| Curso Electivo Teórico-Práctico | Análisis Cuantitativo de Colocalización en Microscopía Confocal |08|2007 Steffen Härtel Programa de Anatomía y Biología del Desarollo, Instituto de Ciencias Biomédicas, Facultad de Medicina, Universidad de Chile, Santiago, Chile ICBM

|-> Rodrigo Castillo: viernes, Measurement of colocalization of objects in dual-color confocal images Manders E. (1993) Journal of Microscopy 169: |-> Ivan Alfarro: lunes, STED-Microscopy: Concepts for nanoscale resolution in fluorescence microscopy Hell S., Dyba, M., Jakobs S (2004) Current Opinion in Neurobiology 4: |-> Valentina Parra: lunes, Automatic and Quantitative Measurement of Protein-Protein Colocalization in Live Cells Costes et al Biophys. J. 86, 3993–4003 |-> Ariel Contreras: martes, A syntaxin 1, Galphao, and N-type calcium channel complex at a presynaptic nerve terminal: analysis by quantitative immunocolocalization Li, Q., Lau, A., Morris, T.J., Guo, L., Fordyce, C.B. & Stanley, E.F. (2004) J. Neurosci. 24, 4070–4081 |-> Barbra Toro: jueves, Co-localization analysis of complex formation among membrane proteins by computerized fluorescence microscopy: application to immunofluorescence co-patching studies Lachmanovich, E., Shvartsman, D.E., Malka, Y., Botvin, C., Henis, Y.I. & Weiss, A.M. (2003) Journal of Microscopy. 212, 122–131 |-> Nancy Leal: jueves, Partial colocalization of glucocorticoid and mineralocorticoid receptors in discrete compartments in nuclei of rat hippocampus neurons Van Steensel, B., van Binnendijk, E., Hornsby, C., van der Voort, H., Krozowski, Z., de Kloet, E. & van Driel, R. (1996) J. Cell Sci. 109, 787–792 |-> Ximena Verges: viernes, Multicolour analysis and local image correlation in confocal microscopy Demandolx, D. & Davoust, J. (1997) Journal of Microscopy 185, 21–36 |-> Leonel Muñoz: viernes, A guided tour into subcellular colocalization analysis in light microscopy Bolte S. & Cordelieres P. (2006) Journal of Microscopy, 224 (3): 213–232 |-> Seminarios

I|-> Intensity The observation volume (femtoliter) defined by the Point Spread Function must be considered as a mini-sprectrofluorimeter. Consider the Offset I(0) in order to calibrate your signal I(0)  0 ! Never saturate the signal: I  I max (255 for 8 bit) ! I(0) > 0 I > I max

II|-> Refractive index Use the right inmersion setup ! n1 = n2 ! Keep refractive index / index of refraction constant !

The sampling frequency (or sampling distance) is a function of the observation volume (femtoliter) defined by the Point Spread Function (PSF): Consider sampling distances in  x and  y  50 nm and  z  nm for later deconvolution, or calculate the explicit sample distances directly. III|-> Nyquist

I. Image Adquisition I.a|-> Fundamentos de la microscopía confocal I.b|-> Fundamentos de la fluorescencia II. Deconvolution III. SegmentationIV. Analisys |-> what‘s up

Interacciones... intra- e inter moleculares... producen cambios... espectrales tiempos de vida polarización intensidad... - Fluorescencia - Fosforescencia Luminescencia:  t ~ s  t ~ s Absorción / Excitación   t  Emissión |-> Fluorescencia

Intercombinación s s Transiciones: con radiación Niveles: vibracionales sin radiación | -> calor electrónicos -> transferencia de energía (FRET) -> radicales... STED s Fluorescencia s Fosforescencia s Absorción s Conversión interna x/y z |-> Jablonski Diagram

ENERGY Distance [r] Frank Condon : ‘Transiciones entre niveles electrónicos ocurren mucho mas rápido que movimientos de núcleos moleculares.‘ (masa electron / masa atom : 1 : 2000) -> Mirror Image Rule Stokes (Shift) : ‘ La energía de emisión es menor a la energía de excitación.‘ |-> Franck Condon

|-> Mirror Image Rule

a: DPH. b: TMA-DPH, c: Carboxyl –DPH. Momentos dipolares µ de absorción y emission son parallelos. Polarización Anisotropía a MAX = 0,39 I II ~cos  ² a(  )  (a) , viscosidad |-> Polarisación

Probabilidad p :p(r DA ) ~ r DA -6 [Förster, 1946] Efectivo sólo para distancias :r DA < 150 Å Molecule 1 ( DONOR )Molecule 2 ( ACCEPTOR ) Energy Transfer (Fluorescence) Resonance Energy Transfer: (F)RET |-> FRET

occurs from the excited-state (*) of a donor (D) to an acceptor (A). h + D + A  D* + A  D + A*  D + A + h ‘ occurs w/o the generation of an intermediate photon h ‘‘....  D* + A  (D + A + h ‘‘)  D + A* ..., (reabsorption ~ 1/r²) occurs as a result of long-range dipole-dipole interactions between the ´oszillating dipoles´ D & A., depends on the spectral overlap between D emission and A excitation., the quantum yield (QY) of D: QY = [h ‘‘] / ([h ‘‘] + k nr ), the relative orientation of D & A transition dipoles. occurs from the excited-state (*) of a donor (D) to an acceptor (A). h + D + A  D* + A  D + A*  D + A + h ‘ occurs from the excited-state (*) of a donor (D) to an acceptor (A). h + D + A  D* + A  D + A*  D + A + h ‘ occurs w/o the generation of an intermediate photon h ‘‘....  D* + A  (D + A + h ‘‘)  D + A* ..., (reabsorption ~ 1/r²) |-> FRET

Transiciones: con radiación Niveles: ---- vibracionales sin radiación electrónicos DonorAcceptor |-> FRET

the rate k T of RET is given by : k T = 1/  D · (R 0 /r) 6,  D decay time of D w/o A. I(t) = I 0 exp(-t/  D ), r distance between D & A., R 0 Förster distance (20-90 Å). R 0 6 = 8.78·10 -5 · QY D ·  ² · n -4 · J( ), n refractive index: n² =  ·  (n ~1.4 biomolecules in aq).  : electric permittivity,  : magnetic permeability,  orientation between transition dipoles,  ² ~2/3 random, J( ) overlap integral between D-emission & A-excitation., QY = [h ‘‘] / ([h ‘‘] + k nr ) |-> FRET

the transfer quantum yield / energy transfer efficiency E is given by: E = k T / (  D -1 + k T ) | (k T = 1/  D · (R 0 /r) 6 ) = 1 / (1 + (r/R 0 ) 6 ), 2D* + 2A  D + D* + A + A*  2D + 2A + h ‘ + h ‘‘, E(r =2R 0 ) = , E(r =0.1R 0 ) = R 0 : Å detecta cambios conformacionales dentro de macromoleculas. detecta proximidades entre moleculas mas alla de la resolución optica. Literature: - Joseph R. Lakowicz, Principles of Fluorescence Spectroscopy, Kluwer Academic Publishers, ISBN: Jares-Erijman E.A. and Jovin T.M. (2003) FRET imaging. Nat. Biotechnol. 21(11): R. Röder, Einführung in die molekulare Photobiophysik, Teubner, ISBN: , E = ½ at r = R 0 : |-> FRET

|-> Colores

PC distance (pixel) PC distance (pixel) PC

|-> Colores

Bastones Conos (S, M, L) -> 10 8 Bastones -> 6·10 6 Conos: L : M : S (primates) 580 nm : 545 nm : 420 nm ~10: ~10: 1 en cantidad y sensitividad La fóvea |-> Colores

-> 10 8 Bastones -> 6·10 6 Conos: [Azul] / [Amarillo]: [+S] / [M+L] [Verde] / [Rojo]: [M-L] / [L-M] S - Conos M - Conos L - Conos ‘Midget-Cell‘ |-> Colores

10 8 Conos & bastones Nervus opticus 8· 10 5 Células horizontales Células bipolares [Glutamato]~1/I Células amacrin Celulas del Ganglio Células del ganglio Se conocen~15 tipos diferentes de células de ganglio. |-> Colores

->[I, L, M, S, x, y, t] - Receptores: - [Glu] ~ [Na + ] en ‘Off-cells‘ bipolares - [Glu] ~ [Na + ] -1 en ‘On-cells‘ bipolares ->[] = [] + [dI/dt, dL/dt, dM/dt, dS/dt] ->[] = [] + [z] - Activación/Inhibición lateral: - Células horizontales emiten GABA, en caso de una excitación homogenia en [x,y] ->[] = [] + [dI/dxy, dL/dxy, dM/dxy, dS/dxy] |-> Colores

Representación simbólica o modelo individual del mundo real

Alessandro Rizzi GIC - Graphic, Imaging and Color research group Università di Milano |-> Colores

RGB (Red Green Blue), (R:0-255, G: 0-255, B:0-255) : R G B H V S HSV (Hue Saturation Value), (H:0-2 , S:0-1, V:0-1) |-> Colores

The Hue Saturation Value (or HSV) model defines a color spacecolor space in terms of three constituent components: HueHue, the color type (such as red, blue, or yellow); Measured in values of by the central tendency of ist wavelength SaturationSaturation, the 'intensity' of the color (or how much greyness is present), Measured in values of 0-100% by the amplitude of the wavelength. ValueValue, the brightness of the color. Measured in values of 0-100% by the spread of the wavelength HSV is a non-linear transformation of the RGB color space.non-lineartransformationRGB color space

-> RGB.....Se puede usar muy bien para fines científicos pero no sirve para obtener una medida para lo que el ser humano califica como diferencia entre colores (R 1 G 1 B 1 ) y (R 2 G 2 B 2 ). -> Diferencia entre (H 1 S 1 I 1 ) y (H 2 S 2 I 2 ): |-> Colores

I( 290,267 ) = 220 r g b 0 : 220 : : 220 : : 220 : 255 Una mesa de color está definida por 3 vectores r, g, b de 8 bits. Amarillo (255, 255, 0) Negro (0, 0, 0) Rojo (255, 0, 0) Verde (0, 255, 0) Blanco (255, 255, 255) Cian (0, 255, 255) Magenta (255, 0, 255) Azul (0, 0, 255) red = [r 0, r 1,…...…...,r 255 ] green = [g 0, g 1,……...,g 255 ] blue = [b 0, b 1,……...,b 255 ] Un canal tiene 8 bits y se pueden codificar 256 colores o intensidades |-> Colores

r g b 000::::0::0000::::0:: : 200 : ::::0::0000::::0::0 |-> Colores

r g b : 220 : ::::0::0000::::0::0 000::::0::0000::::0::0 |-> Colores

r = [r 0, r 1, r 2,…...., r 255 ] = [0, 0, 0,……..,0] g = [g 0, g 1, g 2,…, g 255 ] = [0, 1, 2,…..,255] b = [b 0, b 1, b 2,…., b 255 ] = [0, 0, 0,……..,0] o = [o 0, o 1, o 2,….., o 255 ] = [255, 255,.,255] r = [r 0, r 1, r 2,…...., r 255 ] = [0, 1, 2,…..,255] g = [g 0, g 1, g 2,…, g 255 ] = [0, 0, 0,……..,0] b = [b 0, b 1, b 2,…., b 255 ] = [0, 0, 0,……..,0] o = [o 0, o 1, o 2,….., o 255 ] = [80, 80,…….,80] |-> Colores

|-> Localization Standard error of the mean (SE) The standard error of the mean of a sample from a population is the standard deviation of the sampling distribution of the mean, and may be estimated by the formula:populationstandard deviationsampling distribution Where is an estimate of the standard deviation σ of the population, an n is the size (number of items) of the sample.

|-> ICA or ICQ ICA / ICQ Intensity Correlation Analysis /Quotient

ICQ Є [± 0.5] Intensity Correlation Quotient |-> ICQ Sign Test p ≤ 0.5 Σ ( N!/( i ! (N-i) ! )) i = 1,k (k = min(n+,n-)) One-Sample and Matched-Pairs tests Sign Test McNemar's Test Wilcoxon Matched-Pairs Signed-Ranks Test Student-t test for one sample

|-> ICQ Sign Test p ≤ 0.5 Σ ( N!/( i ! (N-i) ! )) i = 1,k (k = min(n+,n-)) Characteristics: Crudest and most insensitive test. It is also the most convincing and easiest to apply. The level of significance can almost be estimated without the help of a calculator or table... Approximation: For N > 30, the Student t-test can be used. However, the Wilcoxon Matched-Pairs Signed-Ranks test is a better alternative.Student t-testWilcoxon Matched-Pairs Signed-Ranks Remarks: The Sign-test answers the question: How often?, whereas other tests answer the question How much?. It must be kept in mind that these two questions might have different answers. When the problem concerns the sizes of the differences, the Wilcoxon Matched-Pairs Signed-Ranks Test should be preferred.Wilcoxon Matched-Pairs Signed-Ranks Test Finally, the Sign-test is an almost ideal quick-and-dirty test that can be used to browse datasets or to check the results of other tests (e.g., as in: "All six subjects show an increase, so why does your test insist that p > 0.05?").