Quantitative analysis of mitochondrial morphology and membrane potential in living cells using high-content imaging, machine learning, and morphological binning.

Cell Culture 661w photoreceptor cells were generously provided by Dr. M. Al-Ubaidi (University of Oklahoma) [ 10 ] and maintained under standard conditions using DMEM-HG media (Sigma-Aldrich #D-7777) supplemented with 10% fetal calf serum (FCS, Atlanta Biologicals #S11550) and alanylglutamine (GlutaMAX, Life Technologies #35050-061). Cells of passages 15-25 were cultured to 80% confluency before trypsinization and seeded at a cell density of 8,000 cells per well in 96 well plates (Nunc Edge Plate, Thermo Scientific #167314) supplemented with 5% FCS (fetal calf serum) and edge reservoirs filled with phosphate-buffered saline (to prevent hydration-dependent microplate edge effects) (PBS; Life Technologies #14080-055). After cells reached 80% confluency (24 h), cells were washed in PBS, and media was changed to DMEM with 5.5 mM glucose without phenol red (Sigma-Aldrich #D-5030) and supplemented with 1% FCS to induce cell cycle arrest. Cells were used for experiments 24 h after this media switch. Treatments were for either 24 h (morphological analysis) or 1 h (membrane potential analysis). All compounds used for treatment were from Sigma-Aldrich (St. Louis, MO) unless otherwise specified, and were prepared at 1000x concentration in DMSO before administration to cells (final [DMSO] = 0.1% v/v).

Live Cell Staining

Cells were stained with Hoechst 33342 (Anaspec #83218), and MitoTracker Deep Red FM (MTDR, Life Technologies # M22426 ). After determining the lowest concentration of dye necessary to acquire high signal-to-noise ratio (>3) images while maintaining exposure times under 1 sec, cells were stained for 30 min at 37°C with the above dyes in phenol-red-free DMEM supplemented with 1% v/v FCS (Hoechst at 10 μM and MTDR at 50 nM), after which the media was replaced for imaging.

Image

Acquisition and Analysis: Overview Briefly, 661w photoreceptor cells cultured on 96-well plates were stained with Hoechst 33342 and MTDR, and imaged using wide-field fluorescence microscopy (see Figure 1a and Section 2.4 ). A z stack of seven images was collected of MTDR-stained mitochondria and 2-D deconvolution applied to the stack to output a single in-focus field with out-of-focus information removed (a process that simulates confocal microscopy). Mitochondrial objects were identified from deconvolved and preprocessed images using the “object” segmentation algorithm in GE INCell Developer Toolbox 1.9.1, a variation of the “top hat” approach to segmentation ( Figure 1b-c ). Developer Toolbox is available within the GE INCell Investigator 1.6.1 software package (GE Healthcare Bio-Sciences, Pittsburgh, PA). As mitochondria display a variety of shapes which indicate interconnectedness and health, four categories were established: puncta, rod, networked, large & round. Rods are an intermediate phenotype between puncta and networks. The large & round group likely represents a combination of pathologically swollen mitochondria as well as normal mitochondria undergoing fission or fusion (see Section 4 ). To automate classification of mitochondrial objects into these bins, 1386 mitochondria were manually classified, and a subset of 897 with 35 morphometric measures calculated on each were then used to train a classifier using conditional inference recursive partitioning [ 9 ]. The remaining 489 mitochondria (test data, not used to train classifier) were used to test its performance. The R code as well as the training and test data sets are included in Supplementary Files ; the decision tree output is shown in Supplementary Figure 2 . Automated Microscopy—Step 1: Image Acquisition Wide-field fluorescence imaging of live cells was coupled with off-line deconvolution of the MTDR-stained mitochondria to increase acquisition speed ( Figure 1a ). Lateral spatial resolution was 717 nm by Rayleigh criteria and 555 nm (3 pixels) by Nyquist criteria, whereas the average width (narrowest dimension) of an individual mitochondrion is ~500 to 1000 nm [ 11 ]. Stained cells were imaged using filters corresponding to each dye and polychroic mirror (“X”, QUAD1) on the GE INCell 2000 Analyzer automated wide-field fluorescence microscope. The objective used was a 40x Nikon ELWD NA 0.6 matched with the large-format 2048×2048 pixel 12-bit Coolsnap K4 camera (z/sampling height 1.55 μm, xy/lateral pixel dimensions of 0.185 μm). Resolving small objects near resolution limits like mitochondria requires high signal-to-noise ratio images. To achieve this, seven images were acquired in a set of z-stacks 1.55 μm apart on the Cy5 filter set (MTDR-stained mitochondria) to enable 2-D deconvolution (See Section 2.5.1 ). A single z section was obtained for Hoechst-stained nuclei on the DAPI filter set. Two series of images (fields or locations) for each set of wavelengths were acquired in each well, and all conditions were run in duplicate wells. Thus, each treatment condition was represented by 4 to 8 fields per plate, leading to 12 to 72 fields overall acquired for each condition. Each plate was treated as an individual experiment, n=3-9 per condition. Computational Analysis 2.5.1 Step 2: Image Pre-processing Preprocessing alters the rate of detection of mitochondrial objects, namely improving the ability of the segmentation algorithm to identify mitochondrial objects from background ( Section 2.5.2 ). Image stacks were opened in the GE INCell Developer Toolbox 1.9.1 program. Using the advanced track and block feature for plate mapping, the software was configured to visualize all z sections and wavelengths acquired at each time point ( Supplementary Figure 3 and Supplementary Figure 4 ), and the image histogram was expanded from 12-bit to a 16-bit range (65,536 gray levels) to accommodate for deconvolution (written to process 16-bit images) and to accommodate for images previously registered using the RNiftyReg algorithm (also written to process 16-bit images; see Section 2.6, and parallelImageReg.R in Supplementary Files ). Next, the nearest-neighbor deblur (2-D deconvolution) algorithm available in Developer Toolbox was applied to the seven z sections acquired of MTDR to simultaneously remove out-of-focus pixels and increase signal-to-noise of the specific section. This deconvolution algorithm used a point-spread-function (PSF) that characterizes the diffusion pattern (convolution) induced by the optics of the light microscopic objective lens. The PSF was provided by the instrument and software manufacturer (GE). The algorithm used the intensity of pixels across a z stack to determine if each pixel was in focus or out of focus at the predefined level of focus (fixed at the center section in this instance as an infrared laser autofocusing mechanism was employed by the instrument to control focus and determine the center section of each z stack). Following deconvolution, the resulting image was flat-field corrected to remove intensity fluctuations arising from the projection of a curved image onto the flat camera (CCD) chip. Next, the resulting image histogram data was normalized using the Information Equalization transformation to achieve normalized intensity values from one field to another ( Figure 1b ). Normalized intensity values were critical to allow for exclusion of aberrantly detected objects and for inter-experiment comparisons (see text at 2.5.2 and 2.5.3 ). See Figure 1b and Figure 1 a-b for examples of preprocessed images. A graphical overview of the sequence of image processing steps is shown in Supplementary Figure 5 . Step 3: Segmentation To locate mitochondrial objects, the processed images were subjected to the Developer Toolbox object-based segmentation based on local (i.e., relative) intensity variations. GE INCell Developer Toolbox documentation characterizes the “object” segmentation method as a modified top-hat (Laplacian of Gaussian) segmentation algorithm, in that identification of objects of interest (mitochondria) is obtained by application of a kernel point operation that identifies pixels above a certain threshold, given the two parameters of kernel size and sensitivity (in this study, 3 pixel kernel, 75% sensitivity). This segmentation method is subject to bias with regards to absolute intensity levels; therefore, contrast normalization was performed to equalize brightness of images – and therefore sensitivity of mitochondrial object detection – within and amongst experiments. Nuclei were detected with the Developer Toolbox nuclear segmentation algorithm (100% sensitivity, 1.0 to 1.9 sensitivity range, and 100 μm 2 minimum target area) in the Hoechst channel. The cellular cytoplasm was estimated by extending the mitochondrial object area in a process known as opening (dilating objects outward until they touch/overlap followed by eroding the aggregated area toward the centroid of the new aggregated object). To separate cells from one another, clump breaking was then performed on the cytoplasm mask, using the nuclei image as a seed (1 cytoplasm = 1 nucleus). Clump breaking is a process that separates an aggregate of two or more objects by determining one or more local intensity minima within the aggregated object. Example segmentation is shown in Figure 1c . Segmentations were temporarily stored in memory as a “target set” and represent a particular object of interest (e.g., nucleus, punctate mitochondrion, rod mitochondrion, cytoplasm, etc.). Step 4: Denoise-Free Aberrant Object Exclusion Following detection of mitochondrial objects during segmentation, the 1-bit images, or masks ( Figure 1c and Supplementary Figure 5 ) were further processed to remove aberrantly detected objects and improve the masks' representation of the experimental data. Post-processing quality control focused on excluding objects also detected in background noise. To ensure that mitochondrial objects were maximally separated, a watershed clump-breaking algorithm, which uses the presence of local intensity minima within an object, was applied to the mask to separate aggregates of adjacent individual objects. Finally, small objects (