Project description:RNA sequencing (RNA-seq) has become a standard method for quantifying gene expression transcriptome-wide. Due to the extremely high proportion of ribosomal RNA (rRNA) in total RNA, sequencing libraries usually incorporate messenger RNA (mRNA) enrichment. Although polyadenylate (poly(A)) tail selection is widely used, many applications require alternate approaches such as rRNA depletion. Recently, selective rRNA digestion, using RNaseH and antisense DNA oligomers that tile the length of target RNAs, has emerged as an easy, cost-effective alternative to commercial rRNA depletion kits. Here, we present a streamlined RNaseH-mediated rRNA depletion method that uses shorter antisense oligos that only sparsely tile the target RNA, in a digestion reaction of only 5 minutes. We wrote a Web tool, Oligo-ASST, that simplifies oligo design to favor target regions with optimal thermodynamic properties, and additionally allows users to design common oligo pools that can simultaneously target divergent RNAs in their regions of higher sequence similarity. We demonstrate the efficacy of these oligos by building rRNA-depleted sequencing libraries for Xenopus laevis as well as zebrafish, which expresses two distinct versions of the 28S, 18S, 5.8S, and 5S rRNAs during embryogenesis. These libraries efficiently deplete rRNA to <5% of total reads, on par with poly(A) selection, and also reveal expression of many non-adenylated RNA species. Oligo-ASST is freely available at https://mtleelab.pitt.edu/oligo to design antisense oligos for any taxon or to target any abundant RNA for depletion.
Project description:Cells expressing Tap-Tagged PUF3 were used for selection on IgG beads, then released using TEV protease. Samples were input and bound fraction without rRNA removal, and unbound fraction and input after rRNA removal with oligonucleotides and RNase H.
Project description:These experiments were designed to quantify depletion of rRNA sequencing reads from bacterial RNA-seq libraries and verify that mRNA sequencing reads were not altered. Specifically, we tested an rRNA depletion method using custom-designed biotinylated oligonucleotides and compared these results to undepleted (total RNA) libraries and libraries made with the previously-available Ribo-Zero kit (Illumina).
Project description:mRNA sequencing in bacteria is challenging due to the abundance of ribosomal rRNA that cannot be easily removed prior to sequencing. While commercially available kits target specific rRNA sequences found in defined lists of common bacterial species, they are frequently inefficient when applied to other divergent species, including those from environmental isolates. Similar to the commercial kits, other common techniques for rRNA depletion typically employ large probe sets that tile full-length rRNA sequences; however, such approaches are both time consuming and expensive when applied to multiple species or complex consortia of non-model microbes. To overcome these limitations, we present EMBR-seq+, which employs less than twenty target oligonucleotides per rRNA molecule, and builds upon our previous rRNA depletion approach, EMBR-seq, through the addition of an RNase H depletion step, to achieve rRNA removal efficiencies of up to 99%. First, we applied EMBR-seq+ to monocultures of Escherichia coli, Geobacter metallireducens, and Fibrobacter succinogenes strain UWB7 to deplete rRNA to approximately 1-7% of the sequencing reads, demonstrating that the new method can be easily extended to diverse bacterial species. Further, in more complex co-cultures between F. succinogenes strain UWB7 and anerobic fungal species, we applied EMBR-seq+ to deplete both bacterial and fungal rRNA, with an approximately 4-fold improved bacterial rRNA depletion efficiency compared to a previous report using a commercial kit, thereby showing that the method can be effectively translated to non-model microbial mixtures. Notably, we also demonstrate that for microbial species with poorly annotated genomes and unknown rRNA sequences, the RNase H depletion component of EMBR-seq+ enables rapid iterations in probe design without requiring to start experiments from total RNA each time, and was key for depleting fungal rRNA to enrich the bacterial mRNA readout in co-cultures. Finally, efficient depletion of rRNA enabled systematic quantification of the reprogramming of the bacterial transcriptome when cultured in the presence of the anerobic fungi, Anaeromyces robustus and Caecomyces churrovis. We observed that F. succinogenes strain UWB7 transcribes nearly 200 carbohydrate-active enzyme (CAZyme) genes in both monoculture and co-culture conditions, with several lignocellulose-degrading CAZymes downregulated in the presence of an anerobic gut fungus. This finding is consistent with the premise that bacteria and fungi specialize in different aspects of biomass breakdown, such that the presence of one regulates the CAZyme production of the other. This also supports previous findings that the fungi release excess reducing sugars in the supernatant, which benefits other members of the microbial community. Thus EMBR-seq+ provides a new and detailed perspective of a rumen microbiome model system by dramatically improving the efficiency of mRNA sequencing, and more generally also enables high-throughput, cost-effective and rapid quantification of the transcriptome to gain functional insights into less-studied and non-model microbial systems.
Project description:We performed deep RNA sequencing of 60 human lung cell lines: 50 lung adenocarcinoma cell lines, 7 NSCLC (non small cell lung cancer, non-adenocarcinoma) cell lines, 3 non-transformed lung cell lines. Total RNA was isolated and subjected to rRNA depletion to maintain also non-polyadenylated transcripts. Strand-specific next generation sequencing (NGS) using Illumina HiSeq allowed us to distinguish reads in the sense and antisense direction. The cell lines were sequenced in 2 or 3 replicates. Reads were aligned to the human reference genome GRCh38 using STAR mapper. The expression of 58,096 genes was calculated in terms of FPKM for every sample.