Bayesian Model Selection Applied to the Analysis of Fluorescence Correlation Spectroscopy Data of Fluorescent Proteins in Vitro and in Vivo
Fluorescence correlation spectroscopy (FCS) is a powerful technique to investigate molecular dynamics with single molecule sensitivity. In particular, in the life sciences it has found widespread application using fluorescent proteins as molecularly specific labels. However, FCS data analysis and in...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en_US |
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American Chemical Society (ACS)
2015
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| Online Access: | http://hdl.handle.net/1721.1/99237 https://orcid.org/0000-0002-6199-6855 https://orcid.org/0000-0002-9009-6813 |
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| author | Sun, Guangyu Guo, Syuan-Ming Teh, Cathleen Korzh, Vladimir Bathe, Mark Wohland, Thorsten |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Sun, Guangyu Guo, Syuan-Ming Teh, Cathleen Korzh, Vladimir Bathe, Mark Wohland, Thorsten |
| author_sort | Sun, Guangyu |
| collection | MIT |
| description | Fluorescence correlation spectroscopy (FCS) is a powerful technique to investigate molecular dynamics with single molecule sensitivity. In particular, in the life sciences it has found widespread application using fluorescent proteins as molecularly specific labels. However, FCS data analysis and interpretation using fluorescent proteins remains challenging due to typically low signal-to-noise ratio of FCS data and correlated noise in autocorrelated data sets. As a result, naive fitting procedures that ignore these important issues typically provide similarly good fits for multiple competing models without clear distinction of which model is preferred given the signal-to-noise ratio present in the data. Recently, we introduced a Bayesian model selection procedure to overcome this issue with FCS data analysis. The method accounts for the highly correlated noise that is present in FCS data sets and additionally penalizes model complexity to prevent over interpretation of FCS data. Here, we apply this procedure to evaluate FCS data from fluorescent proteins assayed in vitro and in vivo. Consistent with previous work, we demonstrate that model selection is strongly dependent on the signal-to-noise ratio of the measurement, namely, excitation intensity and measurement time, and is sensitive to saturation artifacts. Under fixed, low intensity excitation conditions, physical transport models can unambiguously be identified. However, at excitation intensities that are considered moderate in many studies, unwanted artifacts are introduced that result in nonphysical models to be preferred. We also determined the appropriate fitting models of a GFP tagged secreted signaling protein, Wnt3, in live zebrafish embryos, which is necessary for the investigation of Wnt3 expression and secretion in development. Bayes model selection therefore provides a robust procedure to determine appropriate transport and photophysical models for fluorescent proteins when appropriate models are provided, to help detect and eliminate experimental artifacts in solution, cells, and in living organisms. |
| first_indexed | 2024-09-23T12:02:33Z |
| format | Article |
| id | mit-1721.1/99237 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:02:33Z |
| publishDate | 2015 |
| publisher | American Chemical Society (ACS) |
| record_format | dspace |
| spelling | mit-1721.1/992372022-10-01T07:45:55Z Bayesian Model Selection Applied to the Analysis of Fluorescence Correlation Spectroscopy Data of Fluorescent Proteins in Vitro and in Vivo Sun, Guangyu Guo, Syuan-Ming Teh, Cathleen Korzh, Vladimir Bathe, Mark Wohland, Thorsten Massachusetts Institute of Technology. Department of Biological Engineering Guo, Syuan-Ming Bathe, Mark Fluorescence correlation spectroscopy (FCS) is a powerful technique to investigate molecular dynamics with single molecule sensitivity. In particular, in the life sciences it has found widespread application using fluorescent proteins as molecularly specific labels. However, FCS data analysis and interpretation using fluorescent proteins remains challenging due to typically low signal-to-noise ratio of FCS data and correlated noise in autocorrelated data sets. As a result, naive fitting procedures that ignore these important issues typically provide similarly good fits for multiple competing models without clear distinction of which model is preferred given the signal-to-noise ratio present in the data. Recently, we introduced a Bayesian model selection procedure to overcome this issue with FCS data analysis. The method accounts for the highly correlated noise that is present in FCS data sets and additionally penalizes model complexity to prevent over interpretation of FCS data. Here, we apply this procedure to evaluate FCS data from fluorescent proteins assayed in vitro and in vivo. Consistent with previous work, we demonstrate that model selection is strongly dependent on the signal-to-noise ratio of the measurement, namely, excitation intensity and measurement time, and is sensitive to saturation artifacts. Under fixed, low intensity excitation conditions, physical transport models can unambiguously be identified. However, at excitation intensities that are considered moderate in many studies, unwanted artifacts are introduced that result in nonphysical models to be preferred. We also determined the appropriate fitting models of a GFP tagged secreted signaling protein, Wnt3, in live zebrafish embryos, which is necessary for the investigation of Wnt3 expression and secretion in development. Bayes model selection therefore provides a robust procedure to determine appropriate transport and photophysical models for fluorescent proteins when appropriate models are provided, to help detect and eliminate experimental artifacts in solution, cells, and in living organisms. National Science Foundation (U.S.). Physics of Living Systems Program National Institute of Mental Health (U.S.) (Award U01MH106011) 2015-10-13T19:09:42Z 2015-10-13T19:09:42Z 2015-03 2015-01 Article http://purl.org/eprint/type/JournalArticle 0003-2700 1520-6882 http://hdl.handle.net/1721.1/99237 Sun, Guangyu, Syuan-Ming Guo, Cathleen Teh, Vladimir Korzh, Mark Bathe, and Thorsten Wohland. “Bayesian Model Selection Applied to the Analysis of Fluorescence Correlation Spectroscopy Data of Fluorescent Proteins in Vitro and in Vivo.” Anal. Chem. 87, no. 8 (April 21, 2015): 4326–4333. https://orcid.org/0000-0002-6199-6855 https://orcid.org/0000-0002-9009-6813 en_US http://dx.doi.org/10.1021/acs.analchem.5b00022 Analytical Chemistry Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf American Chemical Society (ACS) PMC |
| spellingShingle | Sun, Guangyu Guo, Syuan-Ming Teh, Cathleen Korzh, Vladimir Bathe, Mark Wohland, Thorsten Bayesian Model Selection Applied to the Analysis of Fluorescence Correlation Spectroscopy Data of Fluorescent Proteins in Vitro and in Vivo |
| title | Bayesian Model Selection Applied to the Analysis of Fluorescence Correlation Spectroscopy Data of Fluorescent Proteins in Vitro and in Vivo |
| title_full | Bayesian Model Selection Applied to the Analysis of Fluorescence Correlation Spectroscopy Data of Fluorescent Proteins in Vitro and in Vivo |
| title_fullStr | Bayesian Model Selection Applied to the Analysis of Fluorescence Correlation Spectroscopy Data of Fluorescent Proteins in Vitro and in Vivo |
| title_full_unstemmed | Bayesian Model Selection Applied to the Analysis of Fluorescence Correlation Spectroscopy Data of Fluorescent Proteins in Vitro and in Vivo |
| title_short | Bayesian Model Selection Applied to the Analysis of Fluorescence Correlation Spectroscopy Data of Fluorescent Proteins in Vitro and in Vivo |
| title_sort | bayesian model selection applied to the analysis of fluorescence correlation spectroscopy data of fluorescent proteins in vitro and in vivo |
| url | http://hdl.handle.net/1721.1/99237 https://orcid.org/0000-0002-6199-6855 https://orcid.org/0000-0002-9009-6813 |
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