Understanding the Benefits of Single-Cell Transcriptomics: The Smoothie Analogy
Imagine sipping a smoothie. At that moment, your taste buds encounter a blend of various ingredients. You may detect the presence of orange in the smoothie, apple, and a hint of strawberry. Yet, the subtleties of blueberry and coconut cream, when used sparingly, might go unnoticed.
Now, imagine figuring out the exact proportions of these ingredients in your smoothie. It is challenging to do this just with the unrefined apparatus of an untrained tongue. You need a more advanced method to precisely discern the components. This analogy aligns with the concept of conventional bulk RNA sequencing.
With bulk RNA seq, you collect data on the transcriptome of many cells simultaneously. This means the transcripts you detect represent an average across all those cells. As a result, the gene expression profile
To delve into the transcriptome of individual cells requires a more sophisticated approach: single-cell transcriptomics.
Single-cell transcriptomics captures gene expression information at the level of individual cells. It empowers you to accurately pinpoint the various cell types, cell subtypes, and cell states within your sample. Unlike bulk transcriptomics, which yields an averaged gene expression profile, single-cell transcriptomics delivers the most accurate view of your sample.
A Brief Word on Terminology
‘Transcriptome’ is actually one of the descriptions used in the single-cell sequencing field. It is also known as the cell’s 'gene expression profile', because it shows which genes are expressed in the cell at the time of analysis. Moreover, it goes by the name of 'transcriptional profile', reflecting the aim to identify the entire profile of mRNA transcripts in the cell.
In addition to single-cell transcriptomics, you can find researchers calling the technology single-cell RNA sequencing (scRNA-seq) or single-cell transcriptional profiling.
How Does Single Cell Transcriptomics Work?
Single-cell RNA sequencing follows a five-step process. This is true whichever sequencing platform or technology one uses. Let's break it down.
Step 1: Preparing Individual Cells
Before we can start sequencing single cells, we need to ensure that our cells are all by themselves, floating freely. If we're working with tissue samples, we have to break them down into individual cells. We can do this in a couple of ways: by using enzymes to dissolve the tissue or by physically breaking it apart. In some applications, we break down tissues and extract nuclei from the cells. This is done when, for example, cells like cardiomyocytes are generally too big for sequencing.
Step 2: Separating the Cells
To make sure we can study and label each cell separately, we have to separate them from one another. There are two main ways to do this:
Method A: Sorting with FACS
We can use a special machine called a Flow Cytometer (FACS) to pick out single, live cells and put them into tiny wells in a plate. We can also use a special dye to check if the cells are alive and healthy. If we're interested in a particular type of cell, we can even use this machine to pick out only those cells.
Method B: Using Microfluidics
Alternatively, we can use microfluidics, where we put our cell mixture onto a chip with tiny beads and chemicals. By squeezing the cell mixture through tiny tubes on the chip, we separate the cells one by one. These isolated cells get paired with the beads and chemicals in tiny droplets for the next step.
Method C: Using Microwells
An option not often utilized is the microwell-based method, which uses chips with micro- or sometimes even pico-sized wells. The main difference is that cells float to the bottom of the wells by gentle gravity. By using the correct concentration of cells per µL, you ensure only one cell per well.
Step 3: Labeling and Copying RNA
Each cell contains a tiny amount of RNA, which requires amplification to study it adequately. We do this by making lots of copies of the RNA using a process called PCR and/or IVT. To tell the cells apart later, we add a unique label, called a cellular barcode, to each cell's RNA.
Step 4: Preparing for Sequencing
Now that we have made copies of the RNA and barcoded them, we mix all RNA copies from different cells into one batch. We add another set of barcodes, this time to show which batch each RNA copy comes from. This helps to identify batch effects without biological meaning. We then prepare the mix as a library for Next Generation Sequencing.
We can perform sequencing in-house in our sequencing facility. It has the most advanced Illumina NovaSeq X Plus as one of the sequencing machines to do the work.
Step 5: Analyzing the Data
Next Generation Sequencing generates a large amount of data for each cell. We can take this raw data and match it with a reference to understand which genes are expressed in each cell. We also run quality control to make sure the data is reliable. This step is like putting all the pieces of a puzzle together.
Once we have organized the data, we break it down to the level of individual cells. This creates a table with rows for all the genes we've detected and columns for each cell. Because there are often hundreds or thousands of cells in our samples, the amount of data can be overwhelming. This requires special tools, like data analysis pipelines, to make sense of it all. To truly understand the data, we use dedicated analysis programs to group similar cells together. This helps us identify different cell types and subgroups within them. Finally, we figure out which genes are more or less active in each group: differentially expressed genes. This gives us valuable insights into cell behavior and single-cell expression profiles.
Some of these genes will be marker genes that help identify a group of cells as a certain cell type. For example, liver macrophage marker genes may designate a number of cells as liver macrophages.
Data Visualization
Finally, we visualize the data as accessible data figures. As you can imagine, you need specific computational approaches to create a picture from very large numbers of transcriptomes. A key step in this process is dimensionality reduction. This generates the tSNE or UMAP plots often seen in single-cell papers.
In short, using an algorithm, we create a simpler data collection that accurately reflects the more complex dataset. We generate a low-dimensionality dataset from a high-dimensionality dataset. We describe this method in more detail in our blogs on cell type identification, t-SNE and UMAP.
And that's the single-cell sequencing process in a nutshell.
Can Single-Cell Transcriptomics help you?
Single-cell sequencing is a tool to answer many scientific questions. In fact, the possibilities are almost endless. If you are wondering whether – or how – to apply single-cell sequencing in your field, we can help.
The History of Single Cell Transcriptomics
Single-cell transcriptomics did not appear overnight. It is the result of different scientific innovations and advances in recent years. Here's an overview of the innovations that got single-cell transcriptomics to where it is today.
Amplification
Single-cell genome or transcriptome sequencing was impossible because of the small amount of RNA in a single cell. This amount was too small to measure.
In 2009, scientists amplified RNA from one cell to high enough volumes to sequence it for the first time. In that year, Tang et al. published the transcriptome of a single mouse blastomere. Until then, acquiring the transcriptome of blastomeres was impossible. One mouse would not contain enough of this cell type, and each cell contained too little RNA for successful sequencing.
Sequencing the transcripts from the material available from a single cell was the first innovation that made single-cell transcriptomics possible.
Barcoding
The next innovation was adding cell-specific barcodes to the primers used in amplification. With the barcodes, researchers could pool multiple cells into a single sequencing library. This process of pooling individual cells bears the name 'multiplexing'.
Amplification with In-Vitro Transcription
Adopting In-Vitro Transcription (IVT) as the first step in the amplification process was another crucial step in single-cell transcriptomics. It's important that IVT amplification is linear, unlike PCR-based amplification, which is exponential. Using linear amplification reduces the bias toward highly expressed genes. This, in turn, increases the sensitivity of the technology.
Growing in scale
Having surmounted most technical obstacles, the subsequent task was to amplify the scale while maintaining low expenses.
This was achieved using lab automation, microfluidics technology, and standardizing protocols. The advances led to the introduction of common commercial technologies such as SORT-seq and 10x Genomics. This opened the way to large-scale single-cell sequencing that can handle high throughput. Currently, the 10x Flex kit can perform single-cell transcriptomic on several thousand to more than a million cells per run.
These innovations have led to a surge in published scientific research across different applications, disease contexts, and areas of interest. In fact, the number of publications linked to single-cell sequencing has doubled on average each year for the past 10 years.
Single-cell atlases
Large collectives of research groups had the ambition to create comprehensive single-cell atlases of entire organisms, disease varieties, and more.
- The Human Cell Atlas (HCA), started in 2016, is one of the most prominent and comprehensive projects. Its aim is to map all cell types in the human body. It focuses on characterizing cell types, states, and their interactions across various tissues and organs.
- The Human Developmental Cell Atlas (HDCA) is a part of the HCA. It aims to map and characterize cell types and their dynamics throughout human development. The HDCA focuses on understanding how cell types change and specialize during different stages of human growth, from conception to adulthood. This project provides crucial insights into embryonic development, tissue differentiation, and organ formation.
- The BRAIN Initiative Cell Census Network (BICCN) aims to create a comprehensive atlas of cell types in the brain. BICCN focuses on understanding brain function and dysfunction at the single-cell level.
- Tabula Muris: Tabula Muris is an atlas project that focuses on the mouse model. It seeks to provide a detailed characterization of cell types across different organs and tissues in the mouse. This project offers valuable insights into the diversity and functions of cells in a model organism.
- The Curated Cancer Atlas connects a total of 77 single-cell RNA-sequencing datasets, which constituted almost 1500 tumors. It is led by the Weizman Institute of Science.
Single Cell Transcriptomics in Drug Development
As technology improved, it became more versatile and compatible with different sample types. Technology has improved and become more versatile and compatible with different sample types. This improvement has led to an increase in its use for various purposes. These purposes include identifying targets, understanding the drugs' mechanisms of action, and optimizing leads.
Recent years have also seen translational applications in preclinical development. The first Phase I, II, and III trials that use single-cell sequencing are also in publication.
Learn how to apply single-cell transcriptomics in drug development on our Drug Development application page.
New tools
In addition to single-cell/nucleus RNA sequencing, single-cell omics (SCO) methods encompass various techniques for studying nuclear epigenetics. These techniques include chromatin accessibility, histone profiling, DNA methylation, and chromatin conformation. These methods also extend to high-throughput single-cell proteomics and lipidomics. New technology allows us to study multiple cellular processes at the same time, improving our understanding of them.
New SCO profiling technologies are rapidly emerging, and researchers publish new analysis methods weekly. As the scale and modality of data sets grow, we need new computational methods and tools. Measured by a recent European effort, there are now more than 1100 tools developed specifically to work with single-cell omics.
How to Supercharge Your Research with Single Cell Transcriptomics
As we explained in the chapters above, single-cell transcriptomics allows you to obtain high-resolution data from your samples. But how can you use single-cell sequencing to improve your research?
Unlocking the Secrets of Biology with Single-Cell Transcriptomics
In the world of complex biological systems like organs, tumors, the immune system, and the brain, there are many different types of cells at play.
Single-cell sequencing is like a powerful microscope for biology. It helps us focus in on these cell types. Here are two ways it helps us do better research compared to regular sequencing:
Checking Disease Models
Imagine you're working with a model of a disease, like a mini-organ in a dish. It's essential to ensure this model truly represents the disease you're studying. Single-cell sequencing lets you compare the cells in your model to those in the real organ. This way, you can be sure that your model is a good representation of the patient.
Creating Maps of Cells
Scientists are now making detailed maps of cells in tissues, organisms, and diseases. The aforementioned Human Cell Atlas is a project that wants to map all the cells in the human body. These maps help us understand health and diseases better.
Understanding How Patients Respond to Treatment
When we study how patients react to treatments, single-cell sequencing lets us look at specific cells in detail. We can compare cells from treated and untreated patients or even track changes in the same patient over time. This helps us see how treatments work and if any cells become resistant.
These examples show how single-cell sequencing can supercharge our research in complex biological systems. Regular sequencing cannot give us this level of insight.
Revisiting Old Ideas with New Tools
Single-cell sequencing provides us with a fresh perspective on your previous research. You can compare it to looking at an old puzzle with a new set of eyes and finding missing pieces.
A Starting Point for New Projects
Single-cell sequencing gives us a ton of data. It's not just an add-on to existing projects; it's a spark that can start new fires of curiosity. That is why it often leads to entirely new research projects.
If you want to know more about what single-cell transcriptomics can mean for your research project, get the information guide.
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