Original Research ARTICLE
A novel methodology for characterizing cell sub-populations in automated time-lapse microscopy
- 1Faculty of Technology, Bielefeld University, Germany
- 2Faculty of Technology, Bielefeld University, Germany
- 3Department of Image Processing and Medical Engineering, Fraunhofer-Institut für Integrierte Schaltungen (IIS), Germany
- 4SYNMIKRO - LOEWE-Centre for Synthetic Microbiology, Philipps University of Marburg, Germany
- 5Department of Computer Science, University of British Columbia, Canada
Time-lapse imaging of cell colonies in microfluidic chambers provides time series of bioimages, i.e. biomovies.
They show the behavior of cells over time under controlled conditions.
One of the main remaining bottlenecks in this area of research is the analysis of experimental data and the extraction of cell growth characteristics, such as lineage information.
The extraction of the cell line by human observers is time-consuming and error-prone.
Previously proposed methods often fail because of their reliance on the accurate detection of a single cell, which is not possible for high density, high diversity of cell shapes and numbers, and high resolution images with high noise.
Our task is to characterize sub-populations in biomovies.
In order to shift the analysis of the data from individual cell level to cellular groups with similar fluorescence or even sub-populations, we propose to represent the cells by two new abstractions: the particle and the patch.
We use a three-step framework: preprocessing, particle tracking, construction of the patch lineage.
First, preprocessing improves the signal-to-noise ratio and spatially aligns the biomovie frames.
Second, cell sampling is performed by assuming particles, which represent a part of a cell, cell or group of contiguous cells in space.
Particle analysis includes: particle tracking, trajectory linking, filtering, and color information, respectively.
Particle tracking consists of following the spatiotemporal position of a particle and gives rise to coherent particle trajectories over time.
Typical tracking problems may occur (e. g. appearance or disappearance of cells, spurious artifacts).
They are effectively processed using trajectory linking and filtering.
Thirdly, the construction of the patch lineage consists in joining particle trajectories that share common attributes (i.e. proximity and fluorescence intensity) and feature common ancestry.
This step is based on: patch finding, patching trajectory propagation, patch splitting and patch merging.
The main idea is to group together the trajectories of particles in order to gain spatial coherence.
The final result of CYCASP is the complete graph of the patch lineage.
Finally, the graph encodes the temporal and spatial coherence of the development of cellular colonies.
We present results showing a computation time of less than five minutes.
Keywords: bioimaging, bioimage informatics, Cell Lineage, Bacteria, Microfluidics, Synthetic Biology, image processing
Received: 30 Aug 2017;
Accepted: 31 Jan 2018.
Edited by:Jie Chen, Augusta University, United States
Reviewed by:Rui Alves, Universitat de Lleida, Spain
Ralf Palmisano, University of Erlangen-Nuremberg, Germany
Pavel Loskot, Swansea University, United Kingdom
Copyright: © 2018 Hattab, Wiesmann, Becker, Munzner and Nattkemper. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mr. Georges Hattab, Bielefeld University, Faculty of Technology, Bielefeld, North Rhine-Westphalia, Germany, firstname.lastname@example.org