Open-source workflow for forskolin-induced swelling (FIS) assay analysis

Quick reference guide

Table of Contents

• 1. Overview
• 2. Setup
  • 2.1. File renaming
  • 2.2. Image analysis
  • 2.3. Data analysis
• 3. Demonstration dataset
  • 3.1. Assay description
  • 3.2. File download
• 4. Analysis of the demonstration dataset
  • 4.1. Generation of TIF files
  • 4.2. Generation of well descriptors (infile)
  • 4.3. Renaming raw files
  • 4.4. Image analysis
    • 4.4.1. CellProfiler
    • 4.4.2. Fiji/ImageJ
      • 4.4.2.1. Test mode
      • 4.4.2.2. Batch analysis mode
  • 4.5. Statistical data analysis
• 5. Interpretation of the results
• 6. License
• 7. Citation
• 8. References

1. Overview

The forskolin-induced swelling (FIS) assay [1] has become the preferential assay to assess the efficacy of established and investigational CFTR-modulating compounds for individuals that suffer from cystic fibrosis (CF) [2]. In this quick reference manual we explain how to use our open-source workflow to perform standardized analysis of CFTR function measurements of intestinal (CF) organoids.

The workflow comprises of three steps:

1. Renaming of raw microscopy images

   • Convert raw microscopy images into a common format, regardless of microscope brand or imaging modality;
   • Include experimental metadata in file names;
   • Implemented in the htmrenamer tool.
     More info

2. Image analysis

   • Measurement of total organoid area per time frame;
   • Implemented in CellProfiler or Fiji/ImageJ.
     More info

3. Statistical data analysis

   • Data visualization, exploratory analysis, quality control and normalization;
   • Implemented in Organoid Analyst.
     More info

The workflow processes a time-lapse imaging dataset and calculates the total organoid cross-sectional area per well and computes the following statistics from the data (see figure below and this link for details):

• AUC (area under the curve): the area under the normalized FIS kinetics curve (baseline: 100%);
• ISR (initial swelling rate): the slope of a fitted line to the region of maximal linear swelling in a normalized FIS kinetics curve;
• Aₜ/A₀: the percentage of area increase during the FIS experiment.

Currently, ZEISS and Leica imaging datasets are supported and the image analysis can be performed by either using CellProfiler or Fiji, as depicted in the following scheme:

This document contains links to all the required software tools, together with a demonstration dataset and the expected results following analysis of the dataset.

2. Setup

This section describes how to install the software that is required for the FIS analysis workflow.

2.1. File renaming

Detailed information

• ZEISS files

  1. Installation of ZEN (blue edition): The LITE version can be downloaded here.

• Leica files

  1. Images must be acquired with the Leica LAS X software, either on the MatrixScreener or Navigator modules.

• Installation of the htmrenamer R package

  1. Install R: Download here.

  2. Install Command Line Tools for Xcode (macOS only): Download and install the latest version matching your macOS version from here (Apple ID required).

  3. Install XQuartz (macOS only): Download here.

  4. Install the htmrenamer package: download the latest htmrenamer release (htmrenamer_xxx.tar.gz). Open R and install via the command line:

  Windows:

   install.packages(c("gWidgets2", "gWidgets2tcltk", "openxlsx", "reshape2", "tiff", "XML"), dependencies=T)
   install.packages("c:/path_to_file/htmrenamer_xxx.tar.gz", repos=NULL)
  

  macOS:

   install.packages(c("gWidgets2", "gWidgets2tcltk", "openxlsx", "reshape2", "tiff", "XML"), dependencies=T)
   install.packages("/path_to_file/htmrenamer_xxx.tar", repos=NULL, type="source")
  

• Describe the contents of the assay plate (infile)

  1. Infile templates can be obtained here or through the following R commands:

  library(htmrenamer)
  newinfile.char(8, 12, show = TRUE, saveto = "myinfile.txt")
  

  The infile has a tabular structure:

  001--A--01--00--00--fsk--0.008
  002--A--02--01--00--fsk--5
  003--A--03--02--00--fsk_vx809--0.008
  004--A--04--03--00--fsk_vx809--5
  ...
  

2.2. Image analysis

Detailed information

• Image analysis using CellProfiler

  1. Install CellProfiler: download here

  2. Download image analysis pipelines: download here

• Image Analysis using Fiji/ImageJ

  1. Install Fiji: download here.

  2. Download image analysis scripts: download here.

  3. Install scripts: (Windows): in the folder \Fiji.app\scripts create a subfolder named FIS and copy the FIS_test....ijm and FIS_analysis....ijm files to this folder.

  4. Install scripts: (macOS): in Finder, go to Applications, locate Fiji, right click and select Show Package Contents. In the Scripts folder create a subfolder named FIS and copy the FIS_test....ijm and FIS_analysis....ijm files to this folder.

2.3. Data analysis

Detailed information

1. A web browser is required to perform the data analysis

2. Install R: see section above.

3. Install Command Line Tools for Xcode (macOS only): see section above.

4. Install Fiji: see section above.

5. Install Organoid Analyst: in the R console type

source("https://raw.githubusercontent.com/hmbotelho/organoid_analyst/master/installer.R")
runGitHub("organoid_analyst", "hmbotelho", launch.browser=T)

3. Demonstration dataset

3.1. Assay description

The demonstration dataset contains data obtained from a FIS assay that was performed using intestinal organoids homozygous for a class II CFTR mutation in the absence (DMSO) or in the presence of VX-809 and/or VX-770 (3.2 μM), as previously described [2]. CFTR was activated by addition of forskolin (Fsk) in a concentration ranging from 0.008 μM – 5 μM. The following conditions were investigated in this experiment, as is depicted below.

The following parameters were applied when acquiring the microscopy images that are included in the demonstration dataset:

• Imaging system: Zeiss confocal microscope
• Number of plates: 1
• Number of imaged wells: 64
• Number of imaging fields per well: 1
• Number of raw images (TIF): 448
• Number of time points: 7
• Time interval between frames: 10 min
• Total experiment time: 60 min
• Image resolution: 512 x 512 pixels
• Pixel dimensions: 4.991 x 4.991 μm
• Image bit depth: 8 bit
• Number of fluorescence channels: 1 (calcein green)

3.2. File download

The demonstration dataset consists of:

1. Raw microscopy images (native CZI format) (00-images_raw/demoplate_01.czi, 169 MB)
2. Raw microscopy images (TIF export) (01-images_exported/demoplate_01, 91.8 MB)
3. Infile (well description) (02-microscope_infile/demoplate_01.txt, 3 KB)
4. Raw microscopy images (renamed) (03-images_renamed/demoplate_01, 91.9 MB)
5. CellProfiler and Fiji image analysis pipelines (pre-configured for the demonstration dataset) (04-analysis_pipelines, 1.2 MB)
6. Image quantification outputs (CellProfiler) (05-images_analysis/demoplate_01--cellprofiler, 14.8 MB)
7. Image quantification outputs (Fiji) (05-images_analysis/demoplate_01--ij, 241 MB)
8. Quantification summary (from CellProfiler data) (05-images_analysis/demoplate_01--cellprofiler--analysis, 37.0 MB)
9. Quantification summary (from Fiji data) (05-images_analysis/demoplate_01--ij--analysis, 37.4 MB)

Download the entire dataset here (735 MB) or here (zip, 294 MB).

4. Analysis of the demonstration dataset

This section explains how to analyze the demonstration dataset using our FIS workflow. This example can be adapted to analyze any FIS dataset. Additional resources are indicated as links in the text.

4.1. Generation of TIF files

TIF files are the required input for the workflow.

1. Open the native CZI file in ZEN Blue.

2. Export as TIF (File > Export/Import > Export > TIFF).

Expected result: 01-images_exported

4.2. Generation of well descriptors (infile)

1. Create a customized infile using this template or the following R commands:

   library(htmrenamer)
   newinfile.char(8, 12, show = TRUE, saveto = "myinfile.txt")
   

Infile for the demonstration dataset: demoplate_01.txt

4.3. Renaming raw files

The renaming process includes relevant metadate in TIF files names.

Detailed information

1. Open R and type

library(htmrenamer)
rename_zeiss_gui()

2. Enter the following information:

   • Location of the raw TIF files (01-images_exported/demoplate_01)
   • Location for renamed files (any folder)
   • Location of the infile (demoplate_01.txt)
   • Number of rows and columns in the assay plate (8 & 12)

3. Click Start renaming and wait for the renaming process to finish.

Expected result: 03-images_renamed

4.4. Image analysis

A description of how to perform image analysis of the demonstration dataset, either with CellProfiler or Fiji, is presented in this section. CellProfiler is recommended for most of the analyses as described here.

Detailed information

4.4.1. CellProfiler

1. Open CellProfiler.

2. Click File > Open Project... and load the cp_pipeline_demo.cpproj file.

3. Click Window > Show All Windows On Run to display all the sequential image processing steps.

4. In the Images module, remove all previously listed files (drag mouse and press delete) and drag raw microscopy images into the white box Drop files and folders here.

Next, the image analysis parameters will be defined interactively:

5. Enter into Test Mode by clicking Test > Start Test Mode. The active module will now appear underlined (e.g. ).

6. Go to Test > Choose Image Group to select a single well to test the image analysis settings. We will use well B8 (well #20) from the demonstration dataset as an example.

7. Click until reaching the IdentifyPrimaryObjects module.

8. The IdentifyPrimaryObjects module performs organoid segmentation and is the most critical step in the analysis. With the exception of Name the primary objects to be identified all settings may need to be adjusted for each experiment, especially the following ones:

   • Typical diameter of objects: in pixel units.

   • Threshold correction factor: controls threshold stringency.

   • Threshold smoothing scale: controls image smoothing before the thresholding step.

   • Suppress local maxima that are closer than...: the approximate radius of the smallest organoid in pixel units.

   • Fill holes in identified objects: we recommend the following settings in this module:

     • Never: when calcein labelling of organoids is intense across all wells and time points.

     • After both thresholding and declumping: when there is significant organoid swelling and calcein fluorescence intensity becomes low in the organoid lumen over time.

     Note: filling holes may result in an overestimation of organoid size if densely packed organoids are touching each other and producing voids (see below). In this case, the Fiji image analysis approach performs better details.

   

   Example fluorescence image before and after thresholding with and without fill holes After both thresholding and declumping. Note that not using the fill holes operation produces an unsatisfactory segmentation with ring-shaped organoids (arrows). When Fill Holes is enabled, most organoids are correctly segmented but in this example a background region is incorrectly classified as object at the 40 min frame (arrowhead). Segmentation masks have been re-coloured to accurately track objects. Panels show a portion of the entire image.

9. Adjust the parameters until satisfactory segmentation of the image is achieved.

10. Click until reaching the DisplayDataOnImage module.

11. The DisplayDataOnImage module allows for overlaying any object feature on top of the microscope image to inform the user of excluded undesired objects following the thresholding criteria for object-level quality control purposes.

12. The FilterObjects module allows for exclusion of individual organoids based on fluorescence intensity of morphological features. In the Category and Measurement boxes select the feature chosen in step 9. In Minimum value and Maximum value insert the range of allowed values. Organoids with values outside this range will be discarded.

    

    An example where objects with FormFactor > 0.3 were approved thereby excluding irregular structures surrounded by cell clumps from the analysis (arrowhead). Segmentation masks show the identified objects from the segmentation step (organoids_prelim) and identified objects by applying the quality control criteria (organoids). Panels show a portion of the images from well H4, #88 from the demonstration dataset.

    Disable the filtering by entering excessively low or high values.

13. Click until reaching the TrackObjects module.

14. The TrackObjects module assigns unique numeric labels to organoids across all time lapse frames. Maximum pixel distance to consider matches should be adjusted to the maximum number of pixels an organoid is expected to drift along two consecutive frames. If Minimum lifetime is adjusted to be n - 1, where n is the number of time points in the time lapse, organoids which are not tracked throughout the entire time lapse will be assigned a NaN label and can optionally be excluded from the data analysis.

15. The CalculateMath module converts the pixel size to micron units. In the Multiply the above operand by field enter the square of the pixel width/height (e.g. if the pixel dimensions are 4.991 × 4.991 μm, the conversion factor is 24.910081).

16. Use Test > Choose Image Group and Test > Choose Image Set to examine additional images, until the analysis parameters are suitable for analysis of all images in the dataset.

17. In View output settings, under Default Output Folder specify where to store the analyzed data files.

18. Save a copy of the CellProfiler project by clicking File > Save Project As....

19. Activate Window > Hide All Windows On Run.

20. Start the analysis of the whole dataset by clicking on the button.

21. CellProfiler will produce an output folder identifiable by the --cellprofiler suffix, containing objects.csv and PNG files.

Expected result: 05-images_analysis/demoplate_01--cellprofiler

4.4.2. Fiji/ImageJ

The Fiji image analysis pipeline comprises of two scripts:

• The test script is used to test single images and determine the analysis parameters for optimal segmentation.
• The analysis script processes a complete dataset using the parameters determined above.

4.4.2.1. Test mode

1. Open Fiji.

2. Open an image (File > Open...) to optimize the analysis settings. In this example the first time point from well B8 (#20) from the demonstration dataset will be used.

3. Start the test mode by selecting FIS > FIS test....

   

4. The test mode window will open.

   

5. Define the analysis parameters for the selected image:

   • Background filter: This filter generates a pseudo-flat field from the fluorescence image, which will be subtracted from the raw fluorescence image to generate a background corrected image.

   • Radius of filter: The radius of the background filter.

   • Offset after background correction: This value will be subtracted from all pixels after background correction.

   • Thresholding method: Select between a manual threshold value or an auto-thresholding method. Thresholding will occur after pseudo-flat field subtraction, offset correction and grey value rescaling to [0 ~ 1].

   • Manual threshold value: Only applies for the 'Manual' thresholding method. All pixels above this grey value will be assigned to the objects (organoids).

   • Fill all holes: When this box is unchecked, an optimized hole filling algorithm (conditional fill holes) will be applied. When this box is checked, a standard fill holes operation will be performed after the thresholding step (all holes are filled example).

   • Remove salt and pepper noise.

   • Declump organoids: separate clustered objects.

   • Exclude objects touching the image border.

   • Minimum/Maximum organoid area: Sets the minimum/maximum allowed organoid area in μm2 units.

   • Minimum/Maximum organoid circularity: Sets the minimum/maximum allowed organoid circularity.

   • Exclude organoids based on measurement: In addition to area and circularity one other feature can be selected for additional object-level quality control purposes.

   • Minimum/Maximum allowed value: The minimum/maximum allowed values for the additional quality control feature.

   • Pixel width/height: Pixel dimension in the raw microscopy image.

6. Click the OK button to test the analysis settings in the open image.

7. Fiji will apply the test settings and display the results of each analysis step. Images are numbered according to the sequence of the operations that have been performed.

   
   

   

   
   

8. The Results window will display the features of all objects, which may be used for object-level quality control. The Log window will display the settings defined in step 5.

9. Inspect the ORGANOIDS_FINAL image to judge the quality of background subtraction, segmentation and object/level quality control.

10. Click the OK button in the box below to return to the test mode.

11. If necessary, adjust the settings to obtain an adequate image segmentation.

12. Click Cancel or X to exit the test mode.

13. Several images should be tested and inspected prior to running the batch analysis mode to ensure that the selected analysis settings are suitable for the entire dataset.

14. The Log window can be saved (File > Save As...) to automatically load the displayed settings in the batch analysis mode.

4.4.2.2. Batch analysis mode

1. Open Fiji.

2. Start the batch analysis mode by selecting FIS > FIS analysis....

3. The batch analysis window will open.

   

   
   

4. Enter the parameters determined in the test mode, together with the following three additional parameters:

   • Regular expression matching all files being analyzed: the default expression .*--C00(?:.ome)??.tif$ will match the images generated by the htmrenamer tool. If required, replace C00 with the channel name for the fluorescence image.

   • Folder location > Raw FIS images: Select the folder containing the renamed fluorescence images (demoplate_01).

   • Folder location > Results: Specify the folder where the results of the analysis will be saved.

   • Output image format: The image format for segmentation count masks (TIF, PNG or both).

   If Load settings? is checked Fiji will ask for the settings file generated during the test mode. This loads all settings except the regular expression and folder locations.

   The demonstration dataset included with this guide was analyzed using the following settings:

   Parameter	Value
   Background filter	Median
   Radius of filter	50
   Offset after background correction	0.005
   Thresholding method	Manual
   Manual threshold value	0.05
   Fill all holes?	No
   Remove salt and pepper noise?	Yes
   Declump organoids?	No
   Exclude objects touching the image border?	No
   Minimum organoid area	500 μm²
   Maximum organoid area	99999999 μm²
   Minimum organoid circularity	0
   Maximum organoid circularity	1
   Exclude organoids based on measurements?	None - Do not exclude
   Minimum allowed value	Irrelevant
   Maximum allowed value	Irrelevant
   Pixel width/height	4.991 μm
   Regular expression	.*--C00(?:.ome)??.tif$
   Output image format	TIF+PNG

5. Click OK to start the analysis.

6. Fiji will produce an output folder identifiable by the --ij suffix, containing objects.csv, TIF and settings_YYYY-MM-DD_HH-MM.log files.

Expected result: 05-images_analysis/demoplate_01--ij

4.5. Statistical data analysis

This section describes how to use the Organoid Analyst application to visualize the image analysis measurements and how to compute summary statistics of the dataset. Organoid Analyst can analyze one dataset (i.e. one 96-well plate) at a time.

Detailed information

1. Open R and type the following commands:

library(shiny)
runGitHub("organoid_analyst", "hmbotelho", launch.browser = T)

2. Organoid Analyst will open in a new browser window

   

   
   

3. Under 1. Load data, click on Choose a '--cellprofiler' or '--ij' folder... and select the demoplate_01--cellprofiler folder.

4. Organoid Analyst concatenates the objects.csv files (one per well) generated during the image analysis process.

5. Under 2. Settings enter or select relevant information of the experiment:

   Parameter	Value
   Experiment Settings
   Time resolution (minutes per timepoint)	10
   Name of the column with AREA values	Math_area_micronsq
   Name of the column with TIME values	Metadata_timeNum
   Name of the column with WELL values	Metadata_wellNum
   Name of the column with COMPOUND names	Metadata_compound
   Name of the column with CONCENTRATION values	Metadata_concentration
   Number of rows	8
   Number of columns	12
   Quality Control Settings
   Name of the column with organoid ID	TrackObjects_Label_4
   ID of invalid organoids	Allow all organoids
   Name of the column with organoid center (X)	AreaShape_Center_X
   Name of the column with organoid center (Y)	AreaShape_Center_Y
   File Remapping Settings
   Column with file path	Metadata_FileLocation
   Image root folder name in table	file:///C:/FIS
   Image root folder name in this computer ¹	C:\FIS
   Segmentation Masks Settings
   Generate segmentation masks?	Yes
   Image root folder name in table	file:///C:/FIS/demo_dataset/03-images_renamed/demoplate_01
   Image root folder name in this computer ²	C:\FIS\demo_dataset\05-images_analysis\demoplate_01--cellprofiler
   Length of image suffix ³	9
   Suffix for segmentation mask files ⁴	--masks.png
   Suffix for Organoid Analyst masks file ⁵	--OAmask
   Suffix for Organoid Analyst labels file ⁶	--OAlabel
   Interaction with Fiji Settings
   Path to Fiji (Windows)	C:/Fiji.app/ImageJ-win64.exe

• ¹ Select the folder that contains the raw microscopy images on your computer (demoplate_01 in the demonstration dataset).*
• ² Select the location of the demoplate_01--cellprofiler folder on your computer.*
• ³ The image analysis process saves object segmentation masks in files which are named as the raw microscopy images, except for a small suffix (or termination). For example: one raw microscopy image file in the demonstration dataset is named demoplate_01--fsk--0.008--W0001--P001--T0000--C00.tif and the corresponding segmentation masks are saved in file demoplate_01--fsk--0.008--W0001--P001--T0000--masks.png. The last 9 characters in the raw file names (--C00.tif) are the mentioned suffix.*
• ⁴ The suffix in the segmentation masks image (see ³)*
• ⁵ Any suffix can be entered, as this does not affect the data analysis process.*
• ⁶ Select the location of the Fiji executable file on your computer. Not required when running macOS.*

6. Click on Normalize data.

7. Organoid Analyst normalizes the data and updates the segmentation masks.

8. Under 3. Plotting interactive data exploration, image visualization and per well quality control can be performed.

   
   

   

   
   

   The following options are available:

   • Analysis settings: Allows for specifying the final time point of the experiment and the time interval that has to be used for calculation of the initial swelling rate.

   • Quality control: The possibility to exclude individual wells from the analysis (e.g. wells with imaging aberrations or insufficient organoids).

   • Time-lapse viewer: Opens images of selected wells as time-lapse sequences in Fiji. Select the wells of interest and click

   

   
   

   • Plots: Organoid Analyst visualizes the quantitative FIS data as five different plots:

     • A multi-well plate layout with the normalized kinetic curves being displayed for each well. In this plot the ISR can also be visualized.

     • A dose-response plot of the AUC measurements.

     • Bar plots representing summarized AUC, ISR and Aₜ/A₀ measurements (average ± SD across identically treated wells).

   The demonstration dataset was analyzed using the following settings:

   Parameter	Value
   Select initial data points	10 ~ 30
   Select final experiment time	60
   Wells excluded from calculations	None

9. Click the Export data button to save the analyzed dataset into the selected output folder.

10. The following files will be generated:

    • Updated segmentation masks
    • Updated organoid labels
    • FIS_normalized.xlsx Data for individual wells: sum of all organoid areas, normalized areas, normalized areas subtracted of the 100% baseline, and cumulative AUC.
    • FIS_rawdata.csv Concatenation of the objects.csv files.
    • FIS_summary_xxmin.xlsx Per-treatment summary of AUC, ISR and Aₜ/A₀ measurements.
    • FISanalysis_dd-mm-yy_hh-ss.log Organoid Analyst settings.
    • plot_AtA0_xxmin.png Bar plot of summarized Aₜ/A₀ measurements.
    • plot_AUC_xxmin.png Bar plot of summarized AUC measurements.
    • plot_initialswellingrate_xxmin.png Bar plot of summarized ISR measurements.
    • plot_overview.png Plate layout with normalized kinetic curves.
    • plot_titration_AUC_xxmin.png Dose-response plot for AUC measurements.

11. Below are the AUC values determined with the demonstration dataset

CellProfiler image analysis

05-images_analysis/demoplate_01--cellprofiler folder.

Compounds	[Fsk] (μM)	AUC (mean)	AUC (sd)
Fsk	0.008	16.64	29.54
Fsk	0.02	-2.07	36.18
Fsk	0.05	14.76	6.87
Fsk	0.128	-64.81	2.29
Fsk	0.32	-29.95	1.13
Fsk	0.8	-1.88	14.57
Fsk	2	43.7	12.3
Fsk	5	47.43	44.05
Fsk + VX-770	0.008	51.47	9.07
Fsk + VX-770	0.02	34.49	7.05
Fsk + VX-770	0.05	5.35	24.62
Fsk + VX-770	0.128	4.7	40.07
Fsk + VX-770	0.32	80.22	14.19
Fsk + VX-770	0.8	359.88	12.5
Fsk + VX-770	2	449.02	11.39
Fsk + VX-770	5	513.1	9.65
Fsk + VX-809	0.008	25.54	18.77
Fsk + VX-809	0.02	72.81	8.57
Fsk + VX-809	0.05	-13.86	11.9
Fsk + VX-809	0.128	-25.36	27.24
Fsk + VX-809	0.32	14.76	13.59
Fsk + VX-809	0.8	171.86	5.63
Fsk + VX-809	2	734.43	114.55
Fsk + VX-809	5	1361.16	74.95
Fsk + VX-770 + VX-809	0.008	30.24	4.7
Fsk + VX-770 + VX-809	0.02	52.6	19.01
Fsk + VX-770 + VX-809	0.05	156.59	73.21
Fsk + VX-770 + VX-809	0.128	477.28	238.58
Fsk + VX-770 + VX-809	0.32	1329.92	155.75
Fsk + VX-770 + VX-809	0.8	2522.41	63.19
Fsk + VX-770 + VX-809	2	2620.19	289.62
Fsk + VX-770 + VX-809	5	2998.54	149.31

Fiji image analysis

Performing an equivalent analysis of the data in 05-images_analysis/demoplate_01--ij folder yields the following results:

Compounds	[Fsk] (μM)	AUC (mean)	AUC (sd)
Fsk	0.008	-14.55	68.73
Fsk	0.02	22.95	38.08
Fsk	0.05	-38.11	14.25
Fsk	0.128	-84.04	0.34
Fsk	0.32	-24.70	15.25
Fsk	0.8	-48.61	19.19
Fsk	2	-10.84	20.23
Fsk	5	33.56	55.63
Fsk + VX-770	0.008	-44.72	6.45
Fsk + VX-770	0.02	-25.54	42.62
Fsk + VX-770	0.05	-49.76	30.29
Fsk + VX-770	0.128	-94.97	55.25
Fsk + VX-770	0.32	40.29	9.84
Fsk + VX-770	0.8	196.14	43.69
Fsk + VX-770	2	254.76	67.97
Fsk + VX-770	5	390.52	1.80
Fsk + VX-809	0.008	-0.42	47.88
Fsk + VX-809	0.02	32.71	1.53
Fsk + VX-809	0.05	-21.92	15.45
Fsk + VX-809	0.128	-47.16	13.48
Fsk + VX-809	0.32	-23.64	3.66
Fsk + VX-809	0.8	97.87	31.26
Fsk + VX-809	2	491.27	79.05
Fsk + VX-809	5	997.04	84.73
Fsk + VX-770 + VX-809	0.008	-4.00	31.17
Fsk + VX-770 + VX-809	0.02	-74.63	32.17
Fsk + VX-770 + VX-809	0.05	89.73	61.40
Fsk + VX-770 + VX-809	0.128	379.10	231.87
Fsk + VX-770 + VX-809	0.32	1122.95	77.37
Fsk + VX-770 + VX-809	0.8	1963.72	60.83
Fsk + VX-770 + VX-809	2	2212.82	323.37
Fsk + VX-770 + VX-809	5	2645.44	139.45

Expected result (CellProfiler image analysis): 05-images_analysis/demoplate_01--cellprofiler--analysis

Expected result (Fiji image analysis): 05-images_analysis/demoplate_01--ij--analysis

5. Interpretation of the results

In organoids of cystic fibrosis patients CFTR activity (i.e. organoid swelling) is absent, or very low, in experiments without treatment with CFTR-modulating compounds (DMSO) due to lack of CFTR activity being the primary biological defect underlying cystic fibrosis. Class II CFTR mutations produce misfolded CFTR molecules, which are retained inside the cell and do not reach their localization at the plasma membrane where they can exert their function. If these defective proteins are rescued by CFTR modulators and do reach the plasma membrane they are unable to open and close the channel (so called gating defects). The data here are indicative of such class II mutations as CFTR activity was only restored by the combined treatment of a CFTR corrector (VX-809) and potentiator compound (VX-770) together and not by either of these compounds alone. We would like to refer the reader also to the publication of Dekkers et al. [2] for additional representative FIS curves of CF organoids with various CFTR mutations.

6. License

This project is licensed under the terms of the GNU General Public License v3.0 (GNU GPLv3).

7. Citation

Hagemeijer MC, Vonk AM, Awatade NT, Silva IAL, Tischer C, Hilsenstein V, Beekman JM, Amaral MD, Botelho HM (2020) An open-source high-content analysis workflow for CFTR function measurements using the forskolin-induced swelling assay. Bioinformatics. DOI: 10.1093/bioinformatics/btaa1073

8. References

[1] Dekkers et al (2013) A functional CFTR assay using primary cystic fibrosis intestinal organoids. Nat Med 19, 939-945. https://doi.org/10.1038/nm.3201

[2] Dekkers et al (2016) Characterizing responses to CFTR-modulating drugs using rectal organoids derived from subjects with cystic fibrosis. Sci Transl Med 8(344), 344ra84. https://doi.org/10.1126/scitranslmed.aad8278