MITOHEALTH Nordic Centre of Excellence Workshop in: Mitochondrial function and metabolic diseases
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1 MITOHEALTH Nordic Centre of Excellence Workshop in: Mitochondrial function and metabolic diseases Quantitative multi-parameter microscopy of mitochondrial function in living cells Werner J.H. Koopman, PhD. Dept. of Biochemistry NCMLS Radboud University Nijmegen Medical Centre Nijmegen, The Netherlands
2 Quantitative life cell technology currently running at our department FLUORESCENT PROTEINS -GFP et al. (dynamics) - GFP-protein et al. (protein dynamics) FLUORESCENT CHEMICAL REPORTERS - Rhodamine 123 (dynamics) - TMRM et al. ( ψ, dynamics, PTP) - Mitotrackers t (dynamics) -rogfp1 (thiol redox status) t - Rhod-2 et al. (mito Ca 2+ ) - Fura-2 et al. (cyto Ca 2+ ) - BCECF et al. (cyto ph) - phluorin (mito ph) - D1ER (ER Ca 2+ ) - Pericam (Ca 2+ ) - Photo-activatable Dendra2 (mito/protein dynamics) -Hydroethidine / MitoSOX RED (superoxide) - CM-DCFDA (ROS) - ER tracker (dynamics) - C11 BODIPY (lipid peroxidation) - NAD(P)H autofluorescence - FADH 2 autofluorescence BIOLUMINESCENCE - Targeted aequorin (Ca 2+ ) - Targeted luciferase (ATP) SOFTWARE - Image analysis: Image Pro Plus Metamorph Imaris IMAGING APPROACHES & DATA HANDLING Volocity - Multispectral confocal laser scanning microscopy ImageJ - Videorate confocal laser scanning microscopy - Numerical analysis: - CCD camera videomicroscopy Origin Pro - Bioluminescence recording (PMT) Excel - Fluorescence correlation spectroscopy - Modeling: - FRAP/FLIP microscopy - Curve fitting - Mathematical cell/data modeling MATLAB/Simulink - Figure preparation CorelDraw
3 Fluorescence: Jablonski diagram Fluorescence Bleaching Higher energy and vibration state Lowest singlet excited state t Absorpt tion Absorpt tion Irreversible oxidation (bleaching) Ground state
4 Fluorescence: Stokes shift George Gabriel Stokes ( ) E = h f = h λ
5 The green fluorescent protein family Shaner et al., J. Cell Sci., 2007
6 The green fluorescent protein family Shaner et al., J. Cell Sci., 2007
7 Protein tagging with GFP Wang et al., Annu. Rev. Biomed. Eng., 2008
8 Getting the fluorescent proteins in the cell: Using the Gateway system (Invitrogen)
9 Fluorescence imaging principle Dichroic mirror Barrier filter Light source Fluorescent sensor measurement Exciter filter Transmis ssion Objective Wavelength (nm) Optimal excitation a elength Optimal emission Specimen wavelength wavelength Adapted from: and
10 XYZ-resolution of various imaging approaches Jaiswal & Simon, Nature Chem. Biol., 2007 Koopman et al., AJP, 2008
11 Not images but numbers : The key paradigm for quantitative image analysis Rubbish Image processing and analysis Rubbish SO FOCUS ON: -Optimal cell quality - Optimal reporter selection - Optimal staining approach - Optimal image acquisition iti Optimal image quality Best quality numerical data
12 What is a good life cell image? The easy answer: That depends on the question you want to address GENERAL RECOMMENDATIONS - Keep your cells happy at all time - Avoid photobleaching / damage: Correct fluorophore, lamp/laser intensity - Use the correct objective: Magnification, numerical aperture, coverslip correction! - Acquire a low noise image with a good signal-to-noise noise ratio: signal averaging - Use an optically aligned microscopy system MORE SPECIFIC RECOMMENDATIONS: I want to measure the intensity of a signal: - Keep your settings identical between experiments - Cell thickness changes? - Ratio imaging possible? I want to measure co-localization - No optical pixel shift (optical quality of the imaging system) - No bleed through between wavelengths (fluorophore choice, spectral unmixing) - How to calculate? (currently the best: The Manders Algorithm)
13 Which fluorescent cation to choose? ph sensitive Binds to proteins Koopman et al., Methods, 2008
14 Acquiring the optimal image: Black-level and intensity gain
15 Stability of the TMRM signal [TMRM] TMRM Leakage TMRM Illumination TMRM Illumination Distelmaier et al., Cytometry A, 2008
16 Automated quantification of mitochondrial parameters
17 Automated quantification of mitochondrial parameters: Algorithm Koopman et al., 2005, AJP Cell Phys.; Koopman et al., 2006, Cytometry A; Koopman et al., 2007, AJP Cell Phys.; Komen et al., 2007, Cell Mol. Life Sci.; Distelmaier et al., 2008, Cytometry A; Koopman et al., 2008, Methods; Eisenberg et al., 2008, Hum Mol. Gen.; Mortiboys et al., (in press) Ann. Neurol.
18 Automated quantification of mitochondrial parameters
19 How does a spatial filter work?
20 Meaning of aspect ratio (AR) and formfactor (F) Koopman et al., 2005a, AJP Cell Phys.
21 Effect of noise in the images Koopman et al., 2006, Cytometry A
22 Branching mitogram Class Objects% Objects Branching µm Koopman et al., 2005a, AJP Cell Phys. Koopman et al., 2005b, AJP Cell Phys. Koopman et al., 2006, Cytometry A
23 Spatial visualization of mitochondrial area and structure 15 µm 15 µm Mitochondrial area Degree of branching Distelmaier et al. (submitted)
24 Automated determination of mitochondrial position
25 Quantification of Ψ during more depolarized conditions Distelmaier et al.,cytometry, 2007
26 Human complex I deficiency: ψ is depolarized Distelmaier et al. (Submitted)
27 BD pathway 855 R123-stained life cells: 2D Z-Stack Optimize contrast (1) 2D pre-processing 3D post-processing Median filter 3x3, S1, P1(2) Optimize contrast (5) Background subtraction (4) - Avg min/max intensity it -Z= No autosharp (3) 3D stack calculation 3D-Stack (6) Quantification - Volume (V) - Surface area (A) A/V ratio - Shape - Connectivity (4D) - Dynamics (4D) - Signaling (4D)
28
29 The next challenge: Quantifying mitochondrial shape and function in multiple dimensions Dimensionality of the data - Spatial dimensions (x,y,z) - Time (t) - Multiple reporters (n 1) - Multiple parameters from same reporter (n 1) Quantification - Volume (V), Surface area (A), A/V ratio - Shape - Colocalization (4D) - Connectivity (4D): Intra/inter mitochondrial, with ER - Dynamics, 3D tracking (4D) - Signaling (4D) Example - Mitochondria stained (GFP), ER stained (RFP): 2 Dimensions - Make 3D stacks in time: 4 Dimensions - Determine co-localization and shape 3 Dimensions
30 Quantifying mitochondrial shape and function in higher dimensions: The right software? Media Cybernetics, 3D Constructor software
31 Quantifying mitochondrial shape and function in higher dimensions: The right software? Media Cybernetics, 3D Constructor software
32 Photoconvertible FPs PA-GFP PS-CFP2 Kaede KikGR Eos Dendra2 Dronpa mtfp0.7 KFP1 Shaner et al., J. Cell Sci., 2007
33 Dendra2 photoconversion mechanism Neutral state Anionic (green) state Red state Red state Anionic (green) state Chudakov et al., Nature Protocols, 2007
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