Classical single cell RNA sequencing (scRNA-Seq) emerged from the necessity to capture the nuances of individual cells within a diverse population. Early efforts paved the way for breakthroughs in gene expression profiling, leading to the development of potent tools like scRNA-Seq in biological research.
Single-cell transcriptomics began with the development of single-cell qPCR, progressing to whole-transcriptome analysis using microarrays. This evolution culminated in the adaptation of bulk RNA sequencing (RNA-Seq) for single-cell analysis, giving rise to scRNA-Seq1.
ScRNA-Seq is a groundbreaking method enabling researchers to explore cellular complexity by examining gene expression dynamics at a cellular level. The first scRNA-Seq transcriptomes were published just two years after the initial use of RNA-Seq in bulk samples2. Since its inception, scRNA-Seq has shed light on transcriptional profiles across various biological domains, offering novel insights into cellular composition and interactions in humans, model organisms, and plants.
In this blog post, we aim to provide an overview of the fundamental principles, benefits, and common applications of traditional scRNA-Seq.
Bulk v scRNA-Seq:
Complex biological systems emerge from the synchronized actions of individual cells, each fulfilling a unique role in the organism's function. Traditional bulk RNA-Seq methods encounter limitations due to this complexity, prompting the adoption of single-cell RNA-Seq approaches.
To illustrate the difference between scRNA-Seq and bulk RNA-Seq, consider a smoothie analogy. Bulk RNA-Seq provides a blended average of gene expression akin to a mixed smoothie, while scRNA-Seq dissects this blend into individual components, revealing precise quantities and subtle variations.
By isolating and analysing individual cells, scRNA-Seq unveils the depth of cellular diversity within tissues or organs. It offers insights into transitional phases and subtle gene expression changes, akin to zooming in on each cell's unique genetic makeup. This approach is crucial for identifying rare cell types and elucidating cellular diversity, particularly in tissues with heterogeneous cell populations. While bulk RNA-Seq offers a broad overview, scRNA-Seq enables researchers to delve into the individual nuances within the cellular landscape.
Why do scRNA-Seq?
ScRNA-Seq facilitates high-resolution transcriptional profiling of thousands of individual cells, shedding light on RNA expression levels within each. This analysis addresses key questions:
- What genes are expressed at single-cell resolution?
- How do transcriptional profiles vary across a heterogeneous cell sample?
- What cell types/varieties exist within the sample?
- The role of individual cells in operating biological systems?
- Modifications to transcriptional profiles during development or treatment?
Since its inception, scRNA-Seq has revealed previously unnoticed heterogeneity within cell populations, particularly in embryonic and immune cells. Investigating cell heterogeneity remains a primary focus of scRNA-Seq studies3.
In addition, comparing gene activity in individual cells has helped identify rare cell types missed by bulk analysis. In cancer research, single cell RNA-Seq is crucial for finding elusive tumour cells, like malignant ones within a tumor4 or hyper-responsive immune cells5 in a seemingly uniform group. It's also perfect for studying unique cells like T lymphocytes with diverse receptors, neurons, or early-stage embryo cells3.
How does scRNA-Seq work?
It's worth noting that while single cell RNA-Seq offers answers to various research questions, the level of detail provided depends on the chosen protocol. Different protocols determine the extent of mRNA data resolution, including the number of detectable genes/transcripts, expression of specific genes of interest, and occurrence of differential splicing. Here are the main steps of scRNA-Seq, which remain consistent across platforms and technologies.
Step 1: Prepare single cells suspension.
In order to isolate individual cells, start by breaking down the tissue using methods like enzymatic digestion or mechanical force. It's crucial to optimize this step to ensure the cells are isolated without altering their genetic material too much.
Step 2: Cell Isolation.
After creating a single-cell suspension, various methods can be used to isolate the cells for scRNA-Seq. Common approaches include fluorescence-activated cell sorting (FACS), magnetic-activated cell sorting, microfluidic systems, and laser microdissection.
FACS sorting involves using a flow cytometer machine to sort live cells into wells of a microtiter plate. Live/dead staining ensures only live cells are sorted. This method is effective when isolating specific cell types by using marker-specific fluorescently labelled antibodies.
Microfluidic technologies are also popular, employing a droplet-based approach. A single-cell suspension is loaded onto a chip with beads for barcoding and reagents. Pressure forces the cells through tiny capillaries, separating them into droplets. These droplets are then combined with barcodes and reagents for the next step.
Step 3: Extract, Process, and Amplify Genetic Material.
In the past, it was challenging to measure the small amount of RNA in a single cell, but now it can be amplified using PCR and/or in vitro transcription. First, the cells are lysed to keep the RNA. Then the mRNA is captured, and reverse transcribed. Poly(T) primers capture mRNA and avoid ribosomal RNA. After converting mRNA into cDNA, sequences like adaptors for sequencing platforms and unique identifiers to mark each mRNA molecule are added. The cDNA is then amplified, sometimes with barcoding to identify individual cells during analysis.
Step 4: Library Preparation and Sequencing.
Following amplification, cDNA from each cell is pooled and sequenced using next-generation sequencing (NGS). Library preparation methods, sequencing platforms, and alignment tools are like those used in bulk RNA-Seq4.
Step 5: Data Analysis.
Bioinformatics tools are employed to evaluate quality, variability, and interpret data. Analysing single-cell datasets poses unique challenges, including addressing sparsity (large fractions of observed zeros in scRNA-Seq datasets), establishing statistical frameworks for detecting complex gene expression differences, accurately mapping cells to a reference atlas, inferring dynamic gene expression patterns, and identifying patterns in spatially resolved measurements.
ScRNA-Seq solutions from Cambridge Bioscience:
ScRNA-Seq has transformed transcriptomics, enabling the study of cellular complexity, transitional states, and gene expression nuances in diverse cell populations, tissues, and organs. This technology has propelled advancements in various fields such as immunology, cancer research, development, neurobiology, and diabetes.
Here at Cambridge bioscience, we are proud to be partnered with RNA experts Lexogen, supplying a portfolio of RNA-sequencing products, including scRNA-Seq. Lexogens LUTHOR HD can detect up to 12,000 genes from as little as 1pg of input RNA. Thanks to their specialist THOR technology directly amplifying the original RNA molecule, LUTHOR HD can detect even genes expressed as lowly as 10 copies/cell with unmatched sensitivity. To discover more about our RNA-Seq range don’t hesitate to get in touch with one of our specialists.
Original content written by Lexogen, read the article here.
References and additional reading:
Kolodziejczyk, A.A., Kim, J.K., and Svensson, V.; Marioni, John C.; Teichmann, Sarah A.201558 (2015). The technology and biology of single-cell RNA sequencing. Molecular Cell 58, 610-620. DOI: 10.1016/j.molcel.2015.04.005.
Tang, F., Barbacioru, C., and Wang, Y.; Nordman, Ellen; Lee, Clarence; Xu, Nanlan; Wang, Xiaohui; Bodeau, John; Tuch, Brian B.; Siddiqui, Asim, et al.20096 (2009). mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6, 377-382. DOI: 10.1038/nmeth.1315.
Haque, A., Engel, J., and Teichmann, S.A.; Lönnberg, Tapio20179 (2017). A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Medicine 9, 75. DOI: 10.1186/s13073-017-0467-4.
Tirosh, I., Izar, B., and Prakadan, S.M.; Wadsworth, Marc H.; Treacy, Daniel; Trombetta, John J.; Rotem, Asaf; Rodman, Christopher; Lian, Christine; Murphy, George, et al.2016352 (2016). Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science (New York, N.Y.) 352, 189-196. DOI: 10.1126/science.aad0501
Shalek, A.K., Satija, R., and Shuga, J.; Trombetta, John J.; Gennert, Dave; Lu, Diana; Chen, Peilin; Gertner, Rona S.; Gaublomme, Jellert T.; Yosef, Nir, et al.2014510 (2014). Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363-369. DOI: 10.1038/nature13437.