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Protocols in Current Issue
0 Q&A 102 Views Jul 5, 2025

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.

0 Q&A 147 Views Jul 5, 2025

This protocol provides a step-by-step approach for generating single-gene knockout in hard-to-transfect suspension immune cell lines like THP1, specifically demonstrated by knocking out the GSDMD gene. By employing CRISPR-Cas9 system delivered via lentivirus, this protocol enables precise gene disruption through targeted single-guide RNAs (sgRNAs). Key steps include designing specific sgRNAs, cloning them into a CRISPR vector, viral packaging, and transducing the target cells, followed by selection and validation. This optimized protocol is particularly useful for functional studies in immune cells, allowing researchers to reliably explore gene function in complex cellular pathways.

0 Q&A 66 Views Jul 5, 2025

Trypanosoma cruzi, the causative agent of Chagas disease, faces significant metabolic challenges due to fluctuating nutrient availability and oxidative stress within its insect vector. Metabolomic techniques, such as gas chromatography–mass spectrometry (GC–MS), have been widely used to study the adaptive mechanisms of the parasite. This article describes a standardized method for the untargeted metabolomics analysis of T. cruzi epimastigote, covering parasite cultivation, sample deproteinization with methanol, metabolite extraction, derivatization with BSTFA, and GC–MS analysis. To ensure robustness and reproducibility, statistical analysis uses univariate tests, as well as multivariate approaches such as principal component analysis (PCA) and partial least squares (PLS) regression. The protocol offers a reliable and sensitive method to study metabolic responses in T. cruzi under environmental stress, with low biological variability and high reproducibility.

0 Q&A 70 Views Jul 5, 2025

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.

Protocols in Past Issues
0 Q&A 341 Views Jun 20, 2025

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.

0 Q&A 173 Views Jun 20, 2025

N6-methyladenosine (m6A) is an abundant internal mRNA modification with roles in regulating cellular and organismal physiology, including development, differentiation, and disease. The deposition of m6A is highly regulated, with various m6A levels across different environmental conditions, cellular states, and cell types. Available methods for measuring bulk m6A levels are often time-consuming, have low throughput, and/or require specialized instrumentation or data analyses. Here, we present a detailed protocol for measuring bulk m6A levels in purified poly(A) RNA samples with m6A-ELISA using a standard-based approach. Critical steps of the protocol are highlighted and optimized, including poly(A) RNA quality controls and antibody specificity testing. The protocol is fast, scalable, adaptable, and cost-effective. It does not require specialized instrumentation, training, or skills in data analysis. We have successfully tested this protocol on mRNAs isolated from budding yeast and mouse cell lines.

0 Q&A 880 Views May 20, 2025

Stable isotopes have frequently been used to study metabolic processes in live cells both in vitro and in vivo. Glutamine, the most abundant amino acid in human blood, plays multiple roles in cellular metabolism by contributing to the production of nucleotides, lipids, glutathione, and other amino acids. It also supports energy production via anaplerosis of tricarboxylic acid cycle intermediates. While 13C-glutamine has been extensively employed to study glutamine metabolism in various cell types, detailed analyses of specific lipids derived from 13C-glutamine via the reductive carboxylation pathway are limited. In this protocol, we present a detailed procedure to investigate glutamine metabolism in human glioblastoma (GBM) cells by conducting 13C-glutamine tracing coupled with untargeted metabolomics analysis using liquid chromatography–mass spectrometry (LC–MS/MS). The method includes step-by-step instructions for the extraction and detection of polar metabolites and long-chain fatty acids (LCFAs) derived from 13C-glutamine in GBM cells. Notably, this approach enables the distinction between isomers of two monounsaturated FAs with identical masses: palmitoleic acid (16:1n-7) (cis-9-hexadecenoic acid) and palmitelaidic acid (16:1n-7) (trans-9-hexadecenoic acid) derived from 13C-glutamine through the reductive carboxylation process. In addition, using this protocol, we also unveil previously unknown metabolic alterations in GBM cells following lysosome inhibition by the antipsychotic drug pimozide.

0 Q&A 267 Views May 5, 2025

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.

0 Q&A 274 Views May 5, 2025

Quantitative proteomic analysis plays a crucial role in understanding microbial co-culture systems. Traditional techniques, such as label-free quantification (LFQ) and label-based proteomics, provide valuable insights into the interactions and metabolic exchanges of microbial species. However, the complexity of microbial co-culture systems often leads to challenges in data normalization, especially when dealing with comparative LFQ data where ratios of different organisms can vary across experiments. This protocol describes the application of LFQRatio normalization, a novel normalization method designed to improve the reliability and accuracy of quantitative proteomics data obtained from microbial co-cultures. The method was developed following the analysis of factors that affect both the identification of proteins and the quantitative accuracy of co-culture proteomics. These include peptide physicochemical characteristics such as isoelectric point (pI), molecular weight (MW), hydrophobicity, dynamic range, and proteome size, as well as shared peptides between species. We then created a normalization method based on LFQ intensity values named LFQRatio normalization. This approach was demonstrated by analysis of a synthetic co-culture of two bacteria, Synechococcus elongatus cscB/SPS and Azotobacter vinelandii ΔnifL. Results showed enhanced accuracy of differentially expressed proteins, allowing for more reliable biological interpretation. This protocol provides a reliable and effective tool with wider application to analyze other co-culture systems to study microbial interactions.

0 Q&A 1376 Views May 5, 2025

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.

0 Q&A 358 Views May 5, 2025

Within a cell, proteins have distinct and highly variable half-lives. As a result, the molecular ages of proteins can range from seconds to years. How the age of a protein influences its environmental interactions is a largely unexplored area of biology. To facilitate such studies, we recently developed a technique termed “proteome birthdating” that differentially labels proteins based on their time of synthesis. Proteome birthdating enables analyses of age distributions of the proteome by tandem mass spectrometry (LC–MS/MS) and provides a methodology for investigating the protein age selectivity of diverse cellular pathways. Proteome birthdating can also provide measurements of protein turnover kinetics from single, sequentially labeled samples. Here, we provide a practical guide for conducting proteome birthdating in in vitro model systems. The outlined workflow covers cell culture, isotopic labeling, protein extraction, enzymatic digestion, peptide cleanup, mass spectrometry, data processing, and theoretical considerations for interpretation of the resulting data.

0 Q&A 326 Views May 5, 2025

Plants rely on metabolite regulation of proteins to control their metabolism and adapt to environmental changes, but studying these complex interaction networks remains challenging. The proteome integral solubility alteration (PISA) assay, a high-throughput chemoproteomic technique, was originally developed for mammalian systems to investigate drug targets. PISA detects changes in protein stability upon interaction with small molecules, quantified through LC–MS. Here, we present an adapted PISA protocol for Arabidopsis thaliana chloroplasts to identify potential protein interactions with ascorbate. Chloroplasts are extracted using a linear Percoll gradient, treated with multiple ascorbate concentrations, and subjected to heat-induced protein denaturation. Soluble proteins are extracted via ultracentrifugation, and proteome-wide stability changes are quantified using multiplexed LC–MS. We provide instructions for deconvolution of LC–MS spectra and statistical analysis using freely available software. This protocol enables unbiased screening of protein regulation by small molecules in plants without requiring prior knowledge of interaction partners, chemical probe design, or genetic modifications.

0 Q&A 460 Views May 5, 2025

Chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq) is a widely used technique for genome-wide analyses of protein–DNA interactions. This protocol provides a guide to ChIP-seq data processing in Saccharomyces cerevisiae, with a focus on signal normalization to address data biases and enable meaningful comparisons within and between samples. Designed for researchers with minimal bioinformatics experience, it includes practical overviews and refers to scripting examples for key tasks, such as configuring computational environments, trimming and aligning reads, processing alignments, and visualizing signals. This protocol employs the sans-spike-in method for quantitative ChIP-seq (siQ-ChIP) and normalized coverage for absolute and relative comparisons of ChIP-seq data, respectively. While spike-in normalization, which is semiquantitative, is addressed for context, siQ-ChIP and normalized coverage are recommended as mathematically rigorous and reliable alternatives.

0 Q&A 548 Views May 5, 2025

Known as the cell’s antenna and signaling hub, the primary cilium is a hair-like organelle with a few micrometers in length and 200–300 nm in diameter. Due to the small size of the primary cilium, it is technically challenging to profile ciliary proteins from mammalian cells. Traditional methods, such as physical isolation of cilia, are susceptible to contamination from other cellular components. Other proximity-based labeling methods via APEX or BioID have been used to map ciliary proteins. However, these approaches have their inherent limitations, including the use of toxic reagents like H2O2 and prolonged labeling kinetics. Here, we show a new proximity-based labeling technique for primary cilia with TurboID. TurboID presents a distinct advantage over BioID and APEX2 due to its expedited labeling kinetics, taking minutes instead of hours, and its use of a non-toxic biotin substrate, which eliminates the need for H2O2. When targeted to the cilium, TurboID selectively labels ciliary proteins with biotin. The biotinylated proteins are then enriched with streptavidin beads and labeled with tandem mass tags (TMT), followed by mass spectrometry (MS) detection. This protocol eliminates the requirement of toxic labeling reagents and significantly reduces the labeling time, thus providing advantages in mapping signaling proteins with high temporal resolution in live cells.




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