Cell Reports Methods最新文献

Cysteine oxidative modifications are critical signaling events regulating cellular functions, but their low abundance and dynamic nature pose technical challenges. We developed the SICyLIA-TMT workflow, which sequentially labels reduced and reversibly oxidized cysteines with light and heavy iodoacetamide (IAA) within the same sample. The inclusion of tandem mass tags (TMTs) enables simultaneous quantification of oxidative modification dynamics and protein levels across multiple conditions using micrograms of material. To improve the detection of low-abundance oxidized cysteines, a dedicated TMT channel serves as a carrier for heavy IAA-labeled peptides (SICyLIA-cTMT), enhancing quantification and enabling precise stoichiometry calculations. We demonstrate the workflow's applicability to cultured cells and full organs under stress. SICyLIA-cTMT achieves unprecedented depth and accuracy in redox proteome analysis while reducing mass spectrometry time. Combining SICyLIA-TMT with latest mass spectrometry technologies further halves the acquisition time without compromising coverage, improving throughput and enabling comprehensive studies of oxidative signaling.

Metagenomic studies have primarily relied on de novo assembly for reconstructing genes and genomes from microbial mixtures. While reference-guided approaches have been employed in the assembly of single organisms, they have not been used in a metagenomic context. Here, we develop an effective approach for reference-guided metagenomic assembly that can complement and improve upon de novo metagenomic assembly methods for certain organisms. Such approaches will be increasingly useful as more genomes are sequenced and made publicly available.

Recent advances in molecular biology have led to large-scale datasets providing new insights into the molecular organization of cells. To fully exploit their potential, computer simulations are essential to gain in-depth understanding of molecular principles. We developed the Python reaction interaction diffusion simulator (PyRID), a Python-based reaction-diffusion simulator designed for the efficient simulation of molecular biological systems. PyRID incorporates unimolecular and bimolecular reactions as well as pair interactions and simulation of individual interacting proteins to polydisperse molecular assemblies. It supports mesh-based compartments and surface diffusion of particles, enabling analyses of interactions between (trans)membrane proteins with intra- and extracellular proteins. Distinctively, PyRID uses hierarchical grids for polydisperse systems, supports rigid bead models, and calculates diffusion tensors internally. Validation against theoretical results and established models confirms PyRID's accuracy in reproducing key physical properties. PyRID is written entirely in Python, making it accessible to the broader scientific community, facilitating customization and integration into diverse research workflows.

Genes associated with the same disease frequently engage in mutual biological interactions, e.g., perturbation within a specific neighborhood in the molecular interactome, often referred to as the disease module. This has propelled the advancement of network-based approaches toward elucidating the molecular bases of human diseases. Although many computational methods have been developed to integrate the molecular interactome and omics profiles to extract such context-dependent disease modules, approaches that leverage multi-omics for disease-module detection are still lacking. Here, we developed a statistical physics approach based on the random-field O(n) model (RFOnM) to fill this gap. We applied the RFOnM approach to integrate gene-expression data and genome-wide association studies or mRNA data and DNA methylation for several complex diseases with the human interactome. We found that the RFOnM approach outperforms existing single omics methods in most of the complex diseases considered in this study.

Active endolysosomal pH regulation is essential for optimal enzymatic activity. To measure acidification, pH sensors can be delivered to acidic compartments using labeled dextran polymers or proteins. However, commercial probes have limited sensitivity in the acidic endolysosomal range or their fluorescence undergoes degradation. Herein, we introduce the new pH-sensitive probe ApHID, a green-emitting sensor with optimal dynamic range matching the acidity of endosomes and lysosomes. Acid pH indicator dye (ApHID) has a pKa near 5, increasing brightness with acidity, and withstands oxidation and photobleaching. We used ApHID dextrans to measure endolysosomal pH in macrophages and compared it to other commercially available sensors. ApHID reported pH accurately and stably over time in cell culture and was sensitive to subtle variations in organelle acidification in real time. Overall, ApHID circumvents limitations of currently available commercial probes and can provide utility in demanding applications such as intravital imaging of tissues.

Spatial proteomic and transcriptomic technologies enable high-throughput phenotyping of cells in situ, enabling quantification of spatial relationships among diverse cell populations. However, the experimental design choice of which regions of a tissue will be imaged can greatly impact the interpretation of spatial quantifications. That is, spatial relationships identified in one region of interest may not be interpreted consistently across other regions. To address this challenge, we introduce Kontextual, a method that considers alternative frames of reference for contextualizing spatial relationships. These contexts may represent landmarks, spatial domains, or groups of functionally similar cells that are consistent across regions. By modeling spatial relationships between cells relative to these contexts, Kontextual produces robust spatial quantifications that are not confounded by the region selected. We demonstrate in spatial proteomics and transcriptomics datasets that modeling spatial relationships this way is biologically meaningful and can improve the prediction of patient prognosis in a classification setting.

Drug-target interaction (DTI) prediction is a crucial component of drug discovery. In recent years, in silico approaches have attracted attention for DTI prediction, primarily because of their potential to mitigate the high costs, low success rates, and extensive timelines of traditional drug development, while efficiently using the growing amount of available data. This review identifies four major factors that influence DTI predictions, highlights persistent challenges, and proposes insights and strategies from the perspectives of data, features, and experimental setups to address these challenges. Furthermore, it emphasizes the importance of refining established approaches-such as the "guilt-by-association" concept-to manage data sparsity, and integrating emerging technologies, including large language models and AlphaFold, to advance feature engineering. We hope that this work will provide valuable guidance and novel perspectives for advancing future research on DTI predictions.

Single-cell sequencing enables genome- and epigenome-wide profiling of thousands of individual cells, offering unprecedented biological insights. However, technical noise and batch effects obscure high-resolution structures, hindering rare-cell-type detection and cross-dataset comparisons. To comprehensively address these challenges, this study upgrades RECODE, a high-dimensional statistics-based tool for technical noise reduction in single-cell RNA sequencing (RNA-seq), to include a function called iRECODE, which simultaneously reduces technical and batch noise. Further, RECODE's applicability is extended to diverse single-cell modalities, including single-cell high-throughput chromosome conformation capture (Hi-C) and spatial transcriptomics. Recent improvements in the algorithm have substantially enhanced both accuracy and computational efficiency. The RECODE platform thus provides a robust and versatile solution for noise mitigation, enabling more accurate downstream analyses across transcriptomic, epigenomic, and spatial domains.

High-definition visualization techniques are critical for understanding the neuroanatomy of the spinal cord, an essential structure for sensorimotor and autonomic functions, in both healthy and pathological conditions. Magnetic resonance imaging (MRI) is a common method for visualizing neural structures in 3D. However, techniques for spinal cord MRI have historically achieved limited visualization of rootlets and nerves, especially at lower spinal levels, due to their highly complex and compact organization. Here, we developed a spinal in situ contrast 3D imaging (SpIC3D) method that allows visualization of spinal compartments in fixed animal and human specimens with high resolution (50 μm) at various spinal levels. Using SpIC3D, we achieved quantification of neuronal cell density in dorsal root ganglia, multi-segment identification of individual rootlets and roots, and volumetric reconstruction of multiple spinal structures for computational modeling. SpIC3D provides a basis for accelerated spinal pathology characterization and personalized spinal cord stimulation treatments.

We developed SpliPath as a generalizable framework to discover disease associations mediated by rare variants that induce experimentally supported mRNA splicing defects. Our approach integrates components of burden tests (BTs), traditional splicing quantitative trait locus (sQTL) analyses, and sequence-to-function AI models (SpliceAI and Pangolin). Central to the workings of SpliPath is our concept of collapsed rare variant splicing QTL (crsQTL). crsQTL groups rare variants that are predicted to alter splicing in the same way, specifically by linking them to shared splice junctions observed in independent (unpaired) RNA sequencing (RNA-seq) datasets. We demonstrate the utility of SpliPath through applications in amyotrophic lateral sclerosis (ALS). Through this, we showcase scenarios where SpliPath detects genetic associations that cannot be recovered by more simplistic combinations of BT and SpliceAI. We also nominate crsQTL for splice defects detected in large-scale analyses of ALS patient tissue.