Pinpointing single molecule positions with in situ point spread function retrieval for three dimensional nanoscopy of whole cells and tissues

A system for point spread function (PSF) retrieval for three-dimensional nanoscopy, comprising:

• a nanoscopy imaging device adapted to provide nano-scale images of objects;
• a 3D encoder adapted to provide 3D location of molecules into a plurality of fluorescence emission patterns expressed as point spread functions (PSFs) detected on a camera;
• a processing system, including memory and a processor, the processor adapted to:
• A) receive one or more images containing a plurality of PSFs generated by molecules within a specimen;
• B) segment the received one or more images into a plurality of sub-regions containing isolated PSFs (sub-PSFs) from point sources;
• C) initialize a plurality of template PSFs, each of which is generated from an initial pupil function based on a corresponding axial distance between an image plane (where the point-source locates) and a focal plane of the objective;
• D) cross correlate each of the plurality of sub-PSFs with each of the plurality of template PSFs, thereby generating a corresponding plurality of cross correlation coefficients;
• E) determine a maximum normalized cross correlation coefficient from the plurality of normalized cross correlation coefficients;
• F) associate each of the plurality of sub-PSFs with a corresponding template PSF of the plurality of template PSFs based on which template PSF produced the maximum normalized cross correlation coefficient and storing each of the plurality of sub-PSFs in an associated bin corresponding to each of the plurality of template PSFs;
• G) align each of the bin-associated sub-PSFs;
• H) average the aligned sub-PSFs;
• I) apply a phase retrieval algorithm to the averaged sub-PSFs to thereby update the pupil function;
• J) regenerate the plurality of template PSFs from the updated pupil function;
• K) repeat steps D-J until a difference between a new and a prior generation pupil function is below a predetermined threshold;
• L) generate in situ PSFs from the last pupil function;
• and
• M) apply a maximum likelihood estimation algorithm based on the in situ PSFs and the plurality of sub-PSFs to thereby generate lateral and axial locations of molecules within the specimen.