Transcriptomics (or more specifically RNA-seq) is one of the most dynamic processes in biology where thousands of RNAs are transcribed and degraded every minute. While RNA takes many different forms and functions – messenger, transfer, ribosomal, non-coding, virus genome, etc. – most commonly scientists are interested in the messenger RNA (mRNA). As the abundance and isoforms of mRNA change dynamically based on conditions (e.g., tissue, disease, treatments) and across different timepoints, this can be very informative. Although proteomics or metabolomics are even more dynamic and responsive, transcriptomics remains a well-understood, sequence-based method that already shows the response to dynamic biological questions.
Currently, technology is developing towards direct RNA sequencing (including RNA modifications) and advancements in spatial transcriptomics. It is also moving towards the integration of transcriptomics together with other multi-omics approaches (e.g., metabolomics or chromatin accessibility or DNA methylation), for the most comprehensive understanding of biology.
Studies Of Transcriptomics changing the world
One of the most widely used applications of single-cell transcriptomics studies focuses on the classification of cell types, whether in the human brain or the mouse microbiome [1], [2], [3].
Early-stage single-cell signs of Alzheimer’s disease [1]. After an analysis of over 80,000 single-nucleus transcriptomes of the prefrontal cortex, highly cell-type-specific Alzheimer’s disease-associated changes were identified (Marthys et al., 2019). These changes appeared early in pathological progression, while genes upregulated at late stages were common across cell types. Myelination was suggested to play a key role in the process in the disease pathophysiology.
Expanding the mammalian proteomics [1]. In 2005, the Fantom Consortium (Carninci et al., 2005) published the transcriptional landscape of the mammalian genome. An impressive team of almost 200 scientists found 16,247 new mouse protein-coding transcripts, including 5,154 encoding previously unidentified proteins as well as many gene regulatory factors (starts, ends, splicing, polyadenylation, alternative promoter usage etc.) which have an immense effect on mammalian differentiation and development.
Explosion of genes and splicing events [1]. Within the very first single-cell mRNA sequencing publication (Tang et al., 2009), the authors detected the expression of 75% more genes than microarray techniques and identified almost 1,800 previously unknown splice junctions in mice.
SARS-CoV-2 entry into human cells [1]. During the COVID-19 pandemic, scientists untangled the expression of genes from various tissues to better understand the viral entry into human cells (Sungnak et al., 2020). Furthermore, they constructed the COVID-19 Cell Atlas, bringing together worldwide efforts to combine invaluable cell-specific RNA sequencing from 39 healthy and 14 patient datasets.
Microbial Metatranscriptomics [1]. Invaluable information about host-microbe and microbe-microbe environmental interactions (the dynamics of energy harvest and chemical cycling, and responses to environmental stresses) has been uncovered by metatranscriptomics (similarly to metagenomics: transcriptomics of microbial communities; reviewed by Zhang et al., 2021).
Spatial Transcriptomics [1]. Spatial information is extremely important for biological processes to occur. Thus spatial transcriptomics is frequently performed with the aim of producing cellular location maps or atlases, whether at a single-cell or even single-nucleus scale, using imaging or NGS-based sequencing. One such study (led by Giles Yeo) focused on the human brain: specifically the hypothalamus (HYPOMAP), with the goal of better understanding obesity and its treatments (Tadross et al., 2025). Additionally, this becomes an extremely valuable resource for identifying new drug targets for a wide range of disorders, even beyond the scope of the original study.
At VUGENE
While the sequencing of total cellular RNA is frequently used to identify significant differences between two groups (e.g., disease and control), there are many other ways to utilize these datasets. At VUGENE, we always advise performing alternative splicing analysis on your transcripts, as different isoforms can have different influences on the condition. It’s not rare to notice that in DEG analysis, splicing-related transcripts or proteins (in proteomics) are affected.
Thus, it’s optimal to examine which splicing events also differ between the conditions. We also perform pathway-specific analyses or sub-analyses of organelle-specific genes (frequently mitochondrial).
This pipeline illustrates the transcriptomics data analysis process, moving from raw sequencing data to differentially expressed genes and enriched biological pathways.
Lastly, among all sequenced RNAs, even if the RNA was poly(A)tail-selected, there are many viral RNA genomes and transcripts. When Dr. Ingrida Olendraite analyzed nearly 3,000 different publicly available transcriptomes, she and her colleagues found almost 6,000 RNA viruses (Olendraite et al., 2023). This is of the highest importance for plant, invertebrate, and fungal datasets (due to differences in the immune system and its response to RNA viruses when compared to vertebrates).
Interested in Learning More
Recently, researchers have increasingly adopted various omics approaches, examining data from transcriptomics, proteomics, metabolomics, and epigenetics. While each is informative in isolation, integrating these vast datasets to produce knowledge remains a significant challenge. Through our work with a wide range of clients, VUGENE has developed deep expertise in multi-omics integration. Our capabilities include integrating different types of transcriptomics, such as bulk, single-cell, and spatial data.
Contact us to discuss how VUGENE can support your research.
Written by: Ingrida Olendraitė, PhD
Cover image credits: Olena / Adobe Stock