Biology subjects

Song, Q.

Publications and source records attributed to Song, Q..

At least 73 records · Page 4Linked to original sources

bioRxiv2022

Global Spatial Transcriptome of Macaque Brain at Single-Cell Resolution

Global profile of gene expression at single-cell resolution remains to be determined for primates. Using a recently developed technology ("Stereo-seq"), we have obtained a comprehensive single-cell spatial transcriptome map at the whole-brain level for cynomolgus monkeys, with [~]600 genes per cell for 10 m-thick coronal sections (up to 15 cm2 in size). Large-scale single-nucleus RNA-seq analysis for [~]1 million cells helped to identify cell types corresponding to Stereo-seq gene expression profiles, providing a 3-D cell type atlas of the monkey brain. Quantitative analysis of Stereo-seq data revealed molecular fingerprints that mark distinct neocortical layers and subregions, as well as domains within subcortical structures including hippocampus, thalamus, striatum, cerebellum, hypothalamus and claustrum. Striking whole-brain topography and coordinated patterns were found in the expression of genes encoding receptors and transporters for neurotransmitters and neuromodulators. These results pave the way for cellular and molecular understanding of organizing principles of the primate brain.

cell biology↗

bioRxiv2022

Cryo-EM structures reveal the activation and substrate recognition mechanism of human enteropeptidase

The enteropeptidase (EP) initiates the intestinal digestion by proteolytic processing of trypsinogen, generating catalytic active trypsin. The dysfunction of EP will cause a series of pancreatic diseases, the most severe of which is acute necrotizing pancreatitis. However, the molecular mechanism of EP activation and substrate recognition remain elusive due to the lack of structural information, hampering the structure-based research of EP and even further EP-targeted drug design. Here we report cryo-EM structures of human EP in multiple states, covering the functional cycle spanning from inactive to active state and eventually to the substrate binding state, with the inactive core region reached an atomic 2.7-[A]-resolution. The heavy chain of EP exhibits a clamping configuration with the CUB2 domain serving for substrate recognition. The N-terminus of light chain induces the surface loop remodeling from inactive to active conformation, resulting in a highly dynamic and active EP. Then the heavy chain performs like a hinge to ensure the flexibility of light chain for substrate recruitment and subsequent cleavage. Our study provides structural insights of EP remodeling and heavy chain dynamics while performing enzymatic function, facilitating our understanding of the pathogenies of EP-related pancreatitis and the EP-targeted treatment of pancreatitis.

cell biology↗

bioRxiv2021

Single particle cryo-EM structure of the outer hair cell motor protein prestin

The mammalian outer hair cell (OHC) protein prestin (Slc26a5), a member of the solute carrier 26 (Slc26) family of membrane proteins, differs from other members of the family owing to its unique piezoelectric-like property that drives OHC electromotility. OHCs require prestin for cochlear amplification, a process that enhances mammalian hearing. Despite substantial biophysical characterization, the mechanistic basis for the prestins electro-mechanical behavior is not fully understood. To gain insight into such behavior, we have used cryo-electron microscopy at subnanometer resolution (overall resolution of 4.0 [A]) to investigate the three-dimensional structure of prestin from gerbil (Meriones unguiculatus). Our studies show that prestin dimerizes with a 3D architecture strikingly similar to the dimeric conformation observed in the Slc26a9 anion transporter in an inside open/intermediate state, which we infer, based on patch-clamp recordings, to reflect the contracted state of prestin. The structure shows two well-separated transmembrane (TM) subunits and two cytoplasmic sulfate transporter and anti-sigma factor antagonist (STAS) domains forming a swapped dimer. The dimerization interface is defined by interactions between the domain-swapped STAS dimer and the transmembrane domains of the opposing half unit, further strengthened by an antiparallel beta-strand at its N terminus. The structure also shows that each one of its two transmembrane subunits consists of 14 transmembrane segments organized in two inverted 7-segment repeats with a topology that was first observed in the structure of bacterial symporter UraA (Lu F, et al., Nature 472, 2011). Finally, the solved anion binding site structural features of prestin are quite similar to that of SLC26a9 and other family members. Despite this similarity, we find that SLC26a9 lacks the characteristic displacement currents (or NonLinear Capacitance(NLC)) found with prestin, and we show that mutation of prestins Cl- binding site removes salicylate competition with anions in the face of normal NLC, thus refuting the yet accepted extrinsic voltage sensor hypothesis and any associated transport-like requirements for voltage-driven electromotility.

neuroscience↗

bioRxiv2021

The first crested duck genome reveals clues to genetic compensation and crest cushion formation

The Chinese crested (CC) duck is a unique indigenous waterfowl breed with a phenotypic crest trait that affects its high survival rate. Therefore, the CC duck is an ideal model to investigate the genetic compensation response to maintain genetic stability. In the present study, we first generated a chromosome-level genome of CC ducks. Comparative genomics revealed genes related to tissue repair, immune function, and tumors were under strong positive selection, which suggested that these adaptive changes might enhance cancer resistance and immune response to maintain the genetic stability of CC ducks. We sub-assembled a Chinese spot-billed duck genome and detected genome-assembled structure variants among three ducks. Functional analysis revealed that a large number of structural variants were related to the immune system, which strongly suggests the occurrence of genetic compensation in the anti-tumor and immune systems to further support the survival of CC ducks. Moreover, we confirmed that the CC duck originated from the mallard ducks. Finally, we revealed the physiological and genetic basis of crest traits and identified a causative mutation in TAS2R40 that leads to crest formation. Overall, the findings of this study provide new insights into the role of genetic compensation in adaptive evolution.

genomics↗

bioRxiv2021

Spike Protein Targeting "Nano-Glue" that Captures and Promotes SARS-CoV-2 Elimination

The global emergency caused by the SARS-CoV-2 pandemics can only be solved with adequate preventive and therapeutic strategies, both currently missing. The electropositive Receptor Binding Domain (RBD) of SARS-CoV-2 spike protein with abundant {beta}-sheet structure serves as target for COVID-19 therapeutic drug design. Here, we discovered that ultrathin 2D CuInP2S6 (CIPS) nanosheets as a new agent against SARS-CoV-2 infection, which also able to promote viral host elimination. CIPS exhibits extremely high and selective binding capacity with the RBD of SARS-CoV-2 spike protein, with consequent inhibition of virus entry and infection in ACE2-bearing cells and human airway epithelial organoids. CIPS displays nano-viscous properties in selectively binding with spike protein (KD < 1 pM) with negligible toxicity in vitro and in vivo. Further, the CIPS-bound SARS-CoV-2 was quickly phagocytosed and eliminated by macrophages, suggesting CIPS could be successfully used to capture and facilitate the virus host elimination with possibility of triggering anti-viral immunization. Thus, we propose CIPS as a promising nanodrug for future safe and effective anti-SARS-CoV-2 therapy, as well as for use as disinfection agent and surface coating material to constrain the SARS-CoV-2 spreading.

immunology↗

bioRxiv2021

Combinatorial engineering of bulk-assembled monodisperse coacervate droplets towards logically integrated protocells

Bottom-up synthetic biology seeks to construct artificial cells from simple building blocks for exploring origin and principles of cellular life and material design. Although cellular life may have emerged spontaneously, programmable integration of building blocks into size, membrane property-controlled compartments (liposome or coacervate) towards cellular organization, without using specialized devices, has proven difficult. Here, we report a self-templated route to monodisperse complex droplets in bulk solution from coacervate-liposome synergy, with nanoliposome controlling coacervate size and coacervate templating on-surface nanoliposome fusion. Nanoliposome-coated monodisperse coacervates are self-assembled within 30 seconds, which are sealed by fusing nanoliposomes into size-controlled giant unilamellar vesicles (GUVs), with model building blocks combinatorially integrated into droplets. Defect-free membrane is established on coacervate, which render these complex GUVs surviving at extreme osmotic, salty and pH conditions (4 M NaCl, 100 mM HCl, 1 M NaOH), while providing homeostasis for enzymatic reactions, reminiscent of extremophiles.

synthetic biology↗

bioRxiv2020

Identification of cell-type marker genes from plant single-cell RNA-seq data using machine learning

An essential step in the analysis of single-cell RNA sequencing data is to classify specific cell types with marker genes. In this study, we have developed a machine learning pipeline called Single cell Predictive markers (SPmarker) to identify novel cell-type marker genes in the Arabidopsis root. Unlike traditional approaches, our method uses interpretable machine learning methods to select marker genes. We have demonstrated that our method can (1) assign cell types based on cells that were labeled using published methods, (2) project cell types identified by trajectory analysis from one dataset to other datasets, and (3) assign cell types based on internal GFP markers. Using SPmarker, we have identified hundreds of new marker genes that were not identified before. As compared to known marker genes, we have found more orthologous genes of these new marker genes in corresponding rice single cell clusters. We have also found 172 new marker genes for trichoblast in five non-Arabidopsis species, which expands number of marker genes for this cell type by 35-154%. Our results represent a new approach to identify cell-type marker genes from scRNA-seq data and pave the way for cross-species mapping of scRNA-seq data in plants.

bioinformatics↗

bioRxiv2020

A versatile resource of 1500 diverse wild and cultivated soybean genomes for post-genomics research

With the advance of next-generation sequencing technologies, over 15 terabytes of raw soybean genome sequencing data were generated and made available in the public. To develop a consolidated, diverse, and user-friendly genomic resource to facilitate post-genomic research, we sequenced 91 highly diverse wild soybean genomes representing the entire US collection of wild soybean accessions to increase the genetic diversity of the sequenced genomes. Having integrated and analyzed the sequencing data with the public data, we identified and annotated 32 million single nucleotide polymorphisms (32mSNPs) with a resolution of 30 SNPs/kb and 12 non-synonymous SNPs/gene in 1,556 accessions (1.5K). Population structure analysis showed that the 1.5K accessions represent the genetic diversity of the 20,087 (20K) soybean accessions in the U.S. collection. Inclusion of wild soybean genomes significantly increased the genetic diversity and shorten linkage disequilibrium distance in the panel of soybean accessions. We identified a collection of paired accessions sharing the highest genomic identity between the 1.5K and 20K accessions as genomically "equivalent" accessions to maximize the use of the genome sequences. We demonstrated that the 32mSNPs in the 1.5K accessions can be effectively used for in-silico genotyping, discovering trait QTL, gene alleles/mutations, identifying germplasms containing beneficial allele and domestication selection of trait alleles. We made the 32mSNPs and 1.5K accessions with detailed annotation available at SoyBase and Ag Data Commons. The dataset could serve as a versatile resource to release the potential of the huge amount of genome sequencing data for a variety of postgenomic research.

plant biology↗

bioRxiv2020

Large-scale single-cell analysis reveals critical immune characteristics of COVID-19 patients

Dysfunctional immune response in the COVID-19 patients is a recurrent theme impacting symptoms and mortality, yet the detailed understanding of pertinent immune cells is not complete. We applied single-cell RNA sequencing to 284 samples from 205 COVID-19 patients and controls to create a comprehensive immune landscape. Lymphopenia and active T and B cell responses were found to coexist and associated with age, sex and their interactions with COVID-19. Diverse epithelial and immune cell types were observed to be virus-positive and showed dramatic transcriptomic changes. Elevation of ANXA1 and S100A9 in virus-positive squamous epithelial cells may enable the initiation of neutrophil and macrophage responses via the ANXA1-FPR1 and S100A8/9-TLR4 axes. Systemic upregulation of S100A8/A9, mainly by megakaryocytes and monocytes in the peripheral blood, may contribute to the cytokine storms frequently observed in severe patients. Our data provide a rich resource for understanding the pathogenesis and designing effective therapeutic strategies for COVID-19. HIGHLIGHTSO_LILarge-scale scRNA-seq analysis depicts the immune landscape of COVID-19 C_LIO_LILymphopenia and active T and B cell responses coexist and are shaped by age and sex C_LIO_LISARS-CoV-2 infects diverse epithelial and immune cells, inducing distinct responses C_LIO_LICytokine storms with systemic S100A8/A9 are associated with COVID-19 severity C_LI

genomics↗

bioRxiv2020

DSTG: Deconvoluting Spatial Transcriptomics Data through Graph-based Artificial Intelligence

Recent development of spatial transcriptomics (ST) is capable of associating spatial information at different spots in the tissue section with RNA abundance of cells within each spot, which is particularly important to understand tissue cytoarchitectures and functions. However, for such ST data, since a spot is usually larger than an individual cell, gene expressions measured at each spot are from a mixture of cells with heterogenous cell types. Therefore, ST data at each spot needs to be disentangled so as to reveal the cell compositions at that spatial spot. In this study, we propose a novel method, named DSTG, to accurately deconvolute the observed gene expressions at each spot and recover its cell constitutions, thus achieve high-level segmentation and reveal spatial architecture of cellular heterogeneity within tissues. DSTG not only demonstrates superior performance on synthetic spatial data generated from different protocols, but also effectively identifies spatial compositions of cells in mouse cortex layer, hippocampus slice, and pancreatic tumor tissues. In conclusion, DSTG accurately uncovers the cell states and subpopulations based on spatial localization.

bioinformatics↗

bioRxiv2020

scGCN: a Graph Convolutional Networks Algorithm for Knowledge Transfer in Single Cell Omics

Single-cell omics represent the fastest-growing genomics data type in the literature and the public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will empower the exploration of the single-cell omics. The current label transfer methods have limited performance, largely due to the intrinsic heterogeneity and extrinsic differences between datasets. Here, we present a robust graph-based artificial intelligence model, single-cell Graph Convolutional Network (scGCN), to achieve effective knowledge transfer across disparate datasets. Benchmarked with other label transfer methods on totally 30 single cell omics datasets, scGCN has consistently demonstrated superior accuracy on leveraging cells from different tissues, platforms, and species, as well as cells profiled at different molecular layers. scGCN is implemented as an integrated workflow as a python software, which is available at https://github.com/QSong-github/scGCN.

bioinformatics↗

bioRxiv2020

Somatic mosaicism in the mature brain reveals clonal cellular distributions during cortical development

The structure of the human neocortex underlies species-specific features and is a reflection of intricate developmental programs. Here we analyzed neocortical cellular lineages through a comprehensive assessment of brain somatic mosaicism--which acts as a neutral recorder of lineage history. We employed deep whole genome and variant sequencing in a single postmortem neurotypical human brain across 25 anatomic regions and three distinct modalities: bulk geographies, sorted cell types, and single nuclei. We identified 259 mosaic variants, revealing remarkable differences in localization, clonal abundance, cell type specificity, and clade distribution. We identified a set of hierarchical cellular diffusion barriers, whereby the left-right axis separation of the neocortex occurs prior to anterior-posterior and dorsal-ventral axis separation. We also found that stochastic distribution is a driver of clonal dispersion, and that rules regarding cellular lineages and anatomical boundaries are often ignored. Our data provides a comprehensive analysis of brain somatic mosaicism across the human cerebral cortex, deconvolving clonal distributions and migration patterns in the human embryo. One Sentence SummaryComprehensive evaluation of brain somatic mosaicism in the adult human identifies rules governing cellular distribution during embryogenesis.

genomics↗

bioRxiv2020

scLM: automatic detection of consensus gene clusters across multiple single-cell datasets

In gene expression profiling studies, including single-cell RNA-seq (scRNAseq) analyses, the identification and characterization of co-expressed genes provides critical information on cell identity and function. Gene co-expression clustering in scRNA-seq data presents certain challenges. We show that commonly used methods for single cell data are not capable of identifying co-expressed genes accurately, and produce results that substantially limit biological expectations of co-expressed genes. Herein, we present scLM, a gene co-clustering algorithm tailored to single cell data that performs well at detecting gene clusters with significant biologic context. scLM can simultaneously cluster multiple single-cell datasets, i.e. consensus clustering, enabling users to leverage single cell data from multiple sources for novel comparative analysis. scLM takes raw count data as input and preserves biological variations without being influenced by batch effects from multiple datasets. Results from both simulation data and experimental data demonstrate that scLM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of scLM, we apply it to our in-house and public experimental scRNA-seq datasets. scLM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers. A user-friendly R package with all the key features of the scLM method is available at https://github.com/QSong-WF/scLM.

bioinformatics↗

bioRxiv2020

Identification of 38 novel loci for systemic lupus erythematosus and genetic heterogeneity that may underly population disparities in this disease

Systemic lupus erythematosus (SLE), a worldwide autoimmune disease with high heritability, shows differences in prevalence, severity and age of onset among different ancestral groups. Previous genetic studies have focused more on European populations, which appear to be the least affected. Consequently, the genetic variations that underly the commonalities, differences and treatment options in SLE among ancestral groups have not been well elucidated. To address this, we undertook a genome-wide association study, increasing the sample size of Chinese populations to the level of existing European studies. Thirty-eight novel SLE-associated loci and incomplete sharing of genetic architecture were identified. Nine disease loci showed clear ancestral group heterogeneity and implicated antibody production as a potential mechanism for differences in disease manifestation. Polygenic risk scores performed significantly better when trained on matched ancestral data sets. These analyses help to reveal the genetic bases for disparities in SLE among ancestral groups.

genetics↗

bioRxiv2019

Single-cell RNA-seq data reveals TNBC tumor heterogeneity through characterizing subclone compositions and proportions

Understanding subclonal architecture and their biological functions poses one of the key challenges to deeply portray and investigative the cause of triple-negative breast cancer (TNBC). Here we combine single-cell and bulk sequencing data to analyze tumor heterogeneity through characterizing subclone compositions and proportions. Based on sing-cell RNA-seq data (GSE118389) we identified five distinct cell subpopulations and characterized their biological functions based on their gene markers. According to the results of functional annotation, we found that C1 and C2 are related to immune functions, while C5 is related to programmed cell death. Then based on subclonal basis gene expression matrix, we applied deconvolution algorithm on TCGA tissue RNA-seq data and observed that microenvironment is diverse among TNBC subclones, especially C1 is closely related to T cells. Whats more, we also found that high C5 proportions would led to poor survival outcome, log-rank test p-value and HR [95%CI] for five years overall survival in GSE96058 dataset were 0.0158 and 2.557 [1.160-5.636]. Collectively, our analysis reveals both intra-tumor and inter-tumor heterogeneity and their association with subclonal microenvironment in TNBC (subclone compositions and proportions), and uncovers the organic combination of subclones dictating poor outcomes in this disease. HighlightsWe applied deconvolution algorithm on subclonal basis gene expression matrix to link single cells and bulk tissue together.

cancer biology↗

bioRxiv2019

Synergistic effect: a common theme in mixed-species litter decomposition

O_LILitter decomposition plays a key role in ecosystem nutrients cycling, yet, to date science is lacking a comprehensive understanding of the non-additive effect in mixing litter decomposition.\nC_LIO_LIIn order to fill that gap, we compiled 69 individual studies for the purpose of performing two sub-meta-analyses on the non-additive effect.\nC_LIO_LIOur results show that a significantly synergistic effect occurs at global scale with the average increase by 2-4% in litter mixture decomposition; In particular, low-quality litter in mixture shows a significantly synergistic effect, while no significant change is observed with high-quality species. Additionally, the synergistic effect turns into the antagonistic effect when soil fauna is absent or litter decomposition enters into humus-near stage. In contrast to temperate and tropical areas, studies in frigid area also show a significantly antagonistic effect.\nC_LIO_LIOur meta-analysis provides a systematic evaluation of the non-additive effect in decomposition mixed litters, which is critical for understanding and improving the carbon forecasts and nutrient dynamics.\nC_LI

ecology↗

bioRxiv2019

Breeding of a high-yield strain for commercial cultivation by crossing Pholiota adiposa and P. limonella

Pholiota adiposa is an edible mushroom with excellent nutritional and medicinal properties. However, fruiting body yields are low, and the commercial cultivation potential of this fungus is limited. In the present study, 279 crossbred strains were obtained by mono-mono crossing of monokaryotic strains derived from P. adiposa HS5 and P. limonella HS4. Ligninolytic enzymes and mycelial growth rate were used as markers to screen the crossbred strains, and 18 were selected for further analysis. Crossbred strain A10B4 displayed the highest yield, i.e., 165.91 {+/-} 12.56 g per bag, which was 31.34 g and 74.48 g more than that of strains HS5 and HS4, respectively. The mycelial colonization time of A10B4 was 25.18 {+/-} 1.33 days, which was 5.64 days shorter than that of HS5. A10B4 was characterized by inter-simple sequence repeat molecular markers and antagonism tests. Differences in PCR products from parental and crossbred strains were observed. Therefore, the newly developed hybrid strain A10B4, named P. adiposa-limonella HS54 and having a high yield and desirable traits, might be suitable for commercial cultivation.

microbiology↗

bioRxiv2019

An All-to-All Approach to the Identification of Sequence-Specific Readers for Epigenetic DNA Modifications on Cytosine

The authors have withdrawn their manuscript whilst they perform additional experiments to test some of their conclusions further. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.Competing Interest StatementThe authors have declared no competing interest.

systems biology↗