New Technique Revolutionizing Single-Cell Analysis with Lower Costs and Greater Scale

Researchers from the University of Adelaide, the National Center for Genomic Analysis, and St. Jude Children’s Research Hospital have developed a single-cell RNA analysis method that is 47 times more cost-effective and scalable than current techniques.

Single-cell RNA sequencing helps scientists study gene expression in both healthy and diseased cells. However, the high cost of the method often limits the number of cells that can be analyzed.

To overcome this limitation, the researchers developed a technique that combines microscopy with single-cell RNA analysis.

The method, called Single-Cell Transcriptomics Analysis and Multimodal Profiling via Imaging (STAMP), can analyze millions of single cells at a much lower cost than existing approaches.

We have created a technique that gives us an advantage in the numbers game of single-cell analysis. It is an order of magnitude more cost-effective and allows us to profile a million cells simultaneously, compared to tens of thousands, typical of current methods, making it far more scalable.

Jasmine Plummer, PhD, Study Co-Corresponding Author and Director, Center for Spatial Omics, St. Jude Children’s Research Hospital

The researchers began by separating tissue into individual, unconnected cells. They then fixed, or “stamped,” the cells onto microscope slides. This process kills the cells but preserves their structure and gene expression at that moment in time. Next, they added compounds that bind to specific RNA sequences and glow under a microscope.

They compared their results with existing gene expression atlases. The method allowed them to identify multiple types of immune cells and track different developmental stages of induced pluripotent stem cells.

They also calculated the cost difference. Analyzing immune cells from 1,000 people using traditional methods would cost about $3.56 million. Using STAMP, the same analysis would cost around $75,000—a 47-fold reduction.

STAMPing out Bias in Single-Cell Analysis while Maintaining Sensitivity

Plummer added, “Current techniques for single-cell RNA sequencing require inference to determine things such as cell type, but STAMP allows us to directly examine the cells. Many current single-cell approaches also bias results based on cell shape, often missing irregularly shaped cells, including neurons.”

Traditional RNA sequencing methods require cells to pass through narrow tubes before analysis. Cells with simple, round shapes pass through more easily, which can introduce bias and limit the diversity of cell types detected. The STAMP method is performed on a microscope slide, avoiding this issue and allowing a wider range of cell types to be captured.

To test the method’s sensitivity, the researchers mixed a small number of cancer cells into a large population of other cells. STAMP was able to detect two cancer cells among approximately 850,000 cells on a single slide.

“Being able to profile a million cells is crucial because it only takes one cell to escape cancer treatment. We have shown STAMP allows us to visualize and detect these cells in a numerically advantageous and accessible way, an important feature for potential future development into clinical applications,” Plummer added.

Single-cell RNA sequencing is still too expensive for many researchers, especially those without access to funding, specialized equipment, or advanced computing resources. In contrast, microscopes are widely available in most research facilities.

Plummer concluded, “We as scientists believe what we can see. STAMP gives us the best of both worlds in single-cell analysis: quantitative gene expression data and the ability to visually examine the cells under a microscope. We hope that these features, combined with its accessibility and cost-effectiveness, enable others to discover new biology and clinical uses in the future.”