Pyramidal CA1 Neurons

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Neurons are only a subset of the billions of cells in our brain, but they are the powerhouse driving everything we do. Common genetic and cellular properties define them as neurons. Beyond these defining features, neurons are heterogeneous in there gene expression, shape, and role in driving behavior (cite Zeisel et al. 2015 and Kamme et al 2003). Even within very homogenous populations such as the pyramidal CA1 neurons, there is significant variation in neuronal properties.
This leads to the question of what is noise and what has a biological function? Eve Marder and colleagues eloquently demonstrated that gene expression changes we think are biologically relevant are often compensated for resulting in the same biological output of the
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This also highlights the necessity to know every transcript in the cell. We also know that not all gene expression is compensated for, specifically synaptic plasticity. This thrives on heterogenous features. Recent work highlights the diversity of neurons and within this diversity, the unique role some neurons play in behavior over other others.
The power of the study above and in this proposal lies in the use of a small model organism (Drosophila melanogaster) with a well-defined behavioral assay (aversive olfactory long-term memory (LTM)) and identified neural circuitry underlying this behavior (olfactory pathway-see Fig. 1A). Using the fruit fly circumvents the complexity comprising the mammalian brain and the distributed nature of behavioral circuits. Drosophila melanogaster (fruit flies) are ideal for this type of work because of their well-defined behavioral output and mapped neural circuitry. Fly brains comprise only ~100,000 neurons, and there are abundant genetic and molecular tools
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Here, I propose a novel set of experiments using Drosophila melanogaster to better understand this noise to function problem. To do this I will make use of the data set outlined above. In an ever expanding world of analysis measures and tools to look at single cells, I propose to use these methods to determine what is noise and what gene changes have a biological function. I believe current methods are reaching the technical acuity needed to address this question. This includes gains in high-throughput sequencing, digital qPCR, enhanced genetic tools for Drosophila melanogaster, better mapping of genomes and a surge in bioinformatics tools to analyze gene expression. While I have previously looked at the broad gene expression differences, this data set is lacking the more subtle analysis of different transcript abundance of specific genes. It is known that a total gene expression count will mask transcript differences, thus occluding potentially significant differences in cell heterogeneity. In addition, it may provide a fuller picture of gene differences across neurons as well as differences within the same type of neuron but in different behavioral states. This is critical for assessing changes in gene transcription following memory formation because we know that different forms of CREB and CAMKII are known to be more important for memory formation than other

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