Identification of haploinsufficient genes from epigenomic data using deep forest

PredGCN: a Pruning-enabled Gene-Cell Net for automatic cell annotation of single cell transcriptome data

MOTIVATION

The annotation of cell types from single-cell transcriptomics is essential for understanding the biological identity and functionality of cellular populations. Although manual annotation remains the gold standard, the advent of automatic pipelines has become crucial for scalable, unbiased, and cost-effective annotations. Nonetheless, the effectiveness of these automatic methods, particularly those employing deep learning, significantly depends on the architecture of the classifier and the quality and diversity of the training datasets.

RESULTS

To address these limitations, we present a Pruning-enabled Gene-Cell Net (PredGCN) incorporating a Coupled Gene-Cell Net (CGCN) to enable representation learning and information storage. PredGCN integrates a Gene Splicing Net (GSN) and a Cell Stratification Net (CSN), employing a pruning operation (PrO) to dynamically tackle the complexity of heterogeneous cell identification. Among them, GSN leverages multiple statistical and hypothesis-driven feature extraction methods to selectively assemble genes with specificity for scRNA-seq data while CSN unifies elements based on diverse region demarcation principles, exploiting the representations from GSN and precise identification from different regional homogeneity perspectives. Furthermore, we develop a multi-objective Pareto pruning operation (Pareto PrO) to expand the dynamic capabilities of CGCN, optimizing the sub-network structure for accurate cell type annotation. Multiple comparison experiments on real scRNA-seq datasets from various species have demonstrated that PredGCN surpasses existing state-of-the-art methods, including its scalability to cross-species datasets. Moreover, PredGCN can uncover unknown cell types and provide functional genomic analysis by quantifying the influence of genes on cell clusters, bringing new insights into cell type identification and characterizing scRNA-seq data from different perspectives.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/IrisQi7/PredGCN and test data is available at https://figshare.com/articles/dataset/PredGCN/25251163.

Heterozygous Nexmif female mice demonstrate mosaic NEXMIF expression, autism-like behaviors, and abnormalities in dendritic arborization and synaptogenesis

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a strong genetic basis. ASDs are commonly characterized by impairments in language, restrictive and repetitive behaviors, and deficits in social interactions. Although ASD is a highly heterogeneous disease with many different genes implicated in its etiology, many ASD-associated genes converge on common cellular defects, such as aberrant neuronal morphology and synapse dysregulation. Our previous work revealed that, in mice, complete loss of the ASD-associated X-linked gene NEXMIF results in a reduction in dendritic complexity, a decrease in spine and synapse density, altered synaptic transmission, and ASD-like behaviors. Interestingly, human females of NEXMIF haploinsufficiency have recently been reported to demonstrate autistic features; however, the cellular and molecular basis for this haploinsufficiency-caused ASD remains unclear. Here we report that in the brains of Nexmif± female mice, NEXMIF shows a mosaic pattern in its expression in neurons. Heterozygous female mice demonstrate behavioral impairments similar to those of knockout male mice. In the mosaic mixture of neurons from Nexmif± mice, cells that lack NEXMIF have impairments in dendritic arborization and spine development. Remarkably, the NEXMIF-expressing neurons from Nexmif± mice also demonstrate similar defects in dendritic growth and spine formation. These findings establish a novel mouse model of NEXMIF haploinsufficiency and provide new insights into the pathogenesis of NEXMIF-dependent ASD.