Amyloid precursor protein-b facilitates cell adhesion during early development in zebrafish

Understanding the biological function of amyloid beta (Aβ) precursor protein (APP) beyond its role in Alzheimer’s disease is emerging. Yet, its function during embryonic development is poorly understood. The zebrafish APP orthologue, Appb, is strongly expressed during early development but thus far has only been studied via morpholino-mediated knockdown. Zebrafish enables analysis of cellular processes in an ontogenic context, which is limited in many other vertebrates. We characterized zebrafish carrying a homozygous mutation that introduces a premature stop in exon 2 of the appb gene. We report that appb mutants are significantly smaller until 2 dpf and display perturbed enveloping layer (EVL) integrity and cell protrusions at the blastula stage. Moreover, appb mutants surviving beyond 48 hpf exhibited no behavioral defects at 6 dpf and developed into healthy and fertile adults. The expression of the app family member, appa, was also found to be altered in appb mutants. Taken together, we show that appb is involved in the initial development of zebrafish by supporting the integrity of the EVL, likely by mediating cell adhesion properties. The loss of Appb might then be compensated for by other app family members to maintain normal development.


Supplementary data5. Details on Image Analysis -aggregation and cell cohesiveness assay
This document will summarize: 1. How the algorithm for aggregate segmentation works. 2. How the calculation of the parameters S and P are done from the ROI images of a segmented aggregate.1.

Aggregate segmentation pipeline -ROI generation:
a. Load ".nd2" file. Image data is treated as a matrix of dimensions x,y,z, where x is the number of row pixels, y is the number of column pixels and z is 2 -the number of color channels (red and green). b. Calculate maximum intensity projection over the z dimension, effectively merging both color channels. c. Image contrast is enhanced via the Normalize Local Contrast method -FIJI d. Further improvement is done via a Gaussian blur -sigma 1 e. Image thresholding via Li's Minimum Cross Entropy method f. Objects smaller than 1000 pixels are removed from the segmented image

Calculations of P and S for each ROI:
Calculations

P -closely related to the electrical dipole moment:
Each image channel, red and green, are loaded as matrices, where the matrix value indicates intensity and the row and column index represent the pixel position. The only difference between the calculations for the red and green channel is that the pixel values of the red channel are considered negative, again in analogy to electrical charges.
For each channel, with intensity matrix ( , ), where r and c are row and column indices, respectively: 1. Image is normalized, so the total sum on the pixel intensities is 1. To allow for the comparison of aggregates of different size, ��⃗ are normalized to the radius of the aggregate. This is done by: Where, is the area of the aggregate in units of pixels.
Finally, the parameter P is calculated by summing the red and green moments and calculating the norm of the resulting vector:

S -closely related to the moment of inertia and ratio of scattering amplitudes:
Each image channel, red and green, are loaded as matrices, where the matrix value indicates intensity and the row, column indices represent the pixel position.
For each channel, with intensity matrix ( , ), where r and c are row and column indices, respectively: 1. Image is normalized, so the total sum on the pixel intensities is 1. 5. Calculate the principal moments of inertia: 6. Calculate the scattering amplitude: Finally, the parameter S is calculated as the ratio of the scattering amplitudes: = S is <1 if the red cells are less scattered, and >1 if the red cell are more scattered around the center of mass than the green cells. S=1 if cells are scattered equally.