Blood transcriptomics and metabolomics for personalized medicine

Abstract

Molecular analysis of blood samples is pivotal to clinical diagnosis and has been intensively investigated since the rise of systems biology. Recent developments have opened new opportunities to utilize transcriptomics and metabolomics for personalized and precision medicine. Efforts from human immunology have infused into this area exquisite characterizations of subpopulations of blood cells. It is now possible to infer from blood transcriptomics, with fine accuracy, the contribution of immune activation and of cell subpopulations. In parallel, high-resolution mass spectrometry has brought revolutionary analytical capability, detecting > 10,000 metabolites, together with environmental exposure, dietary intake, microbial activity, and pharmaceutical drugs. Thus, the re-examination of blood chemicals by metabolomics is in order. Transcriptomics and metabolomics can be integrated to provide a more comprehensive understanding of the human biological states. We will review these new data and methods and discuss how they can contribute to personalized medicine.

Keywords: Blood systems biology; Data integration; Metabolomics; Personalized medicine; Transcriptomics.

Fig. 1

Overview of blood systems biology, the pertinent samples and technologies. After a blood sample is taken, it is easily separated into plasma, white blood cells and red blood cells. The major white blood cells are listed on the left, while each cell type can be analyzed via exquisite protein markers via flow cytometry, giving information on particular subpopulations. Major “omics” technologies are listed on the right. DNA microarrays overlap with both genomics (genotyping arrays) and transcriptomics (expression arrays). DNA sequencing supports genomics (and epigenomics), transcriptomics (RNAseq), and immune repertoires. Immune repertoires include T cell receptor and B cell receptor sequences, whereas the latter represents antibody diversity. Both metabolomics (and environmental chemical exposures) and proteomics are largely dependent on mass spectrometry.

Fig. 2

Testing cell populations and gene modules in blood transcriptomics. This demonstration is based on a paired comparison between day 7 and baseline in MCV4 vaccination . Common statistical methods for pathway analysis are used here, while we replace conventional pathways with cell-specific signatures or custom gene modules. (A) Over-representation test. DNA microarray data are collapsed to the gene level by using the probe set of highest intensity per gene. Gene expression values are compared by paired t-test, and corrected for false discovery rate . Among the significant genes identified here, 7 are found in a predefined signature of plasma cells. These numbers are used to construct a contingency table, and Fisher exact test returns an enrichment p-value < 1E^− 5. (B) The distribution of the same plasma cell signature genes is tested by GSEA. The bottom color bar shows the distribution of all genes, ranked by t-score between two time points. The vertical lines indicate the positions of the 24 genes on the ranked list, which are highly skewed for upregulation. (C) A gene module from the BTM collection provides better measurement of antibody secreting cells, demonstrated on the same data. (D) Additional example of BTM module on PLK1 signaling, showing highly significant enrichment towards upregulation. The p-values in B, C, and D approach zero. A detailed tutorial on BTMs is available as an online supplement to Li et al. .

Fig. 3

Metabolomics as potential alternative to clinical blood test. (A) Partial chart of chemicals in blood test (adopted from [74]). The physiological ranges of several metabolites are shown by log scale. (B) Current coverage on KEGG pathways by LC-MS metabolomics, using data generated from our group. Each black dot is a matched metabolite. The full KEGG metabolic map can be viewed at high resolution at http://www.genome.jp/kegg/pathway/map/map01100.html. As metabolomics technology progresses, it can be expected to quantify over 1000 chemicals in less than 10 min. Such data will be able to support a much more detailed diagnostic chart.