Biology subjects

Li, Q.

Publications and source records attributed to Li, Q..

At least 379 records · Page 21Linked to original sources

bioRxiv2023

Bayesian Hidden Mark Interaction Model forDetecting Spatially Variable Genes inImaging-Based Spatially ResolvedTranscriptomics Data

Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies on ad hoc kernels, or the energy-based Ising model, which requires gene expression to be measured on a lattice grid. To overcome these potential limitations, we developed a generalized energybased framework to model gene expression measured from imaging-based SRT platforms, accommodating the irregular spatial distribution of measured cells. Our Bayesian model applies a zero-inflated negative binomial mixture model to dichotomize the raw count data, reducing noise. Additionally, we incorporate a geostatistical mark interaction model with a generalized energy function, where the interaction parameter is used to identify the spatial pattern. Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. We demonstrated the strength of our method on both simulated and real data. Our simulation study showed that our method captured various spatial patterns with high accuracy; moreover, analysis of a seqFISH dataset and a STARmap dataset established that our proposed method is able to identify genes with novel and strong spatial patterns.

bioinformatics↗

bioRxiv2023

Towards establishing a rapid constant temperature detection method for canine parvovirus based on endonuclease activities

Canine parvovirus can cause a high morbidity and mortality rate in puppies, posing a significant threat to both pet dogs and the breeding industry. Rapid, accurate and convenient detection methods are important for the early intervention and treatment of canine parvovirus. In this study, we propose a visual CPV detection system called nucleic acid mismatch enzyme digestion (NMED). This system combines reverse transcription-loop mediated isothermal amplification (RT-LAMP), endonuclease for gene mismatch detection and colloidal gold lateral chromatography. We demonstrated that NMED can induce the binding of the amplicon from the sample to the specific labeling probe, which in turn triggers digestion by the endonuclease. The sensitivity and visual visibility of RT-LAMP were increased by combining of endonuclease and colloidal gold lateral chromatography assisted by a simple temperature-controlled device. The sensitivity of the NMED assay was 1 copy/L, which was consistent with qPCR. The method was validated with 10 clinical samples that potentially had CPV infection and achieved 100% accuracy. As a rapid, accurate and convenient molecular diagnostic method, NMED has great potential for application in the field of pathogenic microorganism detection.

molecular biology↗

bioRxiv2023

Yeast EndoG prevents genome instability by degrading cytoplasmic DNA

In metazoans release of mitochondrial DNA or retrotransposon cDNA to cytoplasm can cause sterile inflammation and disease. Cytoplasmic nucleases degrade these DNA species to limit inflammation. It remains unknown whether degradation these DNA also prevents nuclear genome instability. To address this question, we decided to identify the nuclease regulating transfer of these cytoplasmic DNA species to the nucleus. We used an amplicon sequencing-based method in yeast enabling analysis of millions of DSB repair products. Nuclear mtDNA (NUMTs) and retrotransposon cDNA insertions increase dramatically in nondividing stationary phase cells. Yeast EndoG (Nuc1) nuclease limits insertions of cDNA and transfer of very long mtDNA (>10 kb) that forms unstable circles or rarely insert in the genome, but it promotes formation of short NUMTs ([~]45-200 bp). Nuc1 also regulates transfer of cytoplasmic DNA to nucleus in aging or during meiosis. We propose that Nuc1 preserves genome stability by degrading retrotransposon cDNA and long mtDNA, while short NUMTs can originate from incompletely degraded mtDNA. This work suggests that nucleases eliminating cytoplasmic DNA play a role in preserving genome stability.

genetics↗

bioRxiv2023

Molecular Determinants and Signaling Effects of PKA RIα Phase Separation

Spatiotemporal regulation of intracellular signaling molecules, such as the 3,5-cyclic adenosine monophosphate (cAMP)-dependent protein kinase (PKA), ensures the specific execution of various cellular functions. Liquid-liquid phase separation (LLPS) of the ubiquitously expressed PKA regulatory subunit RI was recently identified as a major driver of cAMP compartmentation and signaling specificity. However, the molecular determinants of RI LLPS remain unclear. Here, we reveal that two separate dimerization interfaces combined with the cAMP-induced release of the PKA catalytic subunit (PKA-C) from the pseudosubstrate inhibitory sequence are required to drive RI condensate formation in cytosol, which is antagonized by docking to A-kinase anchoring proteins. Strikingly, we find that the RI pseudosubstrate region is critically involved in the formation of a non-canonical R:C complex, which serves to maintain low basal PKA activity in the cytosol by enabling the recruitment of active PKA-C to RI condensates. Our results suggest that RI LLPS not only facilitates cAMP compartmentation but also spatially restrains active PKA-C, thus highlighting the functional versatility of biomolecular condensates in driving signaling specificity.

cell biology↗

bioRxiv2023

Adaptive Regularized Tri-Factor Non-Negative Matrix Factorization for Cell Type Deconvolution

Accurate deconvolution of cell types from bulk gene expression is crucial for understanding cellular compositions and uncovering cell-type specific differential expression and physiological states of diseased tissues. Existing deconvolution methods have limitations, such as requiring complete cellular gene expression signatures or neglecting partial biological information. Moreover, these methods often overlook varying cell-type mRNA amounts, leading to biased proportion estimates. Additionally, they do not effectively utilize valuable reference information from external studies, such as means and ranges of population cell-type proportions. To address these challenges, we introduce an Adaptive Regularized Tri-factor non-negative matrix factorization approach for deconvolution (ARTdeConv). We rigorously establish the numerical convergence of our algorithm. Through benchmark simulations, we demonstrate the superior performance of ARTdeConv compared to state-of-the-art semi-reference-based and reference-free methods. In a real-world application, our method accurately estimates cell proportions, as evidenced by the nearly perfect Pearsons correlation between ARTdeConv estimates and flow cytometry measurements in a dataset from a trivalent influenza vaccine study. Moreover, our analysis of ARTdeConv estimates in COVID-19 patients reveals patterns consistent with important immunological phenomena observed in other studies. The proposed method, ARTdeConv, is implemented as an R package and can be accessed on GitHub for researchers and practitioners.

bioinformatics↗

bioRxiv2023

Reconstructing Spatial Transcriptomics at the Single-cell Resolution with BayesDeep

Spatially resolved transcriptomics (SRT) techniques have revolutionized the characterization of molecular profiles while preserving spatial and morphological context. However, most next-generation sequencing-based SRT techniques are limited to measuring gene expression in a confined array of spots, capturing only a fraction of the spatial domain. Typically, these spots encompass gene expression from a few to hundreds of cells, underscoring a critical need for more detailed, single-cell resolution SRT data to enhance our understanding of biological functions within the tissue context. Addressing this challenge, we introduce BayesDeep, a novel Bayesian hierarchical model that leverages cellular morphological data from histology images, commonly paired with SRT data, to reconstruct SRT data at the single-cell resolution. BayesDeep effectively model count data from SRT studies via a negative binomial regression model. This model incorporates explanatory variables such as cell types and nuclei-shape information for each cell extracted from the paired histology image. A feature selection scheme is integrated to examine the association between the morphological and molecular profiles, thereby improving the model robustness. We applied BayesDeep to two real SRT datasets, successfully demonstrating its capability to reconstruct SRT data at the single-cell resolution. This advancement not only yields new biological insights but also significantly enhances various downstream analyses, such as pseudotime and cell-cell communication.

bioinformatics↗

bioRxiv2023

Vagus nerve stimulation by focused ultrasound attenuates acute myocardial ischemia/reperfusion injury predominantly through cholinergic anti-inflammatory pathway

BackgroundThe invasive method using electrically stimulating vagus nerve has been demonstrated to inhibit the inflammatory response against acute myocardial ischemia/reperfusion(I/R) injury. This neuro-regulatory technique used is invasive implantation of electrical stimulators which have the risk of infection and other complications. A non-invasive method of vagus nerve stimulation applying on acute myocardial I/R injury is particularly important and desirable. ObjectivesThis study aimed to test the hypothesis that focused ultrasound stimulation(FUS) of vagus nerve attenuates acute myocardial I/R injury through cholinergic anti-inflammatory pathway. MethodsThis study was completed with two parts. In the first part, ischemia was induced by left anterior descending coronary artery (LAD) occlusion for 30 minutes (min), followed by 2 hours (h) of reperfusion, FUS lasted for 50 min after reperfusion for 10 min. Infammatory markers, arrhythmia score, histopathology, infammatory markers 7 nicotinic acetylcholine receptor (7nAchR) protein were determined. In the second part, ischemia was induced by LAD occlusion for 30 min, followed by 24h of reperfusion, FUS lasted for 50 min after reperfusion for 10 min. Myocardial fibrosis, infarct size, echocardiographic measurements, biomarkers of pro-oxidative stress, antioxidant and myocardial apoptosis were determined. ResultsFUS of vagus nerve significantly decreased the level of infammatory markers in I/R+FUS group compared with the I/R group, I/R+vagotomy group and I/R+vagotomy+FUS group. FUS decreased the incidence of ventricular tachycardia (VT) and ventricular fibrillation (VF), while also reduced arrhythmia score in I/R+FUS group compared with the I/R group. These improvements were associated with the inhibition of acute inflammatory reaction through cholinergic anti-inflammatory pathway. FUS limited myocardial fibrosis and infarct size, preserved left ventricular function. This cardioprotective effect was associated with alleviation of apoptosis and oxidative stress. ConclusionsThe noninvasive method of ultrasonic neuromodulation using FUS of vagus nerve exerted protective effects on acute myocardial I/R injury. Its potential mechanisms involved the suppression of acute inflammatory reaction through cholinergic anti-inflammatory pathway.

neuroscience↗

bioRxiv2023

Development of a human heart-on-a-chip model using induced pluripotent stem cells, fibroblasts and endothelial cells

In recent years, research on organ-on-a-chip technology has been flourishing, particularly for drug screening and disease model development. Fibroblasts and vascular endothelial cells engage in crosstalk through paracrine signaling and direct cell-cell contact, which is essential for the normal development and function of the heart. Therefore, to faithfully recapitulate cardiac function, it is imperative to incorporate fibroblasts and vascular endothelial cells into a heart-on-a-chip model. Here, we report the development of a human heart-on-a-chip composed of induced pluripotent stem cell (iPSC)-derived cardiomyocytes, fibroblasts, and vascular endothelial cells. Vascular endothelial cells cultured on microfluidic channels responded to the flow of culture medium mimicking blood flow by orienting themselves parallel to the flow direction, akin to in vivo vascular alignment in response to blood flow. Furthermore, the flow of culture medium promoted stronger junction formation between vascular endothelial cells, as evidenced by CD31 staining and lower apparent permeability. The triculture condition of iPSC-derived cardiomyocytes, fibroblasts, and vascular endothelial cells resulted in higher expression of the ventricular cardiomyocyte marker IRX4 and increased contractility compared to the biculture condition with iPSC-derived cardiomyocytes and fibroblasts alone. Such triculture-derived cardiac tissues exhibited cardiac responses similar to in vivo hearts, including an increase in heart rate upon noradrenaline administration. In summary, we have achieved the development of a heart-on-a-chip composed of cardiomyocytes, fibroblasts, and vascular endothelial cells that mimics in vivo cardiac behavior.

cell biology↗

bioRxiv2023

Spatially resolved molecular and cellular atlas of the mouse brain

A comprehensive atlas of genes, cell types, and their spatial distribution across a whole mammalian brain is fundamental for understanding function of the brain. Here, using snRNA-seq and Stereo-seq techniques, we generated a mouse brain atlas with spatial information for 308 cell clusters with single-cell resolution involving over 6 million cells as well as for 29,655 genes. We have identified new astrocyte clusters, and demonstrated that distinct cell clusters exhibit preference for cortical subregions. In addition, we identified 155 genes exhibiting regional specificity in the brainstem, and 513 long non-coding RNA exhibited regional specificity in the adult brain. Parcellation of brain regions based on spatial transcriptomic information showed large overlap with that by traditional method. Furthermore, we have uncovered 411 transcription factor regulons with spatiotemporal specificity during development. Thus, our study has discovered genes and regulon with spatiotemporal specificity, and provided a high-resolution spatial transcriptomic atlas of the mouse brain.

neuroscience↗

bioRxiv2023

An inducible genetic tool for tracking and manipulating specific microglial states in development and disease

Recent single-cell RNA sequencing studies have revealed distinct microglial states in development and disease. These include proliferative region-associated microglia (PAM) in developing white matter and disease-associated microglia (DAM) prevalent in various neurodegenerative conditions. PAM and DAM share a similar core gene signature and other functional properties. However, the extent of the dynamism and plasticity of these microglial states, as well as their functional significance, remains elusive, partly due to the lack of specific tools. Here, we report the generation of an inducible Cre driver line, Clec7a-CreERT2, designed to target PAM and DAM in the brain parenchyma. Utilizing this tool, we profile labeled cells during development and in several disease models, uncovering convergence and context-dependent differences in PAM/DAM gene expression. Through long-term tracking, we demonstrate surprising levels of plasticity in these microglial states. Lastly, we specifically depleted DAM in cuprizone-induced demyelination, revealing their roles in disease progression and recovery.

neuroscience↗

bioRxiv2023

Xanthomonas euvesicatoria effector XeAvrRxo1 triggers a Rxo1-mediated defense response in Nicotiana benthamiana and its chaperone Xe4429 functions as an antitoxin, an expression repressor, and an enhancer of XeAvrRxo1 secretion

Xanthomonas euvesicatoria (X. euvesicatoria) is the causal agent of bacterial spot disease that threatens pepper and tomato production around the globe. X. euvesicatoria gene Xe4428 encodes a type III effector (T3E) that shares 89.67% amino acid identity with Xanthomonas oryzae pv. oryzicola (Xoc) T3E AvrRxo1. Deletion of Xe4428 in the genome of X. euvesicatoria (strain Xcv85-10) compromised its virulence to infect pepper and Nicotiana benthamiana plants. Transient co-expression of Xe4428 and Rxo1 on pepper and N. benthamiana plant leaves results in a robust hypersensitive reaction. Thus, Xe4428, renamed as XeAvrRxo1, is a bona fide orthologue of XocAvrRxo1 that possesses both virulence and avirulence functions. Expression of XeAvrRxo1 in E. coli and X. euvesicatoria is toxic to both bacterial cells. Another X. euvesicatoria gene Xe4429, encodes a putative chaperone of XeAvrRxo1, which can interact with XeAvrRxo1 to suppress its toxicity in X. euvesicatoria and E. coli bacterial cells. Xe4429 also binds to the promoter region of XeavrRxo1 and represses its transcription/translation in X. euvesicatoria bacterial cells. In addition, expression of Xe4429 can enhance the secretion and translocation of XeAvrRxo1 into plant cells. Therefore, Xe4429 functions as an antitoxin, a transcription repressor, and a type III chaperone that is capable of enhancing the secretion and translocation of XeAvrRxo1 during pathogenesis.

pathology↗

bioRxiv2023

Predicting the dynamic interaction between intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) participate in various biological processes. Interactions involving IDPs are usually dynamic and affected by their inherent conformation fluctuations. Comprehensive characterization of these interactions based on current techniques is challenging. Here, we present GSALIDP, a GraphSAGE-LSTM Network to capture the dynamic nature of IDP-involved interactions and predict their behaviors. This framework models multiple conformations of IDP as a dynamic graph which can effectively describe the fluctuation of its flexible conformation. The dynamic interaction between IDPs is studied and the datasets of IDP conformations and their interactions are obtained through atomistic molecular dynamic (MD) simulations. Residues of IDP are encoded through a series of features, including their frustration. GSALIDP can effectively predict the interaction sites of IDP and the contact residue pairs between IDPs. Its performance in predicting IDP interaction is on par with or even better than the conventional models in predicting the interaction of structural proteins. To the best of our knowledge, this is the first model to extend the protein interaction prediction to IDP-involved interactions.

molecular biology↗

bioRxiv2023

The Complete Sequence and Comparative Analysis of Ape Sex Chromosomes

Apes possess two sex chromosomes--the male-specific Y and the X shared by males and females. The Y chromosome is crucial for male reproduction, with deletions linked to infertility1. The X chromosome carries genes vital for reproduction and cognition2. Variation in mating patterns and brain function among great apes suggests corresponding differences in their sex chromosomes. However, due to their highly repetitive nature and incomplete reference assemblies, ape sex chromosomes have been challenging to study. Here, using the methodology developed for the telomere-to-telomere (T2T) human genome, we produced gapless assemblies of the X and Y chromosomes for five great apes (chimpanzee, bonobo, gorilla, Bornean and Sumatran orangutans) and a lesser ape, the siamang gibbon. These assemblies allowed us to untangle the intricacies of ape sex chromosome evolution. We found that, compared to the Xs, the ape Ys vary greatly in size and have low alignability and high levels of structural rearrangements. This divergence on the Y arises from the accumulation of lineage-specific ampliconic regions, palindromes, transposable elements, and satellites. Our analysis of Y chromosome genes revealed expansions of multi-copy gene families and signatures of purifying selection. Thus, the Y exhibits dynamic evolution, while the X is more stable. Mapping short-read sequencing data to these assemblies revealed diversity and selection patterns on sex chromosomes of >100 great ape individuals. These reference assemblies are expected to inform human evolution and conservation genetics of nonhuman apes, all of which are endangered species.

genomics↗

bioRxiv2023

Upstream open reading frames dynamically modulate CLOCK protein translation to regulate circadian rhythm and sleep

The circadian rhythm is an evolutionarily conserved mechanism with translational regulation increasingly recognized as pivotal in its modulation. In this study, we found that upstream open reading frames (uORFs) are enriched in circadian rhythm genes, with particularly conserved uORFs present in core circadian clock genes. We demonstrate evidence that the uORFs of the core clock gene, Clock (Clk), rhythmically and substantially attenuate CLK protein translation in Drosophila, with pronounced suppression occurring during daylight hours. Eliminating Clk uORFs results in elevated CLK protein levels during the day and a compressed circadian cycle, along with a broad shift in clock gene expression rhythms. Interestingly, Clk uORF deletion also augments morning sleep by reducing dopaminergic activity. Beyond daily circadian adjustments, Clk uORFs play a role in modulating sleep patterns in response to the varying day lengths of different seasons, inhibiting translation in a day-length contingent manner. Furthermore, the Clk uORFs act as a master regulator to shape the rhythmic expression of a vast array of genes and influence multifaceted physiological outcomes. Collectively, our research sheds light on the intricate ways uORFs dynamically adjust downstream coding sequences to acclimate to environmental shifts.

neuroscience↗

bioRxiv2023

Benchmarking Machine Learning Methods for Synthetic Lethality Prediction in Cancer

Synthetic lethality (SL) is a type of genetic interaction that occurs when defects in two genes cause cell death, while a defect in a single gene does not. Targeting an SL partner of a gene mutated in cancer can selectively kill tumor cells. Traditional wet-lab experiments for SL screening are resource-intensive. Hence, many computational methods have been developed for virtual screening of SL gene pairs. This study benchmarks recent machine learning methods for SL prediction, including three matrix factorization and eight deep learning models. We scrutinize model performance using various data splitting scenarios, negative sample ratios, and negative sampling methods on both classification and ranking tasks to assess the models generalizability and robustness. Our benchmark analyzed performance differences among the models and emphasized the importance of data and real-world scenarios. Finally, we suggest future directions to improve machine learning methods for SL discovery in terms of predictive power and interpretability.

bioinformatics↗

bioRxiv2023

A Foundation Model for Cell Segmentation

Cells are a fundamental unit of biological organization, and identifying them in imaging data - cell segmentation - is a critical task for various cellular imaging experiments. While deep learning methods have led to substantial progress on this problem, most models are specialist models that work well for specific domains but cannot be applied across domains or scale well with large amounts of data. In this work, we present CellSAM, a universal model for cell segmentation that generalizes across diverse cellular imaging data. CellSAM builds on top of the Segment Anything Model (SAM) by developing a prompt engineering approach for mask generation. We train an object detector, CellFinder, to automatically detect cells and prompt SAM to generate segmentations. We show that this approach allows a single model to achieve human-level performance for segmenting images of mammalian cells, yeast, and bacteria collected across various imaging modalities. We show that CellSAM has strong zero-shot performance and can be improved with a few examples via few-shot learning. Additionally, we demonstrate how CellSAM can be applied across diverse bioimage analysis workflows. A deployed version of CellSAM is available at https://cellsam.deepcell.org/.

bioinformatics↗

bioRxiv2023

Complexity Measures Of Psychotic Brain Activity In The fMRI Signal

When viewing the brain as a sophisticated, nonlinear dynamic system, employing complexity measures offers a valuable way to measure the intricate and dynamic aspects of spontaneous psychotic brain activity. These measures can help us identify irregularities and patterns in complex systems. In our study, we utilized fuzzy recurrence plots and sample entropy to evaluate the dynamic characteristics of psychiatric disorders. This assessment focused on understanding the temporal and spatial neural activity patterns, and more specifically, we applied complexity measures to investigate the functional connectivity within the psychotic brain. This involves understanding how different brain regions synchronize their activity, and complexity measures can reveal the patterns of these connections. It provides a means to understand how different brain regions interact and communicate under resting-state abnormal conditions. This study offers evidence demonstrating that fuzzy recurrence plots can serve as descriptors for functional connectivity and discusses their relevance to sample entropy in the context of the psychotic brain. In summary, complexity measures offer valuable insights that enrich our comprehension of atypical brain activity and the complexities present in the psychotic brain1.

neuroscience↗

bioRxiv2023

Dissecting Multiparametric Cerebral Hemodynamics using Integrated Ultrafast Ultrasound and Multispectral Photoacoustic Imaging

Understanding brain-wide hemodynamic responses to different stimuli at high spatiotemporal resolutions can help study neuro-disorders and brain functions. However, the existing brain imaging technologies have limited resolution, sensitivity, imaging depth and provide information about only one or two hemodynamic parameters. To address this, we propose a multimodal functional ultrasound and photoacoustic (fUSPA) imaging platform, which integrates ultrafast ultrasound and multispectral photoacoustic imaging methods in a compact head-mountable device, to quantitatively map cerebral blood volume (CBV), cerebral blood flow (CBF), oxygen saturation (SO2) dynamics as well as contrast agent enhanced brain imaging with high spatiotemporal resolutions. After systematic characterization, the fUSPA system was applied to quantitatively study the changes in brain hemodynamics and vascular reactivity at single vessel resolution in response to hypercapnia stimulation. Our results show an overall increase in brain-wide CBV, CBF, and SO2, but regional differences in singular cortical veins and arteries and a reproducible anti-correlation pattern between venous and cortical hemodynamics, demonstrating the capabilities of the fUSPA system for providing multiparametric cerebrovascular information at high-resolution and sensitivity, that can bring insights into the complex mechanisms of neurodiseases.

bioengineering↗