Cell Reports Methods最新文献

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.

Mapping brain-wide neuronal connectivity is essential for understanding brain function, and barcoded rabies virus offers a powerful tool for this purpose. However, their application has been hindered by challenges in achieving sufficient barcode diversity and efficient transsynaptic transfer. While the CVS-N2cΔG strain offers improved transsynaptic transfer capabilities, producing barcoded versions of this strain has remained technically demanding. Here, we introduce an alternative one-step method for producing SAD-B19ΔG and CVS-N2cΔG strains. This streamlined approach simplifies the production process, significantly reduces production time, and eliminates background contamination. It improves the diversity and uniformity of the rabies virus barcode library. Moreover, the tracing efficiency of viruses produced by this one-step method matches that of conventional techniques. By addressing these limitations, our approach benefits the future development and application of barcoded-rabies-virus-based connectomic studies.

Longitudinal imaging of 3D cell cultures like tumor organoids and spheroids offers crucial insights into cancer progression and treatment. However, spatial displacement during time-course imaging, caused by matrix detachment or experimental artifacts, can confound analyses. We present TRACE-QC, an application of the Procrustes technique to evaluate data integrity and rectify mislabeling in longitudinal imaging of 3D cell culture. Our algorithm integrates permutation-based optimization with Procrustes analysis. By using X and Y coordinates of images, it accurately reorders, matches, and aligns object positions across time points, correcting for global well rotations and translations, along with local spheroid movements. Validation with simulated data confirmed its accuracy and robustness. Applied to longitudinal imaging of tumor spheroids, our algorithm revealed frequent displacement among the spheroids between time points and corrected many mislabeled images. This computationally efficient and adaptable method needs no experimental adjustments and presents a readily accessible solution for data quality control.

To enable fast and sensitive fusion detection critical for clinical oncology testing, we developed Fuzzion2, a pattern-matching program for detecting targeted gene fusions that employs an index of frequency minimizers and fuzzy matching to accommodate sequence variations. Running against 21,736 reference patterns representing chimeric fusions or internal tandem duplications, Fuzzion2 can analyze an unmapped RNA sequencing (RNA-seq) sample in minutes, at a sensitivity exceeding state-of-the art de novo fusion detection methods as demonstrated by dilution experiments. A comprehensive analysis on 23,478 RNA-seq samples from pediatric cancer, adult cancer, and normal tissues showed cancer type specificity for non-kinase fusions after accounting for multi-tissue recurrences caused by readthrough transcription, germline structural variations, index hopping, and circular RNA expression. Application of Fuzzion2 revealed distinct landscapes of pediatric and adult cancers, and its curated fusion patterns can inform interpretation of fusions detected by other methods.

Head-mounted miniaturized two-photon microscopes enable cellular-resolution recording of neural activity deep in the mouse brain during unrestrained behavior. Two-photon microscopy, however, is traditionally limited in frame rate by the necessity of scanning the excitation beam over a large field-of-view (FOV). Here, we present two types of multiplexed miniaturized two-photon microscopes (M-MINI2Ps) that preserve spatial resolution while increasing frame rate by simultaneously imaging two FOVs and demixing them temporally or computationally. We demonstrate large-scale (500 × 500 μm² FOV) multiplane calcium imaging in visual and prefrontal cortices of freely moving mice during spontaneous exploration, social behavior, and auditory stimulus. The increased speed of M-MINI2Ps also enables two-photon voltage imaging at 400 Hz over a 380 × 150 μm² FOV in freely moving mice. With compact footprints and compatibility with the open-source MINI2P, M-MINI2Ps enable high-speed recording of rapid neural dynamics and large-volume population activity in freely moving mice, providing a powerful tool for systems neuroscience.