The screening procedure and automated image analysis were applied as previously described [38 (link)]. Briefly, the cell culture and treatment and the immunofluorescence staining were performed manually. The multiparametric image analysis was performed using Columbus software version 2.8.0 (PerkinElmer, MA, US). We conducted a phenotypic cell-based assay to autonomously identify the ABCA3 WT-like or mutant-like cells by using machine-learning algorithms. After training, the software identified the linear combination of the most relevant properties that determined an effective discriminator for “WT-like cells” and “mutation-like cells”.
Columbus software version 2
Manufactured by PerkinElmer
Sourced in United States
Columbus software version 2.8.0 is a data management and analysis platform for high-content screening. It provides tools for image acquisition, processing, and analysis of cellular-based assays.
Automatically generated - may contain errors
Lab products found in correlation
2 protocols using columbus software version 2
1
ABCA3 Mutant Cell Identification
2
Image Analysis Pipeline for Cell Segmentation
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Image analysis was performed
using Columbus software version 2.8.0 (PerkinElmer). In the following,
the analysis steps in Columbus are described: the DAPI and FITC signals
were smoothened for the cell segmentation process using Median filters
to reduce noise signals. Nuclei were detected via the DAPI signal.
The FITC channel was used to define the cytoplasm and membrane region.
In a next step, morphology/symmetry features, texture (SER features),
and intensity properties of the DAPI, FITC, and red channel were calculated
for each cell region (nuclei, cytoplasm, and membrane). Moreover,
we applied a filter to remove border objects (nuclei that cross image
borders). For the detailed analysis pipeline in Columbus, please seeFigure S2 and the analysis sequences.
using Columbus software version 2.8.0 (PerkinElmer). In the following,
the analysis steps in Columbus are described: the DAPI and FITC signals
were smoothened for the cell segmentation process using Median filters
to reduce noise signals. Nuclei were detected via the DAPI signal.
The FITC channel was used to define the cytoplasm and membrane region.
In a next step, morphology/symmetry features, texture (SER features),
and intensity properties of the DAPI, FITC, and red channel were calculated
for each cell region (nuclei, cytoplasm, and membrane). Moreover,
we applied a filter to remove border objects (nuclei that cross image
borders). For the detailed analysis pipeline in Columbus, please see
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