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, 111 (20), 7361-6

Noninvasive in Vivo Monitoring of Tissue-Specific Global Gene Expression in Humans

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Noninvasive in Vivo Monitoring of Tissue-Specific Global Gene Expression in Humans

Winston Koh et al. Proc Natl Acad Sci U S A.

Erratum in

  • Proc Natl Acad Sci U S A. 2014 Jul 29;111(30):11223

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.

Keywords: cell-free nucleic acids; genomics; noninvasive diagnostics.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Using microarray expression data from the plasma of four normal controls, quadratic programming was performed using known tissue-specific expression from a publicly available database to obtain the relative tissue contribution of the respective different tissue types. The relative contribution from detected tissues were represented in pie charts for these four subjects. Distribution of contribution from different tissue types are fairly consistent between these subjects, with the major contributor of cell-free RNA originating from whole blood.
Fig. 2.
Fig. 2.
Characterization of maternal plasma transcriptome by RNA-seq and microarray assays. (A) The scatter plot of the correlation between RNA-seq and Affymetrix array assay for samples taken at the third trimester. The Pearson correlation coefficient is 0.78. (B) Venn diagram displaying the genes detected by RNA-seq and microarray. The cutoff for the RNA-Seq is fragments per kilobase of transcript per million mapped reads (FPKM) > 0. The cutoff for the microarray is intensity > 4. Sample P12_T3 is shown here as an example.
Fig. 3.
Fig. 3.
Heatmap of time-varying genes identified from microarray analysis. The color bar on the top of the heatmap corresponds to different time points during pregnancy. Each row of the heatmap refers to a gene, and each column is a sample taken at a particular time point: D, donor; T1, first trimester; T2, second trimester; T3, third trimester; P, postpartum. Unsupervised clustering was performed on genes across different time points. Distinct temporal trends are observed; the cluster of genes belongs to the CGB family of genes, which are known to be expressed at high levels during the first trimester exhibited corresponding high levels of RNA in the first trimester. The other clusters of genes mainly contains genes transcripts that increased throughout the first two trimesters and showed a peak expression in the third.
Fig. 4.
Fig. 4.
Time course of fetal brain-specific genes, measured by qPCR. Plot showing the ΔCt value with respect to the housekeeping gene ACTB across the different trimesters of pregnancy including after birth. Time points across each patient is shown connected by the lines. Two replicates were performed for each patient at each time point. The general trends show elevated levels during the trimesters with a decline to low levels after the baby is born in concordance with the notion that fetal-specific transcripts increased into the pregnancy followed by rapid clearance after birth.
Fig. 5.
Fig. 5.
Measurements of PSD3 and APP cell-free RNA transcripts levels in plasma shows that the levels of these two transcripts are elevated in AD patients and can be used to cleanly group the AD patients from the normal patients.

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