Fluorescence microscopy

Software for analysis, visualization, simulation, and acquisition  of data from spectroscopy and fluorescence microscopy.

• Fluorescence Correlation Spectroscopy (FCS)
• Fluorescence Lifetime Imaging (FLIM) and Phasor plots
• Förster Resonance Energy Transfer (FRET)
• Generalized Polarization (GP) and Spectral Phasors
• Number and Brightness (N&B)
• Photon Counting Histogram (PCH)
• Raster and Spatio-temporal Image Correlation Spectroscopy (RICS and STICS)
• Single Particle and Modulation Tracking (SPT, MT)
• Image Mean Square Displacement (iMSD)
• Pair correlation function (pCF)

"The plugin analyzes fluorescence microscopy images of neurites and nuclei of dissociated cultured neurons. Given user-defined thresholds, the plugin counts neuronal nuclei, and traces and measures neurite length."[...]" NeuriteTracer is a fast simple-to-use ImageJ plugin for the analysis of outgrowth in two-dimensional fluorescence microscopy images of neuronal cultures. The plugin performed well on images from three different types of neurons with distinct morphologies."

This plugin requires parameter setting: Threshold levels and scale (see more details on the related publication)

ORION: Online Reconstruction and functional Imaging Of Neurons: segmentation and tracing of neurons for reconstruction.

A project to develop tools that explore single neuron function via sophisticated image analysis. ORION software bridges advanced optical imaging and compartmental modeling of neuronal function by rapidly, accurately, and robustly generating, from structural image data, a cylindrical morphology model suitable for simulating neuronal function. The goal of this project is to develop a computational and experimental framework to allow real-time mapping of functional imaging data (e.g., spatio-temporal patterns of dendritic voltages or intracellularions) to neuronal structure, during the very limited duration of an acute experiment.

Measures wound-healing assay videos, 

 For each video, the velocity and the order parameter are analyzed in time and space to extract quantitative parameters characterizing the cell motility phenotype. The different conditions (videos) can then be classified according to these parameters.

Summary

QuimP is software for tracking cellular shape changes and dynamic distributions of fluorescent reporters at the cell membrane. QuimP's unique selling point is the possibility to aggregate data from many cells in form of spatio-temporal maps of dynamic events, independently of cell size and shape. QuimP has been successfully applied to address a wide range of problems related to cell movement in many different cell types. 

Introduction

In transmembrane signalling the cell membrane plays a fundamental role in localising intracellular signalling components to specific sites of action, for example to reorganise the actomyosin cortex during cell polarisation and locomotion. The localisation of different components can be directly or indirectly visualised using fluorescence microscopy, for high-throughput screening commonly in 2D. A quantitative understanding demands segmentation and tracking of whole cells and fluorescence signals associated with the moving cell boundary, for example those associated with actin polymerisation at the cell front of locomoting cells. As regards segmentation, a wide range of methods can be used (threshold based, region growing, active contours or level sets) to obtain closed cell contours, which then are used to sample fluorescence adjacent to the cell edge in a straightforward manner. The most critical step however is cell edge tracking, which links points on contours at time t to corresponding points at t+1. Optical flow methods have been employed, but usually fail to meet the requirement that total fluorescence must not change. QuimP uses a method (ECMM, electrostatic contour migration method (Tyson et al., 2010) which has been shown to outperform traditional level set methods. ECMM minimises the sum of path lengths connecting all pairs of points, equivalent to minimising the energy required for cell deformation. The original segmentation based on an active contour method and outline tracking algorithms have been described in (Dormann et al., 2002; Tyson et al., 2010; Tyson et al., 2014).

The Sprout Morphology plugin measures sprout number, length, width and cell density of endothelial cell (EC) sprouts grown in a bead sprouting assay. It optionally includes measuring the coverage of these sprouts with pericytes included in the assay, as well as the endothelial cell/pericyte ratio.

SOAX is an open source software tool to extract the centerlines, junctions and filament lengths of biopolymer networks in 2D and 3D images. It facilitates quantitative, reproducible and objective analysis of the image data. The underlying method of SOAX uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then stretch along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments.

SOAX provides 3D visualization for exploring image data and visually checking results against the image. Quantitative analysis functions based on extracted networks are also implemented in SOAX, including spatial distribution, orientation, and curvature of filamentous structures. SOAX also provides interactive manual editing to further improve the extraction results, which can be saved in a file for archiving or further analysis. Useful for microtubules or actin filaments.

Observation: Depending on the operating system, the installation may or may not require Boost C++, ITK and VTK libraries. Windows has a standalone executable application without the need of those. 

This ImageJ plug-in is a compilation of co-localization tools. It allows:

-Calculating a set of commonly used co-localization indicators:

Pearson's coefficient Overlap coefficient k1 & k2 coefficients Manders' coefficient Generating commonly used visualizations:

-Cytofluorogram

Having access to more recently published methods:

-Costes' automatic threshold

Li's ICA Costes' randomization Objects based methods (2 methods: distances between centres and centre-particle coincidence)

ADAPT is capable of rapid, automated analysis of migration and membrane protrusions, together with associated
fluorescently labeled proteins, across multiple cells. ADAPT can detect and morphologically profile filopodia.

ADAPT (Automated Detection and Analysis of ProTrusions) is a plug-in developed for the ImageJ/Fiji platform to automatically detect and analyse cell migration and morphodynamics. The program provides whole-cell analysis of multiple cells, while also returning data on individual membrane protrusion events. The plug-in accepts as input one or two image stacks and outputs a variety of data. ADAPT may also be run in batch mode.

A workflow in Python to measure muscule fibers corresponding to the method used in Keefe, A.C. et al. Muscle stem cells contribute to myofibres in sedentary adult mice. Nat. Commun. 6:7087 doi: 10.1038/ncomms8087 (2015).

Example image:

muscleQNT/15536_2032_0.tif ...

PSF Lab is a software program that calculates the illumination point spread function of a confocal microscope under various imaging conditions. It is available in 32-bit and 64-bit for Windows and in 64-bit for Mac.

## Short Summary Quote from the plugin page: >LineageTracker offers an ImageJ based framework which is easily extendible and has the capability to track cell lineages while being specifically designed to handle large cell displacements between frames. The methods are designed for fluorescent cells and have been used to analyse Schizosaccharomyces pombe, C2C12 mouse stem cells or migrating RPE cells. This tool also allows flexible cell segmentation and extendable in all aspects. The webpage is detailed with usage from ImageJ macro. Rather than being simply a component, the plugin is indeed a framework with set of components. ## Misc info A tip from the plugin author in ImageJ mailing list (08.Sep.2015): > We have an additional script to export only a selected range of frames. I can send you that if you think LineageTracker is something for you. To be on the safe side I would try it with an older version of ImageJ. We have experienced some problems, mostly related to Java. Java 8 seems to fix most of it. ## References 2630: Application example. 2631: Plugin Paper.

## About TANGO software is an open-source software for Analysis of Nuclear Genome Organization. It is composed of an ImageJ plugin for batch processing and analysis, and a R package for statistical analysis. Reference: 2528 ## Some key features - Image import uses bioimage formats. - Construction of workflow in GUI by choosing filters / segmentation strategy for - Prefiltering - Segmentation - Postfiltering - Isolated nuclei could individually be inspected, deleted from list and subjected for detailed analysis. - Uses MCIB3D library as backend. - Basic usage is to segment nucleus, crop them to single nucleus objects, segment substructures within objects and measure their properties. - Optionally R can be connected to do detailed analysis of results. - Uses MongoDB to manage huge data set.

Quote: *A GUI-based program which manually detects spots and places them into previously detected meshes. Currently the program runs from MATLAB only. *

A collection for tracking microtubule dynamics, written in Python.

Rigid registration of time series in 3D. A video tutorial is available (be careful of sounds, the video automatically starts!): [Sample Drift Correction Following 4D Confocal Time-lapse Imaging](http://www.jove.com/video/51086/sample-drift-correction-following-4d-co…)

Normalize the orientation of the images of the Zebrafish embryos.

In the documentation webpage, the aim of the workflow is to normalize the orientation of the images of the Zebrafish embryos, find the point of injection of tumor cells and measure the distribution of Cy3 stained tumor foci.

ImageJ macro implementation of the Workflow described in Ghotra et al (2012). Note that currently only the angle and orientation normalization is implemented in this version.

Sample images are linked in the documentation webpage. 

A clear tutorial on how to write a MATLAB script to segment clustered cells.

The full script is downloadable near the bottom of the article. 

A workflow template to analyze subcellular structures in fluorescence 2D/3D microscopy images based on a Fiji plugin **Squassh** is described in Rizek et al (2014).

The workflow employs detecting, segmenting, and quantifying subcellular structures. For segmentation, it accounts for the microscope optics and for uneven image background. Further analyses include both colocalization and shape analyses. However, it does not work directly for time-lapse data. A brief summary note can be found here.

CellDetector can detect cells (or other objects) in microscopy images such as histopathology, fluorescence, phase contrast, bright field, etc. It uses a machine learning-based method where a cell model is learned from simple dot annotations on a few images for training and predict on test sets. The installation requires some efforts but the instruction is well explained. Training parameters should be tuned for different datasets, but the default settings could be a good starting point.

Very simple application that lets you load your time-lapse intensity data to generate the normalized FRAP recovery curve and perform exponential curve fitting.

Quote: The user can handle simultaneously large data sets of raw data, visualize fluorescence recovery curves, exclude low quality data, perform data normalization, extract quantitative parameters, perform batch analysis and save the resulting data and figures for further use. Our tool is implemented as a single-screen Graphical User Interface (GUI) and is highly interactive, as it permits parameterization and visual data quality assessment at various points during the analysis.

Oufti (previously named MicrobeTracker) is a MATLAB application / suite of tools for analysing fluorescent spots inside microbes. MicrobeTracker can identify cell outlines and fluorescent foci, and generate plots and statistics based on positions and intensity (kymographs, histograms etc.) The MATLAB code is easy to modify and extend to add additional plots and statistics: see e.g. Lesterlin et al. (2014).

The Outfi Forum is quite active.

In this human cytoplasm-nucleus translocation assay, learn how to load a previously calculated illumination correction function for two separate channels, measure protein content in the nucleus and cytoplasm, and calculate the ratio as a measure of translocation. This is a clumpy cell type, so studying the settings in primary object identification may be helpful for users interested in the more advanced options that module offers. More about these images can be found at the BBBC.

Task

Quantify the length of microtubules (MT) and the MT average density per cell.

Workflow descriptions

Simple two step workflow, allowing visual & manual correction of microtubule between the 2 steps. Batch measurement of microtubule lengths for multiple images is achieved by segmenting the MTs and then their skeletonizations. The number of pixels in the microtubule is proportional to their length, so the length can be estimated.

Script

Workflow is written as an ImageJ macro (Fiji) with following steps:

1. The enhancement of tubular structure by computing eigenvalues of the hessian matrix on a Gaussian filtered version of the image ( sigma 1 pixel), as implemented in the tubeness plugin.

2. The tubules were then thresholded , and structures containing less than 3 pixels were discarded.

3. If needed, a visual check and correction of segmented microtubule is then performed.

4. After correction, segmented MTs were then reduced to a 1-pixel thick line using the skeletonize plugin of Fiji. The length of the skeletonized microtubules was then directly proportional to their length.

5. Data were grouped by condition and converted back to micrometers units under Matlab for the statistical tests.

Pitfalls

Commented but not that general without editing some fields in the macros.

Sample Data

Sample data and workflow (see above URL) can be accessed by - login: biii - password Biii!

Misc

3D version also available here. Use of components Skeletonize and Tubeness Filter

Tracking of focal adhesions includes a number of challenges:

1. Detection of focal adhesion regions in areas of highly variable background
2. Separation of "clumped" adhesions in different objects.
3. Dynamics: Focal adhesions dynamically, grow, shrink, change their shape, they can fuse with neighboring adhesions or one adhesion can be split into multiple children.

Würflinger et al (2011) describe how to detect focal adhesion objects and how to track them over time. Interestingly, tracking results are fed back to segmentation to improve separation of clumped adhesions.

The authors implemented the workflow in Matlab, but do not provide a ready-to-use script.

Generation of Kymographs using 2D+t images. In the generated kymographs, objects can be tracked and the results are visualized.

Simple workflow description for ImageJ, step-by-step description for delineating focal adhesions, count and characterize their positions.  

Measurement of dynamics is not involved.

This macro is meant to segment the cells of a multicellular tissue. It is written for images showing highly contrasted and uniformly stained cell membranes. The geometry of the cells and their organization is automatically extracted and exported to an ImageJ results table. This includes: Cell area, major, minor fitted ellipse radii + major axis orientation and number of neighbors of the cells. Manual correction of the automatic segmentation is supported (merge split cells, split merged cells).

Sample image data is available in the documentation page. 

The Huygens Software Suite consists of different image processing packages with functionalities that include deconvolution, interactive analysis, and volume visualization of 2D-3D multi-channel and time series images from fluorescence microscopes such as widefield, confocal, multi-photon, spinning disk, Array Detector, STED, and Light Sheet

Deconvolution software

Acquiarium is for carrying out the common pipeline of many spatial cell studies using fluorescence microscopy. It addresses image capture, raw image correction, image segmentation, quantification of segmented objects and their spatial arrangement, volume rendering, and statistical evaluation. It is focused on quantification of spatial properties of many objects and their mutual spatial relations in a collection of many 3D images. It can be used for analysis of a collection of 2D images or time lapse series of 2D or 3D images as well. It has a modular design and is extensible via plug-ins. It is a stand-alone, easy to install application written in C++ language. The GUI is written using cross-platform wxWidgets library.

An ImageJ macro for correcting frame drift occurred during image acquisition.

It often happens that you have an image sequence that shows problematic drifting of image frame and at the same time you have some landmarks that could be used for correcting the drift. This ImageJ macro allows you to Manually track the landmark using ImageJ Manual Tracking Plugin. Using the coordinates recorded in the Result window, each frame is shifted back so that the landmark stays in a single place.

‘’’Squassh’’’ is a tool for 2D and 3D segmentation and quantification of subcellular shapes in fluorescence microscopy images. It provides globally optimal detection and segmentation of objects with constant internal intensity distribution, followed by object-based colocalization analysis. The segmentation computed by Region Competition can optionally correct for the PSF of the microscope, hence providing optimally deconvolved segmentations. Part of the mosaic suite

Image segmentation based on the MOSAIC Discrete region competition algorithm. 

Easy-to-use, computationally efficient, two- and three-dimensional, feature point-tracking tool for the automated detection and analysis of particle trajectories as recorded by video imaging in cell biology. 

The tracking process requires no apriori mathematical modelling of the motion, it is self-initialising, it discriminates spurious detections, and it can handle temporary occlusion as well as particle appearance and disappearance from the image region. 

The plugin is well suited for video imaging in cell biology relying on low-intensity fluorescence microscopy. It allows the user to visualize and analyze the detected particles and found trajectories in various ways:

• Preview and save detected particles for separate analysis
• Global non progressive view on all trajectories
• Focused progressive view on individually selected trajectory
• Focused progressive view on trajectories in an area of interest

It also allows the user to find trajectories from uploaded particles position and information text files and then to plot particles parameters vs. time - along a trajectory

This ImageJ plugin creates high resolution PSF images by averaging many bead im- ages as well as exploiting the assumption that the point spread function is rotationally symmetric with respect to the axial axis (z-direction).

This plugin can be used for inferring spatial interactions between patterns of spot-like objects in images or between coordinates read from a file. 

CellTracker software is a platform for tracking nuclear and cytoplasmic fluorescence intensities from live cell microscopy time series data.

Requires visual C++

Histogram-based background subtractor for ImageJ.

The implemented algorithm is based on the assumption that, compared to the background region, object (foreground) regions are small. The plugin builds local histograms and assumes the most occuring intensity to be part of the background.

Three different methods for correcting fluorescence bleaching. 1. Simple (framewise ratio based) 2. Exponential (curve fitting with exponential decay model) 3. Histogram matching (register histogram shape. with 16 bit, it takes long time... it should be improved).

The track manager enables the use of DSP-like trackProcessors. This can affect the display of tracks, selection in time or by ROIs, and also compute some views like the overlaid and animated local flow graph, polar graph.

A TrackProcessor that allows the user to monitor, visualize, and export, the intensity profile of tracks in time lapse sequences of 2D images.

This segmentation method performs a N-class thresholding based on a K-Means classification of the image histogram, then extracts objects in a bottom-up manner using user-defined minimum and maximum object sizes. Very useful to detect clustered objects in fluorescence microscopy.

• Counts the number of 3D objects in a stack.
• quantifies for each found object the following parameters:
  • 3D intensity related measurement (with possible redirection to an image with the actual intensity value to be measured, for example for two channels measurements)
  • Volume and shape factors measurements, surface etc...
• generates results representations such as:
  • Objects' map;
  • Surface voxels' map;
  • Centroids' map;
  • Centres of masses' map.

As ImageJ's “Analyze Particles” function, 3D-OC also has a “redirect to” option, allowing one image to be taken as a mask to quantify intensity related parameters on a second image. But unlike the Analyze particle, it include a thresholding option, meaning that you can start from a gray level  stack, not necessarily a binary mask.

To use it, first set the list of measurements by editing 3D OC Options. Both (3D Object counter and 3D OC Options are now in the default Fiji "Analyze" menu.

Add some noise with customizable characteristics (Gaussian noise, Poisson noise, salt & pepper, etc.) to a sequence. ICY plugin.