Enhancing aC'dots analysis through STORM imaging: development of advanced computational tools for super-resolution microscopy
Super-resolution microscopy has transformed bioimaging by enabling nanoscale visualization of cellular structures. This thesis introduces PulseSTORM, a software application designed to streamline the quantitative analysis of Stochastic Optical Reconstruction Microscopy (STORM) and filament datasets....
- Autores:
-
Salgado Manrique, Alejandro
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2024
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/75307
- Acceso en línea:
- https://hdl.handle.net/1992/75307
- Palabra clave:
- STORM
SMLM
ROI
Stretching open active contours
Point spread function
Fluorescent dye
Core-shell silica nanoparticles
Filament
Ingeniería
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Enhancing aC'dots analysis through STORM imaging: development of advanced computational tools for super-resolution microscopy |
| title |
Enhancing aC'dots analysis through STORM imaging: development of advanced computational tools for super-resolution microscopy |
| spellingShingle |
Enhancing aC'dots analysis through STORM imaging: development of advanced computational tools for super-resolution microscopy STORM SMLM ROI Stretching open active contours Point spread function Fluorescent dye Core-shell silica nanoparticles Filament Ingeniería |
| title_short |
Enhancing aC'dots analysis through STORM imaging: development of advanced computational tools for super-resolution microscopy |
| title_full |
Enhancing aC'dots analysis through STORM imaging: development of advanced computational tools for super-resolution microscopy |
| title_fullStr |
Enhancing aC'dots analysis through STORM imaging: development of advanced computational tools for super-resolution microscopy |
| title_full_unstemmed |
Enhancing aC'dots analysis through STORM imaging: development of advanced computational tools for super-resolution microscopy |
| title_sort |
Enhancing aC'dots analysis through STORM imaging: development of advanced computational tools for super-resolution microscopy |
| dc.creator.fl_str_mv |
Salgado Manrique, Alejandro |
| dc.contributor.advisor.none.fl_str_mv |
Ávila Bernal, Alba Graciela |
| dc.contributor.author.none.fl_str_mv |
Salgado Manrique, Alejandro |
| dc.contributor.jury.none.fl_str_mv |
López Jiménez, Jorge Alfredo |
| dc.subject.keyword.eng.fl_str_mv |
STORM |
| topic |
STORM SMLM ROI Stretching open active contours Point spread function Fluorescent dye Core-shell silica nanoparticles Filament Ingeniería |
| dc.subject.keyword.none.fl_str_mv |
SMLM ROI Stretching open active contours Point spread function Fluorescent dye Core-shell silica nanoparticles Filament |
| dc.subject.themes.spa.fl_str_mv |
Ingeniería |
| description |
Super-resolution microscopy has transformed bioimaging by enabling nanoscale visualization of cellular structures. This thesis introduces PulseSTORM, a software application designed to streamline the quantitative analysis of Stochastic Optical Reconstruction Microscopy (STORM) and filament datasets. PulseSTORM integrates tools like ThunderSTORM, Ridge Detection, and SOAX into a modular platform for robust post-processing analysis. The research addresses the need for a comprehensive, user-friendly tool to analyze blinking statistics and filament structures, combining preprocessing, batch processing, and an analytics dashboard. Validation experiments with Cy5, C’dots, and aC’dots confirmed the accuracy of the software, with results aligning closely with literature values and offering actionable insights for sample preparation. PulseSTORM sets a foundation for advancing super-resolution microscopy, with future goals including direct integration with ThunderSTORM, optimization of computational performance, and exploration of recovery yield metrics. Beyond bioimaging, its potential extends to semiconductor defect detection, bridging biological and industrial applications. |
| publishDate |
2024 |
| dc.date.issued.none.fl_str_mv |
2024-12-14 |
| dc.date.accessioned.none.fl_str_mv |
2025-01-09T20:10:29Z |
| dc.date.available.none.fl_str_mv |
2025-01-09T20:10:29Z |
| dc.type.none.fl_str_mv |
Trabajo de grado - Pregrado |
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info:eu-repo/semantics/bachelorThesis |
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info:eu-repo/semantics/acceptedVersion |
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reponame:Repositorio Institucional Séneca |
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eng |
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eng |
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M. Lelek, M. Gyparaki, G. Beliu et al., “Single-molecule localization microscopy,” Nat Rev Methods Primers, vol. 1, p. 39, 2021. M. J. Rust, M. Bates, and X. Zhuang, “Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (storm),” Nat Methods, vol. 3, no. 10, pp. 793–795, Oct 2006. J. Xu, H. Ma, and Y. Liu, “Stochastic optical reconstruction microscopy (storm),” Curr Protoc Cytom, vol. 81, pp. 12.46.1–12.46.27, 2017. L. Schermelleh, R. Heintzmann, and H. Leonhardt, “A guide to super- resolution fluorescence microscopy,” The Journal of Cell Biology, vol. 190, no. 2, pp. 165–175, 2010. J. A. Erstling, N. Naguib, J. A. Hinckley, R. Lee, G. B. Tallman, L. Tsaur, D. Tang, and U. B. Wiesner, “Antibody functionalization of ultrasmall fluorescent core–shell aluminosilicate nanoparticle probes for advanced intracellular labeling and optical super resolution microscopy,” Chemistry of Materials, vol. 35, no. 3, 2023. M. Ovesn´y, P. Kˇr´ıˇzek, J. Borkovec, Z. ˇSvindrych, and G. M. Hagen, “Thunderstorm: a comprehensive imagej plugin for palm and storm data analysis and super-resolution imaging,” Bioinformatics, vol. 30, no. 16, pp. 2389–2390, 2014. J. Schnitzbauer, M. Strauss, and T. e. a. Schlichthaerle, “Super-resolution microscopy with dna-paint,” Nature Protocols, vol. 12, pp. 1198–1228, 2017. D. T. Nguyen, S. Mun, H. Park, U. Jeong, G. ho Kim, S. Lee, C.-S. Jun, M. M. Sung, and D. Kim, “Super-resolution fluorescence imaging for semiconductor nanoscale metrology and inspection,” Nano Letters, vol. 22, no. 24, pp. 10 080–10 087, December 2022. [Online]. Available: https://doi.org/10.1021/acs.nanolett.2c03848 M. Renz, “Fluorescence microscopy—a historical and technical perspective,” Cytometry Part A, vol. 83, pp. 767–779, 2013.68 S. Bradbury and P. Evennett, Fluorescence Microscopy. Oxford, United Kingdom: BIOS Scientific Publishers, Ltd., 1996. S. M. Hickey, B. Ung, C. Bader, R. Brooks, J. Lazniewska, I. R. D. Johnson, A. Sorvina, J. Logan, C. Martini, C. R. Moore, L. Karageorgos, M. J. Sweetman, and D. A. Brooks, “Fluorescence microscopy-an outline of hardware, biological handling, and fluorophore considerations,” Cells, vol. 11, no. 1, p. 35, 2021. D. Tosi, M. Sypabekova, A. Bekmurzayeva, C. Molardi, and K. Dukenbayev, “11 - evaluation of sensors,” in Optical Fiber Biosensors, D. Tosi, M. Sypabekova, A. Bekmurzayeva, C. Molardi, and K. Dukenbayev, Eds. Academic Press, 2022, pp. 283–291. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ B9780128194676000111 I. M. Khater, I. R. Nabi, and G. Hamarneh, “A review of super-resolution single-molecule localization microscopy cluster analysis and quantification methods,” Patterns, vol. 1, no. 3, p. 100038, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S266638992030043X H. Shroff, H. White, and E. Betzig, “Photoactivated localization microscopy (palm) of adhesion complexes,” Curr Protoc Cell Biol, pp.4.21.1–4.21.28, Mar 2013. R. Schmidt, T. Weihs, C. A. Wurm, I. Jansen, J. Rehman, S. J. Sahl, and S. W. Hell, “Minflux nanometer-scale 3d imaging and microsecond- range tracking on a common fluorescence microscope,” Nature Communications, vol. 12, 2021. J. Valli, A. Garcia-Burgos, L. Rooney, B. Vale de Melo E Oliveira, R. Duncan, and C. Rickman, “Seeing beyond the limit: A guide to choosing the right super-resolution microscopy technique,” J Biol Chem, vol. 297, no. 1, p. 100791, 2021. K. K. H. Chung, Z. Zhang, P. Kidd, Y. Zhang, N. D. Williams, B. Rollins, Y. Yang, C. Lin, D. Baddeley, and J. Bewersdorf, “Fluo- rogenic dna-paint for faster, low-background super-resolution imaging,” Nature Methods, vol. 19, pp. 554–559, 2022. J. A. Erstling, J. A. Hinckley, N. Bag, J. Hersh, G. B. Feuer, R. Lee, H. F. Malarkey, F. Yu, K. Ma, B. A. Baird, and U. B. Wiesner, “Ul- trasmall, bright, and photostable fluorescent core–shell aluminosilicate nanoparticles for live-cell optical super-resolution microscopy,” Advanced Materials, vol. 33, 2021. 69 G. T. Dempsey, J. C. Vaughan, K. H. Chen, M. Bates, and X. Zhuang, “Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging,” Nat Methods, vol. 8, pp. 1027–1036, 2011. [Online]. Available: http://www.ncbi.nlm.nih.gov/ pubmed/22056676 E. Herz, H. Ow, D. Bonner, A. Burns, and U. Wiesner, “Dye structure optical property correlations in near-infrared fluorescent core-shell silica nanoparticles,” Journal of Materials Chemistry, vol. 19, no. 35, pp. 6341–6347, 2009. D. Magde, E. Elson, and W. W. Webb, “Thermodynamic fluctuations in a reacting system—measurement by fluorescence correlation spectroscopy,” Physical Review Letters, vol. 29, no. 11, pp. 705–708, 1972. Syga, D. Spakman, C. Punter et al., “Method for immobilization of living and synthetic cells for high-resolution imaging and single-particle tracking,” Scientific Reports, vol. 8, p. 13789, 2018. C. Santos, C.-W. Chang, M.-A. Mycek, and R. Cardullo, “Frap, flim, and fret: Detection and analysis of cellular dynamics on a molecular scale using fluorescence microscopy,” Molecular reproduction and development, vol. 82, 05 2015. A. Small and S. Stahlheber, “Fluorophore localization algorithms for super-resolution microscopy,” Nature Methods, vol. 11, no. 3, pp. 267–279, 2014. S. Wolter, A. L¨oschberger, and T. e. a. Holm, “rapidstorm: accurate, fast open-source software for localization microscopy,” Nature Methods, vol. 9, pp. 1040–1041, 2012. J. Ries, “Smap: a modular super-resolution microscopy analysis plat- form for smlm data,” Nature Methods, vol. 17, pp. 870–872, 2020. S. Malkusch and M. Heilemann, “Extracting quantitative information from single-molecule super-resolution imaging data with lama – local- ization microscopy analyzer,” Scientific Reports, vol. 6, p. 34486, 2016. N. Brede and M. Lakadamyali, “Graspj: an open source, real-time anal-ysis package for super-resolution imaging,” Optics and Nanotechnology, vol. 1, p. 11, 2012. “The wavelet transform in signal and image processing,” in Frontiers in Computing Technologies for Manufacturing Applications, ser. Springer Series in Advanced Manufacturing. London: Springer, 2007. 70 S. Lee, J. Y. Shin, A. Lee, and C. Bustamante, “Counting single photoactivatable fluorescent molecules by photoactivated localization microscopy (palm),” Proceedings of the National Academy of Sciences of the United States of America, vol. 109, no. 43, pp. 17 436–17 441, 2012. K. E. Binkley and C. Griffin, “Imaging analysis of photoswitching fluorophores using single-molecule microscopy,” SMU Journal of Undergraduate Research, vol. 6, no. 2, p. Article 1, 2021. [Online]. Available: https://doi.org/10.25172/jour.6.2.1 K. Im, S. Mareninov, M. F. P. Diaz, and W. H. Yong, “An introduction to performing immunofluorescence staining,” in Methods in Molecular Biology. Humana Press, New York, NY, 2019, vol. 1897, pp. 299–311. J. A. Erstling, N. Naguib, J. A. Hinckley, R. Lee, G. B. Feuer, J. F. Tallman, L. Tsaur, D. Tang, and U. B. Wiesner, “Antibody functionalization of ultrasmall fluorescent core–shell aluminosilicatenanoparticle probes for advanced intracellular labeling and optical super resolution microscopy,” Chemistry of Materials, vol. 35, no. 3, pp. 1047–1061, 2023. [Online]. Available: https://doi.org/10.1021/acs.chemmater.2c02963 R. Graf, J. Rietdorf, and T. Zimmermann, “Live cell spinning disk microscopy,” in Microscopy Techniques, ser. Advances in Biochemical Engineering/Biotechnology. Springer, 2005, vol. 95, pp. 1–13. C. Steger, “An unbiased detector of curvilinear structures,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 2, pp.113–125, 1998. S. Richerson, A. P. Condurache, J. Lohmeyer, K. Schultz, and P. Ganske,“An initial approach to segmentation and analysis of nerve cells usingridge detection,” in 2008 IEEE Southwest Symposium on Image Analysis and Interpretation, 2008, pp. 113–116. A. Martinez-Sanchez, I. Garcia, and J.-J. Fernandez, “A ridge-based framework for segmentation of 3d electron microscopy datasets,” Journal of Structural Biology, vol. 181, no. 1, pp. 61–70, 2013. M. Robert, J. Bonnaure, M. Paindavoine, and S. Hafdi, “Design of an image processing integrated circuit for real time edge detection,” in Proceedings Euro ASIC ’92, 1992, pp. 280–283. T. Wagner, M. Hiner, and Xraynaud, “thorstenwagner/ij-ridgedetection: Ridge detection 1.4.0,” 2017. [Online]. Available: https://zenodo.org/record/84587471 M. B. Smith, H. Li, T. Shen, X. Huang, E. Yusuf, and D. Vavylonis, “Segmentation and tracking of cytoskeletal filaments using open active contours,” Cytoskeleton, 2010. T. Xu, D. Vavylonis, F.-C. Tsai, G. H. Koenderink, W. Nie, E. Yusuf, K. C. Lee, J.-Q. Wu, and X. Huang, “Soax: A software for quantification of 3d biopolymer networks,” Scientific Reports, vol. 5, p. 9081, 2015. J. Hembrow, M. J. Deeks, and D. M. Richards, “Automatic extraction of actin networks in plants,” PLOS Computational Biology, vol. 19, no. 8, p. e1011407, 2023. K. Jaqaman, D. Loerke, M. Mettlen, H. Kuwata, S. Grinstein, S. L. Schmid, and G. Danuser, “Robust single-particle tracking in live-cell time-lapse sequences,” Nature Methods, vol. 5, no. 8, pp. 695–702, 2008. S. C. Wilschefski and M. R. Baxter, “Inductively coupled plasma mass spectrometry: Introduction to analytical aspects,” Clinical Biochemistry Reviews, vol. 40, no. 3, pp. 115–133, 2019. |
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Ávila Bernal, Alba Gracielavirtual::21893-1Salgado Manrique, AlejandroLópez Jiménez, Jorge Alfredo2025-01-09T20:10:29Z2025-01-09T20:10:29Z2024-12-14https://hdl.handle.net/1992/75307instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Super-resolution microscopy has transformed bioimaging by enabling nanoscale visualization of cellular structures. This thesis introduces PulseSTORM, a software application designed to streamline the quantitative analysis of Stochastic Optical Reconstruction Microscopy (STORM) and filament datasets. PulseSTORM integrates tools like ThunderSTORM, Ridge Detection, and SOAX into a modular platform for robust post-processing analysis. The research addresses the need for a comprehensive, user-friendly tool to analyze blinking statistics and filament structures, combining preprocessing, batch processing, and an analytics dashboard. Validation experiments with Cy5, C’dots, and aC’dots confirmed the accuracy of the software, with results aligning closely with literature values and offering actionable insights for sample preparation. PulseSTORM sets a foundation for advancing super-resolution microscopy, with future goals including direct integration with ThunderSTORM, optimization of computational performance, and exploration of recovery yield metrics. Beyond bioimaging, its potential extends to semiconductor defect detection, bridging biological and industrial applications.PregradoNanotecnología89 páginasapplication/pdfengUniversidad de los AndesIngeniería ElectrónicaFacultad de IngenieríaDepartamento de Ingeniería Eléctrica y ElectrónicaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Enhancing aC'dots analysis through STORM imaging: development of advanced computational tools for super-resolution microscopyTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPSTORMSMLMROIStretching open active contoursPoint spread functionFluorescent dyeCore-shell silica nanoparticlesFilamentIngenieríaM. Lelek, M. Gyparaki, G. Beliu et al., “Single-molecule localization microscopy,” Nat Rev Methods Primers, vol. 1, p. 39, 2021.M. J. Rust, M. Bates, and X. Zhuang, “Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (storm),” Nat Methods, vol. 3, no. 10, pp. 793–795, Oct 2006.J. Xu, H. Ma, and Y. Liu, “Stochastic optical reconstruction microscopy (storm),” Curr Protoc Cytom, vol. 81, pp. 12.46.1–12.46.27, 2017.L. Schermelleh, R. Heintzmann, and H. Leonhardt, “A guide to super- resolution fluorescence microscopy,” The Journal of Cell Biology, vol. 190, no. 2, pp. 165–175, 2010.J. A. Erstling, N. Naguib, J. A. Hinckley, R. Lee, G. B. Tallman, L. Tsaur, D. Tang, and U. B. Wiesner, “Antibody functionalization of ultrasmall fluorescent core–shell aluminosilicate nanoparticle probes for advanced intracellular labeling and optical super resolution microscopy,” Chemistry of Materials, vol. 35, no. 3, 2023.M. Ovesn´y, P. Kˇr´ıˇzek, J. Borkovec, Z. ˇSvindrych, and G. M. Hagen, “Thunderstorm: a comprehensive imagej plugin for palm and storm data analysis and super-resolution imaging,” Bioinformatics, vol. 30, no. 16, pp. 2389–2390, 2014.J. Schnitzbauer, M. Strauss, and T. e. a. Schlichthaerle, “Super-resolution microscopy with dna-paint,” Nature Protocols, vol. 12, pp. 1198–1228, 2017.D. T. Nguyen, S. Mun, H. Park, U. Jeong, G. ho Kim, S. Lee, C.-S. Jun, M. M. Sung, and D. Kim, “Super-resolution fluorescence imaging for semiconductor nanoscale metrology and inspection,” Nano Letters, vol. 22, no. 24, pp. 10 080–10 087, December 2022. [Online]. Available: https://doi.org/10.1021/acs.nanolett.2c03848M. Renz, “Fluorescence microscopy—a historical and technical perspective,” Cytometry Part A, vol. 83, pp. 767–779, 2013.68S. Bradbury and P. Evennett, Fluorescence Microscopy. Oxford, United Kingdom: BIOS Scientific Publishers, Ltd., 1996.S. M. Hickey, B. Ung, C. Bader, R. Brooks, J. Lazniewska, I. R. D. Johnson, A. Sorvina, J. Logan, C. Martini, C. R. Moore, L. Karageorgos, M. J. Sweetman, and D. A. Brooks, “Fluorescence microscopy-an outline of hardware, biological handling, and fluorophore considerations,” Cells, vol. 11, no. 1, p. 35, 2021.D. Tosi, M. Sypabekova, A. Bekmurzayeva, C. Molardi, and K. Dukenbayev, “11 - evaluation of sensors,” in Optical Fiber Biosensors, D. Tosi, M. Sypabekova, A. Bekmurzayeva, C. Molardi, and K. Dukenbayev, Eds. Academic Press, 2022, pp. 283–291. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ B9780128194676000111I. M. Khater, I. R. Nabi, and G. Hamarneh, “A review of super-resolution single-molecule localization microscopy cluster analysis and quantification methods,” Patterns, vol. 1, no. 3, p. 100038, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/ S266638992030043XH. Shroff, H. White, and E. Betzig, “Photoactivated localization microscopy (palm) of adhesion complexes,” Curr Protoc Cell Biol, pp.4.21.1–4.21.28, Mar 2013.R. Schmidt, T. Weihs, C. A. Wurm, I. Jansen, J. Rehman, S. J. Sahl, and S. W. Hell, “Minflux nanometer-scale 3d imaging and microsecond- range tracking on a common fluorescence microscope,” Nature Communications, vol. 12, 2021.J. Valli, A. Garcia-Burgos, L. Rooney, B. Vale de Melo E Oliveira, R. Duncan, and C. Rickman, “Seeing beyond the limit: A guide to choosing the right super-resolution microscopy technique,” J Biol Chem, vol. 297, no. 1, p. 100791, 2021.K. K. H. Chung, Z. Zhang, P. Kidd, Y. Zhang, N. D. Williams, B. Rollins, Y. Yang, C. Lin, D. Baddeley, and J. Bewersdorf, “Fluo- rogenic dna-paint for faster, low-background super-resolution imaging,” Nature Methods, vol. 19, pp. 554–559, 2022.J. A. Erstling, J. A. Hinckley, N. Bag, J. Hersh, G. B. Feuer, R. Lee, H. F. Malarkey, F. Yu, K. Ma, B. A. Baird, and U. B. Wiesner, “Ul- trasmall, bright, and photostable fluorescent core–shell aluminosilicate nanoparticles for live-cell optical super-resolution microscopy,” Advanced Materials, vol. 33, 2021. 69G. T. Dempsey, J. C. Vaughan, K. H. Chen, M. Bates, and X. Zhuang, “Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging,” Nat Methods, vol. 8, pp. 1027–1036, 2011. [Online]. Available: http://www.ncbi.nlm.nih.gov/ pubmed/22056676E. Herz, H. Ow, D. Bonner, A. Burns, and U. Wiesner, “Dye structure optical property correlations in near-infrared fluorescent core-shell silica nanoparticles,” Journal of Materials Chemistry, vol. 19, no. 35, pp. 6341–6347, 2009.D. Magde, E. Elson, and W. W. Webb, “Thermodynamic fluctuations in a reacting system—measurement by fluorescence correlation spectroscopy,” Physical Review Letters, vol. 29, no. 11, pp. 705–708, 1972.Syga, D. Spakman, C. 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