US 12,111,263 B2
Methods for analysis of single molecule localization microscopy to define molecular architecture
Ismail M. Khater, Burnaby (CA); Ghassan Hamarneh, Vancouver (CA); Ivan Robert Nabi, Vancouver (CA); and Fanrui Meng, Shanghai (CN)
Appl. No. 16/769,422
Filed by Simon Fraser University, Burnaby (CA); and University of British Columbia, Vancouver (CA)
PCT Filed Dec. 5, 2018, PCT No. PCT/CA2018/051553
§ 371(c)(1), (2) Date Jun. 3, 2020,
PCT Pub. No. WO2019/109181, PCT Pub. Date Jun. 13, 2019.
Claims priority of provisional application 62/594,642, filed on Dec. 5, 2017.
Prior Publication US 2020/0300763 A1, Sep. 24, 2020
Int. Cl. G02B 21/36 (2006.01); G01N 21/64 (2006.01); G01N 33/50 (2006.01); G01N 33/58 (2006.01); G01N 33/68 (2006.01); G02B 21/00 (2006.01); G02B 27/58 (2006.01); G06T 3/4053 (2024.01)
CPC G01N 21/6458 (2013.01) [G01N 21/6428 (2013.01); G01N 33/5005 (2013.01); G01N 33/582 (2013.01); G01N 33/6803 (2013.01); G01N 2021/6439 (2013.01); G01N 2201/12 (2013.01)] 23 Claims
OG exemplary drawing
 
1. A method of defining molecular architecture of a fluorophore labelled structure in a biological sample, the method comprising:
capturing a series of single molecule localization microscopy (SMLM) optical images of said structure, wherein said structure comprises a protein labelled with a fluorophore;
mapping a three-dimensional (3D) location of each single emission event in a plurality of single emission events from the series of SMLM optical images to create a 3D point cloud;
refining the 3D point cloud by merging two or more single emission events;
removing one or more single emission events that correspond to randomly generated points;
completing a multi-threshold network analysis to identify clusters within the refined 3D point cloud;
identifying non-biological network nano-clusters resulting from multiple emissions from a single fluorophore in the refined 3D point cloud, wherein multiple emissions from the single fluorophore are two or more emission events within 20 nm;
removing the non-biological network nano-clusters from the refined 3D point cloud; and
extracting features for each cluster at a threshold of 60 nm-140 nm to obtain size, shape and topology of each cluster and thereby define the molecular architecture of the fluorophore labelled structure;
wherein the extracting features for each cluster uses machine learning;
wherein non-biological networks are distinguished from biological networks in the 3D point cloud by performing a multi-scale network analysis of the 3D point cloud and determining a network degree distribution for each proximity threshold; and
wherein the refining of the 3D point cloud optionally comprises iterative steps.