Biology subjects

Lin, Y.

Publications and source records attributed to Lin, Y..

At least 163 records · Page 9Linked to original sources

bioRxiv2024

Inhibition and Updating Share Common Resources: Evidence from Drift Diffusion Model

Inhibition and updating are fundamental cognitive functions in humans, yet the nature of their relationship--whether shared or distinct--remains ambiguous. This study investigates the relationship between inhibition and updating within a unified task framework using a novel paradigm that integrates the N-back task with the congruent/incongruent Stroop task, creating conditions that require either updating alone or both inhibition and updating. Employing Signal Detection Theory (SDT) and the hierarchical drift diffusion model (HDDM), the results provided overall extremely strong Bayesian evidence that participants exhibited longer response times and lower accuracy in conditions requiring both inhibition and updating, compared to those requiring only updating. SDT analysis revealed a decline in discriminability, while HDDM analysis showed slower drift rates, longer non-decision times and a lower decision threshold in inhibition-demanding conditions. Even after controlling for the congruency sequence effect and current stimulus attributes, the results remained robust, showing a larger inhibition effect size compared to the traditional Stroop task. These findings suggest that inhibition consumes cognitive resources, impairing updating performance, and implying that both functions may rely on shared cognitive resources. Overall, the results elucidate the relationship between these fundamental executive functions, supporting the notion that inhibition and updating share cognitive resources.

neuroscience↗

bioRxiv2024

Accurate Cell Abundance Quantification using Multi-positive and Unlabeled Self-learning

Quantifying the abundance of different cell types in pathological samples can help to uncover the correlations between cell composition and pathological conditions, offering deeper insights into the roles of different cell types in complex diseases. Conventional methods for cell abundance estimation often employ unsupervised clustering or supervised learning to identify cell types and estimate their proportions. However, these methods face challenges in accurately quantifying cell abundances, as clustering results could be unreliable and supervised methods may misclassify cell types not presented in the training data. We introduce CleverXMBD1 (denoted as Clever) for quantifying cell abundance from complex samples using a multi-class classifier trained with a confidence-based multi-positive and unlabeled loss function. Our evaluations show that Clever consistently and substantially outperforms existing methods in quantifying cell type abundance across multiple single cell datasets derived from different modalities, including CyTOF and image mass cytometry.

bioinformatics↗

bioRxiv2024

scMODAL: A general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links

Recent advancements in single-cell technologies have enabled comprehensive characterization of cellular states through transcriptomic, epigenomic, and proteomic profiling at single-cell resolution. These technologies have significantly deepened our understanding of cell functions and disease mechanisms from various omics perspectives. As these technologies evolve rapidly and data resources expand, there is a growing need for computational methods that can integrate information from different modalities to facilitate joint analysis of single-cell multi-omics data. However, integrating single-cell omics datasets presents unique challenges due to varied feature correlations and technology-specific limitations. To address these challenges, we introduce scMODAL, a deep learning framework tailored for single-cell multi-omics data alignment using feature links. scMODAL integrates datasets with limited known positively correlated features, leveraging neural networks and generative adversarial networks to align cell embeddings and preserve feature topology. Our experiments demonstrate scMODALs effectiveness in removing unwanted variation, preserving biological information, and accurately identifying cell subpopulations across diverse datasets. scMODAL not only advances integration tasks but also supports downstream analyses such as feature imputation and feature relationship inference, offering a robust solution for advancing single-cell multi-omics research.

bioinformatics↗

bioRxiv2024

INSPIRE: interpretable, flexible and spatially-aware integration of multiple spatial transcriptomics datasets from diverse sources

Recent advances in spatial transcriptomics technologies have led to a growing number of diverse datasets, offering unprecedented opportunities to explore tissue organizations and functions within spatial contexts. However, it remains a significant challenge to effectively integrate and interpret these data, often originating from different samples, technologies, and developmental stages. In this paper, we present INSPIRE, a deep learning method for integrative analyses of multiple spatial transcriptomics datasets to address this challenge. With designs of graph neural networks and an adversarial learning mechanism, INSPIRE enables spatially informed and adaptable integration of data from varying sources. By incorporating non-negative matrix factorization, INSPIRE uncovers interpretable spatial factors with corresponding gene programs, revealing tissue architectures, cell type distributions and biological processes. We demonstrate the capabilities of INSPIRE by applying it to human cortex slices from different samples, mouse brain slices with complementary views, mouse hippocampus and embryo slices generated through different technologies, and spatiotemporal organogenesis atlases containing half a million spatial spots. INSPIRE shows superior performance in identifying detailed biological signals, effectively borrowing information across distinct profiling technologies, and elucidating dynamical changes during embryonic development. Furthermore, we utilize INSPIRE to build 3D models of tissues and whole organisms from multiple slices, demonstrating its power and versatility.

bioinformatics↗

bioRxiv2024

Engineering Rubisco Condensation in Chloroplasts to Manipulate Plant Photosynthesis

Although Rubisco is the most abundant enzyme globally, it is inefficient for carbon fixation because of its low turnover rate and limited ability to distinguish CO2 and O2, especially under high O2 conditions. To address these limitations, phytoplankton, including cyanobacteria and algae, have evolved CO2-concentrating mechanisms (CCM) that involve compartmentalizing Rubisco within specific structures, such as carboxysomes in cyanobacteria or pyrenoids in algae. Engineering plant chloroplasts to establish similar structures for compartmentalizing Rubisco has attracted increasing interest for improving photosynthesis and carbon assimilation in crop plants. Here, we present a method to effectively induce the condensation of endogenous Rubisco within tobacco (Nicotiana tabacum) chloroplasts by genetically fusing superfolder green fluorescent protein (sfGFP) to the tobacco Rubisco large subunit (RbcL). By leveraging the intrinsic oligomerization feature of sfGFP, we successfully created pyrenoid-like Rubisco condensates that display dynamic, liquid-like properties within chloroplasts without affecting Rubisco assembly and catalytic function. The transgenic tobacco plants demonstrated comparable autotrophic growth rates and full life cycles in ambient air relative to the wild-type plants. Our study offers a promising strategy for modulating endogenous Rubisco assembly and spatial organization in plant chloroplasts via phase separation, which provides the foundation for generating synthetic organelle-like structures for carbon fixation, such as carboxysomes and pyrenoids, to optimize photosynthetic efficiency.

plant biology↗

bioRxiv2024

Modular Nano-Scaffold Biocatalysis for Superior PET Depolymerization and Valorization

The global crisis of polyethylene terephthalate (PET) waste demands new strategies for sustainable management. Biocatalytic recycling offers a promising route, but direct depolymerization under moderate conditions is often limited by kinetic constrains of individual enzyme. Here, we engineer and optimize modular protein scaffolds that utilize orthogonal interaction domains, including the cohesin-dockerin pairs from bacterial cellulosomes and the SH3 ligand/domain pair from mouse Crk, to assemble complementary hydrolases (PETase, MHETase, and ICCG) into a synergistic multi-enzyme cascade. This architecture overcomes intermediate inhibition, efficiently converting PET hydrolysis products into the final monomers, terephthalic acid (TPA) and ethylene glycol (EG), outperforming previous multi-enzyme systems. The systems application is further evaluated by stability enhancement via immobilization on metal-organic frameworks (MOFs), coupling depolymerization to the valorization of EG into glycolic acid (GA), and adapting the system for scalable whole-cell biocatalysis. This scaffold-based multi-enzyme approach provides a new strategy for developing integrated biocatalytic systems to advance a circular PET economy.

synthetic biology↗

bioRxiv2024

Single cell genome and epigenome co-profiling reveals hardwiring and plasticity in breast cancer

Understanding the impact of genetic alterations on epigenomic phenotypes during breast cancer progression is challenging with unimodal measurements. Here, we report wellDA-seq, the first high-genomic resolution, high-throughput method that can simultaneously measure the whole genome and chromatin accessibility profiles of thousands of single cells. Using wellDA-seq, we profiled 22,123 single cells from 2 normal and 9 tumors breast tissues. By directly mapping the epigenomic phenotypes to genetic lineages across cancer subclones, we found evidence of both genetic hardwiring and epigenetic plasticity. In 6 estrogen-receptor positive breast cancers, we directly identified the ancestral cancer cells, and found that their epithelial cell-of-origin was Luminal Hormone Responsive cells. We also identified cell types with copy number aberrations (CNA) in normal breast tissues and discovered non-epithelial cell types in the microenvironment with CNAs in breast cancers. These data provide insights into the complex relationship between genetic alterations and epigenomic phenotypes during breast tumor evolution.

genomics↗

bioRxiv2024

Quantitative site-specific glycoproteomics by ZenoTOF reveals glyco-signatures for breast cancer diagnosis

Intact glycopeptide characterization by mass spectrometry has proven a versatile tool for site-specific glycoproteomics analysis and biomarker screening. Here, we present a method using the ZenoTOF instrument with optimized fragmentation for intact glycopeptide identification and demonstrate its ability to analyze large-cohort glycoproteomes. From 124 clinical serum samples of breast cancer, non-cancerous diseases, and non-disease controls, a total of 6901 unique site-specific glycans on 807 gly-cosites of proteins were detected. Much more differences of glycoproteome were observed in breast diseases than the proteome. By employing machine learning, 15 site-specific glycans were determined as potential glyco-signatures in detecting breast cancer. The results demonstrate that our method provides a powerful tool in glycoproteomic analyses for biomarker discovery studies.

cancer biology↗

bioRxiv2024

SCRATCH: A programmable, open-hardware, benchtop robot that automatically scratches cultured tissues to investigate cell migration, healing, and tissue sculpting.

Despite the widespread popularity of the scratch assay, where a pipette is dragged through cultured tissue to create an injury gap to study cell migration and healing, the manual nature of the assay carries significant drawbacks. So much of the process depends on individual manual technique, which can complicate quantification, reduce throughput, and limit the versatility and reproducibility of the approach. Here, we present a truly open-source, low-cost, accessible, and robotic scratching platform that addresses all of the core issues. Compatible with nearly all standard cell culture dishes and usable directly in a sterile culture hood, our robot makes highly reproducible scratches in a variety of complex cultured tissues with high throughput. Moreover, we demonstrate how scratching can be programmed to precisely remove areas of tissue to sculpt arbitrary tissue and wound shapes, as well as enable truly complex co-culture experiments. This system significantly improves the usefulness of the conventional scratch assay, and opens up new possibilities in complex tissue engineering and cell biological assays for realistic wound healing and migration research.

bioengineering↗

bioRxiv2024

VISTA Uncovers Missing Gene Expression and Spatial-induced Information for Spatial Transcriptomic Data Analysis

Characterizing cell activities within a spatially resolved context is essential to enhance our understanding of spatially-induced cellular states and features. While single-cell RNA-seq (scRNA-seq) offers comprehensive profiling of cells within a tissue, it fails to capture spatial context. Conversely, subcellular spatial transcriptomics (SST) technologies provide high-resolution spatial profiles of gene expression, yet their utility is constrained by the limited number of genes they can simultaneously profile. To address this limitation, we introduce VISTA, a novel approach designed to predict the expression levels of unobserved genes specifically tailored for SST data. VISTA jointly models scRNA-seq data and SST data based on variational inference and geometric deep learning, and incorporates uncertainty quantification. Using four SST datasets, we demonstrate VISTAs superior performance in imputation and in analyzing large-scale SST datasets with satisfactory time efficiency and memory consumption. The imputation of VISTA enables a multitude of downstream applications, including the detection of new spatially variable genes, the discovery of novel ligand-receptor interactions, the inference of spatial RNA velocity, the generation for spatial transcriptomics with in-silico perturbation, and an improved decomposition of spatial and intrinsic variations.

bioinformatics↗

bioRxiv2024

FT-IR micro-spectroscopy for imaging the extracellular matrix composition in biofilms

Microorganisms form granules by embedding themselves in an extracellular matrix through the secretion of extracellular polymeric substances (EPS). The extracellular matrix is a complex structure comprising of e.g. proteins, polysaccharides, lipids, and extracellular DNA. Understanding the function of individual EPS components within the matrix not only requires knowledge on the composition of the extracellular matrix, but also on the spatial distribution of said components. Molecular imaging like e.g. fluorescence microscopy have been used for the visualization of the extracellular matrix, but these target specific molecules. Untargeted approaches like FT-IR micro-spectroscopy would allow for a broader exploration. In this study FT-IR micro-spectroscopy analysis was implemented on sliced anaerobic granular sludge to explore the EPS distribution. Visualization of single wavenumber absorbance showed a higher polysaccharide content in the EPS at the granule perimeter, shifting to a higher protein concentration toward the centre. The boundary of this shift was approximately 150 {micro}m from the surface, which was in accordance with the layer of fermentative bacteria described in literature. The complexity in the polymer composition meant that many functional groups were overlapping, making FT-IR annotation challenging. To address this, principal component analysis and two-dimensional correlation spectroscopy analysis were included in the analysis. These methods enabled the identification of overlapping functional groups and correlations between functional groups. Positive correlations between protein and polysaccharide functional groups suggested the presence of glycoproteins, which has been regularly described in chemical EPS analysis studies. Additionally, correlations between sulfated compounds and protein/polysaccharide functional groups indicated potential co-localization in the extracellular matrix. Differences in positive correlations of sialic acids with polysaccharides suggest variations in polysaccharide compositions, possibly caused by differences in the microbial community.

microbiology↗

bioRxiv2024

Identification of Meibomian gland stem cell populations and mechanisms of aging

Meibomian glands secrete lipid-rich meibum, which prevents tear evaporation. Aging-related Meibomian gland shrinkage may result in part from stem cell exhaustion and is associated with evaporative dry eye disease, a common condition lacking effective treatment. The identities and niche of Meibomian gland stem cells and the signals controlling their activity are poorly defined. Using snRNA-seq, in vivo lineage tracing, ex vivo live imaging, and genetic studies in mice, we identified markers for stem cell populations that maintain distinct regions of the gland and uncovered Hh signaling as a key regulator of stem cell proliferation. Consistent with this, human Meibomian gland carcinoma exhibited increased Hh signaling. Aged glands displayed decreased Hh and EGF signaling, deficient innervation, and loss of collagen I in niche fibroblasts, indicating that alterations in both glandular epithelial cells and their surrounding microenvironment contribute to age-related degeneration. These findings suggest new approaches to treat aging-associated Meibomian gland loss.

cell biology↗

bioRxiv2024

Extensive mutual influences of SMC complexes shape 3D genome folding

Mammalian genomes are folded by the distinct actions of SMC complexes which include the chromatin loop-extruding cohesin, the sister-chromatid cohesive cohesin, and the mitotic chromosome-associated condensins. While these complexes function at different stages of the cell cycle, they co-exist on chromatin during the G2/M-phase transition, when genome structure undergoes a dramatic reorganization. Yet, how distinct SMC complexes affect each other and how their mutual interplay orchestrates the dynamic folding of 3D genome remains elusive. Here, we engineered all possible cohesin/condensin configurations on mitotic chromosomes to delineate the concerted, mutual influential action of SMC complexes. We find that: (i) The mitotic SMC complex condensin disrupts the focal accumulation of extrusive-cohesin at CTCF binding sites, thereby promoting the disassembly of interphase TADs and chromatin loops during mitotic progression. Conversely, extrusive-cohesin can impair condensin activity and alter mitotic chromosome helicity. (ii) Condensin diminishes cohesive-cohesin focal enrichment and, conversely, cohesive-cohesin can counteract condensin function and impede mitotic chromosome longitudinal shortening. (iii) The co-presence of extrusive- and cohesive-cohesin synergistically antagonizes condensin function and dramatically delays mitotic chromosome condensation. (iv) Extrusive-cohesin positions cohesive-cohesin at CTCF binding sites. However, cohesive-cohesin by itself is insufficient to mediate the formation of TADs or chromatin loop, implying non-overlapping function with extrusive-cohesin. Instead, cohesive-cohesin restricts chromatin loop expansion, potentially by limiting extrusive-cohesin movement. Collectively, our data describe a comprehensive three-way interplay among major SMC complexes that dynamically sculpts chromatin architecture during cell cycle progression.

cell biology↗

bioRxiv2024

Ca2+-driven PDIA6 phase separation to ensure proinsulin quality control

The endoplasmic reticulum (ER) plays key roles in protein quality control1,2 and dynamic Ca2+ storage3,4 in eukaryotic cells. However, the protein homeostasis (proteostasis) system that regulates these ER functions is still incompletely characterised. Previous study revealed the importance of oligomerization in the function PDIA1, an ER-resident disulfide isomerase and molecular chaperone, regulates oligomeric states in accordance with client folding5. This result suggests that at least some of the 20 members of other PDI family may undergo regulated self-assembly in order to optimally function. Here, we show that Ca2+ triggers the phase separation of PDIA6 into liquid-like condensates. In contrast to the condensation mechanism observed for proteins containing low-complexity domains, our results indicate that transient but specific electrostatic interactions occur between the first and the third folded thioredoxin-like domains of PDIA6. We further show that the Ca2+-driven condensation of PDIA6 recruits PDIA3 and proinsulin, thus increasing their local concentrations. This process results in the 30-fold enhancement of proinsulin folding and in the inhibition of proinsulin aggregation. Our findings shed light on a condensation-driven Ca2+-mediated proteostasis cascade in the ER by revealing how the efficiency of the protein folding process can be enhanced within quality control granules.

biochemistry↗

bioRxiv2024

Efficient generation of CAR NK cells from human umbilical cord blood CD34+ stem and progenitors for democratizing affordable immunotherapy

Chimeric antigen receptor (CAR) natural killer cells (CAR NK) cells, leveraging safety and not requiring HLA match in adoptive infusion, have emerged as promising alternative cells to CAR-T cells for immunotherapies. High and multiple doses of CAR NK cell infusions are essential to maintain therapeutic efficacy in clinical trials. This requires efficient methods for generating large-scale CAR NK cells and significantly reducing CAR engineering costs. In this study, we develop a three-step strategy to generate highly high yields of induced NK (iNK) and CAR iNK cells from human umbilical cord blood CD34+ hematopoietic stem and progenitor cells (CD34+ HSPCs). Starting from a single umbilical cord blood CD34+ HSPC, our reliable method efficiently produces 14-83 million mature iNK cells or 7-32 million CAR iNK cells with high expression levels of CD16 and zero T cell contaminations. Introducing CAR expression elements at the HSPC level reduces the quantities of CAR pseudoviruses to 1 / 140.000 - 1 / 600,000 compared to engineering CARs in mature NK cells. The iNK and CAR iNK cells, including fresh cells and thawed cells from cryopreserved conditions, demonstrate remarkable tumoricidal activities against various human cancer cells and significantly prolong the survival of human tumor-bearing animals. The high yields of CAR NK cells and negligible costs of CAR engineering of our method support the broad applications of CAR NK cells for treating cancer patients.

cell biology↗

bioRxiv2024

Spatial dynamics of mammalian brain development and neuroinflammation by multimodal tri-omics mapping

The ability to spatially map multiple layers of the omics information over different time points allows for exploring the mechanisms driving brain development, differentiation, arealization, and alterations in disease. Herein we developed and applied spatial tri-omic sequencing technologies, DBiT ARP-seq (spatial ATAC-RNA-Protein-seq) and DBiT CTRP-seq (spatial CUT&Tag- RNA-Protein-seq) together with multiplexed immunofluorescence imaging (CODEX) to map spatial dynamic remodeling in brain development and neuroinflammation. A spatiotemporal tri-omic atlas of the mouse brain was obtained at different stages from postnatal day P0 to P21, and compared to the regions of interest in the human developing brains. Specifically, in the cortical area, we discovered temporal persistence and spatial spreading of chromatin accessibility for the layer-defining transcription factors. In corpus callosum, we observed dynamic chromatin priming of myelin genes across the subregions. Together, it suggests a role for layer specific projection neurons to coordinate axonogenesis and myelination. We further mapped the brain of a lysolecithin (LPC) neuroinflammation mouse model and observed common molecular programs in development and neuroinflammation. Microglia, exhibiting both conserved and distinct programs for inflammation and resolution, are transiently activated not only at the core of the LPC lesion, but also at distal locations presumably through neuronal circuitry. Thus, this work unveiled common and differential mechanisms in brain development and neuroinflammation, resulting in a valuable data resource to investigate brain development, function and disease.

neuroscience↗

bioRxiv2024

CIB2 as a Ca2+ Sensor for Auditory Mechano-Electrical Transduction and Linked to Genetic-Heterogeneity of TMC1 in Hearing Loss

Non-syndromic sensorineural hearing loss is characterized by genetic heterogeneity, leading to potential clinical misdiagnosis. TMC1, a unique causative gene associated with deafness, exhibits variants with autosomal dominant and recessive inheritance patterns. TMC1 codes for the transmembrane channel-like 1 (TMC1), a key component of the mechano-electrical transduction (MET) machinery for hearing. However, the molecular mechanism of Ca2+ regulation in MET, which is essential for sound perception, remains unclear. CIB2, another MET component associated with deafness, can bind with calcium and may play a role in MET gating. Our study reveals that the TMC1-CIB2 complex undergoes a Ca2+-induced conformational change, highlighting the crucial role of Ca2+ in MET. We identified a vertebrate-specific binding site on TMC1, named CR3, that interacts with apo CIB2, with the binding interface linked to hearing loss. Using an in-vivo animal model, we demonstrated that disruption of the calcium-sensing site of CIB2 perturbs the MET channel conductivity, confirming its role as a Ca2+ sensor for MET. Additionally, after systematically analyzing the hearing-loss variants, we observed that dominant mutations of TMC1 are often located near the ion pore or at the binding interfaces with the Ca2+ sensor CIB2. This provides a detailed mechanism of the genetic heterogeneity of TMC1 in hearing loss at an atomic level.

molecular biology↗

bioRxiv2024

Metabolomics profiling reveals association of fermented herbal feed additive, growth performance and gut microbiota in piglets

Fermented Chinese medicine (FCM), as a functional feed additive, has been widely recognized to play a significant role in protecting the intestinal health of piglets and enhancing productive performance. However, the relationship between the active components of FCM, gut microbiota, and their beneficial effects on animal performance remains unclear. In this study, metabolomics analysis revealed a significant increase in the main contents of lactic acid and propionic acid in FCM, while most glycosides and their derivatives decreased after three days of microbial fermentation. Subsequently, piglets were fed a basic diet supplemented with 1% FCM, while the control group received only the basic diet. The results indicated a significant increase in feed intake and average daily gain within 14 days (P<0.05) due to FCM supplementation. Additionally, FCM significantly improved feed efficiency from 1.76 to 1.50 (P<0.05). Meanwhile, piglets in the FCM group exhibited significantly lower frequencies of diarrhea and coughing, indicating better health. Moreover, high-throughput sequencing analysis revealed higher abundances of Lactobacillus agilis, Megasphaera elsdenii, Ligilactobacillus, and Veillonellaceae in piglets fed with FCM. In summary, FCM increased the content of active ingredients through microbial fermentation and regulated the intestinal microbiota to improve the health of piglets. FCM offers a promising potential for enhancing production performance and economic efficiency in the livestock industry.

microbiology↗