Biology subjects

Park, Y.

Publications and source records attributed to Park, Y..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

Noninvasive time-lapse 3D subcellular analysis of embryo development for machine learning-enabled prediction of blastocyst formation

Accurate embryo quality assessment is central to improving outcomes in in vitro fertilization (IVF), yet current practice relies mainly on subjective two-dimensional (2D) morphology. Here we present a label-free framework for quantitative three-dimensional (3D) embryo phenotyping using low-coherence holotomography (HT). Time-lapse HT enabled volumetric imaging of mouse embryos from the 2-cell stage to the blastocyst without affecting developmental competence, capturing subcellular features at high resolution. Quantitative analysis revealed that matured embryos exhibited higher blastomere counts, greater spatial variability, and tighter nuclear packing, whereas arrested embryos showed enlarged blastomeres, elevated cytoplasmic heterogeneity, and fewer, larger nuclei. Machine learning models trained on these features achieved robust prediction of blastocyst formation (AUC up to 0.958). Together, these findings demonstrate that HT provides objective and interpretable 3D biomarkers that could augment and transform embryo selection in IVF.

developmental biology↗

bioRxiv2024

Sample multiplexing for retinal single-cell RNA-sequencing

Rare cell populations can be challenging to characterize using microfluidic single-cell RNA sequencing (scRNA-seq) platforms. Typically, the population of interest must be enriched and pooled from multiple biological specimens for efficient collection. However, these practices preclude the resolution of sample origin together with phenotypic data and are problematic in experiments in which biological or technical variation is expected to be high (e.g., disease models, genetic perturbation screens, or human samples). One solution is sample multiplexing whereby each sample is tagged with a unique sequence barcode that is resolved bioinformatically. We have established a scRNA-seq sample multiplexing pipeline for mouse retinal ganglion cells using cholesterol-modified-oligos and utilized the enhanced precision to investigate cell type distribution and transcriptomic variance across retinal samples. As single cell transcriptomics are becoming more widely used to research development and disease, sample multiplexing represents a useful method to enhance the precision of scRNA-seq analysis.

genomics↗

bioRxiv2024

Integrated number sense tutoring remediates aberrant neural representations in children with mathematical disabilities

Number sense is essential for early mathematical development but it is compromised in children with mathematical disabilities (MD). Here we investigate the impact of a personalized 4-week Integrated Number Sense (INS) tutoring program aimed at improving the connection between nonsymbolic (sets of objects) and symbolic (Arabic numerals) representations in children with MD. Utilizing neural pattern analysis, we found that INS tutoring not only improved cross-format mapping but also significantly boosted arithmetic fluency in children with MD. Critically, the tutoring normalized previously low levels of cross-format neural representations in these children to pre-tutoring levels observed in typically developing, especially in key brain regions associated with numerical cognition. Moreover, we identified distinct, inverted U-shaped neurodevelopmental changes in the MD group, suggesting unique neural plasticity during mathematical skill development. Our findings highlight the effectiveness of targeted INS tutoring for remediating numerical deficits in MD, and offer a foundation for developing evidence-based educational interventions. Significance StatementFocusing on neural mechanisms, our study advances understanding of how numerical problem-solving can be enhanced in children with mathematical disabilities (MD). We evaluated an integrated number sense tutoring program designed to enhance connections between concrete (e.g. 2 dots) and symbolic (e.g. "2") numerical representations. Remarkably, the tutoring program not only improved these childrens ability to process numbers similarly across formats but also enhanced their arithmetic skills, indicating transfer of learning to related domains. Importantly, tutoring normalized brain processing patterns in children with MD to resemble those of typically developing peers. These insights highlight the neural bases of successful interventions for MD, offering a foundation for developing targeted educational strategies that could markedly improve learning outcomes for children facing these challenges.

neuroscience↗

bioRxiv2024

SLIRP mediates nuclear-mitochondrial communication during antiviral signaling to promote robust interferon response

The abnormal innate immune response is a prominent feature underlying autoimmune diseases. One emerging factor that can trigger dysregulated immune activation is cytosolic mitochondrial double-stranded RNAs (mt-dsRNAs). However, the mechanism by which mt-dsRNAs stimulate immune responses remains poorly understood. Here, we discover SRA stem-loop interacting RNA binding protein (SLIRP) as a key amplifier of mt-dsRNA-triggered antiviral signals. In autoimmune diseases, SLIRP is commonly upregulated, and targeted knockdown of SLIRP dampens the interferon response. We find that the activation of melanoma differentiation-associated gene 5 (MDA5) by exogenous dsRNAs upregulates SLIRP, which then stabilizes mt-dsRNAs and promotes their cytosolic release to activate MDA5 further, augmenting the interferon response. Furthermore, the downregulation of SLIRP partially rescues the abnormal interferon-stimulated gene expression in autoimmune patients primary cells and makes cells vulnerable to certain viral infections. Our study unveils SLIRP as a pivotal mediator of interferon response through positive feedback amplification of antiviral signaling.

molecular biology↗

bioRxiv2024

Mitochondrial stress in GABAergic neurons non-cell autonomously regulates organismal health and aging

Mitochondrial stress within the nervous system can trigger non-cell autonomous responses in peripheral tissues. However, the specific neurons involved and their impact on organismal aging and health have remained incompletely understood. Here, we demonstrate that mitochondrial stress in {gamma}-aminobutyric acid-producing (GABAergic) neurons in Caenorhabditis elegans (C. elegans) is sufficient to significantly alter organismal lifespan, stress tolerance, and reproductive capabilities. This mitochondrial stress also leads to significant changes in mitochondrial mass, energy production, and levels of reactive oxygen species (ROS). DAF-16/FoxO activity is enhanced by GABAergic neuronal mitochondrial stress and mediates the induction of these non-cell-autonomous effects. Moreover, our findings indicate that GABA signaling operates within the same pathway as mitochondrial stress in GABAergic neurons, resulting in non-cell-autonomous alterations in organismal stress tolerance and longevity. In summary, these data suggest the crucial role of GABAergic neurons in detecting mitochondrial stress and orchestrating non-cell-autonomous changes throughout the organism.

genetics↗

bioRxiv2024

A highly scalable approach to topic modelling in single-cell data by approximate pseudobulk projection

Probabilistic topic modelling has become essential in many types of single-cell data analysis. Based on probabilistic topic assignments in each cell, we identify the latent representation of cellular states, and topic-specific gene frequency vectors provide interpretable bases to be compared with known cell-type-specific marker genes. However, fitting a topic model on a large number of cells would require heavy computational resources-specialized computing units, computing time and memory. Here, we present a scalable approximation method customized for single-cell RNA-seq data analysis, termed ASAP, short for Annotating Single-cell data by Approximate Pseudobulk estimation. Our approach is more accurate than existing methods but requires orders of magnitude less computing time, leaving much lower memory consumption. We also show that our approach is widely applicable for atlas-scale data analysis; our method seamlessly integrates single-cell and bulk data in joint analysis, not requiring additional preprocessing or feature selection steps.

bioinformatics↗

bioRxiv2024

Three-dimensional Label-Free Measurements of Adipocyte Differentiation and Lipid Droplet Dynamics

The visualization and tracking of adipocytes and their lipid droplets (LDs) during differentiation are pivotal in developmental biology and regenerative medicine studies. Traditional staining or labeling methods, however, pose significant challenges due to their labor-intensive sample preparation, potential disruption of intrinsic cellular physiology, and limited observation timeframe. This study introduces a novel method for long-term visualization and quantification of biophysical parameters of LDs in unlabeled adipocytes, utilizing the refractive index (RI) distributions of LDs and cells. We employ low-coherence holotomography (HT) to systematically investigate and quantitatively analyze the 42-day redifferentiation process of fat cells into adipocytes. This technique yields three-dimensional, high-resolution refractive tomograms of adipocytes, enabling precise segmentation of LDs based on their elevated RI values. Subsequent automated analysis quantifies the mean concentration, volume, projected area, and dry mass of individual LDs, revealing a gradual increase corresponding with adipocyte maturation. Our findings demonstrate that HT is a potent tool for non-invasively monitoring live adipocyte differentiation and analyzing LD accumulation. This study, therefore, offers valuable insights into adipogenesis and lipid research, establishing HT and image-based analysis as a promising approach in these fields.

biochemistry↗

bioRxiv2023

A "through-DNA" mechanism for metal uptake-vs.-efflux regulation

Transition metals like Zn are essential for all organisms including bacteria, but fluctuations of their concentrations in the cell can be lethal. Organisms have thus evolved complex mechanisms for cellular metal homeostasis. One mechanistic paradigm involves pairs of transcription regulators sensing intracellular metal concentrations to regulate metal uptake and efflux. Here we report that Zur and ZntR, a prototypical pair of regulators for Zn uptake and efflux in E. coli, respectively, can coordinate their regulation through DNA, besides sensing cellular Zn2+ concentrations. Using a combination of live-cell single-molecule tracking and in vitro single-molecule FRET measurements, we show that unmetallated ZntR can enhance the unbinding kinetics of Zur from DNA by directly acting on Zur-DNA complexes, possibly through forming heteromeric ternary and quaternary complexes that involve both protein-DNA and protein-protein interactions. This through-DNA mechanism may functionally facilitate the switching in Zn uptake regulation when bacteria encounter changing Zn environments; it could also be relevant for regulating the uptake-vs.-efflux of various metals across different bacterial species and yeast.

biophysics↗

bioRxiv2023

Three-dimensional virtual H&E staining of label-free colon cancer tissue using holotomography and deep learning

In standard histopathology, hematoxylin and eosin (H&E) staining stands as a pivotal tool for cancer tissue analysis. However, this method is limited to two-dimensional (2D) analysis or requires labor-intensive preparation for three-dimensional (3D) inspection of cancer tissues. In this study, we present a method for 3D virtual H&E staining of label-free cancer tissues, employing holotomography and deep learning. Holotomography is used to measure the 3D refractive index (RI) distribution of the label-free cancer slides. A deep learning-based image-to-image translation framework is integrated into the resulting 3D RI distribution, enabling virtual H&E staining in 3D. Our method has been applied to colon cancer tissue slides with thicknesses up to 20 m, with conventional chemical H&E staining providing a direct validation for the method. This framework not only bypasses the conventional staining process but also provides 3D structures of glands, lumens, and individual nuclei. The results demonstrate enhancement in histopathological efficiency and the extension of the standard histopathology into the 3D realm. To validate the repeatability and scalability of the approach, we applied the framework to the gastric cancer slides obtained from different institute and imaging devices.

cancer biology↗

bioRxiv2023

Antibody-based immunotherapy targeting FAM19A5 reverses synaptic loss and improves cognitive function in Alzheimer's disease.

IntroductionAlzheimers disease (AD) is characterized by the dysregulation of synaptic balance, with progressive loss of synapses outpacing formation, ultimately leading to cognitive decline. However, the lack of effective strategies for restoring lost synapses poses a major barrier to improving clinical outcomes. MethodsWe developed NS101, a monoclonal antibody targeting FAM19A5, a brain-secreted protein. Its preclinical efficacy in restoring synapses and cognition was evaluated using APP/PS1 and P301S mice. The clinical safety and target engagement of NS101 were examined in human participants. ResultsFAM19A5 binds to LRRC4B, a postsynaptic adhesion molecule, leading to synapse reduction. Blocking this interaction with NS101 normalized the rate of synapse elimination in AD mice. This synaptic rebalancing restored the number and function of synapses, resulting in improved cognition. Systemically administered NS101 facilitated the transport of brain FAM19A5 into the bloodstream. DiscussionTargeting FAM19A5 may hold clinical promise for treating AD by restoring synaptic balance.

neuroscience↗

bioRxiv2023

Tree-structured topic modelling of single-cell gene expression data uncovers hierarchical relationships between immune cell types

Immune cells undergo a series of differentiation steps following a lineage-tree structure stemming from hematopoietic stem cells. During differentiation of immune cells in both homeostasis and pathological processes, many gene regulatory mechanisms are shared by fully differentiated immune cell sub-types. In order to characterize these features quantitatively, we propose LaRCH, a tree-structured embedded topic model. In this model, single-cell gene expression profiles are represented by a mixture of topics consisting of latent features that follow an underlying tree structure, mirroring that of cellular differentiation-nested cluster structures. We present findings of our model trained on simulated single-cell RNA sequencing (scRNA-seq) based on cell-sorted bulk RNA-seq data as well as on a scRNA-seq dataset of over 1.2 million cells from healthy individuals and individuals diagnosed with systemic lupus erythematosus (SLE). The cellular topic profiles estimated by our model markedly improve clustering accuracy over traditional latent variable models and illustrate transcriptomic differences between SLE phenotypes, revealing a pivotal role of multiple immune cell types in disease progression and relapse. Ultimately, LaRCH captures the hierarchical context between cellular subtypes by simultaneously identifying shared and distinct latent features amongst subsets of heterogeneous samples of cells.

bioinformatics↗

bioRxiv2023

Antibody-conjugating nanogel (Conjugel) with two immune checkpoint inhibitors for enhanced cancer immunotherapy

Cancer immunotherapy by immune checkpoint inhibitors (ICI) acts on antitumor responses by stimulating the immune system to attack cancer cells. However, this powerful therapy is hampered by its high treatment cost and limited efficacy. Here, we show the development of an antibody-conjugating system (Conjugel) that potentiates the efficacy of bispecific immunotherapy that simultaneously targets CTLA-4 and PD-L1. The Conjugel, consisting of highly deformable nanogels and antibody-binding protein, was loaded with two ICI monoclonal antibodies (mAb). Compared with mAb treatment alone, treatment with a bispecific Conjugel loaded with the both ICIs significantly decreased both the survival of MCF-7 and MDA-MB-231 breast cancer cells in vitro and the size of 4T1-Luc2-derived orthotopic syngeneic tumors in vivo. Furthermore, the ICI-loaded Conjugel was less toxic in vivo than the combination treatment delivered as a bolus. Our findings have important implications for Conjugel-based immunotherapy, developing the safer and higher efficacy of ICIs to treat breast cancers.

bioengineering↗

bioRxiv2023

Long-term three-dimensional high-resolution imaging of live unlabeled small intestinal organoids using low-coherence holotomography

The prevailing challenges in live unlabeled high-resolution imaging of native organoids stem from technical issues like complex sample handling and optical scattering in three-dimensional architectures. In this study, we introduce low-coherence holotomography as an advanced, label-free, quantitative imaging modality, designed to overcome related technical obstacles for long-term live imaging of 3D organoids. We successfully captured high-resolution morphological intricacies and dynamic events within mouse small intestinal organoids at a subcellular resolution. Furthermore, this method provides a unique advantage in differentiating between viable and non-viable organoids, thereby expanding its potential applications in organoid-based research.

developmental biology↗

bioRxiv2023

Virtual biomarkers: predicting immune status using label-free holotomography of individual human monocytes and machine learning analysis

Precise evaluation of immune status is critical for managing diseases such as sepsis, in which the immune system transitions between hyper-inflammatory and immune-suppressed states. However, current biomarkers are limited by low specificity and time-consuming protocols. Here, we present a label-free, imaging-based framework for single-cell immune profiling of human monocytes using three-dimensional holotomography (HT) and deep learning. HT captures subcellular refractive index (RI) distributions of live, unlabeled cells, enabling quantitative extraction of morphological and biophysical features. Using an in vitro LPS-based model, we classified three functional immune states--control, hyper-inflammation, and immune suppression--based on 4,059 holotomograms from 11 donors. Immune state transitions were associated with significant changes in cell volume, surface area, RI variability, and the abundance of lipid droplets. A 3D convolutional neural network trained on HT images achieved 83.7% accuracy for single-cell predictions, increasing to 99.9% with ensemble averaging. This study establishes HT as a scalable, label-free platform for real-time immune monitoring and introduces subcellular RI features as robust correlates of immune dysregulation.

immunology↗

bioRxiv2023

The simplicity of protein sequence-function relationships

How complicated is the genetic architecture of proteins - the set of causal effects by which sequence determines function? High-order epistatic interactions among residues are thought to be pervasive, making a proteins function difficult to predict or understand from its sequence. Most studies, however, used methods that overestimate epistasis, because they analyze genetic architecture relative to a designated reference sequence - causing measurement noise and small local idiosyncrasies to propagate into pervasive high-order interactions - or have not effectively accounted for global nonlinearity in the sequence-function relationship. Here we present a new reference-free method that jointly estimates global nonlinearity and specific epistatic interactions across a proteins entire genotype-phenotype map. This method yields a maximally efficient explanation of a proteins genetic architecture and is more robust than existing methods to measurement noise, partial sampling, and model misspecification. We reanalyze 20 combinatorial mutagenesis experiments from a diverse set of proteins and find that additive and pairwise effects, along with a simple nonlinearity to account for limited dynamic range, explain a median of 96% of total variance in measured phenotypes (and >92% in every case). Only a tiny fraction of genotypes are strongly affected by third- or higher-order epistasis. Genetic architecture is also sparse: the number of terms required to explain the vast majority of variance is smaller than the number of genotypes by many orders of magnitude. The sequence-function relationship in most proteins is therefore far simpler than previously thought, opening the way for new and tractable approaches to characterize it.

genetics↗

bioRxiv2023

SPAG7 deletion causes intrauterine growth restriction, resulting in adulthood obesity and metabolic dysfunction

From a forward mutagenetic screen to discover mutations associated with obesity, we identified mutations in the spag7 gene linked to metabolic dysfunction in mice. Here we show that SPAG7 KO mice are born smaller and develop obesity and glucose intolerance in adulthood. This obesity does not stem from hyperphagia, but a decrease in energy expenditure. The KO animals also display reduced exercise tolerance and muscle function due to impaired mitochondrial function. Furthermore, SPAG7-deficiency in developing embryos leads to intrauterine growth restriction, brought on by placental insufficiency, likely due to abnormal development of the placental junctional zone. This insufficiency leads to loss of SPAG7-deficient fetuses in utero and reduced birth weights of those that survive. We hypothesize that a "thrifty phenotype" is ingrained in SPAG7 KO animals during development that leads to adult obesity. Collectively, these results indicate that SPAG7 is essential for embryonic development and energy homeostasis later in life.

molecular biology↗

bioRxiv2023

Enhanced functionalities of immune cells separated by microfludic lattice: assessment based on holotomography

The isolation of white blood cells (WBCs) from whole blood constitutes a pivotal process for immunological studies, diagnosis of hematologic disorders, and the facilitation of immunotherapy. Despite the ubiquity of density gradient centrifugation in WBC isolation, its influence on WBC functionality remains inadequately understood. This research employs holotomography to explore the effects of two distinct WBC separation techniques, namely conventional centrifugation and microfluidic separation, on the functionality of the isolated cells. We utilize three-dimensional refractive index distribution and time-lapse dynamics to conduct an in-depth analysis of individual WBCs, focusing on their morphology, motility, and phagocytic capabilities. Our observations highlight that centrifugal processes negatively impacts WBC motility and phagocytic capacity, whereas microfluidic separation yields a more favorable outcome in preserving WBC functionality. These findings emphasize the potential of microfluidic separation techniques as a viable alternative to traditional centrifugation for WBC isolation, potentially enabling more precise analyses in immunology research and improving the accuracy of hematologic disorder diagnoses.

biophysics↗

bioRxiv2023

Species-Agnostic Transfer Learning for Cross-species Transcriptomics Data Integration without Gene Orthology

Novel hypotheses in biomedical research are often developed or validated in model organisms such as mice and zebrafish and thus play a crucial role, particularly in studying disease mechanisms and treatment responses. However, due to biological differences between species, translating these findings into human applications remains challenging. Moreover, commonly used orthologous gene information is often incomplete, particularly for non-model organisms, and entails a significant information loss during gene-id conversion. To address these issues, we present a novel methodology for species-agnostic transfer learning with heterogeneous domain adaptation. We built on the cross-domain structure-preserving projection and extended the algorithm toward out-of-sample prediction, a common challenge in biomedical sequencing data. Our approach not only allows knowledge integration and translation across various species without relying on gene orthology but also identifies similar GO biological processes amongst the most influential genes composing the latent space for species integration. Subsequently, this enables the identification and functional annotation of genes missing from public orthology databases. Finally, we evaluated our approach with four different single-cell sequencing datasets focusing on out-of-sample prediction and compared it against related machine-learning approaches. In summary, the developed model outperforms all related methods working without prior knowledge when predicting unseen cell types based on other species data. The results demonstrate that our novel approach allows knowledge transfer beyond species barriers without the dependency on known gene orthology but utilizing the entire gene sets.

bioinformatics↗