The unique and innovative methods of the Potoscnak Center for Undiagnosed and Rare Disorders at Vanderbilt are what has allowed us help countless individuals and families who have been struggling with unexplained medical conditions. The program's success is rooted in the science behind its approach, which includes the use of a multitude of cutting-edge technology and techniques:
Whole genome sequencing (WGS) is a powerful tool that allows for the analysis of an individual's entire genetic makeup. This can be especially useful for identifying genetic variations that may be causing a patient's symptoms. The center uses WGS to analyze genomic data from patients with undiagnosed diseases, which can reveal previously undetected genetic mutations or variations.
Long-read sequencing is a powerful tool that allows for the analysis of long stretches of DNA in a single sequencing read. This is especially useful for identifying large structural variations, such as deletions or duplications, that may be causing a patient's symptoms. We use long-read sequencing to analyze genomic data from patients with undiagnosed diseases, which can reveal previously undetected genetic mutations or variations.
RNA-seq is a technique that allows for the analysis of an individual's transcriptome, which is the set of all the RNA molecules in a cell. This can be useful for identifying changes in gene expression that may be causing a patient's symptoms. We use RNA-seq to analyze transcriptomic data from patients with undiagnosed diseases, which can reveal previously undetected changes in gene expression.
Methylation analysis is a technique that allows for the study of changes in the methylation patterns of an individual's DNA. Methylation patterns can be associated with changes in gene expression and can be useful for identifying the underlying causes of a patient's symptoms. We utilize methylation analysis to study DNA methylation patterns in patients with undiagnosed diseases, which can reveal previously undetected changes in gene expression.
Optical chromosomal analysis is a technique that allows for the study of structural variations in an individual's chromosomes. This can be useful for identifying structural variations, such as deletions or duplications, that may be causing a patient's symptoms. We use optical chromosomal analysis to study chromosomal data from patients with undiagnosed diseases, which can reveal previously undetected structural variations.
Structural biology is a field of science that focuses on the study of the three-dimensional structure of biological molecules, such as proteins. By understanding the structure of a protein, scientists can better understand its function and how it interacts with other molecules. We use structural biology to study the proteins associated with undiagnosed diseases, which can provide insight into the underlying causes of the disease.
Animal models are used to study the biology and pathology of human diseases in a living organism. We use animal models to study the symptoms and progression of undiagnosed diseases, which can provide important information about the underlying causes of the disease.
The BioVU database is a DNA databank that contains information from more than 2 million patients. We utilize the BioVU database to identify genetic variations that may be associated with undiagnosed diseases.
iPSCs are cells that have been reprogrammed to become stem cells, which can differentiate into any type of cell in the body. We use iPSCs to study the effects of genetic mutations on cell development and function, which can provide important information about the underlying causes of undiagnosed diseases.
EHR-based analysis is a technique that uses electronic health records to identify patterns and trends in patient data. We use EHR-based analysis to study large amounts of patient data, which can reveal previously undetected patterns and trends that may be associated with undiagnosed diseases.
Artificial intelligence (AI) tools are used to analyze large amounts of data and identify patterns that may be associated with undiagnosed diseases. We utilize AI tools to analyze genomic data, EHR data, and other forms of patient data, which can reveal previously undetected patterns and trends.
Collaborative research is a critical component of our approach, as it allows us to bring together experts from a wide range of fields to work together on each case. This multidisciplinary approach allows for a comprehensive evaluation of each patient and a more thorough understanding of their condition.
Good old-fashioned detective work is also an important component of our approach. The center's team of specialists use a combination of scientific techniques and critical thinking to investigate each case and identify the underlying cause of the patient's symptoms.