# US 12,111,263 B2 · Methods for analysis of single molecule localization microscopy to define molecular architecture > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,111,263 B2 - Document ID: US12111263B2 - Status: Granted - Kind: B2 - Application number: 16/769,422 - Filed: 2018-12-05 - Published: 2020-09-24 - Granted: 2024-10-08 - Earliest claimed: 2017-12-05 - Applicants: Simon Fraser University; University of British Columbia - Inventors: Ismail M. Khater; Ghassan Hamarneh; Ivan Robert Nabi; Fanrui Meng - Web page: https://patentgrep.com/patent/US12111263B2 ## CPC - G01N 21/6458: {Fluorescence microscopy (fluorescence microscopes per se G02B21/0076 and G02B21/16)} - G06T 7/30: Determination of transform parameters for the alignment of images, i.e. image registration - G01N 21/6428: {Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes" (in vivo A61B5/00; immunoassay G01N33/53)} - G01N 33/5005: {involving human or animal cells (immunoassay G01N33/56966; immunoassays of protozoa G01N33/56905; protozoa in screening assays C12Q1/025)} - G01N 33/582: {with fluorescent label} - G01N 33/6803: {General methods of protein analysis not limited to specific proteins or families of proteins} - G02B 21/0076: {arrangements using fluorescence or luminescence} - G02B 21/367: {providing an output produced by processing a plurality of individual source images, e.g. image tiling, montage, composite images, depth sectioning, image comparison} - G02B 27/58: Optics for apodization or superresolution; Optical synthetic aperture systems - G06T 3/4053: based on super-resolution, i.e. the output image resolution being higher than the sensor resolution - G01N 15/10: Investigating individual particles - G01N 2015/0038: {Investigating nanoparticles} - G01N 2021/6439: {with indicators, stains, dyes, tags, labels, marks} - G01N 2201/12: Circuits of general importance; Signal processing - G06T 2207/10028: Range image; Depth image; 3D point clouds - G06T 2207/10061: from scanning electron microscope - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G02B: OPTICAL ELEMENTS, SYSTEMS OR APPARATUS ## Abstract A method of super-resolution microscopy is provided. The method includes mapping three-dimensional location of each single emission event in a plurality of single emission events from a series of optical images of a sample to create a point cloud. The point cloud is filtered or refined. Clusters within the point cloud are identified and characterized allowing for an assessment of molecular architecture. ## Claims ### Claim 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. ### Claim 2 (depends on 1) The method of claim 1, wherein the merging of two or more single emission events is based on proximity with crisp or fuzzy proximity threshold and optionally wherein single emission events within a specified distance or merge threshold are merged. ### Claim 3 (depends on 2) The method of claim 2, wherein the merge threshold is based on the resolution limit of the SMLM microscopy or merge threshold is determined based on an analysis of non-biological networks in the 3D point cloud. ### Claim 4 (depends on 2) The method of claim 2, wherein single emission events within a specified distance or merge threshold are merged and wherein the specified distance is assessed in two dimensions, three dimensions or is different in different dimensions. ### Claim 5 (depends on 1) The method of claim 1, wherein merging comprises replacing two or more single emission events with one single emission event at the average position of the two or more single emission events. ### Claim 6 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 a series of optical images of a sample to create a 3D point cloud; refining the 3D point cloud by merging two or more single emission events into a node within a merge threshold to create a merged 3D point cloud, wherein the merge threshold is based on the resolution limit of the SMLM microscopy; completing a multi-threshold network analysis to identify clusters within 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. ### Claim 7 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 a series of optical images of a sample to create a 3D point cloud; refining the 3D point cloud by merging two or more single emission events into a node within a merge threshold to create a merged 3D point cloud, wherein the merge threshold is determined based on an analysis of non-biological networks resulting from multiple emissions from a single fluorophore in the 3D point; cloud, wherein multiple emissions from the single fluorophore are two or more emission events within 20 nm; completing a multi-threshold network analysis to identify clusters within 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 a filtering step removes the non-biological network nano-clusters from the 3D point cloud; and 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 the network degree distribution for each proximity threshold. ### Claim 8 (depends on 6) The method of claim 6, comprising acquiring the series of optical images as a function of time. ### Claim 9 (depends on 6) The method of claim 6, comprising converting the 3D point cloud into a network or graph. ### Claim 10 (depends on 9) The method of claim 9, comprising comparing the network or graph or fragment thereof with one or more randomly generated networks or graphs or of fragment thereof. ### Claim 11 (depends on 6) The method of claim 6, wherein optical images are divided into one or more regions of interest. ### Claim 12 (depends on 6) The method of claim 6, wherein distance between nodes is measured. ### Claim 13 (depends on 6) The method of claim 6, wherein the method comprises comparing a merged 3D point cloud or networks with a merged 3D point cloud or network from a reference sample; wherein the reference sample is optionally a negative control. ### Claim 14 (depends on 13) The method of claim 13, wherein the comparison identifies differences in the merged 3D point cloud or network and the merged 3D point cloud or network from the reference sample. ### Claim 15 (depends on 13) The method of claim 13, wherein the comparison is at more than one threshold. ### Claim 16 (depends on 1) A non-transitory computer readable medium containing program instructions executable by a processor, the computer readable medium comprising program instructions to implement the method of claim 1. ### Claim 17 (depends on 6) A non-transitory computer readable medium containing program instructions executable by a processor, the computer readable medium comprising program instructions to implement the method of claim 6. ### Claim 18 (depends on 7) A non-transitory computer readable medium containing program instructions executable by a processor, the computer readable medium comprising program instructions to implement the method of claim 7. ### Claim 19 (depends on 1) The method of claim 1, wherein the protein labelled with a fluorophore is a protein labelled with a fluorophore conjugated antibody, is a fusion protein comprising a fluorescent protein or a photoactivatable fluorescent protein. ### Claim 20 (depends on 6) The method of claim 6, wherein the protein labelled with a fluorophore is a protein labelled with a fluorophore conjugated antibody, is a fusion protein comprising a fluorescent protein or a photoactivatable fluorescent protein. ### Claim 21 (depends on 7) The method of claim 7, wherein the protein labelled with a fluorophore is a protein labelled with a fluorophore conjugated antibody, is a fusion protein comprising a fluorescent protein or a photoactivatable fluorescent protein. ### Claim 22 (depends on 1) The method of claim 1, wherein the machine learning is based on 3D pattern analysis. ### Claim 23 (depends on 1) The method of claim 1, wherein the machine learning is based on the Newman method.