Membrane heterogeneity : from lipid domains to curvature effects
Semrau, S.
Citation
Semrau, S. (2009, October 29). Membrane heterogeneity : from lipid domains to curvature effects. Casimir PhD Series. Retrieved from https://hdl.handle.net/1887/14266
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Membrane heterogeneity
From lipid domains to curvature effects
PROEFSCHRIFT
ter verkrijging van
de graad van Doctor aan de Universiteit Leiden,
op gezag van Rector Magnificus prof. mr. P. F. van der Heijden, volgens besluit van het College voor Promoties
te verdedigen op donderdag 29 oktober 2009 klokke 13.45 uur
door
Stefan Semrau
geboren te Solingen, Duitsland in 1979
Promotiecommissie
Promotor: Prof. dr. T. Schmidt
Copromotor: Dr. C. Storm (TU Eindhoven)
Referent: Prof. dr. O.G. Mouritsen (University of Southern Denmark, Odense) Overige leden: Prof. dr. F. MacKintosh (Vrije Universiteit Amsterdam)
Prof. dr. J. van Ruitenbeek Prof. dr. M. Orrit
Prof. dr. T.J. Aartsma Dr. J. van Noort Dr. M. Overhand
Casimir PhD Series, Delft-Leiden, 2009-11 ISBN 978-90-8593-058-7
An electronic version of this thesis can be found at https://openaccess.leidenuniv.nl
Het onderzoek beschreven in dit proefschrift is onderdeel van het weten- schappelijke programma van de Stichting voor Fundamenteel Onderzoek der Materie (FOM), die financieel wordt gesteund door de Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO).
“There is never any end. There are always new sounds to imagine; new feelings to get at. And always, there is the need to keep purifying these feelings and sounds so that we can really see what we’ve discovered in its pure state. So that we can see more and more clearly what we are. In that way, we can give to those who listen the essence, the best of what we are.
But to do that at each stage, we have to keep on cleaning the mirror.”
John Coltrane (1926-1967), Jazz saxophonist
F¨ur meine Familie
Contents
1 Introduction 1
1.1 Membrane heterogeneity . . . 3
1.2 Artificial membranes . . . 6
1.2.1 Formation and observation . . . 7
1.2.2 Selected results . . . 8
1.3 Living cells . . . 15
1.3.1 Single molecule tracking . . . 16
1.3.2 Selected results . . . 23
1.4 Future directions . . . 28
2 Elastic parameters of multi-component membranes 31 2.1 Introduction . . . 32
2.2 Model . . . 33
2.3 Experiment . . . 35
2.4 Results . . . 42
2.5 Discussion . . . 44
3 Membrane mediated interactions 47 3.1 Introduction . . . 48
3.2 Materials and Methods . . . 49
3.2.1 GUV formation . . . 49
3.2.2 Image analysis . . . 50
3.3 Evidence for interactions . . . 52
3.3.1 Radial distribution function . . . 52
3.3.2 Size distribution . . . 53
3.4 Domain budding . . . 58
3.5 Measuring the interactions . . . 63
3.5.1 Domain position tracking . . . 63
vi CONTENTS
3.5.2 Domain distance statistics . . . 67
3.6 Conclusion . . . 71
4 Membrane mediated sorting 73 4.1 Introduction . . . 74
4.2 Materials and Methods . . . 75
4.3 Analytical model . . . 76
4.4 Simulations . . . 80
4.5 Experimental verification . . . 82
4.6 Conclusion . . . 82
5 Particle image correlation spectroscopy (PICS) 83 5.1 Introduction . . . 84
5.2 Theory . . . 85
5.2.1 Algorithm . . . 85
5.2.2 Relation to diffusion dynamics . . . 87
5.2.3 Figure of merit and achievable accuracy . . . 88
5.2.4 Diffusion modes . . . 90
5.3 Materials and Methods . . . 91
5.3.1 Monte Carlo simulations . . . 91
5.3.2 Single-molecule microscopy . . . 92
5.4 Results . . . 93
5.4.1 Monte Carlo simulations . . . 93
5.4.2 Diffusional behavior of H-Ras mutants . . . 94
5.5 Discussion . . . 98
5.A Beyond the ideal situation . . . 99
5.B Correction for positional correlations due to diffraction . . . 100
6 Adenosine A1 receptor signaling unraveled by PICS 103 6.1 Introduction . . . 104
6.2 Materials and Methods . . . 106
6.2.1 Single-molecule microscopy . . . 106
6.2.2 Particle Image Correlation Spectroscopy (PICS) . . 107
6.2.3 Analysis of MSDs (Mean square displacements) . . . 108
6.2.4 Cell culture . . . 108
6.2.5 Agonist stimulation assay . . . 109
6.2.6 Decoupling of G-protein with Pertussis toxin . . . . 109
6.3 Results . . . 110 6.3.1 The activated receptor translocates to microdomains 111
CONTENTS vii
6.3.2 The observed microdomains are related to the cy-
toskeleton . . . 112
6.3.3 The receptor is partially precoupled to its G-protein 113 6.4 Discussion . . . 113
7 Counting autofluorescent proteins in vivo 117 7.1 Introduction . . . 118
7.2 Materials and Methods . . . 120
7.2.1 DNA constructs . . . 120
7.2.2 Cell culture . . . 120
7.2.3 Single-molecule microscopy . . . 120
7.2.4 Image analysis . . . 121
7.3 Results and Discussion . . . 122
7.3.1 Blinking and bleaching of fluorescent proteins . . . 122
7.3.2 Robust intensity distributions . . . 124
7.3.3 Detection probability . . . 125
7.3.4 Experimental validation . . . 128
7.3.5 Clustering due to membrane heterogeneity . . . 130
7.3.6 Limitations and errors . . . 131
7.4 Conclusion . . . 134
7.5 Acknowledgement . . . 134
7.A Image processing and analysis . . . 135
7.A.1 Definitions . . . 135
7.A.2 Autofluorescent background subtraction . . . 135
7.B Detection probability . . . 138
7.C Intensity distribution . . . 141
7.C.1 Mandel theory . . . 141
7.C.2 2-state model . . . 143
7.C.3 3-state model . . . 144
7.C.4 Robust intensity distributions . . . 146
7.C.5 Complete intensity distribution . . . 148
7.D Overlapping single-molecule signals . . . 149
Bibliography 150
Samenvatting 173
Publications 177
Curriculum Vitae 180
viii CONTENTS
