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Toward Single Neuron Gene Expression Analysis for Studying Behavior

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This talk was given at a workshop in Portugal. Here I describe a variety of molecular techniques used for studying the brain as the insight that each can bring and their limitations.

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Toward Single Neuron Gene Expression Analysis for Studying Behavior

  1. 1. TOWARD SINGLE NEURON GENE EXPRESSION ANALYSIS FOR STUDYING BEHAVIOR (AND OTHER TECHNIQUES) RAYNA M. HARRIS HANS HOFMANN LAB, THE UNIVERSITY OF TEXAS AT AUSTIN http://raynamharris.github.io/ 1
  2. 2. qRT-PCR Microarrays & RNA-seq immunohistochemistryin situ hybridization Common approaches for neural gene expression profiling 2 • How you process the brain for each technique is different • Each technique has its own challenges and opportunities • Each tells you something different
  3. 3. Tradeoffs between spatial resolution and fraction of the genome surveyed 3 0.0001 0.001 0.01 0.1 1 1 10 100 1000 10000 FractionoftheBrain Surveyed Number of Genes Measured in situ Hybridization & Immuno- histochemistry qPCR RNA-seq Nanostring Microarray
  4. 4. Candidate genes vs genomic approaches • Histological approaches allow for co-localization • Histological approaches are low throughput • You may choose the wrong candidate genes • Candidate genes act in networks that are poorly understood • Genomics allows systems-level view of brain and behavior • Genomic approaches lack spatial resolution 4
  5. 5. 5 Mapping gene and protein expression with in situ hybridization and immunohistochemistry Androgen receptors Muchrath & Hofmann 2010 Estrogen  receptors Muchrath & Hofmann 2010 Blue: in situ hybridization (RNA) Brown: immunohistochemistry (protein) Shading Left: RNA, paralog a Shading right: RNA, paralog b Dots: protein
  6. 6. 6 Identifying brain regions that respond to social stimuli O’Connell, Rigney et al. 2013
  7. 7. What are the Neural and Molecular Substrates that Govern These Kinds of Social Decisions? 7O’Connell, Rigney et al. 2013
  8. 8. 8 Identifying brain regions that respond to stimuli O’Connell, Rigney et al. 2013
  9. 9. 9 Maruska et al. (2013) J Neuroendocrinol 25, 145–157 Tissue punches: brain region specific gene expression
  10. 10. 10 Chemical Cue DOM urine SUB urine Pre-ovulatory urine Post-ovulatory urin Simões et al. 2015 10 2 3 4 Hierarchical Clustering: Gene Expression Patterns Across Phenotypes
  11. 11. Laser microdissection for increased spatial resolution 11 O’Connell & Hofmann 2012 1. Does this variation map onto behavior? 2. The POA has multiple cell groups, maybe we should look at individual neurons… No significant difference in candidate gene expression in the POA
  12. 12. Integrating genes, hormones & behavior 12 O’Connell & Hofmann 2012
  13. 13. Using IEG-driven GFP-expressing transgenics 13 Denny et al. 2014 I’m doing this in mouse (Arc-GFP) But, researchers have been using this to study zebrafish development for over a decade Delporte et al. 2008
  14. 14. Micro-aspiration for single neuron gene expression analysis of molecular pathways 14
  15. 15. Nanostring 1. Hybridize – 2. Purify – 3. Count • Step 0: Select 200-800 of your favorite genes from any species with a transcriptome/genome • Step 1. Hybridize probes to target RNA in your sample. • Step 2. Purify the sample and immobilize target-probe complex in special cartidge • Step 3. Count the number of unique reporter probes to infer number of transcripts http://www.nanostring.com/ 15
  16. 16. Singe cell gene expression (and physiology) in learning-recruited neurons 16 Learning- recruited Not recruited Future studies will integrative variation in learning & memory to variation in gene expression
  17. 17. Weighted Gene Co-Expression Network Analysis (WGCNA) 17 Behavior, candidate gene, or physiology measures Langfelder & Horvath (2008); Hilliard et al. 2012
  18. 18. Identifying similar patterns of gene expression across datasets, experiments, or contexts 18 Ghazalpour et al. 2006 Preservation of female mouse liver modules in male data I’m using this approach to identify unique and preserved gene expression patterns that are important for hippocampal-dependent spatial (CA1) and social (CA2) learning
  19. 19. 19 Single cell analysis of teleost Dl might to examine homology with mammalian CA1, CA2, CA3, & DG O’Connell & Hofmann 2012 Hawrylycz et al., 2012; Lein et al., 2004
  20. 20. Each technique provides unique but limited insight into the neuromolecular basis of behavior 20 Kelly & Goodson 2005; O’Connell et al. 2013; Hilliard et al. 2012 Denny et al. 2014 C-fos Immunohistochemistry Arc-driven expression of GFP
  21. 21. A comprehensive research program uses each of these techniques to inform future experiments 21 0.0001 0.001 0.01 0.1 1 1 10 100 1000 10000 FractionoftheBrain Surveyed Number of Genes Measured in situ Hybridization & Immuno- histochemistry qPCR RNA-seq Nanostring Microarray
  22. 22. 22 So, now you have a transcriptome… Harris & Hofmann 2014
  23. 23. A few questions that may help you choose most appropriate technique • What are your molecules of interest? – Candidate mRNA or protein, transcriptomic patterns? – How soon after the stimulus will its activity be altered? • How big is your experiment? – How many groups, animals, brain regions, genes? • What resources do you have at your fingertips? – Core facilities and equipment – Validated PCR primers, riboprobes, antibodies? – A mentor who can help you collect & analyze the data? – Bioinformatic and statistical consulting? 23
  24. 24. Bioinformatics: An Essential Part of Every Biologist’s Toolkit 24 “The ability to harvest the wealth of information contained in biomedical Big Data will advance our understanding of human health and disease. However, lack of appropriate tools, poor data accessibility, and insufficient training, are major impediments to rapid translational impact”.  — NIH Big Data to Knowledge (BD2K) Initiative
  25. 25. Many Thanks! NS&B Students & FacultyLars & Rui for the invitation Hofmann Lab Neuroscience FolksThe CCBB EEB, IB, CMB & MBS Folks 25

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