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Coupling transcriptome with electrophysiology analyses of human Rett syndrome neurons

Funding Type: 
Basic Biology V
Grant Number: 
RB5-07481
Funds requested: 
$1 152 000
Funding Recommendations: 
Not recommended
Grant approved: 
No
Public Abstract: 
The iPSC technology has made it possible to establish patient-specific neurological “disease-in-a dish” models. However, the enormous heterogeneity and variations of the system need to be taken into consideration while utilizing this approach for studying disease mechanisms and/or drug discovery. Unfortunately, with the current differentiation protocol to form functional neural circuits from iPSC-derived neurons, we have very little control over the types and the composition of functionally active circuitries that are formed in culture. We have been studying one of the Autism Spectrum Disorders, Rett syndrome (RTT), by utilizing RTT iPSC-derived neurons. We found that the phenotype in spontaneous neural transmission from control and mutant neurons is difficult to track, because the phenotype is highly dependent on the nature of the circuitry. We hypothesize that one way to circumvent the circuitry-dependent problem of RTT neurons is to perform phenotype analysis via electrophysiology recordings followed by genome-wide transcriptome analysis at the single cell stage. In this application, we will perform recordings and single neuron transcriptome analyses from wild type and mutant RTT neurons. Furthermore, we will use Prox1 to induce hippocampal dentate gyral granule neurons and study electrophysiology paired with single neuron transcriptome analyses in vitro, as well as in vivo, after transplantation and integration of neurons into recipient SCID mouse brains.
Statement of Benefit to California: 
The introduction of iPSC technology has made it possible to establish patient-specific neurological “disease-in-a dish” models to study disease mechanisms and drug discovery. Prior to utilizing these models, it is crucial to solve issues such as heterogeneity and variations within the system, since with the current differentiation protocols to form functional neural circuits from iPSC-derived neurons, we have very little control over the types and the composition of functionally active circuitries that are formed in culture. Using one of the Autism Spectrum Disorders, Rett syndrome (RTT), as a model, we aim to circumvent the circuitry-dependent problem of RTT neurons by performing phenotype analysis via electrophysiology recordings followed by genome-wide transcriptome analysis at the single cell level. Our proposed studies will help us understand the genetic and functional mechanisms underlying diseased neural circuitries, and our results will shed light on the nature of the formation of healthy neural networks. The number of residents of California suffer from diseases that could potentially be cured by stem cell-based therapies. In order to be able to use iPSCs as disease models or for therapy purposes, better understanding of their differentiation, as well as function of their progeny, is required. The information obtained from the results of this proposal will benefit the residents of California with respect to stem cell therapy and the use of iPSCs as disease models.
Review Summary: 
The promise of using patient derived induced pluripotent stem cells (iPSCs) to study disease states is hindered by the heterogeneity of outcomes observed when these cells are induced to differentiate into the relevant cell type for disease modeling. This problem is especially acute in the modeling of neurological disorders. Rett syndrome is one such disorder in which the gene, MeCP2, is mutated, and the goal of this Fundamental Mechanisms Award proposal is to overcome the problem of heterogeneity of outcome in Rett-derived iPSCs and differentiated neurons in order to study differences in neural circuitry that exist between MeCP2-wild type and MeCP2-mutant lineages. The team proposes a single cell level approach where they will use electrophysiological recordings paired with transcriptional profiles to identify neurons. Two aims are proposed to achieve this goal. In Aim 1, pair-recordings from neurons in a dish derived from Rett patients iPSCs, using rescued iPSCs as isogenic controls, will be performed followed by single cell sequencing of the transcriptome. In Aim 2, the team proposes to use a specific transcription factor to induce hippocampal dentate gyral granule neurons from the iPSCs, and these will also be studied using the same methods as in aim1 in vitro, as well as in vivo after transplantation and integration into adult mouse brains. In this way, neuronal properties for a given cell will be associated with its neuronal type. Significance and Innovation - The project addresses a major hurdle in using human patient derived stem cells for studying neurological diseases. - The applicant employs innovative (and very challenging) approaches to address this hurdle particularly the ability to perform single cell sequencing post recording. Feasibility and Experimental Design - One major concern in this proposal is the feasibility of Aim 1, in terms of recording and sequencing enough cells to elucidate differences between MeCP mutants and normal cells. - There was a lack of rationale for the studies proposed in Aim 2. The evidence for the ability of the particular transcription factor proposed in this aim to direct human stem cells to the specific hippocampal neuronal lineage was not provided, raising questions of its feasibility. - Potential difficulties are not acknowledged and alternative plans are not sufficiently addressed. Principal Investigator (PI) and Research Team -The PI has an excellent track record in molecular biology and neuronal development. -The research team has the appropriate expertise to conduct the proposed research. Responsiveness to the RFA - The proposal is responsive to the objective of the RFA to conduct basic biology studies using derivatives of human iPSC cells for disease modeling and functional analyses and evidence of reprogramming and differentiation is presented.
Conflicts: 

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