Real-time Deformability Cytometry (RT-DC)
Real-time deformability cytometry (RT-DC) allows image-based mechanical phenotyping at throughput rates of 1000 cells per second. I am the maintainer of ShapeOut, the open-source analysis software of Zellmechanik Dresden GmbH, the company behind RT-DC.
- M. Urbanska, M. Winzi, K. Neumann, S. Abuhattum, P. Rosendahl, P. Müller, A. Taubenberger, K. Anastassiadis, and J. Guck, “Single-cell mechanical phenotype is an intrinsic marker of reprogramming and differentiation along the mouse neural lineage,” Development 144(23) : 4313–4321, 2017. doi:10.1242/dev.155218. PDF
- M. Herbig, M. Kräter, K. Plak, P. Müller, J. Guck, and O. Otto, “Real-Time Deformability Cytometry: Label-Free Functional Characterization of Cells,” in Flow Cytometry Protocols, 4, eds Teresa S. Hawley and Robert G. Hawley (Springer New York, 347–369), 2017. doi:10.1007/978-1-4939-7346-0_15.
- M. C. Munder, D. Midtvedt, T. Franzmann, E. Nüske, O. Otto, M. Herbig, E. Ulbricht, P. Müller, A. Taubenberger, S. Maharana, L. Malinovska, D. Richter, J. Guck, V. Zaburdaev, and S. Alberti, “A pH-driven transition of the cytoplasm from a fluid- to a solid-like state promotes entry into dormancy,” eLife 5, 2016. doi:10.7554/elife.09347. PDF
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 on a subcellular scale (right image). ODT achieves this in a marker-free manner and introduces the spatially-varying refractive index of biological cells as a structural marker. In the Guck Lab, we measure the refractive using ODT with the Rytov approximation. In the course of my work, 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
- M. Schürmann, G. Cojoc, S. Girardo, E. Ulbricht, J. Guck, and P. Müller, “3D correlative single-cell imaging utilizing fluorescence and refractive index tomography,” Journal of Biophotonics : n/a, 2017. doi:10.1002/jbio.201700145. PDF
- 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
- 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. PDF
- 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
Quantitative Phase Imaging (QPI)
Quantitative phase imaging (QPI) is a fundamental imaging technique that visualizes the retardation of electromagnetic radiation as it passes through an object. The parameter that governs this retardation is called refractive index. In biological imaging, QPI is an important tool to measure the dry mass or the refractive index (related to mass density) of single cells and tissues, which enables a profound characterization of the investigated samples.
As part of my present work in the Guck Lab, I am maintaining several Python libraries for QPI analysis:
- DryMass: user-friendly QPI analysis software
- qpimage: library for basic QPI analysis
- qpsphere: library for the QPI analysis of spherical phase objects
- qpformat: library for opening QPI data
- 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
- 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