Sep 23, 2023

Public workspaceMicroscopy-based bead assay

DOI
dx.doi.org/10.17504/protocols.io.14egn38pzl5d/v1
  • 1Sascha Martens lab, University of Vienna, Max Perutz Labs - Vienna
Elias Adriaenssens
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DOI: dx.doi.org/10.17504/protocols.io.14egn38pzl5d/v1
Protocol Citation: Elias Adriaenssens 2023. Microscopy-based bead assay. protocols.io https://dx.doi.org/10.17504/protocols.io.14egn38pzl5d/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: July 07, 2023
Last Modified: May 31, 2024
Protocol Integer ID: 84651
Keywords: Microscopy-based bead assay, ASAPCRN
Funders Acknowledgement:
Aligning Science Across Parkinson’s (ASAP)
Grant ID: ASAP-000350
Marie Skłodowska-Curie MSCA Postdoctoral fellowship
Grant ID: 101062916
Abstract
This protocol describes the microscopy-based bead assay.
Materials
Materials
  • Glutathione Sepharose 4B beads (GE Healthcare)
  • dH2O
  • MgCl2
  • FIP200-GFP
  • SINTBAD-mCherry
  • TBK1
  • SINTBAD-GFP
  • mCherry-OPTN
  • Zeiss LSM 700 confocal microscope

Bead assay buffer
AB
Tris-HCl pH 7.425 mM
NaCl150 mM
DTT1 mM
Microscopy-based bead assay
Microscopy-based bead assay
6h 30m
6h 30m
We use Glutathione Sepharose 4B beads (GE Healthcare) to bind GST-tagged bait proteins.
To this end, wash Amount20 µL of beads twice with dH2O and equilibrate with bead assay buffer.

Wash
Then, resuspend beads in Amount40 µL bead assay buffer, to which bait proteins are added at a final concentration of Concentration5 micromolar (µM) .
Incubate the Beads with the bait proteins for Duration01:00:00 at Temperature4 °C at a horizontal tube roller.

1h
Incubation
Wash the beads three times to remove unbound GST-tagged bait proteins and resuspend in Amount30 µL bead assay buffer.
Wash
Where needed, also add MgCl2 and ATP to the buffer to allow the phosphorylation of targets by TBK1 or other kinases.
Pipetting
Prepare glass-bottom 384-well microplates (Greiner Bio-One) with Amount20 µL samples containing prey proteins at the concentrations described below and dilute in bead assay buffer, and add Amount3 µL of beads per well.
Pipetting
For the experiments in which full-length FIP200-GFP is recruited to GST-4xUb beads in presence of NDP52 and/or SINTBAD-mCherry, use NDP52 at a final concentration of Concentration50 nanomolar (nM) , FIP200-GFP, SINTBAD-mCherry, and TBK1 are used at a final concentration of Concentration100 nanomolar (nM) .

For recruitment of SINTBAD-GFP to GST-LC3/GABARAP beads, use a final concentration of Concentration200 nanomolar (nM) SINTBAD-GFP.
For the TBK1-binding competition experiment between OPTN and NAP1, use mCherry-OPTN and GFP-TBK1 at a final concentration of Concentration500 nanomolar (nM) , and use NAP1 from Concentration100 nanomolar (nM) to Concentration10 micromolar (µM) .
Incubate the beads with the prey proteins for Duration00:30:00 prior to imaging, with the exception of experiments where full-length FIP200 is recruited, where proteins are co-incubated for Duration04:00:00 before imaging. For recruitment of autophagy components to GST-LC3/GABARAP beads, proteins are co-incubated for Duration01:00:00 before imaging.
5h 30m
Incubation
Image samples with a Zeiss LSM 700 confocal microscope equipped with Plan Apochromat 20X/0.8 WD 0.55 mm objective.
Note
Three biological replicates were performed for each experimental condition.

Imaging
For the quantification, we employ an artificial intelligence (AI) script that automatically quantifies signal intensities from microscopy images by drawing line profiles across beads and recording the difference between the minimum and maximum grey values along the lines.
Note
The AI was trained to recognize beads employing cellpose [60].

Processing is composed of two parts, with the first operating in batch mode. Multichannel input images are split into individual TIFF images and passed to cellpose (running in a Python environment).
The labeled images produced by cellpose are re-assembled into multichannel images.
Circular regions of interest (ROIs) are fitted to the segmented particles, and a pre-defined number of line profiles (here set to 20) are drawn automatically, starting at the center of the ROI and extending beyond the border of the circular ROI.
This results in line profiles from the center of the bead into the inter-bead space of the well, allowing us to quantify the signal intensities at the rim of the beads.
To prevent line profiles from protruding into adjacent beads, a combined ROI containing all beads is used.
Inspect the AI-generated results manually for undetected beads, incorrect line profiles, or false-assigned bead structures.
For each bead, a mean fluorescence and standard deviation are obtained based on the 20 line profiles per bead.
Either exclude or subject the beads with standard deviations equal to or greater than half the mean value to manual inspection for correction.
If needed, correct for inter-experiment variability in absolute values by dividing the mean values for each bead by the average bead intensity of the control condition, to obtain relative values.
These values are then plotted and subjected to statistical significance calculations.
Protocol references
Schneider CA, Rasband WS, Eliceiri KW: NIH Image to ImageJ: 25 years of image analysis. Nat Methods 2012, 9(7):671-675.

Stringer C, Wang T, Michaelos M, Pachitariu M: Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 2021, 18(1):100-106.