Fluorescence fluctuation spectroscopy and microscopy : application to cellular molecular dynamics and organization

Fluorescence fluctuation spectroscopy and microscopy have been powerful tools for studying molecular organization and dynamics in cells. Fluorescence Correlation Spectroscopy (FCS) has been widely applied to probing molecular dynamics in live cells with single molecule sensitivity and the ability to resolve local molecular concentrations, aggregation states, and transport mechanisms. Despite the broad utility of FCS, interpretation of cellular FCS data is often confounded by the heterogeneity in the underlying biological process and the low signal-to-noise ratio. Systematic data evaluation and interpretation become even more challenging for imaging FCS, where hundreds to thousands of FCS curves are generated in a single measurement. This thesis presents an objective Bayesian inference procedure for testing multiple competing FCS models. Bayesian inference determines model probabilities by considering the probability distributions over the full range of parameter values, thereby naturally penalizes model complexity and prevents over-fitting. We applied this procedure to imaging FCS data in order to resolve hIAPP-induced microdomain spatial organization and temporal dynamics in the cell membrane. Our analysis resolved the temporal evolution of multiple diffusing species in the spatially heterogeneous cell membrane, lending support to the "carpet model" for the association mode of hIAPP aggregates with the plasma membrane. Finally, we presented a fluctuation-based microscopy approach, Points Accumulation for Imaging in Nanoscale Topography (PAINT), that enables highly multiplexed super-resolution imaging of synaptic proteins. We employed DNA-PAINT to resolve nano-scale organization of seven targets simultaneously including synaptic proteins and cytoskeletal markers. These approaches demonstrated the broad applicability of fluorescence fluctuation spectroscopy and microscopy in resolving molecular dynamics and organization in cells.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Chemistry.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Guo, Syuan-Ming

Advisor dc:contributor.advisor

• Mark Bathe.

• Chemistry.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/103709

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/103709