scholarly journals Identification of de novo mutations in the Chinese ASD cohort via whole-exome sequencing unveils brain regions implicated in autism

2021 ◽  
Author(s):  
Bo Yuan ◽  
Mengdi Wang ◽  
Xinran Wu ◽  
Peipei Cheng ◽  
Ran Zhang ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
De Novo ◽  
Brain Regions ◽  
Imaging Data ◽  
Genetic Studies ◽  
Single Cell Sequencing ◽  
Genome Wide ◽  
Whole Exome ◽  
Brain Imaging Data

Abstract Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder characterized by deficits in social interactions and repetitive behaviors. Although hundreds of ASD risk genes, implicated in synaptic formation and transcriptional regulation, have been identified through human genetic studies, the East Asian ASD cohorts is still under-represented in the genome-wide genetic studies. Here we performed whole-exome sequencing on 369 ASD trios including probands and unaffected parents of Chinese origin. Using a joint-calling analytical pipeline based on GATK toolkits, we identified numerous de novo mutations including 55 high-impact variants and 165 moderate-impact variants, as well as de novo copy number variations containing known ASD-related genes. Importantly, combining with single-cell sequencing data from the developing human brain, we found that expression of genes with de novo mutations were specifically enriched in pre-, post-central gyrus (PRC, PC) and banks of superior temporal (BST) regions in the human brain. By further analyzing the brain imaging data with ASD and health controls, we found that the gray volume of the right BST in ASD patients significantly decreased comparing to health controls, suggesting the potential structural deficits associated with ASD. Finally, we found that there was decrease in the seed-based functional connectivity (FC) between BST/PC/PRC and sensory areas, insula, as well as frontal lobes in ASD patients. This work indicated that the combinatorial analysis with genome-wide screening, single-cell sequencing and brain imaging data would reveal brain regions contributing to etiology of ASD.

scholarly journals Identification of de novo mutations in the Chinese ASD cohort via whole-exome sequencing unveils brain regions implicated in autism

2021 ◽  
Author(s):  
Bo Yuan ◽  
Mengdi Wang ◽  
Xinran Wu ◽  
Peipei Cheng ◽  
Ran Zhang ◽  
...  
Keyword(s):  
Human Brain ◽  
De Novo ◽  
Brain Regions ◽  
Imaging Data ◽  
De Novo Mutations ◽  
Genetic Studies ◽  
Single Cell Sequencing ◽  
Genome Wide ◽  
Whole Exome ◽  
Brain Imaging Data

Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder characterized by deficits in social interactions and repetitive behaviors. Although hundreds of ASD risk genes, implicated in synaptic formation and transcriptional regulation, have been identified through human genetic studies, the East Asian ASD cohorts is still under-represented in the genome-wide genetic studies. Here we performed whole-exome sequencing on 369 ASD trios including probands and unaffected parents of Chinese origin. Using a joint-calling analytical pipeline based on GATK toolkits, we identified numerous de novo mutations including 55 high-impact variants and 165 moderate-impact variants, as well as de novo copy number variations containing known ASD-related genes. Importantly, combining with single-cell sequencing data from the developing human brain, we found that expression of genes with de novo mutations were specifically enriched in pre-, post-central gyrus (PRC, PC) and banks of superior temporal (BST) regions in the human brain. By further analyzing the brain imaging data with ASD and health controls, we found that the gray volume of the right BST in ASD patients significantly decreased comparing to health controls, suggesting the potential structural deficits associated with ASD. Finally, we found that there was decrease in the seed-based functional connectivity (FC) between BST/PC/PRC and sensory areas, insula, as well as frontal lobes in ASD patients. This work indicated that the combinatorial analysis with genome-wide screening, single-cell sequencing and brain imaging data would reveal brain regions contributing to etiology of ASD.

scholarly journals Reconstructing single-cell karyotype alterations in colorectal cancer identifies punctuated and gradual diversification patterns

2021 ◽  
Author(s):  
Yannik Bollen ◽  
Ellen Stelloo ◽  
Petra van Leenen ◽  
Myrna van den Bos ◽  
Bas Ponsioen ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Single Cell ◽  
Temporal Dynamics ◽  
De Novo ◽  
Tumor Evolution ◽  
Genome Wide ◽  
Aneuploid Tumor ◽  
Evolutionary Paths ◽  
Tumor Biopsies ◽  
Punctuated Evolution

AbstractCentral to tumor evolution is the generation of genetic diversity. However, the extent and patterns by which de novo karyotype alterations emerge and propagate within human tumors are not well understood, especially at single-cell resolution. Here, we present 3D Live-Seq—a protocol that integrates live-cell imaging of tumor organoid outgrowth and whole-genome sequencing of each imaged cell to reconstruct evolving tumor cell karyotypes across consecutive cell generations. Using patient-derived colorectal cancer organoids and fresh tumor biopsies, we demonstrate that karyotype alterations of varying complexity are prevalent and can arise within a few cell generations. Sub-chromosomal acentric fragments were prone to replication and collective missegregation across consecutive cell divisions. In contrast, gross genome-wide karyotype alterations were generated in a single erroneous cell division, providing support that aneuploid tumor genomes can evolve via punctuated evolution. Mapping the temporal dynamics and patterns of karyotype diversification in cancer enables reconstructions of evolutionary paths to malignant fitness.

scholarly journals Topological Segmentation of Time-Varying Functional Connectivity Highlights the Role of Preferred Cortical Circuits

2020 ◽  
Author(s):  
Jacob Billings ◽  
Manish Saggar ◽  
Shella Keilholz ◽  
Giovanni Petri
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Persistent Homology ◽  
Brain Regions ◽  
Topological Data Analysis ◽  
Time Varying ◽  
Imaging Data ◽  
Experimental Conditions ◽  
Common Time ◽  
Brain Imaging Data

Functional connectivity (FC) and its time-varying analogue (TVFC) leverage brain imaging data to interpret brain function as patterns of coordinating activity among brain regions. While many questions remain regarding the organizing principles through which brain function emerges from multi-regional interactions, advances in the mathematics of Topological Data Analysis (TDA) may provide new insights into the brain’s spontaneous self-organization. One tool from TDA, “persistent homology”, observes the occurrence and the persistence of n-dimensional holes presented in the metric space over a dataset. The occurrence of n-dimensional holes within the TVFC point cloud may denote conserved and preferred routes of information flow among brain regions. In the present study, we compare the use of persistence homology versus more traditional TVFC metrics at the task of segmenting brain states that differ across a common time-series of experimental conditions. We find that the structures identified by persistence homology more accurately segment the stimuli, more accurately segment volunteer performance during experimentally defined tasks, and generalize better across volunteers. Finally, we present empirical and theoretical observations that interpret brain function as a topological space defined by cyclic and interlinked motifs among distributed brain regions, especially, the attention networks.

scholarly journals STAB: a spatio-temporal cell atlas of the human brain

2020 ◽  
Vol 49 (D1) ◽  
pp. D1029-D1037
Author(s):  
Liting Song ◽  
Shaojun Pan ◽  
Zichao Zhang ◽  
Longhao Jia ◽  
Wei-Hua Chen ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Temporal Dynamics ◽  
Cell Types ◽  
Brain Regions ◽  
Molecular Characteristics ◽  
Great Effort ◽  
Brain Functions ◽  
Spatio Temporal ◽  
The Brain

Abstract The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.

scholarly journals Identification of a β-Arrestin 2 Mutation Related to Autism by Whole-Exome Sequencing

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yunfei Tang ◽  
Yamei Liu ◽  
Lei Tong ◽  
Shini Feng ◽  
Dongshu Du ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Developmental Stages ◽  
De Novo ◽  
Repetitive Behavior ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Potential Contribution ◽  
De Novo Mutations ◽  
Whole Exome

Autism spectrum disorder (ASD) is a complex neurological disease characterized by impaired social communication and interaction skills, rigid behavior, decreased interest, and repetitive activities. The disease has a high degree of genetic heterogeneity, and the genetic cause of ASD in many autistic individuals is currently unclear. In this study, we report a patient with ASD whose clinical features included social interaction disorder, communication disorder, and repetitive behavior. We examined the patient’s genetic variation using whole-exome sequencing technology and found new de novo mutations. After analysis and evaluation, ARRB2 was identified as a candidate gene. To study the potential contribution of the ARRB2 gene to the human brain development and function, we first evaluated the expression profile of this gene in different brain regions and developmental stages. Then, we used weighted gene coexpression network analysis to analyze the associations between ARRB2 and ASD risk genes. Additionally, the spatial conformation and stability of the ARRB2 wild type and mutant proteins were examined by simulations. Then, we further established a mouse model of ASD. The results showed abnormal ARRB2 expression in the mouse ASD model. Our study showed that ARRB2 may be a risk gene for ASD, but the contribution of de novo ARRB2 mutations to ASD is unclear. This information will provide references for the etiology of ASD and aid in the mechanism-based drug development and treatment.

EPGA-SC : A framework for de novo assembly of single-cell sequencing reads

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Xingyu Liao ◽  
Min Li ◽  
Junwei Luo ◽  
You Zou ◽  
Fangxiang Wu ◽  
...  
Keyword(s):  
Single Cell ◽  
De Novo Assembly ◽  
De Novo ◽  
Single Cell Sequencing

Linking brain imaging signals to visual perception

2013 ◽  
Vol 30 (5-6) ◽  
pp. 229-241 ◽  
Author(s):  
ANDREW E. WELCHMAN ◽  
ZOE KOURTZI
Keyword(s):  
Human Brain ◽  
Brain Imaging ◽  
Imaging Data ◽  
Neural Basis ◽  
3D Perception ◽  
Multimodal Data ◽  
Psychological States ◽  
Perceptual Judgments ◽  
Brain Circuits ◽  
Brain Imaging Data

AbstractThe rapid advances in brain imaging technology over the past 20 years are affording new insights into cortical processing hierarchies in the human brain. These new data provide a complementary front in seeking to understand the links between perceptual and physiological states. Here we review some of the challenges associated with incorporating brain imaging data into such “linking hypotheses,” highlighting some of the considerations needed in brain imaging data acquisition and analysis. We discuss work that has sought to link human brain imaging signals to existing electrophysiological data and opened up new opportunities in studying the neural basis of complex perceptual judgments. We consider a range of approaches when using human functional magnetic resonance imaging to identify brain circuits whose activity changes in a similar manner to perceptual judgments and illustrate these approaches by discussing work that has studied the neural basis of 3D perception and perceptual learning. Finally, we describe approaches that have sought to understand the information content of brain imaging data using machine learning and work that has integrated multimodal data to overcome the limitations associated with individual brain imaging approaches. Together these approaches provide an important route in seeking to understand the links between physiological and psychological states.

scholarly journals Single-Cell, Genome-wide Sequencing Identifies Clonal Somatic Copy-Number Variation in the Human Brain

Cell Reports ◽  
2014 ◽  
Vol 8 (5) ◽  
pp. 1280-1289 ◽  
Author(s):  
Xuyu Cai ◽  
Gilad D. Evrony ◽  
Hillel S. Lehmann ◽  
Princess C. Elhosary ◽  
Bhaven K. Mehta ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Human Brain ◽  
Single Cell ◽  
Copy Number ◽  
Genome Wide ◽  
Number Variation ◽  
Cell Genome ◽  
Single Cell Genome

scholarly journals Self-controlled Sequencing Suggests that Somatic Mutations of Signaling, Transcription and Tumor Suppression Genes are a Precondition for AML Transformation in MDS

2022 ◽  
Author(s):  
Feng Xu ◽  
Ling-Yun Wu ◽  
Juan Guo ◽  
Qi He ◽  
Zheng Zhang ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Tumor Suppressors ◽  
Rna Splicing ◽  
Somatic Mutations ◽  
Target Genes ◽  
Single Cell Sequencing ◽  
Target Sequencing ◽  
Paired Samples ◽  
Whole Exome

Abstract Background The transformation biology of secondary AML from MDS is still not fully understood. Here, we performed a large cohort of paired self-controlled sequences including target, whole-exome and single cell sequencing to search AML transformation-related mutations (TRMs). Methods 39 target genes from paired samples from 72 patients with MDS who had undergone AML transformation were analyzed by next generation target sequencing. Whole exome and single-cell RNA sequencing were used to verify the dynamics of transformation. Results The target sequencing results showed that sixty-four out of the 72 (88.9%) patients presented presumptive TRMs involving activated signaling, transcription factors, or tumor suppressors. Of the 64 patients, most of TRMs (62.5%, 40 cases) emerged at the leukemia transformation point. All three of the remaining eight patients analyzed by paired whole exome sequencing showed TRMs which are not included in the reference targets. No patient with MDS developed into AML only by acquiring mutations involved in epigenetic modulation or RNA splicing. Single-cell sequencing in one pair sample indicated that the activated cell signaling route was related to TRMs which take place prior to phenotypic development. Of note, target sequencing defined TRMs were limited to a small set of seven genes (in the order: NRAS/KRAS, CEBPA, TP53, FLT3, CBL, PTPN11 and RUNX1, accounted for nearly 90.0% of the TRMs). Conclusions Somatic mutations involving in signaling, transcription factors, or tumor suppressors appeared to be a precondition for AML transformation from MDS. The TRMs may be considered as new therapy targets.

scholarly journals An Autism Spectrum Disorder Adaptive Identification Based on the Elimination of Brain Connections: A Proof of Long-Range Underconnectivity

2021 ◽  
Author(s):  
Fatima zahra Benabdallah ◽  
Ahmed Drissi El Maliani ◽  
Dounia Lotfi ◽  
Rachid Jennane ◽  
Mohammed El hassouni
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Long Range ◽  
Data Exchange ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Brain Imaging Data

Abstract Autism spectrum disorder (ASD) is theoretically characterized by alterations in functional connectivity between brain regions. Many works presented approaches to determine informative patterns that help to predict autism from typical development. However, most of the proposed pipelines are not specifically designed for the autism problem, i.e they do not corroborate with autism theories about functional connectivity. In this paper, we propose a framework that takes into account the properties of local connectivity and long range under-connectivity in the autistic brain. The originality of the proposed approach is to adopt elimination as a technique in order to well emerge the autistic brain connectivity alterations, and show how they contribute to differentiate ASD from controls. Experimental results conducted on the large multi-site Autism Brain Imaging Data Exchange (ABIDE) show that our approach provides accurate prediction up to 70% and succeeds to prove the existence of deficits in the long-range connectivity in the ASD subjects brains.

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