Calcium signals are necessary to establish auxin transporter polarity in a plant stem cell niche

In plants mechanical signals pattern morphogenesis through the polar transport of the hormone auxin and through regulation of interphase microtubule (MT) orientation. To date, the mechanisms by which such signals induce changes in cell polarity remain unknown. Through a combination of time-lapse imaging, and chemical and mechanical perturbations, we show that mechanical stimulation of the SAM causes transient changes in cytoplasmic calcium ion concentration (Ca2+) and that transient Ca2+ response is required for downstream changes in PIN-FORMED 1 (PIN1) polarity. We also find that dynamic changes in Ca2+ occur during development of the SAM and this Ca2+ response is required for changes in PIN1 polarity, though not sufficient. In contrast, we find that Ca2+ is not necessary for the response of MTs to mechanical perturbations revealing that Ca2+ specifically acts downstream of mechanics to regulate PIN1 polarity response.

LaCl3 for 15 min before mechanical stimulus and then subjected for 5 min to 5mM LaCl3, which in the continued presence of inhibitors did not cause a calcium wave. After 3 h of incubation on GM without a sample rinse, the SAM was washed with water. A Ca 2+ wave occurs immediately after the water wash. f-j, After 10 min pretreatment with 2mM BAPTA following mechanical stimulus and then subjected to 5 min of 2mM BAPTA, after 3h incubation without sample rinse, the Ca 2+ wave was also restored following washout with 3mM CaCl2. n = 9 of 19 independent experiments. a-j, Time format mm:ss. Scale bar: 20 µm. k, Quantitative analysis of induced Ca 2+ signal recovery 3 h after LaCl3 pretreatment, and immediately after water washout. (I-I0)/I0 shows mean of normalized R-GECO1 fluorescence intensity fold changes. Purple represents SD of 11 independent experiments. Arrow represents water wash initiation. l, Quantitative analysis of Ca 2+ signal recovery 3 h after 2mM BAPTA pretreatment, immediately after 3mM CaCl2 resupply. (I-I0)/I0 shows mean of normalized R-GECO1 fluorescence intensity fold changes.
Purple represents SD of 9 independent experiments. Arrow represents the time the water wash began. Source data for k-l are provided as a Source Data file.

Image processing and data analysis.
Quantitative characterization of the Ca 2+ oscillations (Fig. 3a) in the excised SAM and intact SAMs was performed as follows: for each sample, the mean pixel intensity over a region of interest in the SAM was evaluated ('SAM signal'); to account for non sample-related contributions to the signal, this procedure was repeated in the same frames, with the region-ofinterest defined to exclude the sample region ('BG signal'), which was then subtracted from the SAM signal; to suppress experimental noise and sporadic cellular spikes, natural smoothing cubic splines 6 were applied to the resulting signal. De Boor's smooth factor, ranging from zero to one, was chosen automatically, where the 'improved Akaike information criterion' (AICC) 7 took the role of the usual generalized cross-validation; smooth factor of value 1 leads to no smoothing (interpolating spline), while approaching zero converges to linear least squares. The corresponding open-source Python code, previously introduced 8,9 , can be found at https://github.com/eldad-a/natural-cubic-smoothing-splines . The smooth factor was calculated independently for each dataset. Two smoothing splines were calculated based on the resulting data: first, the smooth factor was evaluated using the AICC, then an over-smoothed spline was evaluated using a 10 -4 times smaller one. The temporal location of local maxima and minima were identified in the over-smoothed spline. The corresponding intensities were evaluated based on the first spline. A baseline intensity was estimated for each local maximum by linear interpolation of the two neighboring local minima (in case one was outside the dataset, only one was used). The FWHM was defined as the spline intersection with the middle value between the peak and the baseline, in case such intersection existed in the time interval between the neighboring local minima. Finally, a filtering step was applied: peaks were sorted based on their height, evaluated as the difference from the corresponding baseline; starting from the next to highest, each peak height was compared to the previous one; if a ratio smaller than 30% was found, that peak and all smaller ones were discarded. The results are presented in Supplementary   Fig. 5a To compare LaCl3 and BAPTA treatments with water controls in signal area change and wave propagation speed, we extracted these measures from images as follows using MATLAB software: individual frames were segmented to extract the calcium signal region in each frame.
First, the signal was treated by a Gaussian smoothing followed by thresholding of the foreground ( Supplementary Fig. 9a). Possible artifacts that remain outside of this mask were removed from the mask by selecting only the largest area representing the tissue (Supplementary Fig. 9b). A secondary, variable thresholding step was used to extract the calcium signal region within the masked tissue region (Supplementary Fig. 9c). Once all frames were segmented, a final visual inspection was performed to discard sequences with segmentation errors. Area changes were normalized by calculating areat / max(area), where max(area) refers to the maximum area found in any of the three individual frames at (t-1) and (t+1). To quantify the propagation speed of the signal front, we manually marked the wave initiation site on the signal boundary on the first frame of each sequence. The initiation site on the first frame was transferred to subsequent frames by mapping it to the closest point on the boundary. We computed the distance dt (in microns) from the initiation site to the boundary pixels in five different directions as -60°, -30°, 0°, 30°, 60°, in frame t (Supplementary Fig. 9d). The speed of propagation was computed as the average of frame-by-frame distances over consecutive frames, e.g. (dt -dt-1). Propagation speeds along each direction were averaged over all sequences within each treatment group.

Quantification of MT orientation and PIN1-GFP localization.
MT orientations were quantified using the FibrilTool plugin in ImageJ as published 1