Different RNA sequencing technologies are available and present an efficient way of studying transcriptase.
The advent of next-generation technologies opened exciting avenues to study complex cellular processes.
RNA sequencing using next-generation technologies has become the standard for studying gene expression, RNA biogenesis, and metabolism (Hrdlickova et al. 2016).
However, bulk RNA sequencing experiments give averaged gene expression across sampled cells, thus masking cell heterogeneity (Chen et al. 2019).
For example, single-cell RNA sequencing is becoming increasingly favored for studying its processes.
Using single-cell RNA sequencing allows for a better understanding of the heterogeneity of gene expression between cells (Angerer et al. 2017).
Thus, single-cell RNA seq analysis is increasing in popularity because it allows for interrogation of individual cell types,
Therefore, being able to uncover patterns of co-expression in genes (Hrdlickova et al. 2016, Angerer et al. 2017).
Sample selection needs to be decided upon before RNA-seq protocol initiation,
As these protocols only focus on the subset of genes expressed in the sample which varies from tissue to tissue.
Single-cell RNA sequencing is based on the premise of bulk RNA-seq,
Therefore, the fundamental steps involved in RNA-seq are present in scRNA seq protocols albeit with some practical modification and additional steps.
Due to the non-specific nature of bulk RNA-seq, more starting material is used compared to scRNA-seq which requires cell isolation steps prior to library preparation (Brennecke et al. 2013).
The most widely used methods of single-cell isolation include using microfluidics
(e.g. Fluidigm C1), droplets (e.g., Drop-Seq), and microplates (Thorsen et al. 2002, Klein et al. 2015, Nichterwitz et al. 2016).
Microfluid and droplet techniques use chips that trap individual cells per well.
while microplates use a laser capture to isolate individual cells into microplates slots (Hwang et al. 2018).
Once trapped in these microplates, subsequent RNA to cDNA conversion is conducted and the rest of the library preparation steps are performed on these platforms which are then sent to sequencers (Hwang et al. 2018).
During library preparation in scRNA seq, individual cells are also marked with barcodes thus allowing for libraries from multiple cells to be prepared simultaneously.
The barcodes inform subsequent single-cell RNA seq analysis.
In bulk RNA seq, the sample under investigation RNA purification follows these general steps:
cell lysis of tissue sample followed by RNA isolation using a collection tube,
Contaminants remain on the membrane and where only the purified RNA collects at the bottom where it is suspended in elution buffer (Thermofisher 2016).
The fine details of protocols vary depending on RNA type, morphology, and prevalence.
From the purified RNA, a small amount is used for library preparation where it is fragmented and converted to cDNA (Wang et al. 2009).
To prepare a DNA library for sequencing, sequencing adaptors are also added to each end of the fragments (Hrdlickova et al. 2016).
The prepared library can then be sequenced by the available sequencing platforms such as Illumina sequencing.
Two sequencing options exist, either sequencing single-read or paired-end, the former is cheaper and quicker (Illumina 2019).
Data processing and analysis
Data processing and analysis of batch RNA-seq experiments are limited by our current understanding and classification of cell types and subtypes.
ScRNA-seq, on the other hand, is informed by per cell transcriptome classification.
However, due to the logistical constraints of scRNA-seq that come with an increased number of cells
(up to millions) experiments end up being conducted at different times or runs (Chen et al. 2019).
This can introduce variations as artifacts of technical handling called the Batch effect (5 and 6 in Haghverdi) that have no biological relevance.
The batch effect can be dealt with prior to data analyses so that it does not bias the results.
In scRNA-seq, this is done with techniques that use linear regression analysis or using the mutual nearest neighbor approach (Haghverdi et al. 2018).
After this correction, similar data analyses pipelines are used in both methods with some steps used for the cell to cell pattern inferences for single-cell RNA seq analysis.
The small starting amount in single-cell RNA-seq leads to more noise in the sequenced data, therefore necessitating additional filtering steps before single-cell RNA seq analysis (Chen et al. 2019).
In single-cell RNA seq analysis cell separation can be performed in silico and grouped in a meaningful manner (cell barcode directed).
Data handling and analysis involves silico-based data pre-processing, dimension reduction, differential analysis, clustering, and hypothesis-driven analyses Stegle et al. 2015).
Commercially automated pipelines include bulk RNA-seq and single-cell RNA seq analysis with Basepair.
Bulk RNA-seq data Analysing pipelines
- DNASTAR (DNASTAR, Madison, WI, USA) and
- Tophat (University of Maryland, College Park, MD, USA)
- R-based packages such as DESeq (EMBL, Heidelberg, Germany).
For example, Khan et al. (2015) ran their analyses using DNASTAR, Tophat, and DESeq.
They preprocessed sequence data through adapter removal using SeqPrep and then aligning sequence reads to a reference genome.
Duplicate reads were removed using SAMtools (Wellcome Trust Sanger Institute, Cambridge, UK). They identified gene ontologies using the PANTHER classification system.
Due to the seniority of bulk RNA-seq bioinformatics tools are more fine-tuned and standardized than those used for single-cell RNA seq analysis.
This makes bulk RNA-seq studies more comparable to each other.
However, as siRNA seq increases in use the protocols and pipelines will become more streamlined as is already evident with single-cell RNA seq analysis with Basepair.
Bulk RNA-seq is still a useful technique for studying tissue dynamics.
The ease with which multiple cells can be individually interrogated makes scRNA-seq increasingly popular in studies focused on cell heterogeneity during development, disease profiles, and therapy development.