Systems Biology

While cell hashing enhances single-cell RNA sequencing (scRNA-seq) efficiency and minimizes batch effects, commercial mouse hashtags often fail in FVB/N and several other strains due to antibody-epitope incompatibility. We describe a robust alternative utilizing biotinylated antibody cocktails and streptavidin-conjugated oligos to enable reliable sample multiplexing. This approach was validated in FVB/N lung tissues, yielding high-quality single-cell libraries. Our protocol offers a practical solution for researchers requiring strain-specific or custom-designed multiplexing strategies for single-cell transcriptomics.

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.

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.

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.