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TOWARD SINGLE NEURON GENE EXPRESSION ANALYSIS

FOR STUDYING BEHAVIOR(AND OTHER TECHNIQUES)

RAYNA M. HARRISHANS HOFMANN LAB, THE UNIVERSITY OF

TEXAS AT AUSTINhttp://raynamharris.github.io/

qRT-PCR Microarrays &RNA-seq

immunohistochemistryin situ hybridization

Common approaches for neural gene expression profiling

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• How you process the brain for each technique is different

• Each technique has its own challenges and opportunities

• Each tells you something different

Tradeoffs between spatial resolution and fraction of the genome surveyed

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100 1000 100000.0001

0.001

0.01

Number of Genes Measured

Frac

tion

of t

he B

rain

Su

rvey

ed

in situ Hybridization

& Immuno-

histochemistry

qPCR

RNA-seq

Nanostring

Microarray

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

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Mapping gene and protein expression with in situ hybridization and immunohistochemistry

Androgen receptorsMuchrath & Hofmann 2010

Estrogen receptorsMuchrath & Hofmann 2010

Blue: in situ hybridization (RNA)Brown: immunohistochemistry (protein)

Shading Left: RNA, paralog aShading right: RNA, paralog bDots: protein

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Identifying brain regions that respond to social stimuli

O’Connell, Rigney et al. 2013

What are the Neural and Molecular Substrates that Govern These Kinds of

Social Decisions?

7O’Connell, Rigney et al. 2013

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Identifying brain regions that respond to stimuli

O’Connell, Rigney et al. 2013

9Maruska et al. (2013)

J Neuroendocrinol 25, 145–157

Tissue punches: brain region specific gene expression

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Chemical CueDOM urineSUB urinePre-ovulatory urinePost-ovulatory urin

Simões et al. 2015

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Hierarchical Clustering: Gene Expression Patterns Across Phenotypes

Laser microdissection for increased spatial resolution

11O’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

Integrating genes, hormones & behavior

12O’Connell & Hofmann 2012

Using IEG-driven GFP-expressing transgenics

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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

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Micro-aspiration for single neuron gene expression analysis of molecular pathways

Nanostring1. 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

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Singe cell gene expression (and physiology) in learning-recruited neurons

Learning-

recruited

Not recruite

dFuture studies will integrative variation in learning &

memory to variation in gene expression

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Weighted Gene Co-Expression Network Analysis (WGCNA)

Behavior, candidate gene, or

physiology measures

Langfelder & Horvath (2008); Hilliard et al. 2012

Identifying similar patterns of gene expression across datasets, experiments,

or contexts

18Ghazalpour et al. 2006

Preservation of female mouse liver modules in male dataI’m using this approach to identify

unique and preserved gene expression patterns

that are important for hippocampal-dependent

spatial (CA1) and social (CA2) learning

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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

Each technique provides unique but limited insight into the neuromolecular basis of behavior

20Kelly & Goodson 2005;

O’Connell et al. 2013; Hilliard et al. 2012 Denny et al. 2014

C-fos Immunohistochemistry Arc-driven expression of GFP

A comprehensive research program uses each of these techniques to inform future

experiments

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100 1000 100000.0001

0.001

0.01

Number of Genes Measured

Frac

tion

of t

he B

rain

Su

rvey

ed

in situ Hybridization

& Immuno-

histochemistry

qPCR

RNA-seq

Nanostring

Microarray

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So, now you have a transcriptome…

Harris & Hofmann 2014

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?

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Bioinformatics: An Essential Part of Every Biologist’s Toolkit

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“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

Many Thanks!NS&B Students & FacultyLars & Rui for the invitation

Hofmann Lab

Neuroscience FolksThe CCBB

EEB, IB, CMB & MBS Folks

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