Systems Biology

Unsaturated fatty acids (UFAs) play key roles in essential cellular functions such as membrane dynamics, metabolism, and animal development. Disruptions in UFA metabolism are linked to metabolic, cardiovascular, and neurodegenerative disorders. Cellular UFAs composition and quantification are normally determined using methods such as gas chromatography and/or mass spectrometry, which require extraction procedures and prevent analysis of live specimens. Here, we describe a protocol that employs uniform ¹³C isotope labeling and high-resolution 2D solution-state nuclear magnetic resonance (NMR) spectroscopy to analyze lipid composition and fatty acid unsaturation directly in the model organism Caenorhabditis elegans. The approach enables in vivo assessment of lipid storage compositions with sufficient resolution and sensitivity to distinguish wild-type animals from those with altered fatty acid desaturation. Complementary analysis of total lipid extracts provides information regarding lipid molecules that are not detected in vivo, such as phospholipid molecules organized in biological membranes. Overall, this non-destructive NMR-based method offers a powerful tool for investigating lipid metabolism in C. elegans and other small model systems that can be isotopically enriched.

Bottom-up proteomics workflows encompass several key stages, including sample preparation, data acquisition, and data analysis. Of these, sample preparation is the initial and critical stage, as it significantly influences the depth, reproducibility, and reliability of subsequent mass spectrometry–based analyses. While several main digestion strategies exist, including in-gel, in-solution, and filter-aided methods, each presents distinct trade-offs in terms of throughput, contamination removal, and applicability to complex biological matrices. The Suspension Trapping (S-Trap) method offers a compelling alternative by efficiently capturing and digesting proteins while removing interferents like sodium dodecyl sulfate (SDS), which can compromise downstream LC–MS/MS performance. This protocol details a S-Trap workflow optimized for biofluid proteomics, specifically plasma, serum, and cerebrospinal fluid (CSF). We describe two complementary formats: a manual tube-based procedure for individual or small-batch samples and a 96-well-plate-based system enabling high-throughput processing. The protocol integrates optional high-abundance protein depletion to enhance coverage of low-abundance analytes and includes steps for reduction, alkylation, digestion, and peptide elution for low total protein content samples, such as plasma, serum, and cerebrospinal fluid. By providing a detailed protocol, this work aims to improve the consistency and accessibility of S-Trap-based sample preparation, facilitating robust and reproducible discoveries in bottom-up proteomics.

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.

The placenta is a metabolically active organ whose mitochondrial activity is tightly linked to fetal growth, oxygenation, and nutrient transport, mediating fetal susceptibility to environmental exposures. Accordingly, aberrant mitochondrial function has been implicated in the progression of placental dysfunction. However, existing respirometry platforms require primarily fresh or cryopreserved placental tissue and offer limited throughput, rendering these platforms impractical in the context of large-scale placental dissections. Here, we describe and validate a Seahorse XF approach for measuring mitochondrial respiration in previously frozen placentae, enabling the functional interrogation of placental mitochondria in prenatal studies. Our protocol fundamentally relies on the restoration of matrix substrates that are depleted due to increased mitochondrial membrane permeability following freeze-thaw cycles. We provide a strategy to assess complex I and II-associated respiration adapted for the Seahorse XFe24 Analyzer and further demonstrate comparable oxygen consumption readouts between fresh and frozen placentae. We further demonstrate distinct differences in the magnitude of oxygen consumption between fresh and frozen placentae in the absence of exogenous NADH. Taken together, we present a simplified and convenient protocol for the assessment of respiratory enzyme complex-associated respiration from archived placental tissue.

DNA language models, such as DNABERT-2, have recently enabled the accurate prediction of functional sequence elements across species. However, the practical, protocol-style steps needed to transform these resources into training datasets, fine-tune the official DNABERT-2 model, and evaluate classifier performance have not been explicitly described. Herein, we present a step-by-step computational protocol for preparing training data, fine-tuning DNABERT-2, and evaluating sequence-level binary classifiers using readily available command-line tools. The protocol has been demonstrated using RNA off-target sites induced by cytosine base editors, detected by our PiCTURE pipeline from RNA sequencing (RNA-seq) data, and extended to core promoter prediction using the EPDnew database. We describe how to derive positive and negative sequence sets into DNABERT-2 compatible datasets, and fine-tune the official pretrained model of DNABERT-2 using the datasets. We also demonstrate how to compute the standard performance metrics and compare the model outputs with the baselines. This protocol will help researchers adapt DNA foundation models to new genomic tasks, including the safety assessment of genome editing tools and the functional annotation of regulatory sequences.

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.

Extracellular vesicles (EVs) are nanoscale particles secreted by all cells and present in all biological fluids, where they carry molecular cargo reflective of health and disease states. Their diagnostic potential is often obscured by the high abundance of non-EV proteins and lipoproteins (e.g., albumin, apolipoproteins) that complicate proteomic analysis of primary biofluids, such as ascites fluid. Conventional isolation strategies face a persistent trade-off between EV purity and yield. To overcome this, a magnetic bead-based protocol (Mag-Net) to enrich EVs according to electrochemical surface charge using strong anion-exchange chemistry (SAX) was adapted for proteomics. Our workflow is specifically adapted to ascites fluid from human or murine sources. This approach effectively separates EVs from high-abundance proteins and lipoproteins, enabling proteomic profiling from as little as 2 μL of ascites fluid. Demonstrated in both murine and human ovarian cancer models, Mag-Net offers a reproducible, scalable, and automation-ready solution for EV isolation from various biofluids.

Spatial proteomics enables the mapping of protein distribution within tissues, which is crucial for understanding cellular functions in their native context. While spatial transcriptomics has seen rapid advancement, spatial proteomics faces challenges due to protein non-amplifiability and mass spectrometry sensitivity limitations. This protocol describes a sparse sampling strategy for spatial proteomics (S4P) that combines multi-angle tissue strip microdissection with deep learning–based image reconstruction. The method achieves whole-tissue slice coverage with significantly reduced sampling requirements, enabling mapping of over 9,000 proteins in mouse brain tissue at 525 μm resolution within 200 h of mass spectrometry time. Key advantages include reduced sample processing time, deep proteome coverage, and applicability to centimeter-sized tissue samples.

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.

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.