Most commonly used single-cell technologies in pharma research

I recently directed this question to pharmaceutical researchers. The answers are for as follows:

I don't use any:   33%

scATAC-Seq:     15%

scMultiomics:  15%

Spatial transcriptomics: 37%

Each technology has its own advantages and limitations. Let’s go over them briefly.

scRNA-Seq:

Single-cell RNA sequencing (scRNA-Seq) is the most widely used single-cell genomic technology. For many researchers, “single-cell sequencing” is almost synonymous with scRNA-Seq. It is supported by a variety of protocols and has been widely adopted across laboratories for profiling tissue heterogeneity. Computational tools like Seurat have streamlined data analysis, making this technology well-established and highly supported.

scRNA-Seq provides transcriptomic profiles of individual cells at a reasonable cost, enabling detailed analysis of cellular heterogeneity. However, despite its popularity, it has limitations. It only captures the abundance of (pre-)messenger RNAs—essentially an intermediate step in the central dogma. These RNAs are transcribed from DNA but have not yet been translated into proteins. As a result, scRNA-Seq offers a snapshot of the transcriptome, not the full biological outcome.

This means that abnormalities detected at the RNA level may not reveal their true causes (which lie in the genome) or consequences (which are reflected in the proteome). Therefore, scRNA-Seq provides only a partial picture of the cellular state. A comprehensive understanding often requires insights into both the genome (e.g., DNA mutations, chromatin accessibility) and the proteome (e.g., final protein products).

scATAC-Seq – 15%:

Advances in molecular genetics have revealed that gene expression is tightly regulated by non-coding regulatory regions in the genome. These elements act as molecular switches and influence cell state, polarization, and differentiation. Typically, these switches are activated by protein complexes like transcription factors and cofactors. For binding and activation to occur, the chromatin must be open and accessible.

scATAC-Seq profiles these accessible regions, which are likely to harbor active regulatory elements capable of initiating gene transcription. In this sense, it captures the epigenetic state of the chromatin—effectively a step upstream of transcription in the central dogma.

Chromatin accessibility tends to be more stable and less stochastic than gene expression, providing a more consistent picture of cell identity. Therefore, scATAC-Seq offers valuable insights, particularly into the regulatory landscape of cells.

However, a significant drawback is the difficulty in annotating cell clusters. Unlike scRNA-Seq, where clusters can be readily identified using known marker genes, scATAC-Seq lacks such clear-cut signatures. Most cell clusters do not show distinctive activation patterns for known markers, making cell-type assignment and downstream analysis more challenging and fragile.

scMultiomics – 15%:

Single-cell multiomics technologies have gained momentum since the late 2010s. These protocols capture more than one type of molecular data—such as combinations of gene expression, chromatin accessibility, DNA methylation, and mutation profiling—from the same cell.

These technologies are particularly powerful when one of the modalities is scRNA-Seq. For most non-transcriptomic single-cell data types, annotating cell populations remains challenging due to the lack of direct gene expression information. However, the integration with scRNA-Seq allows reliable cell-type identification using transcriptomic data.

Despite their robustness and comprehensive insights, scMultiomics approaches can be cost-prohibitive and logistically complex, which may limit their practical application in some settings.

Spatial Transcriptomics – 37%:

Location matters—not just for real estate, but also for cells. The function and behavior of cells are heavily influenced by their spatial context within tissues.

Spatial transcriptomics technologies provide not only single-cell gene expression data but also information about the physical location of cells. This allows for the comparison of expression profiles across different tissue regions, making it an especially powerful tool for studying intercellular interactions.

Currently, a trade-off exists between throughput and resolution. Some protocols offer genome-wide expression coverage but lack single-cell resolution. Others achieve subcellular resolution but are limited to a predefined panel of genes. Nonetheless, rapid advancements are being made in this field, and technologies are steadily approaching high-resolution, high-throughput spatial profiling.