The goal of this U01 is to characterize and understand the variability in the expressed transcriptome of human excitable cells. There are two predominant types of excitable cell in the human body, neurons and muscle cells, including cardiac cells. Many human CNS diseases result from modulation of the electrical responsiveness of neurons while cardiac arrhythmias account for most of heart associated deaths. However, at the level of individual cells there is considerable heterogeneity in function, response, and dysfunction. Here, we present preliminary data showing large-scale single cell variability that is difficult to explain as simple molecular noise. We hypothesize that there is a many-to-one relationship between transcriptome states and a cell's phenotype. In this relationship the functional molecular ratios of the RNA are determined by the cell systems'stoichiometric constraints, which underdetermine the transcriptome state. Because a broad set of multi-genic combinations support a particular phenotype, changes in the transcriptome state do not necessarily lead to changes in the phenotype potentially explaining cellular heterogeneity in phenotype response to variant conditions such as the application of therapeutic molecules. To test this hypothesis we propose to investigate the extent of single cell variation for the whole transcriptome for excitable cells that are in their natural environment using a novel mRNA capture methodology (TIVA-tag), and on a subset of the transcriptome, the mRNAs encoding the therapeutically important and manipulable G protein-coupled receptor (GPRC) pathways. The use of functional genomics techniques developed in the Eberwine and Kim labs (TIPeR) will permit an assessment of the biological role of multigenic transcriptome variation. These studies are truly interdisciplinary involving the collaboration of two clinicians (Drs. Grady, Neurosurgeon and Kuhn, Cardiologist), two genomicists (Drs. Eberwine and Kim) one of whom is a computational scientist (Dr. Kim), a neuro/cardio- pharmacologist (Dr. Bartfai) and a biophotonics expert (Dr. Sul).!

Public Health Relevance

The goal of this proposal is to generate a compendium of single cells sequencing data from live human excitable cells that are in contact with endogenous neighboring cells. These sequencing data will be analyzed for variability in gene expression and the biological function of this variability assessed using a novel in vivo functional genomics methodology.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZRG1-CB-D (52))
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Yao, Yong
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University of Pennsylvania
Schools of Medicine
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Zhu, Qin; Fisher, Stephen A; Dueck, Hannah et al. (2018) PIVOT: platform for interactive analysis and visualization of transcriptomics data. BMC Bioinformatics 19:6
Yester, Jessie Wettig; Kühn, Bernhard (2017) Mechanisms of Cardiomyocyte Proliferation and Differentiation in Development and Regeneration. Curr Cardiol Rep 19:13
Gawronski, Katerina A B; Kim, Junhyong (2017) Single cell transcriptomics of noncoding RNAs and their cell-specificity. Wiley Interdiscip Rev RNA 8:
Spaethling, Jennifer M; Na, Young-Ji; Lee, Jaehee et al. (2017) Primary Cell Culture of Live Neurosurgically Resected Aged Adult Human Brain Cells and Single Cell Transcriptomics. Cell Rep 18:791-803
Morris, Jacqueline; Na, Young-Ji; Zhu, Hua et al. (2017) Pervasive within-Mitochondrion Single-Nucleotide Variant Heteroplasmy as Revealed by Single-Mitochondrion Sequencing. Cell Rep 21:2706-2713
Dueck, Hannah R; Ai, Rizi; Camarena, Adrian et al. (2016) Assessing characteristics of RNA amplification methods for single cell RNA sequencing. BMC Genomics 17:966
Jeong, Heejin; Na, Young-Ji; Lee, Kihwan et al. (2016) High-resolution transcriptome analysis reveals neuropathic pain gene-expression signatures in spinal microglia after nerve injury. Pain 157:964-76
Dueck, Hannah; Eberwine, James; Kim, Junhyong (2016) Variation is function: Are single cell differences functionally important?: Testing the hypothesis that single cell variation is required for aggregate function. Bioessays 38:172-80
Lovatt, Ditte; Bell, Thomas; Eberwine, James (2015) Single-neuron isolation for RNA analysis using pipette capture and laser capture microdissection. Cold Spring Harb Protoc 2015:pdb.prot072439
Dueck, Hannah; Khaladkar, Mugdha; Kim, Tae Kyung et al. (2015) Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation. Genome Biol 16:122

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