Artificial Intelligence Boosted Evolution and Detection of Genetically Encoded Reporters for In Vivo Imaging

人工智能促进体内成像基因编码报告基因的进化和检测

• 批准号: 10533825
• 负责人: CHRISTIAN T FARRAR
• 金额: $ 68.14万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10533825
• 关键词: Algorithms; Amides; Artificial Intelligence; Biological Response Modifier Therapy; Cardiac; Cells; Chemicals; Clinic; Clinical; Clinical assessments; Collaborations; Detection; Development; Diagnosis; Discrimination; Disease; Drug Kinetics; Evolution; Fingerprint; Gene Transfer; Genes; Genetic Programming; Glioblastoma; Goals; Histologic; Image; Infection; Longitudinal Studies; Lysine; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Measurement; Medical Imaging; Metabolic; Methods; Modeling; Monitor; Mus; Oncolytic; Oncolytic viruses; Pathology; Performance; Physiologic pulse; Predisposition; Problem Solving; Productivity; Protein Engineering; Proteins; Protons; Reporter; Reporter Genes; Resistance; Resolution; Schedule; Sensitivity and Specificity; Signal Transduction; Specificity; Technology; Testing; Therapeutic; Therapeutic Agents; Time; Tissues; Tumor Cell Line; Viral; Virus Diseases; Virus Replication; Water; Work; cancer imaging; clinical translation; deep learning; deep neural network; detection method; detection sensitivity; gene therapy; imaging modality; improved; in vivo imaging; neoplastic cell; neural network architecture; novel; oncolytic virotherapy; pre-clinical; radio frequency; rapid technique; response; therapeutic gene; tool; trafficking; transgene expression; tumor; viral gene delivery

PROJECT SUMMARY Magnetic resonance (MR) reporter genes have the potential to monitor transgene expression non-invasively in real time at high resolution. These genes can be applied to interrogate the efficacy of gene therapy, to monitor viral therapeutics and viral gene delivery, to assess cellular differentiation, cell trafficking, and specific metabolic activity, and also assess changes in the microenvironment. Efforts toward the development of MR reporter genes have been made for over a decade, but, despite these efforts, the field is still in its early developmental stage. This reflects the fact that there are numerous complications, caused by the low sensitivity of detection, the need for substrates with their associated undesirable pharmacokinetics, and/or the difficult and, in some cases, delayed interpretation of signal changes. We have previously demonstrated that many of these challenges can be overcome with the use of a lysine rich protein (LRP) reporter gene, that is detectable by chemical exchange saturation transfer (CEST) MRI. However, to mature the CEST reporter gene technology and bring it towards clinical translation, its sensitivity and specificity need to be improved. In particular, the LRP reporter gene specificity is limited by the fact that the lysine amide exchangeable protons of LRP have the same chemical shift as amide protons from endogenous proteins. It is therefore difficult to distinguish the reporter CEST contrast from the background CEST contrast, both of which may be changing with time. The specificity is further limited by the sensitivity of the CEST contrast to intracellular pH where the qualitative CEST contrast cannot distinguish between exchange rate and concentration effects. Finally, a decrease in cytosolic pH, observed in many disease pathologies, reduces the amide proton exchange rate and hence the CEST reporter sensitivity. We therefore propose to develop improved MRI reporter genes and quantitative MRI detection methods that will facilitate the clinical translation of these methods for imaging biological therapeutics, such as oncolytic virotherapy. We hypothesize that CEST reporter genes with improved sensitivity and specificity along with improved quantitative CEST methods will enable viral infection and replication to be monitored longitudinally throughout OV tumor therapy. To test this hypothesis and establish the clinical potential of MRI reporter genes we will capitalize on two transformative technologies developed in our labs; (Aim 1) an artificial intelligence based genetic programming algorithm will be used for optimizing the sensitivity and specificity of the CEST reporter gene and (Aim 2) a CEST magnetic resonance fingerprinting (MRF) method will be used for the rapid quantification of both the reporter protein concentration and chemical exchange rate. (Aim 3) These methods will be validated for imaging oncolytic viral infection and replication in mouse glioblastoma tumor models.

项目总结