Optical Diffraction Tomography (ODT)
Optical diffraction tomography (ODT) uses quantitative phase imaging techniques that record phase and intensity data (left image) of a cell from multiple angles to obtain the spatially resolved 3D refractive index of that cell (right image). I am interested in investigating single cells on the subcellular scale. ODT allows this in a marker-free manner and introduces the spatially-varying refractive index of biological cells as a structural marker. In the GuckLab, we measure the refractive using ODT in the Rytov approximation. In the course of this project, I have written several Python libraries that we are using for pre-processing and tomographic reconstruction of single cells:
- ODTbrain: diffraction tomography with the Born and Rytov approximations
- nrefocus: numerical focusing (refocusing, autofocusing) of complex wave fields
- radontea: classical tomography with the inverse Radon transform
- P. Müller, “Optical Diffraction Tomography for Single Cells,” (PhD thesis, TU Dresden), 2016. url:http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-202261. PDF
- M. Schürmann, J. Scholze, P. Müller, J. Guck, and C. J. Chan, “Cell nuclei have lower refractive index and mass density than cytoplasm,” Journal of Biophotonics : 1068–1076, 2016. doi:10.1002/jbio.201500273. PDF
- P. Müller, M. Schürmann, C. J. Chan, and J. Guck, “Single-cell diffraction tomography with optofluidic rotation about a tilted axis,” Proc. of SPIE 9548: 95480U, 2015. doi:10.1117/12.2191501.
- P. Müller, M. Schürmann, and J. Guck, “ODTbrain: a Python library for full-view, dense diffraction tomography,” BMC Bioinformatics 16(1) : 1–9, 2015. doi:10.1186/s12859-015-0764-0. PDF
- P. Müller, M. Schürmann, and J. Guck, “The Theory of Diffraction Tomography.” 2015. arXiv:1507.00466 [q-bio.QM]. PDF
- M. Schürmann, J. Scholze, P. Müller, C. J. Chan, A. E. Ekpenyong, K. J. Chalut, and J. Guck, “Refractive index measurements of single, spherical cells using digital holographic microscopy,” in Biophysical Methods in Cell Biology, 125, ed Ewa K. Paluch (Academic Press, 143–159), 2015. doi:10.1016/bs.mcb.2014.10.016.
Fluorescence Correlation Spectroscopy (FCS)
I have developed two graphical end-user programs, PyCorrFit and PyScanFCS, for data analysis in fluorescence correlation spectroscopy (FCS). At the time I started working with FCS and needed to process and fit my own experimental data, it was quite difficult to keep track of all the results that I produced with qtiplot. Since I was working with Linux and partially with Windows, I started with the Python programming language which made easy to port the software to any operating system.
In addition to these graphical programs, I have implemented a multiple-tau correlation algorithm for Python. Multipe-tau correlation is computed on a logarithmic scale (less data points are computed) and is thus much faster than conventional correlation.
Binaries of the programs are available at GitHub/FCS-analysis, at the python package index, and have also been debianized by Alexandre Mestiashvili. The initial development of these programs was done in the group of Petra Schwille and a lot of input came from Thomas Weidemann who was my mentor at that time:
- multipletau: python multiple-tau correlation algorithm
- PyCorrFit: data analysis and fitting software for fluorescence correlation spectroscopy
- PyScanFCS: data evaluation in perpendicular line scanning FCS
- P. Müller, P. Schwille, and T. Weidemann, “PyCorrFit – generic data evaluation for fluorescence correlation spectroscopy,” Bioinformatics , 2014. doi:10.1093/bioinformatics/btu328. PDF
- P. Müller, P. Schwille, and T. Weidemann, “Scanning fluorescence correlation spectroscopy (SFCS) with a scan path perpendicular to the membrane plane,” in Methods in Molecular Biology, 1076, (635–51), 2014. doi:10.1007/978-1-62703-649-8_29. PDF