Data Processing for Modern Microscopy: Faster, More Accurate, and More Reproducible
| dc.contributor.advisor | Landes, Christy F. | |
| dc.creator | Shuang, Bo | |
| dc.date.accessioned | 2017-08-07T17:31:34Z | |
| dc.date.available | 2017-08-07T17:31:34Z | |
| dc.date.created | 2016-05 | |
| dc.date.issued | 2016-04-22 | |
| dc.date.submitted | May 2016 | |
| dc.date.updated | 2017-08-07T17:31:34Z | |
| dc.description.abstract | Modern 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.mimetype | application/pdf | |
| dc.identifier.citation | Shuang, 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.uri | https://hdl.handle.net/1911/96601 | |
| dc.language.iso | eng | |
| dc.rights | Copyright 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.subject | single molecule tracking | |
| dc.subject | single molecule imaging | |
| dc.subject | 3D imaging | |
| dc.subject | piecewise constant signal | |
| dc.subject | data processing | |
| dc.title | Data Processing for Modern Microscopy: Faster, More Accurate, and More Reproducible | |
| dc.type | Thesis | |
| dc.type.material | Text | |
| thesis.degree.department | Chemistry | |
| thesis.degree.discipline | Natural Sciences | |
| thesis.degree.grantor | Rice University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |
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