Molecular Biology

Parasitology

Clinical Biochemistry

Proteomics

Extracellular Matrix

Aging

ResearchHub Funding

The Extracellular Matrix Aging Atlas: a knowledgebase of time-resolved matrisome signatures extracted from public proteomic datasets—a proposal

Rakhan Aimbetov [1][1] Overlake Biologics; r@overlake.bio AbstractThe extracellular matrix is a complex substance localized in the extracellular space, serving as a medium where cells reside. Besides providing anchoring support, the extracellular matrix is a mechanical and biochemical environment that directs cellular functions and processes via a collection of various stimuli, prompting gene expression profiles to reflect developmental and physiological contexts. As a dynamic structure, the extracellular matrix composition is subject to change as a function of age. Presently, there is a lack of a unified consensual understanding of the qualitative and quantitative aspects of these changes. A few recent publications look into proteomic alterations that happen in the extracellular matrix of some tissues with aging. Consequently, aggregating the published datasets into a database of the extracellular matrix aging signatures is proposed. IntroductionThe extracellular matrix (ECM) is a complex biological scaffold that gives tissues and organs their structural foundation (1). The ECM and the cells that synthesize and assemble it communicate reciprocally to modulate tissue homeostasis (2). Consequently, any alteration, physiological or not, in either the cell or its microenvironment, represented by the ECM, launches an array of mechanisms to restore balance.The ECM is mainly protein in nature. The sum total of proteins that constitute the ECM is called the matrisome (3). The matrisome comprises 1027 genes for the human genome and 1110 genes for the mouse genome (4, 5) (Fig. 1). Any given tissue consists of over 150 ECM and ECM-associated proteins; with characteristic differences in the ECM composition of different tissues (6, 7). Furthermore, qualitative and quantitative matrisomic alterations in response to insult can be used as a biomarker for undercurrent pathology (8).Fig. 1. The matrisome (5). The core matrisome comprises ECM glycoproteins, collagens, and proteoglycans. Matrisome-associated proteins include ECM-affiliated proteins, ECM regulators, and secreted factors. Hs, Homo sapiens; Mm, Mus musculus.Matrisomes, being a dynamic structure, are subject to compositional variation as a function of age (9). Although we know the cross-sectional structural make-up of different tissues in the norm (10), little is known about how the matrisomes change temporally – at different stages of life. Of note, it is important to describe the ECM on the protein level since transcriptomic signatures might not adequately reflect proteomic changes (11). In the past few years, several papers describing such time-resolved shifts in matrisomic composition for select tissues in humans and mice using protein mass spectrometry have been published (12–24). The creation of a database that would integrate the public datasets on age-associated ECM characteristics consistently and comparably is a natural next step. A database like that would help the matrix biologists to better understand the temporal ECM dynamics, and identify potential targets for interventions.AimsI propose to create such a database from publicly available datasets for human and murine tissues – The ECM Aging Atlas. As a result, a curated database, where each subsequent new dataset is processed according to a standardized schema, complementing and enriching the whole body of information in a harmonized fashion, will be deployed. Materials/methodsStudy typeOtherThis is a bioinformatics and computational project to integrate the available knowledge on age-related extracellular matrix dynamics into a standardized database format, aiming to enhance research accessibility and facilitate new discoveries in the field.DataRegistration prior to analysis of the dataThe data selected for the project are described in the spreadsheet. These include processed datasets and associated raw data files from peer-reviewed research on tissue- or organ-level, time-resolved matrisomic signatures in mice and humans (12–24).Processing and analysisTo efficiently compile and standardize the ECM Aging Atlas database, focusing on the essence and eliminating redundancies, the workflow includes:Database and metadata standards. A relational database schema organizes data into interrelated tables, ensuring efficient storage and query execution. Metadata standards specify the format and content of additional information about data (e.g., experimental conditions, sample preparation), which is crucial for interpreting and reproducing research findings.Data normalization and analysis protocols. Data normalization adjusts measurements to a common scale or reference, compensating for variations in experimental methods. Standardized analysis workflows use consistent procedures for processing and analyzing data, enhancing comparability across studies.Quality control and integrating data. Quality control involves checks to ensure data accuracy and consistency. Data integration merges information from various sources into a unified database, maintaining logical connections among different data elements.Database access and community interaction. A user-friendly web interface facilitates access to the database, allowing researchers to easily explore and extract information. Engaging with the scientific community ensures the database remains relevant and up-to-date, encouraging contributions and feedback.This streamlined approach to database creation supports the harmonization of diverse datasets into a singular, valuable resource for the scientific community, fostering advancements in understanding ECM changes with aging.To re-analyze the raw data, I will use one of the open-source solutions tailored for proteomics workflows. For example, OpenMS is a versatile environment for proteomic analysis (25, 26), compatible with such workflow systems as Nextflow, KNIME, and Galaxy. Nextflow is an open-source workflow system for automating bioinformatics pipelines. It is designed to be scalable, reproducible, and portable, making it suitable for large-scale analyses on cloud platforms, such as Google Cloud (doc). Deployed on Nextflow, quantms is an open-source cloud-based pipeline for massively parallel proteomic data analysis (27). Currently, the workflow supports three major MS-based analytical methods: (i) data-dependent acquisition (DDA) label-free and isobaric quantitation (e.g. TMT, iTRAQ); (ii) data-independent acquisition (DIA) label-free quantification.MaxQuant is an open-source software platform for analyzing large-scale quantitative proteomics data; it provides a comprehensive set of tools for protein identification, quantification, and statistical analysis (28, 29). MaxQuant on Galaxy offers seamless data analysis, eliminating software installation needs (30) (doc).CloudProteoAnalyzer is a cloud-computing platform designed to offer a user-friendly interface and accurate analysis of comprehensive proteomics data (31). This platform harnesses the computational capacity of multiple computing nodes within a supercomputer, thereby ensuring scalability for large datasets. BudgetCostsGoogle Cloud compute (VM + storage): 250 USD/mo. * 6 mo. = 1500 USDChatGPT subscription: 20 USD/mo. * 12 mo. = 240 USDResearcher hourly time: 25 USD/hr. * 130 hrs. = 3250 USDTOTAL: 4990 USD LinksPROJECT DATAROOMTo help me analyze the datasets and build the database, I customized OpenAI’s GPT-4 to guide me in my work – ECM GPT (“Your copilot for everything ECM”).DatabasesECMGAG-DB; https://gagdb.glycopedia.eu/MatriNet; https://www.matrinet.org/Matrisome AnalyzeR; https://matrinet.shinyapps.io/MatrisomeAnalyzer/MatrisomeDB 2.0; https://matrisomedb.org/MatrixDB; http://matrixdb.univ-lyon1.fr/The Human Protein Atlas; https://www.proteinatlas.org/The Matrisome Project; http://matrisome.org/DatasetsGoogle Dataset Search; https://datasetsearch.research.google.com/Mendeley Data; https://data.mendeley.com/OmicsDI; https://www.omicsdi.org/ References1.  Frantz, C., Stewart, K. 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