Much of genomics discovery has been built on the back of sequencing cDNA from collections of cells taken from a tissue. Yet “bulk” sequencing averages out the tissue’s heterogeneity. To understand complex samples such as a tumor or liver, researchers can turn to measuring gene expression at the single-cell level. Here they can find a fount of insights into the makeup and function of tissue by looking at the identity of its individual cells.
The difference between bulk sequencing and single-cell sequencing is akin to comparing a fruit salad to a fruit smoothie—all the same ingredients are in there, but once they’re pureed and blended you can’t tell which part came from the mango and which from the kiwi.
Certainly single-cell sequencing will not replace platforms such RT-PCR or bulk RNA sequencing (RNA-seq) any time soon. It’s often enough to know that a panel of genes is expressed in a tissue, or that expression of gene x is different between healthy and patient samples, or that its level changes with treatment. Here the sample is treated as a homogenous tissue.
Yet in such “grind and find” techniques the heterogeneity of cellular sub-types is lost, and with it the ability to discern by looking individual expression patterns, for example, which types of immune cells are present in a tumor biopsy and what they are doing.
To better establish a transcriptional profile of individual cells, without the guesswork and assumptions inherent in extrapolating from bulk samples, researchers started using homebrewed methods in the 1990s. Such methods were initially technically quite challenging, and allowed even expert laboratories to analyze only a few cells at a time.
Rapid technological advancements followed, and the field has continued to evolve and progress. With the advent of commercialized single-cell solutions researchers can profile the transcriptome of thousands or even millions of cells in a single experiment.
While there are still many approaches used to prepare single cells for sequencing, four have gained the most popularity.
A plate-based approach will typically start by using flow cytometry to sort single cells into individual wells of a 96- or 384-well plate. In each well cells are then lysed, RNA is reverse-transcribed into cDNA and a sequencing library is prepared. This method has produced some highly sensitive data, but is very low throughput and can take up to two days to complete.
Nanowell-based technologies involve depositing cells and oligo-tagged beads into sub-nanoliter wells by gravity as they are flowed over a nanowell array. Lysis, reverse-transcription and library preparation are then performed. Because each oligo bead has a unique sequence that tags the cell, it’s easy to tell which transcripts came from which cell. This method has a moderate throughput, and can be tricky to scale up.
Combinatorial barcoding also uses a 96- or 384-well plate format, but here pools of cells are split into plates containing well-specific barcodes, so all transcripts from each well have the same index. The process is then repeated three to four times so that transcripts from each cell contains a unique combination of indices. This method is scalable, but requires some time-intensive workflow steps involving a lot of handling and pipetting.
Finally, the 10x Genomics platform uses microfluidics to generate nanoliter-sized droplets containing a single cell and all reagents necessary to prepare a library, including a bead with its own unique barcoded oligonucleotide tags. All transcripts from each cell will bear that same barcode. This method can be easily scaled and has a streamlined workflow. It can take about a day to complete.
It’s not just the gene expression itself that can be profiled at the single-cell level.
For example, the 10x platform offers a portfolio of assays that allows a wide variety of species to be interrogated from single cells. Assay for transposase accessible chromatin (ATAC)-seq examines chromatin accessibility, giving insights into cell states and types as well as mechanisms of gene regulation. An immune response can be mapped by simultaneously sequencing paired light and heavy chain of T and B cell receptors. CRISPR screens allow for edits to be mapped to phenotypes. In many cases a single cell can be queried for multiple analytes, for example by combining immunofluorescent protein detection with a transcriptome assay.
If your research can benefit from an understanding of what each cell individually—rather than just in aggregate–contributes to the makeup and function of a tissue or an organ, an immune response or a metastasis, then single-cell assays are likely for you.