An integrated framework to identify and isolate novel, functionally distinct tissue subpopulations using high-resolution single cell analysis
- Complex cell populations are characterized by a level of transcriptional heterogeneity that likely facilitates their biological role. While this heterogeneity is believed to be associated with functional differences, until recently the granularity afforded by measurement tools has been insufficient to efficiently interrogate the transcriptomes of individual cells. In the last several years, high-throughput single cell gene expression analysis has become possible through microfluidic-based devices; however, the potential utility of such platforms has yet to be fully explored. Two issues limit the usefulness of such systems. First, these techniques are generally limited in the number of gene targets that may be simultaneously interrogated (typically < 100), making selection of an appropriate gene panel critical. Second, since these measurement tools require cell lysis, there is no obvious mechanism for functional evaluation of characterized cells. In this thesis proposal, I first describe a novel technique for the detection of transcriptionally-defined subpopulations from complex tissue using single cell gene expression data, which I developed and have successfully applied to characterize a wide variety of target populations. I then present my work in extending these methods to construct a fully integrated pipeline for the systematic identification, isolation, and validation of novel subpopulations. This includes (1) using public microarray data to predict gene targets likely to be either (i) heterogeneous within a single target population or (ii) differentially expressed among multiple target populations; (2) performing single cell transcriptional interrogation against this gene set to identify (i) transcriptionally-defined subpopulations and (ii) surface marker genes whose expression profiles differentiate among these subpopulations; and (3) prospective isolation of select subpopulations using FACS for validation of functional properties in vitro and in vivo. Together, these methods permit the efficient evaluation of previously unknown subpopulations. This approach is highly generalizable and can be applied to characterize essentially any complex cell population, as evidenced by the wide range of tissues and phenotypes examined in the 150 pages that follow.
|Type of resource
|electronic; electronic resource; remote
|1 online resource.
|Stanford University, Program in Biomedical Informatics.
|Butte, Atul J
|Butte, Atul J
|Statement of responsibility
|Submitted to the Program in Biomedical Informatics.
|Thesis (Ph.D.)--Stanford University, 2015.
- © 2015 by Michael Januszyk
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