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Multiplexed high-content analysis of mitochondrial morphofunction using live-cell microscopy

Nature Protocols volume 11, pages 1693–1710 (2016)Cite this article

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Abstract

Mitochondria have a central role in cellular (patho)physiology, and they display a highly variable morphology that is probably coupled to their functional state. Here we present a protocol that allows unbiased and automated quantification of mitochondrial 'morphofunction' (i.e., morphology and membrane potential), cellular parameters (size, confluence) and nuclear parameters (number, morphology) in intact living primary human skin fibroblasts (PHSFs). Cells are cultured in 96-well plates and stained with tetramethyl rhodamine methyl ester (TMRM), calcein–AM (acetoxy-methyl ester) and Hoechst 33258. Next, multispectral fluorescence images are acquired using automated microscopy and processed to extract 44 descriptors. Subsequently, the descriptor data are subjected to a quality control (QC) algorithm based upon principal component analysis (PCA) and interpreted using univariate, bivariate and multivariate analysis. The protocol requires a time investment of ∼4 h distributed over 2 d. Although it is specifically developed for PHSFs, which are widely used in preclinical research, the protocol is portable to other cell types and can be scaled up for implementation in high-content screening.

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Figure 1: Overview of the protocol.
Figure 2: Spectral properties of the fluorescent reporter molecules and microscopy hardware.
Figure 3: Microscopy light path and spectral properties of the excitation source and filters.
Figure 4: Layout of the 96-well plate and image acquisition strategy.
Figure 5: Overall image quantification strategy.
Figure 6: Image processing of the TMRM image.
Figure 7: Descriptor extraction, data visualization and QC procedure.
Figure 8: Quality control and monovariate and bivariate data visualization.

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Acknowledgements

This research was supported by the Marie-Curie Initial Training Networks (ITN) grant 'MEET' (Mitochondrial European Educational Training; FP7-PEOPLE-2012-ITN), a PM-Rare (Priority Medicines Rare Disorders and Orphan Diseases) grant from the Netherlands Organization for Health Research and Development—Medical Sciences (40-41900-98-033), theEnergy4All Foundation (http://www.energy4all.eu) and the CSBR (Centres for Systems Biology Research) initiative from the Netherlands Organisation for Scientific Research (NWO; CSBR09/013V). We thank L. Blanchet and M. Pellegrini (both from Khondrion BV) for training, practical assistance and discussions.

Author information

Authors and Affiliations

  1. Department of Biochemistry, Radboud Institute for Molecular Life Sciences, Nijmegen, the Netherlands

    Eligio F Iannetti, Peter H G M Willems & Werner J H Koopman

  2. Khondrion BV, Nijmegen, the Netherlands

    Eligio F Iannetti, Jan A M Smeitink, Julien Beyrath, Peter H G M Willems & Werner J H Koopman

  3. Department of Pediatrics, Radboud Center for Mitochondrial Medicine, Radboudumc, Nijmegen, the Netherlands

    Jan A M Smeitink

  4. Centre for Systems Biology and Bioenergetics, Radboudumc, Nijmegen, the Netherlands

    Jan A M Smeitink, Peter H G M Willems & Werner J H Koopman

Authors
  1. Eligio F Iannetti
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  2. Jan A M Smeitink
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  3. Julien Beyrath
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  4. Peter H G M Willems
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  5. Werner J H Koopman
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Contributions

W.J.H.K. developed the original microscopy and image processing strategy on which this protocol is based. E.F.I., J.B. and W.J.H.K. designed the experiments. E.F.I. performed the experiments. E.F.I. and W.J.H.K. analyzed the data and prepared the figures. E.F.I., J.A.M.S., J.B., P.H.G.M.W. and W.J.H.K. wrote and proof-read the manuscript. W.J.H.K. supervised the research.

Corresponding author

Correspondence to Werner J H Koopman.

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Competing interests

This research was carried out in a collaborative project of the Department of Biochemistry (Radboud University Medical Center) and Khondrion BV (a Radboud University Medical Center spin-off biotech company). E.F.I. (full time), J.B. (full time) and J.A.M.S. (part time) are employed by Khondrion BV. P.H.G.M.W. and W.J.H.K. are scientific consultants for Khondrion BV.

Integrated supplementary information

Supplementary Figure 1 Importance of the threshold (T) gray value for correct calculation of the BIN and MSK images.

(A) Typical RAW calcein image (left image) and the effect of various thresholds (numerals depict the threshold gray value) on the resulting binary (BIN) image. (B) Same as panel A but now for the RAW TMRM image of the same cells. (C) TMRM MSK image calculated using the BIN images in panel B. (D) Average value of Casum (total area of the objects in the calcein BIN image) and Asum (total area of the mitochondrial objects in the TMRM MSK image) for various threshold values. (E) Average value of AR (aspect ratio of mitochondrial objects), F (form factor: length and degree of branching of mitochondrial objects) and Mm (mitochondrial mass) in the TMRM MSK image for various threshold values. (F) Average value of Dm (mitochondrial TMRM intensity in TMRM MSK image), Ot (the total number of mitochondrial objects in the TMRM MSK image), Am (the area of individual mitochondrial objects in the TMRM MSK image) and Nc (the number of mitochondria per cell; calculated by combining information from the TMRM MSK and Hoechst BIN image) for various threshold values. Using a threshold value of 40 yielded the correct values of key descriptors between 3-6 (F), 2-3 (AR) and 50-150 (Nc), compatible with previous manual analysis (e.g. Koopman et al., 2005a; Koopman et al., 2005b; Koopman et al., 2006; Koopman et al., 2008a; Distelmaier et al., 2008; Distelmaier et al., 2012; Distelmaier et al., 2015).

Supplementary Figure 2 Illustration of too low and too high cell density.

Since images are automatically acquired for each well there is no user-control regarding which cells are imaged. Although this strategy avoids user-induced bias, it requires that the average number of imaged cells is similar between individual wells. The latter is also important to allow standardized image processing, statistical analysis and QC. This figure provides typical examples of too low and too high cell densities (relative to the proper density depicted in Figure 5). The protocol described in the main text was used to acquire the images in this figure.

Supplementary Figure 3 Importance of the presence of extracellular TMRM, well mixing and illumination intensity for TMRM descriptor analysis.

This figure illustrates why it is important to have TMRM present outside the cell during image TMRM acquisition, why wells need to be properly mixed following TMRM addition, and the artefact-inducing effect of too high illumination intensities. Unless stated otherwise, the experimental data in this figure was acquired using the experimental protocol described in the main text. (A) Importance of the presence of extracellular TMRM during TMRM image acquisition. In this experiment the calcein/Hoechst loading and imaging steps were omitted. Three different manoeuvres were tested (using 5 plates/days for each condition), thereby modifying step 15 of the protocol as follows: “Washing” condition (gray symbols; two washing steps were performed using 100 µl assay medium; 100 µl of assay medium was present in each well during image acquisition); “No washing” condition (open symbols; nothing was done to the plate after step 13); “Dilution” condition (black symbols; 100 µl assay medium was added to each well without washing and mixing). Only for the last condition, the fluorescence signal remained stable as indicated by the slope (inset) of a linear fit (red line). All data were normalized to the average of the first row (row A) of the plate. Fitting results: P<0.001, R=-0.83 (Washing condition); P<0.001, R=0.91 (No washing condition); P=0.029, R=-0.22 (Dilution condition). (B) Importance of mixing after TMRM addition to the well. The following steps of the protocol were modified: Step 13 (TMRM was added and mixed by pipetting up and down 4 times, but only in row A and B. For row C to H, TMRM was added but not mixed. Calcein/Hoechst staining and imaging were not required here. Only in the mixed wells (green), the TMRM signal was stable (i.e. the fitted line had no significant slope). (C) Effect of TMRM illumination intensity on the TMRM fluorescence signal. In this experiment, 30 subsequent images of the same well were acquired at the same position (Option in BD AttoVision Software: image acquisition option/acquire button/30 data points. Three different acquisition protocols were used with the following settings: Protocol 1 (Exposure time: 0.1 s; Excitation B: 548/20; Excitation dichroic: 40%; Lamp B intensity: 60%), protocol 2 (Exposure time: 0.1 s; Excitation B: 548/20; Excitation dichroic: open; Lamp B intensity: 80%), protocol 3 (Exposure time: 0.1 s; Excitation B: 548/20; Excitation dichroic: open; Lamp B intensity: 100%). Acquisition protocol 1 (used in the protocol) displayed the lowest drop in TMRM signal. Fitting results: TMRM intensity curves were fitted using a mono-exponential equation (y=y0+A1·e-t/τ, with τ being the decay time constant). The fitted τ values equalled: 44.0±23.4 (acquisition protocol 1), 17.4±0.384 (acquisition protocol 2) and 7.56±0.129 (acquisition protocol 3). (D) Effect of the three illumination protocols on the calculated mitochondrial area (Am) and number of mitochondrial objects per cell (Nc). The more the TMRM signal drops, the greater the erroneous apparent increase in Am and Nc. (E) Similar to panel D but now for the calculated mitochondrial aspect ratio (AR) and form factor (F). (F) Values of calculated key mitochondrial descriptors (Nc, Am, AR and F; y-axis) as a function of TMRM intensity (x-axis) for acquisition protocol 1. About 13 illuminations can be carried out (i.e. 13 images can be acquired) before the (10-15%) drop in TMRM intensity affects descriptor quantification.

Supplementary Figure 4 Illustration of the effect of image defocusing on TMRM-reported mitochondrial morphology descriptors.

It is of the greatest importance that the acquired images are optimally focused, since defocusing will affect the extracted numerical data in a descriptor-dependent manner. This figures illustrates the effects of image defocusing on key mitochondrial descriptors. Typically, defocused images contain “artificial” small non-mitochondrial objects after image processing. The latter results in an apparent increase in the number of mitochondrial objects per cell (Nc), and a decrease in mitochondrial size (Am) and form factor (F). Statistics: Significant differences between the focused and defocused condition were assessed using an unpaired independent Student’s test and presented by: * (P<0.05), the actual P-value (Am) and by non-significant (n.s.). The protocol described in the main text was used to acquire the images in this figure.

Supplementary Figure 5 Control for TMRM autoquenching, detectability of Δψ hyperpolarization and effect of the mitochondrial uncoupler FCCP.

This figure illustrates how to check if the TMRM fluorescence signal is affected by autoquenching and whether Δψ depolarization and hyperpolarization can be detected by decreased and increased mitochondrial TMRM accumulation, respectively. (A) Eighty-five consecutive TMRM images were acquired from the same well. The displayed typical image-pairs illustrate the effect of the mitochondrial uncoupler p-trifluoromethoxy carbonyl cyanide phenyl hydrazine (FCCP, 10 µM; #370-86-5; Sigma-Aldrich, St. Louis, MO, USA) on mitochondrial TMRM staining (upper vs. lower panel). Numerals mark individual cells and regions of interest (ROIs; yellow) mark the nuclear and mitochondrial compartment (yellow circles). (B) Average ratio between TMRM intensity in the mitochondrial and nuclear compartments calculated using the ROIs in panel A for untreated (n=4 cells) and FCCP-treated (n=2) cells. Repeated illumination induces a gradual drop in ratio (photobleaching) in the untreated cells. FCCP induces a smooth drop in the ratio signal, indicating the absence of TMRM autoquenching. If the latter would be the case, FCCP would first induce an increase in the signal (due to TMRM dequenching in the mitochondrial matrix), followed by a decrease. (C) Effect of subsequent acute addition of the FoF1-ATPase inhibitor Oligomycin A (OLI; 1 µM; #1404-19-9; Sigma-Aldrich), the mitochondrial complex I inhibitor Rotenone (ROT; 1 µM; #83-79-4; Sigma-Aldrich) and FCCP (10 µM) on mitochondrial TMRM fluorescence intensity. In a second type of experiment, Bonkrekic acid (BA; 50 µM; #11076-19-0; Sigma-Aldrich) was acutely added to inhibit the electrogenic mitochondrial ATP/ADP translocator (ANT). This was followed by subsequent addition of ROT and FCCP. Statistics: Significant differences with the indicated columns (a,b,c,d,e,f) was assessed using an unpaired independent Student’s test (Origin Pro 6.1) and presented by: *** (P<0.001). Experimental details: Options used in the BD attovision software: “Montage Capture Setup”, “INACTIVE MONTAGE”, “1 Frame”. MACRO on BD attovision software: Experiment 1 (30 (CT) TMRM image acquisitions of the same well; 30 s pause during which 1 µM Oligomycin (OLI) is added by pipetting; 30 TMRM image acquisitions of the same well; 30 s pause during which 1 µM Rotenone (ROT) is added; 30 TMRM image acquisitions of the same well; 30 s pause during which 10 µM FCCP is added; 30 TMRM image acquisitions of the same well); Experiment 2 (30 (CT) TMRM image acquisitions of the same well; 30 s pause during which 50 µM Bongkrekic acid (BA) is added; 30 TMRM image acquisitions of the same well; 30 s pause during which 1 µM Rotenone (ROT) is added; 30 TMRM image acquisitions of the same well; 30 s pause during with 10 µM FCCP is added; 30 TMRM image acquisitions of the same well). Remarks: Care should be taken that during addition of the inhibitors the pipette tip does not touch the plate to avoid altering the field of view. Inhibitors are added at twice their final concentration in a (relatively large) volume of 100 µl to each well (which contains 100 µl of fluid). This ensures complete and rapid mixing.

Supplementary Figure 6 Visualization of the effect of median (MED) and top-hat (THF) spatial filtering on the TMRM image.

Proper use of the MED and THF filters to reduce noise and isolate mitochondrial objects, respectively, is crucial for proper subsequent calculation of the binary image (BIN). The latter image represents white mitochondrial objects on a black background (for the complete processing pipeline see Fig. 5C). In this protocol, we have established the correct size of the MED and THF filter, how often they should be applied (“passes”) and, in case of the THF filter, its strength (“S”). For details about these parameters see the information in Box 1 provided in the main text. (A) Effect of various MED filter settings on the TMRM image (RAW). (B) Effect of various THF filter settings on the TMRM image (RAW).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6, Supplementary Tables 1–3 and Supplementary References (PDF 1914 kb)

Supplementary Software

Dataset for IPP and MATLAB analysis (ZIP 5018 kb)

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Iannetti, E., Smeitink, J., Beyrath, J. et al. Multiplexed high-content analysis of mitochondrial morphofunction using live-cell microscopy. Nat Protoc 11, 1693–1710 (2016). https://doi.org/10.1038/nprot.2016.094

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