Automated Analysis of Dynamic Ca2+ Signals in Image Sequences

Summary

Here a novel region of interest analysis protocol based on sorting best-fit ellipses assigned to regions of positive signal within two-dimensional time lapse image sequences is demonstrated. This algorithm may enable investigators to comprehensively analyze physiological Ca²⁺ signals with minimal user input and bias.

Abstract

Intracellular Ca²⁺ signals are commonly studied with fluorescent Ca²⁺ indicator dyes and microscopy techniques. However, quantitative analysis of Ca²⁺ imaging data is time consuming and subject to bias. Automated signal analysis algorithms based on region of interest (ROI) detection have been implemented for one-dimensional line scan measurements, but there is no current algorithm which integrates optimized identification and analysis of ROIs in two-dimensional image sequences. Here an algorithm for rapid acquisition and analysis of ROIs in image sequences is described. It utilizes ellipses fit to noise filtered signals in order to determine optimal ROI placement, and computes Ca²⁺ signal parameters of amplitude, duration and spatial spread. This algorithm was implemented as a freely available plugin for ImageJ (NIH) software. Together with analysis scripts written for the open source statistical processing software R, this approach provides a high-capacity pipeline for performing quick statistical analysis of experimental output. The authors suggest that use of this analysis protocol will lead to a more complete and unbiased characterization of physiologic Ca²⁺ signaling.

Introduction

Ca²⁺ is a ubiquitous second messenger signaling molecule and cytosolic Ca²⁺ levels are highly regulated. Intracellular Ca²⁺ signals are complex and include isolated transients, oscillations, and propagating waves^1-4. Spatial and temporal control of Ca²⁺ is thought to underlie physiological signal specificity, and therefore the analysis of Ca²⁺ signal patterns is of considerable interest to investigators in multiple fields⁵.

Ca²⁺ indicator dyes such as Fluo-4 and Fura-2 are commonly employed to measure intracellular Ca²⁺ signals with fluorescence microscopy^5-12. Typically, temporal Ca²⁺ signals are evaluated as time-dependent changes in mean fluorescence within a user-defined area, or region of interest (ROI)^5,6,13-16. Currently, manual ROI analysis is both time consuming and labor intensive because it requires users to identify many ROIs and perform repetitive computations^17-19. These techniques may also be subject to considerable user error, including introduction of artificial signal modes and exclusion of low amplitude or diffuse signals^18,20.

Automated ROI detection algorithms have previously been implemented using a variety of statistical approaches to determine optimal ROI placement, but they have generally been limited to analysis of line scan or pseudo-line scan images, which restricts analysis to a single spatial dimension in time^17,19-22. Additionally, many existing algorithms are not adequate to encompass the diversity of Ca²⁺ release events which range from periodic, localized transients to propagating waves^23,24. Comprehensive evaluation of physiological Ca²⁺ signals is often further complicated by the presence of significant image artifact that confounds signal to noise discrimination in many experimental systems.

Previously, an automated ROI detection algorithm solution to Ca²⁺ signal transient detection, implemented as a plugin for NIH ImageJ software (National Institutes of Health, Bethesda, MD), was developed and validated^25,26. This algorithm, called LC_Pro, was designed to identify and analyze ROIs encompassing Ca²⁺ signal transients in two-dimensional time lapse image sequences. Here a practical experimental protocol and representative demonstration of an application of the algorithm in porcine coronary artery endothelium is provided, with additional postprocessing using the open source statistical processing software R to generate usable graphical output.

Protocol

1. Vessel Dissection and Imaging

1. Harvest tissue from domestic juvenile pigs as described in Martens et al²⁷. Place harvested swine right ventricles into a polydimethylsiloxane (PDMS) bottom dissection dish containing HEPES buffered physiological saline solution (PSS).
   1. With the aid of a stereomicroscope, dissect and remove a segment of the left anterior descending coronary artery (~8 mm length, 0.5 mm diameter) from surrounding tissue using forceps and spring scissors by removing the vessel segment from the surrounding cardiac tissue layers. NOTE: Take care to not puncture the vessel wall.
   2. Place a PDMS block (1 x 0.5 x 0.5 cm) into the dissection dish, and use a needle to pin it to the bottom. Cut an approximately 40 μm diameter tungsten wire into twelve ~0.3 cm length segments, forming micropins, and place these in the dish. Using forceps, secure one end of the vessel segment to the block with a micropin.
   3. Carefully insert small spring scissors into the vessel lumen, and cut longitudinally down one side of the vessel to open it completely. Orient the opened vessel segment with the endothelium up.
   4. Use the remaining micropins to secure the borders of the opened vessel segment to block such that the vessel forms a flat rectangle. NOTE: Micropin tops should be bent to 90° and inserted until flush with the block surface. Care should be taken to stretch the vessel preparation without overstretching; final width should be ~1.5x the starting unstretched width.
   5. Prepare a small volume (~1 ml) of Fluo-4 AM loading solution by mixing Fluo-4 AM (10 µM) dissolved in DMSO with pluronic (0.03 %) in a HEPES buffered PSS (containing in mM: 134 NaCl, 6 KCl, 1 MgCl, 10 HEPES, 10 glucose), and insert the entire block into the loading solution for approximately 40 min at room temperature in the dark.
   6. After loading, wash the block in HEPES buffered PSS for 5-10 min.
   7. Mount the block onto 50-100 µm thick spacers in a coverglass bottom chamber containing HEPES buffered PSS. NOTE: Metal pins can be used as spacers and ensure the vessel segment is facing down and not touching the spacers.
   8. Place the chamber on the stage of an inverted microscope equipped for confocal imaging, and focus on the endothelial cell layer.
   9. Capture time lapse image sequences of basal relative fluorescence for ~3 min at 20X magnification and a frame rate of ~8 frames per sec using confocal image sequence aquisition software.
   10. After ~ 3 min of recording basal fluorescence, replace HEPES buffered PSS solution with the same volume (~1 ml) of Substance P (100 pM) dissolved in HEPES buffered PSS, and record for an addtional 3 min.

2. Automated Analysis

1. Render image sequence(s) of fluorescent calcium activity from confocal acquisition software as 8 bit, grayscale '.tif' format files with no scale information.
2. Open ImageJ, click 'open' in the file menu, and select the appropriate image sequence in the explorer window to view the image sequence(s) in ImageJ.
3. Determine an appropriate ROI diameter by using the rectangular ROI tool to estimate the upper and lower boundaries of the spatial spread of activity within the image sequence(s). NOTE: The mean rectangular diameter is a suitable choice for ROI diameter.
4. Create a new folder on the computer hard drive, and add the image sequence(s) into the folder directory.
5. In ImageJ, click the 'plugins' window, then click 'LC_Pro' to start the analysis.
6. Enter the ROI diameter value and select the filter threshold value (either 0.05 or 0.01).
7. Click the 'drug treatment' checkbox and enter the time point values immediately before and after the drug was added (in seconds).
8. Click 'ok', and enter the image sequence directory into the file explorer window.

3. Graphical Output

1. Download R version 3.0.2 from http://www.r-project.org/.
2. Open the 32 bit version of R.
3. Click “file”, then 'open script' and select the 'traceplot.R' R script for generating graphical experimental reports.
4. Install the calibrate, and gplots packages by selecting 'packages', 'install packages', and selecting the appropirate packages.
5. Click 'file', then 'change directory' command, and use the explorer window to select the directory that corresponds to the LC_Pro analysis output.
6. Click on the script window and then the 'run all' command to run the script.

Results

A custom algorithm, LC_Pro, was developed and implemented in order to perform automated analysis of Ca²⁺ dynamics on confocal image sequences. As depicted in Figure 1, the algorithm utilizes sequential processing modules that A) detect and track sites of dynamic Ca²⁺ change above statistical (p < 0.01) noise, B) define regions of interest (ROI) automatically at active site centers, and C) calculate average fluorescence intensities at ROIs to determine specific event parameters. A...

Discussion

Decoding complex Ca²⁺ signals at the cellular and multicellular level will require rigorous experimental and analytical approaches. Here, an approach is described in which time resolved confocal image sequences of Ca²⁺ dependent fluorescence are subjected to an automated analysis that identifies and quantifies statistically relevant Ca²⁺ signals within intact cellular fields In the specific case presented, an artery segment was isolated from pig heart, pinned opened to expose the endothel...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Dissection dish	Fisher Sci	#08-772-70
Polydimethylsiloxane (PDMS)	Fisher Sci	#NC9644388	elastomer kit, must be molded into dishes
HEPES-buffered PSS	Sigma	#H3375-250G	HEPES acid
Stereomicroscope	Nikon Inst.	#MNA42000
Forceps	Fine Science Tools	#11223-20
Spring scissors	Fine Science Tools	15003-08
Tungsten wire	Scientific Inst Svcs	#406
Fluo-4 AM	Life Tech.	#F-14201
Pluronic F-127	Life Tech.	#P3000MP
Metal pins	Fine Science Tools	#26002-10
Cover-glass bottom chamber			Custom designed
Spinning disc confocal microscope	Perkin Elmer	RS-3
ImageJ software			download at: http://rsbweb.nih.gov/ij/download.html
LC_Pro plugin for imageJ			download at: http://rsbweb.nih.gov/ij/plugins/lc-pro/index.html
R software			download at: http://www.r-project.org/
R traceplot script			download at: https://docs.google.com/file/d/0B-PSp1D9e2fjV3NIcGppUzkxdEk/edit?usp=sharing