scholarly journals Constructing Local Cell Specific Networks from Single Cell Data

2021 ◽  
Author(s):  
Xuran Wang ◽  
David Choi ◽  
Kathryn Roeder
Keyword(s):  
Single Cell ◽  
Network Structure ◽  
Gene Networks ◽  
Fetal Brain ◽  
Autism Spectrum ◽  
Cellular Level ◽  
Gene Block ◽  
Local Cell ◽  
The Individual ◽  
Downstream Analysis

ABSTRACTGene co-expression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level. However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene networks. We develop an approach that estimates cell-specific networks (CSN) for each cell using a method inspired by Dai et al. (7). Although individual CSNs are estimated with considerable noise, average CSNs provide stable estimates of network structure, which provide better estimates of gene block structures than traditional measures. The method, called locCSN, is based on a non-parametric investigation of the joint distribution of gene expression, hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. The individual networks preserve information about the heterogeneity of the cells and having repeated estimates of network structure facilitates testing for difference in network structure between groups of cells. The original CSN algorithm showed promise; however, it had shortcomings which locCSN overcomes. Additionally, we propose new downstream analysis methods using CSNs, to utilize more fully the information contained within them. Finally, to further our understanding of autism spectrum disorder we examined the evolution of gene networks in fetal brain cells and compared the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene co-expression changes.

scholarly journals Constructing local cell-specific networks from single-cell data

2021 ◽  
Vol 118 (51) ◽  
pp. e2113178118
Author(s):  
Xuran Wang ◽  
David Choi ◽  
Kathryn Roeder
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Networks ◽  
Fetal Brain ◽  
Autism Spectrum ◽  
Cellular Level ◽  
Cellular Heterogeneity ◽  
Gene Coexpression ◽  
Downstream Analysis ◽  
Coexpression Networks

Gene coexpression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level. However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene networks. We develop an approach, locCSN, that estimates cell-specific networks (CSNs) for each cell, preserving information about cellular heterogeneity that is lost with other approaches. LocCSN is based on a nonparametric investigation of the joint distribution of gene expression; hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. Although individual CSNs are estimated with considerable noise, average CSNs provide stable estimates of networks, which reveal gene communities better than traditional measures. Additionally, we propose downstream analysis methods using CSNs to utilize more fully the information contained within them. Repeated estimates of gene networks facilitate testing for differences in network structure between cell groups. Notably, with this approach, we can identify differential network genes, which typically do not differ in gene expression, but do differ in terms of the coexpression networks. These genes might help explain the etiology of disease. Finally, to further our understanding of autism spectrum disorder, we examine the evolution of gene networks in fetal brain cells and compare the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene coexpression.

EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning

2019 ◽  
Vol 35 (22) ◽  
pp. 4827-4829 ◽  
Author(s):  
Xiao-Fei Zhang ◽  
Le Ou-Yang ◽  
Shuo Yang ◽  
Xing-Ming Zhao ◽  
Xiaohua Hu ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Ensemble Learning ◽  
R Package ◽  
Supplementary Information ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
The Individual ◽  
Downstream Analysis ◽  
Shiny Application

Abstract Summary Imputation of dropout events that may mislead downstream analyses is a key step in analyzing single-cell RNA-sequencing (scRNA-seq) data. We develop EnImpute, an R package that introduces an ensemble learning method for imputing dropout events in scRNA-seq data. EnImpute combines the results obtained from multiple imputation methods to generate a more accurate result. A Shiny application is developed to provide easier implementation and visualization. Experiment results show that EnImpute outperforms the individual state-of-the-art methods in almost all situations. EnImpute is useful for correcting the noisy scRNA-seq data before performing downstream analysis. Availability and implementation The R package and Shiny application are available through Github at https://github.com/Zhangxf-ccnu/EnImpute. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Single-Cell Transcriptomics: Current Methods and Challenges in Data Acquisition and Analysis

2021 ◽  
Vol 15 ◽  
Author(s):  
Asif Adil ◽  
Vijay Kumar ◽  
Arif Tasleem Jan ◽  
Mohammed Asger
Keyword(s):  
Single Cell ◽  
Human Life ◽  
Data Generation ◽  
Rna Seq ◽  
Individual Level ◽  
Transcriptomics Data ◽  
Differential Gene ◽  
The Individual ◽  
Downstream Analysis ◽  
Immense Potential

Rapid cost drops and advancements in next-generation sequencing have made profiling of cells at individual level a conventional practice in scientific laboratories worldwide. Single-cell transcriptomics [single-cell RNA sequencing (SC-RNA-seq)] has an immense potential of uncovering the novel basis of human life. The well-known heterogeneity of cells at the individual level can be better studied by single-cell transcriptomics. Proper downstream analysis of this data will provide new insights into the scientific communities. However, due to low starting materials, the SC-RNA-seq data face various computational challenges: normalization, differential gene expression analysis, dimensionality reduction, etc. Additionally, new methods like 10× Chromium can profile millions of cells in parallel, which creates a considerable amount of data. Thus, single-cell data handling is another big challenge. This paper reviews the single-cell sequencing methods, library preparation, and data generation. We highlight some of the main computational challenges that require to be addressed by introducing new bioinformatics algorithms and tools for analysis. We also show single-cell transcriptomics data as a big data problem.

scholarly journals Enriched expression of genes associated with autism spectrum disorders in human inhibitory neurons

2017 ◽  
Author(s):  
Ping Wang ◽  
Dejian Zhao ◽  
Herbert M. Lachman ◽  
Deyou Zheng
Keyword(s):  
Single Cell ◽  
Gene Networks ◽  
Developmental Stages ◽  
Cell Types ◽  
Autism Spectrum ◽  
Inhibitory Neurons ◽  
Expression Of Genes ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome ◽  
Upregulated Genes

AbstractAutism spectrum disorder (ASD) is highly heritable but genetically heterogeneous. The affected neural circuits and cell types remain unclear and may vary at different developmental stages. By analyzing multiple sets of human single cell transcriptome profiles, we found that ASD candidates showed enriched gene expression in neurons, especially in inhibitory neurons. ASD candidates were also more likely to be the hubs of the co-expressed module that is highly expressed in inhibitory neurons, a feature not detected for excitatory neurons. In addition, we found that upregulated genes in multiple ASD cortex samples were also enriched with genes highly expressed in inhibitory neurons, suggesting a potential increase of inhibitory neurons and an imbalance in the ratio between excitatory and inhibitory neurons. Furthermore, the downstream targets of several ASD candidates, such as CHD8, EHMT1 and SATB2, also displayed enriched expression in inhibitory neurons. Taken together, our analysis of single cell transcriptomic data suggest that inhibitory neurons may be the major neuron subtype affected by the disruption of ASD gene networks, providing single cell functional evidence to support the excitatory/inhibitory (E/I) imbalance hypothesis.

Specialized Diet Therapies: Exploration for Improving Behavior in Autism Spectrum Disorder (ASD)

2020 ◽  
Vol 27 (40) ◽  
pp. 6771-6786
Author(s):  
Geir Bjørklund ◽  
Nagwa Abdel Meguid ◽  
Maryam Dadar ◽  
Lyudmila Pivina ◽  
Joanna Kałużna-Czaplińska ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Neurodevelopmental Disorder ◽  
Caloric Intake ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Nutritional Deficiencies ◽  
Childhood And Adolescence ◽  
Restricted Interests ◽  
Balanced Diet ◽  
The Individual

As a major neurodevelopmental disorder, Autism Spectrum Disorder (ASD) encompasses deficits in communication and repetitive and restricted interests or behaviors in childhood and adolescence. Its etiology may come from either a genetic, epigenetic, neurological, hormonal, or an environmental cause, generating pathways that often altogether play a synergistic role in the development of ASD pathogenesis. Furthermore, the metabolic origin of ASD should be important as well. A balanced diet consisting of the essential and special nutrients, alongside the recommended caloric intake, is highly recommended to promote growth and development that withstand the physiologic and behavioral challenges experienced by ASD children. In this review paper, we evaluated many studies that show a relationship between ASD and diet to develop a better understanding of the specific effects of the overall diet and the individual nutrients required for this population. This review will add a comprehensive update of knowledge in the field and shed light on the possible nutritional deficiencies, metabolic impairments (particularly in the gut microbiome), and malnutrition in individuals with ASD, which should be recognized in order to maintain the improved socio-behavioral habit and physical health.

scholarly journals CB1 antagonism increases excitatory synaptogenesis in a cortical spheroid model of fetal brain development

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexis Papariello ◽  
David Taylor ◽  
Ken Soderstrom ◽  
Karen Litwa
Keyword(s):  
Brain Development ◽  
Spontaneous Activity ◽  
Microelectrode Array ◽  
Cannabinoid Receptor ◽  
Fetal Brain ◽  
Endocannabinoid System ◽  
Autism Spectrum ◽  
Nervous System Development ◽  
Inhibitory Synapses ◽  
Fetal Brain Development

AbstractThe endocannabinoid system (ECS) plays a complex role in the development of neural circuitry during fetal brain development. The cannabinoid receptor type 1 (CB1) controls synaptic strength at both excitatory and inhibitory synapses and thus contributes to the balance of excitatory and inhibitory signaling. Imbalances in the ratio of excitatory to inhibitory synapses have been implicated in various neuropsychiatric disorders associated with dysregulated central nervous system development including autism spectrum disorder, epilepsy, and schizophrenia. The role of CB1 in human brain development has been difficult to study but advances in induced pluripotent stem cell technology have allowed us to model the fetal brain environment. Cortical spheroids resemble the cortex of the dorsal telencephalon during mid-fetal gestation and possess functional synapses, spontaneous activity, an astrocyte population, and pseudo-laminar organization. We first characterized the ECS using STORM microscopy and observed synaptic localization of components similar to that which is observed in the fetal brain. Next, using the CB1-selective antagonist SR141716A, we observed an increase in excitatory, and to a lesser extent, inhibitory synaptogenesis as measured by confocal image analysis. Further, CB1 antagonism increased the variability of spontaneous activity within developing neural networks, as measured by microelectrode array. Overall, we have established that cortical spheroids express ECS components and are thus a useful model for exploring endocannabinoid mediation of childhood neuropsychiatric disease.

scholarly journals The Effects of Maternal Interleukin-17A on Social Behavior, Cognitive Function, and Depression-Like Behavior in Mice with Altered Kynurenine Metabolites

2021 ◽  
Vol 14 ◽  
pp. 117864692110266
Author(s):  
Yuki Murakami ◽  
Yukio Imamura ◽  
Yoshiyuki Kasahara ◽  
Chihiro Yoshida ◽  
Yuta Momono ◽  
...  
Keyword(s):  
Neuronal Development ◽  
Fetal Brain ◽  
Autism Spectrum ◽  
Maternal Serum ◽  
Fetal Plasma ◽  
Maternal Inflammation ◽  
Interleukin 17A ◽  
Behavioral Abnormalities ◽  
Neuroactive Compounds ◽  
The Impact

Viral infection and chronic maternal inflammation during pregnancy are correlated with a higher prevalence of autism spectrum disorder (ASD). However, the pathoetiology of ASD is not fully understood; moreover, the key molecules that can cross the placenta following maternal inflammation and contribute to the development of ASD have not been identified. Recently, the pro-inflammatory cytokine, interleukin-17A (IL-17A) was identified as a potential mediator of these effects. To investigate the impact of maternal IL-17A on offspring, C57BL/6J dams were injected with IL-17A-expressing plasmids via the tail vein on embryonic day 12.5 (E12.5), and maternal IL-17A was expressed continuously throughout pregnancy. By adulthood, IL-17A-injected offspring exhibited behavioral abnormalities, including social and cognitive defects. Additionally, maternal IL-17A promoted metabolism of the essential amino acid tryptophan, which produces several neuroactive compounds and may affect fetal neurodevelopment. We observed significantly increased levels of kynurenine in maternal serum and fetal plasma. Thus, we investigated the effects of high maternal concentration of kynurenine on offspring by continuously administering mouse dams with kynurenine from E12.5 during gestation. Obviously, maternal kynurenine administration rapidly increased kynurenine levels in the fetal plasma and brain, pointing to the ability of kynurenine to cross the placenta and change the KP metabolites which are affected as neuroactive compounds in the fetal brain. Notably, the offspring of kynurenine-injected mice exhibited behavioral abnormalities similar to those observed in offspring of IL-17A-conditioned mice. Several tryptophan metabolites were significantly altered in the prefrontal cortex of the IL-17A-conditioned and kynurenine-injected adult mice, but not in the hippocampus. Even though we cannot exclude the possibility or other molecules being related to ASD pathogenesis and the presence of a much lower degree of pathway activation, our results suggest that increased kynurenine following maternal inflammation may be a key factor in changing the balance of KP metabolites in fetal brain during neuronal development and represents a therapeutic target for inflammation-induced ASD-like phenotypes.

scholarly journals “Guilt by association” is not competitive with genetic association for identifying autism risk genes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Margot Gunning ◽  
Paul Pavlidis
Keyword(s):  
Machine Learning ◽  
Genetic Association ◽  
Gene Networks ◽  
Rare Variants ◽  
Association Studies ◽  
Genetic Disorders ◽  
Autism Spectrum ◽  
Biological Data ◽  
Disease Genes ◽  
Risk Genes

AbstractDiscovering genes involved in complex human genetic disorders is a major challenge. Many have suggested that machine learning (ML) algorithms using gene networks can be used to supplement traditional genetic association-based approaches to predict or prioritize disease genes. However, questions have been raised about the utility of ML methods for this type of task due to biases within the data, and poor real-world performance. Using autism spectrum disorder (ASD) as a test case, we sought to investigate the question: can machine learning aid in the discovery of disease genes? We collected 13 published ASD gene prioritization studies and evaluated their performance using known and novel high-confidence ASD genes. We also investigated their biases towards generic gene annotations, like number of association publications. We found that ML methods which do not incorporate genetics information have limited utility for prioritization of ASD risk genes. These studies perform at a comparable level to generic measures of likelihood for the involvement of genes in any condition, and do not out-perform genetic association studies. Future efforts to discover disease genes should be focused on developing and validating statistical models for genetic association, specifically for association between rare variants and disease, rather than developing complex machine learning methods using complex heterogeneous biological data with unknown reliability.

scholarly journals Savouring as an Intervention to Decrease Negative Affect in Anxious Mothers of Children with Autism and Neurotypical Children

2021 ◽  
Vol 11 (5) ◽  
pp. 652
Author(s):  
Ariel Pereira ◽  
Atiqah Azhari ◽  
Chloe Hong ◽  
Gerin Gaskin ◽  
Jessica Borelli ◽  
...  
Keyword(s):  
Negative Affect ◽  
Positive Affect ◽  
Children With Autism ◽  
Well Being ◽  
Autism Spectrum ◽  
Positive Experience ◽  
Children With Asd ◽  
Mother Child Relationships ◽  
Potential Benefits ◽  
The Individual

Savouring is an emotion regulation strategy and intervention that focuses on the process of attending, intensifying and prolonging positive experiences and positive affect associated with these memories. Personal savouring involves a reflection on positive memories that are specific to the individual and do not involve others. In contrast, relational savouring entails reflecting on instances when people were responsive to the needs of their significant others. Such interventions hold potential benefits in enhancing positive affect (PA) and reducing negative affect (NA) for both parents of children with autism spectrum disorder (ASD) and parents of neurotypical children. Adults with greater symptoms of generalised anxiety have been found to have less PA and more NA. However, no study has investigated the effects of a mother’s anxiety symptoms on the efficacy of savouring in enhancing PA and reducing NA. Thus, this paper combined personal and relational savouring to investigate whether savouring may enhance PA and reduce NA of a pooled sample of mothers of neurotypical children and mothers of children with ASD. 52 mothers of neurotypical children and 26 mothers of children with ASD aged 3–7 years old were given a series of questionnaires and randomly assigned to either relational savouring or personal savouring conditions. In relational savouring, mothers were asked to reflect upon a shared positive experience with their child while in the personal savouring condition, a personal positive experience was recalled. Across mothers of children with ASD and neurotypical children, findings suggest that savouring leads to a decrease in NA (p < 0.01) but not increases in PA. Similarly, mothers with higher levels of anxiety experience a greater decrease in NA (p < 0.001) compared to mothers with lower levels of anxiety post-savouring. This study proposes that a brief savouring intervention may be effective among mothers of preschoolers. As lower levels of negative affect is linked to healthier psychological well-being, mothers might be able to engage in more effective and warm parenting after savouring exercises, which would cultivate positive mother-child relationships that benefit their children in the long-term.

Critical downstream analysis steps for single-cell RNA sequencing data

2021 ◽  
Author(s):  
Zilong Zhang ◽  
Feifei Cui ◽  
Chen Lin ◽  
Lingling Zhao ◽  
Chunyu Wang ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Noisy Data ◽  
Single Cell Level ◽  
Cell Type ◽  
Sequencing Data ◽  
Cell Level ◽  
Bioinformatics Tool ◽  
Single Cell Rna Sequencing ◽  
Downstream Analysis

Abstract Single-cell RNA sequencing (scRNA-seq) has enabled us to study biological questions at the single-cell level. Currently, many analysis tools are available to better utilize these relatively noisy data. In this review, we summarize the most widely used methods for critical downstream analysis steps (i.e. clustering, trajectory inference, cell-type annotation and integrating datasets). The advantages and limitations are comprehensively discussed, and we provide suggestions for choosing proper methods in different situations. We hope this paper will be useful for scRNA-seq data analysts and bioinformatics tool developers.

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