Characterization of cell-cell communication in autistic brains with single-cell transcriptomes

Dysfunction of Endothelial Cell-Mediated Intercellular Communication and Metabolic Pathways in Age-Related Macular Degeneration

PURPOSE

Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly, but the therapies are not satisfactory. This study aimed to find AMD specific features through the analysis of high-throughput sequencing.

METHODS

In this study, we integrated six projects containing single-cell RNA sequencing (scRNA-seq) data to perform a comprehensive analysis for AMD samples in the tissues of retina and retinal pigment epithelium/choroid, and in the positions of macula and periphery. Differentially expressed genes (DEGs) were analyzed and crucial signaling pathways were identified across cell types and between the macula and periphery. The intercellular signaling transduction among cell types were inferred by "CellChat" to build cell-cell communication network under normal and AMD conditions, and verified at the transcriptional level. The CD31+ endothelial cells were obtained to evaluate the enrichment of KEGG pathways in atrophic and neovascular AMD, and GSVA was adopted to discover differential metabolic signals in each AMD type.

RESULTS

Thirteen major cell types were identified in the integrated scRNA-seq data. Although no disease-specific cell type or differential cell proportion was found, DEGs and enriched pathways were shown in cell-type- and position-dependent manners. Severe impairment of endothelial cell-mediated cell interactions was found in the signaling transduction network of the macula, and compromised cell interactions were observed in the periphery. Furthermore, distinct signaling pathways and metabolic states were uncovered in atrophic and neovascular AMD. Striking reduction in energy metabolism, lipid metabolism, and oxidative stress was indicated in the atrophic AMD.

CONCLUSION

Conclusively, we discover aberrant signals and metabolic pathways in AMD samples, providing insight into mechanisms and potential therapeutic targets for the AMD treatment.

Emergence of neuron types

Neuron types are the building blocks of the nervous system, and therefore, of functional circuits. Understanding the origin of neuronal diversity has always been an essential question in neuroscience and developmental biology. While knowledge on the molecular control of their diversification has largely increased during the last decades, it is now possible to reveal the dynamic mechanisms and the actual stepwise molecular changes occurring at single-cell level with the advent of single-cell omics technologies and analysis with high temporal resolution. Here, we focus on recent advances in the field and in technical and analytical tools that enable detailed insights into the emergence of neuron types in the central and peripheral nervous systems.

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.

Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on Autism Spectrum Disorder Neuroimaging

Background

Autism spectrum disorder (ASD) is a chronic developmental disability caused by differences in the brain. The gold standard for the diagnosis of this condition is based on behavioral science, but research on the application of neurological detection to diagnose the atypical nervous system of ASD is ongoing. ASD neuroimaging research involves the examination of the brain's structure, functional connections, and neurometabolic. However, limited medical resource and the unique heterogeneity of ASD have resulted in many challenges when neuroimaging is utilized.

Objective

This bibliometric study is aimed at summarizing themes and trends in research on autism spectrum disorder neuroimaging and at proposing potential directions for future inquiry.

Methods

Citations were downloaded from the Web of Science Core Collection database on neuroimaging published from January 1, 2012, to December 31, 2021. The retrieved information was analyzed using Bibliometric.com, CiteSpace.5.8. R3, and VOS viewer.

Results

A total of 1,363 papers were published across 58 regions. The United States was the leading source of publications. The League of European Research Universities published the largest number of articles (171). Burst keywords from 2018 to 2021 include identification and network. The clusters of references that continued into 2020 included graph theory, functional connectivity, and classification, which represent key research topics.

Conclusions

Imaging data is being used to identify neuro-network models with higher accuracy for ASD discrimination. Functional near-infrared imaging is advantageous compared to other neuroimaging. In the future, research on systematic and accurate computer-aided diagnosis technology should be encouraged. Moreover, the study of neuroimaging of ASD in different psychological and behavioral states can inspire new ideas about the diagnosis and intervention training of ASD and should be explored.

Context-aware deconvolution of cell-cell communication with Tensor-cell2cell

Cell interactions determine phenotypes, and intercellular communication is shaped by cellular contexts such as disease state, organismal life stage, and tissue microenvironment. Single-cell technologies measure the molecules mediating cell-cell communication, and emerging computational tools can exploit these data to decipher intercellular communication. However, current methods either disregard cellular context or rely on simple pairwise comparisons between samples, thus limiting the ability to decipher complex cell-cell communication across multiple time points, levels of disease severity, or spatial contexts. Here we present Tensor-cell2cell, an unsupervised method using tensor decomposition, which deciphers context-driven intercellular communication by simultaneously accounting for multiple stages, states, or locations of the cells. To do so, Tensor-cell2cell uncovers context-driven patterns of communication associated with different phenotypic states and determined by unique combinations of cell types and ligand-receptor pairs. As such, Tensor-cell2cell robustly improves upon and extends the analytical capabilities of existing tools. We show Tensor-cell2cell can identify multiple modules associated with distinct communication processes (e.g., participating cell-cell and ligand-receptor pairs) linked to severities of Coronavirus Disease 2019 and to Autism Spectrum Disorder. Thus, we introduce an effective and easy-to-use strategy for understanding complex communication patterns across diverse conditions.

Expression and structural analysis of human neuroligin 2 and neuroligin 3 implicated in autism spectrum disorders

The development of autism spectrum disorders (ASDs) involves both environmental factors such as maternal diabetes and genetic factors such as neuroligins (NLGNs). NLGN2 and NLGN3 are two members of NLGNs with distinct distributions and functions in synapse development and plasticity. The relationship between maternal diabetes and NLGNs, and the distinct working mechanisms of different NLGNs currently remain unclear. Here, we first analyzed the expression levels of NLGN2 and NLGN3 in a streptozotocin-induced ASD mouse model and different brain regions to reveal their differences and similarities. Then, cryogenic electron microscopy (cryo-EM) structures of human NLGN2 and NLGN3 were determined. The overall structures are similar to their homologs in previous reports. However, structural comparisons revealed the relative rotations of two protomers in the homodimers of NLGN2 and NLGN3. Taken together with the previously reported NLGN2-MDGA1 complex, we speculate that the distinct assembly adopted by NLGN2 and NLGN3 may affect their interactions with MDGAs. Our results provide structural insights into the potential distinct mechanisms of NLGN2 and NLGN3 implicated in the development of ASD.