Data Processing for Modern Microscopy: Faster, More Accurate, and More Reproducible

dc.contributor.advisorLandes, Christy F.
dc.creatorShuang, Bo
dc.date.accessioned2017-08-07T17:31:34Z
dc.date.available2017-08-07T17:31:34Z
dc.date.created2016-05
dc.date.issued2016-04-22
dc.date.submittedMay 2016
dc.date.updated2017-08-07T17:31:34Z
dc.description.abstractModern medicine is currently facing the challenge of improving wellness for the general public, especially the quality of later life that is threatened by aging-associated chronic conditions and diseases. These improvements are heavily dependent on scientific discovery of biological processes occurring on the nanoscale. Single-molecule super-resolution microscopy has made vital advancements to further understand disease mechanisms, revolutionize genome sequencing, and improve drug purification efficiencies. However, single-molecule techniques usually generate large amounts of complex data. Interpretation of this complex data to gather useful information requires sophisticated data processing techniques. In this thesis, several new data processing techniques are presented to extract valuable information in single-molecule data efficiently and accurately. First, maximum likelihood estimators have been proposed to calculate the diffusion coefficient for short single particle tracking trajectories, which can improve the space and time sensitivities of single particle tracking studies. The trade-off between accuracy and precision of different estimation methods is discussed to guide the selection of the developed estimation methods. Secondly, a newly developed single particle tracking package allows users to automatically process large amounts of raw single particle data to produce single-molecule tracking results. This is accomplished by a robust fitting approach that has been developed to localize single emitters to achieve roughly a 10 nm spatial resolution. Moreover, a 3D super-resolution algorithm for general 3D point spread functions has been explored, serving as the first open source program for 3D super-resolution recovery. Finally, an analysis algorithm for single-molecule Förster resonance energy transfer has been created to identify fast step transitions and determine the optimum number of states from single-molecule data. This algorithm outperforms the established cutting-edge algorithm in accuracy and speed. Overall, this thesis offers a broad range of data analysis techniques that benefit the powerful research in single-molecule studies.
dc.format.mimetypeapplication/pdf
dc.identifier.citationShuang, Bo. "Data Processing for Modern Microscopy: Faster, More Accurate, and More Reproducible." (2016) Diss., Rice University. <a href="https://hdl.handle.net/1911/96601">https://hdl.handle.net/1911/96601</a>.
dc.identifier.urihttps://hdl.handle.net/1911/96601
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectsingle molecule tracking
dc.subjectsingle molecule imaging
dc.subject3D imaging
dc.subjectpiecewise constant signal
dc.subjectdata processing
dc.titleData Processing for Modern Microscopy: Faster, More Accurate, and More Reproducible
dc.typeThesis
dc.type.materialText
thesis.degree.departmentChemistry
thesis.degree.disciplineNatural Sciences
thesis.degree.grantorRice University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
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