Boosting Single-Cell RNA Sequencing Analysis with Simple Neural Attention.

A limitation of current deep learning (DL) approaches for single-cell RNA sequencing (scRNAseq) analysis is the lack of interpretability. Moreover, existing pipelines are designed and trained for specific tasks used disjointly for different stages of analysis. We present scANNA, a novel interpretable DL model for scRNAseq studies that leverages neural attention to learn gene associations. After training, the learned gene importance (interpretability) is used to perform downstream analyses (e.g., global marker selection and cell-type classification) without retraining. ScANNA's performance is comparable to or better than state-of-the-art methods designed and trained for specific standard scRNAseq analyses even though scANNA was not trained for these tasks explicitly. ScANNA enables researchers to discover meaningful results without extensive prior knowledge or training separate task-specific models, saving time and enhancing scRNAseq analyses.

ACTIVA: realistic single-cell RNA-seq generation with automatic cell-type identification using introspective variational autoencoders.

MOTIVATION: Single-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. RESULTS: We 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 IMPLEMENTATION: The codes and datasets are hosted on Zenodo (https://doi.org/10.5281/zenodo.5879639). Tutorials are available at https://github.com/SindiLab/ACTIVA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CD8 follicular T cells localize throughout the follicle during germinal center reactions and maintain cytolytic and helper properties.

Follicular CXCR5+ PD-1+ CD8 T cells (CD8 Tfc) arise in multiple models of systemic autoimmunity yet their functional contribution to disease remains in debate. Here we define the follicular localization and functional interactions of CD8 Tfc with B cells during autoimmune disease. The absence of functional T regulatory cells in autoimmunity allows for CD8 Tfc development that then expands with lymphoproliferation. CD8 Tfc are identifiable within the lymph nodes and spleen during systemic autoimmunity, but not during tissue-restricted autoimmune disease. Autoimmune CD8 Tfc cells are polyfunctional, producing helper cytokines IL-21, IL-4, and IFNγ while maintaining cytolytic proteins CD107a, granzyme B, and TNF. During autoimmune disease, IL-2-KO CD8 T cells infiltrate the B cell follicle and germinal center, including the dark zone, and in vitro induce activation-induced cytidine deaminase in naïve B cells via IL-4 secretion. CD8 Tfc represent a unique CD8 T cell population with a diverse effector cytokine repertoire that can contribute to pathogenic autoimmune B cell response.