BRB-seq

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Schematic overview of the MERCURIUS BRB-seq workflow where up to 384 samples can be barcoded and multiplexed per kit.

Bulk RNA barcoding and sequencing (BRB-seq) is an ultra-high-throughput bulk 3' mRNA-seq technology that uses early-stage sample barcoding and unique molecular identifiers (UMIs) to allow the pooling of up to 384 samples in one tube early in the sequencing library preparation workflow. The transcriptomic technology is compatible with both Illumina and MGI short-read sequencing instruments.[1]

In standard RNA-seq, a sequencing library must be prepared for each RNA sample individually.[2] In contrast, in BRB-seq, all samples are pooled early in the workflow for simultaneous processing to reduce the cost and hands-on time associated with the library preparation stage[1]

As BRB-seq is a 3' mRNA-seq technique, short reads are generated only for the 3' region of polyadenylated mRNA molecules instead of the full length of transcripts like in standard RNA-seq. This means that BRB-seq requires a far lower sequencing depth per sample to generate genome-wide transcriptomic data that allows users to detect similar numbers of expressed genes and differentially expressed genes as the standard Illumina TruSeq approach but at a cost up to 25 times cheaper or similar to profiling four genes using RT-qPCR.[1] BRB-seq also has a greater tolerance for lower RNA quality (RIN <6) where transcripts are degraded because only the 3' region is required in library preparation[1]

History[edit]

The BRB-seq technique was first published in April 2019 in the peer-reviewed journal Genome Research in a manuscript entitled 'BRB-seq: ultra-affordable high-throughput transcriptomics enabled by bulk RNA barcoding and sequencing[1]. By the end of 2019, the article was among the top 10 most-read papers in the journal and has been cited over 150 times[3] (April 2024).

The technique was developed at the École Polytechnique Fédérale de Lausanne in Switzerland in the labs of Professor Bart Deplancke and collaborators. In May 2020, a company called Alithea Genomics was established to provide BRB-seq as kits for researchers or as a full service[4]. BRB-seq builds upon technological advances in single-cell transcriptomics, where sample barcoding made the early multiplexing of hundreds to thousands of single cells possible. Sample multiplexing allowed researchers to create single sequencing libraries containing multiple distinct samples, reducing overall experimental costs and hands-on time while dramatically boosting throughput[5].

BRB-seq applies these advancements in sample and mRNA barcoding to mRNAs derived from bulk cell populations to enable ultra-high-throughput studies crucial for drug discovery, population studies, or fundamental research[1][6].

Method[edit]

The fundamental aspect of BRB-seq is the optimized sample barcode primers. Each barcoded nucleotide sequence includes an adaptor for primer annealing, a 14-nt long barcode that assigns a unique identifier to each individual RNA sample, and a random 14-nt long UMI that tags each mRNA molecule with a unique sequence to distinguish between original mRNA transcripts and duplicates that result from PCR amplification bias. BRB-seq allows up to 384 individually barcoded RNA samples to be pooled into one tube early in the workflow to streamline subsequent steps in cDNA library preparation and sequencing.[1][7][8]

Applications[edit]

BRB-seq is suitable for any study requiring genome-wide transcriptomic data. It is especially suited to studies with hundreds or thousands of samples thanks to its scalable, straightforward, and quick workflow, which is suitable for automation.

AI-driven drug discovery and toxicogenomics[edit]

Artificial intelligence requires vast amounts of training data to reach robust and reliable conclusions about a drug's on- or off-target biological effects and their toxicogenomic profiles. BRB-seq is a cost-effective and time-efficient sequencing technology that allows pharmaceutical companies to extract more transcriptomic data at a lower cost to investigate the pharmacological effects of thousands of molecules on cells of interest simultaneously and at scale.[9]

Fundamental research[edit]

BRB-seq has been used to discover a new type of cell that inhibits the formation of fat in humans, with the potential to improve treatments for obesity and type 2 diabetes,[10] to determine the expression of immune genes activated by SARS-CoV-2 at different temperatures in human airway cells[11] and to discover genes that are turned on or off at different times of the day in the fruit fly[12]

Agrigenomics[edit]

Researchers also used Plant BRB-seq in agritranscriptomics to investigate the transcriptomic response of maize to nitrogen fertilizers. They found the differential expression of a subset of stress-responsive genes in response to altering levels of fertilizer[13]

References[edit]

  1. ^ a b c d e f g Alpern, Daniel; Gardeux, Vincent; Russeil, Julie; Mangeat, Bastien; Meireles-Filho, Antonio C. A.; Breysse, Romane; Hacker, David; Deplancke, Bart (2019-04-19). "BRB-seq: ultra-affordable high-throughput transcriptomics enabled by bulk RNA barcoding and sequencing". Genome Biology. 20 (1): 71. doi:10.1186/s13059-019-1671-x. ISSN 1474-760X. PMC 6474054. PMID 30999927.
  2. ^ Stark, Rory; Grzelak, Marta; Hadfield, James (2019-07-24). "RNA sequencing: the teenage years". Nature Reviews Genetics. 20 (11): 631–656. doi:10.1038/s41576-019-0150-2. ISSN 1471-0056. PMID 31341269.
  3. ^ "BRB-seq". Google scholar. Retrieved 27 April 2024.
  4. ^ "Alithea Genomics | Large-scale RNA sequencing". alitheagenomics.com. Retrieved 2024-04-25.
  5. ^ Ziegenhain, Christoph; Vieth, Beate; Parekh, Swati; Reinius, Björn; Guillaumet-Adkins, Amy; Smets, Martha; Leonhardt, Heinrich; Heyn, Holger; Hellmann, Ines; Enard, Wolfgang (February 2017). "Comparative Analysis of Single-Cell RNA Sequencing Methods". Molecular Cell. 65 (4): 631–643.e4. doi:10.1016/j.molcel.2017.01.023. hdl:10230/34928. ISSN 1097-2765. PMID 28212749.
  6. ^ Spreafico, Roberto; Soriaga, Leah B.; Grosse, Johannes; Virgin, Herbert W.; Telenti, Amalio (August 2020). "Advances in Genomics for Drug Development". Genes. 11 (8): 942. doi:10.3390/genes11080942. ISSN 2073-4425. PMC 7465049. PMID 32824125.
  7. ^ Ziegenhain, Christoph; Vieth, Beate; Parekh, Swati; Reinius, Björn; Guillaumet-Adkins, Amy; Smets, Martha; Leonhardt, Heinrich; Heyn, Holger; Hellmann, Ines; Enard, Wolfgang (2017-02-16). "Comparative Analysis of Single-Cell RNA Sequencing Methods". Molecular Cell. 65 (4): 631–643.e4. doi:10.1016/j.molcel.2017.01.023. hdl:10230/34928. ISSN 1097-4164. PMID 28212749.
  8. ^ Sena, Johnny A.; Galotto, Giulia; Devitt, Nico P.; Connick, Melanie C.; Jacobi, Jennifer L.; Umale, Pooja E.; Vidali, Luis; Bell, Callum J. (2018-09-03). "Unique Molecular Identifiers reveal a novel sequencing artefact with implications for RNA-Seq based gene expression analysis". Scientific Reports. 8 (1): 13121. Bibcode:2018NatSR...813121S. doi:10.1038/s41598-018-31064-7. ISSN 2045-2322. PMC 6120941. PMID 30177820.
  9. ^ Singh, Ajay Vikram; Chandrasekar, Vaisali; Paudel, Namuna; Laux, Peter; Luch, Andreas; Gemmati, Donato; Tisato, Veronica; Prabhu, Kirti S.; Uddin, Shahab; Dakua, Sarada Prasad (July 2023). "Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology". Biomedicine & Pharmacotherapy. 163: 114784. doi:10.1016/j.biopha.2023.114784. ISSN 0753-3322. PMID 37121152.
  10. ^ Schwalie, Petra C.; Dong, Hua; Zachara, Magda; Russeil, Julie; Alpern, Daniel; Akchiche, Nassila; Caprara, Christian; Sun, Wenfei; Schlaudraff, Kai-Uwe; Soldati, Gianni; Wolfrum, Christian; Deplancke, Bart (2018-06-20). "A stromal cell population that inhibits adipogenesis in mammalian fat depots". Nature. 559 (7712): 103–108. Bibcode:2018Natur.559..103S. doi:10.1038/s41586-018-0226-8. ISSN 0028-0836. PMID 29925944.
  11. ^ V'kovski, Philip; Gultom, Mitra; Kelly, Jenna; Steiner, Silvio; Russeil, Julie; Mangeat, Bastien; Cora, Elisa; Pezoldt, Joern; Holwerda, Melle (2020-04-27). "Disparate temperature-dependent virus – host dynamics for SARS-CoV-2 and SARS-CoV in the human respiratory epithelium". dx.doi.org. doi:10.1101/2020.04.27.062315. Retrieved 2024-04-25.
  12. ^ Litovchenko, Maria; Meireles-Filho, Antonio C. A.; Frochaux, Michael V.; Bevers, Roel P. J.; Prunotto, Alessio; Anduaga, Ane Martin; Hollis, Brian; Gardeux, Vincent; Braman, Virginie S.; Russeil, Julie M. C.; Kadener, Sebastian; dal Peraro, Matteo; Deplancke, Bart (2021-01-29). "Extensive tissue-specific expression variation and novel regulators underlying circadian behavior". Science Advances. 7 (5): eabc3781. Bibcode:2021SciA....7.3781L. doi:10.1126/sciadv.abc3781. ISSN 2375-2548. PMC 7846174. PMID 33514540.
  13. ^ Ying, Sheng; Webster, Brandon; Gomez-Cano, Lina; Shivaiah, Kiran-Kumar; Wang, Qianjie; Newton, Linsey; Grotewold, Erich; Thompson, Addie; Lundquist, Peter K (2023-10-31). "Multiscale physiological responses to nitrogen supplementation of maize hybrids". Plant Physiology. doi:10.1093/plphys/kiad583. ISSN 0032-0889. PMC 11060684. PMID 37925649.
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