Scaling between cell cycle duration and wing growth is regulated by Fat-Dachsous signaling in Drosophila

The atypical cadherins Fat and Dachsous (Ds) signal through the Hippo pathway to regulate growth of numerous organs, including the Drosophila wing. Here, we find that Ds-Fat signaling tunes a unique feature of cell proliferation found to control the rate of wing growth. The duration of the cell cycle increases in direct proportion to the size of the wing, leading to linear rather than exponential growth. Ds-Fat signaling enhances the rate at which the cell cycle lengthens with wing size, thus diminishing the linear rate of wing growth. We show that this results in a complex but stereotyped relative scaling of wing growth with body growth in Drosophila. Finally, we examine the dynamics of Fat and Ds protein distribution in the wing, observing graded distributions that change during growth. However, the significance of these dynamics is unclear since perturbations in expression have negligible impact on wing growth.

Methods
Data accompanying a published eLife paper titled "Scaling between cell cycle duration and wing growth is regulated by Fat-Dachsous signaling in Drosophila."

Imaging data were acquired on confocal micrscope and processed. Please refer to the Methods section of the accompanying eLife paper for detailed methodology of how images were collected. How the data was processed are descripted below. Code is available at https://github.com/andrewliu321/LarvaSeg and https://github.com/andrewliu321/ProteinIntensity.

Image processing

Raw images were processed using a custom-built Matlab pipeline with no prior preprocessing. The pipeline consists of several modules: 1) surface detection, 2) wing disc, pouch, and midline segmentation, 3) volume measurement, 4) fluorescence intensity measurement, 5) cell segmentation, 6) mitotic index measurement.

Surface detection

Fat-GFP and Ds-GFP proteins localize to the apical region of cells in the wing disc proper. These proteins are also localized in cells of the peripodial membrane, which is positioned near the apical surface of the disc proper. In order to get a 2D projection of the signal in the wing disc proper excluding the peripodial membrane signal, we used an open-source software package called ImSAnE – Image Surface Analysis Environment. The detailed parameters we used have been previously described. Briefly, we used the MIPDetector module to find the brightest z-position of every xy pixel followed by tpsFitter to fit a single layer surface through these identified z-positions. Using the onionOpts function in ImSAnE, we output a 9-layer z-stack, 4 layers above and below the computed surface that capture the entire signal from the wing disc proper. However, this operation still sometimes includes fluorescence signals from the peripodial membrane. Therefore, we manually masked the residual peripodial signal using FIJI 1.53t. The resulting z-stack was sum-projected to form a 2D surface projection of the wing disc proper.

Wing disc, pouch, and midline segmentation

Wing discs were counterstained for both Wg and En proteins, which mark the wing pouch dorsal-ventral (DV) midline and anterior-posterior (AP) midline, respectively. Although both Wg and En proteins were stained with the same Alexa 546 fluorescent antibody, the two signals were readily distinguished by their distinct separation in z space. Wg is apically localized in cells of the disc proper and En is nuclear localized more basally in the disc proper. Moreover, the Wg signal was far stronger than En, allowing for detection of its stripe in the posterior compartment even with a max projection.

We built a semi-automated Matlab script that computationally segments the wing disc into discrete objects. i) Wing disc segmentation. Endogenous Fat-GFP or Ds-GFP signal was used to segment the wing disc from surrounding media. ii) Wing pouch segmentation. The 3D morphology of the wing disc creates narrow and deep tissue folds that surround the wing pouch. Using these morphological landmarks that were visualized by the Fat-GFP or Ds-GFP signals, the Matlab script recorded user-derived mouse-clicks that defined the wing pouch boundary. This method was validated to be 95.5% accurate when compared to a wing pouch boundary that was defined by the expression boundary of a reporter for the vestigial quadrant enhancer, 5x-QE-DsRed. iii) DV midline segmentation. The Wg signal was used to segment the DV midline running through the segmented wing pouch. Since the Wg and En signals are distinguishable in z space, the upper third of the z-stack was max projected to segment Wg. An adaptive threshold of 0.6 was used to binarize the image into Wg-positive pixels. The binarized and raw images were used to inform manual input of the DV midline. iv) AP midine segmentation. The En signal was used to segment the AP midline running through the segmented wing pouch. The lower two-thirds of the z-stack was max projected and binarized in En+ pixels. The binarized and raw images were used to inform manual input of the AP midline.

Volume measurement

Areas of each of the segmented objects were calculated by summing the number of pixels in each object and multiplying by the pixel dimensions in xy physical space. Notum-hinge area was calculated by subtracting the segmented pouch area from total segmented wing disc area. The thickness of the wing pouch was measured at the intersection of the AP and DV midlines using the orthogonal views tool in FIJI. The first layer is defined by the initial signal of Fat-GFP or Ds-GFP at this xy position. The last layer is defined by the first appearance of background signal in the composite image. Thickness of the object was calculated by multiplying the sum of z-slices by the z-separation. To calculate the volume of segmented objects, we multiplied the thickness of the object (in µm) by the object’s surface area (in µm2). Conversion from µm3 to nL units was performed.

Fluorescence intensity measurement of Fat-GFP and Ds-GFP

Fluorescence intensity values were averaged across a vector of 50 pixels length that was orthogonal to the segmented boundary of interest and having 25 pixels residing on each side of the segmented line. These values were then averaged in a sliding window of 100 pixels length that moved along the segmented boundary of interest. Physical distance along the boundaries were measured using ImSAnE function Proper_Dist to account for the curvature of the segmented objects. The intersection of the segmented DV and AP midlines was defined as the center (0,0 µm) of the wing pouch, with the anterior/dorsal annotated in units of negative µm and the posterior/ventral annotated in units of positive µm. A minimum of three wing discs of the same age and genotype were aligned by their (0,0) centers and their fluorescent measurements were averaged along the AP and DV midlines.

Older third instar, WPP, and BPP wing discs begin to evert such that the ventral compartment is partially folded underneath the dorsal compartment. However, the ventral compartment can still be accurately segmented to measure area and thickness even under these conditions. For GFP measurements, the folded specimen resulted in dimmer fluorescent signals from the compartment farthest from the objective due to tissue thickness and light scattering. Thus, measurements were limited to the compartment closest to the objective.

Cell boundary segmentation

To count cell numbers and cell sizes in the wing pouch, we analyzed wing discs imaged from E-cadherin-GFP or E-cadherin-mCherry larvae. We used a machine learning pixel-classification model based on a convolutional neural net to segment cell boundaries in the surface projections. This model was trained on a broad range of image data derived from Cadherin-GFP labeled Drosophila imaginal discs. The model is > 99.5% accurate at segmenting cells when compared to ground truth. Cell size (surface area) and number were computed for specific compartments in the wing pouch.

Mitotic index measurement

Phospho-histone H3 (PHH3) has been used to estimate mitotic index previously. Wing discs were immunostained for PHH3, which labels nuclei undergoing mitosis. These nuclei were manually recorded by user-defined mouse clicks at or near the center of each nucleus. Their Euclidean distances relative to the segmented AP and DV midlines were calculated as was the number of PHH3+ cells. To estimate the total cell number in a wing pouch, we used E-cadherin to computationally segment cells as described above. Each imaged wing pouch had a subset of cell boundaries segmented in a subdomain of the pouch. This was then used to calculate cell density: number of segmented cells divided by subdomain area. The density value was multiplied by total wing pouch area to estimate the total number of wing pouch cells for that sample. We then derived an averaged conversion factor to apply to each volume measurement in order to estimate total cell number. This was done by plotting the estimated total cell number versus wing pouch volume for all discs of a given genotype. Linear regression of the data produced an equation to convert pouch volume to cell number.