Medium to high throughput single cell sequencing: A guide for different scRNAseq applications
Medium and High throughput single cell RNA sequencing are two approaches to obtain single cell transcriptomic profiles. The choice between these methodologies should be made with the biological question and experimental design in mind.
Single cell applications range from building animal wide atlases to analyzing very small populations, from millions of cells to less than a hundred. The technical approaches to these two types of projects must be different.
Atlases involve millions of cells being sequenced to create an average expression profile of every cell, allowing for the identification of cell types and subtypes. This requires a high throughput method that can collect information from all the cells in an easy-to-use and cost-effective manner.
On the other hand, very small and specific populations might be missed from these high throughput studies, if these are rare or fragile cell populations. In these cases, cell enrichment might be necessary. Moreover, when the cellular input is a limiting factor, it is important to obtain the maximum information from every cell, this is one example of where medium throughput will be the best choice in generating in-depth transcriptomic profiles for every cell with full-length transcript sequencing.
To help you choose the best approach for your research let’s dive into the differences between medium and high throughput approaches:
Medium throughput methods like SMARTseq2 (1) involve full-length cDNA amplification to generate high quality, deep sequencing data from individual cells. It aims to capture full-length transcripts present in every cell, allowing for a more precise quantification of the mRNA and isoforms being utilized.
High throughput methods (Singleron, DropSeq, 10X Genomics) use beads labeled with oligos for capturing the transcripts present in each cell, this comes with lower coverage (3’ sequencing) but much higher throughput.
Plate based approaches like SMARTseq require more hands-on labor and time for library generation when compared with high-throughput technologies. Medium throughput method involves cell isolation, lysis, cDNA synthesis and library preparation for each individual cell while high throughput methods can lyse, capture the mRNA and prepare libraries from thousands of single cells in one single reaction. This protocol is not only faster but cheaper as it significantly reduces the volumes of reagents needed.
Medium throughput uses a sequencing depth of around 1 million reads per cell allowing for the detection of rare transcripts while high throughput aims at around 20000-50000 reads/cell which is enough to capture most of the expressed genes but might miss rare transcripts and low-abundance genes.
The information that can be extracted from both options is also different, medium throughput allows for identification of alternative splicing events due to full length sequencing, identifying isoforms and new transcripts with high confidence while high throughput is more suitable for cell type and subtype identification due to the higher number of cells captured.
Let’s look at some examples that illustrate the benefits of both methodologies:
1. High throughput application: Cell-type specific molecular changes in autism.
In this publication, authors used high throughput single cell sequencing to analyze the transcriptome of over 100,000 nuclei of postmortem brains of individuals with and without autism spectrum disorder (ASD). High throughput is an especially good fit when there are patient samples to capture all the cells and identify all cell types present and differences between the two conditions. This allowed for the identification of changes in gene expression associated with ASD in specific cell types, showing that specific gene sets in cortical upper-layer projection neurons and microglia correlates with the clinical severity of ASD. This study provided new insights into the cellular and molecular mechanisms for ASD (2).
2. Medium throughput application: Differential isoform usage defines cell-types.
In this study, the authors used SMARTseq to assay around 6000 mouse primary cortex cells and found examples of cell type specific isoform usage. As mentioned before full-length sequencing is necessary to access the usage of different transcripts isoforms and because the authors combined medium and high throughput approaches in this study, it was possible to connect the specific isoform usage to different cell types. It was also shown that the different isoforms helped refine the cell type (3).
In summary, full-length medium throughput methods with high sequencing depth are powerful to unravel transcriptomics differences that go beyond gene expression, such as isoform usage, however, these are more time consuming and expensive. High throughput methods are very efficient for the analysis of large number of cells, albeit with lower sequencing depth.
The choice of method must be catered to the specific project and information that needs to be collected for the study as both medium and high throughput have different experimental targets and are able to answer different questions. For specific projects the ideal approach can be to potentially combined both methodologies.
1. Picelli, S., et al. Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc 9, (2014). DOI: 10.1038/nprot.2014.006
2. Dmitry Velmeshev et al. Single-cell genomics identifies cell type–specific molecular changes in autism. Science 364, (2019). DOI:1126/science.aav8130
3. Booeshaghi, A.S., Yao, Z., van Velthoven, C. et al. Isoform cell-type specificity in the mouse primary motor cortex. Nature 598, (2021). DOI: 10.1038/s41586-021-03969-3
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