Cell Reports Methods最新文献

Rapid, spatially controlled methods are needed to investigate RNA localization in bacterial cells. APEX2 proximity labeling was shown to be adaptable to rapid RNA labeling in eukaryotic cells and, through the fusion of APEX2 to different proteins targeted to diverse subcellular locations, has been useful to identify RNA localization in these cells. Therefore, we adapted APEX2 proximity labeling of RNA to bacterial cells by generating an APEX2 fusion to the ribonuclease (RNase) E gene, which is necessary and sufficient for bacterial ribonucleoprotein (BR)-body formation. APEX2 fusion is minimally perturbative, and RNA can be rapidly labeled on the sub-minute timescale with alkyne-phenol, outpacing the rapid speed of mRNA decay in bacteria. Alkyne-phenol provides flexibility in the overall application with copper-catalyzed click chemistry for downstream processes, such as fluorescent dye azides or biotin-azides for purification. Altogether, APEX2 proximity labeling of RNA provides a useful method for studying RNA localization in bacteria.

To detect precise DNA methylation patterns in long-read DNA sequencing analysis, an efficient target enrichment method is needed. In this study, we established t-nanoEM, a practical method that integrates a hybridization-based capture step into a long-read enzymatic methyl (EM)-seq library for nanopore sequencing. We achieved a high sequencing coverage of up to ×570 at 5 kb N50 in length. We applied this method to the long-read methylation analysis of cancers. Using breast cancer as an example, we demonstrated that the signature changes in DNA methylation occurring in local cell populations could be displayed in a haplotype-aware manner. In lung cancer, the spatial diversity in gene expression as detected by the spatial expression profiling analysis may be associated with changes in DNA methylation.

Traumatic brain injury (TBI) is the leading environmental risk factor for neurodegenerative diseases, yet its molecular link to chronic neurodegeneration is unclear. While animal models of TBI are commonly used, emerging research suggests that induced pluripotent stem cell (iPSC)-derived brain organoids offer a promising human-specific alternative, particularly for studying processes like cryptic exon splicing. However, widespread use has been limited by methodological variability and the need for expensive and specialized equipment. To address these challenges, we developed a tabletop blast device capable of delivering highly reproducible pressure waves via a gravity-based pressure chamber. We validated the applicability of our approach by assessing the short- and long-term consequences of mechanical stress on brain organoids after pressure wave exposure. Our approach provides a controllable and reproducible method to apply complex pressure cycles on brain organoids, enabling broader accessibility for studying the mechanistic links between TBI and neurodegeneration in a human-relevant context.

Spatial transcriptomics (ST) enables in situ analysis of gene expression patterns and spatial microenvironments. However, current ST technologies are limited by detection sensitivity and gene coverage, posing significant challenges for precise cell type annotation at the single-cell level. To address this, we present stTransfer, a method that integrates reference single-cell RNA sequencing (scRNA-seq) data with ST context using a graph autoencoder and transfer learning. This approach minimizes information transfer loss between scRNA-seq and ST datasets. Benchmark analyses on publicly available spatial transcriptomic datasets demonstrate that stTransfer outperforms existing methods in both accuracy and robustness for cell type annotation. Lastly, we apply stTransfer to annotate neuronal populations in a high-precision Stereo-seq dataset of the zebra finch optic tectum.

Hematoxylin and eosin (H&E) staining has been a standard in clinical histopathology for many decades but lacks molecular detail. Advances in multiplexed spatial proteomics imaging allow cell types and tissues to be annotated by their expression patterns as well as their morphological features. However, these technologies are at present unavailable in most clinical settings. In this work, we present a machine learning framework that leverages histopathology foundation models and paired H&E and spatial proteomic imaging data to enable enhanced cell type annotation on H&E-only datasets. We trained and evaluated our method on kidney datasets with paired H&E and spatial proteomic imaging data and found that models trained using our methods outperform models trained directly on the imaging data. We also show how our framework can be used to study biological differences between two major kidney diseases.

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.