A scalable and cost-efficient rRNA depletion approach to enrich RNAs for molecular biology investigations.

Transcriptomics analyses play pivotal roles in understanding the complex regulatory networks that govern cellular processes. The abundance of rRNAs, which account for 80%-90% of total RNA in eukaryotes, limits the detection and investigation of other transcripts. While mRNAs and long noncoding RNAs have poly(A) tails that are often used for positive selection, investigations of poly(A)- RNAs, such as circular RNAs, histone mRNAs, and small RNAs, typically require the removal of the abundant rRNAs for enrichment. Current approaches to deplete rRNAs for downstream molecular biology investigations are hampered by restrictive RNA input masses and high costs. To address these challenges, we developed rRNA Removal by RNaseH (rRRR), a method to efficiently deplete rRNAs from a wide range of human, mouse, and rat RNA inputs and of varying qualities at a cost 10- to 20-fold cheaper than other approaches. We used probe-based hybridization and enzymatic digestion to selectively target and remove rRNA molecules while preserving the integrity of non-rRNA transcripts. Comparison of rRRR to two commercially available approaches showed similar rRNA depletion efficiencies and comparable off-target effects. Our developed method provides researchers with a valuable tool for investigating gene expression and regulatory mechanisms across a wide range of biological systems at an affordable price that increases the accessibility for researchers to enter the field, ultimately advancing our understanding of cellular processes.

Brain lncRNA-mRNA co-expression regulatory networks and alcohol use disorder.

Prolonged alcohol consumption can disturb the expression of both coding and noncoding genes in the brain. These dysregulated genes may co-express in modules and interact within networks, consequently influencing the susceptibility to developing alcohol use disorder (AUD). In the present study, we performed an RNA-seq analysis of the expression of both long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) in 192 postmortem tissue samples collected from eight brain regions (amygdala, caudate nucleus, cerebellum, hippocampus, nucleus accumbens, prefrontal cortex, putamen, and ventral tegmental area) of 12 AUD and 12 control subjects of European ancestry. Applying the limma-voom method, we detected a total of 57 lncRNAs and 51 mRNAs exhibiting significant differential expression (Padj < 0.05 and fold-change ≥2) across at least one of the eight brain regions investigated. Machine learning analysis further confirmed the potential of these top genes in predicting AUD. Through Weighted Gene Co-expression Network Analysis (WGCNA), we identified distinct lncRNA-mRNA co-expression modules associated with AUD in each of the eight brain regions. Additionally, lncRNA-mRNA co-expression networks were constructed for each brain region using Cytoscape to reveal gene regulatory interactions implicated in AUD. Hub genes within these networks were found to be enriched in several key KEGG pathways, including Axon Guidance, MAPK Signaling, p53 Signaling, Adherens Junction, and Neurodegeneration. Our results underscore the significance of networks involving AUD-associated lncRNAs and mRNAs in modulating neuroplasticity in response to alcohol exposure. Further elucidating these molecular mechanisms holds promise for the development of targeted therapeutic interventions for AUD.

Rational probe design for efficient rRNA depletion and improved metatranscriptomic analysis of human microbiomes.

The microbiota that colonize the human gut and other tissues are dynamic, varying both in composition and functional state between individuals and over time. Gene expression measurements can provide insights into microbiome composition and function. However, efficient and unbiased removal of microbial ribosomal RNA (rRNA) presents a barrier to acquiring metatranscriptomic data. Here we describe a probe set that achieves efficient enzymatic rRNA removal of complex human-associated microbial communities. We demonstrate that the custom probe set can be further refined through an iterative design process to efficiently deplete rRNA from a range of human microbiome samples. Using synthetic nucleic acid spike-ins, we show that the rRNA depletion process does not introduce substantial quantitative error in gene expression profiles. Successful rRNA depletion allows for efficient characterization of taxonomic and functional profiles, including during the development of the human gut microbiome. The pan-human microbiome enzymatic rRNA depletion probes described here provide a powerful tool for studying the transcriptional dynamics and function of the human microbiome.

Nuclease-mediated depletion biases in ribosome footprint profiling libraries.

Ribosome footprint profiling is a high-throughput sequencing-based technique that provides detailed and global views of translation in living cells. An essential part of this technology is removal of unwanted, normally very abundant, ribosomal RNA sequences that dominate libraries and increase sequencing costs. The most effective commercial solution (Ribo-Zero) has been discontinued as a standalone product and a number of new, experimentally distinct commercial applications have emerged on the market. Here we evaluated several commercially available alternatives designed for RNA-seq of human samples and find them generally unsuitable for ribosome footprint profiling. We instead recommend the use of custom-designed biotinylated oligos, which were widely used in early ribosome profiling studies. Importantly, we warn that depletion solutions based on targeted nuclease cleavage significantly perturb the high-resolution information that can be derived from the data, and thus do not recommend their use for any applications that require precise determination of the ends of RNA fragments.

16S ribosomal RNA-depletion PCR and its application in cause analysis of yogurt package shrinkage.

Fermentative bacteria, the main microbiota in yogurt, interfere with the detection of unintended bacterial contaminants. The removal of fermentative bacteria and enrichment of unintended bacterial contaminants is a challenging task in bacterial detection. The present study developed a new 16S rRNA-depletion PCR for such enrichment and detection. Specifically, a single-guide RNA was designed and synthesized based on the 16S rRNA sequence of Streptococcus thermophilus, with the highest DNA abundance in the yogurt. The CRISPR-Cas9 system was used to specifically cleave and remove the genomic DNA of the fermentative bacteria, followed by PCR amplification. This method improved the detection sensitivity, simplified the operation steps, and reduced the detection cost of PCR analysis. We also used the 16S rRNA-depletion PCR to amplify and detect the unintended bacterial contaminants in yogurts with shrunken packages and analyzed the underlying reasons to prevent this issue of product quality.

RNA-seq library preparation for comprehensive transcriptome analysis in cancer cells: The impact of insert size.

With long reads and high coverage, RNA-seq enables comprehensive transcriptome analysis of cancer cells, provided that optimal length of libraries (and their inserts) is assured, to avoid overlap of paired reads and consequent loss of sequencing data. We assessed TruSeq Stranded library preparation protocols (poly(A) enrichment-PA and rRNA depletion-RD) for the thoroughness of transcriptome analysis of a heterogeneous cancer, acute lymphoblastic leukemia. We applied 2x150PE sequencing, >150 M reads/sample on Illumina NovaSeq6000. We show that PA outperforms RD for the analysis of gene expression and structural aberrations. RD is more suitable for detection of various classes of RNAs, mutations or polymorphisms. We demonstrate that reduced RNA fragmentation time (generating longer inserts) positively affects detection of structural RNA changes, without introducing bias into gene expression analysis. We recommend this modification for all RNA-seq studies utilizing reads longer than 75 nt, aimed to go beyond gene expression analysis and to detect also structural changes.

Efficient and cost-effective bacterial mRNA sequencing from low input samples through ribosomal RNA depletion.

BACKGROUND: RNA sequencing is a powerful approach to quantify the genome-wide distribution of mRNA molecules in a population to gain deeper understanding of cellular functions and phenotypes. However, unlike eukaryotic cells, mRNA sequencing of bacterial samples is more challenging due to the absence of a poly-A tail that typically enables efficient capture and enrichment of mRNA from the abundant rRNA molecules in a cell. Moreover, bacterial cells frequently contain 100-fold lower quantities of RNA compared to mammalian cells, which further complicates mRNA sequencing from non-cultivable and non-model bacterial species. To overcome these limitations, we report EMBR-seq (Enrichment of mRNA by Blocked rRNA), a method that efficiently depletes 5S, 16S and 23S rRNA using blocking primers to prevent their amplification. RESULTS: EMBR-seq results in 90% of the sequenced RNA molecules from an E. coli culture deriving from mRNA. We demonstrate that this increased efficiency provides a deeper view of the transcriptome without introducing technical amplification-induced biases. Moreover, compared to recent methods that employ a large array of oligonucleotides to deplete rRNA, EMBR-seq uses a single or a few oligonucleotides per rRNA, thereby making this new technology significantly more cost-effective, especially when applied to varied bacterial species. Finally, compared to existing commercial kits for bacterial rRNA depletion, we show that EMBR-seq can be used to successfully quantify the transcriptome from more than 500-fold lower starting total RNA. CONCLUSIONS: EMBR-seq provides an efficient and cost-effective approach to quantify global gene expression profiles from low input bacterial samples.

A comparative analysis of RNA sequencing methods with ribosome RNA depletion for degraded and low-input total RNA from formalin-fixed and paraffin-embedded samples.

BACKGROUND: Formalin-fixed and paraffin-embedded (FFPE) blocks held in clinical laboratories are an invaluable resource for clinical research, especially in the era of personalized medicine. It is important to accurately quantitate gene expression with degraded and small amounts of total RNA from FFPE materials. RESULTS: High concordance in transcript quantifications were shown between FF and FFPE samples using the same kit. The gene expression using the TaKaRa kit showed a difference with other kits, which may be due to the different principle of rRNA depletion or the amount of input total RNA. For seriously degraded RNA from FFPE samples, libraries could be constructed with as low as 50 ng of total RNA, although there was residual rRNA in the libraries. Data analysis with HISAT demonstrated that the unique mapping ratio, percentage of exons in unique mapping reads and number of detected genes decreased along with the decreasing quality of input RNA. CONCLUSIONS: The method of RNA library construction with rRNA depletion can be used for clinical FFPE samples. For degraded and low-input RNA samples, it is still possible to obtain repeatable RNA expression profiling but with a low unique mapping ratio and high residual rRNA.

Efficient depletion of ribosomal RNA for RNA sequencing in planarians.

BACKGROUND: The astounding regenerative abilities of planarian flatworms prompt steadily growing interest in examining their molecular foundation. Planarian regeneration was found to require hundreds of genes and is hence a complex process. Thus, RNA interference followed by transcriptome-wide gene expression analysis by RNA-seq is a popular technique to study the impact of any particular planarian gene on regeneration. Typically, the removal of ribosomal RNA (rRNA) is the first step of all RNA-seq library preparation protocols. To date, rRNA removal in planarians was primarily achieved by the enrichment of polyadenylated (poly(A)) transcripts. However, to better reflect transcriptome dynamics and to cover also non-poly(A) transcripts, a procedure for the targeted removal of rRNA in planarians is needed. RESULTS: In this study, we describe a workflow for the efficient depletion of rRNA in the planarian model species S. mediterranea. Our protocol is based on subtractive hybridization using organism-specific probes. Importantly, the designed probes also deplete rRNA of other freshwater triclad families, a fact that considerably broadens the applicability of our protocol. We tested our approach on total RNA isolated from stem cells (termed neoblasts) of S. mediterranea and compared ribodepleted libraries with publicly available poly(A)-enriched ones. Overall, mRNA levels after ribodepletion were consistent with poly(A) libraries. However, ribodepleted libraries revealed higher transcript levels for transposable elements and histone mRNAs that remained underrepresented in poly(A) libraries. As neoblasts experience high transposon activity this suggests that ribodepleted libraries better reflect the transcriptional dynamics of planarian stem cells. Furthermore, the presented ribodepletion procedure was successfully expanded to the removal of ribosomal RNA from the gram-negative bacterium Salmonella typhimurium. CONCLUSIONS: The ribodepletion protocol presented here ensures the efficient rRNA removal from low input total planarian RNA, which can be further processed for RNA-seq applications. Resulting libraries contain less than 2% rRNA. Moreover, for a cost-effective and efficient removal of rRNA prior to sequencing applications our procedure might be adapted to any prokaryotic or eukaryotic species of choice.

Cross-site comparison of ribosomal depletion kits for Illumina RNAseq library construction.

BACKGROUND: Ribosomal RNA (rRNA) comprises at least 90% of total RNA extracted from mammalian tissue or cell line samples. Informative transcriptional profiling using massively parallel sequencing technologies requires either enrichment of mature poly-adenylated transcripts or targeted depletion of the rRNA fraction. The latter method is of particular interest because it is compatible with degraded samples such as those extracted from FFPE and also captures transcripts that are not poly-adenylated such as some non-coding RNAs. Here we provide a cross-site study that evaluates the performance of ribosomal RNA removal kits from Illumina, Takara/Clontech, Kapa Biosystems, Lexogen, New England Biolabs and Qiagen on intact and degraded RNA samples. RESULTS: We find that all of the kits are capable of performing significant ribosomal depletion, though there are differences in their ease of use. All kits were able to remove ribosomal RNA to below 20% with intact RNA and identify ~ 14,000 protein coding genes from the Universal Human Reference RNA sample at >1FPKM. Analysis of differentially detected genes between kits suggests that transcript length may be a key factor in library production efficiency. CONCLUSIONS: These results provide a roadmap for labs on the strengths of each of these methods and how best to utilize them.

Integration of quantitated expression estimates from polyA-selected and rRNA-depleted RNA-seq libraries.

BACKGROUND: The availability of fast alignment-free algorithms has greatly reduced the computational burden of RNA-seq processing, especially for relatively poorly assembled genomes. Using these approaches, previous RNA-seq datasets could potentially be processed and integrated with newly sequenced libraries. Confounding factors in such integration include sequencing depth and methods of RNA extraction and selection. Different selection methods (typically, either polyA-selection or rRNA-depletion) omit different RNAs, resulting in different fractions of the transcriptome being sequenced. In particular, rRNA-depleted libraries sample a broader fraction of the transcriptome than polyA-selected libraries. This study aimed to develop a systematic means of accounting for library type that allows data from these two methods to be compared. RESULTS: The method was developed by comparing two RNA-seq datasets from ovine macrophages, identical except for RNA selection method. Gene-level expression estimates were obtained using a two-part process centred on the high-speed transcript quantification tool Kallisto. Firstly, a set of reference transcripts was defined that constitute a standardised RNA space, with expression from both datasets quantified against it. Secondly, a simple ratio-based correction was applied to the rRNA-depleted estimates. The outcome is an almost perfect correlation between gene expression estimates, independent of library type and across the full range of levels of expression. CONCLUSION: A combination of reference transcriptome filtering and a ratio-based correction can create equivalent expression profiles from both polyA-selected and rRNA-depleted libraries. This approach will allow meta-analysis and integration of existing RNA-seq data into transcriptional atlas projects.

High-Throughput Sequencing Methods for the Detection of Two Strawberry Viruses in Post-Entry Quarantine.

High-throughput sequencing (HTS) technologies may be a useful tool for testing imported plant germplasm for multiple pathogens present in a sample, offering strain-generic detection not offered by most PCR-based assays. Metatranscriptomics (RNAseq) and tiled amplicon PCR (TA-PCR) were tested as HTS-based techniques to detect viruses present in low titres. Strawberry mottle virus (SMoV), an RNA virus, and strawberry vein banding virus (SVBV), a DNA virus, were selected for comparison of RNAseq and TA-PCR with quantitative PCR assays. RNAseq of plant ribosomal RNA-depleted samples of low viral titre was used to obtain datasets from 3 M to 120 M paired-end (PE) reads. RNAseq demonstrated PCR-like sensitivity, able to detect as few as 10 viral copies/µL when 60 million (M) PE reads were generated. The custom TA-PCR primer panels designed for each virus were successfully used to recover most of the reference genomes for each virus. Single- and multiple-target TA-PCR allowed the detection of viruses in samples with around 10 viral copies/µL with a minimum continuous sequence length recovery of 500 bp. The limit of detection of the HTS-based protocols described here is comparable to that of quantitative PCR assays. This work lays the groundwork for an increased flexibility in HTS detection of plant viruses.

An improved bacterial mRNA enrichment strategy in dual RNA sequencing to unveil the dynamics of plant-bacterial interactions.

BACKGROUND: Dual RNA sequencing is a powerful tool that enables a comprehensive understanding of the molecular dynamics underlying plant-microbe interactions. RNA sequencing (RNA-seq) poses technical hurdles in the transcriptional analysis of plant-bacterial interactions, especially in bacterial transcriptomics, owing to the presence of abundant ribosomal RNA (rRNA), which potentially limits the coverage of essential transcripts. Therefore, to achieve cost-effective and comprehensive sequencing of the bacterial transcriptome, it is imperative to devise efficient methods for eliminating rRNA and enhancing the proportion of bacterial mRNA. In this study, we modified a strand-specific dual RNA-seq method with the goal of enriching the proportion of bacterial mRNA in the bacteria-infected plant samples. The enriched method involved the sequential separation of plant mRNA by poly A selection and rRNA removal for bacterial mRNA enrichment followed by strand specific RNA-seq library preparation steps. We assessed the efficiency of the enriched method in comparison to the conventional method by employing various plant-bacterial interactions, including both host and non-host resistance interactions with pathogenic bacteria, as well as an interaction with a beneficial rhizosphere associated bacteria using pepper and tomato plants respectively. RESULTS: In all cases of plant-bacterial interactions examined, an increase in mapping efficiency was observed with the enriched method although it produced a lower read count. Especially in the compatible interaction with Xanthmonas campestris pv. Vesicatoria race 3 (Xcv3), the enriched method enhanced the mapping ratio of Xcv3-infected pepper samples to its own genome (15.09%; 1.45-fold increase) and the CDS (8.92%; 1.49-fold increase). The enriched method consistently displayed a greater number of differentially expressed genes (DEGs) than the conventional RNA-seq method at all fold change threshold levels investigated, notably during the early stages of Xcv3 infection in peppers. The Gene Ontology (GO) enrichment analysis revealed that the DEGs were predominantly enriched in proteolysis, kinase, serine type endopeptidase and heme binding activities. CONCLUSION: The enriched method demonstrated in this study will serve as a suitable alternative to the existing RNA-seq method to enrich bacterial mRNA and provide novel insights into the intricate transcriptomic alterations within the plant-bacterial interplay.

Evaluation of rRNA depletion methods for capturing the RNA virome from environmental surfaces.

OBJECTIVE: Metatranscriptomic analysis of RNA viromes on built-environment surfaces is hampered by low RNA yields and high abundance of rRNA. Therefore, we evaluated the quality of libraries, efficiency of rRNA depletion, and viral detection sensitivity using a mock community and a melamine-coated table surface RNA with levels below those required (< 5 ng) with a library preparation kit (NEBNext Ultra II Directional RNA Library Prep Kit). RESULTS: Good-quality RNA libraries were obtained from 0.1 ng of mock community and table surface RNA by changing the adapter concentration and number of PCR cycles. Differences in the target species of the rRNA depletion method affected the community composition and sensitivity of virus detection. The percentage of viral occupancy in two replicates was 0.259 and 0.290% in both human and bacterial rRNA-depleted samples, a 3.4 and 3.8-fold increase compared with that for only bacterial rRNA-depleted samples. Comparison of SARS-CoV-2 spiked-in human rRNA and bacterial rRNA-depleted samples suggested that more SARS-CoV-2 reads were detected in bacterial rRNA-depleted samples. We demonstrated that metatranscriptome analysis of RNA viromes is possible from RNA isolated from an indoor surface (representing a built-environment surface) using a standard library preparation kit.

Transcriptomic Analysis Pipeline (TAP) for quality control and functional assessment of transcriptomes.

Background: RNA-sequencing (RNA-seq) has revolutionized the exploration of biological mechanisms, shedding light on the roles of non-coding RNAs, including long non-coding RNAs (lncRNAs), across various biological processes, including stress responses. Despite these advancements, there remains a gap in our understanding of the implications of different RNA-seq library protocols on comprehensive lncRNA expression analysis, particularly in non-mammalian organisms. Results: In this study, we sought to bridge this knowledge gap by investigating lncRNA expression patterns in Drosophila melanogaster under thermal stress conditions. To achieve this, we conducted a comparative analysis of two RNA-seq library protocols: polyA + RNA capture and rRNA-depletion. Our approach involved the development and application of a Transcriptome Analysis Pipeline (TAP) designed to systematically assess both the technical and functional dimensions of RNA-seq, facilitating a robust comparison of these library protocols. Our findings underscore the efficacy of the polyA + protocol in capturing the majority of expressed lncRNAs within the Drosophila melanogaster transcriptome. In contrast, rRNA-depletion exhibited limited advantages in the context of D. melanogaster studies. Notably, the polyA + protocol demonstrated superior performance in terms of usable read yield and the accurate detection of splice junctions. Conclusions: Our study introduces a versatile transcriptomic analysis pipeline, TAP, designed to uniformly process RNA-seq data from any organism with a reference genome. It also highlights the significance of selecting an appropriate RNA-seq library protocol tailored to the specific research context. Background: Advances in next generation sequencing (NGS) technologies enable the comprehensive analysis of genetic sequences of organisms in a relatively cost-effective manner [1, 2]. Among these technologies, RNA-sequencing (RNA-seq) has emerged as a preeminent method to study fundamental biological mechanisms at the level of cells, tissues, and whole organisms. RNA-seq enables the detection and quantification of various RNA populations, including messenger RNA (mRNA) and various species of non-coding RNA, such as long non-coding RNA (lncRNA), as well as an assessment of features including splice junctions in RNA.

Targeted rRNA depletion enables efficient mRNA sequencing in diverse bacterial species and complex co-cultures.

IMPORTANCE: Microbes present one of the most diverse sources of biochemistry in nature, and mRNA sequencing provides a comprehensive view of this biological activity by quantitatively measuring microbial transcriptomes. However, efficient mRNA capture for sequencing presents significant challenges in prokaryotes as mRNAs are not poly-adenylated and typically make up less than 5% of total RNA compared with rRNAs that exceed 80%. Recently developed methods for sequencing bacterial mRNA typically rely on depleting rRNA by tiling large probe sets against rRNAs; however, such approaches are expensive, time-consuming, and challenging to scale to varied bacterial species and complex microbial communities. Therefore, we developed EMBR-seq+, a method that requires fewer than 10 short oligonucleotides per rRNA to achieve up to 99% rRNA depletion in diverse bacterial species. Finally, EMBR-seq+ resulted in a deeper view of the transcriptome, enabling systematic quantification of how microbial interactions result in altering the transcriptional state of bacteria within co-cultures.

Improved Bacterial Single-Cell RNA-Seq through Automated MATQ-Seq and Cas9-Based Removal of rRNA Reads.

Bulk RNA sequencing technologies have provided invaluable insights into host and bacterial gene expression and associated regulatory networks. Nevertheless, the majority of these approaches report average expression across cell populations, hiding the true underlying expression patterns that are often heterogeneous in nature. Due to technical advances, single-cell transcriptomics in bacteria has recently become reality, allowing exploration of these heterogeneous populations, which are often the result of environmental changes and stressors. In this work, we have improved our previously published bacterial single-cell RNA sequencing (scRNA-seq) protocol that is based on multiple annealing and deoxycytidine (dC) tailing-based quantitative scRNA-seq (MATQ-seq), achieving a higher throughput through the integration of automation. We also selected a more efficient reverse transcriptase, which led to reduced cell loss and higher workflow robustness. Moreover, we successfully implemented a Cas9-based rRNA depletion protocol into the MATQ-seq workflow. Applying our improved protocol on a large set of single Salmonella cells sampled over different growth conditions revealed improved gene coverage and a higher gene detection limit compared to our original protocol and allowed us to detect the expression of small regulatory RNAs, such as GcvB or CsrB at a single-cell level. In addition, we confirmed previously described phenotypic heterogeneity in Salmonella in regard to expression of pathogenicity-associated genes. Overall, the low percentage of cell loss and high gene detection limit makes the improved MATQ-seq protocol particularly well suited for studies with limited input material, such as analysis of small bacterial populations in host niches or intracellular bacteria. IMPORTANCE Gene expression heterogeneity among isogenic bacteria is linked to clinically relevant scenarios, like biofilm formation and antibiotic tolerance. The recent development of bacterial single-cell RNA sequencing (scRNA-seq) enables the study of cell-to-cell variability in bacterial populations and the mechanisms underlying these phenomena. Here, we report a scRNA-seq workflow based on MATQ-seq with increased robustness, reduced cell loss, and improved transcript capture rate and gene coverage. Use of a more efficient reverse transcriptase and the integration of an rRNA depletion step, which can be adapted to other bacterial single-cell workflows, was instrumental for these improvements. Applying the protocol to the foodborne pathogen Salmonella, we confirmed transcriptional heterogeneity across and within different growth phases and demonstrated that our workflow captures small regulatory RNAs at a single-cell level. Due to low cell loss and high transcript capture rates, this protocol is uniquely suited for experimental settings in which the starting material is limited, such as infected tissues.

Metatranscriptomics of cheese microbial communities: Efficiency of RNA extraction from various cheese types and of mRNA enrichment.

Microbial communities from cheeses contribute to the development of typical organoleptic properties. Metatranscriptomic analyses can be used to provide a global picture of the functioning of these communities. Our objective was to evaluate the efficiency of RNA extraction from various cheese types and to evaluate mRNA enrichment procedures for metatranscriptomic analyses. For the 32 tested cheese brands, corresponding to five cheese types, the extraction yield varied from 1 µg to 363 µg RNA per gram of cheese and, overall, the yield was lower for fresh cheeses and for the core of pressed cooked cheeses than for the other cheese types. Pressed cooked cheeses also had a lower RNA integrity than the other cheese types. For total RNA extracts from four cheeses, approximately 99% of the sequencing reads corresponded to ribosomal RNA, and mRNA enrichment by RiboPOOL and FastSelect kits decreased this percentage to a range of 75 to 97% and of 53 to 76%, respectively. Comparison of RNA libraries after mRNA enrichment with libraries of undepleted total RNA showed that the FastSelect mRNA enrichment had a lower impact on the gene expression profiles of five target cheese species than the riboPOOL kit and the oligo (dT) selection method. The procedures that we describe in the present study may be useful for metatranscriptomic analysis of various cheese types.

Efficient and specific DNA oligonucleotide rRNA probe-based rRNA removal in Talaromyces marneffei.

Emerging evidence showed that lncRNAs play important roles in a wide range of biological processes of fungi such as Saccharomyces cerevisiae. However, systemic identification of lncRNAs in non-model fungi is a challenging task as the efficiency of rRNA removal has been proved to be affected by mismatches of universal rRNA-targeting probes of commercial kits, which forces deeper sequencing depth and increases costs. Here, we developed a low-cost and simple rRNA depletion method (rProbe) that could efficiently remove more than 99% rRNA in both yeast and mycelium samples of Talaromyces marneffei. The efficiency and robustness of rProbe were demonstrated to outperform the Illumina Ribo-Zero kit. Using rProbe RNA-seq, we identified 115 differentially expressed lncRNAs and constructed lncRNA-mRNA co-expression network related to dimorphic switch of T. marneffei. Our rRNA removal method has the potential to be a useful tool to explore non-coding transcriptomes of non-model fungi by adjusting rRNA probe sequences species specifically.

Effective ribosomal RNA depletion for single-cell total RNA-seq by scDASH.

A decade since its invention, single-cell RNA sequencing (scRNA-seq) has become a mainstay technology for profiling transcriptional heterogeneity in individual cells. Yet, most existing scRNA-seq methods capture only polyadenylated mRNA to avoid the cost of sequencing non-messenger transcripts, such as ribosomal RNA (rRNA), that are usually not of-interest. Hence, there are not very many protocols that enable single-cell analysis of total RNA. We adapted a method called DASH (Depletion of Abundant Sequences by Hybridisation) to make it suitable for depleting rRNA sequences from single-cell total RNA-seq libraries. Our analyses show that our single-cell DASH (scDASH) method can effectively deplete rRNAs from sequencing libraries with minimal off-target non-specificity. Importantly, as a result of depleting the rRNA, the rest of the transcriptome is significantly enriched for detection.