Systems Biology

Formalin-fixed paraffin-embedded (FFPE) samples remain an underutilized resource in single-cell omics due to RNA degradation from formalin fixation. Here, we present snPATHO-seq, a robust and adaptable approach that enables the generation of high-quality single-nucleus (sn) transcriptomic data from FFPE tissues, utilizing advancements in single-cell genomic techniques. The snPATHO-seq workflow integrates optimized nuclei isolation with the 10× Genomics Flex assay, targeting short RNA fragments to mitigate FFPE-related RNA degradation. Benchmarking against standard 10× 3' and Flex assays for fresh/frozen tissues confirmed robust detection of transcriptomic signatures and cell types. snPATHO-seq demonstrated high performance across diverse FFPE samples, including diseased tissues like breast cancer. It seamlessly integrates with FFPE spatial transcriptomics (e.g., FFPE Visium) for multi-modal spatial and single-nucleus profiling. Compared to workflows like 10× Genomics’ snFFPE, snPATHO-seq delivers superior data quality by reducing tissue debris and preserving RNA integrity via nuclei isolation. This cost-effective workflow enables high-resolution transcriptomics of archival FFPE samples, advancing single-cell omics in translational and clinical research.

RNA sequencing (RNA-Seq) has transformed transcriptomic research, enabling researchers to perform large-scale inspection of mRNA levels in living cells. With the growing applicability of this technique to many scientific investigations, the analysis of next-generation sequencing (NGS) data becomes an important yet challenging task, especially for researchers without a bioinformatics background. This protocol offers a beginner-friendly step-by-step guide to analyze NGS data (starting from raw .fastq files), providing the required codes with an explanation of the different steps and software used. We outline a computational workflow that includes quality control, trimming of reads, read alignment to the genome, and gene quantification, ultimately enabling researchers to identify differentially expressed genes and gain insights on mRNA levels. Multiple approaches to visualize this data using statistical and graphical tools in R are also described, allowing the generation of heatmaps and volcano plots to represent genes and gene sets of interest.

Laser-assisted microdissection (LAM) coupled with next-generation sequencing technologies offers a powerful approach to dissecting the complex cellular heterogeneity within lung adenocarcinoma (LUAD) tumors. This protocol outlines the method for isolating specific high-risk LUAD tissues containing micropapillary/solid (MIP/SOL) patterns, which is linked to poor prognosis. We detail the process of LAM, which involves tissue fixation, microtome sectioning, and the precise dissection and collection of cells of interest under microscopic guidance. The isolated cells are then subjected to RNA extraction, library preparation, and sequencing to profile transfer RNA–derived fragments (tRFs) and tRNA-derived stress-induced RNAs (tiRNAs), which are emerging as key regulators in cancer. This protocol enables researchers to obtain high-quality transcriptomic data from specific LUAD cell populations, aiming to uncover tRF-Val-CAC-024 and tiRNA-Gly-CCC as potential biomarkers for early diagnosis and therapeutic targets for LUAD treatment.

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

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.