Molecular 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.

Membrane-less organelles play essential roles in both physiological and pathological processes by compartmentalizing biomolecules through phase separation to form dynamic hubs. These hubs enable rapid responses to cellular stress and help maintain cellular homeostasis. However, a straightforward and efficient method for detecting and illustrating the distribution and diversity of RNA species within membrane-less organelles is still highly sought after. In this study, we present a detailed protocol for in situ profiling of RNA subcellular localization using Target Transcript Amplification and Sequencing (TATA-seq). Specifically, TATA-seq employs a primary antibody against a marker protein of the target organelle to recruit a secondary antibody conjugated with streptavidin, which binds an oligonucleotide containing a T7 promoter. This design enables targeted, in situ reverse transcription of RNAs with minimal background noise, a key advantage further refined during data analysis by subtracting signals obtained from a parallel IgG control experiment. The subsequent T7 RNA polymerase-mediated linear amplification ensures high-fidelity RNA amplification from low-input material, which directly contributes to optimized sequencing metrics, including a duplication rate of no more than 25% and a mapping ratio of approximately 90%. Furthermore, the modular design of TATA-seq provides broad compatibility with diverse organelles. While initially developed for membrane-less organelles, the protocol can be readily adapted to profile RNA in other subcellular compartments, such as nuclear speckles and paraspeckles, under both normal and pathogenic conditions, offering a versatile tool for spatial transcriptomics.

Single-cell and single-nucleus RNA sequencing are revolutionizing our understanding of cellular biology. The identification of molecular markers, single-cell transcriptomic profiling, and differential gene expression at the cellular level has revealed key functional differences between cells within the same tissue. However, tissue dissociation remains challenging for non-model organisms and for tissues with unique biochemical properties. For example, the mosquito fat body, which serves functions analogous to mammalian adipose and liver tissues, consists of trophocytes—large, adipocyte-like cells whose cytoplasm is filled with lipid droplets. Conventional enzymatic dissociation methods are often too harsh for these fragile cells, and their high lipid content can interfere with reagents required for single-cell transcriptomic analysis. Single-nucleus RNA sequencing (snRNA-seq) offers an alternative strategy when intact cells with high-quality RNA cannot be obtained by enzymatic or mechanical dissociation. Here, we present an optimized reproducible methodology for nuclei isolation from the fat body of Anopheles gambiae mosquitoes, enabling high-quality snRNA-seq. Our approach involves tissue fixation and lipid removal, followed by cell lysis and nuclei purification using a sucrose cushion. We validated this protocol on both sugar-fed and blood-fed samples, established quality metrics to remove potential ambient RNA contamination, and demonstrated that snRNA-seq using this method yields high-quality sequencing results.

Thousands of RNAs are localized to specific subcellular locations, and these localization patterns are often required for optimal cell function. However, the sequences within RNAs that direct their transport are unknown for almost all localized transcripts. Similarly, the RNA content of most subcellular locations remains unknown. To facilitate the study of subcellular transcriptomes, we developed the RNA proximity labeling method OINC-seq. OINC-seq utilizes photoactivatable, spatially restricted RNA oxidation to specifically label RNA in proximity to a subcellularly localized bait protein. After labeling, these oxidative RNA marks are then read out via high-throughput sequencing due to their ability to induce predictable misincorporation events by reverse transcriptase. These induced mutations are then quantitatively assessed for each gene using our software package PIGPEN. The observed mutation rate for a given RNA species is therefore related to its proximity to the localized bait protein. This protocol describes procedures for assaying RNA localization via OINC-seq experiments as well as computational procedures for analyzing the resulting data using PIGPEN.

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.

Traditional tissue dissociation methods for bulk- and single-cell sequencing use various protease and/or collagenase combinations at temperatures ranging from 28 to 37 °C, which cause transcriptional cell stress that may alter data interpretation. Such artifacts can be reduced by dissociating cells in cold-active proteases, but few studies have shown that this improves cell-type specific transcription, particularly in tissues hypersensitive to mechanical integrity and extracellular matrix (ECM) interactions. To address this, we have dissociated zebrafish tendons and ligaments in subtilisin A at 4 °C and compared the results with 37 °C collagenase dissociation using bulk RNA sequencing. We find that high-temperature collagenase dissociation causes general cell stress in tendon fibroblasts (tenocytes) as reported in previous studies with other cell types, but also that high temperature specifically downregulates hallmark genes involved in tenocyte specification and ECM production in vivo. Our results suggest that cold-protease dissociation reduces transcriptional artifacts and increases the robustness of RNA-sequencing datasets such that they better reflect native in vivo tissue microenvironments.

Single-cell transcriptomic analyses have emerged as very powerful tools to query the gene expression changes at the single-cell level in physiological and pathological conditions. The quality of the analysis is heavily dependent on tissue digestion protocols, with the goal of preserving thousands of single live cells to submit to the subsequent processing steps and analysis. Multiple digestion protocols that use different enzymes to digest the tissues have been described. Harsh digestion can damage certain cell types, but this might be required to digest especially fibrotic tissue as in our experimental condition. In this paper, we summarize a collagenase type I digestion protocol for preparing the single-cell suspension from fibrovascular tissues surgically removed from patients with proliferative diabetic retinopathy (PDR) for single-cell RNA sequencing (scRNA-Seq) analyses. We also provide a detailed description of the data analysis that we implemented in a previously published study.

Single-cell RNA sequencing (scRNA-seq) stands as a cutting-edge technology widely used in biological and biomedical research. Existing scRNA-seq methods rely on reverse transcription (RT) and second-strand synthesis (SSS) to convert RNA to cDNA before amplification. However, these methods often suffer from limited RT/SSS efficiency, which compromises the sensitivity of RNA detection. Here, we develop a new method, linearly amplified single-stranded RNA-derived transcriptome sequencing (LAST-seq), which directly amplifies the original single-stranded RNA without prior RT and SSS and offers high-sensitivity RNA detection and a low level of technical noise in single-cell transcriptome analysis. LAST-seq has been applied to quantify transcriptional bursting kinetics in human cells, advancing our understanding of chromatin organization’s role in regulating gene expression.

Pseudouridine (Ψ), the most prevalent modified base in cellular RNAs, has been mapped to numerous sites not only in rRNAs, tRNAs, and snRNAs but also mRNAs. Although there have been multiple techniques to identify Ψs, due to the recent development of sequencing technologies some reagents are not compatible with the current sequencer. Here, we show the updated Pseudo-seq, a technique enabling the genome-wide identification of pseudouridylation sites with single-nucleotide precision. We provide a comprehensive description of Pseudo-seq, covering protocols for RNA isolation from human cells, library preparation, and detailed data analysis procedures. The methodology presented is easily adaptable to any cell or tissue type with high-quality mRNA isolation. It can be used for discovering novel pseudouridylation sites, thus constituting a crucial initial step toward understanding the regulation and function of this modification.

Many single nucleotide polymorphisms (SNPs) identified by genome-wide association studies exert their effects on disease risk as expression quantitative trait loci (eQTL) via allele-specific expression (ASE). While databases for probing eQTLs in tissues from normal individuals exist, one may wish to ascertain eQTLs or ASE in specific tissues or disease-states not characterized in these databases. Here, we present a protocol to assess ASE of two possible target genes (GPNMB and KLHL7) of a known genome-wide association study (GWAS) Parkinson’s disease (PD) risk locus in postmortem human brain tissue from PD and neurologically normal individuals. This was done using a sequence of RNA isolation, cDNA library generation, enrichment for transcripts of interest using customizable cDNA capture probes, paired-end RNA sequencing, and subsequent analysis. This method provides increased sensitivity relative to traditional bulk RNAseq-based and a blueprint that can be extended to the study of other genes, tissues, and disease states.

Key features

• Analysis of GPNMB allele-specific expression (ASE) in brain lysates from cognitively normal controls (NC) and Parkinson’s disease (PD) individuals.

• Builds on the ASE protocol of Mayba et al. (2014) and extends application from cells to human tissue.

• Increased sensitivity by enrichment for desired transcript via RNA CaptureSeq (Mercer et al., 2014).

• Optimized for human brain lysates from cingulate gyrus, caudate nucleus, and cerebellum.

Graphical overview