Comparative evaluation of RNA-Seq library preparation methods for strand-specificity and low input

Correcting 4sU induced quantification bias in nucleotide conversion RNA-seq data

Nucleoside analogues like 4-thiouridine (4sU) are used to metabolically label newly synthesized RNA. Chemical conversion of 4sU before sequencing induces T-to-C mismatches in reads sequenced from labelled RNA, allowing to obtain total and labelled RNA expression profiles from a single sequencing library. Cytotoxicity due to extended periods of labelling or high 4sU concentrations has been described, but the effects of extensive 4sU labelling on expression estimates from nucleotide conversion RNA-seq have not been studied. Here, we performed nucleotide conversion RNA-seq with escalating doses of 4sU with short-term labelling (1h) and over a progressive time course (up to 2h) in different cell lines. With high concentrations or at later time points, expression estimates were biased in an RNA half-life dependent manner. We show that bias arose by a combination of reduced mappability of reads carrying multiple conversions, and a global, unspecific underrepresentation of labelled RNA emerging during library preparation and potentially global reduction of RNA synthesis. We developed a computational tool to rescue unmappable reads, which performed favourably compared to previous read mappers, and a statistical method, which could fully remove remaining bias. All methods developed here are freely available as part of our GRAND-SLAM pipeline and grandR package.

Post-transcriptional regulation shapes the transcriptome of quiescent budding yeast

To facilitate long-term survival, cells must exit the cell cycle and enter quiescence, a reversible non-replicative state. Budding yeast cells reprogram their gene expression during quiescence entry to silence transcription, but how the nascent transcriptome changes in quiescence is unknown. By investigating the nascent transcriptome, we identified over a thousand noncoding RNAs in quiescent and G1 yeast cells, and found noncoding transcription represented a larger portion of the quiescent transcriptome than in G1. Additionally, both mRNA and ncRNA are subject to increased post-transcriptional regulation in quiescence compared to G1. We found that, in quiescence, the nuclear exosome-NNS pathway suppresses over one thousand mRNAs, in addition to canonical noncoding RNAs. RNA sequencing through quiescent entry revealed two distinct time points at which the nuclear exosome controls the abundance of mRNAs involved in protein production, cellular organization, and metabolism, thereby facilitating efficient quiescence entry. Our work identified a previously unknown key biological role for the nuclear exosome-NNS pathway in mRNA regulation and uncovered a novel layer of gene-expression control in quiescence.

Whole-Transcriptome Profiling on Small FFPE Samples: Which Sequencing Kit Should Be Used?

RNA sequencing (RNA-Seq) appears as a great tool with huge clinical potential, particularly in oncology. However, sufficient sample size is often a limiting factor and the vast majority of samples from patients with cancer are formalin-fixed paraffin-embedded (FFPE). To date, several sequencing kits are proposed for FFPE samples yet no comparison on low quantities were performed. To select the most reliable, cost-effective, and relevant RNA-Seq approach, we applied five FFPE-compatible kits (based on 3′ capture, exome-capture and ribodepletion approaches) using 8 ng to 400 ng of FFPE-derived RNA and compared them to Nanostring on FFPE samples and to a reference PolyA (Truseq) approach on flash-frozen samples of the same tumors. We compared gene expression correlations and reproducibility. The Smarter Pico V3 ribodepletion approach appeared systematically the most comparable to Nanostring and Truseq (p < 0.001) and was a highly reproducible technique. In comparison with exome-capture and 3′ kits, the Smarter appeared more comparable to Truseq (p < 0.001). Overall, our results suggest that the Smarter is the most robust RNA-Seq technique to study small FFPE samples and 3′ Lexogen presents an interesting quality−price ratio for samples with less limiting quantities.

A comparison of mRNA sequencing (RNA-Seq) library preparation methods for transcriptome analysis

BACKGROUND

mRNA sequencing is a powerful technique, which is used to investigate the transcriptome status of a gene of interest, such as its transcription level and splicing variants. Presently, several RNA sequencing (RNA-Seq) methods have been developed; however, the relative advantage of each method has remained unknown. Here we used three commercially available RNA-Seq library preparation kits; the traditional method (TruSeq), in addition to full-length double-stranded cDNA methods (SMARTer and TeloPrime) to investigate the advantages and disadvantages of these three approaches in transcriptome analysis.

RESULTS

We observed that the number of expressed genes detected from the TeloPrime sequencing method was fewer than that obtained using the TruSeq and SMARTer. We also observed that the expression patterns between TruSeq and SMARTer correlated strongly. Alternatively, SMARTer and TeloPrime methods underestimated the expression of relatively long transcripts. Moreover, genes having low expression levels were undetected stochastically regardless of any three methods used. Furthermore, although TeloPrime detected a significantly higher proportion at the transcription start site (TSS), its coverage of the gene body was not uniform. SMARTer is proposed to be yielded for nonspecific genomic DNA amplification. In contrast, the detected splicing event number was highest in the TruSeq. The percent spliced in index (PSI) of the three methods was highly correlated.

CONCLUSIONS

TruSeq detected transcripts and splicing events better than the other methods and measured expression levels of genes, in addition to splicing events accurately. However, although detected transcripts and splicing events in TeloPrime were fewer, the coverage at TSS was highest. Additionally, SMARTer was better than TeloPrime with regards to the detected number of transcripts and splicing events among the understudied full-length double-stranded cDNA methods. In conclusion, for short-read sequencing, TruSeq has relative advantages for use in transcriptome analysis.

Protocol variations in run-on transcription dataset preparation produce detectable signatures in sequencing libraries

Background

A variety of protocols exist for producing whole genome run-on transcription datasets. However, little is known about how differences between these protocols affect the signal within the resulting libraries.

Results

Using run-on transcription datasets generated from the same biological system, we show that a variety of GRO- and PRO-seq preparation methods leave identifiable signatures within each library. Specifically we show that the library preparation method results in differences in quality control metrics, as well as differences in the signal distribution at the 5 ^′ end of transcribed regions. These shifts lead to disparities in eRNA identification, but do not impact analyses aimed at inferring the key regulators involved in changes to transcription.

Conclusions

Run-on sequencing protocol variations result in technical signatures that can be used to identify both the enrichment and library preparation method of a particular data set. These technical signatures are batch effects that limit detailed comparisons of pausing ratios and eRNAs identified across protocols. However, these batch effects have only limited impact on our ability to infer which regulators underlie the observed transcriptional changes.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-022-08352-8).

Transcriptome diversity is a systematic source of variation in RNA-sequencing data

RNA sequencing has been widely used as an essential tool to probe gene expression. While standard practices have been established to analyze RNA-seq data, it is still challenging to interpret and remove artifactual signals. Several biological and technical factors such as sex, age, batches, and sequencing technology have been found to bias these estimates. Probabilistic estimation of expression residuals (PEER), which infers broad variance components in gene expression measurements, has been used to account for some systematic effects, but it has remained challenging to interpret these PEER factors. Here we show that transcriptome diversity-a simple metric based on Shannon entropy-explains a large portion of variability in gene expression and is the strongest known factor encoded in PEER factors. We then show that transcriptome diversity has significant associations with multiple technical and biological variables across diverse organisms and datasets. In sum, transcriptome diversity provides a simple explanation for a major source of variation in both gene expression estimates and PEER covariates.

Systematic comparative analysis of strand-specific RNA-seq library preparation methods for low input samples

Despite the recent precipitous decline in the cost of genome sequencing, library preparation for RNA-seq is still laborious and expensive for applications such as high throughput screening. Limited availability of RNA generated by some experimental workflows poses an additional challenge and increases the cost of RNA library preparation. In a search for low cost, automation-compatible RNA library preparation kits that maintain strand specificity and are amenable to low input RNA quantities, we systematically tested two recent commercial technologies—Swift RNA and Swift Rapid RNA, presently offered by Integrated DNA Technologies (IDT) —alongside the Illumina TruSeq stranded mRNA, the de facto standard workflow for bulk transcriptomics. We used the Universal Human Reference RNA (UHRR) (composed of equal quantities of total RNA from 10 human cancer cell lines) to benchmark gene expression in these kits, at input quantities ranging between 10 to 500 ng. We found normalized read counts between all treatment groups to be in high agreement. Compared to the Illumina TruSeq stranded mRNA kit, both Swift RNA library kits offer shorter workflow times enabled by their patented Adaptase technology. We also found the Swift RNA kit to produce the fewest number of differentially expressed genes and pathways directly attributable to input mRNA amount.

Boosting the power of transcriptomics by developing an efficient gene expression profiling approach

Recent advances in plant genomics are scaling up gene expression profiling from the individual level to the population level, making transcriptomics a more powerful tool while deciphering the genome function. This study developed an efficient 3'RNA-seq method, Simplified Poly(A) Anchored Sequencing (SiPAS), to perform large-scale experiments of gene expression quantification. Aside from being cost-effective, by conducting a comprehensive performance assessment of SiPAS in hexaploid wheat, we demonstrated that SiPAS is highly sensitive, accurate, and reproducible while quantifying gene expression. Our method is anticipated to boost studies of population transcriptomics in plants and improve our understanding of genome biology.

MustSeq, an alternative approach for multiplexible strand-specific 3' end sequencing of mRNA transcriptome confers high efficiency and practicality

RNA-seq has been widely used to reveal the molecular mechanism of variants of life process. We have developed an alternative method, MustSeq, which generates multiple second strands along a single 1^st strand cDNA by random-priming initiation, immediately after reverse transcription for each RNA extract using sample-barcoded poly-dT primers, then 3' ends-enriching PCR is applied to construct the library. Unlike the conventional RNA seq, MustSeq avoids procedures such as mRNA isolation, fragmentation and RNA 5'-end capture, enables early pooling of multiple samples, and requires only one twentieth of sequencing reads of full-length sequencing. We demonstrate the power and features of MustSeq comparing with TruSeq and NEBNext RNA-seq, two conventional full-length methods and QuantSeq, an industrial 3' end method. In cancer cell lines, the reads distribution of CDS-exon as well as genes, lncRNAs and GO terms detected by MustSeq are closer than QuantSeq to TruSeq. In mouse hepatocarcinoma and healthy livers, MustSeq enriches the same pathways as by NEBNext, and reveals the molecular profile of carcinogenesis. Overall MustSeq is a robust and accurate RNA-seq method allowing efficient library construction, sequencing and analysis, particularly valuable for analysis of differentially expressed genes with a large number of samples. MustSeq will greatly accelerate the application of bulk RNA-seq on different fields, and potentially applicable for single cell RNA-seq.

Non-coding Natural Antisense Transcripts: Analysis and Application

Non-coding natural antisense transcripts (ncNATs) are regulatory RNA sequences that are transcribed in the opposite direction to protein-coding or non-coding transcripts. These transcripts are implicated in a broad variety of biological and pathological processes, including tumorigenesis and oncogenic progression. With this complex field still in its infancy, annotations, expression profiling and functional characterisations of ncNATs are far less comprehensive than those for protein-coding genes, pointing out substantial gaps in the analysis and characterisation of these regulatory transcripts. In this review, we discuss ncNATs from an analysis perspective, in particular regarding the use of high-throughput sequencing strategies, such as RNA-sequencing, and summarize the unique challenges of investigating the antisense transcriptome. Finally, we elaborate on their potential as biomarkers and future targets for treatment, focusing on cancer.

Transcriptomic Signature of Human Embryonic Thyroid Reveals Transition From Differentiation to Functional Maturation

The human thyroid gland acquires a differentiation program as early as weeks 3-4 of embryonic development. The onset of functional differentiation, which manifests by the appearance of colloid in thyroid follicles, takes place during gestation weeks 10-11. By 12-13 weeks functional differentiation is accomplished and the thyroid is capable of producing thyroid hormones although at a low level. During maturation, thyroid hormones yield increases and physiological mechanisms of thyroid hormone synthesis regulation are established. In the present work we traced the process of thyroid functional differentiation and maturation in the course of human development by performing transcriptomic analysis of human thyroids covering the period of gestation weeks 7-11 and comparing it to adult human thyroid. We obtained specific transcriptomic signatures of embryonic and adult human thyroids by comparing them to non-thyroid tissues from human embryos and adults. We defined a non-TSH (thyroid stimulating hormone) dependent transition from differentiation to maturation of thyroid. The study also sought to shed light on possible factors that could replace TSH, which is absent in this window of gestational age, to trigger transition to the emergence of thyroid function. We propose a list of possible genes that may also be involved in abnormalities in thyroid differentiation and/or maturation, hence leading to congenital hypothyroidism. To our knowledge, this study represent the first transcriptomic analysis of human embryonic thyroid and its comparison to adult thyroid.

Constrained Standardization of Count Data from Massive Parallel Sequencing

In high-throughput omics disciplines like transcriptomics, researchers face a need to assess the quality of an experiment prior to an in-depth statistical analysis. To efficiently analyze such voluminous collections of data, researchers need triage methods that are both quick and easy to use. Such a normalization method for relative quantitation, CONSTANd, was recently introduced for isobarically-labeled mass spectra in proteomics. It transforms the data matrix of abundances through an iterative, convergent process enforcing three constraints: (I) identical column sums; (II) each row sum is fixed (across matrices) and (III) identical to all other row sums. In this study, we investigate whether CONSTANd is suitable for count data from massively parallel sequencing, by qualitatively comparing its results to those of DESeq2. Further, we propose an adjustment of the method so that it may be applied to identically balanced but differently sized experiments for joint analysis. We find that CONSTANd can process large data sets at well over 1 million count records per second whilst mitigating unwanted systematic bias and thus quickly uncovering the underlying biological structure when combined with a PCA plot or hierarchical clustering. Moreover, it allows joint analysis of data sets obtained from different batches, with different protocols and from different labs but without exploiting information from the experimental setup other than the delineation of samples into identically processed sets (IPSs). CONSTANd's simplicity and applicability to proteomics as well as transcriptomics data make it an interesting candidate for integration in multi-omics workflows.

Sex-specific effects of social defeat stress on miRNA expression in the anterior BNST

Women are more likely to suffer from stress-related affective disorders than men, but the underlying mechanisms of sex differences remain unclear. Previous works show that microRNA (miRNA) profiles are altered in stressed animals and patients with depression and anxiety disorders. In this study, we investigated how miRNA expression in the anterior bed nucleus of stria terminalis (BNST) was affected by social defeat stress in female and male California mice (Peromyscus californicus). We performed sequencing to identify miRNA transcripts in the whole brain and anterior BNST followed by qPCR analysis to compare miRNA expression between control and stressed animals. The results showed that social defeat stress induced sex-specific miRNA expression changes in the anterior BNST. Let-7a, let-7f and miR-181a-5p were upregulated in stressed female but not male mice. Our study provided evidence that social stress produces distinct molecular responses in the BNST of males and females.

RNAflow: An Effective and Simple RNA-Seq Differential Gene Expression Pipeline Using Nextflow

RNA-Seq enables the identification and quantification of RNA molecules, often with the aim of detecting differentially expressed genes (DEGs). Although RNA-Seq evolved into a standard technique, there is no universal gold standard for these data's computational analysis. On top of that, previous studies proved the irreproducibility of RNA-Seq studies. Here, we present a portable, scalable, and parallelizable Nextflow RNA-Seq pipeline to detect DEGs, which assures a high level of reproducibility. The pipeline automatically takes care of common pitfalls, such as ribosomal RNA removal and low abundance gene filtering. Apart from various visualizations for the DEG results, we incorporated downstream pathway analysis for common species as Homo sapiens and Mus musculus. We evaluated the DEG detection functionality while using qRT-PCR data serving as a reference and observed a very high correlation of the logarithmized gene expression fold changes.

Detection of MEAF6-PHF1 translocation in an endometrial stromal nodule

Endometrial stromal nodule (ESN) and low-grade endometrial stromal sarcoma (LG-ESS) are rare uterine tumors known as endometrial stromal tumors (ESTs). In addition to their similarity in morphological features, recent studies have shown that these two tumors share common genetic alterations. In particular, JAZF1-SUZ12 fusion is found with high frequency in both ESN and LG-ESS. In LG-ESS, some minor fusions have also been described, which include rearrangements involving PHF1 and its partner genes, such as JAZF1, EPC1, MEAF6, BRD8, EPC2, and MBTD1. Because of the rarity of ESN, genetic alterations other than JAZF1 fusion have not been investigated in detail. In this study, we performed a next-generation sequencing-based analysis in a case of ESN with peripheral metaplastic bone formation and detected MEAF6-PHF1 fusion, which has been reported in a small subset of uterine LG-ESSs and soft tissue ossifying fibromyxoid tumors. The finding that MEAF6-PHF1 fusion is a background genetic abnormality detected both in ESN and LG-ESS, along with JAZF1-SUZ12, provides further support for the similarity and continuum between these two types of ESTs. Furthermore, the association between metaplastic bone formation and MEAF6-PHF1 fusion may not be limited to soft tissue tumors.