Genome-wide association study reveals multiple loci for analgesia and opioid consumption behaviors associated with heroin vulnerability in outbred rats.

The increased prevalence of opioid use disorder (OUD) has made it imperative to disentangle the biological mechanisms contributing to individual differences in susceptibility to OUD. OUD shows strong heritability, however genetic variants contributing toward vulnerability remain poorly defined. We performed a genome-wide association study (GWAS) using over 850 male and female heterogeneous stock rats to identify genes underlying behaviors associated with OUD such as analgesia, as well as heroin-taking, refraining and seeking behaviors. By using an animal model of OUD, we were able to identify genetic variants associated with distinct OUD behaviors while maintaining a uniform environment, an experimental design not easily achieved in humans. Furthermore, we applied an animal model capturing individual variation in OUD propensity to assess if GWAS results were associated with OUD vulnerable versus resilient behavioral phenotypes. Our findings confirm the heritability of several OUD-like behaviors, including overall phenotype. We identified several genetic variants associated with basal analgesia prior to heroin experience, heroin consumption, escalation of intake, and motivation to obtain heroin. Ets2 , a regulator of microglia functional plasticity, and its eQTL PCP4 were identified for heroin consumption, and were associated with an OUD vulnerable phenotype through phenotype wide association study analysis. Furthermore, the coding variant Phd1l2 and the eQTL MMP15 for break point are both known mediators of addiction-related behaviors, and correlated with OUD vulnerability. These findings identify novel genetic markers related to individual differences in the susceptibility to OUD-relevant behaviors.

Discovery of cell type classification marker genes from single cell RNA sequencing data using NS-Forest.

The use of single-cell transcriptomic technologies that quantitively describe cell transcriptional phenotypes using single cell/nucleus RNA sequencing (scRNA-seq) is revolutionizing our understanding of cell biology, leading to new insights in cell type identification, disease mechanisms, and drug development. The tremendous growth in scRNA-seq data has posed new challenges in efficiently characterizing data-driven cell types and identifying quantifiable marker genes for cell type classification. The use of machine learning and explainable artificial intelligence has emerged as an effective approach to study large-scale scRNA-seq data. NS-Forest is a random forest machine learning-based algorithm that aims to provide a scalable data-driven solution to identify minimum combinations of necessary and sufficient marker genes that capture cell type identity with maximum classification accuracy. Here, we describe the latest version, NS-Forest version 4.0 and its companion Python package (https://github.com/JCVenterInstitute/NSForest), with several enhancements, to select marker gene combinations that exhibit selective expression patterns among closely related cell types and more efficiently perform marker gene selection for large-scale scRNA-seq data atlases with millions of cells. By modularizing the final decision tree step, NS-Forest v4.0 can be used to compare the performance of user-defined marker genes with the NS-Forest computationally-derived marker genes based on the decision tree classifiers. To quantify how well the identified markers exhibit the desired pattern of being exclusively expressed at high levels within their target cell types, we introduce the On-Target Fraction metric that ranges from 0 to1, with a metric of 1 given to markers that are only expressed within their target cell types and not in cells of any other cell types. We have applied NS-Forest v4.0 on scRNA-seq datasets from three human organs, including the brain, kidney, and lung. We observe that NS-Forest v4.0 outperforms previous versions on its ability to identify markers with higher On-Target Fraction values for closely related cell types and outperforms other marker gene selection approaches on the classification performance with significantly higher F-beta scores.

Machine learning for cell type classification from single nucleus RNA sequencing data.

With the advent of single cell/nucleus RNA sequencing (sc/snRNA-seq), the field of cell phenotyping is now a data-driven exercise providing statistical evidence to support cell type/state categorization. However, the task of classifying cells into specific, well-defined categories with the empirical data provided by sc/snRNA-seq remains nontrivial due to the difficulty in determining specific differences between related cell types with close transcriptional similarities, resulting in challenges with matching cell types identified in separate experiments. To investigate possible approaches to overcome these obstacles, we explored the use of supervised machine learning methods-logistic regression, support vector machines, random forests, neural networks, and light gradient boosting machine (LightGBM)-as approaches to classify cell types using snRNA-seq datasets from human brain middle temporal gyrus (MTG) and human kidney. Classification accuracy was evaluated using an F-beta score weighted in favor of precision to account for technical artifacts of gene expression dropout. We examined the impact of hyperparameter optimization and feature selection methods on F-beta score performance. We found that the best performing model for granular cell type classification in both datasets is a multinomial logistic regression classifier and that an effective feature selection step was the most influential factor in optimizing the performance of the machine learning pipelines.