DOI: 10.5061/dryad.c59zw3rfd
title: Piezo1 ion channels are capable of conformational signaling

description: Piezo1 is a mechanically activated ion channel that senses forces with
 short latency and high sensitivity. Piezos undergo large conformational
 changes, induce far-reaching deformation onto the membrane, and modulate
 the function of two-pore potassium (K2P) channels. Taken together, this
 led us to hypothesize that Piezos may be able to signal their
 conformational state to other nearby proteins. Here, we use chemical
 control to acutely restrict Piezo1 conformational flexibility and show
 that Piezo1 conformational changes, but not ion permeation through it, are
 required for modulating the K2P channel TREK1. Super-resolution imaging
 and stochastic simulations further reveal that both channels do not
 co-localize, which implies that modulation is not mediated through direct
 binding interactions; however, at high Piezo1 densities, most TREK1
 channels are within the predicted Piezo1 membrane footprint, suggesting
 the footprint may underlie conformational signaling. We speculate that
 physiological roles originally attributed to Piezo1 ionotropic function
 could, alternatively, involve conformational signaling.
description: <strong>Microscopy sample preparation
 </strong> Cells were transiently transfected with
 TREK1<sub>HA</sub> and/or Piezo1<sub>Myc
 </sub>plasmids and plated on No 1.5 coverslips (Warner Instruments:
 CS-12R15, Catalog # 64-0712) 48 hours before staining, fixed in 2%
 formaldehyde, blocked with 10% normal goat serum, and stained with 1:100
 chicken anti-Myc (Novus) primary antibody followed by 1:500 Alexa Fluor
 plus 594 goat anti-chicken (Thermo Fisher Scientific) secondary antibody
 to label Piezo1<sub>Myc </sub>channels and 1:500 rabbit
 anti-HA (Cell Signaling Technology) primary antibody followed by 1:200
 Atto 647N goat anti-rabbit (Rockland Instruments) secondary antibody to
 label TREK1<sub>HA</sub> channels. Coverslips were then
 mounted with ProLong Glass Antifade Mountant (ThermoFisher Scientific) and
 cured at room temperature overnight before image acquisition. STED images
 were collected from at least three coverslips per condition, with n=3
 independent transfections to generate biological replicates.
 <strong>STED image acquisition and
 deconvolution</strong> Two-color STED was
 performed on co-labeled images of either Neuro2A-Piezo1ko cells
 overexpressing Piezo1<sub>Myc </sub>or
 Neuro2A-Piezo1<sub>Myc</sub> cells, both transiently
 transfected with TREK1<sub>HA</sub>. All image collection was
 performed on a Leica SP8 instrument equipped with a 100x/1.4 HCX PL APO
 OIL WD 90 mm objective, pulsed White Light Laser, and HyD detectors, using
 Leica Application Suite Software (3.5). The 594 red channel, corresponding
 to Piezo1<sub>Myc</sub>, was excited using 5% laser power at
 591 nm, and emitted light was collected between 603 and 641 nm with 22%
 gain. The 647 far red channel, corresponding to
 TREK1<sub>HA</sub>, was excited using 3% laser power at 641 nm
 and emitted light was collected between 651 and 779 nm with 42% gain. The
 647 channel was collected using 2x frame averaging, to reduce noise. A
 pulsed 775 nm STED depletion laser at 20% laser power in the 647 channel,
 with gating (0.7 to 4.2), and 70% laser power in 594 channel, with gating
 (0.7 to 4.2), was used to improve image resolution to ~80 nm
 (<strong>Supplemental Table 1-2</strong>). For STED image
 collection, Z-stacks were acquired from the middle to the top of the cell
 in steps of 220 nm with a Märzhäuser linearly encoded piezo Z stage
 (<strong>Supplemental Movie 1</strong>).
 All channels from the STED images were deconvolved using Huygen’s
 Professional (Scientific Volume Imaging). The refractive index was
 corrected to match the immersion oil (1.5) and images were cropped as
 necessary to isolate single cells. Deconvolution was then performed with
 an automatically generated theoretical point spread function and the
 preset Classic Maximum Likelihood Estimation CMLE deconvolution algorithm,
 with the signal-to-noise ratio set to 5.0.
 <strong>Confocal microscopy image
 acquisition</strong> Confocal imaging was used to
 assess antibody specificity (<strong>Figure S5F-I, Figure
 S6B-C</strong>)<strong> </strong>and quantify membrane
 expression levels of all Myc-tagged Piezo constructs (<strong>Figure
 S8D-E</strong>). Antibody specificity was assessed using widefield
 confocal images at 100X magnification acquired with the same objective,
 excitation and emission collection parameters as STED images, as described
 above, but without depletion. For membrane expression quantification,
 images were acquired using an HC PL APO CS2 40x/1.30 Oil objective on the
 above described Leica SP8 scope. The 594 red channel, corresponding to
 Piezo1<sub>Myc</sub>, was collected using 5% laser power at
 591 nm, and emitted light was collected between 603 and 641 nm with 13%
 gain. The 488 green channel, corresponding to GFP, was collected using 7%
 laser power at 490 nm, and emitted light was collected between 501 and 561
 nm with 52% gain. Single Z-slices of the central plane, the midpoint of
 most cells in a field view, were collected over ~12 images, yielding
 approximately 200 cells per condition.
 <strong>Image processing</strong>
 Images were processed in FIJI (version
 2)<sup>66</sup> as 16-bit TIFF files. A single z-slice for
 spatial analysis from the cell surface (top) was manually chosen from the
 Z-stack. Noise thresholds were set for each cell by identifying the
 intersection of Gaussian distributions fit to the intensity histograms of
 segmented puncta from the surface z-slice and a separate z-slice chosen
 from the unlabeled interior of the cell.  Manually drawn ROIs were
 generated around the xy perimeter of each cell, and signal outside the ROI
 was cleared. Both 594 (Piezo1<sub>Myc</sub>) and 647
 (TREK1<sub>HA</sub>) channels were auto-enhanced and filtered
 with a 2.0 pixel Gaussian Blur Filter. Individual puncta from both
 overexpressed TREK1 and Piezo1 channel conditions  were segmented using
 StarDist’s Versatile (fluorescent nuclei) Model with the following
 settings (Normalized Image Percentile 3-100, Probability/Score Threshold:
 0.5, Overlap Threshold: 0.2, Number of Tiles: 1, Boundary Exclusion:
 2)<sup>67</sup>. Automated segmentation parameters were
 developed based on manual segmentation.  Endogenous Piezo1 puncta from
 Neuro2a-Piezo1<sub>Myc</sub> images were manually segmented.
 We note that the size and intensity of individual segmented puncta in
 images varies, particularly for TREK1. This variance primarily stems from
 the variable z position along the axial point spread function of a
 punctum, especially given that our image acquisition settings were
 optimized exclusively to improve lateral resolution in x and y. Additional
 variance may come from two or more channels located closer to each other
 than the resolution limit of our experiments. The center of mass for each
 Piezo1 or TREK1 punctum was identified using the ‘Measure’ function in
 FIJI. Puncta with mean intensity values under the identified threshold
 were excluded, and corresponding XY coordinates were exported for
 analysis. Resolution for each cell was quantified from the Full-Width Half
 Max (FWHM) across n=28-70 puncta from 2-3 2 µm x 2 µm regions in each
 image. Lineplots were used to measure the intensity profile across
 individual points, and gaussian curves were fit to these data [see github
 for Jupyter notebook] using the equation: . The FWHM
 was calculated from Gaussian fits to line intensity profiles of each
 punctum using the equation FWHM = ℴ*2.355 and averaged across the puncta
 for each image. <strong>Spatial distribution
 analysis and modeling </strong> Nearest Neighbor
 Distances (NNDs) between empirical TREK1 and Piezo1 coordinates were
 calculated using the KDtree function from the SciPy package in
 Python3<sup>68</sup>. To compare the empirical spatial
 relationship of TREK1 to Piezo1 puncta, we retained the empirical XY
 locations of Piezo1 puncta and simulated random populations of TREK1
 puncta. Each simulation was performed 1,000 times per image. For random
 TREK1 distributions, for each image, TREK1 positions were simulated from a
 random distribution within the image ROI at a density equivalent to the
 empirical TREK1 density for that respective cell. Local Piezo1 densities
 were calculated for a 564 nm radius (=1 mm<sup>2 </sup>area)
 around each TREK1 punctum using the Ball Query function in
 SciPy. Our estimate of 30 nm as the maximum cutoff for
 TREK1 and Piezo1 binding is based on the central location of the Piezo1
 epitope, the predicted radius for Piezo1 in a biological membrane (14
 nm)<sup>19,21,34</sup>, the location of the TREK1 epitope and
 radius of the TREK1 channel (~5 nm<sup>69</sup>), and some
 accounting for linkage error from our labeling strategy. Our estimate of
 100 nm as the maximum cutoff for the Piezo1 footprint comes from estimates
 of the Piezo1 diameter (10-30 nm)<sup>20,23,25,26,34</sup>
 plus 5-fold the predicted decay length of the Piezo1 footprint in a
 biological membrane (14 nm)<sup>24</sup>.
description: # Piezo1 ion channels are capable of long-range conformational signaling
 ## Description of the data
 and file structure There are two files: One contains data for
 Neuro2A-Piezo1Myc cells overexpressing TREK1-HA ("Endogenous");
 the other contains data for Neuro2a-Piezo1ko cells overexpressing both
 Piezo1-Myc and TREK1-HA ("Overexpression"). Within each folder
 are labelled cells ("Cell B", "Cell C", etc.) that
 correspond to Table 1 (Endogenous) and Table 2 (Overexpression) in the
 manuscript. For the endogenous dataset, the PDF for each cell contains a
 STED image (scale bar: 1 micron) and associated cumulative frequency
 distribution, as in Figure 5A and 5B of the main manuscript. For the
 overexpression dataset, the PDF for each cell contains a STED image and
 corresponding plot showing NNDT1-P1 as a function of local Piezo density,
 as in Figure 7A and 7C of the main manuscript. ## Code/Software Python
 code used to generate random simulations and analyze nearest-neighbor
 distances is available on GitHub (2_Channel_Spatial_Analysis)