Systems Biology

Single-cell RNA sequencing (scRNA-seq) is a powerful technique for exploring cellular heterogeneity and host–pathogen interactions. This protocol details the Zika virus (ZIKV)-targeted scRNA-seq workflow for preparing high-quality single-cell suspensions from the whole brain tissues of neonatal mice, high-quality single-cell sorting, cDNA reverse transcription, amplification, ZIKV enrichment and host transcriptome library preparation, and sequencing dataset integration in downstream analysis to complete the quantification of ZIKV RNA in individual cells.

Weighted gene co-expression network analysis (WGCNA) is widely used in transcriptomic studies to identify groups of highly correlated genes, aiding in the understanding of disease mechanisms. Although numerous protocols exist for constructing WGCNA networks from gene expression data, many focus on single datasets and do not address how to compare module stability across conditions. Here, we present a protocol for constructing and comparing WGCNA modules in paired tumor and normal datasets, enabling the identification of modules involved in both core biological processes and those specifically related to cancer pathogenesis. By incorporating module preservation analysis, this approach allows researchers to gain deeper insights into the molecular underpinnings of oral cancer, as well as other diseases. Overall, this protocol provides a framework for module preservation analysis in paired datasets, enabling researchers to identify which gene co-expression modules are conserved or disrupted between conditions, thereby advancing our understanding of disease-specific vs. universal biological processes.

Glomerular diseases characterized by injury to post-mitotic epithelial cells called podocytes are a leading cause of chronic kidney disease. Yet, isolating podocytes from the kidney for transcriptomic, proteomic, and metabolomic studies has been a major technical challenge. Protocols utilizing glomerular sieving and laser capture methods are of limited use because they are not podocyte-specific but instead capture all four glomerular cell types. Here, we present a magnetic-activated cell sorting (MACS) method where podocytes are isolated from digested whole kidneys using antibodies specific to extracellular antigens on podocytes. Using microbeaded secondary antibodies binding to the podocyte-specific primary antibodies allows sorting of the podocytes using a magnet. This podocyte-only cell fraction is a unique source of in vivo–derived cells for molecular and cellular experiments.

The complexity of the human transcriptome poses significant challenges for complete annotation. Traditional RNA-seq, often limited by sensitivity and short read lengths, is frequently inadequate for identifying low-abundant transcripts and resolving complex populations of transcript isoforms. Direct long-read sequencing, while offering full-length information, suffers from throughput limitations, hindering the capture of low-abundance transcripts. To address these challenges, we introduce a targeted RNA enrichment strategy, rapid amplification of cDNA ends coupled with Nanopore sequencing (RACE-Nano-Seq). This method unravels the deep complexity of transcripts containing anchor sequences—specific regions of interest that might be exons of annotated genes, in silico predicted exons, or other sequences. RACE-Nano-Seq is based on inverse PCR with primers targeting these anchor regions to enrich the corresponding transcripts in both 5' and 3' directions. This method can be scaled for high-throughput transcriptome profiling by using multiplexing strategies. Through targeted RNA enrichment and full-length sequencing, RACE-Nano-Seq enables accurate and comprehensive profiling of low-abundance transcripts, often revealing complex transcript profiles at the targeted loci, both annotated and unannotated.

Single-cell RNA sequencing has revolutionized molecular cell biology by enabling the identification of unique transcription profiles and cell transcription states within the same tissue. However, tissue dissociation presents a challenge for non-model organisms, as commercial kits are often incompatible, and current protocols rely on tissue enzymatic digestion for extended periods. Tissue digestion can alter cell transcription in response to temperature and the stress caused by enzymatic treatment. Here, we propose a protocol to stabilize RNA using a deep eutectic solvent (Vivophix, Rapid Labs) prior to tissue dissociation, thereby avoiding transcription changes induced by the process and preventing RNase activity during incubation. We validated this methodology for three medically important insect vectors: Anopheles gambiae, Aedes aegypti, and Lutzomyia longipalpis. Single-cell RNA sequencing using our insect midgut dissociation protocol yielded high-quality sequencing results, with a high number of cells recovered, a low percentage of mitochondrial reads, and a low percentage of ambient RNA—two hallmark standards of cell quality.

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.