This article aims to present a protocol on how to build a spot variation Fluorescence Correlation Spectroscopy (svFCS) microscope to measure molecular diffusion at the plasma membrane of living cells.
Dynamic biological processes in living cells, including those associated with plasma membrane organization, occur on various spatial and temporal scales, ranging from nanometers to micrometers and microseconds to minutes, respectively. Such a broad range of biological processes challenges conventional microscopy approaches. Here, we detail the procedure for implementing spot variation Fluorescence Correlation Spectroscopy (svFCS) measurements using a classical fluorescence microscope that has been customized. The protocol includes a specific performance check of the svFCS setup and the guidelines for molecular diffusion measurements by svFCS on the plasma membrane of living cells under physiological conditions. Additionally, we provide a procedure for disrupting plasma membrane raft nanodomains by cholesterol oxidase treatment and demonstrate how these changes in the lateral organization of the plasma membrane might be revealed by svFCS analysis. In conclusion, this fluorescence-based method can provide unprecedented details on the lateral organization of the plasma membrane with the appropriate spatial and temporal resolution.
The complexity of plasma membrane organization
The current understanding of cell membrane organization has to take into account several aspects1. First, a complex lipid composition varies not only between cell types, but also within a single cell (membrane organelles/plasma membrane). Besides, associated or intrinsic membrane proteins are mostly organized in dynamic multimeric complexes, with large domains extending outside of the membrane, accounting for a significantly larger area than that of the transmembrane domains alone. Moreover, membrane-associated proteins exhibit specific lipid-binding or lipid-interacting capacities that play roles in regulating protein function. These depend directly on the local composition and accessibility of the lipids2.
Finally, a significant level of asymmetry is observed between two membrane leaflets due to the intrinsic asymmetric structure of membrane proteins and the distribution of lipids. Indeed, a lipid metabolic balance between synthesis and hydrolysis, combined with lipid flip-flop between the leaflets, generates such asymmetric distribution. As any transport across the bilayer is constrained by the free energy required to move the polar head group through the hydrophobic interior of the membranes, it is usually assisted by selective transporters. For each cell type, asymmetry tends to be firmly maintained. Altogether, these factors contribute to lateral inhomogeneity or compartmentalization of the plasma membrane3,4.
We enrich this representation of the plasma membrane by taking into account the intrinsic molecular diffusion within and across the bilayer, which contributes to the dynamic lateral heterogeneity on a scale of tenths to hundreds of nanometers and microseconds to seconds. For instance, lipid-dependent membrane nanodomains—the so-called lipid rafts, defined as cholesterol, and sphingolipid-rich signaling platforms—contribute to the compartmentalization of the plasma membrane5,6. However, the current view of membrane organization is not restricted to lipid rafts alone. Membrane nanodomains are more complex and heterogeneous in composition, origin, and function. Still, their presence at the plasma membrane has to be tightly coordinated, and dynamic interactions between proteins and lipids seem to be important in the spatial distribution and chemical modification of membrane nanodomains1,3,7,8.
The svFCS principle and its application to probe the organization of the plasma membrane
Although much progress has been made in the analysis of membrane domains, mainly through biophysical techniques, the determinants that dictate the local organization of the plasma membrane need to be refined with appropriate spatial and temporal resolution. Determinants based on tracking individual molecules provide excellent spatial precision and allow the characterization of different modes of motion9,10,11,12, but have a limited temporal resolution with classical low camera frame rates and require more experimental effort to record a significant number of trajectories. Alternatively, the diffusion coefficient of membrane components can be evaluated by Fluorescence Recovery After Photobleaching (FRAP)13 or Fluorescence Correlation Spectroscopy (FCS)14. The latter has received more attention, mainly because of its high sensitivity and selectivity, microscopic detection volume, low invasiveness, and wide dynamic range15.
The conceptual basis of FCS was introduced by Magde and colleagues about 50 years ago16,17. It is based on recording the fluctuation of fluorescence emission with a high temporal resolution (from µs to s)18. In its modern version, measurements in living cells are performed by a small confocal excitation volume (~0.3 femtoliters) positioned within a region of interest (e.g., at the plasma membrane); the fluorescence signal generated by diffusing fluorescent molecules going in and out of the observation volume is collected with very high temporal resolution (i.e., the time of arrival of each photon on the detector). Then, the signal is computed to generate the autocorrelation function (ACF), from which the average time td (diffusion time) for which a molecule stays within the focal volume is extracted, together with the mean number of particles, (N), present in the observation volume, which is inversely proportional to the amplitude of the ACF. This last parameter might be useful information on the molecule concentration within the observation volume.
Since then, a growing number of FCS modalities have been implemented thanks to rapidly developing instrumentation in biophotonics, allowing the description of dynamic phenomena occurring in living systems. Still, a molecular species would experience a more overlapping distribution of the diffusion coefficient values, which is usually reflected by an anomalous diffusion characteristic, in which molecules diffuse with a nonlinear relationship in time19, and difficulty in identifying the biological meaning of this anomalous subdiffusion. In the past, this difficulty has been somewhat overcome by recording the molecular diffusion by FRAP from areas of various sizes, rather than from just one area, thereby providing additional spatial information. This enabled, for instance, the conceptualization of membrane microdomains20,21,22.
A translation of this strategy to FCS measurements (i.e., the so-called spot variation Fluorescence Correlation Spectroscopy (svFCS)) was established by varying the size of the focal volume of observation, allowing the fluctuation in fluorescence to be recorded on different spatial scales23. Thus, the svFCS approach provides indirect spatial information allowing for the identification and determination of molecular diffusion modes and type of membrane partitioning (isolated versus contiguous domains24) of studied molecules. By plotting the diffusion time td as a function of the various spatial scales defined by the waist (ω) value, which corresponds to the detection beam radius size in this case23,25, one can characterize the diffusion law of a given molecule in a given physiological condition. The svFCS is, therefore, a perfect analog to single-particle tracking in the time domain26. Under the Brownian diffusion constraint, one should expect a strictly linear relationship between the diffusion time td and the waist ω (Figure 1)23,25. The origin of the deviation of the diffusion law from this scheme can be attributed to nonexclusive reasons, such as cytoskeleton meshwork, molecular crowding, dynamic partitioning in nanodomains, or any combination of these and other effects (Figure 1), and needs to be tested experimentally25.
Here we provide all necessary control checkpoints for the daily use of a custom-made svFCS optical system built from scratch, which complements our previous protocol reviews27,28 on that experimental approach. Further, as a proof of concept, we give guidelines regarding the calibration of the setup, the preparation of cells, data acquisition, and analysis for the establishment of svFCS diffusion law (DL) for Thy1-GFP, a plasma membrane glycosylphosphatidylinositol-anchored protein, which is known to be localized in lipid-raft nanodomains29. Finally, we demonstrate how the partial destabilization of lipid-raft nanodomains by cholesterol oxidase treatment impacts the diffusion properties of Thy1-GFP. Additionally, a detailed description of building a svFCS setup from scratch is provided in Supplementary material.
1. Setting specification for assembling a custom-made svFCS setup
NOTE: The simplicity of the proposed svFCS setup allows easy installation, operation, and maintenance at a low cost while ensuring efficiency in photon recovery. For more details, see Supplementary material.
2. Daily checkpoint before running the experiment
3. General considerations for svFCS data recording and analysis
4. Cell culture and transfection
5. Preparation of cells for svFCS measurements
6. Pharmacological treatment
7. Spot size calibration
8. svFCS data acquisitions on cells
9. Diffusion laws of different experimental condition comparison
NOTE: If necessary, reproduce sections 7 and 8 for different experimental conditions. A dedicated software (MATLAB software 2) was developed to determine whether these diffusion laws are similar or not according to the t0 and Deff values28. It tests two hypotheses: the two values are different, or the two values are not different at a threshold set above a probability of false alarm (PFA). An arbitrary PFA value of 5% (T = 3.8) is considered the upper limit of significance between two parameters (t0 or Deff), indicating that there is only 5% chance that the two values are identical.
10. Cholesterol concentration measurements
We generated a DL for Thy1-GFP expressed in Cos-7 cells (Figure 4, black squares). The diffusion law has a positive t0 value (19.47 ms ± 2 ms), indicating that Thy1-GFP is confined in nanodomain structures of the plasma membrane. The cholesterol oxidase treatment of the cells expressing Thy1-GFP resulted in the shift of the DL t0 value to 7.36 ± 1.34 ms (Figure 4, gray squares). This observation confirms that the nature of Thy1-GFP confinement depends on the cholesterol content and is associated with lipid raft nanodomains. These two diffusion laws are shown to be different according to the statistical test described above (see step 9.1.3) in terms of t0 and Deff values. In addition, we assessed the concentration of total cellular cholesterol in non-treated Cos-7 cells versus the cells treated with COase. A small, but significant, decrease in total cholesterol content is observed upon COase treatment (Figure 5). As this enzyme acts only on the cholesterol pool accessible at the outer leaflet of the plasma membrane, we assume that the observed decrease in cholesterol is associated only with the plasma membrane and results in the destabilization of lipid raft nanodomains.
Figure 1: Simulated fluorescence correlation spectroscopy (FCS) diffusion laws established by spot-variation FCS for different forms of membrane organization. (Upper panels) Schematic representation of membrane organization—(A) free diffusion, (B) meshwork barriers, and (C) trap/domain confinements—with the trajectory drawn for a single molecule (red). Blue circles denote the intersection of the membrane and laser beam of waist ω. (Lower panels) FCS diffusion laws represented by plotting the diffusion time td as a function of the squared radius ω2. Diffusion law projection (green dashed line) intercepts the time axis at (A) the origin (t0 = 0) in the case of free diffusion; (B) in negative axis (t0 < 0) when there are meshwork barriers, or (C) in positive axis (t0 > 0) when there are traps and domains (lipid rafts). D is the lateral diffusion coefficient for Brownian motion; Deff, the effective diffusion coefficient; Dmicro, the microscopic diffusion coefficient inside the meshwork traps; Dde, the diffusion coefficient inside domains; Dout, the diffusion coefficient outside domains; L, the size of the side of a square domain; and rD, the radius of a circular domain. This figure has been modified from He and Marguet6. Please click here to view a larger version of this figure.
Figure 2: Schematic view of svFCS hardware control. The computer controls all the devices through different communication protocols: serial (microscope, external shutter), USB (XYZ piezoelectric stage, correlator), and PCI (acquisition board). DAQ: data acquisition board, APD: avalanche photodiode, SPCM: single-photon counting module, DO: digital output. Please click here to view a larger version of this figure.
Figure 3: Schematic view of excitation and emission optical paths of the svFCS setup. The svFCS setup contains four modules: (1) the output of a fibered 488 nm laser is collimated, (2) a combination of a half-wave plate and polarizing beam-splitter sets the optical power, (3) the laser beam focused on the sample after traveling through a tube-lens free motorized microscope, and (4) the fluorescence is detected through a confocal-like detection path onto an avalanche photodiode coupled to a single photon counting module, which delivers a signal to a hardware correlator. Simplicity gives the system its sensitivity, robustness, and ease of use (widely commented in Supplementary material). Please click here to view a larger version of this figure.
Figure 4: The svFCS diffusion laws generated from diffusion analysis of Thy1-GFP expressed in Cos-7. svFCS diffusion laws of Cos-7 cells without treatment (NT, black squares) and after cholesterol oxidase treatment (COase, gray circles). The insert in the graph represents statistical testing of a significant difference between the two presented svFCS diffusion laws (according to Mailfert et al.28). The test value (T) should be above the threshold set at 3.8 when both diffusion laws are different. The higher it is, the greater is the difference between the diffusion laws. The value of T is color-coded. Please click here to view a larger version of this figure.
Figure 5: Comparison of total cholesterol content in Cos-7 cells. Cos-7 cells were either non-treated (NT) or treated with 1 U/mL of cholesterol oxidase (COase) for 1 h. The data represent an example of one experiment in triplicate. A two-tailed, unpaired t-test was used to assess the statistical difference (α=0.05). Please click here to view a larger version of this figure.
Table of materials: The list of optical elements required for the svFCS setup.
Supplementary material: This document describes the building of a svFCS setup from scratch. Please click here to download this file.
Here, we have described the implementation of the svFCS module on a standard fluorescent microscope, a powerful experimental approach to decipher the dynamics of the plasma membrane organization in living cells thanks to the FCS diffusion law analysis. Conceptually, the svFCS is based on a simple principle: correlation measurements of fluorescence in the time domain while varying the size of the illumination area23. This strategy has been instrumental in deducing nanoscopic information from microscopic measurements, which helps decipher the main physicochemical elements contributing to the plasma membrane organization in steady state25 and physiological processes30,31,32,33. Altogether, these svFCS analyses unambiguously demonstrate the existence of lipid-dependent nanodomains in various cell types and their direct implication in tuning different signaling events.
Within this framework, there are some optical aspects that need to be considered while building the svFCS setup to optimize the photon budget and minimize optical aberrations. Thus, we recommend using a microscope from which the tube lens can be removed when the svFCS measurement is performed. Moreover, a single iris plays a key role in the svFCS setup: it changes the beam size at the back aperture of the objective, thus directly varying the effective waist size (i.e., the effective excitation volume). The beam diameter should fit the objective back pupil to obtain the smallest waist size34. This option, which helps tune the waist size, ensures optimization of the photon budget and is easy to implement. Finally, a minimal number of optical parts are used along the light path; the less complex the system, the fewer the photons that are lost. All of these options significantly improve the robustness of svFCS experiments.
Regarding the protocol itself, a few critical steps have to be considered. The most important is an appropriate alignment of the optical paths that is crucial for successful svFCS measurements (protocol, section 2). This is easy to check by analyzing the fluorescence signal from a 2 nM Rh6G solution, which should be ~200 kHz under 300 µW laser illumination. All irises should be opened, and the ACFs should have an important amplitude (typically G0~1.5–2.0). Another critical point concerns the cells and their preparation for svFCS analysis (protocol, sections 4–8). Their density has to be adapted so that isolated cells to be observed are available for analysis. Non-adherent cells have to be immobilized on a chambered coverglass by using poly-L-lysine solution. The fluorescence signal from cell labeling should not be too strong, or it will result in very flat ACFs that are difficult to fit, and the fit parameters are burdened with an important error. Additionally, nonhomogeneous labeling and fluorescence aggregates in cells make the svFCS measurements extremely difficult to interpret. Finally, cholesterol oxidase treatment affects cell viability, and the svFCS analysis should not exceed one hour after the treatment. It is also better to record the fluorescence fluctuations from the upper plasma membrane as it is not attached to the support, and there is no risk of hindered diffusion of molecules due to the physical interactions with the support.
There have been enough advances in the svFCS technique for its use in different approaches owing to the diversity of modalities for adjusting the detection volume, making it possible to study various biological processes in living cells. An alternative to adjusting the size of the excitation volume is to use a variable beam expander35. It is also possible to simply modulate the size of the illumination area by recording the fluorescent signal from the intercept of the plasma membrane along the z direction36. This can be done on a standard confocal microscope for which a theoretical framework has been developed to derive the diffusion law37,38.
Although the svFCS method offers spatio-temporal resolution, which is necessary for the characterization of the inhomogeneous lateral organization of the plasma membrane, the geometrical modes of confinement are not mutually exclusive. A deviation of t0 in one direction or the other exclusively reveals a dominant mode of confinement25. Moreover, another important limitation of the present svFCS method results from the classical optical diffraction limit (~200 nm). This is unquestionably greater than the domains confining the molecules within the cell plasma membrane. Therefore, the analysis of the confinement is inferred from the t0 value, extrapolated from the diffusion law.
This drawback has been overcome by implementing alternative methods. Initially, using metallic films drilled with nanoapertures offered the possibility of illuminating a very small membrane area (i.e., below the optical diffraction limit of single nanometric apertures of radii varying between 75 and 250 nm)39. The transition regime predicted from the theoretical diffusion law for isolated domain organization was thus reported, and it allowed a refinement of the characteristic size of the nanometric membrane heterogeneities and a quantitative estimate of the surface area occupied by lipid-dependent nanodomains39. Alternatively, nanometric illumination has also been developed using near-field scanning optical microscopy40 or planar optical nanoantennas41. More recently, combining stimulated emission depletion (STED) and FCS has provided a powerful and sensitive tool to document the diffusion law with very high spatial resolution. This STED-FCS gives access to molecular diffusion characteristics on a nanoscale occurring within a short period of time, allowing the study of the dynamic organization of lipid probes at the plasma membrane42,43. However, the incomplete suppression of fluorescence in the STED process challenges the analysis of the auto-correlation curves in FCS.
A new fitting model has been developed to overcome this difficulty, improving the accuracy of the diffusion times and average molecule numbers measurements44. Finally, for slow molecular diffusion at the plasma membrane, the svFCS principle can be applied to data recorded by image correlation spectroscopy45. Recently, it has been demonstrated that combining atomic force microscopy (AFM) with imaging total internal reflection-FCS (ITIR-FCS) contributes to the refinement of the nature of the mechanism hindering molecular diffusion at the plasma membrane, especially near the percolation threshold membrane configuration because of a high density of nanodomains46.
In conclusion, establishing diffusion law by svFCS has provided the experimental evidence to infer local heterogeneity created by dynamic collective lipids and membrane proteins’ associations. As stated by Wohland and co-workers46, “the FCS diffusion law analysis remains a valuable tool to infer structural and organizational features below the resolution limit from dynamic information”. Still, we need to develop new models to refine the interpretation of the diffusion law that should allow for a better understanding of the dynamics of the molecular events occurring at the plasma membrane.
The authors have nothing to disclose.
SB, SM and DM was supported by institutional funding from the CNRS, Inserm and Aix-Marseille University and program grants from the French National Research Agency (ANR-17-CE15-0032-01 and ANR-18-CE15-0021-02) and the French "Investissement d'Avenir" (ANR-10-INBS-04 France-BioImaging, ANR-11-LABX-054 labex INFORM). KW acknowledges "BioTechNan", a program of interdisciplinary environmental doctoral studies KNOW in the field of Biotechnology and Nanotechnology. EB acknowledges the financial support of the National Science Centre of Poland (NCN) under project no. 2016/21/D/NZ1/00285, as well as the French Government and the Embassy of France in Poland. MŁ acknowledges the financial support from the Polish Ministry of Development (CBR POIR.02.01.00-00-0159/15-00/19) and the National Center for Research and Development (Innochem POIR.01.02.00-00-0064/17). TT acknowledges financial support from the National Science Center of Poland (NCN) under project no. 2016/21/B/NZ3/00343 and from the Wroclaw Biotechnology Center (KNOW).
Aligment tool | Spanner Wrench for SM1-Threaded Retaining Rings | Thorlabs | SPW602 |
Avalanche Photodiode and Single Photon Counting Module (SPCM) | Single-Photon Counting Module, Avalanche Photodiode | Excelitas | SPCM-AQRH-15 |
BNC 50 Ω plug to 50 Ω plug lead 2 m | RS Components | 742-4315 | |
Coaxial cable 415 Cinch Connectors, RG-316, 50 Ω With connector, 1.22 m, RoHS2 | RS Components | 885-8172 | |
Tee 50Ω RF Adapter BNC Plug to BNC Socket 0 → 1GHz | RS Components | 546-4948 | |
Brennenstuhl 2.5 m, 8 Socket Type E – French Extension Lead, 230 V | RS Components | 768-5500 | |
Mascot, 6W Plug In Power Supply 5V dc, 1.2A, 1 Output Switched Mode Power Supply, Type C | RS Components | 452-8394 | |
Crystek CCSMACL-MC-24 Reference Oscillator Power Cable RF Adapter | RS Components | 792-4079 | |
Fluorescence filtering | 535/70 ET Bandpass, AOI 0° Chroma Diameter 25 mm | AHF filter | F47-539 |
Laser Beamsplitter zt488 RDC, AOI 45° Chroma 25.5 x 36 x 1 mm | AHF filter | F43-088 | |
496/LP BrightLine HC Longpass Filter, AOI 0° Chroma Diameter 25 | AHF filter | F37-496 | |
Hardware correlator | 80 MHz Digital Correlator | Correlator.com | Flex02-12D |
Laser | LASER LASOS LDM-XT fiber coupled, 488 nm, 65 mW | Lasos | BLD-XT 488100 |
Laser safety | High-Performance Black Masking Tape, 1" x 180' (25 mm x 55 m) Roll | Thorlabs | T743-2.0 |
Lens Tissues, 25 Sheets per Booklet, 5 Booklets | Thorlabs | MC-5 | |
Laser Safety Glasses, Light Orange Lenses, 48% Visible Light Transmission | Thorlabs | LG3B | |
Microscope | Zeiss Axiovert 200M Motorized Inverted Fluorescence Microscope Fine and coarse focusing, reflector turret rotation, objective nosepiece rotation, switching camera ports, and internal light shutters |
Carl Zeiss | |
C-Apochromat 40x/1,2 W Korr.selected for FCS (D=0.14-0.19 mm) (WD=0.28 mm at D=0.17 mm), UV-VIS-IR |
Carl Zeiss | 421767-9971-711 | |
Adapter W0.8 / M27x0.75 H "5" | Carl Zeiss | 000000-1698-345 | |
Middle ring W0.8 – W0.8 H "5" | Carl Zeiss | 000000-1698-347 | |
Optical path | D25.4mm Mirror, Protected Silver | Thorlabs | PF10-03-P01 |
D25.4mm, F=60.0.mm, Visible Achromat | Thorlabs | AC254-060-A | |
D25.4mm, F=35.0.mm, Visible Achromat | Thorlabs | AC254-035-A | |
25 µm mounted pinhole | Thorlabs | P25S I | |
25.4mm Mounted Zero, Order 1/2 Waveplate 488 nm | Thorlabs | WPH10M-488 (HWP) | |
20mm Polarizing Beamsplitter Cube 420-680 nm | Thorlabs | PBS201 | |
Rotation Stage 56 mm x 26 mm Threaded ID | Thorlabs | RSP1/M | |
52 mm x 52 mm Kinematic Platform Mount | Thorlabs | KM100B/M | |
Adjustable Prism Clamp | Thorlabs | PM3/M | |
Beam block – active area 19 mm x 38 mm | Thorlabs | LB1/M | |
Iris Diaphragm 1 mm to 25 mm Aperture | Thorlabs | ID25/M | |
Left-Handed Kinematic Cylindrical Lens Mount | Thorlabs | KM100CL | |
1" Optic Holder, M4 Tap | Thorlabs | MFF101/M | |
1" Stackable Lens Tube | Thorlabs | SM1L03 | |
Stackable Lens Mount for 1" optic-usable depth ½ | Thorlabs | SM1L05 | |
Stackable Lens Mount For 1"Optic-usable Depth 2" | Thorlabs | SM1L20 | |
Small Optical Rails 600mm, metric | Thorlabs | RLA600/M | |
Small Optical Rails 75mm, metric | Thorlabs | RLA075/M | |
Small Optical Rails 150mm, metric | Thorlabs | RLA150/M | |
Rail Carrier, Counterbored Hole 1"x 1" | Thorlabs | RC1 | |
Rail Carrier, Perpendicular Dovetail | Thorlabs | RC3 | |
High Precision Translating Lens Mount for 1 inch | Thorlabs | LM1XY/M | |
½ " (12mm) Dovetail Translation Stage | Thorlabs | DT12/M | |
Rail Clamps | Thorlabs | CL6 | |
Metric XYZ Translation Stage (Includes PT102) | Thorlabs | PT3/M | |
Black Rubberized Fabric | Thorlabs | BK5 | |
Ball Driver kit/ 6 tools | Thorlabs | BD-KIT/M | |
Adapter with External M6 x 1.0 Threads and External M4 x 0.7 Threads | Thorlabs | AP6M4M | |
Mounting Base, 25 mm x 58 mm x 10 mm, 5 Pack | Thorlabs | BA1S/M-P5 | |
Lens Mount for 25.4mm optic | Thorlabs | LMR1/M | |
SM1 FC/APC Adapter | Thorlabs | SM1FCA | |
Kinematic Mirror Mount For 1 inch Optics | Thorlabs | KM100 | |
Silicon Power Head, 400-1100nm, 50mW | Thorlabs | S120C | |
12.7 mm Post Holders, Spring-Loaded Hex-Locking Thumbscrew, L=50 mm, 5 Pack |
Thorlabs | PH50/M-P5 | |
Post Holder with Spring-Loaded Hex-Locking Thumbscrew, L=20 mm | Thorlabs | PH20/M | |
12.7 mm x 50 mm Stainless Steel Optical Post, M4 Stud, M6-Tapped Hole, 5 Pack |
Thorlabs | TR50/M-P5 | |
12.7 mm x 75 mm Stainless Steel Optical Post, M4 Stud, M6-Tapped Hole, 5 Pack |
Thorlabs | TR75/M-P5 | |
USB Power and Energy Meter Interface | Thorlabs | PM100USB | |
12.7 mm x 30 mm Stainless Steel Optical Post, M4 Stud, M6-Tapped Hole |
Thorlabs | TR30/M | |
12.7 mm x 20 mm Stainless Steel Optical Post, M4 Stud, M6-Tapped Hole |
Thorlabs | TR20/M | |
750 mm long Structural Rail (detection box) | Thorlabs | XE25L750/M | |
350 mm long Structural Rail (detection box) | Thorlabs | XE25L350/M | |
Quick Corner Cube for 25 mm Rails | Thorlabs | XE25W3 | |
Right-Angle Bracket for 25 mm Rails | Thorlabs | XE25A90 | |
Black posterboard 20" x 30" (508 mm x 762 mm), 1/16" (1.6 mm) Thick, 5 Sheets | Thorlabs | TB5 | |
Hinge for 25 mm Rail Enclosures | Thorlabs | XE25H | |
Lid Stop for 25 mm Rail Enclosures | Thorlabs | XE25LS | |
M4 Cap Screw Kit | Thorlabs | HW-KIT1/M | |
M6 Cap Screw Kit and Hardware kit | Thorlabs | HW-KIT2/M | |
Table Clamp, L-Shape, 5 Pack | Thorlabs | CL5-P5 | |
SM1 Ring-Actuated Iris Diaphragm (Ø0.8 – Ø12 mm) | Thorlabs | SM1D12D | |
Ø1" SM1-Mounted Frosted Glass Alignment Disk w/Ø1 mm Hole | Thorlabs | DG10-1500-H1-MD | |
Honeycomb Optical Table Top, Standa | Standa | 1HB10-15-12 | |
Optical Table support, Standa | Standa | 1TS05-12-06-AR | |
Sample nano-positionning | Precision XYZ Nanopositioning | Physik Instrumente | PI P527-3.CD |
Digital Multi-Channel Piezo Co, 3 Channels, -30 to 130 V Sub- D Connector(s), Capacitive Sensors, |
Physik Instrumente | PI E727-3.CD | |
Temperature chamber | Zeiss 200M Inverted Microscope Incubator System MATT BLK | Digital Pixel | |
Dual Channel Microprocessor Temperature Controller | Digital Pixel | DP_MTC_2000_DUO | |
Two Vibration Free Heater Modules | Digital Pixel | DP_150_VF | |
PT100 Temperature Sensor | Digital Pixel | DP_P100_TS | |
Biological Reagents and Materials | |||
Cell culture and transfection | Cos7 cells | ATCC® | CRL-1651™ |
8- well Lab-Tek chambers | Thermo Fisher Scientific | 155411PK | |
Dulbecco's Modified Eagle Medium (DMEM) | Thermo Fisher Scientific | 11965092 | |
Fetal bovine serum | Thermo Fisher Scientific | 16000044 | |
L-glutamine | Thermo Fisher Scientific | 25030081 | |
PBS buffer | Thermo Fisher Scientific | 14190144 | |
PenStrep | Thermo Fisher Scientific | 15140122 | |
PolyJet Transfection Reagent | SignaGen Laboratories | SL100688 | |
Cholesterol content measurement | Amplex Red Cholesterol Assay Kit | Thermo Fisher Scientific | A12216 |
Protease Inhibitor Cocktail | Thermo Fisher Scientific | 87786 | |
Phosphatase Inhibitor Cocktail | Thermo Fisher Scientific | 78420 | |
ROTI Nanoquant Working Solution | Roth | K880 | |
GloMax Discover Microplate Reader | Promega | GM3000 | |
svFCS measurements | HBSS buffer | Thermo Fisher Scientific | 14025092 |
Hepes buffer | Thermo Fisher Scientific | 15630080 | |
Cholesterol oxidase | Sigma-Aldrich | C8868 | |
Rhodamine 6G | Sigma-Aldrich | 83697-1G |