Biology subjects

Choi, H.

Publications and source records attributed to Choi, H..

At least 91 records · Page 5Linked to original sources

bioRxiv2023

Stimulus-dependent functional network topology in mouse visual cortex

Information is processed by networks of neurons in the brain. On the timescale of sensory processing, those neuronal networks have relatively fixed anatomical connectivity, while functional connectivity, which defines the interactions between neurons, can vary depending on the ongoing activity of the neurons within the network. We thus hypothesized that different types of stimuli, which drive different neuronal activities in the network, could lead those networks to display stimulus-dependent functional connectivity patterns. To test this hypothesis, we analyzed electrophysiological data from the Allen Brain Observatory, which utilized Neuropixels probes to simultaneously record stimulus-evoked activity from hundreds of neurons across 6 different regions of mouse visual cortex. The recordings had single-cell resolution and high temporal fidelity, enabling us to determine fine-scale functional connectivity. Comparing the functional connectivity patterns observed when different stimuli were presented to the mice, we made several nontrivial observations. First, while the frequencies of different connectivity motifs (i.e., the patterns of connectivity between triplets of neurons) were preserved across stimuli, the identities of the neurons within those motifs changed. This means that functional connectivity dynamically changes along with the input stimulus, but does so in a way that preserves the motif frequencies. Secondly, we found that the degree to which functional modules are contained within a single brain region (as opposed to being distributed between regions) increases with increasing stimulus complexity. This suggests a mechanism for how the brain could dynamically alter its computations based on its inputs. Altogether, our work reveals unexpected stimulus-dependence to the way groups of neurons interact to process incoming sensory information.

neuroscience↗

bioRxiv2023

Unbiased phosphoproteomics analysis unveils modulation of insulin signaling by extramitotic CDK1 kinase activity in human myotubes

Sensitivity and plasticity of insulin signaling and glucose uptake in skeletal muscle depends on determinants such as genetic variation and obesity. We collected muscle biopsies and isolated myoblasts from a multi-ethnic cohort of lean South Asians (N=10), lean Chinese (N=10), and obese Chinese (N=10), and analysed the proteome and phosphoproteome dynamics in terminally differentiated myotubes after a low-dose insulin stimulation (10nM at 0, 5, 30 min). The myotubes initially responded with increased abundance and phosphorylation level changes along the PI3K/AKT/mTOR axis, decreased abundance of translation initiation factors, and increased phosphorylation levels on proteins involved in mRNA processing at 5 min. After the acute response, protein abundance returned to baseline at 30 min, while phosphorylation changes persisted in proteins including AKT, RPS6 and AS160 (TBC1D4). A joint kinase-substrate statistical analysis revealed that protein abundance changes of AKT, PAK1 and CDK1 showed concordant phosphorylation changes in their respective substrates upon insulin stimulation. We also observed increased phosphorylation of some substrates uniquely in each group, particularly the substrates of CDKs showing stronger changes in South Asians than in Chinese. Pharmacological inhibition and siRNA knockdown of CDK1, a non-myogenic kinase, in terminally differentiated myotubes reduced glucose uptake and desensitized several phosphorylation-mediated signaling on protein translation initiation factors, IRS1, and AS160. Our data suggest that basal extramitotic activity of CDK1 is required for PI3K/AKT/mTORC1 signaling cascade and glucose uptake in insulin-stimulated myotubes. The data also provide a rich resource for studying the role of other kinases in the mechanism of insulin resistance in human myotubes.

physiology↗

bioRxiv2023

Generation of Super-resolution Images from Barcode-based Spatial Transcriptomics Using Deep Image Prior

Spatial transcriptomics (ST) has revolutionized the field of biology by providing a powerful tool for analyzing gene expression in situ. However, current ST methods, particularly barcode-based methods, have limitations in reconstructing high-resolution images from barcodes sparsely distributed in slides. Here, we present SuperST, a novel algorithm that enables the reconstruction of dense matrices from low-resolution ST libraries. SuperST based on deep image prior reconstructs spatial gene expression patterns as image matrices. SuperST allows gene expression mapping to better reflect immunofluorescence (IF) images. Compared with previous methods, SuperST generated output images that more closely resembled IF images for given gene expression maps. Additionally, SuperST overcomes the limitations inherent in IF images, highlighting its potential applications in the realm of spatial biology. By providing a more detailed understanding of gene expression in situ, SuperST has the potential to contribute to comprehensively understanding biology from various tissues.

bioinformatics↗

bioRxiv2023

SpatialSPM: Statistical parametric mapping for the comparison of gene expression pattern images in multiple spatial transcriptomic datasets

Spatial transcriptomic (ST) techniques help us understand the gene expression levels in specific parts of tissues and organs, providing insights into their biological functions. Even though ST dataset provides information on the gene expression and its location for each sample, it is challenging to compare spatial gene expression patterns across tissue samples with different shapes and coordinates. Here, we propose a method that reconstructs ST data into multi-dimensional image matrices to ensure comparability across different samples through spatial registration process. We demonstrated the applicability of this method by using two mouse brain ST datasets to investigate and directly compare gene expression in a specific anatomical region of interest, pixel by pixel, across various biological statuses. It can produce statistical parametric maps to find specific regions with differentially expressed genes across tissue samples. Our approach provides an efficient way to analyze ST datasets and may offer detailed insights into various biological conditions.

bioinformatics↗

bioRxiv2023

Mutant analysis of Kcng4b reveals how the different functional states of the voltage-gated potassium channel regulate ear development

The voltage gated (Kv) slow-inactivating delayed rectifier channel regulates the development of hollow organs of the zebrafish. The functional tetramer consists of an electrically active subunit (Kcnb1, Kv2.1) and a modulatory silent subunit (Kcng4b, Kv6.4). The two mutations in zebrafish kcng4b - kcng4b-C1 and kcng4b-C2 (Gasanov et al., 2021) - have been studied during ear development using electrophysiology, developmental biology and in silico structural modelling. kcng4b-C1 mutation causes a C-terminal truncation characterized by mild Kcng4b loss-of-function (LOF) manifested by failure of kinocilia to extend and formation of ectopic otoliths. In contrast, the kcng4b-C2-/- mutation causes the C-terminal domain to elongate and the ectopic seventh transmembrane (TM) domain to form, converting the intracellular C-terminus to an extracellular one. Kcng4b-C2 acts as a Kcng4b gain-of-function (GOF) allele. Otoliths fail to develop and kinocilia are reduced in kcng4b-C2-/-. These results show that different mutations of the silent subunit Kcng4 can affect the activity of the Kv channel and cause a wide range of developmental defects.

developmental biology↗

bioRxiv2023

Differential encoding of temporal context and expectation under representational drift across hierarchically connected areas

The classic view that neural populations in sensory cortices preferentially encode responses to incoming stimuli has been strongly challenged by recent experimental studies. Despite the fact that a large fraction of variance of visual responses in rodents can be attributed to behavioral state and movements, trial-history, and salience, the effects of contextual modulations and expectations on sensory-evoked responses in visual and association areas remain elusive. Here, we present a comprehensive experimental and theoretical study showing that hierarchically connected visual and association areas differentially encode the temporal context and expectation of naturalistic visual stimuli, consistent with the theory of hierarchical predictive coding. We measured neural responses to expected and unexpected sequences of natural scenes in the primary visual cortex (V1), the posterior medial higher order visual area (PM), and retrosplenial cortex (RSP) using 2-photon imaging in behaving mice collected through the Allen Institute Mindscopes OpenScope program. We found that information about image identity in neural population activity depended on the temporal context of transitions preceding each scene, and decreased along the hierarchy. Furthermore, our analyses revealed that the conjunctive encoding of temporal context and image identity was modulated by expectations of sequential events. In V1 and PM, we found enhanced and specific responses to unexpected oddball images, signaling stimulus-specific expectation violation. In contrast, in RSP the population response to oddball presentation recapitulated the missing expected image rather than the oddball image. These differential responses along the hierarchy are consistent with classic theories of hierarchical predictive coding whereby higher areas encode predictions and lower areas encode deviations from expectation. We further found evidence for drift in visual responses on the timescale of minutes. Although activity drift was present in all areas, population responses in V1 and PM, but not in RSP, maintained stable encoding of visual information and representational geometry. Instead we found that RSP drift was independent of stimulus information, suggesting a role in generating an internal model of the environment in the temporal domain. Overall, our results establish temporal context and expectation as substantial encoding dimensions in the visual cortex subject to fast representational drift and suggest that hierarchically connected areas instantiate a predictive coding mechanism.

neuroscience↗

bioRxiv2023

A Spatial Transcriptomics based Label-Free Method for Assessment of Human Stem Cell Distribution and Effects in a Mouse Model of Lung Fibrosis

Recently, cell therapy has emerged as a promising treatment option for various disorders. Given the intricate mechanisms of action (MOA) and heterogenous distribution in target tissues inherent to cell therapy, it is necessary to develop more sophisticated, unbiased approaches to evaluate the distribution of administered cells and the molecular changes at a microscopic level. In this study, we present a label-free approach for assessing the tissue distribution of administered human mesenchymal stem cells (hMSCs) and their MOA, leveraging spatially resolved transcriptomics (ST) analysis. We administered hMSCs to mouse model of lung fibrosis and utilized ST to visualize the spatial distribution of hMSCs within the tissue. This was achieved by capitalizing on interspecies transcript differences between human and mouse. Furthermore, we could examine molecular changes associated with the spatial distribution of hMSCs. We suggest that our method has the potential to serve as an effective tool for various cell-based therapeutic agents.

bioinformatics↗

bioRxiv2023

IAMSAM : Image-based Analysis of Molecular signatures using the Segment-Anything Model

Spatial transcriptomics is a cutting-edge technique that combines gene expression data with spatial information, allowing researchers to study gene expression patterns within tissue architecture. Here, we present IAMSAM, a user-friendly web-based tool for analyzing spatial transcriptomics data focusing on morphological features. IAMSAM accurately segments tissue images using the Segment-anything model, allowing for the semi-automatic selection of regions of interest based on morphological signatures. Furthermore, IAMSAM provides downstream analysis, such as identifying differentially expressed genes, enrichment analysis, and cell type prediction within the selected regions. With its simple interface, IAMSAM empowers researchers to explore and interpret heterogeneous tissues in a streamlined manner.

bioinformatics↗

bioRxiv2023

LRRC8A anion channels modulate vasodilation via association with Myosin Phosphatase Rho Interacting Protein (MPRIP)

BackgroundIn vascular smooth muscle cells (VSMCs), LRRC8A volume regulated anion channels (VRACs) are activated by inflammatory and pro-contractile stimuli including tumor necrosis factor alpha (TNF), angiotensin II and stretch. LRRC8A physically associates with NADPH oxidase 1 (Nox1) and supports its production of extracellular superoxide (O2-*). Methods and ResultsMice lacking LRRC8A exclusively in VSMCs (Sm22-Cre, KO) were used to assess the role of VRACs in TNF signaling and vasomotor function. KO mesenteric vessels contracted normally to KCl and phenylephrine, but relaxation to acetylcholine (ACh) and sodium nitroprusside (SNP) was enhanced compared to wild type (WT). 48 hours of ex vivo exposure to TNF (10ng/ml) markedly impaired dilation to ACh and SNP in WT but not KO vessels. VRAC blockade (carbenoxolone, CBX, 100 M, 20 min) enhanced dilation of control rings and restored impaired dilation following TNF exposure. Myogenic tone was absent in KO rings. LRRC8A immunoprecipitation followed by mass spectroscopy identified 35 proteins that interacted with LRRC8A. Pathway analysis revealed actin cytoskeletal regulation as the most closely associated function of these proteins. Among these proteins, the Myosin Phosphatase Rho-Interacting protein (MPRIP) links RhoA, MYPT1 and actin. LRRC8A-MPRIP co-localization was confirmed by confocal imaging of tagged proteins, Proximity Ligation Assays, and IP/western blots which revealed LRRC8A binding at the second Pleckstrin Homology domain of MPRIP. siLRRC8A or CBX treatment decreased RhoA activity in cultured VSMCs, and MYPT1 phosphorylation at T853 was reduced in KO mesenteries suggesting that reduced ROCK activity contributes to enhanced relaxation. MPRIP was a target of redox modification, becoming oxidized (sulfenylated) after TNF exposure. ConclusionsInteraction of Nox1/LRRC8A with MPRIP/RhoA/MYPT1/actin may allow redox regulation of the cytoskeleton and link Nox1 activation to both inflammation and vascular contractility.

physiology↗

bioRxiv2023

Mapping cell types in the tumor microenvironment from tissue images via deep learning trained by spatial transcriptomics of lung adenocarcinoma

Profiling heterogeneous cell types in the tumor microenvironment (TME) is important for cancer immunotherapy. Here, we propose a method and validate in independent samples for mapping cell types in the TME from only hematoxylin and eosin (H&E)-stained tumor tissue images using spatial transcriptomic data of lung adenocarcinoma. We obtained spatial transcriptomic data of lung adenocarcinoma from 22 samples. The cell types of each spot were estimated using cell type inference based on a domain adaptation algorithm with single-cell RNA-sequencing data. They were used to train a convolutional neural network with a corresponding H&E image patch as an input. Consequently, the five predicted cell types estimated from the H&E images were significantly correlated with those derived from the RNA-sequencing data. We validated our model using immunohistochemical staining results with marker proteins from independent lung adenocarcinoma samples. Our resource of spatial transcriptomics of lung adenocarcinoma and proposed method with independent validation can provide an annotation-free and precise profiling method of tumor microenvironment using H&E images.

cancer biology↗

bioRxiv2023

GeneDART: Extending gene coverage in image-based spatial transcriptomics by deep learning-based domain adaptation with barcode-based RNA-sequencing data

Spatial transcriptomics (ST) technologies provide comprehensive biological insights regarding cell-cell interactions and peri-cellular microenvironments. ST technologies are divided into two categories: imaging-based (I-B) and barcode-based (B-B). I-B ST technologies provide high resolution and sensitivity but have limited gene coverage. B-B ST technologies can analyze the whole transcriptome but have lower spatial resolution. To address these limitations, we propose a deep learning-based model that integrates I-B and B-B ST technologies to increase gene coverage while preserving high resolution. A model, trained by a neural network with an adversarial loss based on I-B and B-B datasets from human breast cancer tissue, was able to extend gene coverage to whole transcripts-level and accurately predict gene expression patterns in the I-B dataset with a high resolution. This novel methodology, named GeneDART, could enable researchers to utilize B-B and I-B ST datasets in a complementary way.

bioinformatics↗

bioRxiv2023

Spatial transcriptomic brain imaging reveals the effects of immunomodulation therapy upon specific regional brain cells in mouse dementia model

Increasing evidence of brain-immune crosstalk raises expectations for the efficacy of novel immunotherapies in Alzheimers disease (AD), but the lack of methods to understand brain tissues make it difficult to examine therapeutics. Here, we investigated the changes of spatial transcriptomic signatures and brain cell type using the 10x Genomics Visium platform in immune modulated AD models by various treatments. To proceed with an analysis suitable for a single spot-based transcriptomics, we first organized a workflow for segmentation of neuroanatomical regions, establishment of appropriate gene combinations, and comprehensive review of altered brain cell signatures. Ultimately, we investigated spatial transcriptomic changes following administration of immunomodulators, NK cell supplements and anti-CD4 antibody, that ameliorate behavior impairment, and designated brain cells and regions showing probable associations with behavior changes. We provided the customized analytic pipeline into an application named STquantool. Thus, we anticipate that our approach can help researchers to interpret real action of drug candidate by simultaneously investigating the dynamics of all transcripts for development of novel AD therapeutics.

neuroscience↗

bioRxiv2023

Predictive Motor Control Based on a Generative Adversarial Network

Predictive processing models suggest that a brain decides actions through inference using its internal generative model over the worlds states and their transitions. Most of the predictive processing models have been formalized using explicit representations of the probability distributions, with explicit structures and parameters. They are difficult to learn in general and needs explanation about representation of structure and parameters of distributions, and the method for statistical arithmetic on them, each of which are questions not easy to answer. In this study, we explore an alternative representation for predictive processing which is based on an implicit model known as generative adversarial networks, which has been widely explored recently in machine learning studies as they can learn a distribution directly from data. We demonstrate how a generative adversarial network can be trained to learn an implicit generative model of motor dynamics. And then, we show that such a model can perform approximate inference using the trained model, providing the necessary computations for both the forward and inverse model of motor control. Our framework may provide another formalization for brains inference model, especially for learning process. Additionally, we suggest that the functional architecture of the cortical-basal ganglia circuit may modeled as the generator and discriminator in the generative adversarial network model.

neuroscience↗

bioRxiv2023

On the physiological and structural contributors to the dynamic balance of excitation and inhibition in local cortical networks

Overall balance of excitation and inhibition in cortical networks is central to their functionality and normal operation. Such orchestrated co-evolution of excitation and inhibition is established through convoluted local interactions between neurons, which are organized by specific network connectivity structures and are dynamically controlled by modulating synaptic activities. Therefore, identifying how such structural and physiological factors contribute to establishment of overall balance of excitation and inhibition is crucial in understanding the homeostatic plasticity mechanisms that regulate the balance. We use biologically plausible mathematical models to extensively study the effects of multiple key factors on overall balance of a network. We characterize a networks baseline balanced state by certain functional properties, and demonstrate how variations in physiological and structural parameters of the network deviate this balance and, in particular, result in transitions in spontaneous activity of the network to high-amplitude slow oscillatory regimes. We show that deviations from the reference balanced state can be continuously quantified by measuring the ratio of mean excitatory to mean inhibitory synaptic conductances in the network. Our results suggest that the commonly observed ratio of the number of inhibitory to the number of excitatory neurons in local cortical networks is almost optimal for their stability and excitability. Moreover, the values of inhibitory synaptic decay time constants and density of inhibitory-to-inhibitory network connectivity are critical to overall balance and stability of cortical networks. However, network stability in our results is sufficiently robust against modulations of synaptic quantal conductances, as required by their role in learning and memory. SummaryWe leverage computational tractability of a biologically plausible conductance-based meanfield model to perform a comprehensive bifurcation and sensitivity analysis that demonstrates how variations in key synaptic and structural parameters of a local cortical network affect networks stability and overall excitation-inhibition balance. Our results reveal optimality and criticality of baseline biological values for several of these parameters, and provide predictions on their effects on networks dynamics which can inform identifying pathological conditions and guide future experiments.

neuroscience↗

bioRxiv2022

Korea4K: whole genome sequences of 4,157 Koreans with 107 phenotypes derived from extensive health check-ups

We present 4,157 whole-genome sequences (Korea4K) coupled with 107 health check-up parameters as the largest whole genomic resource of Koreans. Korea4K provides 45,537,252 variants and encompasses most of the common and rare variants in Koreans. We identified 1,356 new geno-phenotype associations which were not found by the previous Korea1K dataset. Phenomics analyses revealed 24 genetic correlations, 1,131 pleiotropic variants, and 127 causal relationships from Mendelian randomization. Moreover, the Korea4K imputation reference panel showed a superior imputation performance to Korea1K. Collectively, Korea4K provides the most extensive genomic and phenomic data resources for discovering clinically relevant novel genome-phenome associations in Koreans.

genomics↗

bioRxiv2022

STopover captures spatial colocalization and interaction in the tumor microenvironment using topological analysis in spatial transcriptomics data

Unraveling the spatial configuration of the tumor microenvironment (TME) is key to understanding tumor-immune interactions to translate them into immuno-oncology. With the advent of spatially resolved transcriptomics (SRT), the TME could be dissected for whole cell types across numerous RNAs. We suggest a novel approach, STopover, which performs topological analysis to compute the colocalization patterns between cell types and map the location of cell{square}cell interactions. While gradually lowering the threshold for the feature, the connected components (CCs) were extracted based on the spatial distance between the unit tissue region and the persistence of the CCs. Local and global Jaccard indices were calculated between the CCs of a feature pair to measure the extent of spatial overlap. The STopover was applied to various lung cancer data obtained from SRT platforms, both barcode and image-based SRT, and could explain the infiltration patterns of immune and stromal cells in the TME. Moreover, the method predicted the top cell{square}cell communication based on the ligand{square}receptor database and highlighted the main region of the interaction. STopover is a tool to decipher spatial interaction in the tissue and shed light on the pathophysiology underlying the microenvironment.

genomics↗

bioRxiv2022

Complex changes in serum protein levels upon recovery from SARS-CoV2 infection

The COVID-19 pandemic, triggered by severe acute respiratory syndrome coronavirus 2, has affected millions of people worldwide. Much research has been dedicated to our understanding of COVID-19 disease heterogeneity and severity, but less is known about recovery associated changes. To address this gap in knowledge, we quantified the proteome from serum samples from 29 COVID-19 convalescents and 29 age-, race-, and sex-matched healthy controls. Samples were acquired within the first months of the pandemic. Many proteins from pathways known to change during acute COVID-19 illness, such as from the complement cascade, coagulation system, inflammation and adaptive immune system, had returned to levels seen in healthy controls. In comparison, we identified 22 and 15 proteins with significantly elevated and lowered levels, respectively, amongst COVID-19 convalescents compared to healthy controls. Some of the changes were similar to those observed for the acute phase of the disease, i.e. elevated levels of proteins from hemolysis, the adaptive immune systems, and inflammation. In contrast, some alterations opposed those in the acute phase, e.g. elevated levels of CETP and APOA1 which function in lipid/cholesterol metabolism, and decreased levels of proteins from the complement cascade (e.g. C1R, C1S, and VWF), the coagulation system (e.g. THBS1 and VWF), and the regulation of the actin cytoskeleton (e.g. PFN1 and CFL1) amongst COVID-19 convalescents. We speculate that some of these shifts might originate from a transient decrease in platelet counts upon recovery from the disease. Finally, we observed race-specific changes, e.g. with respect to immunoglobulins and proteins related to cholesterol metabolism.

immunology↗

bioRxiv2022

Proteomic signatures of the serological response to flu vaccination in a large human cohort study

The serological response to the influenza virus vaccine is highly heterogeneous for reasons that are not entirely clear. While the impact of demographic factors such as age, body mass index (BMI), sex, prior vaccination and titer levels are known to impact seroconversion, they only explain a fraction of the response. To identify signatures of the vaccine response, we quantified 273 proteins from serum samples of 160 flu vaccine recipients (2019-2020 season). We found that levels of proteins functioning in cholesterol transport were positively associated with seroconversion, likely linking to the known impact of BMI. When adjusting seroconversion for the demographic factors, we identified additional, unexpected signatures: proteins regulating actin cytoskeleton dynamics were significantly elevated in participants with high adjusted seroconversion. Viral strain specific analysis showed that this trend was largely driven by the H3N2 strain. Further, we identified complex associations between adjusted seroconversion and other factors: levels of proteins of the complement system associated positively with adjusted seroconversion in younger participants, while they were associated negatively in the older population. We observed the opposite trends for proteins of high density lipoprotein remodeling, transcription, and hemostasis. In sum, careful integrative modeling can extract new signatures of seroconversion from highly variable data that suggest links between the humoral response as well as immune cell communication and migration.

immunology↗