Colocalization of 2D to 3D point patterns

I have serial sections representing an ROI. Around 10x10um sections at 100um apart have been immunostained for a marker of interest that localizes to the cell nucleus, along with DAPI. The X,Y coords of the 2D section have been obtained for the positive-cells and DAPI-positive cells.

  1. I am interested in having a “3D” view of the spatial data by creating a stack of the 2D X, Y coords (also have other data associated with points, like cell area and perimeter). I know there are plugins to create an aligned 3D stack from 2D images, but wondering if it works for the masks of 2D images. That is, how do I account for rotation when creating the “3D” data. My current thought is to do a Procrustes-fit (geometric morphometrics). Another option is I create the “3D” image stack first, then extract spatial data, since multiple channels need to be rotated…
  2. How to I overlay the masks for the positive-signal and DAPI-signal? The logic is the X, Y of DAPI and X,Y of positive-cells will be overlapped within the area/perimeter of the DAPI cell, so assigning a colocalization metric should be attainable.
  3. Does colocalization analysis (plugins) work only on pre-processed images? What is best for examining multi-labeled cells?