ABSTRACT
RNA sequencing (RNA-seq) has recently been used in translational research settings to facilitate diagnoses of Mendelian disorders. A significant obstacle for clinical laboratories in adopting RNA-seq is the low or absent expression of a significant number of disease-associated genes/transcripts in clinically accessible samples. As this is especially problematic in neurological diseases, we developed a clinical diagnostic approach that enhanced the detection and evaluation of tissue-specific genes/transcripts through fibroblast-to-neuron cell transdifferentiation. The approach is designed specifically to suit clinical implementation, emphasizing simplicity, cost effectiveness, turnaround time, and reproducibility. For clinical validation, we generated induced neurons (iNeurons) from 71 individuals with primary neurological phenotypes recruited to the Undiagnosed Diseases Network. The overall diagnostic yield was 25.4%. Over a quarter of the diagnostic findings benefited from transdifferentiation and could not be achieved by fibroblast RNA-seq alone. This iNeuron transcriptomic approach can be effectively integrated into diagnostic whole-transcriptome evaluation of individuals with genetic disorders.
Subject(s)
Cell Transdifferentiation , Fibroblasts , Neurons , Sequence Analysis, RNA , Humans , Cell Transdifferentiation/genetics , Fibroblasts/metabolism , Fibroblasts/cytology , Sequence Analysis, RNA/methods , Neurons/metabolism , Neurons/cytology , Transcriptome , Reproducibility of Results , Nervous System Diseases/genetics , Nervous System Diseases/diagnosis , RNA-Seq/methods , Female , MaleABSTRACT
Osteosarcoma is the most common type of bone cancer. Osteosarcoma is commonly associated with TP53 inactivation (around 95% of cases) and RB1 inactivation (around 28% of cases). With the discovery of reprogramming factors to induce pluripotency even in terminally differentiated cells, induced pluripotent stem cells (iPSCs) have emerged as a promising disease model. iPSC-based disease modeling uniquely recapitulates disease phenotypes and can support discoveries into disease etiology and is used extensively today to study a variety of diseases, including cancers. This paper focuses on iPSC-based modeling of Li-Fraumeni syndrome (LFS), an autosomal dominant disorder commonly associated with TP53 mutation and high osteosarcoma incidence. As iPSCs are increasingly utilized as a platform for cancer modeling, the experimental approaches that we discuss here may serve as a guide for future studies.
