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Abstract MotivationSingle-cell RNA sequencing (scRNAseq) technologies allow for measurements of gene expression at a single-cell resolution. This provides researchers with a tremendous advantage for detecting heterogeneity, delineating cellular maps or identifying rare subpopulations. However, a critical complication remains: the low number of single-cell observations due to limitations by rarity of subpopulation, tissue degradation or cost. This absence of sufficient data may cause inaccuracy or irreproducibility of downstream analysis. In this work, we present Automated Cell-Type-informed Introspective Variational Autoencoder (ACTIVA): a novel framework for generating realistic synthetic data using a single-stream adversarial variational autoencoder conditioned with cell-type information. Within a single framework, ACTIVA can enlarge existing datasets and generate specific subpopulations on demand, as opposed to two separate models [such as single-cell GAN (scGAN) and conditional scGAN (cscGAN)]. Data generation and augmentation with ACTIVA can enhance scRNAseq pipelines and analysis, such as benchmarking new algorithms, studying the accuracy of classifiers and detecting marker genes. ACTIVA will facilitate analysis of smaller datasets, potentially reducing the number of patients and animals necessary in initial studies. ResultsWe train and evaluate models on multiple public scRNAseq datasets. In comparison to GAN-based models (scGAN and cscGAN), we demonstrate that ACTIVA generates cells that are more realistic and harder for classifiers to identify as synthetic which also have better pair-wise correlation between genes. Data augmentation with ACTIVA significantly improves classification of rare subtypes (more than 45% improvement compared with not augmenting and 4% better than cscGAN) all while reducing run-time by an order of magnitude in comparison to both models. Availability and implementationThe codes and datasets are hosted on Zenodo (https://doi.org/10.5281/zenodo.5879639). Tutorials are available at https://github.com/SindiLab/ACTIVA. Supplementary informationSupplementary data are available at Bioinformatics online. 

Abstract Glutaredoxins (GRXs) are small oxidoreductase enzymes that can reduce disulfide bonds in target proteins. The class III GRX gene family is unique to land plants, andArabidopsis thalianahas 21 class III GRXs, which remain largely uncharacterized. About 80% ofA. thalianaclass III GRXs are transcriptionally regulated by nitrate, and several recent studies have suggested roles for these GRXs in nitrogen signaling. Our objective was to functionally characterize two nitrate‐induced GRX genes,AtGRXS5andAtGRXS8, defining their roles in signaling and development in theA. thalianaroot. We demonstrated thatAtGRXS5andAtGRXS8are primarily expressed in root and shoot vasculature (phloem), and that the corresponding GRX proteins display nucleo‐cytosolic subcellular localization. Ectopic expression ofAtGRXS8in transgenic plants caused major alterations in root system architecture: Normal primary root development, but a near absence of lateral roots. RNA sequencing demonstrated that the roots ofAtGRXS8‐overexpressing plants show strongly reduced transcript abundance for many primary nitrate response genes, including the major high‐affinity nitrate transporters. Correspondingly, high‐affinity nitrate uptake and the transport of nitrate from roots to shoots are compromised inAtGRXS8‐overexpressing plants. Finally, we demonstrated that the AtGRXS8 protein can physically interact with the TGA1 and TGA4 transcription factors, which are central regulators of early transcriptional responses to nitrate inA. thalianaroots. Overall, these results suggest thatAtGRXS8acts to quench both transcriptional and developmental aspects of primary nitrate response, potentially by interfering with the activity of the TGA1 and TGA4 transcription factors.