29 July 2026
As part of our “Multiomics Explained” series, we’re unpacking one ome at a time. To get an overall picture, read our first piece, “What is multiomics? A simple guide to the future of biology.” You’ll learn how the omes work together to help solve some of the toughest questions in human health and see how researchers are already capitalizing on the advantages of a multiomic approach. And don’t forget to read our second installment in the series, “What is proteomics?”
Now, let’s focus on transcriptomics and its power to tell us about gene expression and cellular behavior.
What is transcriptomics?
Transcriptomics is the complete study of the transcriptome, which includes all RNA molecules found within an organism. Reading the complete set of RNA molecules in a sample (transcriptomics) can give scientists incredible insights into cell biology.
While DNA is mostly static, staying put in the cell’s nucleus, RNA is dynamic and versatile, and provides direct information about differential gene expression. Messenger RNA (mRNA) transcribes DNA’s genetic code and carries it into the cytoplasm to help manufacture proteins. Yet mRNA is just one part of the story. There are thousands of non-coding RNAs (ncRNAs), for example, that perform important regulatory functions.
RNA tells entire stories about how cells function, interact, and sometimes become diseased. RNA has become a critical piece of the biological insights ecosystem. While genomics provides the biological playbook, transcriptomics reveals which plays are currently being called, and proteomics shows how those plays are being executed on the field.
What Illumina transcriptomics can tell us
Researchers can study transcriptomics alongside genomics using the same next-generation sequencing (NGS) instruments. Depending on the application, Illumina’s NovaSeq, NextSeq, and MiSeq systems can all be used for RNA sequencing (RNA-seq). The RNA samples are first converted into complementary DNA (cDNA) using reverse transcription, allowing them to be processed by standard NGS workflows.
RNA-seq provides a comprehensive view of the transcriptome, not just a few pre-selected transcripts, providing unbiased readouts on gene expression and cellular behavior. This complete view has given researchers a powerful tool to detect gene expression changes; characterize multiple forms of non-coding RNAs; and identify alternatively spliced isoforms, gene fusions, single nucleotide variants, and other molecular features.
Illumina’s approach to transcriptomics
Single cell transcriptomics
While RNA-seq has enabled many novel discoveries, it does have limitations. Tissue samples are dissociated and processed to release their RNA, much like blueberries and strawberries are blended together in a smoothie.
As a result, traditional RNA-seq measures average gene expression from all cells in a sample. Investigators know which RNA transcripts are present, they just don’t know which specific cells generated them. This is important because individual cells, even adjacent cells, can have different expression patterns, a common trait in tumors.
To reveal this granular data, the field has evolved toward single-cell transcriptomics. Single-cell RNA-seq (scRNA-seq), enabled by the Illumina Single Cell 3' RNA Prep, can characterize the transcriptomes in hundreds, thousands, or millions of individual cells, revealing cellular heterogeneity, tissue composition, and even rare cell types. As a result, scRNA-seq is now an essential tool to study cancer, immunology, developmental biology, and many other areas.
Reading Perturb-seq expression changes
Perturb-seq is a new take on an old scientific method—modulating specific genes to analyze their functions. In this case, rather than up- or down-regulating genes in an entire organism, researchers can use CRISPR-Cas9 to simultaneously edit gene function in millions of individual cells and monitor the associated phenotypic changes.
In this process, scRNA-seq is essential to measure expression changes. Together, high-throughput sequencing and single cell technologies allow researchers to analyze the molecular consequences of gene perturbations at an unprecedented scale, providing big picture views of the phenotypic impacts on each individual cell.
Hear how Illumina’s high-throughput single-cell CRISPR prep makes gene editing a reality.
Spatial transcriptomics
Spatial transcriptomics combines molecular profiling with spatial context to profile gene activity from intact tissue samples. Researchers can study gene expression in intact tissue while preserving the sample’s natural structure and context. This spatial resolution helps labs study cellular interactions in tumor microenvironments and other complex tissue samples, compare normal and diseased regions, and build comprehensive tissue function atlases.
Imaging-based spatial transcriptomic techniques, such as immunohistochemistry (IHC) and in situ hybridization, paved the way, but these low-throughput, hypothesis-driven methods require investigators to choose which transcripts they wish to study. NGS-based spatial transcriptomics has built on these early wins by increasing throughput and adding objectivity. With NGS-based spatial transcriptomics, researchers can:
· Map gene expression within intact tissues to connect molecular activity with tissue structure and reveal how cellular function varies across regions
· Quantify thousands of genes per sample, without bias, to capture a more complete view of cellular ecosystems
· Combine multiple omics datasets to reveal biological pathways, cell states, and the molecular mechanisms driving cellular functions
Illumina’s StrataMap Spatial solution combines RNA-seq, spatial barcoding, DRAGEN secondary analysis, and Connected Multiomics to profile gene expression across millions of cells in large tissue regions while preserving spatial context. This gives researchers a scalable way to see not only which genes are active, but where that activity occurs—helping to reveal how cells behave, interact, and contribute to tissue organization or disease.
How researchers use transcriptomics
RNA-seq, scRNA-seq, Perturb-seq, and spatial transcriptomics are transforming how the life sciences community studies gene expression and noncoding RNAs. These advances have helped researchers reveal more actionable data in oncology, infectious diseases, human development, and many other areas.
Gene expression studies are laying the groundwork to support the development of precision medicines by identifying therapeutic biomarkers and drug targets. RNA-seq has shown that noncoding RNAs play important roles in diabetes, diabetic kidney disease, metastatic breast cancer, and many other conditions.
The five-year Molecular and Genomic Interrogation of Childhood Cancer – Ireland (MAGIC-I) study is using whole genome and transcriptome sequencing analysis to better understand cancer in young patients.
In addition, scRNA-seq approaches are revealing granular information about cancer
immunology, and developmental biology. One scRNA-seq study on renal cell carcinoma has shown how tumor microenvironments influence disease progression and responses to therapy.
Another study affiliated with the Human Cell Atlas, a major effort to understand human development, profiled transcriptomes from two million cells to investigate how organs develop. In addition, Illumina and Broad Clinical Labs are leveraging scRNA-seq, Perturb-seq, and other methods to rapidly develop a five billion cell atlas.
The future of transcriptomics in precision medicine
Perturb-seq is a powerful tool for drug discovery because it allows researchers to systematically study the effects of gene perturbations on individual cells, providing a high-resolution view of cellular responses to identify drug targets and dissect the pathways associated with treatment resistance.
The fundamental discovery research these varied transcriptomic methods are enabling will ultimately have a profound impact on drug development and human health. This deeply granular understanding of cellular biology could help catalyze the next generation of medicines.
