Time-resolved RNA sequencing methods are applications of RNA-seq that allow for observations of RNA abundances over time in a biological sample or samples. Second-Generation DNA sequencing has enabled cost effective, high throughput and unbiased analysis of the transcriptome. Normally, RNA-seq is only capable of capturing a snapshot of the transcriptome at the time of sample collection. This necessitates multiple samplings at multiple time points, which increases both monetary and time costs for experiments. Methodological and technological innovations have allowed for the analysis of the RNA transcriptome over time without requiring multiple samplings at various time points.
Background
While DNA encodes all of the functional elements of life, the information encoded must be converted into functional form. Following the central dogma of molecular biology, messenger RNA encodes genetic information for producing proteins, which, alongside functional RNA carry out the majority of cellular processes required for life. Changes in RNA abundance may be used as a measurement of changes in cellular behavior, such as heat stress, infection by virus, or oncogenesis. Knowledge of how the transcriptome changes during cellular processes allows for greater understanding of the exact mechanisms underlying these processes. Originally, transcriptome-wide RNA abundance could only be assessed using methods such as DNA microarrays or serial analysis of gene expression (SAGE). These methods are prohibitive in differing regards; microarrays, while cheap, provide inconsistent results and SAGE is based on sanger sequencing, which provides limited throughput. Using second generation sequencing, instead of measuring relative hybridization of sequences to probes in the case of microarrays or sequencing short segments in the case of SAGE, a researcher can simply sequence the bulk RNA within a sample and measure relative abundances of specific types of RNA by comparing the number of times each RNA molecule was sequenced in a given sample. Normally, in a traditional RNA-seq, microarray, or SAGE experiment RNA is extracted from a biological sample such as cultured cells, and the RNA is analyzed using the chosen method. The data obtained from such an experiment corresponds to abundance of RNA under the given experimental conditions at the time of harvest. For many applications, such as comparing the abundance of mRNA molecules between cells exposed to a drug and those not exposed to the drug, this type of experimental approach is sufficient. However, many cellular processes of scientific and medical interest are processes which occur over time, such as cellular differentiation or phagocytosis. Studying such processes requires analysis of RNA abundance across a series of time points.
Methods
Time series samples
Sample preparation and data processing The simplest approach towards assessing RNA abundance over time is to simply use multiple samples which are treated in exactly the same way, except for the duration of treatment. For example, to investigate a biological process which is estimated to occur for an hour, a researcher might design an experiment where the process is triggered for five minutes, 15 minutes, 30 minutes, 45 minutes, one hour, and two hours in separate cell culture samples before harvesting the cells for RNA-seq analysis. The researcher would then have measurements of the transcriptome at each of these time points, and comparing between these samples would indicate which cellular processes are activated and deactivated over time.
Strengths This method is the most common for measurement of RNA over time in cell culture models, mainly due to its simplicity. Each biological sample need only be processed in exactly the same way, and the factor of time is easily adjusted in most experimental protocols. Furthermore, since each time point is its own sample, more RNA can be harvested and sequenced for a study.
Weaknesses The requirement of multiple samples for time-resolved data collection increases the cost of the experiment as well as introducing a greater potential for technical errors. While the price of massively parallel sequencing has decreased greatly since its introduction, it is still prohibitively expensive for many laboratories to conduct large scale RNA-seq studies. This issue is compounded by additional time points increasing the number of samples by a multiple of the number of time points; using two time points rather than one doubles the number of samples required in an experiment. Consequently, many studies which use time series RNA-seq become limited in either their sample size, which reduces statistical power, or the number of time points, which reduces their time resolution, or both. Finally, by requiring a greater number of biological samples, there is greater risk for human error to affect the results, which may lead to spurious conclusions
Affinity Purification
Sample preparation and data processing In this approach, cell culture samples are cultured with tagged nucleotides which allow for selective purification of newly synthesized RNA molecules. One popular approach is pulse labeling with 4-thiouridine (4-sU), a uracil analogue that is incorporated in newly synthesized RNA molecules. In this type of experiment, a researcher would supplement cells with 4-sU at the time of the experiment or shortly beforehand. When the experimental treatment presumably affects RNA expression, newly synthesized RNA would be labeled with 4-sU. Newly synthesized RNA is labeled with a reactive thiol group, making it possible to link useful molecules to the RNA. Biotin is a popular molecule for use in this type of assay, as it is inexpensive and binds incredibly strongly and selectively to streptavidin. Incubation of biotinylated RNA with beads containing streptavidin allows for the selective purification of newly synthesized RNA. From here, newly synthesized and total RNA are sequenced separately and compared for differences.
Strengths Affinity purification makes use of the incredibly popular biotin-streptavidin system for fractionation of biological materials. Binding of biotin to streptavidin is incredibly strong (Kd < 10−14 mol/L). It is also highly specific, which results in minimal background signal from non-specific binding events. Furthermore, time resolution is obtained in a single biological sample, resulting in reduced biological variability compared to using separate samples for each time point.
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