Archive pour 7 juillet 2008

Colocalization and confocal images and imageJ Matlab

The laser scanning confocal microscope (LSCM) generates images of multiple labelled fluorescent samples. Colocalization of fluorescent labels is frequently examined.

Colocalization is usually evaluated by visual inspection of signal overlap or by using commercially available software tools, but there are limited possibilities to automate the analysis of large amounts of data.

–formation

www.picin.u-bordeaux2.fr/Cours/formation_2006/cerner_la_colocalisation_2006.pdf

—————————————————————–

–Colocalization image processing imageJ

Colocalisation analysis is an subject plagued with errors and contention. The literature is full of different methods for colocalisation analysis which probably reflects the fact that one approach does not necessarily fit all circumstances.

Analysis can be considered qualitative or quantitative. However, opinions differ as to which category the different approaches fall!

Qualitative analysis can be thought of as « highlighting overlapping pixels ». Although this is often given as a number (« percentage overlap ») suggesting quantification, the qualitative aspect arises when the user has to define what is considered « overlapping ». The two channels have a threshold set and any areas where they overlap is considered « colocalised ». Qualitative analysis has the benefit of being readily understood with little expert knowledge but suffers from the intrinsic user bias of « setting the threshold ». There are algorithms available which will automate the thresholding without user intervention but these rely on analysis of the image’s histogram which is subject to user intervention during acquisition.

Quantitative analysis removes user bias by analysing all the pixels based on of their intensity (it must be noted that some authors consider this a draw back rather than an advantage due to the intrinsic uncertainty of pixel intensity; see Lachmanovich et al. (2003) J. Microscopy, 212, 122-131). There are a number of coefficients detailed in the literature which can be calculated using ImageJ; each coefficient has it’s strengths and weaknesses and should be thoroughly researched before being used. It is this requirement for the coefficient to be fully understood which is a disadvantage when trying to convey information to research peers who are experts in biology, and not necessarily mathematics.

One key issue that can confound colocalisation analysis is bleed through. Colocalisation typically involves determining how much the green and red colours overlap. Therefore it is essential that the green emitting dye does not contribute to the red signal (typically, red dyes do not emit green fluorescence but this needs to be experimentally verified). One possible way to avoid bleed-through is to acquire the red and green images sequentially, rather than simultaneously (as with normal dual channel confocal imaging) and the use of narrow band emission filters. Single and unlabelled controls must be used to assess bleed-through.

Intensity Correlation Analysis

This plugin generates Mander’s coefficients (see below) as well as performing Intensity Correlation Analysis as described by Li et al. To fully understand this analysis you should read:
Li, Qi, Lau, Anthony, Morris, Terence J., Guo, Lin, Fordyce, Christopher B., and Stanley, Elise F. (2004). A Syntaxin 1, G{alpha}o, and N-Type Calcium Channel Complex at a Presynaptic Nerve Terminal: Analysis by Quantitative Immunocolocalization. Journal of Neuroscience 24, 4070-4081.

It is bundled with WCIF ImageJ and can be downloaded alone here.

reference:

http://www.uhnresearch.ca/facilities/wcif/imagej/colour_analysis.htm

Manders’ Coefficient » (formerly Image Correlator plus<!–[if supportFields]> XE « Image Correlator plus: Wayne Rasband, Tony Collins«  <![endif]–> ” and “Red-Green Correlator” plugins)

This plugin generates various colocalisation coefficients for two 8 or 16-bit images or stacks.

The plugins generate a scatter plots plus correlation coefficients. In each scatter plot, the first (channel 1) image component is represented along the x-axis, the second image (channel 2) along the y-axis. The intensity of a given pixel in the first image is used as the x-coordinate of the scatter-plot point and the intensity of the corresponding pixel in the second image as the y-coordinate.

The intensities of each pixel in the “Correlation Plot” image represent the frequency of pixels that display those particular red/green values. Since most of you image will probably be background, the highest frequency of pixels will have low intensities so the brightest pixels in the scatter plot are in the bottom left hand corner – i.e. x~ zero, y ~ zero. The intensities in the “Red-Green correlation plot” image represent the actual colour of the pixels in the image.

Mito-DsRed; ER-EGFP

Pearson’s correlation (R)=0.34

Overlap coefficient (R)=0.40

Nred ÷ Ngreen pixels=0.66

Colocalisation coefficient for red (Mred)=0.96

Colocalisation coefficient for green (Mgreen)=0.49

TMRE (red) plus Mito-pericam (Green)

Pearson’s correlation Rr=0.93

Overlap coefficient R=0.94

Nred ÷ Ngreen pixels=0.93

Colocalisation coefficient (red) Mred=0.99

Colocalisation coefficient (green) Mgreen=0.98

Both plugins generate various colocalisation coefficients: Pearson’s (Rr), Overlap (R) and Colocalisation (M1, M2) See Manders, E.E.M., Verbeek, F.J. & Aten, J.A. ‘Measurement of co-localisation of objects in dual-colour confocal images’,  (1993) J. Microscopy, 169, 375-382. See tutorial sheet ‘Colocalisation’ for details. The threshold is also reported (0,0 means no threshold was used).

Colocalisation Test

When a coefficient is calculated for two images, it is often unclear quite what this means, in particular for intermediate values. This raises the following question: how does this value compare with what would be expected by chance alone?

There are several approaches that can be used to compare an observed coefficient with the coefficients of randomly generated images. Van Steensel (3) adopted an approach where the observed colocalisation between channel 1 and channel 2 was compared to colocalisation between channel 1 and a number of channel 2 images that had been translated (i.e. displaced by a number of pixels) in increments along the image’s X-axis. Fay et al (4) extended this approach by translating channel 2 in 5-pixel increments along the X- and Y-axis (i.e., –10, –5, 0, 5, and 10) and ± 1 slices in the Z-axis. This results in 74 randomisations (plus one original channel 2). The observed correlation was compared to these 74 and considered significant if it was greater than 95% of them.

Costes et al. (5) subsequently adopted a different approach, based on “scrambling” channel 2. The original channel 1 image was compared to 200 “scrambled” channel 2 images; the observed correlations between channel 1 and channel 2 were considered significant if they were greater than 95% of the correlations between channel 1 and scrambled channel 2s.

Costes’ scrambled images were generated by randomly rearranging blocks of the channel-2 image. The size of these blocks was chosen to equal the point spread function (PSF) of the image.

An approximation of Costes’ approach is used by Bitplane’s Imaris and also the Colocalisation Test plugin. For Imaris, a white noise image is smoothed with a Gaussian filter the width of the image’s PSF. The Colocalisation Test plugin generates a randomized image by taking random pixels from the channel-2 image; it then smoothes the image with a Gaussian filter, which is again the width of the image’s PSF.

The Colocalisation Test plugin calculates Pearson’s correlation coefficient for the two selected channels (Robs) and compares this to Pearson’s coefficients for channel 1 against a number of randomized channel-2 images (Rrand).

–Colocalization image processing matlab:

doi:10.1016/S0169-2607(03)00071-3

Automated high through-put colocalization analysis of multichannel confocal images

M. Kreft , I. Milisav , M. Potokar and R. Zorec

Lab. Neuroendocrinology-Molecular Cell Physiology, Inst. Pathophysiology, Medical Faculty, Zaloska 4, 1000 Ljubljana and Celica Biomed. Sciences Center, Stegne 21, 1000, Ljubljana, Slovenia

accepted 20 April 2003.

Available online 15 July 2003.

We developed a simple tool using Matlab to automate the colocalization procedure and to exclude the biased estimations resulting from visual inspections of images. The script in Matlab language code automatically imports confocal images and converts them into arrays. The contrast of all images is uniformly set by linearly reassigning the values of pixel intensities to use the full 8-bit range (0–255). Images are binarized on several threshold levels. The area above a certain threshold level is summed for each channel of the image and for colocalized regions. As a result, count of pixels above several threshold levels in any number of images is saved in an ASCII file. In addition Pearson’s r correlation coefficient is calculated for fluorescence intensities of both confocal channels. Using this approach quick quantitative analysis of colocalization of hundreds of images is possible. In addition, such automated procedure is not biased by the examiner’s subject visualization.

kreft-2004-colocalization

imageJ import export file format

2. Importing Image Files

ImageJ primarily uses TIFF as the image file format. The menu command “File/Save” will save in TIFF format. The menu command “File/Open” will open TIFF files and import a number of other common file formats (e.g. JPEG, GIF, BMP, PGM, PNG). These natively supported files can also be opened by drag-and-dropping the file on to the ImageJ toolbar. MetaMorph *.STK files can also be opened directly.

Several more file formats can be imported via ImageJ plugins (e.g. Biorad, Noran, Zeiss, Leica). When you subsequently save these files within ImageJ they will no longer be in their native format. Bear this in mind; ensure you do not overwrite original data.

There are further file formats such as PNG, PSD (Photoshop), ICO (Windows icon), PICT, which can be imported via the menu command File/Import/*.PNGJimi Reader… .

2.1 Importing Zeiss LSM files

Files acquired on the Zeiss confocal are can be opened directly (with the “Handle Extra File Types” plugin installed) via the “File/Open” menu command, or by dropping them on the ImageJ toolbar. They can also be imported via the “Zeiss LSM Import Panel” which is activated by the menu command “File/Import/*.LSM”. This plugin has the advantage of being able to access extra image information stored with the LSM file, but it is an extra mouse click.

Images are opened as 8-bit colour images with the “no-palette” pseudocolour (!) from the LSM acquisition software. Each channel is imported as a separate image/stack. Lambda stacks are therefore imported as multiple images, not a single stack. They can be converted to a stack with the menu command: “Image/Stacks/Covert Images to stack”.

Once opened, the file information can be accessed and the z/t/lambda information can be irreversibly stamped in to the images or exported to a text file.

2.2 Importing Zeiss ZVI files

ZVI files can be imported via the menu command « File/Import/*.ZVI« . The files are opened as a single stack with the different channels interleaved. The channels can be separated with the « Plugins/Stacks-Shuffling/DeInterleave » plugin.

2.3 Importing Noran SGI file

Noran movies can be opened in several ways:

File/Import/Noran movie… opens the entire movie as an image stack.
File/Import/Noran Selection…
allows you to specify a range of frames to be opened as a stack.

The Noran SGI plugins are not bundled with the ImageJ package. To receive them, please contact tonyc@uhnresearch.ca or their author, Greg Joss, so he can keep track of users. Greg Joss gjoss AT bio.mq.edu.au is in the Dept of Biology, Macquarie University, Sydney, Australia.

2.4 Importing Biorad PIC files

Biorad PIC files can be now be imported directly via the menu command “File/Open”. Experimental information, calibration, and other useful information can be accessed via Image/Show Info. Biorad PIC files can also be opened by drag-and-dropping the file on to the ImageJ toolbar. The PIC file is opened with the same LUT with which it was saved in the original acquisition software.

2.5 Importing multiple files from folder

Each time point of an experiment acquired with software such as Perkin Elmer’s UltraVIEW or Scion Image’s time lapse macro is saved by the acquisition software as a single TIF file. The experimental sequence can be imported to ImageJ via the menu command “File/Import/Image Sequence…”.

Locate the directory, click on the first image in the sequence and OK all dialogs. (You may get a couple of error messages while ImageJ tries to open any non-image files in the experimental directory.) The stack will “interleave” the multiple channels you recorded, and can be de-interleaved via “Plugins/Stacks – Shuffling/Deinterleave.

Selected images that are not the same size can be imported as individual images windows using “File/Import/Selected files to open… ” or as a stack with the “File/Import/Selected files for stack… ”. Unlike the “File/Import/Image Sequence…” function, the images need not be of the same dimensions. If memory is limited, stacks can be opened as Virtual-Stacks with most of the stack remaining on the disk until it is required “File/Import/Disk based stack” .

2.6 Importing Multi-RAW sequence from folder

To form an image, ImageJ needs to know the image dimensions, bit-depth, slice number per file and any extraneous information in the file format (offset and header size). All you really need to tell it is the image dimension in x and y. These values should be obtainable from the software in which the images were acquired. Armed with this information follow these steps:

1. File/Import/Raw…

2. Select experimental directory.

3. Typical values for the dialog box are:

Image type = 16-bit unsigned            (or 8 bit typically)

width and height as determined earlier

offset = 0, number of image = 1, gap = 0, ‘white’ is zero = off

‘Little-endian byte order’ = on, ‘open all files in folder’ = on to open all files in folder.

Non-image files will also be opened and may appear as blank images and need deleting: “Image/Stacks/Delete slice”. The stack will “interleave” the multiple channels you recorded, and can be de-interleaved via “Plugins/Stacks – Shuffling/DeInterleave.

2.7 Importing AVI and MOV files

There are two plugins which can open uncompressed AVIs and some types of MOV file.

For opening (and writing) QuickTime you need a custom installation of QuickTime to include QT for Java (see section 1.3). QuickTime movies are then opened via “File/Import/*.MOV ”.

Uncompressed AVIs can be opened via “File/Import/*.AVI ”.

2.8 Importing Scanalytics IPLab IPL files

IPLab files can be imported directly by ImageJ. File/Import/*.IPL . Allows Windows IPLab files to be opened directly with the “File/Open” menu command, or drag-and-drop.

The spatial calibration should be imported correctly from your IPLab software.

2.9 Importing Leica SP2 LEI series

Leica SP2 experiments are saved as multiple tiff files in a single folder. This can contain many different series acquired during the one experiment. Along with many tiffs, the folder also contains a text description in the *.TXT files and a Leica proprietary file *.LEI.

Double clicking, drag/dropping of « File/Open« ‘ing the *.LEI files should run the Leica TIFF Sequence plugin. Alternatively, run the menu command « File/Import/*.TXT Leica SP2 series » and select the experiment’s TXT file from the open dialog.

A second dialog will open listing the names of the series in the folder. The user can then select those that are to be opened. The appropriate spatial calibration should be read form the txt file and applied to the image. Leica ‘Snapshots’ do not have spatial calibration saved with them. The entry in the TXT file for the series is written to the ‘Notes’ for the image and can be access by the menu command « Image/Show Info…« .

Folders with large numbers of series in could potentially generate a dialog so large that some names are « off screen ». The maximum number of series names per column can be set by running the plugin with the alt-key down.

2.10 Other Import functions

These import plugins import the image data as well as meta-data.

Leica SP- Leica multi-colour images are tiffs. They can be opened as multiple files to a a single stack. Each channel can be imported separately by adding ‘c1′ or c2’ etc. as the import string. Alternatively, they can be all imported to the one stack then separated by de-interleaving them (« Plugins/Stack – shuffling/Deinterleave« ).

Olympus Fluoview – available from http://rsb.info.nih.gov/ij/plugins/ucsd.html. Not bundled with the current download.

Animated GI F – This plugin opens an animated GIF file as an RGB stack. Also opens single GIF images.

File/Import/ICS,IDS Image Cytometry Standard file format from Nico Sturman.

File/Import/*.DV *.DV files generated on DeltaVision system format from Fabrice Cordelieres

File/Import/*.ND *.ND files created with MetaMorph’s ‘Multidimensional acquisition”. From Fabrice Cordelieres

leica SP2 tiff sequence – ressources

formats:

  • tiff
  • volume tiff
  • raw
  • avi

———————————–

plug-in imageJ

This plugin opens multi-TIFF series acquired with Leica SP2 confocal.

Run the plugin; select the TXT file associated with the TIFF series and select the series to be opened. This information is then passed to the native « Import Sequence » function.

Optional ability to split the channels.

The plugin should apply the spatial calibration found in the TXT file.

ImageJ primarily uses TIFF as the image file format. The menu command “File/Save” will save in TIFF format. The menu command “File/Open” will open TIFF files and import a number of other common file formats (e.g. JPEG, GIF, BMP, PGM, PNG). These natively supported files can also be opened by drag-and-dropping the file on to the ImageJ toolbar. MetaMorph *.STK files can also be opened directly.

Several more file formats can be imported via ImageJ plugins (e.g. Biorad, Noran, Zeiss, Leica). When you subsequently save these files within ImageJ they will no longer be in their native format. Bear this in mind; ensure you do not overwrite original data.

There are further file formats such as PNG, PSD (Photoshop), ICO (Windows icon), PICT, which can be imported via the menu command File/Import/*.PNGJimi Reader…<!–[if supportFields]> XE « Jimi Reader…:Wayne Rasband and Ulf Dittmer (udittmer at mac.com) » <![endif]–> .

exemple pour Zeiss LSM:

Importing Leica SP2 LEI series

Leica SP2 experiments are saved as multiple tiff files in a single folder. This can contain many different series acquired during the one experiment. Along with many tiffs, the folder also contains a text description in the *.TXT files and a Leica proprietary file *.LEI.

Double clicking, drag/dropping of « File/Open« ‘ing the *.LEI files should run the Leica TIFF Sequence plugin. Alternatively, run the menu command « File/Import/*.TXT Leica SP2 series » and select the experiment’s TXT file from the open dialog.

A second dialog will open listing the names of the series in the folder. The user can then select those that are to be opened. The appropriate spatial calibration should be read form the txt file and applied to the image. Leica ‘Snapshots’ do not have spatial calibration saved with them. The entry in the TXT file for the series is written to the ‘Notes’ for the image and can be access by the menu command « Image/Show Info…« .

Folders with large numbers of series in could potentially generate a dialog so large that some names are « off screen ». The maximum number of series names per column can be set by running the plugin with the alt-key down.

plug-in download:

http://rsbweb.nih.gov/ij/plugins/leica-tiff.html

2006/02/16:First version
2006/03/02:Fixed error arising from series with similar names containing spaces; errors arising from images with Gray LUT.
2006/03/20:Filenames listed in multiple columns (max number of rows per column can be set by running the plugin with the alt-key down.).

—–author

Author: Tony Collins (tonyc at uhnresearch.ca)
Wright Cell Imaging Facility, Toronto, Canada

http://www.uhnresearch.ca/facilities/wcif/software/Plugins/LeicaTIFF.html

Save to plugins folder; compile and run plugin.
The compiled version is bundled with latest WCIF ImageJ bundle along with modified HandleExtraFileTypes.class (courtesy of Greg Jefferis) to allow double-clicking of the *.LEI file to open the sequence.

Code for HandleExtraFileTypes.java from Greg:

//  Leica SP confocal .lei file handler
        if (name.endsWith(".lei")) {
            int dotIndex = name.lastIndexOf(".");
            if (dotIndex>=0)
                name = name.substring(0, dotIndex);
            path = directory+name+".txt";
            File f = new File(path);
            if(!f.exists()){
                IJ.error("Cannot find the Leica information file: "+path);
                return null;
            }
            IJ.runPlugIn("Leica_TIFF_sequence", path);
            width = IMAGE_OPENED;
            return null;
        }

———————-Huygens Software reads and writes

The Huygens Software reads and writes (among other → File Formats) TIFF series with Leica style numbering if there are more channels (different wavelength), slices or frames (in a Time Series) than in a simple Numbered Tiff series.

An image of four slices and two frames is named with Leica style numbering as follows:

c_t00_z000.tif
c_t00_z001.tif
c_t00_z002.tif
c_t00_z003.tif
c_t01_z000.tif
c_t01_z001.tif
c_t01_z002.tif
c_t01_z003.tif

And an image sTCh of four slices, three frames and two channels:

sTCh_t00_z000_ch00.tif
sTCh_t00_z000_ch01.tif
sTCh_t00_z001_ch00.tif
sTCh_t00_z001_ch01.tif
sTCh_t00_z002_ch00.tif
sTCh_t00_z002_ch01.tif
sTCh_t00_z003_ch00.tif
sTCh_t00_z003_ch01.tif
sTCh_t01_z000_ch00.tif
sTCh_t01_z000_ch01.tif
sTCh_t01_z001_ch00.tif
sTCh_t01_z001_ch01.tif
sTCh_t01_z002_ch00.tif
sTCh_t01_z002_ch01.tif
sTCh_t01_z003_ch00.tif
sTCh_t01_z003_ch01.tif
sTCh_t02_z000_ch00.tif
sTCh_t02_z000_ch01.tif
sTCh_t02_z001_ch00.tif
sTCh_t02_z001_ch01.tif
sTCh_t02_z002_ch00.tif
sTCh_t02_z002_ch01.tif
sTCh_t02_z003_ch00.tif
sTCh_t02_z003_ch01.tif
------------

-----------------------quelques ressources des centres
http://www-ijpb.versailles.inra.fr/fr/lcc/fichiers/equip-sp2.htm
http://www-ijpb.versailles.inra.fr/fr/lcc/fichiers/pdf/instruction-utilisation-SP2.pdf

http://www.itg.uiuc.edu/ms/equipment/microscopes/lscm.htm
leica LCS lite

http://microscopy.unc.edu/How-to/leicasp2/default-viewing.html

http://ijm2.ijm.jussieu.fr/imagerie/fichiers

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