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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Advisor:
Ahmet F. Coskun, PhD (BME, Georgia Tech & Emory University)
Committee Members:
Ghassan AlRegib (ECE, Georgia Institute of Technology)
Hang Lu, PhD (ChBE, Georgia Institute of Technology)
Melissa L. Kemp, PhD (BME, Georgia Tech & Emory University)
Rapid microfluidic multiplexing of proteins for deciphering spatial organelle networks
New technologies in the advancing biotechnology and biomedical engineering have shown us that organelles play many roles in human health and disease. Being the building blocks of the smallest unit of life, organelles play an important role in the health and well-beings of humans. Cell diversity not only exists between cell types but also between individual cells, thus it is important to understand the distribution of organelles at single-cell level. Mesenchymal stem cells, being multipotent, have been explored as a therapeutic for treating a variety of diseases. Studying how organelles are arranged in these cells will answer questions about their function and potential. This thesis aims to understand the spatial organization of organelles and the interactions between them in mesenchymal stem cells using multiplexed imaging of proteins. In this work, rapidly run cyclic immunofluorescence staining was performed to decipher the subcellular localization of ten organelle markers in bone marrow and umbilical cord stem cells. An automated microfluidic system was developed that handles the repetitive manual processes of blocking, washing, staining and bleaching the sample on coverslip during each cycle. Spatial correlations, colocalization analysis, heatmaps and network analysis were carried out on the 10-plex data to explore relations between the organelles. In future, this data-driven single-cell approach can enable personalized stem cell therapeutics.
