Biology subjects

Song, D.

Publications and source records attributed to Song, D..

At least 73 records · Page 4Linked to original sources

bioRxiv2022

An alpaca-derived nanobody neutralizes the SARS-CoV-2 omicron variant

The SARS-CoV2 Omicron variant sub-lineages spread rapidly through the world, mostly due to their immune-evasive properties. This has put a significant part of the population at risk for severe disease and underscores the need for anti-SARS-CoV-2 agents that are effective against emergent strains in vulnerable patients. Camelid nanobodies are attractive therapeutic candidates due to their high stability, ease of large-scale production and potential for delivery via inhalation. Here, we characterize the RBD-specific nanobody W25, which we previously isolated from an alpaca, and show superior neutralization activity towards Omicron lineage BA.1 in comparison to all other SARS-CoV2 variants. Structure analysis of W25 in complex with the SARS-CoV2 spike surface glycoprotein shows that W25 engages an RBD epitope not covered by any of the antibodies previously approved for emergency use. Furthermore, we show that W25 also binds the spike protein from the emerging, more infectious Omicron BA.2 lineage with picomolar affinity. In vivo evaluation of W25 prophylactic and therapeutic treatments across multiple SARS-CoV-2 variant infection models, together with W25 biodistribution analysis in mice, demonstrates favorable pre-clinical properties. Together, these data endorse prioritization of W25 for further clinical development.

microbiology↗

bioRxiv2022

Electrical Stimulation Human Mesenchymal Stem Cells on Conductive Chitosan-Polyaniline Substrates Promotes Neural Priming

Electrical stimulation (ES) within conductive polymer substrates has been suggested to promote the differentiation of stem cells toward a neuronal phenotype. The use of conductive scaffolds in tissue regeneration provides a unique and attractive new option to control the amount and location of ES delivery. Scaffold stiffness has also been shown to be an important regulator of stem cells behavior and fate. Therefore, to improve stem cell-based regenerative therapies, it is essential to characterize the simultaneous effects of electroconductive substrate stiffness and electric field stimuli on stem cell fate processes. In this study, biodegradable electroconductive substrates based on chitosan-polyaniline (CS-g-PANI) were fabricated with different stiffnesses. Human mesenchymal stem cells (hMSCs) seeded on these scaffolds were electrically stimulated for 14 days with 100 mV/ cm (20 min every day). For hMSCs cultured on soft conductive scaffolds, a morphological change with significant filopodial elongation was observed after 2 weeks of electrically stimulated culture. Compared with stiff conductive CS-g-PANI scaffolds and non-conductive CS scaffolds, for soft conductive CS-g-PANI scaffolds microtubule-associated protein 2 (MAP2) and neurofilament (NF-H) expression increased after application of ES. At the same time, there was a decrease in the expression of the glial markers glial fibrillary acidic protein (GFAP) and vimentin after ES. Furthermore, the elevation of intracellular calcium [Ca2+] during spontaneous, cell-generated Ca2+ transients further suggested that electric field stimulation of hMSCs cultured on conductive CS-g-PANI substrates can promote a neural-like phenotype. Our findings propose that the combination of the soft conductive CS-g-PANI substrate and ES is a promising new tool for enhancing nerve tissue engineering outcomes.

bioengineering↗

bioRxiv2022

Defining Multiple Layers of Intratumor Heterogeneity Based on Variations of Perturbations in Multi-omics Profiling

Intratumor heterogeneity (ITH) is associated with tumor progression, relapse, immunoevasion, and drug resistance. Existing algorithms for measuring ITH are limited to at a single molecular level. We proposed a set of algorithms for measuring ITH at the genome (somatic copy number alterations (CNAs) and mutations), mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), protein, and epigenome level, respectively. These algorithms were designed based on a common concept: information entropy. By analyzing 33 TCGA cancer types, we demonstrated that these ITH measures had the typical properties of ITH, namely their significant correlations with unfavorable prognosis, tumor progression, genomic instability, antitumor immunosuppression, and drug resistance. Furthermore, we showed that the correlations between ITH measures at identical molecular levels were stronger than those at different molecular levels. The mRNA ITH showed stronger correlations with the miRNA, lncRNA, and epigenome ITH than with the genome ITH, supporting the regulatory relationships of miRNA, lncRNA, and DNA methylation towards mRNA. The protein ITH displayed stronger correlations with the transcriptome-level ITH than with the genome-level ITH, supporting the central dogma of molecular biology. Finally, we integrated the seven ITH measures into an ITH measure, which displayed more prominent properties of ITH than the ITH measures at a single molecular level. This analysis of multi-level ITH provides novel insights into tumor biology and potential values in clinical practice for pan-cancer.

bioinformatics↗

bioRxiv2022

A unified framework of realistic in silico data generation and statistical model inference for single-cell and spatial omics

In the single-cell and spatial omics field, computational challenges include method benchmarking, data interpretation, and in silico data generation. To address these challenges, we propose an all-in-one statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs, and feature modalities, by learning interpretable parameters from real datasets. Furthermore, using a unified probabilistic model for single-cell and spatial omics data, scDesign3 can infer biologically meaningful parameters, assess the goodness-of-fit of inferred cell clusters, trajectories, and spatial locations, and generate in silico negative and positive controls for benchmarking computational tools.

bioinformatics↗

bioRxiv2022

A small-molecule myosin inhibitor as a targeted multi-stage antimalarial

Malaria is a devastating disease that resulted in an estimated 627,000 deaths in 2020. About 80% of those deaths were among children under the age of five. Our approach is to develop small molecule inhibitors against cytoskeletal targets that are vital components of parasite function, essential at multiple stages of parasite infection, can be targeted with high specificity, and are highly druggable. Here we describe KNX-115, which inhibits purified Plasmodium falciparum myosin A (PfMyoA) actin-activated ATPase with a potency in the 10s of nanomolar range and >50-fold selectivity against cardiac, skeletal, and smooth muscle myosins. KNX-115 inhibits the blood and liver stages of Plasmodium with an EC50 of about 100 nanomolar, with negligible liver cell toxicity. In addition, KNX-115 inhibits sporozoite cell traversal and blocks the gametocyte to oocyst conversion in the mosquito. KNX-115 displays a similar killing profile to pyrimethamine and parasites are totally killed after 96 hours of treatment. In line with its novel mechanism of action, KNX-115 is equally effective at inhibiting a panel of Plasmodium strains resistant to experimental and marketed antimalarials. In vitro evolution data likely suggests a refractory potential of KNX-115 in developing parasite resistance.

pharmacology and toxicology↗

bioRxiv2022

CGAN-Cmap: protein contact map prediction using deep generative adversarial neural networks

Protein contact maps represent spatial pairwise inter-residue interactions, providing a proteins translationally and rotationally invariant topological representation. Accurate contact map prediction has been a critical driving force for improving protein structure prediction, one of computational biologys most challenging problems in the last half-century. While many computational tools have been developed to this end, most fail to predict accurate contact maps for proteins with insufficient homologous protein sequences, and exhibit low accuracy for long-range contacts. To address these limitations, we develop a novel hybrid model, CGAN-Cmap, that uses a generative adversarial neural network embedded with a series of modified squeeze and excitation residual networks. To exploit features of different dimensions, we build the generator of CGAN-Cmap via two parallel modules: sequential and pairwise modules to capture and interpret distance profiles from 1D sequential and 2D pairwise feature maps, respectively, and combine them during the training process to generate the contact map. This novel architecture helps to improve the contact map prediction by surpassing redundant features and encouraging more meaningful ones from 1D and 2D inputs simultaneously. We also introduce a new custom dynamic binary cross-entropy (BCE) as the loss function to extract essential details from feature maps, and thereby address the input imbalance problem for highly sparse long-range contacts in proteins with insufficient numbers of homologous sequences. We evaluate the performance of CGAN-Cmap on the 11th, 12th, 13th, and 14th Critical Assessment of protein Structure Prediction (CASP 11, 12, 13, and 14) and CAMEO test sets. CGAN-Cmap significantly outperforms state-of-the-art models, and in particular, it improves the precision of medium and long-range contact by at least 3.5%. Furthermore, our model has a low dependency on the number of homologous sequences obtained via multiple sequence alignment, suggesting that it can predict protein contact maps with good accuracy for those proteins that lack homologous templates. These results demonstrate an efficient approach for fast and highly accurate contact map prediction toward construction of protein 3D structure from protein sequence. Data availabilityAll datasets and source codes are provided in: https://github.com/mahan-fcb/CGAN-Cmap-A-protein-contact-map-predictor

bioinformatics↗

bioRxiv2022

A study of hyperelastic continuum models for isotropic athermal fibrous networks

Many biological materials contain fibrous protein networks as their main structural components. Understanding the mechanical properties of such networks is important for creating biomimicking materials for cell and tissue engineering, and for developing novel tools for detecting and diagnosing disease. In this work, we develop continuum models for isotropic, athermal fibrous networks by combining a single-fiber model that describes the axial response of individual fibers, with network models that assemble individual fiber properties into overall network behavior. In particular, we consider four different network models, including the affine, three-chain, eight-chain, and micro-sphere models, which employ different assumptions about network structure and kinematics. We systematically investigate the ability of these models to describe the mechanical response of athermal collagen and fibrin networks by comparing model predictions with experimental data. We test how each model captures network behavior under three different loading conditions: uniaxial tension, simple shear, and combined tension and shear. We find that the affine and three-chain models can accurately describe both the axial and shear behavior, whereas the eight-chain and micro-sphere models fail to capture the shear response, leading to an unphysical zero shear moduli at infinitesimal strains. Our study is the first to systematically investigate the applicability of popular network models for describing the macroscopic behavior of athermal fibrous networks, offering insights for selecting efficient models that can be used for large-scale, finite-element simulations of athermal networks.

biophysics↗

bioRxiv2022

NSUN2-mediated mRNA m5C Modification Regulates the Progression of Hepatocellular Carcinoma

RNA modification affects many biological processes and physiological diseases. The 5-methylcytosine (m5C) modification regulates the progression of multiple tumors. However, its characteristics and functions in hepatocellular carcinoma (HCC) remain largely unknown. Here, we found that HCC tissues had a higher m5C methylation level than the adjacent normal tissues. Transcriptome analysis revealed that a major fraction of the hypermethylated genes participated in the phosphokinase signaling pathways, such as the Ras and PI3K-Akt pathways. The m5C methyltransferase NSUN2 was highly expressed in HCC tissues consistently. Interestingly, the expression of many oncogenes was positively correlated with the expression of NSUN2, including GRB2, RNF115, AATF, ADAM15, RTN3, and HDGF. Real-time PCR assays further revealed that the expression of the mRNA of GRB2, RNF115, and AATF decreased significantly with the depletion of NSUN2 in HCC cells. Furthermore, NSUN2 could regulate the cellular sensitivity of HCC cells to sorafenib via modulating the Ras signaling pathway. Moreover, knocking down NSUN2 caused cell cycle arrest. Our study demonstrated a vital role of NSUN2 in the progression of HCC.

cancer biology↗

bioRxiv2022

MUC5B mobilizes and MUC5AC spatially aligns mucociliary transport on human airway epithelium

Airway mucus acts as a protective barrier and vehicle for clearance of pathogens, providing the lungs with a defense mechanism called mucociliary clearance (MCC). Airway mucus is composed of two mucins, mucin 5B (MUC5B) and 5AC (MUC5AC) that form a hydrogel that enables functional clearance in health. However, abnormalities in mucin expression, specifically increases in MUC5AC is observed in chronic respiratory diseases and leading to defective MCC. Our current understanding of MCC impairment focuses on mucin concentration, while the impact of mucin composition remains unclear. Here, we use MUC5AC/B-knock out (KO) human airway epithelial (HAE) tissue cultures to understand the role and contribution of individual secreted mucins on MCC mechanisms. We find that KO cultures result in impaired or discoordinated mucociliary transport demonstrating the importance of each of these mucins to effective MCC and shedding light on a new mechanism of mucin composition-dependent airway clearance.

physiology↗

bioRxiv2022

scSampler: fast diversity-preserving subsampling of large-scale single-cell transcriptomic data

SummaryThe number of cells measured in single-cell transcriptomic data has grown fast in recent years. For such large-scale data, subsampling is a powerful and often necessary tool for exploratory data analysis. However, the easiest random subsampling is not ideal from the perspective of preserving rare cell types. Therefore, diversity-preserving subsampling is required for fast exploration of cell types in a large-scale dataset. Here we propose scSampler, an algorithm for fast diversity-preserving subsampling of single-cell transcriptomic data. Using simulated and real data, we show that scSampler consistently outperforms existing subsam-pling methods in terms of both the computational time and the Hausdorff distance between the full and subsampled datasets. AvailabilityscSampler is implemented in Python and is published under the MIT source license. It can be installed by pip install scsampler and used with the Scanpy pipline. The code is available on GitHub: https://github.com/SONGDONGYUAN1994/scsampler. Contactlinwang@gwu.edu; jli@stat.ucla.edu

bioinformatics↗

bioRxiv2021

Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime

MotivationModeling single-cell gene expression trends along cell pseudotime is a crucial analysis for exploring biological processes. Most existing methods rely on nonparametric regression models for their flexibility; however, nonparametric models often provide trends too complex to interpret. Other existing methods use interpretable but restrictive models. Since model interpretability and flexibility are both indispensable for understanding biological processes, the single-cell field needs a model that improves the interpretability and largely maintains the flexibility of nonparametric regression models. ResultsHere we propose the single-cell generalized trend model (scGTM) for capturing a genes expression trend, which may be monotone, hill-shaped, or valley-shaped, along cell pseudotime. The scGTM has three advantages: (1) it can capture non-monotonic trends that are still easy to interpret, (2) its parameters are biologically interpretable and trend informative, and (3) it can flexibly accommodate common distributions for modeling gene expression counts. To tackle the complex optimization problems, we use the particle swarm optimization algorithm to find the constrained maximum likelihood estimates for the scGTM parameters. As an application, we analyze several single-cell gene expression data sets using the scGTM and show that it can capture interpretable gene expression trends along cell pseudotime and reveal molecular insights underlying the biological processes. Availability and implementationThe Python package scGTM is open-access and available at https://github.com/ElvisCuiHan/scGTM. Contactjli@stat.ucla.edu

bioinformatics↗

bioRxiv2021

Retinoids rescue ceruloplasmin secretion and alleviate oxidative stress in Wilson's disease-specific hepatocytes

Wilsons disease (WD) is a copper metabolic disorder, which is caused by defective ATP7B function. Here, we have generated induced pluripotent stem cells (iPSCs) from WD patients carrying compound heterozygous mutations on ATP7B. ATP7B loss- and gain-of-functions were further manifested with ATP7B-deficient iPSCs and heterozygously-corrected R778L WD patient-derived iPSCs using CRISPR-Cas9-based gene editing. Transcriptome analysis identified abnormalities of retinoid signaling pathway and lipid metabolism in WD-specific hepatocytes. Although the expression level of ATP7B protein was variable among WD-specific hepatocytes, the expression and secretion of ceruloplasmin (Cp), which is a downstream copper carrier in plasma, were consistently decreased. Cp secretion-based drug screening identified all-trans retinoic acid (ATRA) as promising candidates for rescuing Cp secretion. ATRA also alleviated reactive oxygen species (ROS) production induced by lipid accumulation in WD-specific hepatocytes. Our patient-derived iPSC-based hepatic models provide potential therapeutics for liver steatosis in WD and other fatty liver diseases.

cell biology↗

bioRxiv2021

TMEM120A contains a specific coenzyme A-binding site and might not mediate poking- or stretch-induced channel activities in cells

TMEM120A, a member of the Transmembrane protein 120 (TMEM120) family, has pivotal function in adipocyte differentiation and metabolism, and may also contribute to sensing mechanical pain by functioning as an ion channel named TACAN. Here we report that expression of TMEM120A is not sufficient in mediating poking- or stretch-induced currents in cells, and have solved cryo-EM structures of human TMEM120A (HsTMEM120A) in complex with an endogenous metabolic cofactor (coenzyme A, CoASH) and in the apo form. HsTMEM120A forms a symmetrical homodimer with each monomer containing an amino-terminal coiled-coil motif followed by a transmembrane domain with six membrane-spanning helices. Within the transmembrane domain, a CoASH molecule is hosted in a deep cavity and forms specific interactions with nearby amino acid residues. Mutation of a central tryptophan residue involved in binding CoASH dramatically reduced the binding affinity of HsTMEM120A with CoASH. HsTMEM120A exhibits distinct conformations at the states with or without CoASH bound. Our results suggest that TMEM120A may have alternative functional roles potentially involved in CoASH transport, sensing or metabolism.

biophysics↗

bioRxiv2021

Loss of Smad4 promotes aggressive lung cancer metastasis by de-repression ofPAK3 via miRNA regulation

Over 85% of lung cancer patients harbor overt or subclinical metastases at diagnosis, and therefore most patients die of progressive metastatic disease despite aggressive local and systemic therapies. Somatic mutations in the Smad4 gene have been found in non-small-cell lung cancer, but the underlying mechanism by which Smad4 loss-of-function (LOF) accelerates lung cancer metastasis is yet to be elucidated. Here, we generated a highly aggressive lung cancer mouse model bearing conditional KrasG12D, p53fl/fl LOF and/or Smad4 fl/fl LOF mutations. The Smad4fl/fl; p53 fl/fl; KrasG12D (SPK) mutant mice manifested a much higher incidence of tumor metastases than the p53 fl/fl; KrasG12D (PK) mice. Molecularly, PAK3 was identified as a novel downstream effector of Smad4, mediating metastatic signal transduction via the PAK3-JNK-Jun pathway. Upregulation of PAK3 by Smad4 LOF in SPK mice was achieved by attenuating Smad4-dependent transcription of miR-495 and miR-543. These microRNAs (miRNAs) directly bind to the PAK3 3UTR for blockade of PAK3 production, ultimately regulating lung cancer metastasis. An inverse correlation between Smad4 and PAK3 pathway components suggests clinical use of Smad4 LOF as a potential marker for prognosis in human lung cancer. Our study highlights the Smad4-PAK3 regulation as a point of potential therapy in metastatic lung cancer.

cancer biology↗

bioRxiv2021

Rhinovirus C replication is associated with the endoplasmic reticulum and triggers cytopathic effects in an in vitro model of human airway epithelium

The clinical impact of rhinovirus C (RV-C) is well-documented; yet the viral life cycle remains poorly defined. Thus, we characterized RV-C15 replication at the single-cell level and its impact on the human airway epithelium (HAE) using a physiologically-relevant in vitro model. RV-C15 replication was restricted to ciliated cells where viral RNA levels peaked at 12 hours post-infection (hpi), correlating with elevated titers in the apical compartment at 24 hpi. Notably, infection was associated with a loss of polarized expression of the RV-C receptor, cadherin-related family member 3. Visualization of double-stranded RNA (dsRNA) during RV-C15 replication revealed two distinct replication complex arrangements within the cell, likely corresponding to different time points in infection and correlating with the formation of large intracellular vesicles. To further define RV-C15 replication sites, we analyzed the expression of giantin, phosphatidylinositol-4- phosphate, and calnexin, as well as the colocalization of these markers with dsRNA. Fluorescence levels of all three cellular markers were elevated during infection and altered giantin distribution further indicated Golgi fragmentation. However, unlike previously characterized RVs, the high ratio of calnexin-dsRNA colocalization implicated the endoplasmic reticulum as the primary site for RV-C15 replication in HAE. RV-C15 infection was also associated with elevated stimulator of interferon genes (STING) expression, facilitating replication, and the induction of incomplete autophagy, a mechanism used by other RVs to promote non-lytic release of progeny virions. Finally, RV-C15 infection resulted in a temporary loss in epithelial barrier integrity and the translocation of tight junction proteins while a reduction in mucociliary clearance indicated cytopathic effects on epithelial function. Together, our findings identify both shared and unique features of RV-C replication compared to related rhinoviruses and define the impact of RV-C on both epithelial cell organization and tissue functionality - aspects of infection that may contribute to pathogenesis in vivo. Author summaryRhinovirus C has a global distribution and significant clinical impact - especially in those with underlying lung disease. Although RV-C is genetically, structurally, and biologically distinct from RV-A and -B viruses, our understanding of the RV-C life cycle has been largely inferred from these and other related viruses. Here, we performed a detailed analysis of RV-C15 replication in a physiologically-relevant model of human airway epithelium. Our single-cell, microscopy-based approach revealed that - unlike other RVs - the endoplasmic reticulum is the primary site for RV- C15 replication. RV-C15 replication also stimulated STING expression, which was proviral, and triggered dramatic changes in cellular organization, including altered virus receptor distribution, fragmented Golgi stacks, and the induction of incomplete autophagy. Additionally, we observed a loss of epithelial barrier function and a decrease in mucociliary clearance, a major defense mechanism in the lung, during RV-C15 infection. Together, these data reveal novel insight into RV-C15 replication dynamics and resulting cytopathic effects in the primary target cells for infection, thereby furthering our understanding of the pathogenesis of RV-C. Our work highlights similar, as well as unique, aspects of RV-C15 replication compared to related pathogens, which will help guide future studies on the molecular mechanisms of RV-C infection.

microbiology↗

bioRxiv2021

Membrane-Tethered Mucin 1 is Stimulated by Interferon in Multiple Cell Types and Antagonizes Influenza A Virus Infection in Human Airway Epithelium

Influenza A virus (IAV) causes seasonal epidemics and periodic pandemics, resulting in significant morbidity and mortality in the human population. Tethered mucin 1 (MUC1) is highly expressed in airway epithelium, the primary site of IAV replication, and also by other cell types that influence IAV infection, including macrophages. MUC1 has the potential to influence infection dynamics through physical interactions and/or signaling activity, and recent work suggests MUC1 acts as a releasable decoy receptor and anti-inflammatory molecule during IAV infection. Still, the modulation of MUC1 and its impact during viral pathogenesis remains unclear. Thus, we sought to further investigate the interplay between MUC1 and IAV in an in vitro model of primary human airway epithelium (HAE). Our data indicate that a recombinant IAV hemagglutinin (H3) and H3N2 virus can bind endogenous HAE MUC1. We find that infection of HAE cultures with H1N1 or H3N2 IAV strains does not trigger enhanced MUC1 shedding, but instead stimulates an increase in cell-associated MUC1 protein. We observed a similar increase after stimulation with either type I or type III interferon (IFN); however, inhibition of IFN signaling during H1N1 infection only partially abrogated this increase, indicating multiple soluble factors contribute to MUC1 upregulation during the antiviral response. We expanded these findings and demonstrate that in addition to HAE, primary human monocyte-derived macrophages also upregulate MUC1 protein in response to both IFN treatment and conditioned media from IAV-infected HAE cultures. We then developed HAE genetically depleted for MUC1 to determine its impact on IAV pathogenesis, finding that MUC1 knock-out cultures exhibited enhanced viral growth compared to control cultures for several IAV strains. Together, our data support a model whereby MUC1 antagonizes productive uptake of IAV in HAE. Infection then stimulates MUC1 expression on multiple cell types through IFN-dependent and -independent mechanisms that may further impact infection dynamics. Author SummaryThe mucosal surface of the respiratory epithelium is an important site of first contact for viral respiratory pathogens. Large and heavily glycosylated molecules known as tethered mucins extend from the cell surface and may physically restrict access to underlying cells. Recently, one of these tethered mucins, MUC1, has also been shown to influence cell signaling and inflammation. Still, despite its abundance in the airway and multifunctional capability, the role of MUC1 during influenza virus infection in the human respiratory tract remains unclear. Here, we demonstrate that influenza virus directly interacts with MUC1 in a physiologically-relevant model of human airway epithelium and find that MUC1 protein expression is elevated throughout the epithelium and in primary human monocyte-derived macrophages in response to important antiviral signals produced during infection. Using genetically-modified human airway cultures lacking MUC1, we then provide evidence of more efficient influenza virus infection in the absence of this mucin. Our data suggest that MUC1 not only physically restricts influenza virus uptake, but also represents a dynamic component of the host response that acts to further stem viral spread.

microbiology↗

bioRxiv2021

scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling

Single-cell RNA sequencing (scRNA-seq) captures whole transcriptome information of individual cells. While scRNA-seq measures thousands of genes, researchers are often interested in only dozens to hundreds of genes for a closer study. Then a question is how to select those informative genes from scRNA-seq data. Moreover, single-cell targeted gene profiling technologies are gaining popularity for their low costs, high sensitivity, and extra (e.g., spatial) information; however, they typically can only measure up to a few hundred genes. Then another challenging question is how to select genes for targeted gene profiling based on existing scRNA-seq data. Here we develop the single-cell Projective Non-negative Matrix Factorization (scPNMF) method to select informative genes from scRNA-seq data in an unsupervised way. Compared with existing gene selection methods, scPNMF has two advantages. First, its selected informative genes can better distinguish cell types. Second, it enables the alignment of new targeted gene profiling data with reference data in a low-dimensional space to facilitate the prediction of cell types in the new data. Technically, scPNMF modifies the PNMF algorithm for gene selection by changing the initialization and adding a basis selection step, which selects informative bases to distinguish cell types. We demonstrate that scPNMF outperforms the state-of-the-art gene selection methods on diverse scRNA-seq datasets. Moreover, we show that scPNMF can guide the design of targeted gene profiling experiments and cell-type annotation on targeted gene profiling data.

bioinformatics↗

bioRxiv2021

A human antibody with blocking activity to RBD proteins of multiple SARS-CoV-2 variants including B.1.351 showed potent prophylactic and therapeutic efficacy against SARS-CoV-2 in rhesus macaques

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which causes coronavirus disease-2019 (COVID-19), interacts with the host cell receptor angiotensin-converting enzyme 2 (hACE2) via its spike 1 protein for infection. After the virus sequence was published, we identified two potent antibodies against SARS-CoV-2 RBD from antibody libraries using a phage-to-yeast (PtY) display platform in only 10 days. Our lead antibody JMB2002, now in a phase I clinical trial, showed broad-spectrum in vitro blocking activity against hACE2 binding to the RBD of multiple SARS-CoV-2 variants including B.1.351 that was reportedly much more resistant to neutralization by convalescent plasma, vaccine sera and some clinical stage neutralizing antibodies. Furthermore, JMB2002 has demonstrated complete prophylactic and potent therapeutic efficacy in a rhesus macaque disease model. Prophylactic and therapeutic countermeasure intervention of SARS-CoV-2 using JMB2002 would likely slow down the transmission of currently emerged SARS-CoV-2 variants and result in more efficient control of the COVID-19 pandemic.

microbiology↗