Cell Reports Methods最新文献

CRISPR-Cas9 is rapidly expanding across diverse organisms. Among these advances, in-frame knockins of reporter genes have become essential for studying gene expression and protein localization. However, in hemimetabolan insects such as the German cockroach Blattella germanica, a phylogenetically basal and relevant pest species, functional fusion proteins have remained technically difficult to obtain. We present a streamlined gene-editing strategy to knock in a reporter gene in-frame with the distal-less gene, generating a functional fusion protein in B. germanica. By combining direct parental CRISPR with donor constructs designed for homology-directed repair carrying the mCherry gene, we successfully achieved targeted integration at the distal-less locus. The resulting fusion protein was functional and heritable and enabled live visualization of Distal-less protein distribution, showing fluorescence in developing appendages and the nervous system. This simple and robust methodology opens the door to generating fusion proteins in non-model insects, providing a valuable molecular tool for ecological, developmental, and pest-management research.

Neural organoids are invaluable model systems for studying neurodevelopment, generated by either guided or unguided approaches. Despite the importance for the field, the resulting differences between these models are unclear. To obtain an unbiased comparison, we performed a multi-omic analysis of forebrain organoids generated in parallel with two widely applied guided and unguided protocols. The guided forebrain organoids contained a larger proportion of neurons, including GABAergic interneurons, whereas the unguided organoids contained significantly more choroid plexus, radial glia, and astrocytes at later stages. Substantial differences in metabolic profiles were identified, pointing to increased levels of oxidative phosphorylation and fatty acid β-oxidation in the unguided forebrain organoids and a higher reliance on glycolysis in the guided forebrain organoids. Overall, our study comprises a thorough description of the multi-omic differences between these guided and unguided forebrain organoids and provides an important resource for the neural organoid field studying neurodevelopment and disease.

Populations of neurons form assemblies at many scales and display recurring spatiotemporal patterns of activity. In the cerebral cortex, these patterns of activity involve coordinated activity spanning large distances and anatomical regions subserving distinct functions. The constraints governing how these activity motifs transition over time is not known because conventional computational modeling and analyses collapse either the spatial or the temporal properties of the dynamics. Here, we use a continuous-time Markov chain (CTMC) modeling framework to probabilistically describe the temporal sequences elicited in large-scale complex cortical activity recorded with mesoscale imaging. This reveals a conserved dynamical structure across animals, with modular transitions serving as pseudo-"absorbing states." The parameters of the CTMC model are readily analyzed and used as a "neural barcode," a low-dimensional description of neural dynamics that is sensitive to cortical imaging applications, including pathological brain dynamics. This neural barcode provides a powerful computational tool to characterize cortical dynamics.

Functional imaging using genetically encoded indicators has become a foundational tool for cellular dynamics and communication analysis. However, large or complex experiments pose analytical challenges. Many programs address these challenges; however, most require proprietary software, impose restrictions, or require programming knowledge, which limits their utility. To address this, we designed MCA (Multicellular Analysis toolkit) to work with ImageJ, a widely used open-source software. MCA utilizes ImageJ to generate new images based on completed tasks, allowing visualization of the analysis pipeline. MCA also implements a user-friendly graphical user interface (GUI) resembling native ImageJ plugins. We incorporated rigid registration for motion correction, cell prediction algorithms, and data annotation and exporting features. We validated MCA using previously published zebrafish visual response calcium imaging data. To further show MCA's versatility, we also tested multiple sensory responses, brain regions, and model organisms, including Drosophila and mouse. Altogether, MCA is a user-friendly environment viable for multiple forms of functional imaging analysis.

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.