New gene-sequencing tool cuts costs by 6-fold while matching commercial rivals
Researchers have released inDrops-2, an open-source technology that analyzes genetic activity in individual cells at a fraction of commercial system prices. The breakthrough could democratize single-cell research for hospitals and biotech firms, enabling larger studies on patient samples without prohibitive equipment costs.
Originaltitel: inDrops-2: a flexible, versatile and cost-efficient droplet microfluidic approach for high-throughput scRNA-seq of fresh and preserved clinical samples
<p>The expansion of single-cell analytical techniques has empowered the exploration of diverse biological questions at the individual cells. Droplet-based single-cell RNA sequencing (scRNA-seq) methods have been particularly widely used due to their high-throughput capabilities and small reaction volumes. While commercial systems have contributed to the widespread adoption of droplet-based scRNA-seq, their relatively high cost limits the ability to profile large numbers of cells and samples. Moreover, as the scale of single-cell sequencing continues to expand, accommodating diverse workflows and cost-effective multi-biospecimen profiling becomes more critical. Herein, we present inDrops-2, an open-source scRNA-seq technology designed to profile live or preserved cells with a sensitivity matching that of state-of-the-art commercial systems but at a 6-fold lower cost. We demonstrate the flexibility of inDrops-2, by implementing two prominent scRNA-seq protocols, based on exponential and linear amplification of barcoded-complementary DNA, and provide useful insights into the advantages and disadvantages inherent to each approach. We applied inDrops-2 to simultaneously profile multiple human lung carcinoma samples that had been subjected to cell preservation, long-term storage and multiplexing to obtain a multiregional cellular profile of the tumor microenvironment. The scalability, sensitivity and cost efficiency make inDrops-2 stand out among other droplet-based scRNA-seq methods, ideal for large-scale studies on rare cell molecular signatures.</p>