School of Medicine
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Andres Plata Stapper
Postdoctoral Research Fellow, Otolaryngology - Head & Neck Surgery
Current Research and Scholarly Interests I aim to discern the molecular mechanisms driving lineage specification during embryonic development, neurogenesis, organogenesis and disease, with a long term goal of the development of tools for precision medicine at single cell, tissue, individual and population level.
My current research uses inner ear development as a model system. The inner ear is a complex structural and functional interconnected collection of sensory organs, responsible for our perception of sound, acceleration and balance. The inner ear semi-autonomously originates in early embryonic development from a patch of thickened ectoderm known as the otic placode. Advances in the understanding on otic development and lineage specification could lead to medical applications such as treating and identifying developmental disorders, as well as the development of in-vitro and in-vivo protocols for guided differentiation of sensory hair cells to cure deafness.
I am working to generate a cell atlas specific to the initiation of inner ear development, when the otic placode thickens and undergoes molecular and morphological changes to form an otocyst, which eventually develops into a fully functional inner ear. I aim to identify early otic-specific lineages, the molecular signatures specific to each, and describe the spatio-temporal dynamics of cells and genes during this developmental time frame.
I use computational tools to identify otic from non-otic cells in transgenic and wild type model organisms from multi-parallel qrtPCR from single cells, and concentrate on the otic populations for deep learning to accurately identify, characterize, and classify otic specific subpopulations. Computational approaches also allows us to determine the lineages composing the developing otic placode, to generate spatial and temporally accurate 3d models of organogenesis, and to design an otic cellular classifier using machine learning.
I use multidisciplinary approaches including: 1) microfluidic technology for the generation of single cell gene expression data, 2) computational and statistical multi-dimensional data analysis approaches in the form of supervised and unsupervised machine learning, 3) molecular biology tools such as transgenics, multi-parallel qRT-PCR, immuno-histochemistry, single molecule in-situ hybridization and 4) confocal microscopy.