Novel technologies for cellular age profiling and validation

Project Summary Objective. This project aims to address a critical challenge in biomedical research – the reliable identification of cell line age and division/passage history. Leveraging recent advances in synthetic biology and genome editing, we propose the development of an innovative technology using genetic PUFs, a novel concept inspired by the semiconductor industry, for precise quantification of cellular age. Our technology is expected to significantly enhance the reproducibility and reliability of experimental outcomes in biomedical research. Today there are no commercially available methods to calculate and attest cellular age. Methods such as simple passage tracking are not standardized across labs and can be prone to errors or mislabeling. Moreover, STR profiling, while effective for confirming cell line identity (but not clonality), does not provide information about passage history or genomic drift that arise from aging. Specific Aims: Development of Genetic PUFs. Utilizing a combination of molecular barcoding and CRISPR-induced DNA repair mechanisms, we will embed unique, robust, and unclonable genetic identifiers within cell lines. This will allow for accurate tracking of cell line provenance and age, ensuring the integrity of biomedical experiments. Establishing a Cellular Clock. By characterizing the temporal drift space of genetic PUFs and employing advanced machine learning algorithms, we will develop a "cellular clock". This clock will provide a standardized method to monitor and record critical cellular events and divisions, thereby accurately determining the biological age of cell lines. Health Relatedness. The proposed technology directly addresses the pervasive issue of cell line misidentification and contamination in biomedical research. By ensuring the authenticity and age of cell lines, our innovation will contribute to more accurate and reliable research outcomes, ultimately benefiting health- related studies and pharmaceutical developments. Research Design and Methods. Our approach integrates cutting-edge genome editing techniques and computational methodologies. Our first aim will focus on the creation and characterization of genetic PUFs in a variety of cell lines over multiple divisions/passages. Our second aim will extend these findings to develop a comprehensive platform for cellular age identification, integrating machine learning models for predicting and validating the age of PUFed cell lines. We will conduct rigorous testing and validation across different cell lines and conditions to ensure the robustness and applicability of our technology. Impact. The successful completion of the proposed work will be a significant contribution to the field of cell line authentication, paving the way for more reliable and reproducible biomedical research. Our technology has the potential to enhance the quality and reliability of biomedical research and to become a standard tool in labs worldwide.