Novel technologies based on RNA-sequencing (RNA-seq) are now streamlined to sequence hundreds of whole blood transcriptomes simultaneously. These ‘ultra-high-throughput’ RNA-seq technologies generate high-quality transcriptional information at low-cost. Therefore, they have the potential to revolutionize both large-scale investigative studies and non-invasive, blood-based diagnostic identification of diseases.
In this article we discuss previous discoveries made using whole blood transcriptome sequencing with RNA-seq, and explore advances in technology that could accelerate future whole blood transcriptomic studies.
The whole blood transcriptome
Blood is the pipeline of the body and contains many different cell types such as red blood cells (erythrocytes) and the white blood cell component of the immune system (leukocytes). In addition, it also contains plasma, non-blood cells, small vesicles and diverse molecular signals.
Whole blood RNA-seq identifies and quantifies mRNA from cells contained in blood. This provides insight into the physiological state of a system. Variations in mRNA levels detected by whole blood RNA-seq can indicate both systemic disease and localized tissue-specific changes (Chaussabel 2015).
The whole blood transcriptome is highly dynamic. It responds rapidly to environmental changes such as disease, infection or injury (Chaussabel 2015). Studies therefore use the whole blood transcriptome to provide a snapshot of the levels of mRNA present at any one time.
Discoveries from whole blood transcriptomics
Comparative whole blood transcriptome studies have characterized gene expression changes in diverse conditions such as sepsis, aging and infection (Chaussabel 2015; Peters et al., 2015; Hopp et al., 2018). A recent whole blood transcriptomic study even found that residents of the highest city in the world had time-of-day- and altitude-dependent responses of several immune cell types (Manella et al., 2022).
Whole blood transcriptomes provide a holistic representation of systemic perturbations in the body. This is particularly useful for complex, multiorgan disorders.
Recent studies used whole blood transcriptomics combined with computational analyses to predict tissue-specific gene expression (Basu et al., 2021). Researchers could use this blood-based, tissue-specific transcriptome to predict disease state for complex, multiorgan disorders such as hypertension and type-2 diabetes (Basu et al., 2021).
Novel techniques for whole blood transcriptome analyses
Collection of whole blood is routine, non-invasive and cost effective. Therefore, it is attractive for large-scale studies or to detect disease in organs that are difficult to sample. Despite this, until recently, the RNA-seq stage of whole blood transcriptomics remained prohibitively expensive.
Novel ultra-high-throughput bulk RNA-seq approaches, such as Bulk RNA Barcoding and Sequencing (MERCURIUS™ Blood BRB-seq), are optimized for whole blood transcriptome sequencing with RNA-seq (Alpern et al., 2019). This technology uses sample barcoding which allows researchers to pool samples for simultaneous processing.
When combined with standard whole blood extraction from PAXgene™ blood RNA tubes (PreAnalytiX) or Tempus™ blood RNA tubes (Applied Biosystems), researchers can perform larger-scale cohort studies at a lower cost than ever before.
MERCURIUS™ Blood BRB-seq kits seamlessly integrate a human globin blocker reagent into the sample processing pipeline.
RNA extracted from whole blood samples contains high levels of hemoglobin RNAs from the red blood cell component (Mastrokolias et al., 2012; Mele et al., 2015). These hemoglobin transcripts can affect measurements of the rest of the blood transcriptome. The human globin blocker reagents therefore ensure maximum sensitivity to detect relevant and interesting gene candidates (Harrington et al., 2020).
To find out more about MERCURIUS™ Blood BRB-seq please contact us at info@alitheagenomics.com.
References
- Alpern, D., Gardeux, V., Russeil, J., Mangeat, B., Meireles-Filho, A.C., Breysse, R., Hacker, D. and Deplancke, B., 2019. BRB-seq: ultra-affordable high-throughput transcriptomics enabled by bulk RNA barcoding and sequencing. Genome biology, 20(1), pp.1-15.
- Chaussabel, D., 2015. Assessment of immune status using blood transcriptomics and potential implications for global health. In Seminars in immunology, 27(1), pp. 58-66.
- Basu, M., Wang, K., Ruppin, E. and Hannenhalli, S., 2021. Predicting tissue-specific gene expression from whole blood transcriptome. Science Advances, 7(14), p.eabd6991.
- Harrington, C.A., Fei, S.S., Minnier, J., Carbone, L., Searles, R., Davis, B.A., Ogle, K., Planck, S.R., Rosenbaum, J.T. and Choi, D., 2020. RNA-Seq of human whole blood: Evaluation of globin RNA depletion on Ribo-Zero library method. Scientific reports, 10(1), pp.1-12.
- Hopp, L., Loeffler-Wirth, H., Nersisyan, L., Arakelyan, A. and Binder, H., 2018. Footprints of sepsis framed within community acquired pneumonia in the blood transcriptome. Frontiers in immunology, 9, p.1620.
- Manella, G., Ezagouri, S., Champigneulle, B., Gaucher, J., Mendelson, M., Lemarie, E., Stauffer, E., Pichon, A., Howe, C.A., Doutreleau, S. and Golik, M., 2022. The human blood transcriptome exhibits time-of-day-dependent response to hypoxia: Lessons from the highest city in the world. Cell Reports, 40(7), p.111213.
- Mastrokolias, A., den Dunnen, J.T., van Ommen, G.B., t Hoen, P.A. and van Roon-Mom, W., 2012. Increased sensitivity of next generation sequencing-based expression profiling after globin reduction in human blood RNA. Bmc Genomics, 13(1), pp.1-9.
- Melé, M., Ferreira, P.G., Reverter, F., DeLuca, D.S., Monlong, J., Sammeth, M., Young, T.R., Goldmann, J.M., Pervouchine, D.D., Sullivan, T.J. and Johnson, R., 2015. The human transcriptome across tissues and individuals. Science, 348(6235), pp.660-665.
- Peters, M.J., Joehanes, R., Pilling, L.C., Schurmann, C., Conneely, K.N., Powell, J., Reinmaa, E., Sutphin, G.L., Zhernakova, A., Schramm, K. and Wilson, Y.A., 2015. The transcriptional landscape of age in human peripheral blood. Nature communications, 6(1), pp.1-14.
