Lilah Toker

Secondary neurotransmitter deficiencies in epilepsy caused by voltage-gated sodium channelopathies: A potential treatment target?

We describe neurotransmitter abnormalities in two patients with drug-resistant epilepsy resulting from deleterious de novo mutations in sodium channel genes. Whole exome sequencing identified a de novo SCN2A splice-site mutation (c.2379+1G>A, p.Glu717Gly.fs*30) resulting in deletion of exon 14, in a 10-year old male with early onset global developmental delay, intermittent ataxia, autism, hypotonia, epileptic encephalopathy and cerebral/cerebellar atrophy. In the cerebrospinal fluid both homovanillic acid and 5-hydroxyindoleacetic acid were significantly decreased; extensive biochemical and genetic investigations ruled out primary neurotransmitter deficiencies and other known inborn errors of metabolism. In an 8-year old female with an early onset intractable epileptic encephalopathy, developmental regression, and progressive cerebellar atrophy, a previously unreported de novo missense mutation was identified in SCN8A (c.5615G>A; p.Arg1872Gln), affecting a highly conserved residue located in the C-terminal of the Nav1.6 protein. Aside from decreased homovanillic acid and 5-hydroxyindoleacetic acid, 5-methyltetrahydrofolate was also found to be low. We hypothesize that these channelopathies cause abnormal synaptic mono-amine metabolite secretion/uptake via impaired vesicular release and imbalance in electrochemical ion gradients, which in turn aggravate the seizures. Treatment with oral 5-hydroxytryptophan, l-Dopa/Carbidopa, and a dopa agonist resulted in mild improvement of seizure control in the male case, most likely via dopamine and serotonin receptor activated signal transduction and modulation of glutamatergic, GABA-ergic and glycinergic neurotransmission. Neurotransmitter analysis in other sodium channelopathy patients will help validate our findings, potentially yielding novel treatment opportunities.

Transcriptomic correlates of neuron electrophysiological diversity

How neuronal diversity emerges from complex patterns of gene expression remains poorly understood. Here we present an approach to understand electrophysiological diversity through gene expression by integrating pooled- and single-cell transcriptomics with intracellular electrophysiology. Using neuroinformatics methods, we compiled a brain-wide dataset of 34 neuron types with paired gene expression and intrinsic electrophysiological features from publically accessible sources, the largest such collection to date. We identified 420 genes whose expression levels significantly correlated with variability in one or more of 11 physiological parameters. We next trained statistical models to infer cellular features from multivariate gene expression patterns. Such models were predictive of gene-electrophysiological relationships in an independent collection of 12 visual cortex cell types from the Allen Institute, suggesting that these correlations might reflect general principles relating expression patterns to phenotypic diversity across very different cell types. Many associations reported here have the potential to provide new insights into how neurons generate functional diversity, and correlations of ion channel genes like Gabrd and Scn1a (Nav1.1) with resting potential and spiking frequency are consistent with known causal mechanisms. Our work highlights the promise and inherent challenges in using cell type-specific transcriptomics to understand the mechanistic origins of neuronal diversity.

Cross-Laboratory Analysis of Brain Cell Type Transcriptomes with Applications to Interpretation of Bulk Tissue Data

Visual Abstract Establishing the molecular diversity of cell types is crucial for the study of the nervous system. We compiled a cross-laboratory database of mouse brain cell type-specific transcriptomes from 36 major cell types from across the mammalian brain using rigorously curated published data from pooled cell type microarray and single-cell RNA-sequencing (RNA-seq) studies. We used these data to identify cell type-specific marker genes, discovering a substantial number of novel markers, many of which we validated using computational and experimental approaches. We further demonstrate that summarized expression of marker gene sets (MGSs) in bulk tissue data can be used to estimate the relative cell type abundance across samples. To facilitate use of this expanding resource, we provide a user-friendly web interface at www.neuroexpresso.org.

NeuroExpresso: A cross-laboratory database of brain cell-type expression profiles with applications to marker gene identification and bulk brain tissue transcriptome interpretation

The identification of cell type marker genes, genes highly enriched in specific cell types, plays an important role in the study of the nervous system. In particular, marker genes can be used to identify cell types to enable studies of their properties. Marker genes can also aid the interpretation of bulk tissue expression profiles by revealing cell type specific changes. We assembled a database, NeuroExpresso, of publicly available mouse brain cell type-specific gene expression datasets. We then used stringent criteria to select marker genes highly expressed in individual cell types. We found a substantial number of novel markers previously unknown in the literature and validated a subset of them using in silico analyses and in situ hybridization. We next demonstrate the use of marker genes in analysis of whole tissue data by summarizing their expression into “cell type profiles” that can be thought of as surrogates for the relative abundance of the cell types across the samples studied. Further analysis of our cell type-specific expression database confirms some recent findings about brain cell types along with revealing novel properties, such as Ddc expression in oligodendrocytes. To facilitate further use of this expanding database, we provide a user-friendly web interface for the visualization of expression data. Significance Statement Cell type markers are powerful tools in the study of the nervous system that help reveal properties of cell types and acquire additional information from large scale expression experiments. Despite their usefulness in the field, known marker genes for brain cell types are few in number. We present NeuroExpresso, a database of brain cell type specific gene expression profiles, and demonstrate the use of marker genes for acquiring cell type specific information from whole tissue expression. The database will prove itself as a useful resource for researchers aiming to reveal novel properties of the cell types and aid both laboratory and computational scientists to unravel the cell type specific components of brain disorders.Establishing the molecular diversity of cell types is crucial for the study of the nervous system. We compiled a cross-laboratory database of mouse brain cell type-specific transcriptomes from 36 major cell types from across the mammalian brain using rigorously curated published data from pooled cell type microarray and single cell RNA-sequencing studies. We used these data to identify cell type-specific marker genes, discovering a substantial number of novel markers, many of which we validated using computational and experimental approaches. We further demonstrate that summarized expression of marker gene sets in bulk tissue data can be used to estimate the relative cell type abundance across samples. To facilitate use of this expanding resource, we provide a user-friendly web interface at Neuroexpresso.org.

Metaanalysis of flawed expression profiling data leading to erroneous Parkinson's biomarker identification

Santiago and Potashkin (1) propose that two RNAs in blood, hepatocyte nuclear factor 4 alpha (HNF4A) and polypyrimidine tract binding protein 1 (PTBP1), might be clinically useful biomarkers for diagnosing and tracking the progression of Parkinson’s disease (PD), even speculating that they are better than neurological examination. Many have proposed biomarkers based on gene expression in blood, including the same authors (2, 3), but none of the studies propose the same biomarkers as each other, and to our knowledge such studies have not led to any substantial inroads in changing PD diagnosis methods. Given this backdrop, the bar to propose new blood-based biomarkers for PD should be quite high. In our opinion, this bar was not reached in this case.

Data-driven approaches for improving the interpretability of patch-seq data

Patch-seq, enabling simultaneous measurement of a transcriptomic, electrophysiological, and morphological features, has recently emerged as a powerful tool for neuronal characterization. However, we show the method is susceptible to technical artifacts, including the presence of mRNA contaminants from multiple cells, that limit the interpretability of the data. We present a straightforward marker gene-based approach for controlling for these artifacts and show that our method improves the correspondence between gene expression and electrophysiological features.Patch-seq, combining patch-clamp electrophysiology with single-cell RNA-sequencing (scRNAseq), enables unprecedented single-cell access to a neuron’s transcriptomic, electrophysiological, and morphological features. Here, we present a systematic review and re-analysis of scRNAseq profiles from 4 recent patch-seq datasets, benchmarking these against analogous profiles from cellular-dissociation based scRNAseq. We found an increased likelihood for off-target cell-type mRNA contamination in patch-seq, likely due to the passage of the patch-pipette through the processes of adjacent cells. We also observed that patch-seq samples varied considerably in the amount of mRNA that could be extracted from each cell, strongly biasing the numbers of detectable genes. We present a straightforward marker gene-based approach for controlling for these artifacts and show that our method improves the correspondence between gene expression and electrophysiological features. Our analysis suggests that these technical confounds likely limit the interpretability of patch-seq based single-cell transcriptomes. However, we provide concrete recommendations for quality control steps that can be performed prior to costly RNA-sequencing to optimize the yield of high quality samples.

Assessing transcriptome quality in patch-seq datasets

Patch-seq, combining patch-clamp electrophysiology with single-cell RNA-sequencing (scRNAseq), enables unprecedented access to a neurons transcriptomic, electrophysiological, and morphological features. Here, we present a re-analysis of five patch-seq datasets, representing cells from ex vivo mouse brain slices and in vitro human stem-cell derived neurons. Our objective was to develop simple criteria to assess the quality of patch-seq derived single-cell transcriptomes. We evaluated patch-seq transcriptomes for the expression of marker genes of multiple cell types, benchmarking these against analogous profiles from cellular-dissociation based scRNAseq. We found an increased likelihood of off-target cell-type mRNA contamination in patch-seq cells from acute brain slices, likely due to the passage of the patch-pipette through the processes of adjacent cells. We also observed that patch-seq samples varied considerably in the amount of mRNA that could be extracted from each cell, strongly biasing the numbers of detectable genes. We developed a marker gene-based approach for scoring single-cell transcriptome quality post-hoc. Incorporating our quality metrics into downstream analyses improved the correspondence between gene expression and electrophysiological features. Our analysis suggests that technical confounds likely limit the interpretability of patch-seq based single-cell transcriptomes. However, we provide concrete recommendations for quality control steps that can be performed prior to costly RNA-sequencing to optimize the yield of high-quality samples.

Transcriptomic Evidence for Alterations in Astrocytes and Parvalbumin Interneurons in Subjects With Bipolar Disorder and Schizophrenia

BACKGROUND High-throughput expression analyses of postmortem brain tissue have been widely used to study bipolar disorder and schizophrenia. However, despite the extensive efforts, no consensus has emerged as to the functional interpretation of the findings. We hypothesized that incorporating information on cell type-specific expression would provide new insights. METHODS We reanalyzed 15 publicly available bulk tissue expression datasets on schizophrenia and bipolar disorder, representing various brain regions from eight different cohorts of subjects (unique subjects: 332 control, 129 bipolar disorder, 341 schizophrenia). We studied changes in the expression profiles of cell type marker genes and evaluated whether these expression profiles could serve as surrogates for relative abundance of their corresponding cells. RESULTS In both bipolar disorder and schizophrenia, we consistently observed an increase in the expression profiles of cortical astrocytes and a decrease in the expression profiles of fast-spiking parvalbumin interneurons. No changes in astrocyte expression profiles were observed in subcortical regions. Furthermore, we found that many of the genes previously identified as differentially expressed in schizophrenia are highly correlated with the expression profiles of astrocytes or fast-spiking parvalbumin interneurons. CONCLUSIONS Our results indicate convergence of transcriptome studies of schizophrenia and bipolar disorder on changes in cortical astrocytes and fast-spiking parvalbumin interneurons, providing a unified interpretation of numerous studies. We suggest that these changes can be attributed to alterations in the relative abundance of the cells and are important for understanding the pathophysiology of the disorders.

Large-scale analysis of brain-wide electrophysiological diversity reveals novel characterization of mammalian neuron types

Brains achieve efficient function through implementing a division of labor, in which different neurons serve distinct computational roles. One striking way in which neuron types differ is in their electrophysiology properties. These properties arise through expression of combinations of ion channels that collectively define the computations that a neuron performs on its inputs and its role within its larger circuit. Though the electrophysiology of many neuron types has been previously characterized, these data exist across thousands of journal articles, making cross-study neuron-to-neuron comparisons difficult. Here, we present NeuroElectro, a public database where physiological properties for the majority of mammalian neuron types have been compiled through semi-automated literature text-mining and expert curation. The corresponding web application, at http://www.neuroelectro.org, provides a rich dynamic interface for visualizing and comparing physiological information across neuron types; conveniently linking extracted data back to its primary reference. Mining the database content after normalization for methodological differences, we show that there exist but 5-9 major neuron classes in terms of electrophysiological properties, which separate largely based on cell size and basal levels of excitability (Figure ​(Figure11). Figure 1 Hierarchical clustering of diverse neuron types on the basis of electrophysiological similarity. Electrophysiological parameters are obtained from the NeuroElectro database via literature-mining and are normalized to account for variability in experimental ... As an example of how this resource can help answer fundamental questions in neuroscience, we integrate NeuroElectro with neuronal gene expression from public datasets like the Allen Brain Atlas. We show that simple statistical models can accurately predict features of a neurons electrophysiological phenotype given information of its gene expression alone. We further investigate these models to ask which genes, of the 20K in the genome, are most predictive of neuron physiology. We find that while ion channel-related genes provide significant predictive power, the most predictive gene classes surprisingly correspond to G-proteins and transcription factors, suggesting the involvement of hundreds of diverse genes in regulating a neurons computational function.