系统生物学

Brain endothelial cells, which constitute the cerebrovasculature, form the first interface between the blood and brain and play essential roles in maintaining central nervous system (CNS) homeostasis. These cells exhibit strong apicobasal polarity, with distinct luminal and abluminal membrane compositions that crucially mediate compartmentalized functions of the vasculature. Existing transcriptomic and proteomic profiling techniques often lack the spatial resolution to discriminate between these membrane compartments, limiting insights into their distinct molecular compositions and functions. To overcome these limitations, we developed an in vivo proteomic strategy to selectively label and enrich luminal cerebrovascular proteins. In this approach, we perfuse a membrane-impermeable biotinylation reagent into the vasculature to covalently tag cell surface proteins exposed on the luminal side. This is followed by microvessel isolation and streptavidin-based enrichment of biotinylated proteins for downstream mass spectrometry analysis. Using this method, we robustly identified over 1,000 luminally localized proteins via standard liquid chromatography–tandem mass spectrometry (LC–MS/MS) techniques, achieving substantially improved enrichment of canonical luminal markers compared with conventional vascular proteomic approaches. Our method enables the generation of a high-confidence, compartment-resolved atlas of the luminal cerebrovascular proteome and offers a scalable platform for investigating endothelial surface biology in both healthy and disease contexts.

Protein synthesis and degradation (i.e., turnover) forms an important part of protein homeostasis and has been implicated in many age-associated diseases. Different cellular locations, such as organelles and membraneless compartments, often contain individual protein quality control and degradation machineries. Conventional methods to assess protein turnover across subcellular compartments require targeted genetic manipulation or isolation of specific organelles. Here we describe a protocol for simultaneous proteome localization and turnover (SPLAT) analysis, which combines protein turnover measurements with unbiased subcellular spatial proteomics to measure compartment-specific protein turnover rates on a proteome-wide scale. This protocol utilizes dynamic stable isotope labeling of amino acids in cell culture (dynamic SILAC) to resolve the temporal information of protein turnover and multi-step differential ultracentrifugation to assign proteins to multiple subcellular localizations. We further incorporate 2D liquid chromatography fractionation to greatly increase analytical depth while multiplexing with tandem mass tags (TMT) to reduce acquisition time 10-fold. This protocol resolves the spatial and temporal distributions of proteins and can also reveal temporally distinct spatial localizations within a protein pool.

This manuscript details protocols for the ZnCl₂ precipitation-assisted sample preparation (ZASP) for proteomic analysis. By inducing protein precipitation with ZASP precipitation buffer (ZPB, final concentration of ZnCl ₂ at 100 mM and 50% methanol), ZASP depletes harsh detergents and impurities, such as sodium dodecyl sulfate (SDS), Triton X-100, and urea, at high concentrations in solution from protein solutions prior to trypsin digestion. It is a practical, robust, and cost-effective approach for proteomic sample preparation. It has been observed that 90.2% of the proteins can be recovered from lysates by incubating with an equal volume of ZPB at room temperature for 10 min. In 1 h of data-dependent acquisition (DDA) analysis on an Exploris 480, 4,037 proteins and 25,626 peptides were quantified from 1 μg of mouse small intestine proteins, reaching a peak of 4,500 proteins and up to 30,000 peptides with 5 μg of input. Additionally, ZASP outperformed other common sample preparation methods such as sodium deoxycholate (SDC)-based in-solution digestion, acetone precipitation, filter-aided sample preparation (FASP), and single-pot, solid-phase-enhanced sample preparation (SP3). It demonstrated superior performance in protein (4,456 proteins) and peptide identification (29,871 peptides), lower missing cleavage rates (16.3%), and high reproducibility (Pearson correlation coefficients of 0.96 between replicates) with similar protein distributions and cellular localization patterns. Significantly, the cost of ZASP per sample with 100 μg of protein as input is lower than 30 RMB, including the expense of trypsin.

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.

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.

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.

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.

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.

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.

With reduced genotyping costs, genome-wide association studies (GWAS) face more challenges in diverse populations with complex structures to map genes of interest. The complex structure demands sophisticated statistical models, and increased marker density and population size require efficient computing tools. Many statistical models and computing tools have been developed with varied properties in statistical power, computing efficiency, and user-friendly accessibility. Some statistical models were developed with dedicated computing tools, such as efficient mixed model analysis (EMMA), multiple loci mixed model (MLMM), fixed and random model circulating probability unification (FarmCPU), and Bayesian-information and linkage-disequilibrium iteratively nested keyway (BLINK). However, there are computing tools (e.g., GAPIT) that implement multiple statistical models, retain a constant user interface, and maintain enhancement on input data and result interpretation. In this study, we developed a protocol utilizing a minimal set of software tools (BEAGLE, BLINK, and GAPIT) to perform a variety of analyses including file format conversion, missing genotype imputation, GWAS, and interpretation of input data and outcome results. We demonstrated the protocol by reanalyzing data from the Rice 3000 Genomes Project and highlighting advancements in GWAS model development.