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An end-to-end software solution for the analysis of high-throughput single-cell migration data

View Article: PubMed Central - PubMed

ABSTRACT

The systematic study of single-cell migration requires the availability of software for assisting data inspection, quality control and analysis. This is especially important for high-throughput experiments, where multiple biological conditions are tested in parallel. Although the field of cell migration can count on different computational tools for cell segmentation and tracking, downstream data visualization, parameter extraction and statistical analysis are still left to the user and are currently not possible within a single tool. This article presents a completely new module for the open-source, cross-platform CellMissy software for cell migration data management. This module is the first tool to focus specifically on single-cell migration data downstream of image processing. It allows fast comparison across all tested conditions, providing automated data visualization, assisted data filtering and quality control, extraction of various commonly used cell migration parameters, and non-parametric statistical analysis. Importantly, the module enables parameters computation both at the trajectory- and at the step-level. Moreover, this single-cell analysis module is complemented by a new data import module that accommodates multiwell plate data obtained from high-throughput experiments, and is easily extensible through a plugin architecture. In conclusion, the end-to-end software solution presented here tackles a key bioinformatics challenge in the cell migration field, assisting researchers in their high-throughput data processing.

No MeSH data available.


Single-cells trajectories acquired by time-lapse imaging and image processing.Cell trajectories (colored lines) obtained from applying tracking software on a time-lapse image sequence and overlaid on the last image of the sequence for Ba/F3 cells expressing p210 Bcr-Abl (left, experiment 1, sparsely seeded cells, Table 2) and for untreated MDA-MB-231 cells (right, experiment 2, cell exclusion zone assay, Table 2). Numbers indicate individual cell trajectories. Colors indicate temporal evolution of x and y cell positions (blue: earlier in time, red: later in time). The red line in the right image indicates the border between the confluent cell layer and the cell-free zone that has shifted in time. Single cells are only identified as such in experiment 2 when they escape the expanding cell sheet.
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f3: Single-cells trajectories acquired by time-lapse imaging and image processing.Cell trajectories (colored lines) obtained from applying tracking software on a time-lapse image sequence and overlaid on the last image of the sequence for Ba/F3 cells expressing p210 Bcr-Abl (left, experiment 1, sparsely seeded cells, Table 2) and for untreated MDA-MB-231 cells (right, experiment 2, cell exclusion zone assay, Table 2). Numbers indicate individual cell trajectories. Colors indicate temporal evolution of x and y cell positions (blue: earlier in time, red: later in time). The red line in the right image indicates the border between the confluent cell layer and the cell-free zone that has shifted in time. Single cells are only identified as such in experiment 2 when they escape the expanding cell sheet.

Mentions: Once the data associated with an experiment are loaded, the software provides the following four key functions: (i) data inspection and visualization, (ii) parameter extraction, (iii) quality control, and (iv) statistical data analysis. The following sections describe these functionalities in detail using two multi-sample data sets as examples. These data sets were selected because they were generated via different assay types (Fig. 3), using different cell types (immune cells versus cancer cells). Furthermore, these data sets reflect a different level of complexity (i.e., number of samples tested in parallel). The details for each experiment are reported in Table 2. Single-cell trajectories from a single replicate in each of the experiments are visualized in Fig. 3. The experiments were performed in phase-contrast, but other imaging techniques are of course also fully supported. The complete single-cell migration analyses for these experiments are available on figshare (see “Methods” section for details).


An end-to-end software solution for the analysis of high-throughput single-cell migration data
Single-cells trajectories acquired by time-lapse imaging and image processing.Cell trajectories (colored lines) obtained from applying tracking software on a time-lapse image sequence and overlaid on the last image of the sequence for Ba/F3 cells expressing p210 Bcr-Abl (left, experiment 1, sparsely seeded cells, Table 2) and for untreated MDA-MB-231 cells (right, experiment 2, cell exclusion zone assay, Table 2). Numbers indicate individual cell trajectories. Colors indicate temporal evolution of x and y cell positions (blue: earlier in time, red: later in time). The red line in the right image indicates the border between the confluent cell layer and the cell-free zone that has shifted in time. Single cells are only identified as such in experiment 2 when they escape the expanding cell sheet.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5304333&req=5

f3: Single-cells trajectories acquired by time-lapse imaging and image processing.Cell trajectories (colored lines) obtained from applying tracking software on a time-lapse image sequence and overlaid on the last image of the sequence for Ba/F3 cells expressing p210 Bcr-Abl (left, experiment 1, sparsely seeded cells, Table 2) and for untreated MDA-MB-231 cells (right, experiment 2, cell exclusion zone assay, Table 2). Numbers indicate individual cell trajectories. Colors indicate temporal evolution of x and y cell positions (blue: earlier in time, red: later in time). The red line in the right image indicates the border between the confluent cell layer and the cell-free zone that has shifted in time. Single cells are only identified as such in experiment 2 when they escape the expanding cell sheet.
Mentions: Once the data associated with an experiment are loaded, the software provides the following four key functions: (i) data inspection and visualization, (ii) parameter extraction, (iii) quality control, and (iv) statistical data analysis. The following sections describe these functionalities in detail using two multi-sample data sets as examples. These data sets were selected because they were generated via different assay types (Fig. 3), using different cell types (immune cells versus cancer cells). Furthermore, these data sets reflect a different level of complexity (i.e., number of samples tested in parallel). The details for each experiment are reported in Table 2. Single-cell trajectories from a single replicate in each of the experiments are visualized in Fig. 3. The experiments were performed in phase-contrast, but other imaging techniques are of course also fully supported. The complete single-cell migration analyses for these experiments are available on figshare (see “Methods” section for details).

View Article: PubMed Central - PubMed

ABSTRACT

The systematic study of single-cell migration requires the availability of software for assisting data inspection, quality control and analysis. This is especially important for high-throughput experiments, where multiple biological conditions are tested in parallel. Although the field of cell migration can count on different computational tools for cell segmentation and tracking, downstream data visualization, parameter extraction and statistical analysis are still left to the user and are currently not possible within a single tool. This article presents a completely new module for the open-source, cross-platform CellMissy software for cell migration data management. This module is the first tool to focus specifically on single-cell migration data downstream of image processing. It allows fast comparison across all tested conditions, providing automated data visualization, assisted data filtering and quality control, extraction of various commonly used cell migration parameters, and non-parametric statistical analysis. Importantly, the module enables parameters computation both at the trajectory- and at the step-level. Moreover, this single-cell analysis module is complemented by a new data import module that accommodates multiwell plate data obtained from high-throughput experiments, and is easily extensible through a plugin architecture. In conclusion, the end-to-end software solution presented here tackles a key bioinformatics challenge in the cell migration field, assisting researchers in their high-throughput data processing.

No MeSH data available.