系统生物学

Capturing produced, consumed, or exchanged metabolites (metabolomics) and the result of gene expression (transcriptomics) require the extraction of metabolites and RNA. Multi-omics approaches and, notably, the combination of metabolomics and transcriptomic analyses are required for understanding the functional changes and adaptation of microorganisms to different physico-chemical and environmental conditions. A protocol was developed to extract total RNA and metabolites from less than 6 mg of a kind of phototrophic biofilm: oxygenic photogranules. These granules are aggregates of several hundred micrometers up to several millimeters. They harbor heterotrophic bacteria and phototrophs. After a common step for cell disruption by bead-beating, a part of the volume was recovered for RNA extraction, and the other half was used for the methanol- and dichloromethane-based extraction of metabolites. The solvents enabled the separation of two phases (aqueous and lipid) containing hydrophilic and lipophilic metabolites, respectively. The ¹H nuclear magnetic resonance (NMR) analysis of these extracts produced spectra that contained over a hundred signals with a signal-to-noise ratio higher than 10. The quality of the spectra enabled the identification of dozens of metabolites per sample. Total RNA was purified using a commercially available kit, yielding sufficient concentration and quality for metatranscriptomic analysis. This novel method enables the co-extraction of RNA and metabolites from the same sample, as opposed to the parallel extraction from two samples. Using the same sample for both extractions is particularly advantageous when working with inherently heterogeneous complex biofilm. In heterogeneous systems, differences between samples may be substantial. The co-extraction will enable a holistic analysis of the metabolomics and metatranscriptomics data generated, minimizing experimental biases, including technical variations and, notably, biological variability. As a result, it will ensure more robust multi-omics analyses, particularly by improving the correlation between metabolic changes and transcript modifications.

Bone repair is a complex regenerative process relying on skeletal stem/progenitor cells (SSPCs) recruited predominantly from the periosteum. Activation and differentiation of periosteal SSPCs occur in a heterogeneous environment, raising the need for single cell/nucleus transcriptomics to decipher the response of the periosteum to injury. Enzymatic cell dissociation can induce a stress response affecting the transcriptome and lead to overrepresentation of certain cell types (i.e., immune and endothelial cells) and low coverage of other cell types of interest. To counteract these limitations, we optimized a protocol to isolate nuclei directly from the intact periosteum and from the fracture callus to perform single-nucleus RNA sequencing. This protocol is adapted for fresh murine periosteum, fracture callus, and frozen human periosteum. Nuclei are isolated using mechanical extraction combined with fluorescence-based nuclei sorting to obtain high-quality nucleus suspensions. This protocol allows the capture of the full diversity of cell types in the periosteum and fracture environment to better reflect the in vivo tissue composition.

Dual RNA-Seq technology has significantly advanced the study of biological interactions between two organisms by allowing parallel transcriptomic analysis. Existing analysis methods employ various combinations of open-source bioinformatics tools to process dual RNA-Seq data. Upon reviewing these methods, we intend to explore crucial criteria for selecting standard tools and methods, especially focusing on critical steps such as trimming and mapping reads to the reference genome. In order to validate the different combinatorial approaches, we performed benchmarking using top-ranking tools and a publicly available dual RNA-Seq Sequence Read Archive (SRA) dataset. An important observation while evaluating the mapping approach is that when the adapter trimmed reads are first mapped to the pathogen genome, more reads align to the pathogen genome than the unmapped reads derived from the traditional host-first mapping approach. This mapping method prevents the misalignment of pathogen reads to the host genome due to their shorter length. In this way, the pathogenic read information found at lesser proportions in a complex eukaryotic dataset is precisely obtained. This protocol presents a comprehensive comparison of these possible approaches, resulting in a robust unified standard methodology.

Information on RNA localisation is essential for understanding physiological and pathological processes, such as gene expression, cell reprogramming, host–pathogen interactions, and signalling pathways involving RNA transactions at the level of membrane-less or membrane-bounded organelles and extracellular vesicles. In many cases, it is important to assess the topology of RNA localisation, i.e., to distinguish the transcripts encapsulated within an organelle of interest from those merely attached to its surface. This allows establishing which RNAs can, in principle, engage in local molecular interactions and which are prevented from interacting by membranes or other physical barriers. The most widely used techniques interrogating RNA localisation topology are based on the treatment of isolated organelles with RNases with subsequent identification of the surviving transcripts by northern blotting, qRT-PCR, or RNA-seq. However, this approach produces incoherent results and many false positives. Here, we describe Controlled Level of Contamination coupled to deep sequencing (CoLoC-seq), a more refined subcellular transcriptomics approach that overcomes these pitfalls. CoLoC-seq starts by the purification of organelles of interest. They are then either left intact or lysed and subjected to a gradient of RNase concentrations to produce unique RNA degradation dynamics profiles, which can be monitored by northern blotting or RNA-seq. Through straightforward mathematical modelling, CoLoC-seq distinguishes true membrane-enveloped transcripts from degradable and non-degradable contaminants of any abundance. The method has been implemented in the mitochondria of HEK293 cells, where it outperformed alternative subcellular transcriptomics approaches. It is applicable to other membrane-bounded organelles, e.g., plastids, single-membrane organelles of the vesicular system, extracellular vesicles, or viral particles.

Key features

• Tested on human mitochondria; potentially applicable to cell cultures, non-model organisms, extracellular vesicles, enveloped viruses, tissues; does not require genetic manipulations or highly pure organelles.

• In the case of human cells, the required amount of starting material is ~2,500 cm² of 80% confluent cells (or ~3 × 10⁸ HEK293 cells).

• CoLoC-seq implements a special RNA-seq strategy to selectively capture intact transcripts, which requires RNases generating 5′-hydroxyl and 2′/3′-phosphate termini (e.g., RNase A, RNase I).

• Relies on nonlinear regression software with customisable exponential functions.

Graphical overview

Accessible chromatin regions modulate gene expression by acting as cis-regulatory elements. Understanding the epigenetic landscape by mapping accessible regions of DNA is therefore imperative to decipher mechanisms of gene regulation under specific biological contexts of interest. The assay for transposase-accessible chromatin sequencing (ATAC-seq) has been widely used to detect accessible chromatin and the recent introduction of single-cell technology has increased resolution to the single-cell level. In a recent study, we used droplet-based, single-cell ATAC-seq technology (scATAC-seq) to reveal the epigenetic profile of the transit-amplifying subset of thymic epithelial cells (TECs), which was identified previously using single-cell RNA-sequencing technology (scRNA-seq). This protocol allows the preparation of nuclei from TECs in order to perform droplet-based scATAC-seq and its integrative analysis with scRNA-seq data obtained from the same cell population. Integrative analysis has the advantage of identifying cell types in scATAC-seq data based on cell cluster annotations in scRNA-seq analysis.

Due to overlapping sequences with linear cognates, identifying internal sequences of circular RNA (circRNA) remains a challenge. Recently, we have developed a full-length circRNA sequencing method (circFL-seq) and computational pipeline, to profile ordinary and fusion circRNA at the isoform level. Compared to short-read RNA-seq, rolling circular reverse transcription and nanopore long-read sequencing of circFL-seq make circRNA reads more than tenfold enriched, and show advantages for identification of both short (<100 nt) and long (>2,000 nt) circRNA transcripts. circFL-seq allows identification of differential alternative splicing suggested wide application prospects for functional studies of internal sequences in circRNAs. In addition, the experimental protocol and computational pipeline of circFL-seq shows better sensitivity and precision for identification of back-splicing junctions than current long-read sequencing methods. Together, the accurate identification and quantification of full-length circRNAs makes circFL-seq a potential tool for large-scale screening of functional circRNAs.

In neurons, local translation in dendritic and axonal compartments allows for the fast and on-demand modification of the local proteome. As the last few years have witnessed dramatic advancements in our appreciation of the brain’s neuronal diversity, it is increasingly relevant to understand how local translation is regulated according to cell type. To this end, both sequencing-based and imaging-based techniques have recently been reported. Here, we present a subcellular single cell RNA sequencing protocol that allows molecular quantification from the soma and dendrites of single neurons, and which can be scaled up for the characterization of several hundreds to thousands of neurons. Somata and dendrites of cultured neurons are dissected using laser capture microdissection, followed by cell lysis to release mRNA content. Reverse transcription is then conducted using an indexed primer that allows the downstream pooling of samples. The pooled cDNA library is prepared for and sequenced in an Illumina platform. Finally, the data generated are processed and converted into a gene vs. cells digital expression table. This protocol provides detailed instructions for both wet lab and bioinformatic steps, as well as insights into controls, data analysis, interpretations, and ways to achieve robust and reproducible results.

Graphic abstract:

Subcellular Single Cell RNA-seq in Neurons.

High-throughput RNA sequencing (RNA-seq) has extraordinarily advanced our understanding of gene expression and disease etiology, and is a powerful tool for the identification of biomarkers in a wide range of organisms. However, most RNA-seq methods rely on retroviral reverse transcriptases (RTs), enzymes that have inherently low fidelity and processivity, to convert RNAs into cDNAs for sequencing. Here, we describe an RNA-seq protocol using Thermostable Group II Intron Reverse Transcriptases (TGIRTs), which have high fidelity, processivity, and strand-displacement activity, as well as a proficient template-switching activity that enables efficient and seamless RNA-seq adapter addition. By combining these activities, TGIRT-seq enables the simultaneous profiling of all RNA biotypes from small amounts of starting material, with superior RNA-seq metrics, and unprecedented ability to sequence structured RNAs. The TGIRT-seq protocol for Illumina sequencing consists of three steps: (i) addition of a 3' RNA-seq adapter, coupled to the initiation of cDNA synthesis at the 3' end of a target RNA, via template switching from a synthetic adapter RNA/DNA starter duplex; (ii) addition of a 5' RNA-seq adapter, by using thermostable 5' App DNA/RNA ligase to ligate an adapter oligonucleotide to the 3' end of the completed cDNA; (iii) minimal PCR amplification, to add capture sites and indices for Illumina sequencing. TGIRT-seq for the Illumina sequencing platform has been used for comprehensive profiling of coding and non-coding RNAs in ribodepleted, chemically fragmented cellular RNAs, and for the analysis of intact (non-chemically fragmented) cellular, extracellular vesicle (EV), and plasma RNAs, where it yields continuous full-length end-to-end sequences of structured small non-coding RNAs (sncRNAs), including tRNAs, snoRNAs, snRNAs, pre-miRNAs, and full-length excised linear intron (FLEXI) RNAs.

Graphic abstract:

Figure 1. Overview of the TGIRT-seq protocol for Illumina sequencing. Major steps are: (1) Template switching from a synthetic R2 RNA/R2R DNA starter duplex with a 1-nt 3' DNA overhang (a mixture of A, C, G, and T residues, denoted N) that base pairs to the 3' nucleotide of a target RNA, and upon initiating reverse transcription by adding dNTPs, seamlessly links an R2R adapter to the 5' end of the resulting cDNA; (2) Ligation of an R1R adapter to the 3' end of the completed cDNA; and (3) Minimal PCR amplification with primers that add Illumina capture sites (P5 and P7) and barcode sequences (indices 5 and 7). The index 7 barcode is required, while the index 5 barcode is optional, to provide unique dual indices (UDIs).

Gene expression within the mitochondria of African trypanosomes and other protozoan organisms relies on a nucleotide-specific RNA-editing reaction. In the process exclusively uridine (U)-nucleotides are site-specifically inserted into and deleted from sequence-deficient primary transcripts to convert them into translatable mRNAs. The reaction is catalyzed by a 0.8 MDa multiprotein complex termed the editosome. Here we describe an improved in vitro test to quantitatively explore the catalytic activity of the editosome. The assay uses synthetic, fluorophore-derivatized oligoribonucleotides as editing substrates, which enable the automated electrophoretic separation of the reaction products by capillary electrophoresis (CE) coupled to laser-induced fluorescence (LIF) detection systems. The assay is robust, it requires only nanogram amounts of materials and by using multicapillary CE/LIF-instruments it can be executed in a highly parallel layout. Further improvements include the usage of phosphorothioate-modified and thus RNase-resistant substrate RNAs as well as multiplex-type fluorophore labeling strategies to monitor the U-insertion and U-deletion reaction simultaneously. The assay is useful for investigating the mechanism and enzymology of the editosome. However, it can also be executed in high-throughput to screen for RNA editing-specific inhibitors.

Graphic abstract:

Characteristics of the fluorescence-based in vitro U-insertion/U-deletion RNA-editing (FIDE) assay

Next generations sequencing (NGS) has become an important tool in biomedical research. The Primer ID approach combined with the MiSeq platform overcomes the limitation of PCR errors and reveals the true sampling depth of population sequencing, making it an ideal tool to study mutagenic effects of potential broad-spectrum antivirals on RNA viruses. In this report we describe a protocol using Primer ID sequencing to study the mutations induced by antivirals in a coronavirus genome from an in vitro cell culture model and an in vivo mouse model. Viral RNA or total lung tissue RNA is tagged with Primer ID-containing cDNA primers during the initial reverse transcription step, followed by two rounds of PCR to amplify viral sequences and incorporate sequencing adaptors. Purified and pooled libraries are sequenced using the MiSeq platform. Sequencing data are processed using the template consensus sequence (TCS) web-app. The Primer ID approach provides an accurate sequencing protocol to measure mutation error rates in viral RNA genomes and host mRNA. Sequencing results suggested that β-D-N4-hydroxycytidine (NHC) greatly increased the transition substitution rate but not the transversion substitution rate in the viral RNA genomes, and cytosine (C) to uridine (U) was found as the most frequently seen mutation.