Cell Reports Methods最新文献

The novel object recognition (NOR) test is widely used to assess memory in rodents, offering strong ethological validity, cross-species relevance, and specificity for hippocampal-parahippocampal function. However, standard implementations are often confounded by uncontrolled factors. Here, we present a fully automated, homecage-based NOR test for evaluating long-term object memory in mice. Our empirically informed computational model demonstrates the robustness of this approach despite uncertainties in defining exploratory behavior. Mice reliably preferred novel over familiar objects after both 24-h and 7-day delays, with recognition emerging already at a distance. Results were replicated across two facilities. Notably, recognition after 24 h depended on prior interactions with the replaced object, but not after 7 days. We also show that external factors can bias exploration, which can be mitigated using relative discrimination measures. This automated paradigm enhances standardization, reproducibility, and our understanding of the factors influencing object exploratory behaviors and object memory.

Polyploidy (whole-genome duplication) is a common yet under-surveyed property of tissues across multicellular organisms. Polyploidy plays a critical role during tissue development, following acute stress, and during disease progression. Common methods to reveal polyploidy involve either destroying tissue architecture by cell isolation or tedious identification of individual nuclei in intact tissue. Therefore, there is a critical need for rapid and high-throughput ploidy quantification using images of nuclei in intact tissues. Here, we present iSPy (inferring Spatial Ploidy), an unsupervised learning pipeline that is designed to create a spatial map of nuclear ploidy across a tissue of interest. We demonstrate the use of iSPy in Arabidopsis, Drosophila, and human tissue. iSPy can be adapted for a variety of tissue preparations, including whole mount and sectioned. This high-throughput pipeline will facilitate rapid and sensitive identification of nuclear ploidy in diverse biological contexts and organisms.

Here, we introduce CRISPR and transcriptomics-assay for transposase-accessible chromatin (CAT-ATAC), a technique that adds CRISPR guide RNA (gRNA) capture to the existing 10× Genomics Multiome assay, generating linked transcriptome, chromatin accessibility, and perturbation identity data from the same individual cells. We demonstrate up to 77% capture rate for both arrayed and pooled delivery of lentiviral gRNAs in induced pluripotent stem cells (iPSCs) and cancer cell lines. This capability allows us to construct gene regulatory networks (GRNs) in cells under drug and genetic perturbations. By applying CAT-ATAC, we identified a GRN associated with dasatinib resistance, indirectly activated by the HIC2 gene. Using loss-of-function experiments, we further validated that ZFPM2, a component of the predicted GRN, also contributes to dasatinib resistance. CAT-ATAC can thus be used to generate high-content multidimensional genotype-phenotype maps to reveal gene and cellular interactions and functions.

Spatial mapping of multi-slice multi-omics data enables the identification of shared and slice-specific cellular components across spatiotemporal axes. However, conventional graph neural networks assume uniform contributions from neighboring cells, neglecting directional and angular influences that shape central cell states and limiting their ability to dissect complex spatial structures. Here, we present stLVG, a vector-guided lightweight graph model for spatial mapping, label transfer, and niche identification across multi-slice multi-omics datasets. Specifically, stLVG (1) learns two distinct shared feature spaces across slices by aggregating neighbor information through adversarial learning with distance- and direction-informed weights and (2) integrates these features via a multi-view contrastive learning framework. Compared to existing methods, stLVG achieves superior performance across technologies, modalities, and resolutions; it accurately delineates tumor edge regions in breast cancer samples. Notably, it uses pre-computed weights and can be efficiently executed on a standard laptop within minutes, ensuring scalability to large-scale spatial omics analyses.

In this study, four dynamic magnetic resonance imaging (MRI) sequences were first developed to collect data. On this basis, we presented DVR-NeMF, a neural magnetic field framework that enables real-time, high-dimensional (4D) MRI reconstruction from synchronized dynamic 2D image slices and physiological signals. By embedding spatiotemporal priors into an implicit representation of the imaging space, DVR-NeMF reconstructs temporally consistent 3D volumes over time with high anatomical fidelity. Comprehensive evaluations across a cardiovascular phantom, cardiac dynamic bio-simulators, and living human hearts, including external out-of-distribution validation on the Automated Cardiac Diagnosis Challenge (ACDC) dataset, demonstrated that DVR-NeMF outperforms both autoencoder- and generative adversarial network (GAN)-based baselines in terms of reconstruction accuracy and computational efficiency. Comparative analysis with paired cardiac ultrasound data in terms of key left ventricular function parameters further confirmed its reliability. This work offers a promising paradigm for extending MRI to dynamic, high-dimensional imaging, with potential for real-time functional assessment in clinical settings.

Simple behavioral tests like the forced swim test (FST) and tail suspension test (TST) are widely used to assess depression-like behaviors in rodents, primarily measuring immobility time. However, this approach can oversimplify behavioral readouts and obscure cognitive processes driving behavior, leaving the relationship between increased immobility and cognitive biases unclear. Here, we developed the SwimStruggleTracker (SST) to extract fine-grained behavioral trajectories and integrate computational modeling to systematically analyze behavior. Our findings show that behavior in the FST and TST follows reinforcement learning principles involving learning, consequence perception, and decision-making. Notably, the cognitive processes underlying behavior differ between the two tests, challenging the assumption that they are interchangeable for cross-validation. Regression analyses identify distinct behavior phases: early behavior is primarily influenced by learning-related factors, while later stages are more affected by consequence sensitivity. These findings suggest that traditional analyses may underestimate the role of learning and overemphasize consequence sensitivity.

Coding mutations can cause neurodevelopmental disorders (NDDs), including autism. Yet, predicting which non-coding (e.g., 5' untranslated region [UTR]) mutations are functional is challenging. We tested assays of various throughput for the assessment of 997 mutations from NDD families. A massively parallel reporter assay (MPRA) using polysomes from cell lines identified >100 altering translation, with a subset subsequently altering endogenous protein production in patient lymphoblastoid cell lines. Next, since UTR function varies by cell type, we optimized Cre-dependent MPRAs, enabling assessment in neurons in vivo. We demonstrate that neurons have different principles of regulation by 5' UTRs and discover mutations altering translational activity. Finally, we tested whether polysome-MPRAs predict changes in canonical open reading frame (ORF) protein production. Only for mutations altering UTR structure was there a reasonable correlation. Overall, we benchmarked a variety of approaches for assessing impacts of 5' UTR mutation and identified functional 5' UTR mutations from known NDD genes, including LRRC4 and ZNF644.

Mapping the input connections of a single neuron, or the "inputome," is crucial for constructing mesoscopic connectomes at the cellular resolution of the brain. By combining retrograde viral tracing with single-cell RNA sequencing, we developed a barcoded rabies viral tracing (BRT) method that enables mapping both local and long-range input connections to transcriptome-defined neurons at the single-cell level. When applied to the mouse medial prefrontal cortex (mPFC), BRT revealed that certain starter cells were innervated by a large number of input cells while others received fewer than expected inputs. Interestingly, for each inputome, the number of local input neurons was positively correlated with the number of distant input regions, suggesting a dependence of local circuit complexity on distant input diversity. Thus, the BRT method provides a valuable foundation for constructing comprehensive mesoscopic connectomes of the brain.

Seizures are caused by abnormal synchronous brain activity. The resulting changes in muscle tone, such as twitching, stiffness, or jerking, are used in visual scoring systems such as the Racine scale to quantify seizure intensity. However, visual inspection is time consuming, low throughput, and partially subjective, and there is a need for scalable and rigorous quantitative approaches. We used supervised machine learning approaches to develop automated classifiers to predict seizure severity directly from non-invasive video data. Using the pentylenetetrazole (PTZ)-induced seizure model in mice, we trained video-only classifiers to predict ictal events and combined these events to predict composite seizure intensity for a recording session, as well as time-localized seizure intensity scores. Our results show that seizure events and overall intensity can be rigorously quantified directly from overhead video of mice in a standard open field using supervised approaches. These results enable high-throughput, non-invasive, and standardized seizure scoring for neurogenetics and therapeutic discovery.

Effective systemic therapies against brain metastases are severely limited. To understand and target vulnerabilities of human metastases in a brain niche context, we developed reproducible melanoma brain metastasis (MBM) models for metastasis-integrating drug screening. We co-cultured A375 melanoma cells or tumor regional lymph node-derived disseminated cancer cells (DCCs) in close proximity with human induced pluripotent stem cell-derived cortical organoids (hCOs). In these, RNA sequencing revealed an upregulation of metastasis-associated features. First, A375 cells and DCCs were screened against an anti-cancer library containing 315 compounds. Hits were ranked by neurotoxicity, central nervous system permeation, and anti-DCC efficacy. Only a minority of hits effectively targeted A375-MBMs, with the first-in-class XPO1 inhibitor selinexor emerging as top hit. Selinexor also demonstrated efficacy in DCC-MBM models and low toxicity on hCOs, suggesting a promising therapeutic window in clinically applied doses. Collectively, the MBM model provides a tool for identifying candidate therapies counteracting metastatic progression.