Single-cell sequencing: the Ultimate Guide

 

ContentsStart reading

As a scientist, you have probably come across the use of single-cell sequencing in your field. But what is it exactly? And also how can you apply it to your research, which technology should you choose, and what do you need to think about before starting your project?

This ultimate guide to single-cell RNA sequencing answers all these questions and more.

 

What is single-cell RNA sequencing?

First of all: What is it?

Single-cell RNA sequencing measures gene expression right down to the level of individual cells. A simple way of looking at this is to use a smoothie as an analogy.

Take a sip of your smoothie and you taste a combination of all the ingredients. Maybe you'll be able to guess some of them, but there's no way you can deconstruct your smoothie entirely and list its ingredients and their ratios by taste alone.

You need more detail. You need a more advanced method.

This is similar to the limitations of conventional (bulk) RNA sequencing. Because you analyze thousands to millions of cells together, you'll only get an average gene expression level for all the cells combined.

While this might be enough for some research questions, sometimes you need more than just an average. You need more detail. You need a more advanced method.

Enter single-cell RNA sequencing.

Single-cell sequencing allows you to work out the recipe for your cellular smoothies. You'll not only discover the exact ingredient list, but also the ratio between them: You now know how many apples, oranges, strawberries, and bananas were used to make it.

With gene expression information at single-cell level, single-cell RNA sequencing allows you to precisely determine the different cell types and subtypes in your sample.

Compared to the average gene expression level obtained with bulk RNA sequencing, single-cell sequencing gives you an unprecedented high-resolution view of your samples - right down to their most fundamental building block: the single cell.

Back to contents

Where does single-cell RNA sequencing come from?

Single-cell RNA sequencing didn't just appear overnight. It's the result of many different scientific innovations and advances in recent years. Here's an overview of the innovations that got us to where we are today.

Amplification

The first challenge to overcome before single-cell genome or transcriptome sequencing was possible was that the tiny amount of RNA contained within a single cell was too low to measure.

And in 2009, researchers managed to amplify the RNA from a single cell sufficiently for it to be sequenced for the very first time.

Barcoding

The next innovation was the addition of cell-specific barcodes to the primers used in the amplification. This allowed for multiple cells to be pooled into a single sequencing library. This process of pooling cells is called multiplexing.

Amplification with In-Vitro Transcription

Another important innovation was using an In-Vitro Transcription (IVT) as the first step in the amplification process. This is important because IVT amplification is linear, unlike PCR-based amplification, which is exponential.

Using linear amplification reduces the bias towards highly-expressed genes and increases the sensitivity of the technology.

Upscaling

With most of the technical hurdles overcome, the next challenge was to scale up while keeping costs down.

This was achieved by using lab automation, microfluidics, and highly standardized protocols, which led to the introduction of common technologies such as SORT-seq and 10x Genomics. And this opened the way to cost-effective single-cell sequencing of several hundred to tens of thousands of cells per sample.

All these innovations have led to a real surge in published scientific research across many different applications and research areas. In fact, the number of publications linked to single-cell sequencing has doubled on average each year for the past 10 years.

Graph showing the number of papers per year on single-cell sequencing.

Source: Pubmed, September 2020

Back to contents

What are the key concepts of single-cell RNA sequencing?

Before we take a closer look at how single-cell RNA sequencing works, we need to cover a number of key factors crucial to understanding single-cell sequencing projects.

The first two of these factors help determine the cost of your single-cell experiment, and even how it's designed. These factors are; the number of cells per sample, and the number of reads per cell.

Number of cells per sample

The number of cells per sample means the final number of cells that can be used in the data analysis stage.

In most single-cell sequencing projects this can be anywhere between a few hundred to 10,000 cells per sample. You set a target number of cells before you start your experiment, but it's not guaranteed you'll meet that target.

Many things can influence the outcome, such as the quality of the input material, the platform used, and the cell type in question. You can take various precautions to make sure the target is more likely to be met, but the results often fluctuate.

We'll look at how many cells per sample you'll need for your analysis later in this guide.

Sequencing depth

The sequencing depth is defined as the number of raw sequencing reads per cell and is something you decide before you start your experiment. This means sequencing depth is closely related to the concept of the number of cells per sample we described above.

The number of reads usually varies between 30,000 and 150,000 per cell in a typical single-cell RNA sequencing project, so the sequencing depth, and the number of cells per sample, both have a significant impact on the costs of your experiment.

We'll also look at how deep you should sequence your samples later in this guide.

Number of detected genes per cell

Another important factor in determining the quality or complexity of a dataset is the number of detected genes per cell, expressed as how many genes are detected on average over all the cells in a sample.

Typical numbers vary based on the sample type: Inactivated immune cells might have about 1,200 genes per cell, whereas the same cells in an activated state can show up to 4,000 genes per cell.

Back to contents

How does single-cell RNA sequencing work?

There are several different technologies and platforms that can be used for single-cell sequencing, and each follows the same 5-step process. We go into this process in greater detail in our post 'How does single-cell RNA sequencing work?', but for the purposes of this guide, here's a brief overview.

Step 1: Generate a single-cell suspension

The sample is dissociated into single cells floating in suspension.

Step 2: Isolate the cells

The cells need to be isolated from each other and this is either done by sorting them with a Flow Cytometer (FACS) machine, or with microfluidics.

Step 3: Cell barcoding and amplification

The individual cells are barcoded so they can be identified and amplified so that the RNA can be sequenced.

Step 4: NGS library preparation and sequencing

All the material from all the cells is pooled into a single library, barcoded again (to mark the sample) and the library sent for sequencing.

Step 5: Data analysis

The raw sequencing data is mapped to the appropriate reference transcriptome and analyzed using specialized data analysis pipelines.

Read more: How does single-cell sequencing work?

Back to contents

What are common applications of single-cell sequencing?

Single-cell RNA sequencing can be used in almost every biological question that requires a detailed understanding of a cell population. This means that single-cell RNA sequencing has many different applications.

We describe this in greater detail in our post 'Single-cell sequencing: Common applications'.

This is what you can do:

Read more: Single-cell sequencing: Common applications

Back to contents

In which research areas is single-cell sequencing used?

Single-cell sequencing is also used in many different research fields. You can find out more in our post 'Single-cell sequencing: Common research areas'. Here's a short summary:

Of course, single-cell sequencing is not limited to these applications and research areas. As we said earlier, the possibilities are endless!

Read more: Single-cell sequencing: Common research areas

Back to contents

What single-cell RNA sequencing technology should I use?

There are several different technologies that can be used in single-cell sequencing projects. But which should you choose?

The answer to this depends on your project. Different biological questions and hypotheses may require different approaches, and therefore a different technological solution.

We have a post that explains in-depth the most important technologies, their benefits, and how they compare to each other. You can read it here: 'Which single-cell RNA sequencing technology should I use?'

The technology/platform options are:

Read more: Which single-cell RNA sequencing technology should I use?

Technology comparison

SORT-seq VASA-seq 10x Genomics
Platform 384-well plates 384-well plates Microfluidics
FACS sorting Required Required Optional
Optimal cell number 384 – 1,500 384 – 1,500 3,000 – 10,000
Coverage 3’ Full length 3’ and 5’
Detected RNA species mRNA (immature) mRNA & non-coding RNA mRNA
Immune Repertoire Sequencing No No Yes
Suitable for all sample types Yes Yes No

Back to contents

Book a meeting

Schedule a free call with one of our specialists to discuss your project

Book a meeting

Book a meeting with one of our specialists

How much does single-cell sequencing cost?

Single-cell sequencing can be expensive, especially when compared to conventional bulk RNA sequencing methods. But there is a good reason why this technology is expensive, and you can mitigate these costs if you carefully consider the design of your experiment.

Here's a quick overview of the most important factors of the high price of this relatively new technology.

The costs of the reagents

Because all the biomolecular reactions happen at a single-cell level, a higher volume of reagent is needed for single-cell sequencing, which drives up the price. Generally speaking, reagent costs are 10-20 times higher than for bulk RNA sequencing experiments.

The costs of sequencing

Transcriptome analysis at single-cell level requires a high sequencing depth to obtain a statistically significant dataset. As with the reagents, a typical single-cell sequencing experiment also requires 10 to 20 times more sequencing reads per sample.

The technology

To enable scaling and standardization of the process, advanced devices are needed for single-cell sequencing. Furthermore, a lot of money has been invested in researching and developing the reagent kits, which are not very straightforward.

The set-up of your experiment

The eventual price of your experiment depends on three factors, which you control:

    • the number of samples
    • the number of cells per sample
    • the required number of reads per cell

The math is pretty simple: more samples, cells, and reads results in a higher price for your project. Therefore, it is critical to carefully consider the set-up of your experiment in relation to the biological question you're trying to answer.

A well-designed experiment can be a real budget saver.

Read more: How much does single-cell sequencing cost?

Back to contents

Request a quote

Request a quotation to receive your price.

Get your price

Things to consider before you start with single-cell sequencing

Ready to get started with single-cell sequencing?

  • Your biological question: This is the most important consideration, as it dictates the set-up of your experiment. Once you know exactly what you are trying to find out, we will advise you on how best to set up the experiment. Before consulting us, don't forget to check the literature on single-cell sequencing in your field.
  • Your sample type: Think about what cell-type you are interested in and how many cells you can collect. You need to know this from the start because your sample type can determine which technology you should choose. For example, a small number of cells or large cells are not compatible with 10x Genomics.
  • Your budget: Single-cell sequencing is expensive, so before reaching out to us, make sure to have a clear idea on your budget. Is your budget limited? Schedule a call with one of our specialists today to see how we can help.

Read more: Things to consider before you start with single-cell sequencing

Back to contents

Wrapping it up

So now you have an overview of single-cell RNA sequencing and how it can be used. If you're wondering whether to - or how to - apply single-cell sequencing in your field, we can help.

Book a free intake meeting today to discuss your ideas or request a quote below.

Back to contents

Download pricing information

Ready to proceed? Use the form below to download pricing information on our services.

Bastiaan Bijl

Business development

Not sure what solutions is right for you?
Contact us and we will help.

Book a meeting