Noninvasive in vivo monitoring of tissue-specific global gene expression in humans PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Koh, W., Pan, W., Gawad, C., Fan, H. C., Kerchner, G. A., Wyss-Coray, T., Blumenfeld, Y. J., El-Sayed, Y. Y., Quake, S. R. 2014; 111 (20): 7361-7366

Abstract

Circulating cell-free RNA in the blood provides a potential window into the health, phenotype, and developmental programs of a variety of human organs. We used high-throughput methods of RNA analysis such as microarrays and next-generation sequencing to characterize the global landscape circulating RNA in a cohort of human subjects. By focusing on genes whose expression is highly specific to certain tissues, we were able to identify the relative contributions of these tissues to circulating RNA and to monitor changes in tissue development and health. As one application of this approach, we performed a longitudinal study on pregnant women and analyzed their combined cell-free RNA transcriptomes across all three trimesters of pregnancy and after delivery. In addition to the analysis of mRNA, we observed and characterized noncoding species such as long noncoding RNA and circular RNA transcripts whose presence had not been previously observed in human plasma. We demonstrate that it is possible to track specific longitudinal phenotypic changes in both the mother and the fetus and that it is possible to directly measure transcripts from a variety of fetal tissues in the maternal blood sample. We also studied the role of neuron-specific transcripts in the blood of healthy adults and those suffering from the neurodegenerative disorder Alzheimer's disease and showed that disease specific neural transcripts are present at increased levels in the blood of affected individuals. Characterization of the cell-free transcriptome in its entirety may thus provide broad insights into human health and development without the need for invasive tissue sampling.

View details for DOI 10.1073/pnas.1405528111

View details for Web of Science ID 000336168100048