系统生物学

The cellular compartments of eukaryotic cells are defined by their specific protein compositions. Different strategies are used for the identification of the subcellular proteomes, such as fractionation by differential centrifugation of cellular extracts. The localization of mitochondrial proteins is particularly challenging, as mitochondria consist of two membranes of different protein composition and two aqueous subcompartments, the intermembrane space (IMS) and the matrix. Previous studies identified subcompartment-specific proteomes by using combinations of hypotonic swelling and protease digestion followed by mass spectrometry. Here, we present an alternative, more unbiased method to identify the proteomes of mitochondrial subcompartments by use of an improved ascorbate peroxidase (APEX2) that is targeted to the IMS and the matrix. This method allows the subcompartment-specific labeling of proteins in mitochondria isolated from cells of the baker’s yeast Saccharomyces cerevisiae, followed by their purification on streptavidin beads. With this method, the proteins located in the different mitochondrial subcompartments of yeast cells can be efficiently and comprehensively identified.

Serial spatial omics technologies capture genome-wide gene expression patterns in thin tissue sections but lose spatial continuity along the third dimension. Reconstructing these two-dimensional measurements into coherent three-dimensional volumes is necessary to relate molecular domains, gradients, and tissue architecture within whole organs or embryos. sc3D is an open-source Python framework that registers consecutive spatial transcriptomic sections, interpolates bead coordinates in three dimensions, and stores the result in an AnnData object compatible with Scanpy. The workflow performs slice alignment, 3D reconstruction, optional downsampling, and interactive visualization in a napari-sc3D-viewer, enabling virtual in situ hybridization and spatial differential gene expression analysis. We tested sc3D on Slide-seq and Stereo-seq datasets, including E8.5 and E16.5 mouse embryos, recovering continuous tissue morphologies, cardiac anatomical markers, and the expected anterior–posterior gradients of Hox gene expression. These results show that sc3D allows reproducible reconstruction and analysis of volumetric spatial omics data across different samples and experimental platforms.

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.

Pinpointing causal genes for complex traits from genome-wide association studies (GWAS) remains a central challenge in crop genetics, particularly in species with extensive linkage disequilibrium (LD) such as rice. Here, we present CisTrans-ECAS, a computational protocol that overcomes this limitation by integrating population genomics and transcriptomics. The method’s core principle is the decomposition of gene expression into two distinct components: a cis-expression component (cis-EC), regulated by local genetic variants, and a trans-expression component (trans-EC), influenced by distal genetic factors. By testing the association of both components with a phenotype, CisTrans-ECAS establishes a dual-evidence framework that substantially improves the reliability of causal inference. This protocol details the complete workflow, demonstrating its power not only to identify causal genes at loci with weak GWAS signals but also to systematically reconstruct gene regulatory networks. It provides a robust and powerful tool for advancing crop functional genomics and molecular breeding.

The cellular secretome is a rich source of biomarkers and extracellular signaling molecules, but proteomic profiling remains challenging, especially when processing culture volumes greater than 5 mL. Low protein abundance, high serum contamination, and sample loss during preparation limit reproducibility and sensitivity in mass spectrometry–based workflows. Here, we present an optimized and scalable protocol that integrates (i) 50 kDa molecular weight cutoff ultrafiltration, (ii) spin column depletion of abundant serum proteins, and (iii) acetone/TCA precipitation for protein recovery. This workflow enables balanced recovery of both low- and high-molecular-weight proteins while reducing background from serum albumin, thereby improving sensitivity, reproducibility, and dynamic range for LC–MS/MS analysis. Validated in human mesenchymal stromal cell cultures, the protocol is broadly applicable across diverse cell types and experimental designs, making it well-suited for biomarker discovery and extracellular proteomics.

Protein S-nitrosylation is a critical post-translational modification that regulates diverse cellular functions and signaling pathways. Although various biochemical methods have been developed to detect S-nitrosylated proteins, many suffer from limited specificity and sensitivity. Here, we describe a robust protocol that combines a modified biotin-switch technique (BST) with streptavidin-based affinity enrichment and quantitative mass spectrometry to detect and profile nitrosylated proteins in cultured cells. The method involves blocking free thiols, selective reduction of nitrosothiols, biotin labeling, enrichment of biotinylated proteins, and identification by tandem mass tag (TMT)-based quantitative mass spectrometry. Additionally, site-directed mutagenesis is employed to generate “non-nitrosylable” mutants for functional validation of specific nitrosylation sites. This protocol provides high specificity, quantitative capability, and versatility for both targeted and global analysis of protein nitrosylation.

In plants, the apoplast contains a diverse set of proteins that underpin mechanisms for maintaining cell homeostasis, cell wall remodeling, cell signaling, and pathogen defense. Apoplast protein composition is highly regulated, primarily through the control of secretory traffic in response to endogenous and environmental factors. Dynamic changes in apoplast proteome facilitate plant survival in a changing climate. Even so, the apoplast proteome profiles in plants remain poorly characterized due to technological limitations. Recent progress in quantitative proteomics has significantly advanced the resolution of proteomic profiling in mammalian systems and has the potential for application in plant systems. In this protocol, we provide a detailed and efficient protocol for tandem mass tag (TMT)-based quantitative analysis of Arabidopsis thaliana secretory proteome to resolve dynamic changes in leaf apoplast proteome profiles. The protocol employs apoplast flush collection followed by protein cleaning using filter-aided sample preparation (FASP), protein digestion, TMT-labeling of peptides, and mass spectrometry (MS) analysis. Subsequent data analysis for peptide detection and quantification uses Proteome Discoverer software (PD) 3.0. Additionally, we have incorporated in silico–generated spectral libraries using PD 3.0, which enables rapid and efficient analysis of proteomic data. Our optimized protocol offers a robust framework for quantitative secretory proteomic analysis in plants, with potential applications in functional proteomics and the study of trafficking systems that impact plant growth, survival, and health.

In neuropharmacology and drug development, in silico methods have become increasingly vital, particularly for studying receptor–ligand interactions at the molecular level. Membrane proteins such as GABA (A) receptors play a central role in neuronal signaling and are key targets for therapeutic intervention. While experimental techniques like electrophysiology and radioligand binding provide valuable functional data, they often fall short in resolving the structural complexity of membrane proteins and can be time-consuming, costly, and inaccessible in many research settings. This study presents a comprehensive computational workflow for investigating membrane protein–ligand interactions, demonstrated using the GABA (A) receptor α5β2γ2 subtype and mitragynine, an alkaloid from Mitragyna speciosa (Kratom), as a case study. The protocol includes homology modeling of the receptor based on a high-resolution template, followed by structure optimization and validation. Ligand docking is then used to predict binding sites and affinities at known modulatory interfaces. Finally, molecular dynamics (MD) simulations assess the stability and conformational dynamics of receptor–ligand complexes over time. Overall, this workflow offers a robust, reproducible approach for structural analysis of membrane protein–ligand interactions, supporting early-stage drug discovery and mechanistic studies across diverse membrane protein targets.

Nociception is critically shaped by descending modulation of spinal circuits, yet its cellular and synaptic mechanisms remain poorly defined. Elucidating these mechanisms is technically challenging, as it requires simultaneous activation of primary afferents and descending fibers while monitoring the functioning of individual spinal neurons. Here, we present a method to investigate the influence of the rostral ventromedial medulla (RVM), a principal supraspinal structure mediating descending modulation, on the activity of spinal lamina I neurons. Our approach combines electrophysiological recordings in ex vivo intact spinal cord preparation with optogenetics, granting several advantages. First, ex vivo preparation spares rostrocaudal and mediolateral spinal architecture, preserving lamina I as well as primary afferent and descending inputs. Second, virally mediated channelrhodopsin-2 (ChR2) expression enables selective photostimulation of RVM-originating fibers. When coupled with patch-clamp recordings, this photostimulation allows identifying postsynaptic inputs from RVM to spinal neurons and revealing RVM-dependent presynaptic inhibition of primary afferent inputs. Overall, our approach is well-suited for investigating both pre- and postsynaptic mechanisms of descending modulation in physiological and pathological pain conditions.

DNA methylation is a fundamental epigenetic mark with critical roles in epigenetic regulation, development, and genome stability across diverse organisms. Whole genome bisulfite sequencing (WGBS) enables single-base resolution mapping of cytosine methylation patterns and has become a standard method in epigenomics. This protocol provides a detailed, step-by-step workflow for WGBS library construction starting from genomic DNA. It includes steps of RNaseA treatment, DNA shearing, end-repair and A-tailing, adapter ligation, bisulfite conversion, library amplification, and quantification. Notably, the method uses self-prepared reagents and customizable index systems, avoiding the constraints of commercial library preparation kits. This flexibility supports cost-effective, scalable methylome profiling, suitable for diverse experimental designs, including high-throughput multiplexed sequencing.