Biology subjects

Shao, X.

Publications and source records attributed to Shao, X..

64 records · Page 4Linked to original sources

bioRxiv2020

Single-cell immune landscape of human recurrent spontaneous abortion

Successful pregnancy in placental mammals substantially depends on the establishment of maternal immune tolerance to the semi-allogenic fetus. Disorders in this process are tightly associated with adverse pregnancy outcomes including recurrent spontaneous abortion (RSA). However, an in-depth understanding of the disorders from the aspect of systematic and decidual immune environment in RSA remains largely lacking. In this study, we utilized single-cell RNA-sequencing to comparably analyze the cellular and molecular signatures of decidual and peripheral leukocytes in normal and RSA pregnancies at the early stage of gestation. Integrative analysis identified 22 distinct cell clusters in total, and a dramatic difference in leukocyte subsets and molecular properties in RSA cases was revealed. Specifically, the cytotoxic properties of CD8T effector, NK, and MAIT cells in peripheral blood indicated apparently enhanced immune inflammatory status, and the subpopulation proportions and ligand-receptor interactions of the decidual leukocyte subsets demonstrated preferential immune activation in RSA patients. The molecular features, spatial distribution and the developmental trajectories of five decidual NK (dNK) subsets were illustrated. The proportion of a dNK subset responsible for fetal protection was reduced, while the ratio of another dNK subset with cytotoxic and immune-active signature was significantly increased. Notably, a unique pro-inflammatory CD56+CD16+ dNK subpopulation was substantially accumulated in RSA decidua. These findings reveal a comprehensive cellular and molecular atlas of decidual and peripheral leukocytes in human early pregnancy, which provides an in-depth insight into the immune pathogenesis for early pregnancy loss.

immunology↗

bioRxiv2020

Genome-wide effects of the antimicrobial peptide apidaecin on translation termination

Biochemical studies suggested that the antimicrobial peptide apidaecin (Api) inhibits protein synthesis by binding in the nascent peptide exit tunnel and trapping the release factor associated with a terminating ribosome. The mode of Api action in bacterial cells had remained unknown. Here, genome-wide analysis revealed that Api arrests translating ribosomes at stop codons and causes pronounced queuing of the trailing ribosomes. By sequestering the available release factors, Api promotes pervasive stop codon bypass, leading to expression of proteins with C-terminal extensions. Api-mediated translation arrest leads to futile activation of the ribosome rescue systems. Understanding the unique mechanism of Api action in living cells may facilitate development of new medicines and research tools for genome exploration.

biochemistry↗

bioRxiv2020

Reference-free Cell-type Annotation for Single-cell Transcriptomics using Deep Learning with a Weighted Graph Neural Network

Advances in single-cell RNA sequencing (scRNA-seq) have furthered the simultaneous classification of thousands of cells in a single assay based on transcriptome profiling. In most analysis protocols, single-cell type annotation relies on marker genes or RNA-seq profiles, resulting in poor extrapolation. Here, we introduce scDeepSort (https://github.com/ZJUFanLab/scDeepSort), a reference-free cell-type annotation tool for single-cell transcriptomics that uses a deep learning model with a weighted graph neural network. Using human and mouse scRNA-seq data resources, we demonstrate the feasibility of scDeepSort and its high accuracy in labeling 764,741 cells involving 56 human and 32 mouse tissues. Significantly, scDeepSort outperformed reference-dependent methods in annotating 76 external testing scRNA-seq datasets, including 126,384 cells (85.79%) from ten human tissues and 134,604 cells from 12 mouse tissues (81.30%). scDeepSort accurately revealed cell identities without prior reference knowledge, thus potentially providing new insights into mechanisms underlying biological processes, disease pathogenesis, and disease progression at a single-cell resolution.

bioinformatics↗

bioRxiv2020

CTLA-4 blockade reverses the Foxp3+ T-regulatory-cell suppression of anti-tuberculosis T-cell effector responses

BackgroundsIt has been well described that Foxp3+ T regulatory (Treg) cells suppress immune responses and that murine cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) can control the function of Foxp3+Treg cells. However, it remains unknown about the role of CTLA-4 pathway in Treg suppression of T cell responses in tuberculosis (TB). MethodsWe assessed TB-driven changes in CTLA-4-expressing Foxp3+ Treg and conducted CTLA-4 blocking mechanistic studies ex vivo in 126 subjects with active TB, latent TB or uninfected statuses. ResultsFrequencies of CTLA-4-expressing Treg cells were increased in the circulation of pulmonary TB patients and in the pleural compartment of TB pleuritis. Six-month anti-TB treatment significantly reduced CTLA-4+ Treg subset. Notably, antibody blocking of CTLA-4 pathway (CTLA-4 blockade) reversed the ability of Treg cells to suppress anti-TB Th1 responses and abrogated the Treg-mediated suppression of TB antigen-stimulated proliferative response. The CTLA-4 blockade reversed the Treg suppression of the ability of T cells to restrict intracellular BCG and M. tuberculosis growth in macrophages. InterpretationThe study uncovered previously-unreported observations implicating that the CTLA-4 blockade abrogates the capability of Treg cells to suppress anti-TB immune responses or immunity. Findings support the rationale for exploring the CTLA-4 blockade as potential host-directed therapy against TB. FundThis work is supported in part by the National Natural Science Foundation of China (30901277, 81671553, 81501359), and the Key Technologies Research and Development Program for Infectious Diseases of China (2017ZX10201302-004).

immunology↗

bioRxiv2020

Experimental study of tissue-engineered urethra with collagen / chitosan composite as scaffolds

In this article we investigated the preparation of tissue-engineered urethra by using the urethral epithelial subculture cells of male New Zealand young rabbits. We inoculated the epithelial cells of urinary mucosa of male New Zealand young rabbits on collagen, chitosan and collagen chitosan composite as scaffolds to prepare tissue-engineered urethra. The results of inverted phase contrast microscope, HE staining and scanning electron microscope of three kinds of tissue-engineered urethra were compared. Whats more, we reported a new method for quantitative and rapid detection of epithelial cell activity of urinary mucosa in situ by Interactive Laser Cytometer. The collagen chitosan composite was more similar to the extracellular matrix of mammalian. Its three-dimensional porous structure had a high area volume ratio, which was conducive to cell adhesion, growth and metabolism. In vitro, the urethral epithelial cells had been cultured on collagen chitosan composite, and the tissue-engineered urethra was successfully prepared, which laid a solid foundation for further in vivo experiments.

bioengineering↗

bioRxiv2020

Patient-specific Alzheimer-like pathology in trisomy 21 cerebral organoids reveals BACE2 as a gene-dose-sensitive AD-suppressor in human brain

A population of >6 million people worldwide at high risk of Alzheimers disease (AD) are those with Down Syndrome (DS, caused by trisomy 21 (T21)), 70% of whom develop dementia during lifetime, caused by an extra copy of {beta}-amyloid-(A{beta})-precursor-protein gene. We report AD-like pathology in cerebral organoids grown in vitro from non-invasively sampled strands of hair from 71% of DS donors. The pathology consisted of extracellular diffuse and fibrillar A{beta} deposits, hyperphosphorylated/pathologically conformed Tau, and premature neuronal loss. Presence/absence of AD-like pathology was donor-specific (reproducible between individual organoids/iPSC lines/experiments). Pathology could be triggered in pathology-negative T21 organoids by CRISPR/Cas9-mediated elimination of the third copy of chromosome-21-gene BACE2, but prevented by combined chemical {beta} and {gamma}-secretase inhibition. We found that T21-organoids secrete increased proportions of A{beta}-preventing (A{beta}1-19) and A{beta}-degradation products (A{beta}1-20 and A{beta}1-34). We show these profiles mirror in cerebrospinal fluid of people with DS. We demonstrate that this protective mechanism is mediated by BACE2-trisomy and cross-inhibited by clinically trialled BACE1-inhibitors. Combined, our data prove the physiological role of BACE2 as a dose-sensitive AD-suppressor gene, potentially explaining the dementia delay in [~]30% of people with DS. We also show that DS cerebral organoids could be explored as pre-morbid AD-risk population detector and a system for hypothesis-free drug screens as well as identification of natural suppressor genes for neurodegenerative diseases.

neuroscience↗

bioRxiv2019

A novel machine learning based approach for iPS progenitor cell identification

Identification of induced pluripotent stem (iPS) progenitor cells, the iPS forming cells in early stage of reprogramming, could provide valuable information for studying the origin and underlying mechanism of iPS cells. However, it is very difficult to identify experimentally since there are no biomarkers known for early progenitor cells, and only about 6 days after reprogramming initiation, iPS cells can be experimentally determined via fluorescent probes. What is more, the ratio of progenitor cells during early reprograming period is below 5%, which is too low to capture experimentally in the early stage.\n\nIn this paper, we propose a novel computational approach for the identification of iPS progenitor cells based on machine learning and microscopic image analysis. Firstly, we record the reprogramming process using a live cell imaging system after 48 hours of infection with retroviruses expressing Oct4, Sox2 and Klf4, later iPS progenitor cells and normal murine embryonic fibroblasts (MEFs) within 3 to 5 days after infection are labeled by retrospectively tracing the time-lapse microscopic image. We then calculate 11 types of cell morphological and motion features such as area, speed, etc., and select best time windows for modeling and perform feature selection. Finally, a prediction model using XGBoost is built based on the selected six types of features and best time windows. Our model allows several missing values/frames in the sample datasets, thus it is applicable to a wide range of scenarios.\n\nCross-validation, holdout validation and independent test experiments showed that the minimum precision is above 52%, that is, the ratio of predicted progenitor cells within 3 to 5 days after viral infection is above 52%. The results also confirmed that the morphology and motion pattern of iPS progenitor cells is different from that of normal MEFs, which helps with the machine learning methods for iPS progenitor cell identification.\n\nAuthor SummaryIdentification of induced pluripotent stem (iPS) progenitor cells could provide valuable information for studying the origin and underlying mechanism of iPS cells. However, it is very difficult to identify experimentally since there are no biomarkers known for early progenitor cells, and only after about 6 days of induction, iPS cells can be experimentally determined via fluorescent probes. What is more, the percentage of the progenitor cells during the early induction period is below 5%, too low to capture experimentally in early stage. In this work, we proposed an approach for the identification of iPS progenitor cells, the iPS forming cells, based on machine learning and microscopic image analysis. The aim is to help biologists to enrich iPS progenitor cells during the early stage of induction, which allows experimentalists to select iPS progenitor cells with much higher probability, and furthermore to study the biomarkers which trigger the reprogramming process.

bioinformatics↗

bioRxiv2018

GenPipes: an open-source framework for distributed and scalable genomic analyses

With the decreasing cost of sequencing and the rapid developments in genomics technologies and protocols, the need for validated bioinformatics software that enables efficient large-scale data processing is growing. Here we present GenPipes, a flexible Python-based framework that facilitates the development and deployment of multi-step workflows optimized for High Performance Computing clusters and the cloud. GenPipes already implements 12 validated and scalable pipelines for various genomics applications, including RNA-Seq, ChIP-Seq, DNA-Seq, Methyl-Seq, Hi-C, capture Hi-C, metagenomics and PacBio long read assembly. The software is available under a GPLv3 open source license and is continuously updated to follow recent advances in genomics and bioinformatics. The framework has been already configured on several servers and a docker image is also available to facilitate additional installations. In summary, GenPipes offers genomic researchers a simple method to analyze different types of data, customizable to their needs and resources, as well as the flexibility to create their own workflows.

bioinformatics↗

bioRxiv2018

Quantitative Analysis of Interactive Behavior of Mitochondria and Lysosomes Using Structured Illumination Microscopy

Super-resolution optical microscopy has extended the spatial resolution of cell biology from the cellular level to the nanoscale, enabling the observation of the interactive behavior of single mitochondria and lysosomes. Quantitative parametrization of interaction between mitochondria and lysosomes under super-resolution optical microscopy, however, is currently unavailable, which has severely limited our understanding of the molecular machinery underlying mitochondrial functionality. Here, we introduce an M-value to quantitatively investigate mitochondria and lysosome contact (MLC) and mitophagy under structured illumination microscopy. We found that the M-value for an MLC is typically less than 0.4, whereas in mitophagy it ranges from 0.5 to 1.0. This system permits further investigation of the detailed molecular mechanism governing the interactive behavior of mitochondria and lysosomes.

cell biology↗

bioRxiv2018

Super-resolution tracking of mitochondrial dynamics with a third-row transition metal complex dye

Combining luminescent transition metal complex (LTMC) with super-resolution microscopy is an excellent strategy for the long-term visualization of the dynamics of subcellular structures in living cells. However, it remains unclear whether iridium(III) complexes are applicable for a particular type of super-resolution technique, structured illumination microscopy (SIM), to image subcellular structures.\n\nAs described herein, we developed an iridium(III) dye, to track mitochondrial dynamics in living cells under SIM. The dye demonstrated excellent specificity and photostability and satisfactory cell permeability. While using SIM to image mitochondria, we achieved an approximately 80-nm resolution that allowed the clear observation of the structure of mitochondrial cristae. We used the dye to monitor and quantify mitochondrial dynamics relative to lysosomes, including fusion involved in mitophagy, and newly discovered mitochondria-lysosome contact (MLC) under different conditions. MLC remained intact and fusion vanished when five receptors, p62, NDP52, OPTN, NBR1, and TAX1BP1, were knocked out, suggesting that these two processes are independence.

biophysics↗