Quantitative Normalization of Spatial Metabolomics for Molecular Signatures of Tissue Heterogeneity

组织异质性分子特征的空间代谢组学定量标准化

• 批准号: 10603667
• 负责人: Leila Hejazi
• 金额: $ 35.65万
• 依托单位: SYGNAMAP, INC.
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-12 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10603667
• 关键词: Address; Adoption; Algorithms; Architecture; Area; Automation; Award; Biochemical; Biochemical Pathway; Biological Markers; Biopsy; Biotechnology; Calibration; Chronic; Chronic Disease; Chronic Kidney Failure; Data; Data Set; Data Sources; Development; Disease; Fibrosis; Future; Goals; Health Sciences; Heterogeneity; Histologic; Histopathology; Human; Image; Inflammation; Ions; Kidney; Kidney Diseases; Letters; Link; Liver; Liver diseases; Maps; Metabolic Pathway; Methodology; Mitochondria; Molecular; Molecular Profiling; National Institute of Diabetes and Digestive and Kidney Diseases; Normal Range; Normal tissue morphology; Organ failure; Outcome; Pathologic; Pathology; Pathway interactions; Pharmacologic Substance; Phase; Precision therapeutics; Proteomics; Reference Values; Regional Anatomy; Residual state; Resolution; Sampling; Small Business Innovation Research Grant; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization; Statistical Data Interpretation; Structure; Technology; Testing; Texas; Therapeutic; Time; Tissues; United States National Institutes of Health; Universities; Variant; biomarker discovery; computational platform; disease phenotype; drug development; drug discovery; improved; innovation; interest; mass spectrometric imaging; metabolomics; therapeutic development; therapeutic target; transcriptomics

PROJECT SUMMARY The molecular and biochemical basis of tissue heterogeneity in normal and disease states can be addressed with spatial omic analysis. Advances in spatial metabolomic analysis of tissue biopsies using MALDI-MSI could enable the identification of numerous biochemical pathways as signatures of specific histopathology features. However, to extract the most robust disease- relevant biomarkers and pathways from spatial omics platforms will require quantitation of spatial molecules for automation and statistical analysis. A key obstacle to adoption of spatial metabolomics is the lack of rigorous normalization of biomolecules across tissue sections. SygnaMap proposes to build MSI-DeepPath as a computational platform to quantify spatially distinct metabolite abundancies with histopathologic features. Data from MALDI-MSI and bulk metabolomics from serial sections of human kidney and liver tissue will be used as the data source for our platform development. SygnaMap is developing MSI-DeepPath, a drug discovery and development computational platform that incorporates ground-breaking improvements in MALDI- MSI with computational pathology. Quantitation of spatial metabolomics for automation and statistical analysis as presented in this SBIR proposal will enable the extraction of the most robust disease-relevant pathways from metabolites. The rigorous deconvolution of structure-relevant pathways will enable a highly impactful therapeutic target discovery and drug development platform for use by biotech and pharmaceutical companies for development of therapeutics alongside biomarkers.

项目摘要 正常和疾病状态下组织异质性的分子和生物化学基础可以 用空间组学分析来解决。组织空间代谢组学研究进展 使用MALDI-MSI的活组织检查可以鉴定许多生物化学途径， 特定组织病理学特征的签名。然而，要提取最强大的疾病- 来自空间组学平台的相关生物标志物和途径将需要定量空间 分子的自动化和统计分析。采用空间技术的一个主要障碍 代谢组学的缺点是缺乏跨组织切片的生物分子的严格标准化。 SygnaMap建议将MSI-DeepPath构建为计算平台，以在空间上量化 具有组织病理学特征的不同代谢物丰度。MALDI-MSI和原液数据 来自人类肾脏和肝脏组织连续切片的代谢组学将用作数据源 我们的平台开发。SygnaMap正在开发MSI-DeepPath，这是一种药物发现， 开发计算平台，结合了MALDI的突破性改进- MSI与计算病理学。空间代谢组学的自动化和定量 本SBIR提案中提出的统计分析将能够提取最可靠的 来自代谢物的疾病相关途径。结构相关的严格反卷积 途径将使高度有影响力的治疗靶点发现和药物开发成为可能 生物技术和制药公司用于开发治疗方法的平台 以及生物标志物。