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 FR-Match: Robust matching of cell type clusters from single cell RNA sequencing data using the Friedman-Rafsky non-parametric test

2020 ◽  
Author(s):  
Yun Zhang ◽  
Brian D. Aevermann ◽  
Trygve E. Bakken ◽  
Jeremy A. Miller ◽  
Rebecca D. Hodge ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
R Package ◽  
Brain Regions ◽  
Cortical Layer ◽  
Middle Temporal Gyrus ◽  
Cell Type ◽  
Sequencing Data

AbstractSingle cell/nucleus RNA sequencing (scRNAseq) is emerging as an essential tool to unravel the phenotypic heterogeneity of cells in complex biological systems. While computational methods for scRNAseq cell type clustering have advanced, the ability to integrate datasets to identify common and novel cell types across experiments remains a challenge. Here, we introduce a cluster-to-cluster cell type matching method – FR-Match – that utilizes supervised feature selection for dimensionality reduction and incorporates shared information among cells to determine whether two cell type clusters share the same underlying multivariate gene expression distribution. FR-Match is benchmarked with existing cell-to-cell and cell-to-cluster cell type matching methods using both simulated and real scRNAseq data. FR-Match proved to be a stringent method that produced fewer erroneous matches of distinct cell subtypes and had the unique ability to identify novel cell phenotypes in new datasets. In silico validation demonstrated that the proposed workflow is the only self-contained algorithm that was robust to increasing numbers of true negatives (i.e. non-represented cell types). FR-Match was applied to two human brain scRNAseq datasets sampled from cortical layer 1 and full thickness middle temporal gyrus. When mapping cell types identified in specimens isolated from these overlapping human brain regions, FR-Match precisely recapitulated the laminar characteristics of matched cell type clusters, reflecting their distinct neuroanatomical distributions. An R package and Shiny application are provided at https://github.com/JCVenterInstitute/FRmatch for users to interactively explore and match scRNAseq cell type clusters with complementary visualization tools.

scholarly journals Cellular diversity in the Drosophila midbrain revealed by single-cell transcriptomics

2017 ◽  
Author(s):  
Vincent Croset ◽  
Christoph D Treiber ◽  
Scott Waddell
Keyword(s):  
Single Cell ◽  
Neural Circuit ◽  
Cell Types ◽  
Brain Regions ◽  
Neurotransmitter Receptor ◽  
Neural Processes ◽  
Subunit Expression ◽  
Circuit Function ◽  
Fast Acting ◽  
The Brain

AbstractTo understand the brain, molecular details need to be overlaid onto neural wiring diagrams so that synaptic mode, neuromodulation and critical signaling operations can be considered. Single-cell transcriptomics provide a unique opportunity to collect this information. Here we present an initial analysis of thousands of individual cells from Drosophila midbrain, that were acquired using Drop-Seq. A number of approaches permitted the assignment of transcriptional profiles to several major brain regions and cell-types. Expression of biosynthetic enzymes and reuptake mechanisms allows all the neurons to be typed according to the neurotransmitter or neuromodulator that they produce and presumably release. Some neuropeptides are preferentially co-expressed in neurons using a particular fast-acting transmitter, or monoamine. Neuromodulatory and neurotransmitter receptor subunit expression illustrates the potential of these molecules in generating complexity in neural circuit function. This cell atlas dataset provides an important resource to link molecular operations to brain regions and complex neural processes.

scholarly journals A spatiotemporal complexity architecture of human brain activity

2021 ◽  
Author(s):  
Stephan Krohn ◽  
Nina von Schwanenflug ◽  
Leonhard Waschke ◽  
Amy Romanello ◽  
Martin Gell ◽  
...  
Keyword(s):  
Human Brain ◽  
Neural Activity ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Activity ◽  
Brain Regions ◽  
Brain Organization ◽  
Functional Connections ◽  
Signal Complexity ◽  
The Brain

The human brain operates in large-scale functional networks, collectively subsumed as the functional connectome1-13. Recent work has begun to unravel the organization of the connectome, including the temporal dynamics of brain states14-20, the trade-off between segregation and integration9,15,21-23, and a functional hierarchy from lower-order unimodal to higher-order transmodal processing systems24-27. However, it remains unknown how these network properties are embedded in the brain and if they emerge from a common neural foundation. Here we apply time-resolved estimation of brain signal complexity to uncover a unifying principle of brain organization, linking the connectome to neural variability6,28-31. Using functional magnetic resonance imaging (fMRI), we show that neural activity is marked by spontaneous "complexity drops" that reflect episodes of increased pattern regularity in the brain, and that functional connections among brain regions are an expression of their simultaneous engagement in such episodes. Moreover, these complexity drops ubiquitously propagate along cortical hierarchies, suggesting that the brain intrinsically reiterates its own functional architecture. Globally, neural activity clusters into temporal complexity states that dynamically shape the coupling strength and configuration of the connectome, implementing a continuous re-negotiation between cost-efficient segregation and communication-enhancing integration9,15,21,23. Furthermore, complexity states resolve the recently discovered association between anatomical and functional network hierarchies comprehensively25-27,32. Finally, brain signal complexity is highly sensitive to age and reflects inter-individual differences in cognition and motor function. In sum, we identify a spatiotemporal complexity architecture of neural activity — a functional "complexome" that gives rise to the network organization of the human brain.

scholarly journals Spatio-temporal parameters for optical probing of neuronal activity

2021 ◽  
Author(s):  
Vincent R. Daria ◽  
Michael Lawrence Castañares ◽  
Hans-A. Bachor
Keyword(s):  
Neuronal Activity ◽  
Brain Regions ◽  
Mammalian Brain ◽  
Neuronal Circuits ◽  
Temporal Parameters ◽  
Brain Functions ◽  
Temporal Properties ◽  
Optical Probing ◽  
Spatio Temporal ◽  
The Brain

AbstractThe challenge to understand the complex neuronal circuit functions in the mammalian brain has brought about a revolution in light-based neurotechnologies and optogenetic tools. However, while recent seminal works have shown excellent insights on the processing of basic functions such as sensory perception, memory, and navigation, understanding more complex brain functions is still unattainable with current technologies. We are just scratching the surface, both literally and figuratively. Yet, the path towards fully understanding the brain is not totally uncertain. Recent rapid technological advancements have allowed us to analyze the processing of signals within dendritic arborizations of single neurons and within neuronal circuits. Understanding the circuit dynamics in the brain requires a good appreciation of the spatial and temporal properties of neuronal activity. Here, we assess the spatio-temporal parameters of neuronal responses and match them with suitable light-based neurotechnologies as well as photochemical and optogenetic tools. We focus on the spatial range that includes dendrites and certain brain regions (e.g., cortex and hippocampus) that constitute neuronal circuits. We also review some temporal characteristics of some proteins and ion channels responsible for certain neuronal functions. With the aid of the photochemical and optogenetic markers, we can use light to visualize the circuit dynamics of a functioning brain. The challenge to understand how the brain works continue to excite scientists as research questions begin to link macroscopic and microscopic units of brain circuits.

scholarly journals Cellular diversity in the Drosophila midbrain revealed by single-cell transcriptomics

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Vincent Croset ◽  
Christoph D Treiber ◽  
Scott Waddell
Keyword(s):  
Single Cell ◽  
Neural Circuit ◽  
Cell Types ◽  
Brain Regions ◽  
Neurotransmitter Receptor ◽  
Neural Processes ◽  
Subunit Expression ◽  
Circuit Function ◽  
Fast Acting ◽  
The Brain

To understand the brain, molecular details need to be overlaid onto neural wiring diagrams so that synaptic mode, neuromodulation and critical signaling operations can be considered. Single-cell transcriptomics provide a unique opportunity to collect this information. Here we present an initial analysis of thousands of individual cells from Drosophila midbrain, that were acquired using Drop-Seq. A number of approaches permitted the assignment of transcriptional profiles to several major brain regions and cell-types. Expression of biosynthetic enzymes and reuptake mechanisms allows all the neurons to be typed according to the neurotransmitter or neuromodulator that they produce and presumably release. Some neuropeptides are preferentially co-expressed in neurons using a particular fast-acting transmitter, or monoamine. Neuromodulatory and neurotransmitter receptor subunit expression illustrates the potential of these molecules in generating complexity in neural circuit function. This cell atlas dataset provides an important resource to link molecular operations to brain regions and complex neural processes.

scholarly journals 80. SINGLE-CELL RNA-SEQUENCING OF HUMAN BRAIN REGIONS IDENTIFIES CELL TYPES AND STATES RELEVANT TO DISEASE

2021 ◽  
Vol 51 ◽  
pp. e83-e84
Author(s):  
Sherif Gerges ◽  
Melissa Goldman ◽  
Sabina Berretta ◽  
Steven McCarroll ◽  
Mark Daly
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Brain Regions ◽  
Single Cell Rna Sequencing

scholarly journals FR-Match: robust matching of cell type clusters from single cell RNA sequencing data using the Friedman–Rafsky non-parametric test

2020 ◽  
Author(s):  
Yun Zhang ◽  
Brian D Aevermann ◽  
Trygve E Bakken ◽  
Jeremy A Miller ◽  
Rebecca D Hodge ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
R Package ◽  
Brain Regions ◽  
Cortical Layer ◽  
Middle Temporal Gyrus ◽  
Cell Type ◽  
Sequencing Data

Abstract Single cell/nucleus RNA sequencing (scRNAseq) is emerging as an essential tool to unravel the phenotypic heterogeneity of cells in complex biological systems. While computational methods for scRNAseq cell type clustering have advanced, the ability to integrate datasets to identify common and novel cell types across experiments remains a challenge. Here, we introduce a cluster-to-cluster cell type matching method—FR-Match—that utilizes supervised feature selection for dimensionality reduction and incorporates shared information among cells to determine whether two cell type clusters share the same underlying multivariate gene expression distribution. FR-Match is benchmarked with existing cell-to-cell and cell-to-cluster cell type matching methods using both simulated and real scRNAseq data. FR-Match proved to be a stringent method that produced fewer erroneous matches of distinct cell subtypes and had the unique ability to identify novel cell phenotypes in new datasets. In silico validation demonstrated that the proposed workflow is the only self-contained algorithm that was robust to increasing numbers of true negatives (i.e. non-represented cell types). FR-Match was applied to two human brain scRNAseq datasets sampled from cortical layer 1 and full thickness middle temporal gyrus. When mapping cell types identified in specimens isolated from these overlapping human brain regions, FR-Match precisely recapitulated the laminar characteristics of matched cell type clusters, reflecting their distinct neuroanatomical distributions. An R package and Shiny application are provided at https://github.com/JCVenterInstitute/FRmatch for users to interactively explore and match scRNAseq cell type clusters with complementary visualization tools.

scholarly journals A High-Resolution In Vivo Atlas of the Human Brain’s Benzodiazepine Binding Site of GABAA Receptors

2020 ◽  
Author(s):  
Martin Nørgaard ◽  
Vincent Beliveau ◽  
Melanie Ganz ◽  
Claus Svarer ◽  
Lars H Pinborg ◽  
...  
Keyword(s):  
High Resolution ◽  
Human Brain ◽  
Mrna Expression ◽  
Brain Regions ◽  
Mrna Levels ◽  
Gamma Aminobutyric Acid ◽  
Healthy Human ◽  
Brain Functions ◽  
The Brain

ABSTRACTGamma-aminobutyric acid (GABA) is the main inhibitory neurotransmitter in the human brain and plays a key role in several brain functions and neuropsychiatric disorders such as anxiety, epilepsy, and depression. The binding of benzodiazepines to the benzodiazepine receptor sites (BZR) located on GABAA receptors (GABAARs) potentiates the inhibitory effect of GABA leading to the anxiolytic, anticonvulsant and sedative effects used for treatment of those disorders. However, the function of GABAARs and the expression of BZR protein is determined by the GABAAR subunit stoichiometry (19 genes coding for individual subunits), and it remains to be established how the pentamer composition varies between brain regions and individuals.Here, we present a quantitative high-resolution in vivo atlas of the human brain BZRs, generated on the basis of [11C]flumazenil Positron Emission Tomography (PET) data. Next, based on autoradiography data, we transform the PET-generated atlas from binding values into BZR protein density. Finally, we examine the brain regional association with mRNA expression for the 19 subunits in the GABAAR, including an estimation of the minimally required expression of mRNA levels for each subunit to translate into BZR protein.This represents the first publicly available quantitative high-resolution in vivo atlas of the spatial distribution of BZR densities in the healthy human brain. The atlas provides a unique neuroscientific tool as well as novel insights into the association between mRNA expression for individual subunits in the GABAAR and the BZR density at each location in the brain.

scholarly journals Parallel RNA and DNA analysis after Deep-sequencing (PRDD-seq) reveals cell type specific lineage patterns in human brain

2020 ◽  
Author(s):  
August Yue Huang ◽  
Pengpeng Li ◽  
Rachel E. Rodin ◽  
Sonia N. Kim ◽  
Yanmei Dou ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Cell Lineage ◽  
Neuronal Cell ◽  
Cell Types ◽  
Inhibitory Neurons ◽  
Cell Type ◽  
Cell Divisions ◽  
Excitatory Neurons ◽  
The Brain

AbstractElucidating the lineage relationships among different cell types is key to understanding human brain development. Here we developed Parallel RNA and DNA analysis after Deep-sequencing (PRDD-seq), which combines RNA analysis of neuronal cell types with analysis of nested spontaneous DNA somatic mutations as cell lineage markers, identified from joint analysis of single cell and bulk DNA sequencing by single-cell MosaicHunter (scMH). PRDD-seq enables the first-ever simultaneous reconstruction of neuronal cell type, cell lineage, and sequential neuronal formation (“birthdate”) in postmortem human cerebral cortex. Analysis of two human brains showed remarkable quantitative details that relate mutation mosaic frequency to clonal patterns, confirming an early divergence of precursors for excitatory and inhibitory neurons, and an “inside-out” layer formation of excitatory neurons as seen in other species. In addition our analysis allows the first estimate of excitatory neuron-restricted precursors (about 10) that generate the excitatory neurons within a cortical column. Inhibitory neurons showed complex, subtype-specific patterns of neurogenesis, including some patterns of development conserved relative to mouse, but also some aspects of primate cortical interneuron development not seen in mouse. PRDD-seq can be broadly applied to characterize cell identity and lineage from diverse archival samples with single-cell resolution and in potentially any developmental or disease condition.Significance StatementStem cells and progenitors undergo a series of cell divisions to generate the neurons of the brain, and understanding this sequence is critical to studying the mechanisms that control cell division and migration in developing brain. Mutations that occur as cells divide are known as the basis of cancer, but have more recently been shown to occur with normal cell divisions, creating a permanent, forensic map of the clonal patterns that define the brain. Here we develop new technology to analyze both DNA mutations and RNA gene expression patterns in single cells from human postmortem brain, allowing us to define clonal patterns among different types of human brain neurons, gaining the first direct insight into how they form.

scholarly journals Systematic investigation of imprinted gene expression and enrichment in the mouse brain explored at single-cell resolution

2020 ◽  
Author(s):  
M. J. Higgs ◽  
M. J. Hill ◽  
R. M. John ◽  
A. R. Isles
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Networks ◽  
Cell Types ◽  
Brain Regions ◽  
Systematic Investigation ◽  
Adult Brain ◽  
Imprinted Genes ◽  
Imprinted Gene ◽  
The Brain

AbstractAlthough a number of imprinted genes are known to be highly expressed in the brain, and in certain brain regions in particular, whether they are truly over-represented in the brain has never been formally tested. Using fifteen single-cell RNA sequencing datasets we take a systematic approach to investigate imprinted gene over-representation at the organ, brain region, and cell-specific levels. We establish that imprinted genes are indeed over-represented in the adult brain, and in neurons particularly compared to other brain cell-types. We then examine brain-wide datasets to examine enrichment within distinct regions of the brain and demonstrate over-representation of imprinted genes in the hypothalamus, ventral midbrain, pons and medulla. Finally, using datasets focusing on these regions of enrichment, we were able to identify hypothalamic neuroendocrine populations and the monoaminergic hindbrain neurons as specific hotspots of imprinted gene expression. These analyses provide the first robust assessment of the neural systems on which imprinted genes converge. Moreover, the unbiased approach, with each analysis informed by the findings of the previous level, permits highly informed inferences about the functions on which imprinted genes converge. Our findings indicate the neuronal regulation of motivated behaviours such as feeding, parental behaviour and sleep as functional hotspots for imprinting, thus adding statistically rigour to prior assumptions and providing testable predictions for novel neural and behavioural phenotypes associated with specific genes and imprinted gene networks. In turn, this work sheds further light on the potential evolutionary drivers of genomic imprinting in the brain.

Sign in / Sign up

Export Citation Format

Share Document