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. 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. 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. 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
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
Identifying brain regions that respond to social stimuli
O’Connell, Rigney et al. 2013

7. What are the Neural and Molecular
Substrates that Govern These Kinds of
Social Decisions?
7O’Connell, Rigney et al. 2013

8. 8
Identifying brain regions that respond to stimuli
O’Connell, Rigney et al. 2013

9. 9
Maruska et al. (2013)
J Neuroendocrinol 25, 145–157
Tissue punches: brain region specific gene expression

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. 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. Integrating genes, hormones & behavior
12
O’Connell & Hofmann 2012

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. Micro-aspiration for single neuron gene
expression analysis of molecular pathways
14

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. 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. Weighted Gene Co-Expression Network Analysis
(WGCNA)
17
Behavior,
candidate
gene, or
physiology
measures
Langfelder & Horvath (2008); Hilliard et al. 2012

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
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. 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. 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
So, now you have a transcriptome…
Harris & Hofmann
2014

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. 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. Many Thanks!
NS&B Students & FacultyLars & Rui for the invitation
Hofmann Lab
Neuroscience FolksThe CCBB
EEB, IB, CMB & MBS Folks
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