Cell Reports Methods最新文献

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.

Under low-coverage or error-prone sequencing conditions, existing assembly frameworks often fail to simultaneously preserve genome integrity and biological variation. To address these, this work introduces a dynamic variable-order unitig-level assembly graph (DVOUG), which constructs an initial precise unitig-level assembly graph using a high k-value and progressively lowers the k-value in regions with low coverage or high noise. Experimental results show that DVOUG solves the problem of path entanglement when reconstructing short sequences under low coverage and significantly outperforms previous graphs in both genome assembly and DNA storage data reconstruction tasks, even under low coverage. In addition, DVOUG achieves more than 99% recall rate by graph neural networks (GNNs) for edge prediction, exceeding both unitig-level assembly graphs and traditional DBGs, while also reducing training time by 4×. In summary, DVOUG excels in handling complex noisy data, enhancing assembly accuracy, connectivity, and learnability, with strong potential for practical applications.

Numerous software tools have been published to aid organoid quantification. These tools generate estimates of total organoid number and morphological characteristics in images. However, there remains a need to estimate the number of organoid cells in a well for use in organoid-based experiments (e.g., co-cultures). We present OSCAR (organoid segmentation and cell number approximation using regression), a workflow for estimating organoid cell numbers from bright-field images. Step one is a Mask-R-CNN-based convolutional neural network for identifying organoids in bright-field images and estimating the area of each organoid. Step two is an empirical multiple linear regression model relating the number of cells in an organoid to its area. OSCAR's estimate of the total number of cells in a well was within ±16% of the real number of organoid cells. OSCAR is an online tool capable of generating this key metric and will contribute to the increased reliability of organoid-based assays.

Accurate measurement of biomolecular condensates' mechanical properties is essential to understand their behavior within cells. We present FlickerPrint, an open-source Python package to determine the interfacial tension and bending rigidity of thousands of condensates using flicker spectroscopy by analyzing their shape fluctuations in confocal microscopy images. We detail the workflow and computational requirements of FlickerPrint to scale up these individual measurements to the population level. Examples of experiments in live cells and in vitro that are suitable for analysis with FlickerPrint are provided, as well as scenarios where the package cannot be used. Using these examples, we show that the results obtained are robust to changes in imaging setup, including frame rate. This implementation enables a step change in measurement capability for two key properties of biomolecular condensates: interfacial tension and bending rigidity. Moreover, the tools in FlickerPrint are also applicable for analyzing other soft, fluctuating bodies, demonstrated here using vesicles.

Studying intercellular and interorgan interactions in animal models is key to understanding development, physiology, and disease. We introduce EyaHOST, a system for clonal combinatorial loss- and gain-of-function genetics in fluorescently labeled cells under QF2-QUAS eya promoter control. Distinct from mosaic analysis with a repressible cell marker (MARCM), it reserves the use of genome-wide GAL4-UAS tools to manipulate any host tissue. EyaHOST-driven Ras^V12 overexpression with scribble knockdown recapitulates key cancer features, including systemic catabolic switching and organ wasting. We demonstrate effective tissue-specific manipulation of host compartments, including homotypic epithelial neighbors, immune cells, fat body, and muscle. Organ-specific inhibition of autophagy or stimulation of growth signaling via PTEN knockdown in fat body or muscle prevents cachexia-like wasting. Additionally, tumors trigger caspase-driven apoptosis in the neighboring epithelium, and blocking apoptosis with p35 enhances tumor growth. EyaHOST provides a modular platform to dissect mechanisms of intercellular and interorgan communication under physiological or disease conditions.