Biology subjects

Pang, Y.

Publications and source records attributed to Pang, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Human transcription factor combinations mapped by genome-wide footprinting with deaminase

An individuals somatic cells have the same genome but exhibit cell-type-specific transcriptome regulated by a combination of transcription factors (TFs) for each gene. Mapping of TF sites on the human genome is critically important for understanding functional genomics. Here we report a novel technique to measure human TFs binding sites genome-wide with single-base resolution by footprinting with deaminase (FOODIE). Single-molecule sequencing reads from thousands of cells after in situ deamination yielded site-specific TF binding fractions and the cooperativity among adjacent TFs. In a human lymphoblastoid cell line, we found that genes in a correlated gene module (CGM) share TF(s) in their cis-regulatory elements to participate a particular biological function. Finally, single-cell resolved experiments (scFOODIE) allow cell-type-specific TF footprinting in heterogeneous brain tissues.

genomics↗

bioRxiv2024

PharaCon: A new framework for identifying bacteriophages via conditional representation learning

MotivationIdentifying bacteriophages (phages) within metagenomic sequences is essential for understanding microbial community dynamics. Transformer-based foundation models have been successfully employed to address various biological challenges. However, these models are typically pre-trained with self-supervised tasks that do not consider label variance in the pre-training data. This presents a challenge for phage identification as pre-training on mixed bacterial and phage data may lead to information bias due to the imbalance between bacterial and phage samples. ResultsTo overcome this limitation, this study proposed a novel conditional BERT framework that incorporates labels during pre-training. We developed an approach using a conditional BERT model for pre-training labeled data, incorporating label constraints with modified language modeling tasks. This approach allows the model to acquire label-conditional sequence representations. Additionally, we proposed a solution that utilizes conditional BERT in the fine-tuning phase as a classifier. We applied this conditional BERT framework to identify phages using a novel fine-tuning strategy, introducing PharaCon. We evaluated PharaCon against several existing methods on both simulated sequence datasets and real metagenomic contig datasets. The results demonstrate PharaCon's potential as an effective and efficient method for phage identification, highlighting the effectiveness of conditional B ERT as a solution for learning label-specific representations during pre-training on mixed sequence data. AvailabilityThe codes of PharaCon are now available in: https://github.com/Celestial-Bai/PharaCon. Contactyaozhong@ims.u-tokyo.ac.jp and imoto@hgc.jp

bioinformatics↗

bioRxiv2024

Clostridium ablates tumors by degrading collagen mesh andinducing ferroptosis

The supportive role of extracellular matrix in tumor growth and the drug resistance of tumor cells stand the major challenges in treating cancer. Here, we report the discovery of an oncolytic Clostridium strain, named RJ-1, which simultaneously expresses collagenases and produces iron-containing particles through intrabacterial mineralization. The secreted collagenases degrade pre-existing collagen mesh and show a collagen concentration-dependent degradation feature. Iron-containing particles released from RJ-1 by autolysis generate ferrous ions in the lysosomal acid environment following cell uptake and subsequently induce ferroptosis via the Fenton reaction. Due to selective germination in tumor sites, systemic administration of RJ-1 spores synchronously destructs dense tumor extracellular matrix and causes non-apoptotic tumor cell death. In collagen-dense tumor-bearing mice, a single intravenous injection achieves a favorable tolerance and rapid oncolysis of giant tumors with a size of [~]1000 mm3. This work finds a dual collagenase-expressing and iron-concentrating bacterium, proposing an alternative strategy to combat tumors.

microbiology↗

bioRxiv2024

Evolutionary analysis of paired box gene family and biological function exploration of Lr.Pax7 in lamprey (Lethenteron reissneri)

The Pax(paired box)gene family comprises highly conserved transcription factors that play a crucial regulatory role in embryonic development, tissue morphogenesis, and cell differentiation. The gene family comprises of nine members, namely Pax1 to Pax9, each exhibiting distinct functionalities and expression patterns. Among the members of the Pax family, Pax7 is particularly involved in key regulatory functions related to skeletal muscle development and satellite cell biology in vertebrates. However, the precise role of Pax7 and its downstream targets in jawless vertebrates such as lamprey remains relatively understudied. Therefore, investigating the expression and function of lampreys Pax genes holds significant value as a model organism for understanding developmental processes, evolution, and interactions with other vertebrates. In this study, we identified Pax7 in Lethentero reissneri and multiple bioinformatics analyses suggested that it may represent the ancestral gene of the Pax7 shared among higher vertebrates. To further elucidate the function and regulatory mechanism of Pax7, we performed transcriptome sequencing analysis after silencing Lr.Pax7 expression which revealed alterations in metabolic processes along with associated genes(Plcy,Jagged2,Hes,Myod,Frizzled2,etc) involved in various immune pathways(Notch, Wnt, IL-17, TNF, etc), myogenesis process, adipocyte formation process consistent with those observed in higher vertebrates. Comparative studies between lamprey and high vertebrates could provide valuable insight into understanding the functional role of Pax7 and its downstream targets across diverse biological process as well as disease states.

evolutionary biology↗

bioRxiv2023

A Fuzzy sequencer for rapid DNA fragment counting and genotyping

High-throughput sequencing technologies generate a vast number of DNA sequence reads simultaneously, which are subsequently analyzed using the information contained within these fragmented reads. The assessment of sequencing technology relies on information efficiency, which measures the amount of information entropy produced per sequencing reaction cycle. In this study, we propose a fuzzy sequencing strategy that exhibits information efficiency more than twice of currently prevailing cyclic reversible terminator sequencing methods. To validate our approach, we developed a fully functional and high-throughput fuzzy sequencer. This sequencer implements a highly efficient fluorogenic sequencing-by-synthesis chemistry and underwent testing across various application scenarios, including copy-number variation detection, noninvasive prenatal testing, transcriptome profiling, mutation genotyping, and metagenomic profling. Our findings unequivocally demonstrate that the fuzzy sequencing strategy outperforms existing methods in terms of information efficiency and delivers accurate resequencing results with faster turnaround times. One Sentence SummaryA fuzzy sequencer can exceed current limit of information efficiency of DNA sequencers for resequencing applications.

genomics↗

bioRxiv2023

SHRIMP: Genetically Encoded mScarlet-derived Red Fluorescent Hydrogen Peroxide Sensor with High Brightness and Minimal Photoactivation

Red fluorescent protein (RFP) derived indicators are popular due to advantages such as increased imaging depth and reduced autofluorescence and cytotoxicity. However, most RFP-based indicators have low brightness and are susceptible to blue-light-induced photoactivation. In this study, we aimed to overcome the limitations of existing red fluorescent indicators. We utilized mScarlet-I, a highly bright and robust monomeric RFP, to develop a circularly permuted variant called cpmScarlet. We further engineered cpmScarlet into a novel red fluorescent indicator specifically for hydrogen peroxide (H2O2), a crucial reactive oxygen species (ROS) involved in redox signaling and oxidative stress. The resultant indicator, SHRIMP (mScarlet-derived H2O2 Redox Indicator with Minimal Photoactivation), exhibited excitation and emission peaks at [~]570 and 595 nm, respectively, and demonstrated a maximum five-fold fluorescence turn-off response to H2O2. Importantly, SHRIMP was not susceptible to blue-light-induced photoactivation and showed high brightness both in its purified protein form and when expressed in mammalian cells. We successfully employed SHRIMP to visualize H2O2 dynamics in mammalian cells with exogenously added H2O2 and in activated macrophages. Additionally, we demonstrated its utility for multiparameter imaging by co-expressing SHRIMP with GCaMP6m, a green fluorescent calcium indicator, enabling simultaneous monitoring of H2O2 and calcium dynamics in mammalian cells in response to thapsigargin (TG) and epidermal growth factor (EGF) stimulation. Furthermore, we expressed SHRIMP in isolated primary mouse islet tissue, and SHRIMP exhibited excellent brightness and capability for effective detection of H2O2 production during streptozotocin (STZ)-induced {beta}-cell damage. This study successfully transformed mScarlet-I, a bright and robust monomeric RFP, into a circularly permuted variant (cpmScarlet) and developed the first cpmScarlet-based genetically encoded fluorescent indicator called SHRIMP. SHRIMP exhibits high brightness and insensitivity to photoactivation and is a valuable tool for real-time monitoring of H2O2 dynamics in various biological systems. Further research may yield an expanded family of cpmScarlet-based red fluorescent indicators with enhanced photophysical properties.

biochemistry↗

bioRxiv2023

Exploiting the therapeutic vulnerability of IDH-mutant gliomas with zotiraciclib

Isocitrate dehydrogenase (IDH)-mutant gliomas have distinctive metabolic and biological traits that may render them susceptible to targeted treatments. Here, by conducting a high-throughput drug screen, we pinpointed a specific susceptibility of IDH-mutant gliomas to zotiraciclib (ZTR). ZTR exhibited selective growth inhibition across multiple IDH-mutant glioma in vitro and in vivo models. Mechanistically, ZTR at low doses suppressed CDK9 and RNA Pol II phosphorylation in IDH-mutant cells, disrupting mitochondrial function and NAD+ production, causing oxidative stress. Integrated biochemical profiling of ZTR kinase targets and transcriptomics unveiled that ZTR-induced bioenergetic failure was linked to the suppression of PIM kinase activity. We posit that the combination of mitochondrial dysfunction and an inability to adapt to oxidative stress resulted in significant cell death upon ZTR treatment, ultimately increasing the therapeutic vulnerability of IDH-mutant gliomas. These findings prompted a clinical trial evaluating ZTR in IDH-mutant gliomas towards precision medicine (NCT05588141). HighlightsO_LIZotiraciclib (ZTR), a CDK9 inhibitor, hinders IDH-mutant glioma growth in vitro and in vivo. C_LIO_LIZTR halts cell cycle, disrupts respiration, and induces oxidative stress in IDH-mutant cells. C_LIO_LIZTR unexpectedly inhibits PIM kinases, impacting mitochondria and causing bioenergetic failure. C_LIO_LIThese findings led to the clinical trial NCT 05588141, evaluating ZTR for IDH-mutant gliomas. C_LI

cancer biology↗

bioRxiv2023

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

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

neuroscience↗

bioRxiv2023

Development, Characterization, and Structural Analysis of a Genetically Encoded Red Fluorescent Peroxynitrite Biosensor

Boronic acid-containing fluorescent molecules have been widely used to sense hydrogen peroxide and peroxynitrite, which are important reactive oxygen and nitrogen species in biological systems. However, it has been challenging to gain specificity. Our previous studies developed genetically encoded, green fluorescent peroxynitrite biosensors by genetically incorporating a boronic acid-containing noncanonical amino acid (ncAA), p-boronophenylalanine (pBoF), into the chromophore of circularly permuted green fluorescent proteins (cpGFPs). In this work, we introduced pBoF to amino acid residues spatially close to the chromophore of an enhanced circularly permuted red fluorescent protein (ecpApple). Our effort has resulted in two responsive ecpApple mutants: one bestows reactivity toward both peroxynitrite and hydrogen peroxide, while the other, namely pnRFP, is a selective red fluorescent peroxynitrite biosensor. We characterized pnRFP in vitro and in live mammalian cells. We further studied the structure and sensing mechanism of pnRFP using X-ray crystallography, 11B-NMR, and computational methods. The boron atom in pnRFP adopts an sp2-hybridization geometry in a hydrophobic pocket, and the reaction of pnRFP with peroxynitrite generates a product with a twisted chromophore, corroborating the observed "turn-off" fluorescence response. Thus, this study extends the color palette of genetically encoded peroxynitrite biosensors, provides insight into the response mechanism of the new biosensor, and demonstrates the versatility of using protein scaffolds to modulate chemoreactivity.

biochemistry↗

bioRxiv2023

Genetically Encoded Boronolectin as a Specific Red Fluorescent UDP-GlcNAc Biosensor

There is great interest in developing boronolectins, which are synthetic lectin mimics containing a boronic acid functional group for reversible recognition of diol-containing molecules, such as glycans and ribonucleotides. However, it remains a significant challenge to gain specificity. Here, we present a genetically encoded boronolectin, which is a hybrid protein consisting of a noncanonical amino acid (ncAA) p-boronophenylalanine (pBoF), natural-lectin-derived peptide sequences, and a circularly permuted red fluorescent protein (cpRFP). The genetic encodability permitted a straightforward protein engineering process to derive a red fluorescent biosensor that can specifically bind uridine diphosphate N-acetylglucosamine (UDP-GlcNAc), an important nucleotide sugar involved in metabolic sensing and cell signaling. We further characterized the resultant boronic acid-and peptide-assisted UDP-GlcNAc sensor (bapaUGAc) both in vitro and in live mammalian cells. Because UDP-GlcNAc in the endoplasmic reticulum (ER) and Golgi apparatus plays essential roles in glycosylating biomolecules in the secretory pathway, we genetically expressed bapaUGAc in the ER and Golgi and validated the sensor for its responses to metabolic disruption and pharmacological inhibition. In addition, we combined bapaUGAc with UGAcS, a recently reported green fluorescent UDP-GlcNAc sensor based on an alternative sensing mechanism, to monitor UDP-GlcNAc level changes in the ER and cytosol simultaneously. We expect our work to facilitate the future development of specific boronolectins for carbohydrates. In addition, this newly developed genetically encoded bapaUGAc sensor will be a valuable tool for studying UDP-GlcNAc and glycobiology.

biochemistry↗

bioRxiv2022

KBPRNA: A novel method integrating bulk RNA-seq data and LINCS-L1000 gene signatures to predict kinase activity based on machine learning

BackgroundKinases are a type of enzymes which can transfer phosphate groups from high-energy and phosphate-donating molecules to specific substrates. Kinase activities could be utilized to be represented as specific biomarkers of specific cancer types. Nowadays novel algorithms have already been developed to compute kinase activities from phosphorylated proteomics data. However, phosphorylated proteomics sequencing could be costly expensive and need valuable samples. Moreover,not methods which could achieve kinase activities from bulk RNA-sequence data have been developed. Here we propose KBPRNA, a general computational framework for extracting specific kinase activities from bulk RNA-sequencing data in cancer samples. KBPRNA also achieves better performance in predicting kinase activities from bulk RNA-sequence data under cancer conditions benchmarking against other models. ResultsIn this study, we used LINCS-L1000 dataset which was used to be reported as efficient gene signatures in defining bulk RNA-seq data as input dataset of KBPRNA. Also, we utilized eXtreme Gradient Boosting (XGboost) as the main algorithm to extract valuable information to predict kinase activities. This model outperforms other methods such as linear regression and random forest in predicting kinase activities from bulk RNA-seq data. KBPRNA integrated tissue samples coming from breast invasive carcinoma, hepatocellular carcinoma, lung squamous cell carcinoma, Glioblastoma multiforme and Uterine Corpus Endometrial Carcinoma. It was found that KBPRNA achieved good performance with an average R score above threshold of 0.5 in kinase activity prediction. ConclusionsModel training and testing process showed that KBPRNA outperformed other machine learning methods in predicting kinase activities coming from various cancer types tissue samples. This model could be utilized to approximate basic kinase activities and link it with specific biological functions, which in further promoted the progress of cancer identification and prognosis.

bioinformatics↗

bioRxiv2022

Rapid and Signal Crowdedness-Robust In-Situ Sequencing through Hybrid Block Coding

Spatial transcriptomics technology has revolutionized our understanding of cell types and tissue organization, opening new possibilities for researchers to explore transcript distributions at subcellular levels. However, existing methods have limitations in resolution, sensitivity, or speed. To overcome these challenges, we introduce SPRINTseq (Spatially Resolved and signal-diluted Next-generation Targeted sequencing), an innovative in situ sequencing strategy that combines hybrid block coding and molecular dilution strategies. Our method enables fast and sensitive high-resolution data acquisition, as demonstrated by recovering over 142 million transcripts using a 108 gene panel from 453,843 cells from four mouse brain coronal slices in less than two days. Using this advanced technology, we uncover the cellular and subcellular molecular architecture of Alzheimers disease, providing additional information into abnormal cellular behaviors and their subcellular mRNA distribution. This improved spatial transcriptomics technology holds great promise for exploring complex biological processes and disease mechanisms.

genomics↗

bioRxiv2022

How do Vampires Suck Blood?

Lampreys are blood-sucking vampires in the marine. From a survival perspective, it is expected that lamprey buccal gland exhibits a repository of pharmacologically active components to modulate the hosts homeostasis, inflammatory and immune responses. Several proteins have been found to function as anticoagulants, ion channel blockers, and immune suppressors in lampreys, while small metabolites have never been explored in detail. In this study, by analyzing the metabolic profiles of 14 different lamprey tissues, we have identified two groups of blood-sucking-associated metabolites, i.e., kynurenine pathway metabolites and prostaglandins, in the buccal gland and they can be injected into the host fish to ensure a steady and sustained blood flow to the feeding site. These findings demonstrate the complex nature of lamprey buccal gland and highlight the diversity in the mechanisms utilized for blood-sucking in lampreys. In addition, a lamprey spatial metabolomics database (https://www.lampreydb.com) was constructed to assist studies using lampreys as model animal. The database contains detailed qualitative, quantitative, and spatial distribution information of each detected metabolite, and users can easily query and check their metabolites of interest, and/or identify unknown peaks using the database. Significance StatementLampreys are one of the two surviving jawless vertebrate groups that hold the key to our understanding of the early vertebrate evolution, adaptive immune origin, and developmental neurobiology. Here, we applied a spatial metabolomics approach to study the lamprey-host interaction. Two groups of metabolites, i.e., kynurenine pathway metabolites and prostaglandins, were found in the lamprey buccal gland, which modulate the hosts homeostasis, inflammatory and immune responses. The establishment of the first tissue-wide spatial lamprey metabolomics database in this study facilitate future studies in biochemistry, clinical chemistry, natural product discovery, medicine, and metabolomics using lampreys as a model animal.

biochemistry↗

bioRxiv2022

miR-148a-3p inhibits osteogenesis by targeting Itga11 via PI3K/Akt/GSK3/β-catenin pathway

In previous research, miR-148a-3p deficiency was observed in bone malformation in hemifacial microsomia. Nevertheless, the mechanism of miR-148a regulating osteogenesis remains unclear. Herein, in this article, we probed into the role of miR-148a-3p in bone physiology by utilizing miR-148a knock-out (KO) mice. Compared with wild-type (WT) or heterozygotic (HE) littermates, miR-148a knock-out mice manifested lower body weight, bone dysplasia with increased bone mass. Through in-vitro experiments, in terms of miR-148a-3p overexpression (miRNA mimic transfection) and knockout (primary cells from WT and KO littermates), we found that miR-148a-3p can suppress osteogenesis, either in the ALP activity or bone nodules formation. Afterward, by means of proteomics, combined with RNA-sequencing and prediction databases of microRNA targets (miRDB and TargetScan), nine candidate genes targeted by miR-148a-3p were identified. Among them, only Itga11 was regulated by mRNA degradation, while the others were modulated via post-transcriptional inhibition. Based on several online databases (GenePaint, BioGPS, STRING), Integrin Subunit Alpha 11 (Itga11) was suggested to play an essential role in osteogenesis and it was confirmed as one direct target of miR-148a-3p by dual-luciferase reporter assay. Meanwhile, gene set enrichment analysis (GSEA) indicated activation of PI3K-Akt signaling pathway and WNT signaling pathway in miR-148a KO mice. The thereafter western blot confirmed that PI3K/Akt/GSK3/{beta}-catenin signaling pathway was involved. Finally, with the help of esterase-response bone-affiliative liposome with the Itga11 siRNA delivery system, the enhanced osteogenesis in miR-148a KO mice was partly rescued. Taken together, we demonstrated that miR-148a-3p can inhibit osteogenesis by targeting Itga11 via PI3K/Akt/GSK3/{beta}-catenin pathway.

cell biology↗

bioRxiv2022

On the origin of R2 orientation dependence angle offsets in white matter

Purposeto identify the origin of confounding angle offset{varepsilon} 0 in R2 orientation dependence and to propose a novel framework for better characterizing anisotropic R2 in brain white matter (WM). MethodsAnisotropic ({varepsilon}) rather than principal diffusivity direction ({Phi}) was theorized along axon fiber, with{varepsilon} determined by all eigenvalues and eigenvectors from diffusion tensor. An extra parameter{varepsilon} 0 was introduced into generalized magic angle effect function to account for any offset in R2 orientation dependence derived from T2-weighted image (b=0). These dependences referenced by{varepsilon} were compared to those referenced as usual by {Phi} at b-values of 1000 and 2000 (s/mm2) on both linear and planar tensor image voxels in WM, based on public domain ultrahigh-resolution (760 {micro}m3) Connectome DTI datasets of a healthy young adult brain. A Students t-test was used to assess the mean differences and the statistical significance was considered at P < 0.05. ResultsFitted{varepsilon} 0 became zero if referenced by{varepsilon} or nonzero if referenced by {Phi}, signifying the origin of{varepsilon} 0. Nonzero{varepsilon} 0 relied on b-values and tensor shapes so did other model parameters, e.g., the amplitude of anisotropic R2 (1/s) significantly increased when using a higher b-value for the linear tensor image voxels, i.e., 3.3{+/-}0.1 vs. 1.8{+/-}0.1, P < 0.01. ConclusionThe origin of R2 orientation dependence angle offsets has been identified and the combined anisotropic diffusion and transverse relaxation models have fully quantified R2 orientational anisotropies, thus providing novel insights otherwise unattainable on microstructural alterations in WM.

biophysics↗

bioRxiv2022

TransDFL: Identification of Disordered Flexible Linkers in Proteins by Transfer Learning

Disordered flexible linkers (DFLs) are the functional disordered regions in proteins, which are the sub-regions of intrinsically disordered regions (IDRs) and play important roles in connecting domains and maintaining inter-domain interactions. Trained with the limited available DFLs, the existing DFL predictors based on the machine learning techniques tend to predict the ordered residues as DFLs leading to a high false-positive rate (FPR) and low prediction accuracy. Previous studies have shown that DFLs are the extremely flexible disordered regions, which are usually predicted as disordered residues with high confidence [P(D) > 0.9] by an IDR predictor. Therefore, transferring an IDR predictor to an accurate DFL predictor is of great significance for understanding the functions of IDRs. In this study, we proposed a new predictor called TransDFL for identifying DFLs by transferring the RFPR-IDP predictor for IDR identification to the DFL prediction. The RFPR-IDP was pre-trained with IDR sequences to learn the general features between IDRs and DFLs, which is helpful to reduce the false positives in the ordered regions. RFPR-IDP was fine-tuned with the DFL sequences to capture the specific features of DFLs so as to be transferred into the TransDFL. Experimental results of two application scenarios (prediction of DFLs only in the IDRs or prediction of DFLs in the entire proteins) showed that TransDFL consistently outperforms the other exiting DFL predictors with higher accuracy. The corresponding web server of TransDFL can be freely accessed from http://bliulab.net/TransDFL/.

bioinformatics↗

bioRxiv2022

Development and optimization of a Barley stripe mosaic virus (BSMV)-mediated gene editing system to improve Fusarium head blight (FHB) resistance in wheat

Fusarium head blight (FHB) is a devastating disease in wheat that causes million dollars of wheat yield losses annually in the U.S.A. Recently we demonstrated that wheat carry FHB susceptible genes and knocking out the susceptible gene increased FHB resistance, which suggests that manipulating susceptible genes using gene editing may open a new avenue to create new sources of FHB resistance. However, wheat genome editing uses gene transformation to deliver CRISPR/Cas9 and gRNA into plants, and most wheat genotypes are not suitable for transformation due to low rates of callus induction and regeneration, therefore it cannot be used in practical wheat breeding. In this study, we developed a new Barley stripe mosaic virus (BSMV)-mediated gRNA delivery system that does not need the gene transformation and tissue culture and it can be used in any genotypes for gene function validation and editing. We used this system edited the susceptible allele of Fhb1, a major FHB resistance gene. We demonstrated that the edited trait is heritable in different genetic backgrounds and knocking out Fhb1 susceptible allele improved FHB resistance in wheat. We also modified system to improve editing efficiency by using floral dip agroinfiltration and adding RNA mobility sequences to the gRNA in the viral vectors. We believe this work will facilitate wheat FHB resistance research and gene editing in cereal crops and will benefit cereal crop researchers and breeders worldwide.

plant biology↗

bioRxiv2021

SHERRY2: A method for rapid and sensitive single cell RNA-seq

Prevalent single cell transcriptomic profiling (scRNA-seq) mechods are mainly based on synthesis and enrichment of full-length double-stranded complementary DNA. These approaches are challenging to generate accurate quantification of transcripts when their abundance is low or their full-length amplifications are difficult. Based on our previous finding that Tn5 transposase can directly cut-and-tag DNA/RNA hetero-duplexes, we present SHERRY2, a specifically optimized protocol for scRNA-seq without second strand cDNA synthesis. SHERRY2 is free of pre-amplification and eliminates the sequence-dependent bias. In comparison with other widely-used scRNA-seq methods, SHERRY2 exhibits significantly higher sensitivity and accuracy even for single nuclei. Besides, SHERRY2 is simple and robust, and can be easily scaled up to high-throughput experiments. When testing single lymphocytes and neuron nuclei, SHERRY2 not only obtained accurate countings of transcription factors and long non-coding RNAs, but also provided bias-free results that enriched genes in specific cellular components or functions, which outperformed other protocols. With a few thousand cells sequenced by SHERRY2, we confirmed expression and dynamics of Myc in different cell types of germinal centers, which were previously only revealed by gene-specific amplification methods. SHERRY2 is able to provide high sensitivity, high accuracy, and high throughput for those applications that require high number of genes identified in each cell. It can reveal the subtle transcriptomic difference between cells and facilitate important biological discoveries.

genomics↗