Bulk and Single-nucleus Transcriptomics Highlight Intra-telencephalic and Somatostatin Neurons in Alzheimer's Disease

Background: Cortical neuron loss is a pathological hallmark of late-onset Alzheimer's disease (AD). However, it remains unclear which neuronal subtypes are most vulnerable to degeneration and contribute most to cognitive decline. Methods: We analyzed postmortem bulk brain RNA-sequencing (RNAseq) data collected from three studies of aging and AD comprising six neocortical regions (704 individuals; 1037 samples). We estimated relative cell type proportions from each brain sample using neuronal subclass-specific marker genes derived from ultra-high depth single-nucleus RNAseq data (snRNAseq). We associated cell type proportions with AD across all samples using mixed-effects mega-analyses. Bulk tissue analyses were complemented by analyses of three AD snRNAseq datasets using the same cell type definitions and diagnostic criteria (51 individuals). Lastly, we identified cell subtype associations with specific neuropathologies, cognitive decline, and residual cognition. Results: In our mega-analyses, we identified the strongest associations of AD with fewer somatostatin (SST) inhibitory neurons (β=-0.48, pbonf=8.98x10-9) and intra-telencephalic (IT) excitatory neurons (β=-0.45, pbonf =4.32x10-7). snRNAseq-based cell type proportion analyses especially supported the association of SST neurons. Analyses of cell type proportions with specific AD-related phenotypes in ROS/MAP consistently implicated fewer SST neurons with greater brain-wide postmortem tau and beta amyloid (β=-0.155, pFDR=3.1x10-4) deposition, as well as more severe cognitive decline prior to death (β=0.309, pFDR=3.9x10-6). Greater IT neuron proportions were associated strongly with improved cognition (β=0.173, pFDR=8.3x10-5) and residual cognition (β=0.175, pFDR=1.2x10-5), but not canonical AD neuropathology. Conclusions: Proportionally fewer SST and IT neurons were significantly associated with AD diagnosis across multiple studies and cortical regions. These findings support seminal work implicating somatostatin and pyramidal neurons in the pathogenesis of AD and improves our current understanding of neuronal vulnerability in AD.

Graph Based Feature Selection for Reduction of Dimensionality in Next-Generation RNA Sequencing Datasets

Analysis of high-dimensional data, with more features () than observations () (), places significant demand in cost and memory computational usage attributes. Feature selection can be used to reduce the dimensionality of the data. We used a graph-based approach, principal component analysis (PCA) and recursive feature elimination to select features for classification from RNAseq datasets from two lung cancer datasets. The selected features were discretized for association rule mining where support and lift were used to generate informative rules. Our results show that the graph-based feature selection improved the performance of sequential minimal optimization (SMO) and multilayer perceptron classifiers (MLP) in both datasets. In association rule mining, features selected using the graph-based approach outperformed the other two feature-selection techniques at a support of 0.5 and lift of 2. The non-redundant rules reflect the inherent relationships between features. Biological features are usually related to functions in living systems, a relationship that cannot be deduced by feature selection and classification alone. Therefore, the graph-based feature-selection approach combined with rule mining is a suitable way of selecting and finding associations between features in high-dimensional RNAseq data.

Detection of A-to-I RNA Editing in SARS-COV-2

ADAR1-mediated deamination of adenosines in long double-stranded RNAs plays an important role in modulating the innate immune response. However, recent investigations based on metatranscriptomic samples of COVID-19 patients and SARS-COV-2-infected Vero cells have recovered contrasting findings. Using RNAseq data from time course experiments of infected human cell lines and transcriptome data from Vero cells and clinical samples, we prove that A-to-G changes observed in SARS-COV-2 genomes represent genuine RNA editing events, likely mediated by ADAR1. While the A-to-I editing rate is generally low, changes are distributed along the entire viral genome, are overrepresented in exonic regions, and are (in the majority of cases) nonsynonymous. The impact of RNA editing on virus–host interactions could be relevant to identify potential targets for therapeutic interventions.

CIM-seq

CIM-seq is a tool for deconvoluting RNA-seq data from cell multiplets (clusters of two or more cells) in order to identify physically interacting cell in a given tissue. The method requires two RNAseq data sets from the same tissue: one of single cells to be used as a reference, and one of cell multiplets to be deconvoluted. CIM-seq is compatible with both droplet based sequencing methods, such as Chromium Single Cell 3′ Kits from 10x genomics; and plate based methods, such as Smartseq2. The pipeline consists of three parts: 1) Dissociation of the target tissue, FACS sorting of single cells and multiplets, and conventional scRNA-seq 2) Feature selection and clustering of cell types in the single cell data set - generating a blueprint of transcriptional profiles in the given tissue 3) Computational deconvolution of multiplets through a maximum likelihood estimation (MLE) to determine the most likely cell type constituents of each multiplet.

TRAWLING: a Transcriptome Reference Aware of spLIciNG events

Alternative splicing is critical for human gene expression regulation and plays an important role in multiple human diseases. In this context, RNA sequencing has emerged as powerful approach to detect alternative splicing events.In parallel, fast alignment-free methods have emerged as a viable alternative to quantify gene and transcript level abundance from RNAseq data. However, the ability to detect differential splicing events is dependent on the annotation of the transcript reference provided by the user.Here, we introduce a new reference transcriptome aware of splicing events, TRAWLING, which simplifies the detection of aberrant splicing events in a fast and simple way. In addition, we evaluate the performances and the benefits of aligning transcriptome data to TRAWLING using three different RNA sequencing datasets: whole transcriptome sequencing, single cell RNA sequencing and Digital RNA with pertUrbation of Genes.Collectively, our comprehensive evaluation underlines the value of using TRAWLING in transcriptomic data analysis.Availability and implementationOur code is available at https://github.com/Novartis/TRAWLING

SARS-CoV-2 infection reduces human nasopharyngeal commensal microbiome with inclusion of pathobionts

The microbiota of the nasopharyngeal tract (NT) play a role in host immunity against respiratory infectious diseases. However, scant information is available on interactions of SARS-CoV-2 with the nasopharyngeal microbiome. This study characterizes the effects of SARS-CoV-2 infection on human nasopharyngeal microbiomes and their relevant metabolic functions. Twenty-two (n = 22) nasopharyngeal swab samples (including COVID-19 patients = 8, recovered humans = 7, and healthy people = 7) were collected, and underwent to RNAseq-based metagenomic investigation. Our RNAseq data mapped to 2281 bacterial species (including 1477, 919 and 676 in healthy, COVID-19 and recovered metagenomes, respectively) indicating a distinct microbiome dysbiosis. The COVID-19 and recovered samples included 67% and 77% opportunistic bacterial species, respectively compared to healthy controls. Notably, 79% commensal bacterial species found in healthy controls were not detected in COVID-19 and recovered people. Similar dysbiosis was also found in viral and archaeal fraction of the nasopharyngeal microbiomes. We also detected several altered metabolic pathways and functional genes in the progression and pathophysiology of COVID-19. The nasopharyngeal microbiome dysbiosis and their genomic features determined by our RNAseq analyses shed light on early interactions of SARS-CoV-2 with the nasopharyngeal resident microbiota that might be helpful for developing microbiome-based diagnostics and therapeutics for this novel pandemic disease.

Asthma reduces glioma formation by T cell decorin-mediated inhibition of microglia

To elucidate the mechanisms underlying the reduced incidence of brain tumors in children with Neurofibromatosis type 1 (NF1) and asthma, we leverage Nf1 optic pathway glioma (Nf1OPG) mice, human and mouse RNAseq data, and two different experimental asthma models. Following ovalbumin or house dust mite asthma induction at 4–6 weeks of age (WOA), Nf1OPG mouse optic nerve volumes and proliferation are decreased at 12 and 24 WOA, indicating no tumor development. This inhibition is accompanied by reduced expression of the microglia-produced optic glioma mitogen, Ccl5. Human and murine T cell transcriptome analyses reveal that inhibition of microglia Ccl5 production results from increased T cell expression of decorin, which blocks Ccl4-mediated microglia Ccl5 expression through reduced microglia NFκB signaling. Decorin or NFκB inhibitor treatment of Nf1OPG mice at 4–6 WOA inhibits tumor formation at 12 WOA, thus establishing a potential mechanistic etiology for the attenuated glioma incidence observed in children with asthma.

Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis

Background: Biological processes are based on complex networks of cells and molecules. Single cell multi-omics is a new tool aiming to provide new incites in the complex network of events controlling the functionality of the cell. Methods: Since single cell technologies provide many sample measurements, they are the ideal environment for the application of Deep Learning and Machine Learning approaches. An autoencoder is composed of an encoder and a decoder sub-model. An autoencoder is a very powerful tool in data compression and noise removal. However, the decoder model remains a black box from which is impossible to depict the contribution of the single input elements. We have recently developed a new class of autoencoders, called Sparsely Connected Autoencoders (SCA), which have the advantage of providing a controlled association among the input layer and the decoder module. This new architecture has the benefit that the decoder model is not a black box anymore and can be used to depict new biologically interesting features from single cell data. Results: Here, we show that SCA hidden layer can grab new information usually hidden in single cell data, like providing clustering on meta-features difficult, i.e. transcription factors expression, or not technically not possible, i.e. miRNA expression, to depict in single cell RNAseq data. Furthermore, SCA representation of cell clusters has the advantage of simulating a conventional bulk RNAseq, which is a data transformation allowing the identification of similarity among independent experiments. Conclusions: In our opinion, SCA represents the bioinformatics version of a universal “Swiss-knife” for the extraction of hidden knowledgeable features from single cell omics data.

Meta-Transcriptome Detector (MTD): a novel pipeline for metatranscriptome analysis of bulk and single-cell RNAseq data

RNA-seq data contains not only host transcriptomes but also non-host information that comprises transcripts from active microbiota in the host cells. Therefore, metatranscriptomics can reveal gene expression of the entire microbial community in a given sample. However, there is no single tool that can simultaneously analyze host-microbiota interactions and to quantify microbiome at the single-cell level, particularly for users with limited expertise of bioinformatics. Here, we developed a novel software program that can comprehensively and synergistically analyze gene expression of the host and microbiome as well as their association using bulk and single-cell RNA-seq data. Our pipeline, named Meta-Transcriptome Detector (MTD), can identify and quantify microbiome extensively, including viruses, bacteria, protozoa, fungi, plasmids, and vectors. MTD is easy to install and is user-friendly. This novel software program empowers researchers to study the interactions between microbiota and the host by analyzing gene expressions and pathways, which provides further insights into host responses to microorganisms.