# US 12,183,098 B2 · Computational systems pathology spatial analysis platform for in situ or in vitro multi-parameter cellular and subcellular imaging data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,183,098 B2 - Document ID: US12183098B2 - Status: Granted - Kind: B2 - Application number: 18/437,612 - Filed: 2024-02-09 - Published: 2024-06-06 - Granted: 2024-12-31 - Earliest claimed: 2018-12-19 - Assignees: University of Pittsburgh—Of the Commonwealth System of Higher Education - Applicants: University of Pittsburgh—of the Commonwealth System of Higher Education - Inventors: Srinivas C. Chennubhotla; Filippo Pullara; Douglass L. Taylor - Web page: https://patentgrep.com/patent/US12183098B2 ## CPC - G06V 20/698: {Matching; Classification} - A61B 5/00: Measuring for diagnostic purposes (radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00); Identification of persons - G01N 33/48: Biological material, e.g. blood, urine (G01N33/02, G01N33/26, G01N33/44, G01N33/46 take precedence); Haemocytometers (counting blood corpuscules distributed over a surface by scanning the surface G06M11/02) - G01N 33/575: for cancer - G06F 18/22: Matching criteria, e.g. proximity measures - G06T 7/0012: {Biomedical image inspection} - G06V 10/426: Graphical representations - G06V 10/761: {Proximity, similarity or dissimilarity measures} - G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro - G06T 2207/30096: Tumor; Lesion - G06V 2201/03: Recognition of patterns in medical or anatomical images - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - 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) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A computational systems pathology spatial analysis platform includes: (i) a spatial heterogeneity quantification component configured for generating a global quantification of spatial heterogeneity among cells of varying phenotypes in multi-parameter cellular and subcellular imaging data; (ii) a microdomain identification component configured for identifying a plurality of microdomains for tissue samples based on the global quantification, each microdomain being associated with a tissue sample; and (iii) a weighted graph component configured for constructing a weighted graph for the multi-parameter cellular and subcellular imaging data, the weighted graph having a plurality of nodes and a plurality of edges each being located between a pair of the nodes, wherein in the weighted graph each node is a particular one of the microdomains and the edge between each pair of microdomains in the weighted graph is indicative of a degree of similarity between the pair of the microdomains. ## Claims ### Claim 1 A method of generating a representation of spatially informed heterocellular communication from multi-parameter cellular and subcellular imaging data obtained from a number of tissue samples from a number of patients or a number of multicellular in vitro models, comprising: generating a quantification of a spatial heterogeneity among cells of certain varying predetermined phenotypes in the multi-parameter cellular and subcellular imaging data and identifying a microdomain for one of the tissue samples based on the quantification, including performing cell phenotyping on the multi-parameter cellular and subcellular imaging data to identify the certain varying predetermined phenotypes; and constructing a communications graph for the microdomain, wherein each of the phenotypes is a node in the communications graph, and wherein an edge between each pair of the phenotypes in the communications graph is indicative of an influence of one phenotype in the pair on a presence of the other phenotype in the pair, wherein each phenotype in the communications graph is represented by a data vector obtained from the multi-parameter cellular and subcellular imaging data, and wherein the method includes: for each pair of the phenotypes, establishing a numerical relationship between the phenotypes in the pair by determining a strength of association or influence value between the data vectors of the phenotypes in the pair, and establishing a directionality for each of the numerical relationships, wherein the edge between each pair of the phenotypes is indicative of the numerical relationship between the phenotypes in the pair and the directionality of the numerical relationship between the phenotypes in the pair. ### Claim 2 (depends on 1) The method according to claim 1, wherein each strength of association or influence value is a linear or non-linear correlation coefficient value. ### Claim 3 (depends on 1) The method according to claim 1, wherein each data vector is a vector of expression values for a number of predetermined biomarkers, wherein the predetermined biomarkers were used in generating the multi-parameter cellular and subcellular imaging data. ### Claim 4 (depends on 3) The method according to claim 3, wherein each vector of expression values is a biomarker intensity pattern for the number of predetermined biomarkers. ### Claim 5 (depends on 3) The method according to claim 3, wherein the establishing the directionality for each of the numerical relationships is based on a number of receptor-ligand databases for the predetermined biomarkers. ### Claim 6 (depends on 1) The method according to claim 1, further comprising interpreting each data vector to determine a number of states of activation for each of the phenotypes, and including each of the number of states of activation in the communications graph. ### Claim 7 (depends on 1) The method according to claim 1, wherein the determining the strength of association or influence value between the data vectors of the phenotypes in the pair comprises removing confounding effects of phenotypes other than the phenotypes in the pair. ### Claim 8 (depends on 1) A non-transitory computer readable medium storing one or more programs, including instructions, which when executed by a computer, causes the computer to perform the method of claim 1. ### Claim 9 A computerized system for generating a representation of spatially informed heterocellular communication from multi-parameter cellular and subcellular imaging data obtained from a number of tissue samples from a number of patients or a number of multicellular in vitro models, comprising: a processing apparatus, wherein the processing apparatus includes: a spatial heterogeneity quantification component configured for generating a quantification of a spatial heterogeneity among cells of certain varying predetermined phenotypes in the multi-parameter cellular and subcellular imaging data, including performing cell phenotyping on the multi-parameter cellular and subcellular imaging data to identify the certain varying predetermined phenotypes; a microdomain identification component configured for identifying a plurality of microdomains for the number of tissue samples based on the quantification, each microdomain being associated with one of the tissue samples; and a communication network component configured for constructing a communications graph for a selected one of the microdomains, wherein each of the phenotypes is a node in the communications graph, and wherein an edge between each pair of the phenotypes in the communications graph is indicative of an influence of one phenotype in the pair on a presence of the other phenotype in the pair, wherein each phenotype in the communications graph is represented by a data vector obtained from the multi-parameter cellular and subcellular imaging data, and wherein communication network component is configured for: for each pair of the phenotypes, establishing a numerical relationship between the phenotypes in the pair by determining a strength of association or influence value between the data vectors of the phenotypes in the pair, and establishing a directionality for each of the numerical relationships, wherein the edge between each pair of the phenotypes is indicative of the numerical relationship between the phenotypes in the pair and the directionality of the numerical relationship between the phenotypes in the pair. ### Claim 10 (depends on 9) The system according to claim 9, wherein each strength of association or influence value is a linear or non-linear correlation coefficient value. ### Claim 11 (depends on 9) The system according to claim 9, wherein each data vector is a vector of expression values for a number of predetermined biomarkers, wherein the predetermined biomarkers were used in generating the multi-parameter cellular and subcellular imaging data. ### Claim 12 (depends on 11) The system according to claim 11, wherein each vector of expression values is a biomarker intensity pattern for the number of predetermined biomarkers. ### Claim 13 (depends on 11) The system according to claim 11, wherein the establishing the directionality for each of the numerical relationships is based on a number of receptor-ligand databases for the predetermined biomarkers. ### Claim 14 (depends on 9) The system according to claim 9, wherein the communication network component is further configured for interpreting each data vector to determine a number of states of activation for each of the phenotypes, and including each of the number of states of activation in the communications graph. ### Claim 15 (depends on 9) The system according to claim 9, wherein the determining the strength of association or influence value between the data vectors of the phenotypes in the pair comprises removing confounding effects of phenotypes other than the phenotypes in the pair. ### Claim 16 A method of creating a personalized medicine strategy for a specific patient, wherein the specific patient is one of a number of patients, and wherein the number of patients has associated therewith multi-parameter cellular and subcellular imaging data obtained from a number of tissue samples from the number of patients, the method comprising: identifying a plurality of microdomains in one of the tissue samples that is associated with the specific patient, wherein the plurality of microdomains are based on the multi-parameter cellular and subcellular imaging data and wherein each microdomain includes a plurality of phenotypes; generating a heterocellular communication graph for each of the microdomains, wherein each heterocellular communication graph comprises a representation of spatially informed heterocellular communication for the microdomain, wherein each of the phenotypes of the microdomain is a node in the communication graph, and wherein an edge between each pair of the phenotypes in the communications graph is indicative of an influence of one phenotype in the pair on a presence of the other phenotype in the pair, wherein each phenotype in the communications graph is represented by a data vector obtained from the multi-parameter cellular and subcellular imaging data, and wherein the method includes, for each pair of the phenotypes, establishing a numerical relationship between the phenotypes in the pair by determining a strength of association or influence value between the data vectors of the phenotypes in the pair, and establishing a directionality for each of the numerical relationships, wherein the edge between each pair of the phenotypes is indicative of the numerical relationship between the phenotypes in the pair and the directionality of the numerical relationship between the phenotypes in the pair; generating a quantification of an interdependence of the microdomains in space and time based on the heterocellular communication graphs; and designing the medicine strategy for the specific patient based on the quantification. ### Claim 17 (depends on 16) The method according to claim 16, wherein each microdomain is identified by generating a quantification of a spatial heterogeneity among cells of certain varying predetermined phenotypes in the multi-parameter cellular and subcellular imaging data and identifying the microdomain based on the quantification, including performing cell phenotyping on the multi-parameter cellular and subcellular imaging data to identify the certain varying predetermined phenotypes. ### Claim 18 (depends on 16) The method according to claim 16, wherein each microdomain carries a low risk of an outcome variable of interest, but spatial proximity and relationships between the microdomains increases overall risk of the outcome variable of interest. ### Claim 19 (depends on 16) The method according to claim 16, wherein the heterocellular communication graphs inform which pathways are activated within the microdomains and what the relationships are between the heterocellular communication graphs across the microdomains. ### Claim 20 (depends on 16) The method according to claim 16, wherein the heterocellular communication graphs reveal information flow that has to be inhibited by drugs to slow down or reverse disease progression for the specific patient. ### Claim 21 A method of representing a time evolution of disease progression in a specific patient, wherein the specific patient is one of a number of patients, and wherein the number of patients has associated therewith multi-parameter cellular and subcellular imaging data obtained from a number of tissue samples from the number of patients, the method comprising: generating a geometrical representation of a disease landscape for the specific patient, wherein the geometrical representation includes a plurality of points, wherein each point on the geometrical representation: (i) describes a disease status of the specific patient at a particular time and is based on a particular one of the tissue samples that is associated the selected patient, (ii) is based on a microdomain in the particular one of the tissue samples that is based on the multi-parameter cellular and subcellular imaging data, wherein the microdomain includes a plurality of phenotypes, and (iii) includes a heterocellular communication graph for the microdomain that comprises a representation of spatially informed heterocellular communication for the microdomain, wherein each of the phenotypes of the microdomain is a node in the communication graph, and wherein an edge between each pair of the phenotypes in the communications graph is indicative of an influence of one phenotype in the pair on a presence of the other phenotype in the pair, wherein each phenotype in the communications graph is represented by a data vector obtained from the multi-parameter cellular and subcellular imaging data, and wherein the method includes, for each pair of the phenotypes, establishing a numerical relationship between the phenotypes in the pair by determining a strength of association or influence value between the data vectors of the phenotypes in the pair, and establishing a directionality for each of the numerical relationships, wherein the edge between each pair of the phenotypes is indicative of the numerical relationship between the phenotypes in the pair and the directionality of the numerical relationship between the phenotypes in the pair. ### Claim 22 (depends on 21) The method according to claim 21, wherein each heterocellular communication graph is associated with a systems biology model comprising a system of ordinary differential equations. ### Claim 23 (depends on 22) The method according to claim 22, wherein for each heterocellular communication graph, the system of ordinary differential equations defines kinetics of the disease landscape for predicting a temporal evolution on the disease landscape for the specific patient. ### Claim 24 (depends on 21) The method according to claim 21, wherein each microdomain is identified by generating a quantification of a spatial heterogeneity among cells of certain varying predetermined phenotypes in the multi-parameter cellular and subcellular imaging data and identifying the microdomain based on the quantification, including performing cell phenotyping on the multi-parameter cellular and subcellular imaging data to identify the certain varying predetermined phenotypes.