Chapter 4 — Transcriptomics

What is Transcriptomics?

The transcriptome is the collection of RNA transcripts produced by the genome¹. The transcriptome consists of messenger RNA (mRNA) that codes for protein but also includes types of non-coding RNA². Transcriptomics is the branch of molecular biology focused on the comprehensive study of the transcriptome. This -omics is concerned with the dynamic aspects of gene expression, helping researchers understand how genes are regulated and expressed in different cells and tissues under various conditions. Analyzing the transcriptome can lead scientists to a deeper understanding of cellular functions and the complex regulatory mechanisms that govern biological processes.

Types of RNA

RNA plays a pivotal role in the central dogma of molecular biology, which describes the flow of genetic information within a biological system. The process begins with DNA, the storage unit of genetic information, which is transcribed into RNA. RNA then serves as a template for protein synthesis through the process of translation. However, RNA doesn’t serve only as a messenger molecule (mRNA). There are a variety of RNA classes, each with their own unique functions, such as ribosomal RNA (rRNA), transfer RNA (tRNA), microRNAs (miRNAs), and a diverse array of non-coding RNAs (ncRNAs).  

While rRNA and tRNA are important for the translation process, miRNAs and long non-coding RNAs (lncRNAs) are crucial for regulating gene expression at the transcriptional and post-transcriptional levels. These molecules can modulate the stability of mRNA and influence the translational efficiency, thereby controlling the amount of protein produced. This regulatory capability is essential for maintaining cellular homeostasis and for responding to environmental stimuli.  

Each of these technologies has its strengths and limitations, which method or combination of methods that works best depends on the specific research question, the type of sample available, and the resolution required. The continuous development and integration of these technologies are driving the field of transcriptomics forward, enhancing our understanding of complex biological systems and disease mechanisms.

How is Transcriptomics Studied?

Advancements in technology have evolved the study of transcriptomics. Technologies can be broadly classified as microarray, sequencing, or PCR-based. Because RNA is less stable than DNA, these approaches involve reverse-transcribing RNA into complementary DNA (cDNA) and then analyzing the cDNA.

Microarrays

One of the earliest tools used in transcriptomics, DNA microarrays measure the expression levels of thousands of genes simultaneously. While microarrays are less commonly used today due to their limitations in dynamic range and detection of only known gene sequences, they were pivotal in the early expansion of transcriptomics.  

RNA sequencing (RNA-seq)

This technique, leveraging next-generation sequencing (NGS) technologies, has become the standard in transcriptomics. Unlike microarrays, RNA-Seq doesn’t rely on predefined sequences. RNA-seq can detect novel transcripts, splice variants, and non-coding RNAs and quantitatively measure transcript expression levels. The technology’s high resolution and sensitivity make it ideal for detecting low-abundance transcripts and subtle changes in gene expression.

Single-cell RNA sequencing (scRNA-seq)

This extension of RNA-seq analyzes the transcriptomes of individual cells rather than a population in aggregate and is a key piece of single-cell multiomics^3,4. Single-cell resolution allows researchers to discover new cell types and lineage relationships. It also allows interrogation of cellular heterogeneity within tissues, crucial for understanding complex tissues, developmental processes, and tumor evolution. Single-cell transcriptomics is currently being used to create cell-level maps of the human body⁴.

Spatial transcriptomics

This collection of techniques maps gene expression data spatially within tissues, which is essential for understanding the tissue architecture and cellular microenvironment. Multiplexed error robust fluorescence in situ hybridization (MERFISH) and sequencing FISH (seqFISH) can localize thousands of RNA targets simultaneously^5,6. Slide-seq is a recent technique that uses DNA barcoding to generate high-resolution transcriptome maps in tissues⁷. This method provides a powerful tool for studying tissue morphology and pathology in conjunction with gene expression.

Isoform sequencing (Iso-seq)

Unlike conventional RNA-Seq, which often requires assembly of short reads, Iso-seq sequences full-length cDNA molecules, aiding in identifying and quantifying different RNA isoforms produced by alternative splicing. This method, primarily powered by third-generation sequencing technologies like those from Pacific Biosciences and Oxford Nanopore, offers a complete view of transcript diversity.

Quantitative reverse transcription PCR (qRT-PCR)

Although not a high-throughput technology, qRT-PCR is widely used to validate and quantify the expression of selected genes identified by other transcriptomic methods. It is highly sensitive and specific, making it an excellent tool for confirming the results of RNA-seq or microarray analyses.

Digital PCR

Similar to qRT-PCR, digital PCR is not high throughput or comprehensive of the entire transcriptome but offers absolute quantification of targeted transcripts and high sensitivity to low abundance targets. Droplet digital PCR (ddPCR), the premiere format of digital PCR, is capable of detecting small (<2-fold) changes in abundance, even with small amounts of the target transcript.

Experimental Considerations

Transcriptomics applications include biological research, drug discovery, diagnosis and classification of disease, and more. For example, gene expression patterns linked to specific diseases can aid in early diagnosis and implementation of treatment strategies,² and—as a part of multiomics—are one of the Bold Goals for US Biotechnology and Biomanufacturing⁸. In cancer research, transcriptomics has been used to classify cancer aggressiveness and understand pathways involved in progression. In the clinic, lab-developed tests that measure RNA gene fusions are being developed to guide cancer treatment⁹.

Each of the available transcriptomics technologies have strengths and limitations. Your chosen technique will depend on your biological question and factors such as sample characteristics, accuracy requirements, and cost. For example, microarrays for broad detection and qPCR for fewer targets are cost-effective, while RNA-seq and scRNA-seq offer comprehensive coverage at a higher price. Iso-seq would be more costly per sample than other sequencing methods due to long-read sequencing technologies but would be ideal for identifying and measuring splice variants. For maximum sensitivity to low abundance target variants, ddPCR would be an effective choice and could complement RNA-seq for broad coverage. While gene expression markers in scRNA-seq can give cell-type specificity, spatial transcriptomics techniques are your best bet when your biological question involves complex tissues.

Use Cases of Transcriptomics in Multiomics Research: Case Studies

Metabolome modulation of host adaptive immunity in malaria

Malaria is a serious mosquito-borne illness that infects millions of people each year, mostly in tropical and subtropical regions. The mosquito is a vector for Plasmodium falciparum, a microscopic parasite that infects host red blood cells and causes the immune response behind the signature symptoms associated with the disease.

In a study published in Nature Metabolism, researchers integrated metabolomics and transcriptomics data to better understand P. falciparum pathogenesis and identify potential new therapeutic targets10.

Samples were collected before and after malaria infection from children from two West African ethnic groups (Gouin and Fulani in Burkina Faso). Using Metabolon’s Global Discovery panel, the researchers identified 92 parasitemia-associated metabolites, highlighting significant changes in the host’s metabolic processes due to infection, with especially major perturbations in lipid and amino acid metabolism that affected the steroid metabolome (Figure 1). Notably, stage-specific metabolic signatures differentiated between acute and chronic malaria.

Ethnic differences were also revealed through metabolomic analyses, showing distinct metabolic responses in the Gouin and Fulani children, two ethnic groups with different malaria susceptibility. The less susceptible Fulani group exhibited decreased levels of steroid metabolites versus the more susceptible Gouin group, which showed increased steroid abundance. Metabolic profiling provided insights into the immunosuppressive role of endogenous steroid production during malaria infection, particularly in the Gouin ethnic group.

Using transcriptomics, the researchers connected parasitemia-associated pregnenolone steroids detected via metabolomics with decreased expression of key immunoregulatory lymphocyte genes in Gouin children. The transcriptomic data highlighted the inhibition of T cell signaling pathways during infection, suggesting that elevated steroids due to infection suppressed T cell function and limited the immune system’s ability to fight infection. Additionally, cross-correlation analysis between steroid levels and gene expression identified thousands of significant steroid-transcript associations, indicating infection-specific coordinated responses.

This study exemplifies how transcriptomics can link gene expression changes to specific metabolic perturbations observed during infection using multiomics, including metabolomics analysis, and provides a deeper understanding of the host-parasite interaction.

Integration of metabolomics and transcriptomics reveals novel biomarkers in the blood for tuberculosis diagnosis in children.

In a study published in Scientific Reports, researchers leveraged metabolomics and transcriptomics data to gain valuable new insights into the diagnosis and treatment of pediatric tuberculosis (TB)¹¹.

Samples were collected from children with active infections and members of the same household who had been exposed but were not experiencing active infection. Metabolomics analysis of plasma revealed unique metabolic signatures associated with active TB infections. Computational algorithms revealed three key metabolites—N-acetylneuraminate (AUC 0.66 at diagnosis), quinolinate (AUC 0.77 at 1 month of treatment), and pyridoxate (AUC 0.87 post-treatment)—that correctly identified disease status at different times during treatment. Additionally, four metabolites (gamma-glutamylalanine, gamma-glutamylglycine, glutamine, and pyridoxate) successfully identified treatment response (AUC 0.86).

Transcriptomics data complemented these findings, enabling the correlation of metabolic changes with gene expression (see Figure 1). For example, the researchers found that N-acetylneuraminate is associated with immunoregulatory interactions between lymphoid and non-lymphoid cells, while pyridoxate is linked to p53-regulated metabolic genes and mitochondrial translation, providing new knowledge on metabolic dysregulation in pediatric TB.

Multiomic evaluation of metabolic alterations in multiple sclerosis identifies shifts in aromatic amino acid metabolism

Researchers combined metabolomics and transcriptomics analyses in a cohort of multiple sclerosis (MS) patients to identify key associations between metabolite shifts and human immune cell function in MS¹².

In a cohort of over 600 MS patients and over 300 healthy controls, metabolomics analysis revealed a unique metabolite signature in MS (Figure 3). Specifically, metabolites such as phenyllactate, 3-(4-hydroxyphenyl)-lactate, and indolelactate were significantly reduced, while others like p-cresol sulfate and phenylacetylglutamine were elevated. Significantly, shifts in aromic amino acid (AAA) metabolism were associated with reduced immunomodulatory metabolites and increased production of metabotoxins.

Integration of transcriptomics data revealed that these metabolite changes were linked to functional changes in immune cells, leading to increased production of pro-inflammatory cytokines and altered endocytosis in monocytes, which may contribute to the pathogenesis and progression of MS. These functional associations were confirmed by altered monocytic endocytosis and pro-inflammatory cytokine production after treatment with AAA-derived metabolites in vitro¹².

The multiomics approach used in this study underscores the advantages of integrating metabolomics and transcriptomics by providing a comprehensive understanding of metabolic dysregulation in MS and its impact on immune cell function. This integrated analysis offers valuable insights into potential therapeutic targets and biomarkers for disease severity in multiple sclerosis.

Transcriptomics in a Multiomics Workflow

Transcriptomics bridges the gap between static genomic data and the functional dynamics observed in proteomics and metabolomics datasets. This crucial link between genotype and phenotype helps elucidate the mechanisms by which genetic information is translated into functional outcomes. The temporal data provided by transcriptomics, capturing rapid changes in gene expression in response to stimuli, adds a vital dimension to multiomics studies, essential for understanding developmental processes, disease progression, and response to treatments.

References

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3. Baysoy A, Bai Z, Satija R, Fan R. The technological landscape and applications of single-cell multi-omics. Nat Rev Mol Cell Biol. 2023;24(10):695-713. doi:10.1038/s41580-023-00615-w
4. Aldridge S and Teichmann SA. Single cell transcriptomics comes of age. Nat Commun. 2020;11(1):4307. doi:10.1038/s41467-020-18158-5
5. Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science. 2015;348(6233):aaa6090. doi:10.1126/science.aaa6090
6. Shah S, Lubeck E, Zhou W, et al. seqFISH accurately detects transcripts in single cells and reveals robust spatial organization in the hippocampus. Neuron. 2017;94(4):752-758.e1. doi:10.1016/j.neuron.2017.05.008
7. Rodriques SG, Stickels RR, Goeva A, et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science. 2019;363(6434):1463-1467. doi:10.1126/science.aaw1219
8. The White House Office of Science and Technology Policy. Bold Goals for U.S. Biotechnology and Biomanufacturing: Harnessing Research and Development to Further Societal Goals. Accessed January 25, 2024. https://www.whitehouse.gov/wp-content/uploads/2023/03/Bold-Goals-for-U.S.-Biotechnology-and-Biomanufacturing-Harnessing-Research-and-Development-To-Further-Societal-Goals-FINAL.pdf
9. FoundationOne®RNA. Foundation Medicine. Accessed July 23, 2024. https://www.foundationmedicine.com/test/foundationone-rna
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11. Dutta NK, Tornheim JA, Fukutani KF, et al. Integration of metabolomics and transcriptomics reveals novel biomarkers in the blood for tuberculosis diagnosis in children. Sci Rep. 2020;10(1):19527. doi:10.1038/s41598-020-75513-8
12. Fitzgerald KC, Smith MD, Kim S, et al. Multi-omic evaluation of metabolic alterations in multiple sclerosis identifies shifts in aromatic amino acid metabolism. Cell Reports Medicine. 2021;2(10):100424. doi:10.1016/j.xcrm.2021.100424