A pipeline for multidimensional confocal analysis of mitochondrial morphology, function, and dynamics in pancreatic β-cells

Abstract

Live-cell imaging of mitochondrial function and dynamics can provide vital insights into both physiology and pathophysiology, including of metabolic diseases like type 2 diabetes. However, without super-resolution microscopy and commercial analysis software, it is challenging to accurately extract features from dense multilayered mitochondrial networks, such as those in insulin-secreting pancreatic β-cells. Motivated by this, we developed a comprehensive pipeline and associated ImageJ plugin that enables 2D/3D quantification of mitochondrial network morphology and dynamics in mouse β-cells and by extension other similarly challenging cell types. The approach is based on standard confocal microscopy and shareware, making it widely accessible. The pipeline was validated using mitochondrial photolabeling and unsupervised cluster analysis and is capable of morphological and functional analyses on a per-organelle basis, including in 4D (xyzt). Overall, this tool offers a powerful framework for multiplexed analysis of mitochondrial state/function and provides a valuable resource to accelerate mitochondrial research in health and disease.

Keywords: cell metabolism; diabetes; fluorescence microscopy; image analysis; live-cell imaging.

Fig. 1.

General workflow and comparison of mitochondrial identification using global vs. adaptive thresholding methods. A: schematic of the general workflow required for mitochondrial analysis by confocal microscopy. Shaded boxes represent the steps that are addressed and detailed in this paper. B: 2 representative examples of object identification using global thresholding (“default” method) vs. adaptive thresholding (radius = 1.25 μm, C = 11) on images of MIN6-cell mitochondria labeled with mitochondria-targeted yellow fluorescent protein (mito-YFP). The number of identified objects (mitochondria) and their total area are indicated below the images. Scale bar, 1 μm. C: part of the mitochondrial network in a MIN6 cell co-transfected with mito-dsRed and mitochondria-targeted photoactivatable green fluorescent protein (mito-PAGFP). Top: all mitochondria imaged in the mito-dsRed channel. Bottom left: a single mitochondrion (green) was labeled by laser-based mito-PAGFP activation at the point indicated by the arrow. Bottom right: object identification using global vs. adaptive threshold algorithms applied to the dsRed channel; in each image, the object that is identified as contiguous with the PAGFP-labeled mitochondrion is shown in green. Comparison with the original image shows that the adaptive method more accurately distinguished the photo-labeled mitochondrion, whereas global thresholding artificially merged it with adjacent mitochondria. Scale bar, 1 μm. D: quantitative comparison of the degree to which global and adaptive thresholding under- or overestimated the PAGFP-labeled mitochondrion in 5 test images. The corresponding images and details of the estimation algorithm are shown in Supplemental Fig. S3. 2D, 2-dimensional; 3D, 3-dimensional; 4D, 4-dimensional.

Fig. 2.

Quantitative comparison of mitochondrial morphology and network connectivity in 2D (2-dimensional). Based on visual inspection of their mitochondria, 84 images of mitochondria-targeted yellow fluorescent protein (mito-YFP)-expressing MIN6 cells were categorized into 3 morphological groups: fragmented (20 cells), intermediate (46 cells), or filamentous (18 cells). A and B: examples of the YFP-labeled mitochondria in representative cells from each group (A) and the objects identified by application of adaptive thresholding to the images (B). C: 2D morphological analysis of all cells in each of the categories. D: skeletonization of the mitochondrial objects identified in B. E: quantitative analysis and comparison of mitochondrial network connectivity performed on all cells in each morphological category. Data are represented by means ± SE. One-way ANOVA with Sidak post hoc test was used to compare the groups; **P < 0.01; ***P < 0.001; ****P < 0.0001. AR, aspect ratio; AU, arbitrary units; FF, form factor.

Fig. 3.

Unsupervised categorization of mitochondrial features using spanning tree progression analysis of density-normalized events (SPADE). A: a SPADE tree was generated based on the same set of 84 images used in Fig. 2 and then automatically subdivided into 3 groups; group 1 contains 19 nodes/cells, group 2 contains 47 nodes/cells, and group 3 contains 18 nodes/cells. B: representative images extracted from each of the 3 SPADE-generated groups. C and D: comparison of the mitochondrial morphology and network parameters between the 3 SPADE-identified cell groups. All data are represented by means ± SE. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001 as determined by 1-way ANOVA with Sidak post hoc test; n = 84 images.

Fig. 4.

Limitations of 2-dimensional (2D) morphometric analysis and the importance of deconvolution for the quality and accuracy of 3-dimensional (3D) mitochondrial analysis. A: schematic illustrating the effect of object orientation in 3D space on the image capture in a horizontal 2D slice. The apparent 2D morphology of the same tubular object (shown in green) will depend on its orientation relative to the confocal plane. If a curved object (shown in blue) intersects the confocal plane at several locations, it will erroneously be identified as separate objects. B: MIN6 cells were co-transfected with mito-dsRed and mitochondria-targeted photoactivatable green fluorescent protein (mito-PAGFP) and photoactivation induced at the point indicated by an arrowhead. Scale bars, 3 µm. Top: 2D image of Mito-dsRed and mito-PAGFP channels after photoactivation. Bottom: objects identified after preprocessing and thresholding of the 2D cross-section. C: full 3D imaging and reconstruction (rendered using Huygens Professional software) of the same mitochondrial population shown in B. Note that the photo-labeled mitochondrion in 2D appears as a series of separate mitochondria, whereas 3D visualization correctly identifies it as 1 contiguous organelle. D: a full z-stack was acquired from a mitochondria-targeted yellow fluorescent protein (mito-YFP)-expressing MIN6 cell that was 11 μm in height. Top: maximum projection views of the z-stack before and after deconvolution. The confocal image stack was deconvolved using either ImageJ DeconvolutionLab (Richardson-Lucy algorithm) or Huygens Professional (Classical Maximum Likelihood Estimation) software for 40 iterations. Dotted line indicates the position of the axial section shown below. Bottom: axial sections (xz-plane) of the raw and deconvolved image stacks. The reduction in axial stretching of objects can be seen in the deconvolved stacks, with the best improvement achieved using the Huygens algorithm (see additional details in Supplemental Fig. S6 and Supplemental Table S1). E: 3D renderings of the z-stack before and after deconvolution with ImageJ or Huygens Professional. All 3D visualizations were generated using the Huygens 3D object renderer, with a unique color assigned to separate objects.

Fig. 5.

Quantitative comparison of mitochondrial morphology and network connectivity in 3D (3-dimensional). Image stacks of mitochondria-targeted yellow fluorescent protein (mito-YFP)-expressing MIN6 cells were visualized in 3D and their mitochondria manually categorized as fragmented, intermediate, or filamentous. A: 3D renderings (produced using Huygens Professional) of representative mito-YFP-expressing MIN6 cells from each of the morphological categories. B: quantitative 3D analysis and comparison of mitochondrial morphology between cells in each category. C: 3D renderings of the skeletonized mitochondrial network of the cells depicted in A. D: quantitative 3D analysis and comparison of mitochondrial network connectivity between cells in each category. All data are represented by means ± SE. **P < 0.01, ***P < 0.001, and ****P < 0.0001, as determined by 1-way ANOVA with Sidak post hoc test; n = 10 cells in each category.

Fig. 6.

Summary of pipeline for 2-dimensional (2D) and 3-dimensional (3D) mitochondrial analysis in ImageJ/Fiji. For illustration, an image stack was acquired from a MIN6 cell expressing mitochondria-targeted yellow fluorescent protein (mito-YFP); a representative slice is shown as the 2D input and the entire stack (after deconvolution) as the 3D input. 3D stacks are represented as maximum projections here. Scale bars, 5 μm. See materials and methods and results for additional details and parameter values. AR, aspect ratio; FF, form factor.

Fig. 7.

Two-dimensional (2D) analysis shows that glucose stimulation is associated with mitochondrial fission in pancreatic islet cells. Dispersed mouse islet cells were treated in either 3 (3G) or 17 mM glucose (17G) for 60 min and then labeled with Hoechst 33342, MitoTracker Green FM (MTG), and tetramethylrhodamine (TMRE) before 2D imaging. A: representative images of an MTG- and TMRE-stained islet cell in 3G and 17G. B: TMRE/MTG ratio (normalized to average 3G), indicating the degree of mitochondrial hyperpolarization. Mitochondrial morphology and polarization were quantified using our 2D analysis pipeline in Fiji/ImageJ (see materials and methods). C and D: comparison of mitochondrial morphometry (C) and mitochondrial network connectivity (D) demonstrates significant differences between cells acutely treated with low and stimulatory glucose. All data are represented by means ± SE. **P < 0.01, ***P < 0.001, and ****P < 0.0001 as determined by Student’s t-test; n = 49 cells in each glucose treatment from 4 mice.

Fig. 8.

Time lapse 3-dimensional (3D) imaging (xyzt) and analysis of mitochondrial dynamics. Mitochondria-targeted yellow fluorescent protein (mito-YFP)-expressing MIN6 cells were imaged in a stage top incubator, with 1 full image stack acquired every 45 s. A: 3D renderings (produced in Huygens Professional) of the mitochondrial network in a single cell at different time points. A high concentration FCCP (25 µM) was added to the incubation media around the 13-min mark. B: quantitative analysis of the time-dependent effects of FCCP on mitochondrial number, sphericity, total and mean volume, and network characteristics in the cell.