Plants are complex systems hierarchically organized and composed of various cell types.To understand the molecular underpinnings of complex plant systems,single-cell RNA sequencing(scRNA-seq)has emerged as a powerful ...Plants are complex systems hierarchically organized and composed of various cell types.To understand the molecular underpinnings of complex plant systems,single-cell RNA sequencing(scRNA-seq)has emerged as a powerful tool for revealing high resolution of gene expression patterns at the cellular level and investigating the cell-type heterogeneity.Furthermore,scRNA-seq analysis of plant biosystems has great potential for generating new knowledge to inform plant biosystems design and synthetic biology,which aims to modify plants genetically/epigenetically through genome editing,engineering,or re-writing based on rational design for increasing crop yield and quality,promoting the bioeconomy and enhancing environmental sustainability.In particular,data from scRNA-seq studies can be utilized to facilitate the development of high-precision Build-Design-Test-Learn capabilities for maximizing the targeted performance of engineered plant biosystems while minimizing unintended side effects.To date,scRNA-seq has been demonstrated in a limited number of plant species,including model plants(e.g.,Arabidopsis thaliana),agricultural crops(e.g.,Oryza sativa),and bioenergy crops(e.g.,Populus spp.).It is expected that future technical advancements will reduce the cost of scRNA-seq and consequently accelerate the application of this emerging technology in plants.In this review,we summarize current technical advancements in plant scRNA-seq,including sample preparation,sequencing,and data analysis,to provide guidance on how to choose the appropriate scRNA-seq methods for different types of plant samples.We then highlight various applications of scRNA-seq in both plant systems biology and plant synthetic biology research.Finally,we discuss the challenges and opportunities for the application of scRNA-seq in plants.展开更多
Plants adapt to their changing environments by sensing and responding to physical,biological,and chemical stimuli.Due to their sessile lifestyles,plants experience a vast array of external stimuli and selectively perc...Plants adapt to their changing environments by sensing and responding to physical,biological,and chemical stimuli.Due to their sessile lifestyles,plants experience a vast array of external stimuli and selectively perceive and respond to specific signals.By repurposing the logic circuitry and biological and molecular components used by plants in nature,genetically encoded plant-based biosensors(GEPBs)have been developed by directing signal recognition mechanisms into carefully assembled outcomes that are easily detected.GEPBs allow for in vivo monitoring of biological processes in plants to facilitate basic studies of plant growth and development.GEPBs are also useful for environmental monitoring,plant abiotic and biotic stress management,and accelerating design-build-test-learn cycles of plant bioengineering.With the advent of synthetic biology,biological and molecular components derived from alternate natural organisms(e.g.,microbes)and/or de novo parts have been used to build GEPBs.In this review,we summarize the framework for engineering different types of GEPBs.We then highlight representative validated biological components for building plant-based biosensors,along with various applications of plant-based biosensors in basic and applied plant science research.Finally,we discuss challenges and strategies for the identification and design of biological components for plant-based biosensors.展开更多
Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios(IFR;the number of deaths caused by an infection per 1,000 infected people)when the availability and qua...Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios(IFR;the number of deaths caused by an infection per 1,000 infected people)when the availability and quality of data on disease burden are limited during an epidemic.Methods We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek testing.We demonstrate the robustness,accuracy,and precision of this framework,and apply it to the United States(U.S.)COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs.Results The estimators for the numbers of infections and IFRs showed high accuracy and precision;for instance,when applied to simulated validation data sets,across counties,Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928,respectively,and they showed strong robustness to model misspecification.Applying the county-level estimators to the real,unsimulated COVID-19 data spanning April 1,2020 to September 30,2020 from across the U.S.,we found that IFRs varied from 0 to 44.69,with a standard deviation of 3.55 and a median of 2.14.Conclusions The proposed estimation framework can be used to identify geographic variation in IFRs across settings.展开更多
Plant phenotyping is typically a time-consuming and expensive endeavor,requiring large groups of researchers to meticulously measure biologically relevant plant traits,and is the main bottleneck in understanding plant...Plant phenotyping is typically a time-consuming and expensive endeavor,requiring large groups of researchers to meticulously measure biologically relevant plant traits,and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale.In this work,we address these challenges by leveraging few-shot learning with convolutional neural networks to segment the leaf body and visible venation of 2,906 Populus trichocarpa leaf images obtained in the field.In contrast to previous methods,our approach(a)does not require experimental or image preprocessing,(b)uses the raw RGB images at full resolution,and(c)requires very few samples for training(e.g.,just 8 images for vein segmentation).Traits relating to leaf morphology and vein topology are extracted from the resulting segmentations using traditional open-source image-processing tools,validated using real-world physical measurements,and used to conduct a genome-wide association study to identify genes controlling the traits.In this way,the current work is designed to provide the plant phenotyping community with(a)methods for fast and accurate image-based feature extraction that require minimal training data and(b)a new population-scale dataset,including 68 different leaf phenotypes,for domain scientists and machine learning researchers.All of the few-shot learning code,data,and results are made publicly available.展开更多
基金supported by the Center for Bioenergy Innovation(CBI),which is a U.S.Department of Energy(DOE)Bioenergy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science,and the DOE Genomic Science Program,as part of the Secure Ecosystem Engineering and Design Scientific(SEED)Focus Area.Oak Ridge National Laboratory is man-aged by UT-Battelle,LLC for the U.S.DOE under Contract Number DE-AC05-00OR22725This material is based on work supported by the U.S.Department of Energy,Ofice of Science,Biological and Environmental Research Program under Award Number DE-SC0023338 to CRB.
文摘Plants are complex systems hierarchically organized and composed of various cell types.To understand the molecular underpinnings of complex plant systems,single-cell RNA sequencing(scRNA-seq)has emerged as a powerful tool for revealing high resolution of gene expression patterns at the cellular level and investigating the cell-type heterogeneity.Furthermore,scRNA-seq analysis of plant biosystems has great potential for generating new knowledge to inform plant biosystems design and synthetic biology,which aims to modify plants genetically/epigenetically through genome editing,engineering,or re-writing based on rational design for increasing crop yield and quality,promoting the bioeconomy and enhancing environmental sustainability.In particular,data from scRNA-seq studies can be utilized to facilitate the development of high-precision Build-Design-Test-Learn capabilities for maximizing the targeted performance of engineered plant biosystems while minimizing unintended side effects.To date,scRNA-seq has been demonstrated in a limited number of plant species,including model plants(e.g.,Arabidopsis thaliana),agricultural crops(e.g.,Oryza sativa),and bioenergy crops(e.g.,Populus spp.).It is expected that future technical advancements will reduce the cost of scRNA-seq and consequently accelerate the application of this emerging technology in plants.In this review,we summarize current technical advancements in plant scRNA-seq,including sample preparation,sequencing,and data analysis,to provide guidance on how to choose the appropriate scRNA-seq methods for different types of plant samples.We then highlight various applications of scRNA-seq in both plant systems biology and plant synthetic biology research.Finally,we discuss the challenges and opportunities for the application of scRNA-seq in plants.
基金the Biological and Environmental Research(BER)program.Oak Ridge National Laboratory is managed by UT-Battelle,LLC for the U.S.Department of Energy under Contract Number DE-AC05-00OR22725The support to Chang-jun Liu was partially from the DOE Office of Basic Energy Sciences,specifically through the Physical Biosciences program of the Chemical Sciences,Geosciences and Biosciences Division,under contract number DE-SC0012704.
文摘Plants adapt to their changing environments by sensing and responding to physical,biological,and chemical stimuli.Due to their sessile lifestyles,plants experience a vast array of external stimuli and selectively perceive and respond to specific signals.By repurposing the logic circuitry and biological and molecular components used by plants in nature,genetically encoded plant-based biosensors(GEPBs)have been developed by directing signal recognition mechanisms into carefully assembled outcomes that are easily detected.GEPBs allow for in vivo monitoring of biological processes in plants to facilitate basic studies of plant growth and development.GEPBs are also useful for environmental monitoring,plant abiotic and biotic stress management,and accelerating design-build-test-learn cycles of plant bioengineering.With the advent of synthetic biology,biological and molecular components derived from alternate natural organisms(e.g.,microbes)and/or de novo parts have been used to build GEPBs.In this review,we summarize the framework for engineering different types of GEPBs.We then highlight representative validated biological components for building plant-based biosensors,along with various applications of plant-based biosensors in basic and applied plant science research.Finally,we discuss challenges and strategies for the identification and design of biological components for plant-based biosensors.
基金K.A.and J.L.were supported by a grant from the Benioff Center for Microbiome MedicineThis research used resources of the Oak Ridge Leadership Computing Facility,which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725+5 种基金This manuscript has been coauthored by UT-Battelle,LLC under contract no.DE-AC05-00OR22725 with the U.S.Department of EnergyThe United States Government retains and the publisher,by accepting the article for publication,acknowledges that the United States Government retains a nonexclusive,paid-up,irrevocable,world-wide license to publish or reproduce the published form of this manuscript,or allow others to do so,for United States Government purposesThe Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan(http://energy.gov/downloads/doe-public-access-plan,last accessed September 16,2020)Work at Oak Ridge and Lawrence Berkeley National Laboratories was supported by the DOE Office of Science through the National Virtual Biotechnology Laboratory,a consortium of DOE national laboratories focused on response to COVID-19,with funding provided by the Coronavirus CARES Actwas facilitated by previous breakthroughs obtained through the Laboratory Directed Research and Development Programs of the Lawrence Berkeley and Oak Ridge National Laboratories.M.P.J.was supported by a grant from the Laboratory Directed Research and Development(LDRD)Program of Lawrence Berkeley National Laboratory under U.S.Department of Energy Contract No.DE-AC02-05CH11231Oak Ridge National Laboratory would also like to acknowledge funding from the U.S.National Science Foundation(EF-2133763).
文摘Objectives We aim to estimate geographic variability in total numbers of infections and infection fatality ratios(IFR;the number of deaths caused by an infection per 1,000 infected people)when the availability and quality of data on disease burden are limited during an epidemic.Methods We develop a noncentral hypergeometric framework that accounts for differential probabilities of positive tests and reflects the fact that symptomatic people are more likely to seek testing.We demonstrate the robustness,accuracy,and precision of this framework,and apply it to the United States(U.S.)COVID-19 pandemic to estimate county-level SARS-CoV-2 IFRs.Results The estimators for the numbers of infections and IFRs showed high accuracy and precision;for instance,when applied to simulated validation data sets,across counties,Pearson correlation coefficients between estimator means and true values were 0.996 and 0.928,respectively,and they showed strong robustness to model misspecification.Applying the county-level estimators to the real,unsimulated COVID-19 data spanning April 1,2020 to September 30,2020 from across the U.S.,we found that IFRs varied from 0 to 44.69,with a standard deviation of 3.55 and a median of 2.14.Conclusions The proposed estimation framework can be used to identify geographic variation in IFRs across settings.
基金funded by the Artificial Intelligence(AI)Initiative,an ORNL Laboratory Directed Research and Development programby the Center for Bioenergy Innovation(CBI),which is a U.S.DOE Bioenergy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science.
文摘Plant phenotyping is typically a time-consuming and expensive endeavor,requiring large groups of researchers to meticulously measure biologically relevant plant traits,and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale.In this work,we address these challenges by leveraging few-shot learning with convolutional neural networks to segment the leaf body and visible venation of 2,906 Populus trichocarpa leaf images obtained in the field.In contrast to previous methods,our approach(a)does not require experimental or image preprocessing,(b)uses the raw RGB images at full resolution,and(c)requires very few samples for training(e.g.,just 8 images for vein segmentation).Traits relating to leaf morphology and vein topology are extracted from the resulting segmentations using traditional open-source image-processing tools,validated using real-world physical measurements,and used to conduct a genome-wide association study to identify genes controlling the traits.In this way,the current work is designed to provide the plant phenotyping community with(a)methods for fast and accurate image-based feature extraction that require minimal training data and(b)a new population-scale dataset,including 68 different leaf phenotypes,for domain scientists and machine learning researchers.All of the few-shot learning code,data,and results are made publicly available.
