The present protocol describes sample preparation and data analysis to quantify protein phosphorylation using an improved single-molecule pull-down (SiMPull) assay.
Phosphorylation is a necessary posttranslational modification that regulates protein function and directs cell signaling outcomes. Current methods to measure protein phosphorylation cannot preserve the heterogeneity in phosphorylation across individual proteins. The single-molecule pull-down (SiMPull) assay was developed to investigate the composition of macromolecular complexes via immunoprecipitation of proteins on a glass coverslip followed by single-molecule imaging. The current technique is an adaptation of SiMPull that provides robust quantification of the phosphorylation state of full-length membrane receptors at the single-molecule level. Imaging thousands of individual receptors in this way allows for quantifying protein phosphorylation patterns. The present protocol details the optimized SiMPull procedure, from sample preparation to imaging. Optimization of glass preparation and antibody fixation protocols further enhances data quality. The current protocol provides code for the single-molecule data analysis that calculates the fraction of receptors phosphorylated within a sample. While this work focuses on phosphorylation of the epidermal growth factor receptor (EGFR), the protocol can be generalized to other membrane receptors and cytosolic signaling molecules.
Membrane-associated signaling is tuned by a combination of ligand-induced membrane receptor activation and recruitment of downstream accessory proteins that propagate the signal. Phosphorylation of key tyrosines in receptor cytoplasmic tails is critical to initiating the formation of signaling complexes, or signalosomes1,2. Therefore, an important question in biology is how phosphorylation patterns are created and maintained to recruit signaling partners and dictate cellular outcomes. This includes understanding the heterogeneity of receptor phosphorylation, both in abundance and in the specific phosphotyrosine patterns that can provide a means of manipulating signaling outputs by dictating the composition of the signalosome3,4,5,6,7. However, there are limitations in current methods to interrogate protein phosphorylation. Western blot analysis is excellent for describing trends of protein phosphorylation but is semi-quantitative8 and does not provide information on the heterogeneity of the system because thousands to millions of receptors are averaged together. While western blots allow probing a sample using phospho-specific antibodies to specific tyrosines, they cannot provide information on multisite phosphorylation patterns within the same protein. Quantitative phosphoproteomics report on phosphotyrosine abundance, but there are limitations to detecting multisite phosphorylation, as the residues of interest need to be located within the same peptide (typically 7-35 amino acids) that is generated by enzymatic digestion9,10,11.
To overcome the limitations mentioned above, the single-molecule pull-down (SiMPull) assay has been adapted to quantify the phosphorylation states of intact receptors at the single-molecule level. SiMPull was first demonstrated as a powerful tool for interrogating macromolecular complexes by Jain et al.12,13. In SiMPull, macromolecular complexes were immunoprecipitated (IP) on antibody-functionalized glass coverslips and then analyzed through single-molecule microscopy for protein subunit number and co-IP with complex components12. A modification by Kim et al.14, termed SiMBlot, was the first to use a variation of SiMPull to analyze phosphorylation of denatured proteins. The SiMBlot protocol relies on capturing biotinylated cell surface proteins using NeutrAvidin-coated coverslips, which are then probed for phosphorylation with phospho-specific antibody labeling14. Despite these advances, improvements were needed to make the quantification of posttranslational modification more robust and applicable to a broader range of proteins.
The present protocol describes an optimized SiMPull approach that was used to quantify phosphorylation patterns of intact epidermal growth factor receptor (EGFR) in response to a range of ligand conditions and oncogenic mutations15. While this work focuses on EGFR, this approach can be applied to any membrane receptor and cytosolic proteins of interest (POI), for which quality antibodies are available. The protocol includes steps to reduce sample autofluorescence, a sample array design that requires minimal sample volume with simultaneous preparation of up to 20 samples, and optimization of antibody labeling and fixation conditions. Data analysis algorithms have been developed for single-molecule detection and quantification of phosphorylated proteins.
1. Coverslip preparation
NOTE: For this step, one needs to wear personal protective equipment (PPE), which includes a double layer of nitrile gloves, safety glasses or face shield, and a lab coat.
2. Preparation of SiMPull lysate
CAUTION: The required PPE for the remaining steps of the protocol are nitrile gloves, safety glasses, and lab coats.
NOTE: The lysates were prepared from the adherent CHO cells expressing EGFR-GFP. The cells were plated in a 60 mm tissue culture (TC60) dish overnight12,13. CHO cells were cultured in DMEM supplemented with 10% fetal bovine serum, 1% L-glutamine, 1% Penicillin-Streptomycin, and 500 ng/mL of geneticin (see Table of Materials). Other adherent cell lines or suspension cells can also be used.
3. Functionalization of the array with the biotinylated antibody
4. SiMPull of POI from whole-cell lysates
NOTE: Place the TC100 dish of functionalized SiMPull arrays on ice for the remainder of the SiMPull preparation. This step is the pull-down of a POI from total protein lysate. The lysate must not be reused after thawing.
5. Image acquisition
NOTE: Single-molecule image acquisition is performed using a 150x TIRF objective and an image splitter that captures each spectral channel in a specific quadrant of the emCCD camera (see Table of Materials). Calibration images are first acquired to allow for channel registration and camera gain calibration with a nanopatterned channel alignment grid (nanogrid) that contains 20 x 20 arrays of 200 ± 50 nm holes at an intrahole distance of 3 ± 1 µm (total size ~60 µm × 60 µm).
6. Data analysis
A cartoon depicting the SiMPull process is shown in Figure 1A. Coverslips are functionalized using NeutrAvidin as an anchor for biotinylated anti-EGFR antibodies to capture EGFR-GFP from total protein lysates. After washing away unbound protein, the phosphorylated receptors are labeled with an anti-phosphotyrosine (anti-PY) antibody15. Figure 1B shows an image of the hydrophobic array, where multiple samples can be prepared and imaged on the same coverslip. One advantage of this sample holder is that minimal sample volumes of ~10 µL are required. The coverslip can be imaged by placing it directly on the microscope stage. However, it is helpful to stabilize the coverslip by using a coverslip holder. The coverslip holder shown in Figure 1B was created using a 3D printer, and the blueprint is provided in Supplementary Coding File 5. The autofluorescence of the hydrophobic ink is a useful guide to finding the focal plane of the sample (Figure 1C). An example of a multichannel raw image is shown in Figure 1D. An overlay of the raw green and far-red channels is shown in Figure 1E.
Figure 2 outlines the analysis workflow and provides representative data. Data acquisition first starts with acquiring fiducials for channel registration, which is used to overlay the individual spectral channels data (Figure 2A). Bright-field images are taken using a nanogrid pattern that passes white light and is detected in each spectral channel of the image splitter (not shown). The green channel acts as the reference channel, and the far-red channel is the shifted channel. The local weighted mean transform is calculated using the fitgeotrans20 function in MATLAB and is used to shift far-red coordinates into the coordinate frame of the green channel. This transform uses a second-order polynomial model at every control point. Multichannel data of the SiMPull array is then acquired. This workflow consisted of a semi-automated acquisition, where a starting ROI was selected for the specific sample square, and three regions around this area were imaged, such that each dataset contains the full quad-view image from three independent ROIs (Figure 1D). In each spectral channel, the emitter candidate locations are found by applying a difference of Gaussian filter to images and identifying local maxima. Subregions (boxes, Figure 2B) are drawn around local maxima, and emitter photon counts are estimated by assuming each subregion contains only one emitter. Subregions holding emitter candidates with photon counts above a minimum value are retained for fitting. A Gaussian point spread function (PSF) fits each emitter candidate within small subregions roughly centered around each emitter. The resulting localizations are thresholded based on their photon count, background, Cramér-Rao lower bound of the fit coordinates, PSF variance (i.e., PSF width), and a p-value describing the goodness of fit of the PSF model. A Gaussian image is created for each spectral channel, with uniform intensity Gaussian blobs placed at the coordinates for each good fit (Figure 2C). Colocalization is visualized by overlaying the Gaussian images from each spectral channel using the transform calculated from the fiducial sample (Figure 2D). It is important to fluorescently label the receptor for identification since there is still non-specific binding of the anti-phosphotyrosine antibodies to the surface when cell lysate is present. The EGFR-GFP (green channel) is used to generate a mask of the receptor locations, and only the AF647-anti-PY signal (far-red channel) within that mask is counted (Figure 2D). Pairs within 1 pixel (106.7 nm pixel size) are considered to be colocalized and saved to a list containing the reference channel coordinates. The percentage of AF647 colocalized with GFP is calculated to determine the fraction of phosphorylated receptors (Figure 2E).
There are several critical steps to ensure good data quality. One such effort is to incubate the coverslip array with NaBH4 as described in the protocol to quench autofluorescence in the green channel. This autofluorescence refers to the non-specific signal due to possible impurities on the glass, containing single or conjugated π bonds21. Such impurities are potentially from the aminosilane and PEG reagents used in the functionalization process or dust from the air, and tend to fluoresce in the green spectral channel. Despite efforts to keep glass stored under nitrogen, these molecules may also be generated through oxidation that occurs in storage. NaBH4 has also been used to reduce fluorescence from impurities on slides and microarrays, including those with silane coating16. Figure 3A shows the reduction in the number of background detections that occur when the piranha etched glass is treated with NaBH4. While NaBH4 reduces background fluorescence dramatically, some emitters are still detected in the green channel. One can correct this by acquiring background images from lysate-free samples (Figure 3D) and subtracting the average number of background localizations from the GFP-containing samples. Fluorescence from impurities was not detected in the far-red channel. If the receptor density is too high, multiple GFP emitters can be found within a single diffraction-limited spot (data not shown). Using step-photobleaching to identify the number of GFPs per spot, we found that a receptor density between 0.04-0.08 proteins/µm2 provided sufficient spacing between single emitters to remove the potential of finding multiple emitters per spot12. The receptor density can be optimized by varying the amount of IP antibody bound to the glass surface or the amount of lysate added. It is critical to ensure that the antibody targeting the POI is used at saturating levels. It is recommended to acquire an antibody concentration curve on phosphorylated samples to determine the appropriate labeling conditions (Figure 3B). In addition, the phospho-specificity of an antibody needs to be validated with resting samples and/or treatment with protein-specific kinase inhibitors (Figure 3B). Antibodies will dissociate from the receptor during the imaging time window. Treating the sample with a combination of PFA and GA prevented signal loss (Figure 3C).
Finally, it is important to optimize the single-molecule fitting parameters. The first "box finding" step that identifies potential emitter candidates (Figure 2B) needs to be generous to allow many candidates to undergo the Gaussian Fitting. Thus, the minimum photon threshold for box finding can be relatively low to capture all real emitters and some background spots. It is also important to not set the box size and overlap allowance too small. Keeping the box size within 5-7 pixels and allowing two-pixel overlap is ideal for emitters at the recommended density. After box finding, the minimum photons threshold in the fitting step needs to be optimized. The minimum photons parameter contributes to determining which Gaussian fitted emitters pass as a true fit. To determine the proper minimum photon threshold for true GFP fits, the code includes a histogram plotting function to examine the photons/localization in both background (no cell lysate) and GFP-containing (plus cell lysate) samples (Figure 3D). This step is important because, while NaBH4 reduces the amount of fluorescence from impurities, it does not remove all background localizations. Figure 3D demonstrates the need to set a minimum photon threshold to reduce the number of detections from impurities. To determine this threshold, a histogram of background emitter intensities is calculated from imaging a sample that is not exposed to cell lysate (Figure 3D, top left). The majority of the background emitters were found to have values less than 475 photons. In comparison, the sample containing true GFP emitters showed a significant fraction of the distribution above 475 (Figure 3D, top right). The threshold is chosen by inspection to remove as many background counts as possible while minimizing the amount of signal loss from the lysate sample (Figure 3D, bottom row). The remaining background count density at this threshold is accounted for in the quantitative analysis.
Figure 1: Overview of sample preparation. (A) Cartoon depicting the SiMPull approach. Coverslips are functionalized with an antibody that recognizes the POI to capture that POI from whole cell lysates. The glass is first coated with PEG and biotin-PEG. NeutrAvidin is then bound to the biotin-PEG and acts as an anchor for the biotinylated anti-POI antibody. Phosphorylated proteins are then detected with a fluorescently labeled anti-PY antibody. (B) Photograph of the coverslip holder (red) with coverslip array in place and mounted on the microscope stage. The multisample arrays are generated using hydrophobic ink to create up to 20 individual sample squares on a single glass coverslip. The coverslip is 60 mm x 24 mm. (C) Example images of the hydrophobic ink autofluorescence (magenta) and fluorescent beads (green). The autofluorescence of the hydrophobic ink is a useful guide to find the focal plane at the coverslip surface. (D) Example of a raw data image with spectral channels separated on the camera chip by the Quad-view image splitter. The Quad-view filter set includes the following emission filters: blue (445/45 nm), green (525/45 nm), red (600/37 nm), far-red (685/40 nm). (E) Raw overlay of green and far-red channels. The white box indicates the region further examined in Figure 2B–D. Scale bar (C-E) = 2 µm. Please click here to view a larger version of this figure.
Figure 2: Data analysis workflow. (A) Channel registration is first performed on images acquired from the nanogrid. After cropping the two spectral channels of interest (here, green and far-red), the fiducial images for each channel are overlaid (left). Enlargement of the box in the left image (Inset) shows that the images are not yet truly registered. The emitters in each channel fit a Gaussian model and are localized (Registration). Localization of emitters is shown as circles for the far-red channel and crosses for the green channel. The final step is to apply a local weighted mean transform to shift the far-red channel localization coordinates into the green channel reference frame (Aligned). The calculated local weighted mean transform is then used to register the subsequent SiMPull data. (B) Representative images of the green/EGFR-GFP channel and the far-red/AF647-anti-PY channel. Single emitters above the background photon count are identified and marked with boxes. (C) The emission profile within each selected box is fit to a Gaussian model, and the emitters that fit the model of a single fluorophore PSF are kept. (D) A mask is created from the GFP emitters to identify the location of EGFR-GFP (green). Colocalization of EGFR-GFP and AF647-anti-PY identifies phosphorylated receptors (white). (E) The fraction of phosphorylated receptors is calculated from the colocalized EGFR-GFP and AF647-anti-PY fits. The bar graph compares PV + EGF treatment to resting cells, averaged for multiple measurements. Error bars represent standard error calculated assuming a binomial distribution. Scale bar = 2 µm. Please click here to view a larger version of this figure.
Figure 3: Critical steps to ensure data quality. (A) From left to right, the first three panels are representative images of the autofluorescence on glass under the respective conditions: after piranha etching, with PEG, and PEG plus NaBH4 treatment (indicated with +). Additionally, surface functionalization is retained after NaBH4 treatment as demonstrated by minimal non-specific PY99-AF647 binding while retaining robust binding of EGFR-GFP from the lysate. (B) A saturation curve must be acquired for each batch of antibodies used to ensure optimal antibody labeling. This figure shows the concentration curve for labeling EGFR with the site-specific phosphotyrosine antibody, anti-EGFR-pY1173. Minimal phosphorylation is detected in untreated cells (Resting, gray diamond). As a control for non-specific binding, cells were also treated with the EGFR kinase inhibitor, Lapatinib, before adding 100 nM of EGF (magenta triangle), which shows the expected prevention of EGFR phosphorylation. Error bars represent standard error assuming a binomial distribution. (C) Fixation of the sample with a combination of PFA and GA prevents antibody dissociation over time. Error bars represent standard error assuming a binomial distribution. (D) False positives are excluded by selecting the appropriate threshold for Gaussian fitting. Comparing the histogram of fit intensities at a low threshold (Threshold = 0; top) between background (no lysate) and real data (plus cell lysate) allows for selection of appropriate value (Threshold = 475; bottom) to remove fits from autofluorescent spots in the green channel. The vertical magenta line indicates a 475 photon threshold. Histograms are calculated from the same number of ROIs for each sample type (n = 3). Scale bar = 2 µm. Please click here to view a larger version of this figure.
Supplementary Coding File 1: zip file containing scripts and utilities for running SiMPull analysis. Please click here to download this File.
Supplementary Coding File 2: zip file containing the smite single-molecule analysis package. Please click here to download this File.
Supplementary Coding File 3: zip file containing the sample data. Please click here to download this File.
Supplementary Coding File 4: zip file containing representative sample data analysis outputs. Please click here to download this File.
Supplementary Coding File 5: Coverslip holder blueprint for 3-D printing. Please click here to download this File.
The protocol described here was optimized to enable quantitative measurements of receptor phosphorylation at the single protein level. Several straightforward but important modifications to the SiMPull protocol were developed that improved the reliability of the measurement for phospho-tyrosine detection, including reduction of autofluorescence with NaBH4 treatment and postfixing of the sample to prevent antibody dissociation. Using the green channel mask to identify receptor locations for calculation of colocalization with the anti-PY antibody also improves the measurement accuracy by removing potential artifacts from the non-specific binding of the antibody to the cell lysate. The two-color imaging was utilized to detect the fraction of receptors phosphorylated. In this scenario, the receptor was genetically tagged with GFP, and the antibody was directly labeled with a far-red dye. The SiMPull approach applies to other protein targets for which specific antibodies are available, including intracellular proteins. In addition, because denaturing conditions are not required, multisubunit receptors/complexes can also be captured. However, denaturation may be incorporated if the PTMs of interest are located in structured regions of the protein14. Ultimately, SiMPull can be readily expanded to include simultaneous labeling of distinct phospho-tyrosines on individual receptors to quantify multisite phosphorylation patterns15. The interrogation of full-length, intact receptors in such a way cannot be achieved by other standard methods, including western blotting and phospho-mass spectrometry.
Along with the advantages of SiMPull, some limitations need to be considered. As with any antibody-based technique, the affinity and specificity of antibodies used are critical to the success of the measurement. Therefore, it is important to optimize antibody labeling conditions and ideally avoid secondary antibodies by using directly-labeled primary antibodies. Furthermore, the surface-bound antibodies will precipitate proteins localized to the plasma membrane and within cytosolic compartments. This can underestimate phosphorylation since cytosol-localized proteins are not accessible to the exogenously added ligand. Extra steps must be taken to correct the receptor surface levels (step 6.2.10). The anti-phosphotyrosine antibodies exhibited some non-specific binding once lysate was present. To avoid this artifact, the EGFR was genetically-tagged with GFP to identify the location of receptors, which allowed us to exclude the anti-PY signal off the receptor. If endogenous proteins are to be interrogated, then counterstaining with a total protein antibody can provide the mask image, with appropriate correction for any non-specific binding. Finally, while SiMPull provides information on heterogeneity at the protein level, the lysate generated in this protocol is from thousands of cells, and cell-to-cell variability is lost. However, advances towards single-cell SiMPull have been made using a flow chamber consisting of a coverslip and a microscope side with a 10 µm gap; bacteria were sparsely plated on the coverslip while the slide was functionalized with antibody to capture the desired proteins. Upon lysis of the bacteria, the proteins from each cell were captured in a confined area on the antibody-coated slide22. Similar single-cell SiMPull analysis of mammalian cells and protein phosphorylation may be possible in the future.
The SiMPull protocol contains several critical steps required to ensure high-quality data. For example, the protocol includes an elaborate preparation of the coverslip glass. Piranha etching coverslips thoroughly cleans the glass and increases hydroxyl groups and hydrophilicity, which are needed to optimize the coverslip surface. Following several washes with organic solvents, KOH treatment provides additional hydroxyl groups for aminosilanization13,23, which coats the glass with amine groups for PEG and biotin-PEG binding. Improper cleaning or functionalization at any of these steps will interfere with protein pull-down. Control of the molar ratio of PEG:biotin-PEG, along with lysate concentration, are key factors in obtaining appropriate protein IP density on the SiMPull substrate. As with any biological assay, there is variability between cell lysate preparations, and small differences between phosphorylation percentages may be seen between sample replicates. Therefore, it is important to measure the phosphorylation levels of different tyrosine sites within the same sample. The sample chamber described in this protocol provides a system to collect many data points in one imaging session and allows for averaging over multiple SiMPull experiments.
On the image acquisition side, it is important to obtain the fiducial sample to ensure an accurate channel overlay; otherwise, colocalization will not be accurate. It is also important to optimize laser power and camera settings to maximize signal-to-noise while at the same time minimizing photobleaching. Lastly, while the sample array requires a small amount of sample and reagents, the low volumes are susceptible to evaporation during the imaging session. It is important to periodically check the sample array (~30-45 min) and add buffer as needed to prevent samples from drying.
The present protocol demonstrated the use of SiMPull to quantify membrane receptor phosphorylation states. While focused on EGFR, the approach can be applied to other cell surface receptors and intracellular proteins and protein complexes, as long as appropriate antibodies are available. Another potential use for SiMPull is to interrogate the contents and phosphorylation status of phase-separated condensates. In addition, SiMPull can be used to measure other PTMs, such as ubiquitination. Therefore, SiMPull provides a unique tool for cell biologists to interrogate PTMs on intact proteins and correlate PTM patterns with cellular outcome.
The authors have nothing to disclose.
This work was supported by the National Institutes of Health R35GM126934, R01AI153617, and R01CA248166 to DSL. EMB was supported through the ASERT-IRACDA program (NIH K12GM088021) and JAR by the UNM MARC program (NIH 2T34GM008751-20). We gratefully acknowledge using the University of New Mexico Comprehensive Cancer Center fluorescence microscopy shared resource, supported by NIH P30CA118100. We want to acknowledge Drs. Ankur Jain and Taekijip Ha, whose original development of SiMPull inspired this work.
ES-C present address: Immunodynamics Group, Laboratory of Integrative Cancer Immunology, Center for Cancer Research, National Cancer Institute, Bethesda
1.5 mL microcentrifuge tubes | MTC Bio | C2000 | |
10 mM Tris-HCl pH 7.4 | |||
10 mM Tris-HCl pH 8.0/ 50 mM NaCl | T50 Buffer | ||
100 mm Tissue Culture dish | CELLTREAT | 229620 | Storage of piranha etched glass/arrays |
15 mL conical tube | |||
16% Paraformaldehyde Aqueous Solution | Electron Microscopy Sciences | 15710 | Hazardous |
50 mL conical tube | Functionalized Glass storage/ KOH reuse | ||
50 mM Tris-HCl pH 7.2/150 mM NaCl | Lysis Buffer Component | ||
60 mm Tissue Culture dish | Corning | 430166 | |
8% Glutaraldehyde Aqueous Solution | Electron Microscopy Sciences | 16020 | Hazardous |
Acetone (C3H6O) | Millipore Sigma | 270725 | Hazardous |
Alexa Fluor 647 NHS Ester | Thermo Fisher Scientific | A-20006 | |
Animal-Free Recombinant Human EGF | Peprotech | AF-100-15 | |
Anti-Human EGFR (External Domain) – Biotin | Leinco Technologies, Inc | E101 | |
Anti-p-Tyr Antibody (PY99) Alexa Fluor 647 | Santa Cruz Biotechnology | sc-7020 AF647 | |
Bath-sonicator | Branson | 1200 | |
BCA Protein Assay Kit | Pierce | 23227 | |
Biotin-PEG | Laysan Bio | Biotin-PEG-SVA, MW 5,000 | |
Bovine serum albumin | Gold Biotechnology | A-420-1 | Tyrode's Buffer Component |
Buchner funnel | |||
Bunsen burner | |||
Calcium Chloride (CaCl2) | Millipore Sigma | C4901 | Tyrode's Buffer Component |
Cell Scraper | Bioworld | 30900017-1 | |
Conical Filtering Flask | Fisher Scientific | S15464 | |
Coplin Jar | WHEATON | 900470 | |
Countess II Automated Cell Counter | Thermo Fisher Scientific | AMQAX1000 | |
Coverslips 24 x 60 #1.5 | Electron Microscopy Sciences | 63793 | |
DipImage | https://diplib.org/ | ||
DMEM | Caisson Labs | DML19-500 | |
emCCD camera | Andor iXon | ||
Fetal Bovine Serum, Optima | Bio-Techne | S12450H | Heat Inactivated |
Fusion 360 software | Autodesk | ||
Geneticin G418 Disulfate | Caisson Labs | G030-5GM | |
Glacial Acetic Acid (CH3COOH) | JT Baker | JTB-9526-01 | Hazardous |
Glass serological pipettes | |||
Glass Stir Rod | |||
Glucose (D-(+)-Glucose) | Millipore Sigma | D9434 | Tyrode's Buffer Component |
Halt Phosphotase and Protease Inhibitor Cocktail (100X) | Thermo Fisher Scientific | 78446 | Lysis Buffer Component |
HEPES | Millipore Sigma | H3375 | Tyrode's Buffer Component |
Hydrochloric Acid (HCl) | VWR | BDH7204-1 | Hazardous |
Hydrogen Peroxide (H2O2) (3%) | HX0645 | ||
Hydrogen Peroxide (H2O2) (30%) | EMD Millipore | HX0635-2 | |
Ice | |||
IGEPAL CA-630 (NP-40) | Sigma Aldrich | I8896 | Lysis Buffer Component |
ImmEdge Hydrophobic Barrier Pen | Vector Laboratories | H-4000 | |
Immersol 518F immersion oil | Zeiss | 444960-0000-000 | |
in-house vacuum line | |||
L-glutamine | Thermo Fisher Scientific | 25030-164 | |
Magnessium Chloride Hexahydrate (MgCl2-6H2O) | MPBio | 2191421 | Tyrode's Buffer Component |
Matlab | Mathworks | Curve Fitting Toolbox, Parallel Computing Toolbox, and Statistics and Machine Learning toolbox | |
Methanol (CH3OH) | IBIS Scientific | MX0486-1 | Hazardous |
Milli-Q water | |||
Mix-n-Stain CF Dye Antibody Labeling Kits | Biotium | 92245 | Suggested conjugation kit |
mPEG | Laysan Bio | mPEG-succinimidyl valerate, MW 5,000 | |
N-(2-aminoethyl)-3-aminopropyltrimethoxysilane | UCT United Chemical | A0700 | Hazardous |
Nanogrid | Miraloma Tech | ||
NeutrAvidin Biotin Binding Protein | Thermo Fisher Scientific | 31000 | |
Nitrogen (compressed gas) | |||
NVIDIA GPU with CUDA | Look for compatibility at https://www.mathworks.com/help/parallel-computing/gpu-support-by-release.html | ||
Olympus iX71 Microscope | Olympus | ||
Parafilm M Sealing Film | The Lab Depot | HS234526C | |
PBS pH 7.4 | Caisson Labs | PBL06 | |
PC-200 Analog Hot Plate | Corning | 6795-200 | |
Penicillin-Streptomycin (10,000 U/mL) | Thermo Fisher Scientific | 15140-163 | |
Phospho-EGF Receptor (Tyr1068) (1H12) Mouse mAb | Cell Signaling Technology | 2236BF | |
Potassium Chloride (KCl) | Millipore Sigma | 529552 | Tyrode's Buffer Component |
Potassium Hydroxide (KOH) | Millipore Sigma | 1050330500 | Hazardous |
Premium PLA Filament, 1.75 mm diameter | Raise 3D | PMS:2035U/RAL:3028 | Printing temperature range: 205-235 °C |
Pro2 3D printer | Raise 3D | ||
Pyrex 1 L beaker | |||
PYREX 100 mL storage bottles | Corning | 1395-100 | CH3OH/C3H6O reuse |
Pyrex 250 mL beakers | |||
Pyrex 4 L beaker | |||
Quad-view Image Splitter | Photometrics | Model QV2 | |
Refrigerated centrifuge | Eppendorf | EP-5415R | |
RevCount Cell Counters, EVE Cell Counting Slides | VWR | 10027-446 | |
Semrock emission filters: blue (445/45 nm), green (525/45 nm), red (600/37 nm), far-red (685/40 nm) | Semrock | LF405/488/561/635-4X4M-B-000 | |
Serological pipette controller | |||
Serological Pipettes | |||
smite single molecule analysis package | https://github.com/LidkeLab/smite.git | ||
Sodium Bicarbonate (NaHCO3) | Sigma Aldrich | S6014 | Hazardous |
Sodium Borohydride (NaBH4) | Millipore Sigma | 452874 | Tyrode's Buffer Component |
Sodium Chloride (NaCl) | Millipore Sigma | S9625 | Activate by successive heat and pH cycling |
Sodium Hydroxide | VWR | BDH3247-1 | |
Sodium Orthovanadate (Na3VO4) | Millipore Sigma | S6508 | Hazardous |
Sulfuric Acid (H2SO4) | Millipore Sigma | 258105 | Hazardous |
TetraSpeck Microspheres | Thermo Fisher Scientific | T7279 | multi-fluorescent beads |
Tris (Trizma) base | Millipore Sigma | T1503 | |
Trypan blue stain, 0.4% | Thermo Fisher Scientific | 15250061 | |
Trypsin-EDTA 0.05% | Thermo Fisher Scientific | 25300120 |