Coexpression enrichment analysis at the single-cell level reveals convergent defects in neural progenitor cells and their cell-type transitions in neurodevelopmental disorders

Disruption of the gene regulatory programme in neurodevelopmental disorders

Cortical development consists of a series of synchronised events, including fate transition of cortical progenitors, neuronal migration, specification and connectivity. It is becoming clear that gene expression programs governing these events rely on the interplay between signalling molecules, transcription factors and epigenetic mechanisms. When genetic or environmental factors disrupt expression of genes involved in important brain development processes, neurodevelopmental disorders can occur. This review aims to highlight how recent advances in technologies have helped uncover and imitate the gene regulatory mechanisms commonly disrupted in neurodevelopmental disorders.

Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer’s disease

Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.

Alzheimer’s Disease (AD) is the leading cause of dementia. It affects parts of the brain that control language, behavior, and memory. The human brain is comprised of tens of billions of cells, such as neuronal cells that transmit information via electrical and chemical signals, and glial cells that maintain the brain’s immune system. Researchers have found that AD causes changes in the expression of genes within the brain cells. Gene expression is a tightly regulated process involving interconnected networks of multiple genes. Understanding how these gene networks change in AD is critical to identifying genetic biomarkers and potential drug targets. Using genomic data of post-mortem brains diagnosed with AD and healthy individuals, we identified gene networks that play a crucial role in regulating biological processes within neuronal and glial cells. We utilized these gene networks to make predictions on existing FDA approved drugs that could potentially be repurposed for AD. Furthermore, we used a machine learning strategy to identify novel genes that are more likely to be involved in AD pathology. The systems-level approach lends itself to analysis of single-cell genomics data of other human diseases.

Protein interactome and cell-type expression analyses reveal that cytoplasmic FMR1-interacting protein 1 (CYFIP1), but not CYFIP2, associates with astrocytic focal adhesion

The two members of the cytoplasmic FMR1-interacting protein family, CYFIP1 and CYFIP2, are evolutionarily conserved multifunctional proteins whose defects are associated with distinct types of brain disorders. Even with high sequence homology between CYFIP1 and CYFIP2, several lines of evidence indicate their different functions in the brain; however, the underlying mechanisms remain largely unknown. Here, we performed reciprocal immunoprecipitation experiments using CYFIP1-2×Myc and CYFIP2-3×Flag knock-in mice and found that CYFIP1 and CYFIP2 are not significantly co-immunoprecipitated with each other in the knock-in brains compared to negative control wild-type brains. Moreover, CYFIP1 and CYFIP2 showed different size distributions by size-exclusion chromatography of wild-type mouse brains. Specifically, mass spectrometry-based analysis of CYFIP1-2×Myc knock-in brains identified 131 proteins in the CYFIP1 interactome. Comparison of the CYFIP1 interactome with the previously identified brain region- and age-matched CYFIP2 interactome, consisting of 140 proteins, revealed only eight common proteins. Investigations using single-cell RNA-sequencing databases suggested non-neuronal cell- and neuron-enriched expression of Cyfip1 and Cyfip2, respectively. At the protein level, CYFIP1 was detected in both neurons and astrocytes, while CYFIP2 was detected only in neurons, suggesting the predominant expression of CYFIP1 in astrocytes. Bioinformatic characterization of the CYFIP1 interactome, and co-expression analysis of Cyfip1 with astrocytic genes, commonly linked CYFIP1 with focal adhesion proteins. Immunocytochemical analysis and proximity ligation assay suggested partial co-localization of CYFIP1 and focal adhesion proteins in cultured astrocytes. Together, these results suggest a CYFIP1-specific association with astrocytic focal adhesion, which may contribute to the different brain functions and dysfunctions of CYFIP1 and CYFIP2.

Single-cell transcriptome identifies molecular subtype of autism spectrum disorder impacted by de novo loss-of-function variants regulating glial cells

BACKGROUND

In recent years, several hundred autism spectrum disorder (ASD) implicated genes have been discovered impacting a wide range of molecular pathways. However, the molecular underpinning of ASD, particularly from the point of view of 'brain to behaviour' pathogenic mechanisms, remains largely unknown.

METHODS

We undertook a study to investigate patterns of spatiotemporal and cell type expression of ASD-implicated genes by integrating large-scale brain single-cell transcriptomes (> million cells) and de novo loss-of-function (LOF) ASD variants (impacting 852 genes from 40,122 cases).

RESULTS

We identified multiple single-cell clusters from three distinct developmental human brain regions (anterior cingulate cortex, middle temporal gyrus and primary visual cortex) that evidenced high evolutionary constraint through enrichment for brain critical exons and high pLI genes. These clusters also showed significant enrichment with ASD loss-of-function variant genes (p < 5.23 × 10^-11) that are transcriptionally highly active in prenatal brain regions (visual cortex and dorsolateral prefrontal cortex). Mapping ASD de novo LOF variant genes into large-scale human and mouse brain single-cell transcriptome analysis demonstrate enrichment of such genes into neuronal subtypes and are also enriched for subtype of non-neuronal glial cell types (astrocyte, p < 6.40 × 10^-11, oligodendrocyte, p < 1.31 × 10^-09).

CONCLUSION

Among the ASD genes enriched with pathogenic de novo LOF variants (i.e. KANK1, PLXNB1), a subgroup has restricted transcriptional regulation in non-neuronal cell types that are evolutionarily conserved. This association strongly suggests the involvement of subtype of non-neuronal glial cells in the pathogenesis of ASD and the need to explore other biological pathways for this disorder.

SPEN haploinsufficiency causes a neurodevelopmental disorder overlapping proximal 1p36 deletion syndrome with an episignature of X chromosomes in females

Deletion 1p36 (del1p36) syndrome is the most common human disorder resulting from a terminal autosomal deletion. This condition is molecularly and clinically heterogeneous. Deletions involving two non-overlapping regions, known as the distal (telomeric) and proximal (centromeric) critical regions, are sufficient to cause the majority of the recurrent clinical features, although with different facial features and dysmorphisms. SPEN encodes a transcriptional repressor commonly deleted in proximal del1p36 syndrome and is located centromeric to the proximal 1p36 critical region. Here, we used clinical data from 34 individuals with truncating variants in SPEN to define a neurodevelopmental disorder presenting with features that overlap considerably with those of proximal del1p36 syndrome. The clinical profile of this disease includes developmental delay/intellectual disability, autism spectrum disorder, anxiety, aggressive behavior, attention deficit disorder, hypotonia, brain and spine anomalies, congenital heart defects, high/narrow palate, facial dysmorphisms, and obesity/increased BMI, especially in females. SPEN also emerges as a relevant gene for del1p36 syndrome by co-expression analyses. Finally, we show that haploinsufficiency of SPEN is associated with a distinctive DNA methylation episignature of the X chromosome in affected females, providing further evidence of a specific contribution of the protein to the epigenetic control of this chromosome, and a paradigm of an X chromosome-specific episignature that classifies syndromic traits. We conclude that SPEN is required for multiple developmental processes and SPEN haploinsufficiency is a major contributor to a disorder associated with deletions centromeric to the previously established 1p36 critical regions.

ESCO: single cell expression simulation incorporating gene co-expression

MOTIVATION

Gene-gene co-expression networks (GCN) are of biological interest for the useful information they provide for understanding gene-gene interactions. The advent of single cell RNA-sequencing allows us to examine more subtle gene co-expression occurring within a cell type. Many imputation and denoising methods have been developed to deal with the technical challenges observed in single cell data; meanwhile, several simulators have been developed for benchmarking and assessing these methods. Most of these simulators, however, either do not incorporate gene co-expression or generate co-expression in an inconvenient manner.

RESULTS

Therefore, with the focus on gene co-expression, we propose a new simulator, ESCO, which adopts the idea of the copula to impose gene co-expression, while preserving the highlights of available simulators, which perform well for simulation of gene expression marginally. Using ESCO, we assess the performance of imputation methods on GCN recovery and find that imputation generally helps GCN recovery when the data are not too sparse, and the ensemble imputation method works best among leading methods. In contrast, imputation fails to help in the presence of an excessive fraction of zero counts, where simple data aggregating methods are a better choice. These findings are further verified with mouse and human brain cell data.

AVAILABILITY

The ESCO implementation is available as R package ESCO. Users can either download the development version via github (https://github.com/JINJINT/ESCO) or the archived version via Zenodo (https://zenodo.org/record/4455890).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell network biology for resolving cellular heterogeneity in human diseases

Understanding cellular heterogeneity is the holy grail of biology and medicine. Cells harboring identical genomes show a wide variety of behaviors in multicellular organisms. Genetic circuits underlying cell-type identities will facilitate the understanding of the regulatory programs for differentiation and maintenance of distinct cellular states. Such a cell-type-specific gene network can be inferred from coregulatory patterns across individual cells. Conventional methods of transcriptome profiling using tissue samples provide only average signals of diverse cell types. Therefore, reconstructing gene regulatory networks for a particular cell type is not feasible with tissue-based transcriptome data. Recently, single-cell omics technology has emerged and enabled the capture of the transcriptomic landscape of every individual cell. Although single-cell gene expression studies have already opened up new avenues, network biology using single-cell transcriptome data will further accelerate our understanding of cellular heterogeneity. In this review, we provide an overview of single-cell network biology and summarize recent progress in method development for network inference from single-cell RNA sequencing (scRNA-seq) data. Then, we describe how cell-type-specific gene networks can be utilized to study regulatory programs specific to disease-associated cell types and cellular states. Moreover, with scRNA data, modeling personal or patient-specific gene networks is feasible. Therefore, we also introduce potential applications of single-cell network biology for precision medicine. We envision a rapid paradigm shift toward single-cell network analysis for systems biology in the near future.

Gene regulatory networks reconstructed from single-cell RNA sequencing datasets are allowing researchers to better understand the molecular circuits and cell states that contribute to complex human disease. Junha Cha and Insuk Lee from Yonsei University in Seoul, South Korea, review the concept of ‘single-cell network biology’, which involves using computational algorithms on genetic expression data from thousands of cells to infer functional interactions in various biological contexts. This systems biology approach to analyzing the profiles of messenger RNA in single cells is helping researchers discover new signaling pathways that could serve as disease biomarkers or therapeutic targets. In the future, patient-specific models of personal gene networks could explain why certain genetic variants affect disease risk. This research could also eventually lead to new types of individualized medical treatments.

Cell Development Deficiency and Gene Expression Dysregulation of Trisomy 21 Retina Revealed by Single-Nucleus RNA Sequencing

Retina is a crucial tissue for capturing and processing light stimulus. It is critical to describe the characteristics of retina at the single-cell level for understanding its biological functions. A variety of abnormalities in terms of morphology and function are present in the trisomy 21 (T21) retina. To evaluate the consequences of chromosome aneuploidy on retina development, we identified the single-cell transcriptional profiles of a T21 fetus and performed comprehensive bioinformatic analyses. Our data revealed the diversity and heterogeneity of cellular compositions in T21 retina, as well as the abnormal constitution of T21 retina compared to disomic retina. In total, we identified seven major cell types and several subtypes within each cell type, followed by the detection of corresponding molecular markers, including previously reported ones and a series of novel markers. Through the analysis of the retinal differentiation process, subtypes of retinal progenitor cells (RPCs) exhibiting the potential of different retinal cell-type commitments and certain Müller glial cells (MGs) with differentiating potency were identified. Moreover, the extensive communication networks between cellular types were confirmed, among which a few ligand–receptor interactions were related to the formation and function of retina and immunoregulatory interactions. Taken together, our data provides the first ever single-cell transcriptome profiles for human T21 retina, which facilitates the understanding on the dosage effects of chromosome 21 on the development of retina.