Depletion of Ribosomal RNA Sequences from Single‐Cell RNA‐Sequencing Library

Isolation of high-quality total RNA and RNA sequencing of single bovine oocytes

Studying individual mammalian oocytes has been extremely valuable for the understanding of the molecular composition of oocytes including RNA storage. Here, a detailed protocol for isolation of oocytes, extraction of total RNA from single oocytes followed by full-length cDNA amplification, and library preparation is presented. The procedure permits the production of cost-effective and high-quality sequencing libraries. This protocol can be adapted for transcriptome analysis of oocytes from other species and be used to generate high-quality data from single embryos.

For complete details on the use and execution of this protocol, please refer to Biase and Kimble (2018).

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Isolation of high-quality total RNA from single bovine oocytes

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Detailed procedures for amplification of complementary DNA for library preparation

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A bioinformatic pipeline for the quantitation of transcript abundance

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The protocol also enables high-quality data production from single embryos

Metagenomics next-generation sequencing tests take the stage in the diagnosis of lower respiratory tract infections

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The applications of mNGS for LRIs span a wide range of areas including LRI diagnosis, airway microbiome analyses, human host response analyses, and prediction of drug resistance.

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The workflow of mNGS used in clinical practice involves the wet-lab pipeline and dry-lab pipeline, the complex workflow poses challenges for its extensive use.

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mNGS will become an important tool in the field of infectious disease diagnosis in the next decade.

Metagenomic next-generation sequencing (mNGS) has changed the diagnosis landscape of lower respiratory tract infections (LRIs). With the development of newer sequencing assays, it is now possible to assess all microorganisms in a sample using a single mNGS analysis. The applications of mNGS for LRIs span a wide range of areas including LRI diagnosis, airway microbiome analyses, human host response analyses, and prediction of drug resistance. mNGS is currently in an exciting transitional period; however, before implementation in a clinical setting, there are several barriers to overcome, such as the depletion of human nucleic acid, discrimination between colonization and infection, high costs, and so on.

Aim of Review: In this review, we summarize the potential applications and challenges of mNGS in the diagnosis of LRIs to promote the integration of mNGS into the management of patients with respiratory tract infections in a clinical setting.

Key Scientific Concepts of Review: Once its analytical validation, clinical validation and clinical utility been demonstrated, mNGS will become an important tool in the field of infectious disease diagnosis.

ZWA: Viral genome assembly and characterization hindrances from virus-host chimeric reads; a refining approach

Viral metagenomics, also known as virome studies, have yielded an unprecedented number of novel sequences, essential in recognizing and characterizing the etiological agent and the origin of emerging infectious diseases. Several tools and pipelines have been developed, to date, for the identification and assembly of viral genomes. Assembly pipelines often result in viral genomes contaminated with host genetic material, some of which are currently deposited into public databases. In the current report, we present a group of deposited sequences that encompass ribosomal RNA (rRNA) contamination. We highlight the detrimental role of chimeric next generation sequencing reads, between host rRNA sequences and viral sequences, in virus genome assembly and we present the hindrances these reads may pose to current methodologies. We have further developed a refining pipeline, the Zero Waste Algorithm (ZWA) that assists in the assembly of low abundance viral genomes. ZWA performs context-depended trimming of chimeric reads, precisely removing their rRNA moiety. These, otherwise discarded, reads were fed to the assembly pipeline and assisted in the construction of larger and cleaner contigs making a substantial impact on current assembly methodologies. ZWA pipeline may significantly enhance virus genome assembly from low abundance samples and virus metagenomics approaches in which a small number of reads determine genome quality and integrity.

For years now the study of viruses and their genetic composition has been important in their identification and classification. Especially in these times of the pandemic turmoil, accurate knowledge of a virus’ exact genetic composition can help identify its strengths and weaknesses allowing us to track its evolution and assist in the development of vaccines and antiviral agents. The reconstruction of these genomic sequences is called the assembly process, a bioinformatics approach which can be complicated and full of pitfalls. This work identifies one such issue, concerning artifacts introduced in viral genomes from the new technologies of nucleic acid sequencing. The proposed algorithm helps alleviate this problem by tentatively removing these problematic regions while keeping the vast majority of the genetic information required to produce a more complete viral genome. This work is anticipated to assist in the submission of higher integrity and accuracy viral genomes in public databases used for novel virus identification and characterization.

Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes

Microbiomes are complex and ubiquitous networks of microorganisms whose seemingly limitless chemical transformations could be harnessed to benefit agriculture, medicine, and biotechnology. The spatial and temporal changes in microbiome composition and function are influenced by a multitude of molecular and ecological factors. This complexity yields both versatility and challenges in designing synthetic microbiomes and perturbing natural microbiomes in controlled, predictable ways. In this review, we describe factors that give rise to emergent spatial and temporal microbiome properties and the meta-omics and computational modeling tools that can be used to understand microbiomes at the cellular and system levels. We also describe strategies for designing and engineering microbiomes to enhance or build novel functions. Throughout the review, we discuss key knowledge and technology gaps for elucidating the networks and deciphering key control points for microbiome engineering, and highlight examples where multiple omics and modeling approaches can be integrated to address these gaps.

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.

Misuse of RPKM or TPM normalization when comparing across samples and sequencing protocols

In recent years, RNA-sequencing (RNA-seq) has emerged as a powerful technology for transcriptome profiling. For a given gene, the number of mapped reads is not only dependent on its expression level and gene length, but also the sequencing depth. To normalize these dependencies, RPKM (reads per kilobase of transcript per million reads mapped) and TPM (transcripts per million) are used to measure gene or transcript expression levels. A common misconception is that RPKM and TPM values are already normalized, and thus should be comparable across samples or RNA-seq projects. However, RPKM and TPM represent the relative abundance of a transcript among a population of sequenced transcripts, and therefore depend on the composition of the RNA population in a sample. Quite often, it is reasonable to assume that total RNA concentration and distributions are very close across compared samples. Nevertheless, the sequenced RNA repertoires may differ significantly under different experimental conditions and/or across sequencing protocols; thus, the proportion of gene expression is not directly comparable in such cases. In this review, we illustrate typical scenarios in which RPKM and TPM are misused, unintentionally, and hope to raise scientists' awareness of this issue when comparing them across samples or different sequencing protocols.

Capturing the 'ome': the expanding molecular toolbox for RNA and DNA library construction

All sequencing experiments and most functional genomics screens rely on the generation of libraries to comprehensively capture pools of targeted sequences. In the past decade especially, driven by the progress in the field of massively parallel sequencing, numerous studies have comprehensively assessed the impact of particular manipulations on library complexity and quality, and characterized the activities and specificities of several key enzymes used in library construction. Fortunately, careful protocol design and reagent choice can substantially mitigate many of these biases, and enable reliable representation of sequences in libraries. This review aims to guide the reader through the vast expanse of literature on the subject to promote informed library generation, independent of the application.