The analysis of gene expression from bulk tissue samples, which yields a single, averaged profile from a heterogeneous cell population, has been a fundamental technique used in research and early stage drug development. However, bulk methods mask the profound cellular heterogeneity of most samples, especially those from cancer tissues. The alternative is called single-cell RNA sequencing (scRNA-seq) which enables the quantification of RNA transcripts within individual cells. This scRNA-seq data enables scientists to understand the expression profiles of each individual cell within a tissue sample that can help with target identification and validation, mechanism of action (MoA), patient stratification, and even assess the safety and efficacy of a cell & gene therapy.
Of the different scRNA-seq methods, there are two primary ones used. The first method leverages Whole Transcriptome Sequencing which is an unbiased, discovery-oriented method that aims to capture the expression of all genes to construct a comprehensive cellular map. The second is targeted gene expression profiling, a method that focuses sequencing resources on a pre-defined set of genes to achieve superior sensitivity and quantitative accuracy. The selection between these methodologies is a critical decision determined by the specific research goals, the phase of the drug development workflow, and practical considerations of scale and cost.
Here we will compare whole transcriptome sequencing and targeted gene expression profiling, evaluate their distinct applications, relative advantages, and the strategic rationale for their use, particularly within the context of pharmaceutical and therapeutic development.
What Is Single-Cell Whole Transcriptome Sequencing?
The goal of single-cell whole transcriptome sequencing is to provide a comprehensive and unbiased measurement of a cell’s transcriptional state by capturing and sequencing its entire transcriptome. This approach is intentionally agnostic, requiring no prior knowledge of specific genes, making it a tool for de novo discovery and exploratory research. The process involves isolating individual cells, capturing their mRNA, converting it to barcoded cDNA, and then sequencing this pooled library. The resulting data is processed through a bioinformatics pipeline that demultiplexes the reads, aligns them to a reference genome, and quantifies the expression of every gene in every cell. This generates a digital gene expression matrix, or large dataset, which serves as the basis for downstream analysis, including cell clustering, differential expression analysis, and trajectory inference.
Primary applications of single-cell whole transcriptome sequencing
The unbiased nature of single-cell whole transcriptome sequencing makes it a good tool for exploratory research and discovery.
- De novo cell type identification and atlasing: single-cell whole transcriptome sequencing helps create comprehensive cell atlases. By profiling tissues without preconceived notions of their composition, it allows for the discovery of entirely new cell types and transient cell states. This is the foundational technology for global initiatives like the Human Cell Atlas (HCA), which aims to create a reference map of every cell in the human body to serve as a basis for understanding health and disease.
- Uncovering novel disease pathways: Comparing single-cell whole transcriptome sequencing data from healthy and diseased tissues provides a high-resolution map of pathology. This can reveal which specific cell populations are driving a disease, identify dysregulated signaling pathways, and uncover novel therapeutic targets.
- Mapping developmental and dynamic processes: By capturing cellular “snapshots” at different time points, single-cell whole transcriptome sequencing can reconstruct cellular development pathways, track the progression of disease, or monitor the evolution of an immune response.
Key limitations of single-cell whole transcriptome sequencing analysis
Despite its power, single-cell whole transcriptome sequencing is not without significant challenges that can limit its utility, particularly in translational and clinical settings.
- Cost and scalability: The need to sequence the entire transcriptome of each cell to a reasonable depth makes single-cell whole transcriptome sequencing expensive on a per-cell basis. This high cost can limit the number of samples or patients that can be included in a study, posing a barrier for large-scale clinical research.
- Computational complexity: The massive, high-dimensional datasets generated by single-cell whole transcriptome sequencing require substantial computational infrastructure for storage and processing, as well as specialized bioinformatics expertise for analysis and interpretation.
- The “gene dropout” problem: This is arguably the most critical technical limitation of single-cell whole transcriptome sequencing. Due to the minuscule amount of RNA in a single cell and the relatively low efficiency of the mRNA capture process, many genes that are truly expressed fail to be detected. This results in a false negative in the data, an event known as a “gene dropout”. This issue is particularly pronounced for genes expressed at low levels, a category that includes many of the most important regulatory genes, such as transcription factors. The very goal of single-cell whole transcriptome sequencing, to measure all RNA expression, creates an inherent trade-off between breadth and depth. By spreading a finite number of sequencing reads across all ~20,000 genes, the coverage for any single gene is necessarily shallow, making the method prone to missing these crucial but low-abundance signals. Consequently, while single-cell whole transcriptome sequencing excels at generating a lot of data, its utility as a precise quantitative tool for any single gene can be limited by this inherent noise and data sparsity.
What Is Single-Cell Targeted Gene Expression Profiling?
In direct contrast to the broad exploratory nature of single-cell whole transcriptome sequencing, single-cell targeted gene expression profiling is more focused. Single-cell targeted gene expression profiling measures RNA expression of a pre-selected list of genes, ranging from a few dozen to several thousand in each individual cell of a sample. This makes it perfectly suited for validating previous discoveries early in drug development, interrogating specific biological pathways, or the analytical development of robust quantitative assays to assess the safety of a therapy.
Advantages of single-cell targeted gene expression profiling
Having a more focused target list of genes to understand expression for, single-cell targeted gene expression profiling provides benefits in sensitivity, cost, and efficiency scientists want.
- Superior sensitivity and accuracy: By channeling all sequencing reads to a small subset of target genes, single-cell targeted gene expression profiling plausibly achieves a much greater sequencing depth for each gene of interest. This dramatically increases sensitivity for the target genes, effectively minimizing the “gene dropout” problem of genes seen when using single-cell whole transcriptome sequencing. Therefore, scientists can reliably quantify low-abundance transcripts being targeted.
- Significant cost-effectiveness and throughput: Because far fewer sequencing reads are required per cell to achieve deep coverage of a small gene panel, the cost per sample could be reduced compared to whole transcriptome sequencing. This economic advantage enables researchers to scale up their studies to include hundreds or thousands of samples, making large clinical cohorts or high-throughput drug screens feasible.
- Streamlined bioinformatics: Analyzing data from a few hundred genes is computationally trivial compared to the analysis of the entire transcriptome. This reduces the need for high-performance computing clusters and simplifies the data analysis pipeline, making single-cell targeted gene expression profiling more accessible to labs without dedicated bioinformatics support. With that said, this does not minimize the amount of work required by bioinformatics teams to analyze and read the data from single-cell targeted gene expression profiling.
The principal drawback of single-cell targeted gene expression profiling is a direct consequence of its design. It is completely blind to any gene not included in the pre-defined panel.
How Single-Cell Targeted Gene Expression Profiling Is Transforming Drug Development and Clinical Diagnostics
The journey of a drug from an initial idea to a clinical therapy is long, expensive, and fraught with a high rate of failure. Success depends on making accurate, data-driven decisions at every phase of development. While single-cell whole transcriptome sequencing plays a vital role in initial target discovery, the unique demands of the pharmaceutical pipeline for robustness, scalability, and cost-effectiveness make single-cell targeted gene expression profiling the indispensable method for nearly every subsequent phase.
Target Identification and Validation
The process starts with identifying a gene or pathway that can be modulated to treat a disease. Single-cell whole transcriptome sequencing is a powerful method for this initial discovery, comparing diseased and healthy tissues to pinpoint novel genes expressed in specific cell types that are driving pathology. However, a potential target identified in a small single-cell whole transcriptome study must be rigorously validated. Single-cell targeted gene expression profiling provides the ideal validation tool, allowing researchers to confirm the target’s RNA expression and relevance across a large, statistically significant cohort of patient samples before committing to a costly development program. Leveraging custom, fit-for-purpose assays that are highly sensitive ensures that the target’s expression is a real biological signal, not a technical artifact of single-cell whole transcriptome sequencing dropouts.
Mechanism of Action (MoA) and Off-Target Effects
Understanding how a drug works (its mechanism of action) and what unintended effects it might have on a cell is critical for patient safety. After treating cells with a drug candidate, a targeted RNA expression panel focused on the intended biological pathway can provide a highly sensitive and quantitative readout of on-target activity. By including genes from known toxicity pathways on the same panel, researchers can simultaneously screen for potential off-target effects, gaining crucial safety data early in the development process. Taking a step further, only some single-cell multiomic platforms can link a specific drug-resistance mutation at the genomic level to its functional consequence on gene expression within the same cell, providing a direct and unambiguous view of resistance mechanisms. Being able to evaluate MoA and off-target effects across multiple patient samples with high throughput makes single-cell targeted gene expression profiling as a favorable method of choice.
Clinical Biomarker Validation and Patient Stratification
Modern precision medicine relies on biomarkers to stratify patients, identifying those most likely to respond to a given therapy for inclusion in clinical trials. While single-cell whole transcriptome sequencing can discover candidate biomarkers, a targeted RNA expression panel provides the robust, reproducible, and cost-effective assay required for clinical application. A single-cell targeted gene expression assay can be rigorously validated and used to screen thousands of patients for trial enrollment or for companion diagnostics. For instance, after single-cell whole transcriptome sequencing identified specific immune cell biosignatures in acute myeloid leukemia samples that predicted a positive response to specific drug therapy, these signatures could be included into a single-cell targeted RNA expression panel to prospectively select patients for treatment, increasing the likelihood of clinical trial success.
Monitoring Therapeutic Response and Pharmacodynamics
Single-cell targeted gene expression profiling enables real-time insight into how a therapy engages its target and changes the cellular behavior impacting the patient’s health. By profiling gene expression changes at the single-cell level, these panels reveal pharmacodynamic responses such as tracking T cell activation following immunotherapy or monitoring vector-driven transgene expression. This approach allows researchers and clinicians to detect early molecular indicators of efficacy, resistance, or off-target effects, providing a deeper understanding of therapeutic mechanisms long before conventional clinical endpoints like tumor regression or symptom improvement are observed.
Ultimately, single-cell targeted gene expression profiling serves as the essential “bridge” for single-cell genomics to phenotypic readouts. With the added ability to multiplex both genomic and expression readouts within the same assay from the same cell, we can get a unified understanding of cellular function. It takes the complex, often noisy discoveries from single-cell whole transcriptome sequencing and converts them into robust, scalable, and cost-effective assays that meet the stringent demands of the pharmaceutical industry and companies developing cell and gene therapies.
What Is the Right Single-Cell RNA Sequencing Method To Use?
Single-cell targeted gene expression profiling offers a more practical and clinically translatable approach than single-cell whole transcriptome sequencing for therapeutic development. While single-cell whole transcriptome sequencing provides an unbiased view of global gene expression, it generates high-dimensional, noisy datasets that require extensive computational analysis and are difficult to standardize across studies or clinical settings. Single-cell targeted RNA expression panels focus on predefined, meaningful gene targets such as immune activation, cell differentiation, or vector response pathways allowing for quantitative, reproducible measurement of pharmacodynamic and mechanistic biomarkers. This single-cell targeted approach improves understanding for low-abundance transcripts, reduces sequencing depth requirements, and simplifies regulatory validation and assay transferability. As a result, single-cell targeted gene expression profiling enables rigorous, high-throughput monitoring of therapeutic response and mode of action, making it the preferred strategy for late-stage translational and clinical applications where precision, reproducibility, and scalability are critical.
If you’re interested in learning how targeted gene expression profiling that combines DNA + RNA analysis can advance your research or clinical programs, contact us to start the conversation.
