Cell Reports Methods最新文献

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.

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.

Generating a large number of progenitors that can repopulate the immune system of a recipient is one of the key steps toward efficient cancer immunotherapy. Here, we describe the engineering of T cell progenitors capable of direct and long-term reconstitution of the thymus. In the thymus, human pluripotent stem cell (hPSC)-derived progenitor T cells (pro-T cells) developed into single-positive human T cells that entered circulation and settled in the spleen. Single-cell transcriptome analysis of differentiating hPSCs attested to the emergence of cells that displayed the transcription signature of the early T cell progenitors. Comparative transcription profiling revealed the similarity of the hPSC-pro-T cells with the early T cell precursors of the human thymus. The in vitro generation of T cell progenitors provides a powerful model for studying the molecular mechanisms of human T cell development and improves the perspectives for T cell regenerative medicine, including chimeric antigen receptor T (CAR-T) cell therapies.

CRISPR-mediated gene editing using engineered virus-like particles (eVLPs) can achieve high efficiency, but performance varies with reduced effectiveness often seen in primary cells or when generating polyclonal models at scale. We developed a faster, accurate and 4-fold more efficient CRISPR-Cas9 (FAME-CRISPR) method using pan-histone deacetylase inhibitors with eVLP transduction compared to previous reports using other histone deacetylase inhibitors. Combined optimization of pan-HDACi treatment with eVLP enhanced double-strand break (DSB)-mediated CRISPR and base editing gave significantly edited populations within 2- to 3-cell mean population doublings, reducing the need for post-editing selection in immortalized cancer cells and in primary diploid fibroblasts that have limited replicative lifespans.

Understanding human cell metabolism through genome-scale flux profiling is of interest to diverse research areas of human health and disease. Metabolic modeling using genome-scale metabolic models (GEMs) has the potential to achieve this, but has been limited by a lack of appropriate input data as model constraints. Here, we compare the commonly used consumption and release (CORE) method to a regression-based method (regression during exponential growth phase; REGP). We found that the CORE method is not reliable despite being prevalent in human studies, whereas the exchange fluxes determined by REGP provide constraints that substantially improve GEM simulations for human cell lines. Our results show that the GEM-simulated feasible flux space is constrained to a biologically plausible region, allowing an exploration of the basic organizing principles of the feasible flux space. These improvements help to fulfill the promise of GEMs as a valuable tool in the study of human metabolism and future development of translational applications.

AGO-APP through the expression of the T6B peptide permits the isolation of Ago-bound microRNAs (miRNAs). Here, we present the generation and characterization of two transgenic mouse lines that enable AGO-APP to be performed in vivo. First, we generated mice for CRE-dependent T6B expression throughout the cell. Using this line, we performed AGO affinity purification (AGO-APP) in olfactory bulb (OB) inhibitory interneurons and cerebral cortex excitatory neurons. Bioinformatic analysis validated the high reproducibility of the approach. It also demonstrated that, despite global miRNome conservation between the two cell types, a set of miRNAs, including the miR-200 family and the miR-183/96/182 cluster, is massively enriched in OB interneurons, which aligns with previous observations. In the second mouse line, T6B is fused to the postsynaptic protein PSD95. Isolation of T6B-PSD95 fractions from OB and cortical neurons identified specific sets of postsynapse-enriched miRNAs. Gene ontology analyses confirmed that these miRNAs preferentially target mRNAs related to synaptic functions.

Real-time monitoring of microbial communities offers valuable insights into microbial dynamics across diverse environments. However, many existing metagenome analysis tools require advanced computational expertise and are not designed for monitoring. We present MMonitor, an open-source software platform for real-time analysis and visualization of metagenomic Oxford Nanopore Technologies (ONT) sequencing data. MMonitor includes two components: a desktop application for running bioinformatics pipelines through a graphical user interface (GUI) or command-line interface (CLI) and a web-based dashboard for interactive result inspection. The dashboard provides taxonomic composition over time, quality scores, diversity indices, and taxonomy-metadata correlations. Integrated pipelines enable automated de novo assembly and reconstruction of metagenome-assembled genomes (MAGs). To validate MMonitor, we tracked human gut microbial populations in three bioreactors using 16S rRNA gene sequencing and applied it to whole-genome sequencing (WGS) data to generate high-quality annotated MAGs. We compare MMonitor with other real-time metagenomic tools, outlining their strengths and limitations.

Nuclear magnetic resonance (NMR) spectroscopy is often used for the analysis of metabolites in proteinaceous biological specimens. However, the binding of metabolites to proteins impedes accurate quantitation of total metabolite concentrations by NMR, unless protein binding is disrupted by organic solvent precipitation, which increases variance and may result in the loss of volatile metabolites during post-extraction drying. Here, we present an approach for the inference of total metabolite concentrations from Carr-Purcell-Meiboom-Gill NMR spectra via computation of metabolite and sample-specific factors derived from the individual broadening of spectral peaks due to protein-metabolite binding. The method was validated on both synthetic proteinaceous samples and plasma and urine specimens including a certified reference plasma. Furthermore, results were compared with those obtained for methanol extracts of plasma specimens. In summary, our approach obviates the need for protein precipitation, is easy to use, and allows precise and reliable determination of total metabolite concentrations.