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.

[1]  B. Sabatini,et al.  Corelease of acetylcholine and GABA from cholinergic forebrain neurons , 2015, eLife.

[2]  M. Ugrumov Brain neurons partly expressing dopaminergic phenotype: location, development, functional significance, and regulation. , 2013, Advances in pharmacology.

[3]  Mary Kay Lobo,et al.  FACS-array profiling of striatal projection neuron subtypes in juvenile and adult mouse brains , 2006, Nature Neuroscience.

[4]  Ash A. Alizadeh,et al.  Robust enumeration of cell subsets from tissue expression profiles , 2015, Nature Methods.

[5]  F. Middleton,et al.  Transcriptional analysis of multiple brain regions in Parkinson's disease supports the involvement of specific protein processing, energy metabolism, and signaling pathways, and suggests novel disease mechanisms , 2005, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.

[6]  A. Ramasamy,et al.  Widespread sex differences in gene expression and splicing in the adult human brain , 2013, Nature Communications.

[7]  F. C. Bennett,et al.  New tools for studying microglia in the mouse and human CNS , 2016, Proceedings of the National Academy of Sciences.

[8]  Nelson Spruston,et al.  Hipposeq: a comprehensive RNA-seq database of gene expression in hippocampal principal neurons , 2016, eLife.

[9]  Sacha B. Nelson,et al.  A Quantitative Comparison of Cell-Type-Specific Microarray Gene Expression Profiling Methods in the Mouse Brain , 2011, PloS one.

[10]  G. Tononi,et al.  Effects of Sleep and Wake on Oligodendrocytes and Their Precursors , 2013, The Journal of Neuroscience.

[11]  M. Peters,et al.  Cell specific eQTL analysis without sorting cells , 2014, bioRxiv.

[12]  Stanley J. Watson,et al.  Post-mortem molecular profiling of three psychiatric disorders , 2016, Genome Medicine.

[13]  Magdalena Götz,et al.  In vivo fate mapping and expression analysis reveals molecular hallmarks of prospectively isolated adult neural stem cells. , 2010, Cell stem cell.

[14]  Paul Pavlidis,et al.  Neuron-Enriched Gene Expression Patterns are Regionally Anti-Correlated with Oligodendrocyte-Enriched Patterns in the Adult Mouse and Human Brain , 2013, Front. Neurosci..

[15]  P. Greengard,et al.  Molecular adaptations of striatal spiny projection neurons during levodopa-induced dyskinesia , 2014, Proceedings of the National Academy of Sciences.

[16]  Yuko Saito,et al.  TMEM119 marks a subset of microglia in the human brain , 2016, Neuropathology : official journal of the Japanese Society of Neuropathology.

[17]  S. Nelson,et al.  Cell-Type-Specific Repression by Methyl-CpG-Binding Protein 2 Is Biased toward Long Genes , 2014, The Journal of Neuroscience.

[18]  Barry Halliwell,et al.  Oxidative stress in cell culture: an under‐appreciated problem? , 2003, FEBS letters.

[19]  Stuart C. Sealfon,et al.  CellCODE: a robust latent variable approach to differential expression analysis for heterogeneous cell populations , 2015, Bioinform..

[20]  Rafael A. Irizarry,et al.  A framework for oligonucleotide microarray preprocessing , 2010, Bioinform..

[21]  Ken Sugino,et al.  Transcriptional and Electrophysiological Maturation of Neocortical Fast-Spiking GABAergic Interneurons , 2009, The Journal of Neuroscience.

[22]  Ling Lin,et al.  Cell type-specific gene expression of midbrain dopaminergic neurons reveals molecules involved in their vulnerability and protection. , 2005, Human molecular genetics.

[23]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[24]  Peter Jonas,et al.  Fast-spiking, parvalbumin+ GABAergic interneurons: From cellular design to microcircuit function , 2014, Science.

[25]  C. Shaw,et al.  CGG repeats in RNA modulate expression of TDP-43 in mouse and fly models of fragile X tremor ataxia syndrome. , 2014, Human molecular genetics.

[26]  Jens Hjerling-Leffler,et al.  Disentangling neural cell diversity using single-cell transcriptomics , 2016, Nature Neuroscience.

[27]  Jee Hoon Roh,et al.  Translational profiling of hypocretin neurons identifies candidate molecules for sleep regulation. , 2013, Genes & development.

[28]  H. Boddeke,et al.  Glia Open Access Database (GOAD): A comprehensive gene expression encyclopedia of glia cells in health and disease , 2015, Glia.

[29]  Oliver von Ahsen,et al.  Global Transcriptome Analysis of Genetically Identified Neurons in the Adult Cortex , 2006, The Journal of Neuroscience.

[30]  Matthew E. Ritchie,et al.  limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[31]  J. Dougherty,et al.  Reexposure to nicotine during withdrawal increases the pacemaking activity of cholinergic habenular neurons , 2013, Proceedings of the National Academy of Sciences.

[32]  D. Reis,et al.  Monoamine-synthesizing enzymes in central dopaminergic, noradrenergic and serotonergic neurons. Immunocytochemical localization by light and electron microscopy. , 1976, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[33]  Martin Stangel,et al.  Regional differences between grey and white matter in cuprizone induced demyelination , 2009, Brain Research.

[34]  Geoffrey C. Gurtner,et al.  Evaluating the Effect of Cell Culture on Gene Expression in Primary Tissue Samples Using Microfluidic-Based Single Cell Transcriptional Analysis , 2015, Microarrays.

[35]  R. Steinman,et al.  Flt3L controls the development of radiosensitive dendritic cells in the meninges and choroid plexus of the steady-state mouse brain , 2011, The Journal of experimental medicine.

[36]  Staci A. Sorensen,et al.  Adult Mouse Cortical Cell Taxonomy Revealed by Single Cell Transcriptomics , 2016 .

[37]  P. Rousseeuw Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[38]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..

[39]  M. Ronaghi,et al.  Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain , 2016, Science.

[40]  Z. Josh Huang,et al.  Developmental Coordination of Gene Expression between Synaptic Partners During GABAergic Circuit Assembly in Cerebellar Cortex , 2012, Front. Neural Circuits.

[41]  M. Maier,et al.  Astrocyte decrease in the subgenual cingulate and callosal genu in schizophrenia , 2013, European Archives of Psychiatry and Clinical Neuroscience.

[42]  Lydia Ng,et al.  Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system , 2012, Nucleic Acids Res..

[43]  N. Uranova,et al.  Oligodendroglial density in the prefrontal cortex in schizophrenia and mood disorders: a study from the Stanley Neuropathology Consortium , 2004, Schizophrenia Research.

[44]  R. Faull,et al.  Population-specific expression analysis (PSEA) reveals molecular changes in diseased brain , 2011, Nature Methods.

[45]  D. Maraganore,et al.  A Genomic Pathway Approach to a Complex Disease: Axon Guidance and Parkinson Disease , 2007, PLoS genetics.

[46]  N. Heintz,et al.  Layer 2/3 pyramidal cells in the medial prefrontal cortex moderate stress induced depressive behaviors , 2015, eLife.

[47]  M. Kobor,et al.  Adjusting for Cell Type Composition in DNA Methylation Data Using a Regression-Based Approach. , 2015, Methods in molecular biology.

[48]  E. P. Gardner,et al.  Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex , 2008, Nature Reviews Neuroscience.

[49]  Robert C. Thompson,et al.  Inference of Cell Type Composition from Human Brain Transcriptomic Datasets Illuminates the Effects of Age, Manner of Death, Dissection, and Psychiatric Diagnosis , 2016, bioRxiv.

[50]  Xiang Wan,et al.  Gemma: a resource for the reuse, sharing and meta-analysis of expression profiling data , 2012, Bioinform..

[51]  G. Eichele,et al.  Identification of novel spinal cholinergic genetic subtypes disclose Chodl and Pitx2 as markers for fast motor neurons and partition cells , 2010, The Journal of comparative neurology.

[52]  J. Morgan,et al.  Identification of candidate Purkinje cell-specific markers by gene expression profiling in wild-type and pcd(3J) mice. , 2004, Brain research. Molecular brain research.

[53]  S. Nelson,et al.  Molecular taxonomy of major neuronal classes in the adult mouse forebrain , 2006, Nature Neuroscience.

[54]  T. Maniatis,et al.  An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex , 2014, The Journal of Neuroscience.

[55]  G. Gonye,et al.  VTA neurons show a potentially protective transcriptional response to MPTP , 2010, Brain Research.

[56]  C. Margulies,et al.  Designing Cell-Type-Specific Genome-wide Experiments. , 2015, Molecular cell.

[57]  Paul Pavlidis,et al.  Transcriptomic correlates of neuron electrophysiological diversity , 2017, bioRxiv.

[58]  G. Rosoklija,et al.  Whole-transcriptome brain expression and exon-usage profiling in major depression and suicide: evidence for altered glial, endothelial and ATPase activity , 2016, Molecular Psychiatry.

[59]  E. Chang,et al.  Purification and Characterization of Progenitor and Mature Human Astrocytes Reveals Transcriptional and Functional Differences with Mouse , 2016, Neuron.

[60]  Joshua L Plotkin,et al.  MicroRNA-128 Governs Neuronal Excitability and Motor Behavior in Mice , 2013, Science.

[61]  Jingyuan Fu,et al.  Cell Specific eQTL Analysis without Sorting Cells , 2014, bioRxiv.

[62]  Yasuo Kawaguchi,et al.  Fast spiking cells in rat hippocampus (CA1 region) contain the calcium-binding protein parvalbumin , 1987, Brain Research.

[63]  N. Perrone-Bizzozero,et al.  Increased expression of axogenesis-related genes and mossy fibre length in dentate granule cells from adult HuD overexpressor mice , 2011, ASN neuro.

[64]  I. Amit,et al.  Host microbiota constantly control maturation and function of microglia in the CNS , 2015, Nature Neuroscience.

[65]  Seth G. N. Grant,et al.  Identification of Vulnerable Cell Types in Major Brain Disorders Using Single Cell Transcriptomes and Expression Weighted Cell Type Enrichment , 2016, Front. Neurosci..

[66]  M. Yoshida,et al.  Effects of ageing on microglia in the normal rat brain: immunohistochemical observations. , 1994, Neuroreport.

[67]  Cynthia C. Hession,et al.  Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons , 2016, Science.

[68]  D. Lewis,et al.  Large-scale estimates of cellular origins of mRNAs: Enhancing the yield of transcriptome analyses , 2008, Journal of Neuroscience Methods.

[69]  J. Winters,et al.  Functional classification of skeletal muscle networks. II. Applications to pathophysiology. , 2012, Journal of applied physiology.

[70]  C. Heizmann,et al.  Calcium-binding protein parvalbumin as a neuronal marker , 1981, Nature.

[71]  Scott J. Tebbutt,et al.  Enumerateblood – an R package to estimate the cellular composition of whole blood from Affymetrix Gene ST gene expression profiles , 2017, BMC Genomics.

[72]  J. Dougherty,et al.  Cell type-specific analysis of human brain transcriptome data to predict alterations in cellular composition , 2013, Systems biomedicine.

[73]  D. Geschwind,et al.  Progress in Realizing the Promise of Microarrays in Systems Neurobiology , 2005, Neuron.

[74]  C. Brennan,et al.  Recruited Cells Can Become Transformed and Overtake PDGF-Induced Murine Gliomas In Vivo during Tumor Progression , 2011, PloS one.

[75]  P. Greengard,et al.  Identification of the Cortical Neurons that Mediate Antidepressant Responses , 2012, Cell.

[76]  Sean R. Davis,et al.  NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..

[77]  S. Nelson,et al.  Cell-type–based model explaining coexpression patterns of genes in the brain , 2011, Proceedings of the National Academy of Sciences.

[78]  Y. Xing,et al.  A Transcriptome Database for Astrocytes, Neurons, and Oligodendrocytes: A New Resource for Understanding Brain Development and Function , 2008, The Journal of Neuroscience.

[79]  Stefanie Seiler,et al.  Finding Groups In Data , 2016 .

[80]  Nathaniel G Mahieu,et al.  The Disruption of Celf6, a Gene Identified by Translational Profiling of Serotonergic Neurons, Results in Autism-Related Behaviors , 2013, The Journal of Neuroscience.

[81]  S. Linnarsson,et al.  Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq , 2015, Science.

[82]  Minmin Luo,et al.  Habenula “Cholinergic” Neurons Corelease Glutamate and Acetylcholine and Activate Postsynaptic Neurons via Distinct Transmission Modes , 2011, Neuron.

[83]  X. Morin,et al.  Expression and interactions of the two closely related homeobox genes Phox2a and Phox2b during neurogenesis. , 1997, Development.

[84]  Elyssa B. Margolis,et al.  The ventral tegmental area revisited: is there an electrophysiological marker for dopaminergic neurons? , 2006, The Journal of physiology.

[85]  S. Quake,et al.  A survey of human brain transcriptome diversity at the single cell level , 2015, Proceedings of the National Academy of Sciences.

[86]  D. Koller,et al.  Conservation and divergence in the transcriptional programs of the human and mouse immune systems , 2013, Proceedings of the National Academy of Sciences.

[87]  M Yuzaki,et al.  Purification of Purkinje cells by fluorescence‐activated cell sorting from transgenic mice that express green fluorescent protein , 2001, The European journal of neuroscience.

[88]  B. Barres,et al.  Genomic Analysis of Reactive Astrogliosis , 2012, The Journal of Neuroscience.

[89]  H. Hultborn,et al.  Production of Dopamine by Aromatic l-Amino Acid Decarboxylase Cells after Spinal Cord Injury. , 2016, Journal of neurotrauma.

[90]  E. Nestler,et al.  G9a influences neuronal subtype specification in striatum , 2014, Nature Neuroscience.

[91]  L. Moran,et al.  Whole genome expression profiling of the medial and lateral substantia nigra in Parkinson’s disease , 2006, Neurogenetics.