Bulk RNA-seq has been used since the late 2000s as a tool to study the whole transcriptome. However, the data generated with this approach represents an average of the gene expression within a sample. To bring higher resolution to this experimental approach, single-cell RNA-sequencing (scRNA-seq) was developed to provide the same utility of transcriptomic characterization to individual cells. scRNA-seq is becoming more and more common in biological studies as it provides an unprecedented view of biological changes within specific cell subtypes in a heterogenous population.

Though scRNA-seq is becoming more prevalent, the exact methods and terminology used to describe these techniques are constantly evolving and can be hard to differentiate. Bulk and single-cell sequencing approaches share similar library preparation and sequencing steps, but are very different in terms of sample preparation, particularly in methods used to isolate single cells. Here, we’ll provide an overview and comparison of commonly used single-cell isolation methods.
 

Single-Cell Sorting and Microfluidics

One of the earliest methods to sort single cells was using limiting dilutions or single-cell fluorescence-activated cell sorting (FACS). These approaches are lower throughput as there is a limit to the number of cells that can be analyzed before the experiment becomes too difficult to manage.

Another slightly higher-throughput method involves using microfluidic systems like the Fluidigm C1™ and Takara ICell8 nanowell chips. In this nanowell-based method, wells are loaded by passing a suspension of cells over the wells. The wells are often examined by microscopy to ensure they contain single cells before continuing. Once in the wells, the samples are prepared using a technique like Smart-seq2^1,2, which independently isolates the RNA of each cell. It is essentially the same as a bulk RNA-seq experiment, except that the input is a single cell instead of a larger sample. These techniques are advantageous because they capture all RNA, not just mRNA, and the efficiency of measuring many transcripts is very high compared to other methods. However, they are limited by the number of cells that can analyzed, and the process can be labor intensive.