Functional Organization of the Transcriptome in Human Brain Michael C. Oldham Laboratory of Daniel H. Geschwind, UCLA BIOCOMP ‘08, Las Vegas, NV July 15, 2008

NeuronsAstrocytesOligodendrocytesMicroglia ~10 11 ~10 12

Microarray studies of the human brain face a number of challenges Cellular heterogeneity –mRNA extracted from tissue homogenates Sample quality –Post-mortem tissue Sample size –Typically, <10 individuals per group Focus on differential expression

What’s wrong with differential expression?

Methodological overview: WGCNA

The data Network 1 (U133 cortex): 67 samples, 67 individuals Network 2 (U95 cortex): 42 samples, 32 individuals Network 3 (U133 caudate nucleus): 27 samples, 27 individuals Network 4 (U133 cerebellum): 24 samples, 24 individuals * Raw data obtained in collaboration with Dr. Kazuya Iwamoto and Dr. Tadafumi Kato at RIKEN. ** Following outlier removal, there were 11 pairs of samples from the same individuals in Iwamoto et al. and Ryan et al. One unique sample per individual was retained.

M1A M2 M3 M4A M5A M6A M7A M8A M9A M10A M11A M12A M13A M14A M15A M16A M17A M18A M19A M5B M20 M21 M17B M22 M23 M24 M4B M9B M16B M10B M15B M25 M8B M19B M26 M18B M27 M4C M11C M28 M29 M30 M31 M19C M9C M1C M32 M13C M18C M33 M34 M35 M36 M8C M16C M37 M5C M15C M38 M1D M12D M19D M39 M18D M40 M41 M6D M10D M42 M14D M11D M4D M7D M43 M44 M45 M16D M46 M9D M15D M47 A: CTX B: CTX_95 C: CN D: CB The networks

Many modules are preserved across networks

k me is the Pearson correlation between the expression level of a given probe set and a given eigengene, e.g.: Quantifying module membership with k me

A B C k me is significantly correlated across networks

Conserved modules are enriched for markers of major cell classes 1 Cahoy, J.D. et al. J Neurosci 28: (2008) 3 Nielsen, J.A. et al. J Neurosci 26: (2006) 5 Genes2Cognition Consortium 2 Lein, E.S. et al. Nature 445: (2007) 4 Bachoo, R.M. et al. PNAS 101: (2004) 6 Morciano, M. et al. J Neurochem 95: (2005)

Thought experiment Sample Amount

The cortical transcriptome is organized into functional modules M1A M2 M3 M4A M5A M6A M7A M8A M9A M10A M11A M12A M13A M14A M15A M16A M17A M18A M19A

NeuronsOligodendrocytesAstrocytes Ribosomes Glutamatergic synapses Purkinje neurons PVALB+ interneurons Mitochondria Neurogenesis Gender Meningeal cells Hypoxia Microglia Modules are organized into a functional meta-network

Applications 1)Context-specific annotation for genes expressed in the human brain ( “ guilt-by-association ” ) Rationale: genes with the strongest evidence of membership for the same module are likely to be driven by the same underlying factors

M6A: PVALB+ interneurons

Applications 1)Context-specific annotation for genes expressed in the human brain ( “ guilt-by-association ” ) 2) In silico comparisons of cellular specificity of gene expression across brain regions Rationale: genes with the most significant differences in membership for cell-type modules between brain regions imply differences in the cellular specificity and/or consistency of gene expression

Applications 1)Context-specific annotation for genes expressed in the human brain ( “ guilt-by-association ” ) 2) In silico comparisons of cellular specificity of gene expression across brain regions 3)Cellular phenotype discovery Rationale: unsupervised analysis of gene coexpression patterns can identify novel distinctions among cell types within brain regions

M15CM13C Caudate nucleus 1 Cahoy, J.D. et al. J Neurosci 28: (2008) 2 Lein, E.S. et al. Nature 445: (2007) 3 Bachoo, R.M. et al. PNAS 101: (2004)

M13C (CN) identifies genes that are coexpressed in the SVZ

Conclusions The human brain transcriptome is organized into modules of coexpressed genes –Many modules are reproducible across microarrays, individuals, and brain regions Several highly conserved modules are enriched for markers of major cell classes –‘ Core ’ transcriptional programs for neurons, oligodendrocytes, astrocytes, and microglia –Context-specific annotation for thousands of genes expressed in the human brain Potential to leverage consistency of k me for comparisons with other conditions of interest (e.g. disease)

Acknowledgements Dan Geschwind Steve Horvath Gena Konopka Peter Langfelder Collaborators: Dr. Kazuya Iwamoto Dr. Tadafumi Kato The Geschwind lab