Systems Biology

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

T cells are endowed with T-cell antigen receptors (TCR) that give them the capacity to recognize specific antigens and mount antigen-specific adaptive immune responses. Because TCR sequences are distinct in each naïve T cell, they serve as molecular barcodes to track T cells with clonal relatedness and shared antigen specificity through proliferation, differentiation, and migration. Single-cell RNA sequencing provides coupled information of TCR sequence and transcriptional state in individual cells, enabling T-cell clonotype-specific analyses. In this protocol, we outline a computational workflow to perform T-cell states and clonal analysis from scRNA-seq data based on the R packages Seurat, ProjecTILs, and scRepertoire. Given a scRNA-seq T-cell dataset with TCR sequence information, cell states are automatically annotated by reference projection using the ProjecTILs method. TCR information is used to track individual clonotypes, assess their clonal expansion, proliferation rates, bias towards specific differentiation states, and the clonal overlap between T-cell subtypes. We provide fully reproducible R code to conduct these analyses and generate useful visualizations that can be adapted for the needs of the protocol user.

Key features

• Computational analysis of paired scRNA-seq and scTCR-seq data

• Characterizing T-cell functional state by reference-based analysis using ProjecTILs

• Exploring T-cell clonal structure using scRepertoire

• Linking T-cell clonality to transcriptomic state to study relationships between clonal expansion and functional phenotype

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).

Transcription errors can substantially affect metabolic processes in organisms by altering the epigenome and causing misincorporations in mRNA, which is translated into aberrant mutant proteins. Moreover, within eukaryotic genomes there are specific Transcription Error-Enriched genomic Loci (TEELs) which are transcribed by RNA polymerases with significantly higher error rates and hypothesized to have implications in cancer, aging, and diseases such as Down syndrome and Alzheimer’s. Therefore, research into transcription errors is of growing importance within the field of genetics. Nevertheless, methodological barriers limit the progress in accurately identifying transcription errors. Pro-Seq and NET-Seq can purify nascent RNA and map RNA polymerases along the genome but cannot be used to identify transcriptional mutations. Here we present background Error Model-coupled Precision nuclear run-on Circular-sequencing (EmPC-seq), a method combining a nuclear run-on assay and circular sequencing with a background error model to precisely detect nascent transcription errors and effectively discern TEELs within the genome.

Transcriptome analysis can provide clues to biological processes affected in different genetic backgrounds or/and under various conditions. The price of RNA sequencing (RNA-seq) has decreased enough so that medium- to large-scale transcriptome analyses in a range of conditions are feasible. However, the price and variety of options for library preparation of RNA-seq can still be daunting to those who would like to use RNA-seq for their first time or for a single experiment. Among the criteria for selecting a library preparation protocol are the method of RNA isolation, nucleotide fragmentation to obtain desired size range, and library indexing to pool sequencing samples for multiplexing. Here, we present a high-quality and a high-throughput option for preparing libraries from polyadenylated mRNA for transcriptome analysis. Both high-quality and high-throughput protocol options include steps of mRNA enrichment through magnetic bead-enabled precipitation of the poly-A tail, cDNA synthesis, and then fragmentation and adapter addition simultaneously through Tn5-mediated ‘tagmentation’. All steps of the protocols have been validated with Arabidopsis thaliana leaf and seedling tissues and streamlined to work together, with minimal cost in money and time, thus intended to provide a beginner-friendly start-to-finish RNA-seq library preparation for transcriptome analysis.

RNA-Seq is a powerful method for transcriptome analysis used in varied field of biology. Although several commercial products and hand-made protocols enable us to prepare RNA-Seq library from total RNA, their cost are still expensive. Here, we established a low-cost and multiplexable whole mRNA-Seq library preparation method for illumine sequencers. In order to reduce cost, we used cost-effective and robust commercial regents with small reaction volumes. This method is a whole mRNA-Seq, which can be applied even to non-model organisms lacking the transcriptome references. In addition, we designed large number of 3′ PCR primer including 8 nucleotides barcode sequences for multiplexing up to three hundreds samples. To summarize, it is possible with this protocol to prepare 96 directional RNA-Seq libraries from purified total RNA in three days and can be pooled for up to three hundred libraries. This is beneficial for large scale transcriptome analysis in many fields of animals and plant biology.

Screening with CRISPR/Cas9 technology has already led to significant discoveries in the fields of cancer biology, cell biology and virology. Because of the relatively low false discovery rates and the ability to perform high-throughput, pooled approaches, it has rapidly become the assay of choice for screening studies, including whole-genome screens. Here, we describe a CRISPR screening protocol that allows for efficient screening of the entire life cycle of HIV-1 through packaging of the HIV-CRISPR lentiviral genomes by infecting HIV-1 virus in trans.