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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

ACTINN: automated identification of cell types in single cell RNA sequencing

Abstract Motivation Cell type identification is one of the major goals in single cell RNA sequencing (scRNA-seq). Current methods for assigning cell types typically involve the use of unsupervised clustering, the identification of signature genes in each cluster, followed by a manual lookup of these genes in the literature and databases to assign cell types. However, there are several limitations associated with these approaches, such as unwanted sources of variation that influence clustering and a lack of canonical markers for certain cell types. Here, we present ACTINN (Automated Cell Type Identification using Neural Networks), which employs a neural network with three hidden layers, trains on datasets with predefined cell types and predicts cell types for other datasets based on the trained parameters. Results We trained the neural network on a mouse cell type atlas (Tabula Muris Atlas) and a human immune cell dataset, and used it to predict cell types for mouse leukocytes, human PBMCs and human T cell sub types. The results showed that our neural network is fast and accurate, and should therefore be a useful tool to complement existing scRNA-seq pipelines. Availability and implementation The codes and datasets are available at https://figshare.com/articles/ACTINN/8967116. Tutorial is available at https://github.com/mafeiyang/ACTINN. All codes are implemented in python. Supplementary information Supplementary data are available at Bioinformatics online.

Ma, Feiyang (ORCID:0000000262600787)↗
DOE OSTI2018

A method to improve prediction of secondary structure for large single RNA sequences

The function of a particular RNA molecule within an organic system is principally determined by its structure. The current physical methods available for structure determination are time consuming and expensive. Hence, computational methods for structure prediction are often used. }The prediction of the structure of a large single sequence of RNA needs a lot of research work. In the present work, a method is introduced to improve the prediction of large single sequence RNA secondary structure obtained by Mfold program using the concept of minimum free energy. The Mfold program contains a constraint option that allows forcing some helices in the predicted structure. In our method, some of the firstly formed hairpins that are expected, by a statistical study, to be present in the real structure are forced in the Mfold predicted structure. The results show improvement, toward the real structure, in the Mfold predicted structure and this gives evidence to the RNA kinetic folding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Long-read RNA sequencing atlas of human microglia isoforms elucidates disease-associated genetic regulation of splicing

Microglia, the innate immune cells of the central nervous system, have been genetically implicated in multiple neurodegenerative diseases. Mapping the genetics of gene expression in human microglia has identified several loci associated with disease-associated genetic variants in microglia-specific regulatory elements. However, identifying genetic effects on splicing is challenging because of the use of short sequencing reads. Here, we present the isoform-centric microglia genomic atlas (isoMiGA), which leverages long-read RNA sequencing to identify 35,879 novel microglia isoforms. We show that these isoforms are involved in stimulation response and brain region specificity. We then quantified the expression of both known and novel isoforms in a multi-ancestry meta-analysis of 555 human microglia short-read RNA sequencing samples from 391 donors, and found associations with genetic risk loci in Alzheimer’s and Parkinson’s disease. We nominate several loci that may act through complex changes in isoform and splice-site usage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Analysis of Transcriptome and Epitranscriptome in Plants Using PacBio Iso-Seq and Nanopore-Based Direct RNA Sequencing

Nanopore sequencing from Oxford Nanopore Technologies (ONT) and Pacific BioSciences (PacBio) single-molecule real-time (SMRT) long-read isoform sequencing (Iso-Seq) are revolutionizing the way transcriptomes are analyzed. These methods offer many advantages over most widely used high-throughput short-read RNA sequencing (RNA-Seq) approaches and allow a comprehensive analysis of transcriptomes in identifying full-length splice isoforms and several other post-transcriptional events. In addition, direct RNA-Seq provides valuable information about RNA modifications, which are lost during the PCR amplification step in other methods. Here, we present a comprehensive summary of important applications of these technologies in plants, including identification of complex alternative splicing (AS), full-length splice variants, fusion transcripts, and alternative polyadenylation (APA) events. Furthermore, we discuss the impact of the newly developed nanopore direct RNA-Seq in advancing epitranscriptome research in plants. Additionally, we summarize computational tools for identifying and quantifying full-length isoforms and other co/post-transcriptional events and discussed some of the limitations with these methods. Sequencing of transcriptomes using these new single-molecule long-read methods will unravel many aspects of transcriptome complexity in unprecedented ways as compared to previous short-read sequencing approaches. Analysis of plant transcriptomes with these new powerful methods that require minimum sample processing is likely to become the norm and is expected to uncover novel co/post-transcriptional gene regulatory mechanisms that control biological outcomes during plant development and in response to various stresses.

alternative polyadenylation↗
DOE OSTI2019

A comparison between whole transcript and 3’ RNA sequencing methods using Kapa and Lexogen library preparation methods

Background: 3’ RNA sequencing provides an alternative to whole transcript analysis. However, we do not know a priori the relative advantage of each method. Thus, a comprehensive comparison between the whole transcript and the 3' method is needed to determine their relative merits. To this end, we used two commercially available library preparation kits, the KAPA Stranded mRNA-Seq kit (traditional method) and the Lexogen QuantSeq 3’ mRNASeq kit (3' method), to prepare libraries from mouse liver RNA. We then sequenced and analyzed the libraries to determine the advantages and disadvantages of these two approaches. Results: We found that the traditional whole transcript method and the 3’ RNA-Seq method had similar levels of reproducibility. As expected, the whole transcript method assigned more reads to longer transcripts, while the 3' method assigned roughly equal numbers of reads to transcripts regardless of their lengths. We found that the 3’ RNA-Seq method detected more short transcripts than the whole transcript method. With regard to differential expression analysis, we found that the whole transcript method detected more differentially expressed genes, regardless of the level of sequencing depth. Conclusions: The 3’ RNA-Seq method was better able to detect short transcripts, while the whole transcript RNA-Seq was able to detect more differentially expressed.

3’ RNA-Seq↗
DOE OSTI2018

Global regulatory function of the low oxygen-induced transcriptional regulator LoiA in Salmonella Typhimurium revealed by RNA sequencing

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a major intestinal pathogen that can infect both humans and a variety of animals. LoiA, a novel virulence-regulating protein encoded in Salmonella pathogenicity island (SPI)-14, has been shown to be induced under low oxygen conditions and contribute to S. Typhimurium invasion into intestinal epithetical cells by activating the SPI-1 invasion genes. However, the global regulatory network of LoiA remains unknown. Here, we used high-throughput RNA sequencing (RNA-seq) technology to investigate the regulatory function of LoiA in S. Typhimurium under low oxygen conditions. A total of 1250 genes were differentially expressed between the loiA mutant and the wild-type strain; 413 genes were up-regulated and 837 were down-regulated. SPI-1 gene expression was down-regulated in the loiA mutant, consistent with previous results. SPI-2 gene expression was not affected by deletion of loiA; the expression of most genes involved in flagellar basal body and hook biosynthesis was up-regulated in the loiA mutant, while the expression of genes associated with flagellin, motility, and chemotaxis was down-regulated; the expression of lon, encoding an ATP-dependent protease, was up-regulated in the mutant. This study indicates that LoiA regulates a variety of virulence-associated genes in S. Typhimurium. The negative regulation of Lon protease by LoiA indicates that LoiA can regulates several virulence-associated genes in S. Typhimurium via the Lon protease.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Optimal Bayesian supervised domain adaptation for RNA sequencing data

Abstract Motivation When learning to subtype complex disease based on next-generation sequencing data, the amount of available data is often limited. Recent works have tried to leverage data from other domains to design better predictors in the target domain of interest with varying degrees of success. But they are either limited to the cases requiring the outcome label correspondence across domains or cannot leverage the label information at all. Moreover, the existing methods cannot usually benefit from other information available a priori such as gene interaction networks. Results In this article, we develop a generative optimal Bayesian supervised domain adaptation (OBSDA) model that can integrate RNA sequencing (RNA-Seq) data from different domains along with their labels for improving prediction accuracy in the target domain. Our model can be applied in cases where different domains share the same labels or have different ones. OBSDA is based on a hierarchical Bayesian negative binomial model with parameter factorization, for which the optimal predictor can be derived by marginalization of likelihood over the posterior of the parameters. We first provide an efficient Gibbs sampler for parameter inference in OBSDA. Then, we leverage the gene-gene network prior information and construct an informed and flexible variational family to infer the posterior distributions of model parameters. Comprehensive experiments on real-world RNA-Seq data demonstrate the superior performance of OBSDA, in terms of accuracy in identifying cancer subtypes by utilizing data from different domains. Moreover, we show that by taking advantage of the prior network information we can further improve the performance. Availability and implementation The source code for implementations of OBSDA and SI-OBSDA are available at the following link. https://github.com/SHBLK/BSDA. Supplementary information Supplementary data are available at Bioinformatics online.

Biochemistry & Molecular Biology↗
DOE OSTI2018

RNAcentral: a hub of information for non-coding RNA sequences

RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences, collating information on ncRNA sequences of all types from a broad range of organisms. We have recently added a new genome mapping pipeline that identifies genomic locations for ncRNA sequences in 296 species. We have also added several new types of functional annotations, such as tRNA secondary structures, Gene Ontology annotations, and miRNA-target interactions. A new quality control mechanism based on Rfam family assignments identifies potential contamination, incomplete sequences, and more. The RNAcentral database has become a vital component of many workflows in the RNA community, serving as both the primary source of sequence data for academic and commercial groups, as well as a source of stable accessions for the annotation of genomic and functional features. These examples are facilitated by an improved RNAcentral web interface, which features an updated genome browser, a new sequence feature viewer, and improved text search functionality.

Sweeney, Blake A.↗
DOE OSTI2021

Single-cell RNA sequencing of batch Chlamydomonas cultures reveals heterogeneity in their diurnal cycle phase

The photosynthetic unicellular alga Chlamydomonas (Chlamydomonas reinhardtii) is a versatile reference for algal biology because of its ease of culture in the laboratory. Genomic and systems biology approaches have previously described transcriptome responses to environmental changes using bulk data, thus representing the average behavior from pools of cells. Here, we apply single-cell RNA sequencing (scRNA-seq) to probe the heterogeneity of Chlamydomonas cell populations under three environments and in two genotypes differing by the presence of a cell wall. First, we determined that RNA can be extracted from single algal cells with or without a cell wall, offering the possibility to sample natural algal communities. Second, scRNA-seq successfully separated single cells into nonoverlapping cell clusters according to their growth conditions. Cells exposed to iron or nitrogen deficiency were easily distinguished despite a shared tendency to arrest photosynthesis and cell division to economize resources. Notably, these groups of cells not only recapitulated known patterns observed with bulk RNA-seq but also revealed their inherent heterogeneity. A substantial source of variation between cells originated from their endogenous diurnal phase, although cultures were grown in constant light. We exploited this result to show that circadian iron responses may be conserved from algae to land plants. We document experimentally that bulk RNA-seq data represent an average of typically hidden heterogeneity in the population.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A robust method of nuclei isolation for single-cell RNA sequencing of solid tissues from the plant genus Populus

Single-cell transcriptome analysis has been extensively applied in humans and animal models to uncover gene expression heterogeneity between the different cell types of a tissue or an organ. It demonstrated its capability to discover key regulatory elements that determine cell fate during developmental programs. Single-cell analysis requires the isolation and labeling of the messenger RNA (mRNA) derived from each cell. These challenges were primarily addressed in mammals by developing microfluidic-based approaches. For plant species whose cells contain cell walls, these approaches have generally required the generation of isolated protoplasts. Many plant tissues’ secondary cell wall hinders enzymatic digestion required for individual protoplast isolation, resulting in an unequal representation of cell types in a protoplast population. This limitation is especially critical for cell types located in the inner layers of a tissue or the inner tissues of an organ. Consequently, single-cell RNA sequencing (scRNA-seq) studies using microfluidic approaches in plants have mainly been restricted to Arabidopsis roots, for which well-established procedures of protoplast isolation are available. Here we present a simple alternative approach to generating high-quality protoplasts from plant tissue by characterizing the mRNA extracted from individual nuclei instead of whole cells. We developed the protocol using two different plant materials with varying cellular complexity levels and cell wall structure, Populus shoot apices, and more lignified stems. Using the 10× Genomics Chromium technology, we show that this procedure results in intact mRNA isolation and limited leakage, with a broad representation of individual cell transcriptomes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Single cell RNA sequencing reveals shifts in cell maturity and function of endogenous and infiltrating cell types in response to acute intervertebral disc injury

Intervertebral disc (IVD) degeneration contributes to disabling back pain. Degeneration can be initiated by injury and progressively leads to an irreversible loss of cells and function. IVD function restoration through cell replacement therapies have had limited success due to knowledge gaps in the critical cell populations important for repair. Here, in this study, we used single cell RNA sequencing to identify the transcriptional changes of IVD resident and infiltrating cell populations from Control and Injured coccygeal IVDs extracted from 12-week-old female C57BL/6J mice 7 days post injury. Clustering, gene ontology, and pseudotime trajectory analyses determined transcriptomic divergences with injury, flow cytometry identified they types of infiltrating immune cells, and immunofluorescence was utilized to define mesenchymal stem cell (MSC) localization. We identified 11 distinct clusters that included IVD, immune, vascular cells, and MSCs. Differential gene expression analysis determined that Outer Annulus Fibrosus, Neutrophils, Saa2-High MSCs, Macrophages, and Krt18 + Nucleus Pulposus (NP) cells were the major drivers of transcriptomic differences between Control and Injured cells. Gene ontology revealed that the most upregulated biological pathways were angiogenesis and T cell-related while wound healing and ECM regulation were downregulated. Pseudotime trajectory analyses revealed that IVD injury directed cells towards increased differentiation in all clusters, except for Krt18 + NP cells which remained in a less mature cell state. Saa2-High and Grem1-High MSCs populations shifted towards more differentiated IVD cells profiles with injury and localized distinctly within the IVD. This study revealed novel MSC populations with the potential to be leveraged for future IVD repair studies.

Cartilage↗
DOE OSTI2024

Dual-RNA-sequencing to elucidate the interactions between sorghum and Colletotrichum sublineola

In warm and humid regions, the productivity of sorghum is significantly limited by the fungal hemibiotrophic pathogen Colletotrichum sublineola , the causal agent of anthracnose, a problematic disease of sorghum ( Sorghum bicolor (L.) Moench) that can result in grain and biomass yield losses of up to 50%. Despite available genomic resources of both the host and fungal pathogen, the molecular basis of sorghum− C. sublineola interactions are poorly understood. By employing a dual-RNA sequencing approach, the molecular crosstalk between sorghum and C. sublineola can be elucidated. In this study, we examined the transcriptomes of four resistant sorghum accessions from the sorghum association panel (SAP) at varying time points post-infection with C. sublineola . Approximately 0.3% and 93% of the reads mapped to the genomes of C. sublineola and Sorghum bicolor , respectively. Expression profiling of in vitro versus in planta C. sublineola at 1-, 3-, and 5-days post-infection (dpi) indicated that genes encoding secreted candidate effectors, carbohydrate-active enzymes (CAZymes), and membrane transporters increased in expression during the transition from the biotrophic to the necrotrophic phase (3 dpi). The hallmark of the pathogen-associated molecular pattern (PAMP)-triggered immunity in sorghum includes the production of reactive oxygen species (ROS) and phytoalexins. The majority of effector candidates secreted by C. sublineola were predicted to be localized in the host apoplast, where they could interfere with the PAMP-triggered immunity response, specifically in the host ROS signaling pathway. The genes encoding critical molecular factors influencing pathogenicity identified in this study are a useful resource for subsequent genetic experiments aimed at validating their contributions to pathogen virulence. This comprehensive study not only provides a better understanding of the biology of C. sublineola but also supports the long-term goal of developing resistant sorghum cultivars.

Vela, Saddie↗
DOE OSTI2021

Platform for Single-Cell Dual RNA Sequencing of Host-Pathogen Interactions

The aim of this project was to advance single-cell RNA-Seq methods toward the establishment of a platform that may be used to simultaneously interrogate the gene expression profiles of mammalian host cells and bacterial pathogens. Existing genetic sequencing methods that measure bulk groups of cells do not account for the heterogeneity of cell-microbe interactions that occur within a complex environment, have limited efficiency, and cannot simultaneously interrogate bacterial sequences. In order to overcome these challenges, separate biochemistry workflows were developed based on a No-So-Random hexamer priming strategy or libraries of targeted molecular probes. Computational tools were developed to facilitate these methods, and feasibility was demonstrated for single-cell RNA-Seq for both bacterial and mammalian transcriptomes. This work supports cross-agency national priorities on addressing the threat of biological pathogens and understanding the role of the microbiome in modulating immunity and susceptibility to infection.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Microbiome characterization by high-throughput transfer RNA sequencing and modification analysis

Advances in high-throughput sequencing have facilitated remarkable insights into the diversity and functioning of naturally occurring microbes; however, current sequencing strategies are insufficient to reveal physiological states of microbial communities associated with protein translation dynamics. Transfer RNAs (tRNAs) are core components of protein synthesis machinery, present in all living cells, and are phylogenetically tractable, which make them ideal targets to gain physiological insights into environmental microbes. Here we report a direct sequencing approach, tRNA-seq, and a software suite, tRNA-seq-tools, to recover sequences, abundance profiles, and post-transcriptional modifications of microbial tRNA transcripts. Our analysis of cecal samples using tRNA-seq distinguishes high-fat-and low-fat-fed mice in a comparable fashion to 16S ribosomal RNA gene amplicons, and reveals taxon- and diet-dependent variations in tRNA modifications. Our results provide taxon-specific in situ insights into the dynamics of tRNA gene expression and post-transcriptional modifications within complex environmental microbiomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high recall or high specificity. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility, and adaptability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Rare ribosomal RNA sequences from archaea stabilize the bacterial ribosome

The ribosome serves as the universally conserved translator of the genetic code into proteins and supports life across diverse temperatures ranging from below freezing to above 120°C. Ribosomes are capable of functioning across this wide range of temperatures even though the catalytic site for peptide bond formation, the peptidyl transferase center, is nearly universally conserved. Here we find that Thermoproteota, a phylum of thermophilic Archaea, substitute cytidine for uridine at large subunit rRNA positions 2554 and 2555 (Escherichia coli numbering) in the A loop, immediately adjacent to the binding site for the 3'-end of A-site tRNA. We show by cryo-EM that E. coli ribosomes with uridine to cytidine mutations at these positions retain the proper fold and post-transcriptional modification of the A loop. Additionally, these mutations do not affect cellular growth, protect the large ribosomal subunit from thermal denaturation, and increase the mutational robustness of nucleotides in the peptidyl transferase center. This work identifies sequence variation across archaeal ribosomes in the peptidyl transferase center that likely confers stabilization of the ribosome at high temperatures and develops a stable mutant bacterial ribosome that can act as a scaffold for future ribosome engineering efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Identification of a Fusarium ear rot resistance gene in maize by QTL mapping and RNA sequencing

Fusarium ear rot (FER) caused by Fusarium verticillioides is a prevalent maize disease. To comprehensively characterize the genetic basis of the natural variation in FER resistance, a recombinant inbred line (RIL) population was used to map quantitative trait loci (QTL) for FER resistance. A total of 17 QTL were identified by linkage mapping in eight environments. These QTL were located on six chromosomes and explained 3.88–15.62% of the total phenotypic variation. Moreover, qFER1.03 had the strongest effect and accounted for 4.98–15.62% of the phenotypic variation according to analyses of multiple environments involving best linear unbiased predictions. The chromosome segment substitution lines (CSSLs) derived from a cross between Qi319 (donor parent) and Ye478 (recurrent parent) were used to verify the contribution of qFER1.03 to FER resistance. The line CL171, which harbored an introgressed qFER1.03 , was significantly resistant to FER. Further fine mapping of qFER1.03 revealed that the resistance QTL was linked to insertion/deletion markers InDel 8 and InDel 2, with physical distances of 43.55 Mb and 43.76 Mb, respectively. Additionally, qFER1.03 differed from the previous resistance QTL on chromosome 1. There were three annotated genes in this region. On the basis of the RNA-seq data, which revealed the genes differentially expressed between the FER-resistant Qi319 and susceptible Ye478, GRMZM2G017792 (MPK3) was preliminarily identified as a candidate gene in the qFER1.03 region. The Pr-CMV-VIGS system was used to decrease the GRMZM2G017792 expression level in CL171 by 34–57%, which led to a significant decrease in FER resistance. Using RIL and CSSL populations combined with RNA-seq and Pr-CMV-VIGS, the candidate gene can be dissected effectively, which provided important gene resource for breeding FER-resistant varieties.

Xia, Yusheng↗