I developed and maintain several software libraries and graphical user interfaces (GUIs) for the various projects I was involved in. This page covers the most relevant topics.
Real-Time Deformability Cytometry (RT-DC)
RT-DC is an image-based flow-cytometry technique that allows mechanical phenotyping at throughput rates of ~1000 cells per second. I am currently maintaining the RT-DC analysis pipeline for Zellmechanik Dresden GmbH, used in the Guck Lab, which consists of three Python packages:
- ShapeOut: GUI for RT-DC data analysis
- dclab: core library for RT-DC analysis which is used for scripting beyond ShapeOut and also comes with a set of convenient command-line programs.
- fcswrite: library for writing fcs (flow cytometry standard) files, mainly used by us for data export and subsequent analysis in third-party applications
Quantitative Phase Imaging (QPI)
QPI in biophysics visualizes and quantifies the phase delay of light when it passes through a cell. The phase delay is governed by the refractive index of the cell which is connected to protein or DNA density. Thus, QPI can be used to characterize and monitor cells in a broad range of applications. 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
A simple QPI analysis is restricted to the recorded 2D phase data (transmission phase image). To resolve the 3D refractive index structure of a cell (right image), optical diffraction tomography (ODT) can be used. ODT can be combined with fluorescence tomography in colocalization studies to map out the characteristics of intracellular compartments. In the course of my work in the Guck Lab, I have written several Python libraries for the tomographic reconstruction of single cells:
- ODTbrain: library for ODT with the Born and Rytov approximations
- nrefocus: library for numerical focusing (refocusing, autofocusing) of complex wave fields
- radontea: library for classical tomography with the inverse Radon transform
Fluorescence Correlation Spectroscopy (FCS)
I have developed two GUIs, 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.
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: