Single cell methods have opened up a vast array of granular information about the organisation, function, and interaction of cells within tissue. This information can then be applied to better understand diseases at a single cell level. Scale Biosciences offers single cell tools with an emphasis on their flexibility and throughput. In this presentation, Patrick Boyd pointed out n that customers had often been limited by the scale of their experiments, only being able to process relatively low numbers of cells and samples. That’s why Scale Bio offers tools that go beyond those limits.
QuantumScale scRNA Kit
QuantumScale is Scale Bio’s brand new single cell kit, capable of processing millions of cells and thousands of samples simultaneously at a cost of under a penny per cell. The kit uses methanol-based fixation which can hold up to 100 different sample types at a time, amounting to thousands of individual samples being processed at once. After the samples are loaded into a 96 well plate, they are barcoded and reverse transcribed, ultimately enabling sequencing with flexibility for different sequencing technologies.
ScalePlex Technology
Highter throughput can be achieved using Scale Bio’s ScalePlex technology. It’s a 96 well plate with each well containing a unique oligo which binds to the loaded cells. Then each of the 96 samples can be combined together into one, which can be used in downstream processing. The modular approach of the kits allows users to conduct multiple experiments or break down larger experiments into smaller ones, providing flexibility for various research needs
Single Cell Methylation Kit
The presentation also highlights Scale Bio’s Single Cell Methylation Kit, the first of its kind on the market. The kit which enables analysis of methylation across the genome in individual cells and features a streamlined workflow with formaldehyde fixation and the ability to store samples for up to four weeks. Boyd further pointed out the robust performance across different sample types, including human and mouse cells, with high correlation between total reads and unique reads. It also allows for the identification of major cell types and differential methylation patterns
