Eran A. Mukamel - US grants

DESCRIPTION (provided by applicant): The proposed research combines biophysics, statistical signal processing, computational data analysis, and neurobiology, to address a fundamentally interdisciplinary problem: How are global brain states organized? The brain's ability to operate in distinct global states, including waking, sleep, and general anesthesia, is critical for human health and medicine. This project aims to understand how cortical networks, interacting via millisecond -scale electrical signals, self-organize over macroscopic spatial dimensions to sustain global states for minutes to hours. In humans, electro- and magnetoencephalography (EEG, MEG) and intracranial electrocorticography (ECoG) probe neural dynamics with ms-scale resolution, but interpretation is challenging due to the ambiguity of their microscopic current sources. This project will use new, computationally sophisticated analyses and realistic biophysical modeling, combined with large-scale physiological recordings, to provide insight into the nature of cortical neural activity, in particular the global organizatin of sleep and general anesthesia. The mentored postdoctoral phase will build on preliminary results from electrophysiological studies of human cortical dynamics during induction of general anesthesia. By using advanced statistical signal processing of high-density EEG recordings, this research showed that the unconscious brain during general anesthesia generates two categorically distinct types of rhythmic activity. These patterns are indistinguishable by classica power spectral methods and hence were not observed previously. These results indicate propofol general anesthesia is not a unitary state, but comprises multiple global mode. The implications of these findings will be pursued by analyzing how auditory stimulus processing is altered during each state of unconsciousness evoked by propofol general anesthesia. Through computational and statistical analysis of cortical event-related potentials this project will probe the time course of neural activity following controlled auditory events to test whether the induction and emergence from anesthesia modulate sensory processing differentially. The preliminary results obtained in these studies will lead directly to the R00 independent research. Using intracranial recordings, obtained from patients implanted with arrays of electrodes in the course of treatment for epilepsy, this study will provide the first map in humans of the fine-scale spatial organization of specific activity patterns associated with general anesthesia. The propagation of currents and magnetic flux through the multiple layers of dielectric tissue in the head of a human subject will be measured empirically. Finally, the knowledge and tools resulting from these studies of general anesthesia will be leveraged to investigate the organization of rhythmic activity in physiological sleep, specifically aiming to test a new hypothesis for the circuit under- lying sleep spindles. Together, these studies will provide an empirically validated framework for understanding the global organization of neuronal activity throughout the brain within waking and unconscious states. PUBLIC HEALTH RELEVANCE: The proposed research will investigate how brain activity changes during transitions between different states such as waking and sleep, as well as during the induction of general anesthesia. We aim to understand how the normal communication between neurons in different parts of the brain is disrupted following the loss of consciousness during sleep or general anesthesia. Such an understanding will help design better techniques for monitoring patients during induction of general anesthesia, and will be useful for diagnosing and treating disorders that affect sleep.

Project Summary/Abstract Our project will create a computational resource, the Single Neuron Analyzer, to support the neuroscience community?s efforts to build a reproducible, comprehensive, data-driven atlas of brain cell types. Laboratories in the BRAIN Initiative Cell Census Network and others are generating large-scale molecular datasets from multiple regions of the mouse and human brain using single cell sequencing technology. These datasets include single cell and single nucleus transcriptomes (RNA-Seq), as well as single nucleus DNA methylomes (mC-Seq) and chromatin accessibility (ATAC-Seq). Each data type provides complementary information about the molecular identity of brain cells: transcriptomes directly measure gene expression, while epigenomic data indicates both gene expression levels and the activity of intergenic regulatory regions such as enhancers. However, there is no computational resource for integrating these data from these multiple modalities and for statistically validating the reliability and reproducibility of the cell types defined based on each dataset. The Single Neuron Analyzer will work within the framework of the Single Cell Portal, which provides horizontally-scalable, highly performant solutions that allow researchers to efficiently scale with the growing size of datasets as the technology for single cell sequencing advances. In Aim 1, we will use machine learning and cross-validation to study the reproducibility of cell types defined by researchers based on one or more datasets. The Single Neuron Analyzer will allow users to compute a quantitative score, corresponding to the area under the receiver operating characteristic (AUROC), which quantifies the degree to which cell type labels can be predicted based on independent data such as experimental replicates or complementary molecular assays. Aim 2 will build a data integration system that can jointly analyze single cells profiled by different technologies and modalities, including transcriptomic and epigenomic data. We will take advantage of the reliable correlation of gene expression with low gene body DNA methylation and high chromatin accessibility, to link cells measured in one modality with their closest matching neighbors in the other two modalities. The resulting neighbor graphs will be used to impute the missing data, followed by joint cluster analysis and low-dimensional projection of the integrated dataset. Following joint analysis, the system will provide a variety of visualizations and downloadable reports about key markers for each cell type. By combining transcriptomic and epigenomic information, the system will predict cell type specific genes as well as putative enhancers. Single Neuron Analyzer will offer researchers across the neuroscience community a resource for rigorous multi-modal molecular analysis of neuronal cell types, helping to advance the goal of comprehensively understanding the brain?s cellular parts list.

Project Summary: Most genetic variants associated with disease in genome-wide association studies (GWAS) lie in non-coding gene regulatory elements (GRE; e.g., promoters and enhancers). GREs are tissue- and cell type-specific and are identified through their epigenomic signatures, including low DNA methylation (DNAm), DNA accessibility and certain histone modifications. The PsychENCODE Consortium has characterized brain GREs across brain regions and developmental time points, as well as in the brains of psychiatric patients using mostly DNA accessibility and histone modification marks. These marks, however, identify large regions of enrichment (~300-2,000bp), providing only low resolution coverage of the important regulatory nucleotides, e.g., transcription factor binding sites. DNAm (especially cell type-specific DNAm) has received less attention, although it has been linked to psychiatric disorders, including schizophrenia (SZ) and bipolar disorder (BD). In the adult human brain, ~80% of CG and 1.5% of non-CG (CH) sites are methylated, and can be converted to hydroxymethylcytosine (hmC) and further demethylated. In postmitotic neuronal genomes, mCH and hmC accumulate to a significantly higher level than in other tissues--a distinct feature of the brain's epigenome. Bisulfite sequencing (BS)-based approaches that are used to measure (h)mC can pinpoint GREs with single base resolution, presenting a unique opportunity to refine the gene regulatory landscape of the brain cell types. Here we aim to create reference DNAm maps [mC and hmC, using BS and oxidative (ox)BS sequencing] and transcriptional profiles (using RNA-seq) in two major subtypes of neurons in the human dorsolateral prefrontal cortex (DLPFC), namely excitatory glutamatergic (Glu) and inhibitory GABA-ergic neurons. The proposed work is based on fluorescence-activated nuclear sorting (FANS) methods that we recently developed to separate nuclei of different cell types from autopsied human brain, and on our recent findings that showed unexpected relationships between DNA(h)m, GREs, and gene expression in the DLPFC Glu and GABA neurons. We will perform these studies at key time points of postnatal brain development and adulthood to map DNA(h)m within active neuron subtype-specific GREs that may be vulnerable to disruption during childhood and adolescence periods that coincide with the critical processes of brain maturation and circuit refinement (Aim 1). This work will be complemented with single nucleus mC profiling, which will allow us to define the developmental trajectories of mC in discrete subpopulations of Glu and GABA neurons (Aim2). Finally, we will profile Glu and GABA neurons in 150 autopsied DLPFC samples obtained from controls and cases of SZ and BD, and will then map neuron subtype-specific gene expression and (hydroxy)methylation quantitative trait loci (eQTL, mQTLS, hmQTLs) (Aim3). We will integrate QTL, transcriptome, and DNA(h)m data with the results of SZ and BP GWAS to reveal DNA(h)m and gene expression-mediated causal risk variants and genes, and to distinguish specific neuronal subtype(s) that are critical in the etiology of these disorders.