Cell Reports Methods最新文献

Cancer cachexia, a devastating metabolic wasting syndrome affecting up to 80% of solid cancer patients, remains incurable despite advances in tumor biology understanding. This study introduces neuromuscular organoids (NMOs) derived from human-induced pluripotent stem cells (hiPSCs) as a platform to investigate cancer-driven muscle cachexia. We found that NMOs respond well to atrophic stimuli and replicate the key features of cancer cachexia when treated with conditioned media derived from cachexia-inducing cancer cells. Specifically, cachectic NMOs showed muscle mass loss, impairment of muscle contraction, alteration of intracellular calcium homeostasis, appearance of mitochondrial dysfunction with a metabolic shift, and enhancement of autophagy. Based on these results, we propose NMOs derived from hiPSCs as an in vitro tool for investigating human muscle cachexia, with potential future avenues of patient-specific modeling and therapeutic screening.

Differential gene expression (DGE) analysis is a crucial step in identifying senescent cells using single-cell RNA sequencing (scRNA-seq) data. However, few studies have evaluated the performance of DGE methods-particularly those implemented in the widely used Seurat package. In this study, we systematically assessed 10 DGE methods available in Seurat-Wilcox, Wilcox-limma, bimod, roc, t, negbinom, Poisson, LR, MAST, and DESeq2-using simulated and real scRNA-seq datasets. We evaluated each method's performance across varying sample sizes, levels of sparsity, and proportions of truly differentially expressed genes. Metrics assessed included false discovery rate (FDR), sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC), and area under the precision-recall curve (AUPRC). Among all methods, DESeq2 consistently demonstrated the best overall performance, showing the highest AUC and AUPRC across all tested conditions. Based on our findings, we recommend DESeq2 as the preferred method for DGE analysis in scRNA-seq data.

Combining molecular profiling with imaging techniques has advanced the field of spatial biology, offering new insights into complex biological processes. Focusing on diffuse IDH-mutated glioma, this study presents a workflow for spatial multi-omics integration (SMINT) specifically combining spatial transcriptomics and spatial metabolomics. Our workflow incorporates both existing and custom-developed computational tools to enable cell segmentation and registration of spatial coordinates from both modalities to a common coordinate framework. During our investigation of cell segmentation strategies, we found that nuclei-only segmentation, while containing only 40% of segmented cell transcripts, enables accurate cell-type annotation but does not account for scenarios including delineation of multinucleated cells. Our integrative workflow including cell-morphology segmentation identified distinct cellular neighborhoods at the infiltrating edge of IDH-mutated gliomas, which were enriched in multinucleated and oligodendrocyte-lineage tumor cells and associated with differentially abundant metabolites.

Rigid anatomical mapping is a necessity in current neuroscience research. Here, we introduce DMC-BrainMap, an open-source napari plugin designed as a user-friendly tool for streamlined processing and whole-brain analysis of anatomical data. Its core functionalities include all steps after image acquisition, i.e., preprocessing of images, registration of images to a reference atlas, segmentation of different anatomical features, and data analysis/visualization. DMC-BrainMap can be applied to histological data obtained from a variety of model organisms at different developmental stages to map a diverse range of features. We demonstrate the utility of DMC-BrainMap by mapping and quantifying the location of cell bodies, axonal densities, injection sites, optical fiber and Neuropixels tracts, (single-cell) spatial transcriptomics, as well as neuron morphology data in mice, rats, and zebrafish. By eliminating the need for programming by the user, DMC-BrainMap provides an easy-to-use tool for increased rigor, reproducibility, and data sharing in neuroscientific research involving animal models.

We present a modeling framework that can perform real-time estimation of per-cell metabolic rates of T cells expanded ex vivo in a reactor. We validate our estimated rates using metabolic assays, show how average rates can be deconvoluted to rates of individual T cell phenotypes, and demonstrate applicability to different reactor types. Applying our tool to the expansion of both healthy and patient-derived cells in a perfusion-based microbioreactor, we offer proof-of-principle to show that correlations exist between early metabolic rates of T cells in culture and cellular attributes related to growth, differentiation, and exhaustion of the final product. Given the biological variation that exists in the growth and dynamics of patient-derived cells in culture, such modeling contributes to the overarching goal of improving the consistency of cell therapy through adaptive process control (APC).

Understanding and mitigating laboratory hazards is essential for fostering safe and inclusive research environments. However, conducting risk assessments can be challenging and time consuming, especially for scientists who have new or specific concerns about hazard susceptibility, such as pregnant women. In response, using reproductive hazards as our primary example, we developed HazardPyMatch, a laboratory hazard screening tool designed to be implemented in laboratories across scientific disciplines to support efficient hazard management. HazardPyMatch is an accessible and user-friendly tool that enables scientists to quickly and easily systematically identify chemical hazards in laboratory chemical inventories and categorize these hazards in laboratory protocols.

With the rapid advancement of protein structure prediction techniques and the explosive growth of predicted structural data, existing estimation of model accuracy (EMA) methods struggle to balance computational efficiency with estimation performance. Here, we present MViewEMA, a single-model EMA method that leverages a multi-view representation learning framework to integrate residue-residue interaction features from micro-environment, meso-environment, and macro-environment levels for global accuracy assessment of protein complex models. Benchmark results demonstrate that MViewEMA outperforms state-of-the-art EMA methods in global accuracy assessment, achieving more than a 10-fold improvement in computational efficiency compared to our previous method, DeepUMQA3. This method enables efficient selection of high-quality protein complex models from large-scale structural datasets and achieved top performance in model selection tracks during the CASP16 blind test, demonstrating its potential to enhance the accuracy of complex structure prediction when integrated into modern frameworks such as AlphaFold-Multimer, AlphaFold3, and DiffDock-PP.

Poly(ADP-ribose) polymerase inhibitors (PARPi) have revolutionized the treatment of ovarian high-grade serous carcinoma (HGSC), particularly in homologous recombination-deficient tumors. However, the emergence of resistance poses a critical challenge, as over 50% of patients relapse within 3 years. The mechanisms underlying changes in PARP trapping, a central aspect of PARPi efficacy, are not well understood, as current experimental methodologies lack resolution and throughput. To address this, we develop an intramolecular fluorescence resonance energy transfer (FRET)-based biosensor by CRISPR-Cas9 dual labeling of endogenous PARP1 with EGFP and mCherryFP in OVCAR4 cells. This biosensor enables real-time, single-cell analysis of PARP trapping dynamics. Using fluorescence lifetime imaging microscopy (FLIM), we reveal dose-dependent PARP trapping, differentiate the trapping efficiencies of four clinically approved PARPi, and observe reduced trapping in PARPi-resistant models in vitro and in vivo. This biosensor provides critical insights into PARPi resistance mechanisms, with implications for developing more effective therapies and advancing personalized treatment for ovarian cancer patients.

Polygenic risk score models (PRSs) are important tools in precision medicine, enabling personalized risk prediction; however, they raise privacy concerns. Fully homomorphic encryption (FHE) provides a potential solution, allowing computation on encrypted genomic data. Here, we develop an open-source implementation of FHE for PRS (HEPRS), available online. HEPRS involves a three party system: clients (clinicians handling sensitive genetic data), modelers developing a PRS (academics), and evaluators (a local hospital running the models while maintaining confidentiality). We apply HEPRS to synthetic datasets and a 110,000 single-nucleotide-polymorphism (SNP) model for schizophrenia and show that encrypted and plaintext PRSs agree closely. We investigate encryption parameters that influence computational accuracy, memory, and time, demonstrating that HEPRS is practical to use on a single CPU. These results show that FHE enables realistic, privacy-preserving PRSs with negligible accuracy loss, supporting secure and scalable genomic analytics.

The human gut virome represents a critical yet underexplored component that regulates bacterial communities and maintains gut health. However, virome analysis remains challenging due to the vast diversity and genomic variability. Existing profiling methods often struggle with accuracy and efficiency, hindering novel viral species detection and large-scale analyses. Here, we present signature-protein-based virome profiling (SinProVirP), a signature-protein-based genus-level virome profiling tool. By analyzing 275,202 phage genomes to establish a database of 109,221 signature proteins across 6,780 viral clusters (VCs), SinProVirP achieves genus-level phage quantification with accuracy comparable to the benchmark method while reducing computational demands by over 80%. Crucially, SinProVirP outperforms existing tools in detecting novel viruses, achieving over 80% recall. Applied to inflammatory bowel disease (IBD) cohorts, SinProVirP revealed disease-specific virome dysbiosis, identified high-confidence phage-host interactions, and improved the performance of bacteria-only disease classification models. SinProVirP enables robust cross-cohort virome analysis and improves our understanding of the virome's role in health.