Biology subjects

Jin, X.

Publications and source records attributed to Jin, X..

At least 127 records · Page 7Linked to original sources

bioRxiv2023

Homeostatic regulation of REM sleep by the preoptic area of the hypothalamus

Rapid-eye-movement sleep (REMs) is characterized by activated electroencephalogram (EEG) and muscle atonia, accompanied by vivid dreams. REMs is homeostatically regulated, ensuring that any loss of REMs is compensated by a subsequent increase in its amount. However, the neural mechanisms underlying the homeostatic control of REMs are largely unknown. Here, we show that GABAergic neurons in the preoptic area of the hypothalamus projecting to the tuberomammillary nucleus (POAGAD2[->]TMN neurons) are crucial for the homeostatic regulation of REMs. POAGAD2[->]TMN neurons are most active during REMs, and inhibiting them specifically decreases REMs. REMs restriction leads to an increased number and amplitude of calcium transients in POAGAD2[->]TMN neurons, reflecting the accumulation of REMs pressure. Inhibiting POAGAD2[->]TMN neurons during REMs restriction blocked the subsequent rebound of REMs. Our findings reveal a hypothalamic circuit whose activity mirrors the buildup of homeostatic REMs pressure during restriction and that is required for the ensuing rebound in REMs.

neuroscience↗

bioRxiv2023

Unified cross-modality integration and analysis of T-cell receptors and T-cell transcriptomes

Single-cell RNA sequencing and T-cell receptor sequencing (scRNA-seq and TCR-seq, respectively) technologies have emerged as powerful tools for investigating T-cell heterogeneity. However, the integrated analysis of gene expression profiles and TCR sequences remains a computational challenge. Herein, we present UniTCR, a unified framework designed for the cross-modality integration and analysis of TCRs and T-cell transcriptomes for a series of challenging tasks in computational immunology. By utilizing a dual-modality contrastive learning module and a single-modality preservation module to effectively embed each modality into a common latent space, UniTCR demonstrates versatility across various tasks, including single-modality analysis, modality gap analysis, epitope-TCR binding prediction and TCR profile cross-modality generation. Extensive evaluations conducted on multiple scRNA-seq/TCR-seq paired datasets showed the superior performance of UniTCR. Collectively, UniTCR is presented as a unified and extendable framework to tackle diverse T-cell-related downstream applications for exploring T-cell heterogeneity and enhancing the understanding of the diversity and complexity of the immune system.

bioinformatics↗

bioRxiv2023

Reciprocal polarization imaging of complex media.

The vectorial evolution of polarized light interaction with a medium can reveal its microstructure and anisotropy beyond what can be obtained from scalar light interaction. Anisotropic properties (diattenuation, retardance, and depolarization) of a complex medium can be quantified by polarization imaging by measuring the Mueller matrix. However, polarization imaging in the reflection geometry, ubiquitous and often preferred in diverse applications, has suffered a poor recovery of the mediums anisotropic properties due to the lack of suitable decomposition of the Mueller matrices measured inside a backward geometry. Here, we present reciprocal polarization imaging of complex media after introducing reciprocal polar decomposition for backscattering Mueller matrices. Based on the reciprocity of the optical wave in its forward and backward scattering paths, the anisotropic diattenuation, retardance, and depolarization of a complex medium are determined by measuring the backscattering Mueller matrix. We demonstrate reciprocal polarization imaging in various applications for quantifying complex non-chiral and chiral media, uncovering their anisotropic microstructures with remarkable clarity and accuracy. Reciprocal polarization imaging will be instrumental in imaging complex media from remote sensing to biomedicine and will open up new applications of polarization optics in reflection geometry.

biophysics↗

bioRxiv2023

Accelerating the Characterization of Dynamic DNA Origami Devices with Deep Neural Networks

Mechanical characterization of dynamic DNA nanodevices is essential to facilitate their use in applications like molecular diagnostics, force sensing, and nanorobotics that rely on device reconfiguration and interactions with other materials. A common approach to evaluate the mechanical properties of dynamic DNA nanodevices is by quantifying conformational distributions, where the magnitude of fluctuations correlates to the stiffness. This is generally carried out through manual measurement from experimental images, which is a tedious process and a critical bottleneck in the characterization pipeline. While many tools to support analysis of static of molecular structures, there is a need for tools to facilitate the rapid characterization of dynamic DNA devices that undergo large conformational fluctuations. Here, we develop a data processing pipeline based on Deep Neural Networks (DNNs) to address this problem. The YOLOv5 and Resnet50 network architecture were used for the two key subtasks: particle detection and pose (i.e. conformation) estimation. We demonstrate effective network performance (F1 score of 0.85 in particle detection) and good agreement with experimental distributions with limited user input and small training sets. We also demonstrate this pipeline can be applied to multiple nanodevices, providing a robust approach for the rapid characterization of dynamic DNA devices.

bioengineering↗

bioRxiv2023

Brain-wide Correspondence Between Neuronal Epigenomics and Long-Distance Projections

Single-cell genetic and epigenetic analyses parse the brains billions of neurons into thousands of "cell-type" clusters, each residing in different brain structures. Many of these cell types mediate their unique functions by virtue of targeted long-distance axonal projections to allow interactions between specific cell types. Here we have used Epi-Retro-Seq to link single cell epigenomes and associated cell types to their long-distance projections for 33,034 neurons dissected from 32 different source regions projecting to 24 different targets (225 source [->]target combinations) across the whole mouse brain. We highlight uses of this large data set for interrogating both overarching principles relating projection cell types to their transcriptomic and epigenomic properties and for addressing and developing specific hypotheses about cell types and connections as they relate to genetics. We provide an overall synthesis of the data set with 926 statistical comparisons of the discriminability of neurons projecting to each target for every dissected source region. We integrate this dataset into the larger, annotated BICCN cell type atlas composed of millions of neurons to link projection cell types to consensus clusters. Integration with spatial transcriptomic data further assigns projection-enriched clusters to much smaller source regions than afforded by the original dissections. We exemplify these capabilities by presenting in-depth analyses of neurons with identified projections from the hypothalamus, thalamus, hindbrain, amygdala, and midbrain to provide new insights into the properties of those cell types, including differentially expressed genes, their associated cis-regulatory elements and transcription factor binding motifs, and neurotransmitter usage.

neuroscience↗

bioRxiv2023

Spatiotemporal transcriptome atlas of human embryos after gastrulation

The spatial and temporal atlas of gene expression in the human embryo at early gestation is critical in understanding embryo development, organogenesis, and disease origins. We obtained the spatiotemporal transcriptome from 90 sagittal sections of 16 whole human embryos from 3 to 8 post-conception weeks by Stereo-seq with high resolution and ultra-large field, establishing the development trajectory/regulatory profiling of 49 organs. We uncovered the organ-specific regulons as potential lineage-determining factors and identified the new regulatory networks during heart and brain development. The atlas refines the key organs/cell types vulnerable to virus infection and genetic disorders, and, reveals the dynamics of allelic gene expression in specific organs at different stages. These results present the first comprehensive delineation of the spatiotemporal transcriptomic dynamics of human organogenesis. One Sentence SummaryThe spatiotemporal transcriptome atlas presents a comprehensive delineation of human embryogenesis after gastrulation.

developmental biology↗

bioRxiv2023

Key common genes and pathways in ulcerative colitis and ankylosing spondylitis based on bioinformatics analysis

Associations between ulcerative colitis (UC) and ankylosing spondylitis (AS) have been observed in multiple studies, but the common etiology of UC and AS remain unknown. Thus, the current research was conducted to investigate the shared genes and relevant mechanisms in UC and AS. GSE87466 and GSE25101 datasets were used to identify DEGs involved in UC and AS, respectively. The clusterProfiler R package was utilized to detect the biological processes of DEGs in UC and AS. The performance of common DEGs in distinguishing UC or AS samples from control ones were evaluated by ROC curves. The miRWalk, Cistrome and TransmiR database were utilized to construct the network of TF-miRNA-diagnostic biomarker. GSEA method and CTD database were used to investigate the common KEGG pathways shared by UC and AS. In addition, CTD database was also used to detect the interaction score between diagnostic biomarkers and diseases associated with UC or AS. Moreover, prospective diagnostic biomarker-targeting drugs were identified using the DGIdb database. A total of 20 common DEGs were obtained by analyzing data in GSE97466 and GSE25101 datasets. ROC curves revealed that GMFG, GNG11, CLEC4D, CMTM2, VAMP5, S100A8, S100A12 and DGKQ may serve as diagnostic biomarkers for the individuals with AS and UC. A network of TF-miRNA-diagnostic biomarker, composed of 212 nodes and 721 edges, was constructed and visualized by Cytoscape software. Toll-like receptor signaling pathway, antigen processing and presentation, allograft rejection, viral myocarditis, pathways in cancer, graft versus host disease and natural killer cell mediated cytotoxicity were identified as common pathways in UC and AS. For the first time, our study identified 8 common key genes and 7 common pathways in UC and AS. These findings may help to clarify the relationship between UC and AS, and provide guidance in the diagnosis and treatment of UC and AS patients. Author SummaryAnkylosing spondylitis (AS) and Ulcerative colitis (UC) are two types of autoimmune diseases that often co-occur. The simultaneous onset of both diseases often results in severe clinical manifestations and limited therapeutic efficacy. So there is an urgent need to gain a deeper understanding of the causes of UC and AS in order to develop more effective treatment strategies. In this study, we explored the genes and pathways commonly involved in two diseases, constructed a transcriptional network, and further investigated potential drugs. The discoveries could potentially offer insights into the connection between UC and AS, and assist in identifying and managing patients with UC and AS.

bioinformatics↗

bioRxiv2023

A Comprehensive Benchmark Study on Biomedical Text Generation and Mining with ChatGPT

In recent years, the development of natural language process (NLP) technologies and deep learning hardware has led to significant improvement in large language models(LLMs). The ChatGPT, the state-of-the-art LLM built on GPT-3.5, shows excellent capabilities in general language understanding and reasoning. Researchers also tested the GPTs on a variety of NLP related tasks and benchmarks and got excellent results. To evaluate the performance of ChatGPT on biomedical related tasks, this paper presents a comprehensive benchmark study on the use of ChatGPT for biomedical corpus, including article abstracts, clinical trials description, biomedical questions and so on. Through a series of experiments, we demonstrated the effectiveness and versatility of Chat-GPT in biomedical text understanding, reasoning and generation.

bioinformatics↗

bioRxiv2023

Compensation or preservation? Different roles of functional lateralization in speech perception in older non-musicians and musicians

Musical training can offset age-related decline of speech perception in noisy environments. However, whether functional compensation or functional preservation the older musicians adopt to counteract the adverse effects of aging is unclear yet, so do older non-musicians. Here, we employed the fundamental brain organization feature named functional lateralization, and calculated network-based lateralization indices (LIs) of resting-state functional connectivity (FC) in 23 older musicians (OM), 23 older non-musicians (ONM), and 24 young non-musicians (YNM). OM outperformed ONM and almost equalized YNM in speech-in-noise/speech tasks. In parallel, ONM exhibited reduced lateralization than YNM in LI of intrahemispheric FC (LI_intra) in cingulo-opercular network (CON) and interhemispheric heterotopic FC (LI_he) in language network (LAN). Moreover, OM showed higher neural alignment to YNM (i.e., similar lateralization pattern) than ONM in LI_intra in CON, LAN, frontoparietal network (FPN) and default mode network (DMN) and LI_he in DMN. These findings suggest that musical training contributes to the preservation of youth-like lateralization in older adults. Furthermore, stronger left-lateralized and lower alignment-to-young of LI_intra in somatomotor network (SMN) and dorsal attention network (DAN) and LI_he in DMN correlated with better speech performance in ONM. In contrast, stronger right-lateralized LI_intra in FPN and DAN and higher alignment-to-young of LI_he in LAN correlated with better performance in OM. Thus, functional preservation and compensation of lateralization may play different roles in speech perception in noise for the elderly with and without musical expertise, respectively. Our findings provide insight into successful aging theories from the unique perspective of functional lateralization and speech perception. Significance statementAs a positive lifestyle which contributes to neural resource enrichment, musical training experience may mitigate age-related decline in speech perception in noise through both functional compensation and preservation. What is unknown is whether older musicians rely more on one of these mechanisms, and how is it different from older non-musicians. From a unique perspective of functional lateralization, we found that high-performing older musicians showed stronger preservation of youth-like lateralization with a more right-lateralized pattern whereas high-performing older non-musicians were associated with stronger scaffolding of compensatory networks with a more left-lateralized pattern. Our findings suggest that older musicians and non-musicians exhibit different coping strategies in terms of functional lateralization against aging, which would largely enrich aging theories and inspire training intervention.

neuroscience↗

bioRxiv2023

Multiple dynamic interactions from basal ganglia direct and indirect pathways mediate action selection

The basal ganglia are known to be essential for action selection. However, the functional role of basal ganglia direct and indirect pathways in action selection remains unresolved. Here by employing cell-type-specific neuronal recording and manipulation in mice trained in a choice task, we demonstrate that multiple dynamic interactions from the direct and indirect pathways control the action selection. While the direct pathway regulates the behavioral choice in a linear manner, the indirect pathway exerts a nonlinear inverted-U-shaped control over action selection, depending on the inputs and the network state. We propose a new center (direct) - surround (indirect) - context (indirect) "Triple control" functional model of basal ganglia, which can replicate the physiological and behavioral experimental observations that cannot be simply explained by either the traditional "Go/No-go" or more recent "Co-activation" model. These findings have important implications on understanding the basal ganglia circuitry and action selection in health and disease. In BriefUsing behavior analysis, in vivo electrophysiology, optogenetics and computational modeling in mice, Li and Jin unveiled the neuronal dynamics of basal ganglia direct and indirect pathways underlying action selection, and proposed a new "Triple-control" functional model of basal ganglia.

neuroscience↗

bioRxiv2023

Library size confounds biology in spatial transcriptomics data

Spatial molecular technologies have revolutionised the study of disease microenvironments by providing spatial context to tissue heterogeneity. Recent spatial technologies are increasing the throughput and spatial resolution of measurements, resulting in larger datasets. The added spatial dimension and volume of measurements poses an analytics challenge that has, in the short-term, been addressed by adopting methods designed for the analysis of single-cell RNA-seq data. Though these methods work well in some cases, not all necessarily translate appropriately to spatial technologies. A common assumption is that total sequencing depth, also known as library size, represents technical variation in single-cell RNA-seq technologies, and this is often normalised out during analysis. Through analysis of several different spatial datasets, we noted that this assumption does not necessarily hold in spatial molecular data. To formally assess this, we explore the relationship between library size and independently annotated spatial regions, across 23 samples from 4 different spatial technologies with varying throughput and spatial resolution. We found that library size confounded biology across all technologies, regardless of the tissue being investigated. Statistical modelling of binned total transcripts shows that tissue region is strongly associated with library size across all technologies, even after accounting for cell density of the bins. Through a benchmarking experiment, we show that normalising out library size leads to sub-optimal spatial domain identification using common graph-based clustering algorithms. On average, better clustering was achieved when library size effects were not normalised out explicitly, especially with data from the newer sub-cellular localised technologies. Taking these results into consideration, we recommend that spatial data should not be specifically corrected for library size prior to analysis unless strongly motivated. We also emphasise that spatial data are different to single-cell RNA-seq and care should be taken when adopting algorithms designed for single cell data.

bioinformatics↗

bioRxiv2023

Fast and accurate DNASeq Variant Calling workflow composed of LUSH toolkit

BackgroundWhole genome sequencing (WGS) is becoming increasingly prevalent for molecular diagnosis, staging and prognosis because of its declining costs and the ability to detect nearly all genes associated with a patients disease. The currently widely accepted variant calling pipeline, GATK, is limited in terms of its computational speed and efficiency, which cannot meet the growing analysis needs. MethodsIn this study, we propose a fast and accurate DNASeq variant calling workflow that is purely composed of tools from LUSH toolkit. The LUSH pipeline is highly optimized for the WGS pipeline based on SOAPnuke, BWA and GATK which can be deployed on any general-purpose CPU-based computing system. We validated the accuracy, speed and scalability of the LUSH pipeline on several standard WGS datasets. ResultsOur test results show that the LUSH pipeline and the GATK pipeline are highly consistent in terms of accuracy, achieving over 99% precision and recall on NA12878. For speed, the LUSH pipeline completes 30x WGS data in 1.6 hours, which is about 17x faster than the GATK pipeline. From BAM to VCF, LUSH_HC even takes only 12 minutes, about 76x faster than GATK. Moreover, the LUSH pipeline shows favorable scalability in terms of thread and sequencing depth. ConclusionThe LUSH pipeline provides far superior computational speed to GATK while maintaining a high level of accuracy comparable to that of GATK, which greatly facilitates bedside analysis of acute patients, large-scale cohort data analysis, and variant calling in crop breeding programs.

bioinformatics↗

bioRxiv2023

Distinct tumor architectures for metastatic colonization of the brain

Brain metastasis is a dismal cancer complication, hinging on the initial survival and outgrowth of disseminated cancer cells. To understand these crucial early stages of colonization, we investigated two prevalent sources of cerebral relapse, triple-negative (TNBC) and HER2+ breast cancer (HER2BC). We show that these tumor types colonize the brain aggressively, yet with distinct tumor architectures, stromal interfaces, and autocrine growth programs. TNBC forms perivascular sheaths with diffusive contact with astrocytes and microglia. In contrast, HER2BC forms compact spheroids prompted by autonomous extracellular matrix components and segregating stromal cells to their periphery. Single-cell transcriptomic dissection reveals canonical Alzheimers disease-associated microglia (DAM) responses. Differential engagement of tumor-DAM signaling through the receptor AXL suggests specific pro-metastatic functions of the tumor architecture in both TNBC perivascular and HER2BC spheroidal colonies. The distinct spatial features of these two highly efficient modes of brain colonization have relevance for leveraging the stroma to treat brain metastasis.

cancer biology↗

bioRxiv2022

Analysis of spatial transcriptomics at varying resolution levels using the unified framework of SpaSEG

Spatially resolved transcriptomics (SRT) for characterizing cellular heterogeneities and activities requires systematic analysis approaches to decipher gene expression variations in physiological contexts. Here we develop SpaSEG, an unsupervised convolutional neural network-based model for multiple SRT analysis tasks by jointly learning the transcriptional similarity of spots and their spatial dependence. SpaSEG adopts an edge strength constraint to encourage spatial domain coherence and allows integrative analysis by automatically aligning the spatial domains across multiple adjacent sections. It also enables the detection of domain-specific gene expression patterns and the inference of intercellular interactions and colocalizations within a tissue. In an invasive ductal carcinoma sample analysis, SpaSEG facilitates the unraveling of intratumor heterogeneity and the understanding of immunoregulatory mechanisms. Through comprehensive evaluation over a collection of SRT datasets generated by different platforms at various resolutions, SpaSEG shows superior reliability and computational efficiency over existing methods, endowing it with a great potential for the exploration of tissue architectures and pathological biology.

bioinformatics↗

bioRxiv2022

Cell-type specific developmental defects in PTEN-mutant cortical organoids converge on abnormal circuit activity

De novo heterozygous loss-of-function mutations in PTEN are strongly associated with Autism spectrum disorders (ASD); however, it is unclear how heterozygous mutations in this gene affects different cell types during human brain development, and how these effects vary across individuals. Here, we used human cortical organoids from different donors to identify cell-type-specific developmental events that are affected by heterozygous mutations in PTEN. We profiled individual organoids by single-cell RNA-seq, proteomics and spatial transcriptomics, and revealed abnormalities in developmental timing in human outer radial glia progenitors and deep layer cortical projection neurons, which varied with the donor genetic background. Calcium imaging in intact organoids showed that both accelerated and delayed neuronal development phenotypes resulted in similar abnormal activity of local circuits, irrespective of genetic background. The work reveals donor-dependent, cell-type specific developmental phenotypes of PTEN heterozygosity that later converge on disrupted neuronal activity.

neuroscience↗

bioRxiv2022

Breast cancer mutations HER2 V777L and PIK3CA H1047R activate the p21/CDK4/6/Cyclin D1 axis driving tumorigenesis and drug resistance.

In metastatic breast cancer, HER2 activating mutations frequently co-occur with mutations in the PIK3CA, TP53, or E-cadherin genes. Of these co-occurring mutations, HER2 and PIK3CA mutations are the most prevalent gene pair, with approximately 40% of HER2 mutated breast cancers also having activating mutations in PIK3CA. To study the effects of co-occurring HER2 and PIK3CA mutations, we bred genetically engineered mice with the HER2V777L; PIK3CAH1047Rtransgenes (HP mice) and studied the resulting breast cancers both in vivo as well as ex vivo using cancer organoids. HP breast cancers show accelerated tumor formation in vivo and increased invasion and migration in in vitro assays. HP breast cancers have resistance to the pan-HER tyrosine kinase inhibitor, neratinib, but are effectively treated by neratinib plus trastuzumab deruxtecan. Proteomic and RNA-Seq analysis of HP breast cancers showed increased gene expression of Cyclin D1 and p21WAF1/Cip1 and changes in cell cycle markers. Combining neratinib with CDK4/6 inhibitors was another effective strategy for HP breast cancers with neratinib plus palbociclib showing a statistically significant reduction in mouse HP tumors as compared to either drug alone. We validated both the neratinib plus trastuzumab deruxtecan and neratinib plus palbociclib combinations using a human breast cancer patient-derived xenograft that has very similar HER2 and PIK3CA mutations. Both of these drug combinations are being tested in phase 1 clinical trials and this study provides valuable preclinical evidence for them.

cancer biology↗

bioRxiv2022

Integrative multispecies omics reveals a hierarchy of cold-responsive network launched by circadian components in rosids

Elucidating regulators and molecular mechanisms underlying gene transcriptional and post-transcriptional co-regulatory network is key to understand plant cold-stress responses. Previous studies were mainly conducted on single species and whether the regulators and mechanisms are conserved across different species remains elusive. Here, we selected three species that diverged at early evolution of rosids (93-115 million years ago) and integrated phylotranscriptome and ChIP/DAP-seq datasets to identify cold-responsive regulators and their regulatory networks. First, we found over ten thousand cold-responsive genes including differentially expressed genes (DEGs) and alternative splicing genes (DASGs) in each species. Among the DEGs, genes encoding a set of transcription factors (TFs) (AP2/ERF, MYB, WRKY, NAC, etc.) and RNA binding proteins (RBPs) (Ribosomal, RRM, DEAD, Helicase_C, etc.) are conserved in cold responses in rosids. Compared to TFs, RBPs show a delayed cold-responsive pattern, likely suggesting a hierarchical regulation of DEGs and DASGs. Between DEGs and DASGs, we identified 259 overlapping DE-DASG orthogroups and interestingly, pathway analysis on each dataset of DEGs, DASGs, and DE-DASGs coincidently shows an enrichment of circadian rhythm. Evidentially, many circadian components are cold-regulated at both transcriptional and post-transcriptional levels. Moreover, we reasoned 226 cold-responsive genes regulated by at least two of five circadian components (CCA1, LHY, RV4, RVE8, and RVE7) in rosids. Finally, we unveiled a conserved hierarchical network in dynamic transcriptional and post-transcriptional regulation of cold-responsive genes launched by circadian components in rosids. Together, our results provide insights into core regulators and mechanisms underlying cold-responsive regulatory network across rosids, despite a long evolutionary history.

plant biology↗

bioRxiv2022

Guild-level microbiome signature associated with COVID-19 severity and prognosis

COVID-19 severity has been associated with alterations of the gut microbiota. However, the relationship between gut microbiome alterations and COVID-19 prognosis remains elusive. Here, we performed a genome-resolved metagenomic analysis on fecal samples collected from 300 in-hospital COVID-19 patients at time of admission. Among the 2,568 high quality metagenome-assembled genomes (HQMAGs), Redundancy Analysis identified 33 HQMAGs which showed differential distribution among mild, moderate, and severe/critical severity groups. Random Forest model based on these 33 HQMAGs classified patients from different severity groups (average AUC = 0.79). Co-abundance network analysis found that the 33 HQMAGs were organized as two competing guilds. Guild 1 harbored more genes for short-chain fatty acid biosynthesis, and fewer genes for virulence and antibiotic resistance, compared with Guild 2. Random Forest regression showed that these 33 HQMAGs at admission had the capacity to predict 8 clinical parameters, which are predictors for COVID-19 prognosis, at Day 7 in hospital. Moreover, the dominance of Guild 1 over Guild 2 at admission predicted the death/discharge outcome of the critical patients (AUC = 0.92). Random Forest models based on these 33 HQMAGs classified patients with different COVID-19 symptom severity, and differentiated COVID-19 patients from healthy subjects, non-COVID-19, and pneumonia controls in three independent datasets. Thus, this genome-based guild-level signature may facilitate early identification of hospitalized COVID-19 patients with high risk of more severe outcomes at time of admission.

microbiology↗