundreds of thousands of cells are becoming increasingly common, overwhelming existing analysis pipelines. Here, we describe how to enhance and accelerate single-cell data analysis by summarizing the transcriptomic heterogeneity within a data set using a small subset of cells, which we refer to as a geometric sketch. Our sketches provide more comprehensive visualization of transcriptional diversity, capture rare cell types with high sensitivity, and accurately reveal biological cell types via clustering. Our sketch of umbilical cord blood cells uncovers a rare subpopulation of inflammatory macrophages, which we experimentally validated in vitro. The construction of our sketches is extremely fast, which enabled us to accelerate other crucial resource-intensive tasks such as scRNA-seq data integration. We anticipate that our algorithm will become an increasingly essential step when sharing and analyzing the rapidly-growing volume of scRNA-seq data and help enable the democratization of single-cell omics.

bioinformatics↗

bioRxiv2018

Deep-learning-based label-free segmentation of cell nuclei in time-lapse refractive index tomograms

In order to identify cell nuclei, fluorescent proteins or staining agents has been widely used. However, use of exogenous agents inevitably prevents from long-term imaging of live cells and rapid analysis, and even interferes with intrinsic physiological conditions. In this work, we proposed a method of label-free segmentation of cell nuclei in optical diffraction tomography images by exploiting a deep learning framework. The proposed method was applied for precise cell nucleus segmentation in two, three, and four-dimensional label-free imaging. A novel architecture with optimised training strategies was validated through cross-modality and cross-laboratory experiments. The proposed method would bring out broad and immediate biomedical applications with our framework publicly available.

biophysics↗

bioRxiv2018

Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells

We present a rapid and label-free method for hematologic screening for diseases and syndromes, utilizing quantitative phase imaging (QPI) and machine learning. We aim to establish an efficient blood examination framework that does not suffer from the drawbacks of conventional blood assays, which are incapable of profiling single cells or using labeling procedures. Our method involves the synergistic employment of QPI and machine learning. The high-dimensional refractive index information arising from the QPI-based profiling of single red blood cells is processed to screen for diseases and syndromes using machine learning, which can utilize high-dimensional data beyond the human level. Accurate screening for iron-deficiency anemia, reticulocytosis, hereditary spherocytosis, and diabetes mellitus is demonstrated (>99% accuracy) using the proposed method. Furthermore, we highlight the synergy between QPI and machine learning in the proposed method by analyzing the performance of the method.

biophysics↗

bioRxiv2018

An activation pathway governs cell wall polymerization by a bacterial morphogenic machine

Cell elongation in rod-shaped bacteria is mediated by the Rod system, a conserved morphogenic complex that spatially controls cell wall (CW) assembly. In Escherichia coli, alterations in a CW synthase component of the system called PBP2 were identified that overcome other inactivating defects. Rod system activity was stimulated in the suppressors in vivo, and purified synthase complexes with these changes showed more robust CW synthesis in vitro. Polymerization of the actin-like MreB component of the Rod system was also found to be enhanced in cells with the activated synthase. The results suggest an activation pathway governing Rod system function in which PBP2 conformation plays a central role in stimulating both CW glycan polymerization by its partner RodA and the formation of cytoskeletal filaments of MreB to orient CW assembly. An analogous activation pathway involving similar enzymatic components is likely responsible for controlling CW synthesis by the division machinery.

microbiology↗

bioRxiv2018

Neural Data Visualization for Scalable and Generalizable Single Cell Analysis

Single-cell RNA sequencing is becoming effective and accessible as emerging technologies push its scale to millions of cells and beyond. Visualizing the landscape of single cell expression has been a fundamental tool in single cell analysis. However, standard methods for visualization, such as t-stochastic neighbor embedding (t-SNE), not only lack scalability to data sets with millions of cells, but also are unable to generalize to new cells, an important ability for transferring knowledge across fast-accumulating data sets. We introduce net-SNE, which trains a neural network to learn a high quality visualization of single cells that newly generalizes to unseen data. While matching the visualization quality of t-SNE on 14 benchmark data sets of varying sizes, from hundreds to 1.3 million cells, net-SNE also effectively positions previously unseen cells, even when an entire subtype is missing from the initial data set or when the new cells are from a different sequencing experiment. Furthermore, given a \"reference\" visualization, net-SNE can vastly reduce the computational burden of visualizing millions of single cells from multiple days to just a few minutes of runtime. Our work provides a general framework for newly bootstrapping single cell analysis from existing data sets.

bioinformatics↗

bioRxiv2017

Modeling acute myeloid leukemia in a continuum of differentiation states

Here we present a mathematical model of movement in an abstract space representing states of cellular differentiation. We motivate this work with recent examples that demonstrate a continuum of cellular differentiation using single cell RNA sequencing data to characterize cellular states in a high-dimensional space, which is then mapped into [R]2 or [R]3 with dimension reduction techniques. We represent trajectories in the differentiation space as a graph, and model directed and random movement on the graph with partial differential equations. We hypothesize that flow in this space can be used to model normal differentiation processes as well as predict the evolution of abnormal differentiation processes such as those observed during pathogenesis of acute myeloid leukemia (AML).

cancer biology↗

bioRxiv2017

KDM2B is a histone H3K79 demethylase and induces transcriptional repression via SIRT1-mediated chromatin silencing

The methylation of histone H3 lysine 79 (H3K79) is an active chromatin marker and is prominant in actively transcribed regions of the genome. However, demethylase of H3K79 remains unknown despite intensive research. Here, we show that KDM2B (also known as FBXL10), a member of the Jumonji C family of proteins and known for its histone H3K36 demethylase activity, is a di- and tri-methyl H3K79 demethylase. We demonstrate that KDM2B induces transcriptional repression of HOXA7 and MEIS1 via occupancy of promoters and demethylation of H3K79. Furthermore, genome-wide analysis suggests that H3K79 methylation levels increase when KDM2B is depleted, indicating that KDM2B functions as an H3K79 demethylase in vivo. Finally, stable KDM2B-knockdown cell lines exhibit displacement of NAD+-dependent deacetylase SIRT1 from chromatin, with concomitant increases in H3K79 methylation and H4K16 acetylation. Our findings identify KDM2B as an H3K79 demethylase and link its function to transcriptional repression via SIRT1-mediated chromatin silencing.

genetics↗

bioRxiv2017

Targeting the Untargetable: Predicting Pramlintide Resistance Using a Neural Network Based Cellular Automata

De novo resistance is a major issue for the use of targeted anticancer drugs in the clinic. By integrating experimental data we have created a hybrid neural network/agent-based model to simulate the evolution and spread of resistance to the drug Pramlintide in cutaneous squamous cell carcinoma. Our model can eventually be used to predict patient responses to the drug and thus enable clinicians to make decisions regarding personalized, precision treatment regimes for patients.

cancer biology↗