![]() ![]() Our own benchmarking efforts of bulk SCRB-seq revealed however important quality issues, prompting us to test and improve key steps of this workflow (Additional file 1: Figure S1b), including the barcoded primer design, initial RNA amount, number of amplification cycles, and tagmentation strategies, culminating into the presented Bulk RNA Barcoding and sequencing (BRB-seq) approach. Moreover, its unaltered workflow had already been used in several studies for bulk RNA profiling. As our experimental foundation, we selected the SCRB-seq approach, a single-cell transcriptomics protocol that we deemed to be the most time- and cost-effective amongst all early multiplexing approaches (Additional file 1: Figure S1a,b). In this study, we set out to generate a method for affordable, efficient, and accurate bulk RNA profiling of a large number of samples that combines the high-throughput capacity of single-cell transcriptomics and the high performance of standard RNA-seq. This unnecessarily inflates the overall experimental cost, rationalizing why 3′-end profiling approaches such as the 3′ digital gene expression (3′DGE) assay have already been proven effective to determine genome-wide gene expression levels, although with a slightly lower sensitivity than conventional mRNA-seq. However, most transcriptomics studies do not require or exploit full transcript information, implying that standard RNA-seq methods tend to generate more information than is typically required. Therefore, the principal limitation of this group of methods is the incapacity to address splicing, fusion genes, or RNA editing-related research questions. Since the label is introduced to the terminal part of the transcript prior to fragmentation, the reads solely cover the 3′ or 5′ end of the transcripts. After individual samples have been labeled, they are pooled together, and the remaining steps are performed in bulk, thus shortening the time and cost of library preparation. This is achieved by introducing a sample-specific barcode during the RT reaction using a 6–8 nt tag carried by either the oligo-dT or the template switch oligo (TSO). In contrast, early multiplexing protocols designed for single-cell RNA profiling (CEL-seq2, SCRB-seq, and STRT-seq) provide a great capacity for transforming large sets of samples into a unique sequencing library. Although providing a significantly faster and cheaper alternative, other approaches such as QuantSeq (Lexogen) and LM-seq still require the user to handle every sample individually (Additional file 1: Figure S1a). However, this protocol involves rRNA-depletion and bias-prone RNA adapter ligation, which is relatively cumbersome and expensive. To overcome this limitation, the RNAtag-seq protocol implemented the barcoding of fragmented RNA samples, which allows for early multiplexing and generation of a sequencing library covering entire transcripts. Both procedures evoke late multiplexing, which necessitates the processing of samples on a one-by-one basis. ![]() Currently, the de facto standard workflow for bulk transcriptomics is the directional dUTP approach and its commercial adaptation “Illumina TruSeq Stranded mRNA”. However, when compared side by side, one can observe variation in the order and refinement of these steps (Additional file 1: Figure S1a). However, there have been surprisingly few efforts to explicitly adapt and validate the early-stage multiplexing protocols for reliable and cheap profiling of bulk RNA samples.Īll RNA-seq library preparation methods are globally relying on the same molecular steps, such as reverse transcription (RT), fragmentation, indexing, and amplification. Such a strategy could also be of value to reduce the cost and processing time of bulk RNA sequencing of large sets of samples. This reduces both the RNA-seq cost and preparation time by allowing the generation of a single sequencing library that contains multiple distinct samples/cells. To alleviate this high cost, the emerging single-cell transcriptomics field implemented the sample barcoding/early multiplexing principle. Nevertheless, the high cost of standard RNA library preparation and the complexity of the underlying data analysis still prevent this approach from becoming as routine as quantitative (q) PCR, especially when many samples need to be analyzed. High-throughput sequencing has become the method of choice for genome-wide transcriptomic analyses as its price has substantially decreased over the last years. ![]()
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