Cell Reports Methods最新文献

Non-invasive and high-temporal resolution methods for characterizing blood flow in mouse cranial arteries, such as the ophthalmic artery (OphA), are lacking. We present an application of pulsed Doppler ultrasound to provide real-time, non-invasive measurement of blood flow velocity in the OphA through an identified soft tissue window in the mouse head. We confirmed the identity of the artery and mapped its origin from the internal carotid artery by a combination of microcomputed tomography (microCT) vascular imaging and transient occlusion of the internal carotid artery. Application of our approach demonstrated sex differences in the OphA vasodilative response to agonists. We also evaluated real-time flow characteristics in the OphA in response to transient carotid artery ligation. The method will provide a simple and low-cost approach for screening drugs targeting ophthalmic blood flow and can be used as a more accessible surrogate of cerebral blood flow in both acute and longitudinal imaging studies.

The need for a deeper understanding of adverse drug reaction (ADR) mechanisms is vital for improving drug safety and repurposing. This study introduces Drug Adverse Reaction Mechanism Explainer (DREAMER), a network-based framework that uses a comprehensive knowledge graph to uncover molecular mechanisms underlying ADRs and disease phenotypes. By examining shared phenotypes of drugs and diseases and their effects on protein-protein interaction networks, DREAMER identifies proteins linked to ADR mechanisms. Applied to 649 ADRs, DREAMER identified molecular mechanisms for 67 ADRs, including ventricular arrhythmia and metabolic acidosis, and emphasized pathways like GABAergic signaling and coagulation proteins in personality disorders and intracranial hemorrhage. We further demonstrate the application of DREAMER in drug repurposing and propose sotalol, ranolazine, and diltiazem as candidate drugs to be repurposed for cardiac arrest. In summary, DREAMER effectively detects molecular mechanisms underlying phenotypes, emphasizing the importance of network-based analyses with integrative data for enhancing drug safety and accelerating the discovery of novel therapeutic strategies.

Probabilistic graphical models are powerful tools to quantify, visualize, and interpret network dependencies in complex biological systems such as high-throughput -omics. However, many graphical models assume sample homogeneity, limiting their effectiveness. We propose a flexible Bayesian approach called graphical regression (GraphR), which (1) incorporates sample heterogeneity at different scales through a regression-based formulation, (2) enables sparse sample-specific network estimation, (3) identifies and quantifies potential effects of heterogeneity on network structures, and (4) achieves computational efficiency via variational Bayes algorithms. We illustrate the comparative efficiency of GraphR against existing state-of-the-art methods in terms of network structure recovery and computational cost across multiple settings. We use GraphR to analyze three multi-omic and spatial transcriptomic datasets to investigate inter- and intra-sample molecular networks and delineate biological discoveries that otherwise cannot be revealed by existing approaches. We have developed a GraphR R package along with an accompanying Shiny App that provides comprehensive analysis and dynamic visualization functions.

Cell-cell interaction (CCI) networks are key to understanding disease progression and treatment response. However, existing methods for inferring these networks often aggregate data across patients or focus on cell-type level interactions, providing a generalized overview but overlooking patient heterogeneity and local network structures. To address this, we introduce "random cell-cell interaction generator" (RaCInG), a model based on random graphs to derive personalized networks leveraging prior knowledge on ligand-receptor interactions and bulk RNA sequencing data. We applied RaCInG to 8,683 cancer patients to extract 643 network features related to the tumor microenvironment and unveiled associations with immune response and subtypes, enabling prediction and explanation of immunotherapy responses. RaCInG demonstrated robustness and showed consistencies with state-of-the-art methods. Our findings highlight RaCInG's potential to elucidate patient-specific network dynamics, offering insights into cancer biology and treatment responses. RaCInG is poised to advance our understanding of complex CCI s in cancer and other biomedical domains.

Ewing sarcoma (EwS) cell line culture largely relies on standard techniques, which do not recapitulate physiological conditions. Here, we report on a feasible and cost-efficient EwS cell culture technique with increased physiological relevance employing an advanced medium composition, reduced fetal calf serum, and spheroidal growth. Improved reflection of the transcriptional activity related to proliferation, hypoxia, and differentiation in EwS patient tumors was detected in EwS cells grown in this refined in vitro condition. Moreover, transcriptional signatures associated with the oncogenic activity of the EwS-specific FET::ETS fusion transcription factors in the refined culture condition were shifted from proliferative toward metabolic gene signatures. The herein-presented EwS cell culture technique with increased physiological relevance provides a broadly applicable approach for enhanced in vitro modeling relevant to advancing EwS research and the validity of experimental results.

We present an easy-to-use, disposable, thermoplastic microwell chip designed to support screening and high-resolution imaging of single-cell behavior in two- and three-dimensional (2D and 3D) cell cultures. We show that the chip has excellent optical properties and provide simple protocols for efficient long-term cell culture of suspension and adherent cells, the latter grown either as monolayers or as hundreds of single, uniformly sized spheroids. We then demonstrate the applicability of the system for single-cell analysis by correlating the dynamic cytotoxic response of single immune cells grown under different metabolic conditions to their intracellular cytolytic load at the end of the assay. Additionally, we illustrate highly multiplex cytotoxicity screening of tumor spheroids in the chip, comparing the effect of environment cues characteristic of the tumor microenvironment on natural killer (NK)-cell-induced killing. Following the functional screening, we perform high-resolution 3D immunofluorescent imaging of infiltrating NK cells within the spheroid volumes.

Single-cell assay of transposase-accessible chromatin sequencing (scATAC-seq) unbiasedly profiles genome-wide chromatin accessibility in single cells. In single-cell tumor studies, identification of normal cells or tumor clonal structures often relies on copy-number alterations (CNAs). However, CNA detection from scATAC-seq is difficult due to the high noise, sparsity, and confounding factors. Here, we describe AtaCNA, a computational algorithm that accurately detects high-resolution CNAs from scATAC-seq data. We benchmark AtaCNA using simulation and real data and find AtaCNA's superior performance. Analyses of 10 scATAC-seq datasets show that AtaCNA could effectively distinguish malignant from non-malignant cells. In glioblastoma, endometrial, and ovarian cancer samples, AtaCNA identifies subclones at distinct cellular states, suggesting an important interplay between genetic and epigenetic plasticity. Some tumor subclones only differ in small-scale (10-20 Mb) CNAs, demonstrating the importance of high-resolution CNA detection. These data show that AtaCNA can aid in integrative analysis to understand the complex heterogeneity in cancer.

Identifying key regulators of important genes in non-model crop species is challenging due to limited multi-omics resources. To address this, we introduce the network-enabled gene discovery pipeline NEEDLE, a user-friendly tool that systematically generates coexpression gene network modules, measures gene connectivity, and establishes network hierarchy to pinpoint key transcriptional regulators from dynamic transcriptome datasets. After validating its accuracy with two independent datasets, we applied NEEDLE to identify transcription factors (TFs) regulating the expression of cellulose synthase-like F6 (CSLF6), a crucial cell wall biosynthetic gene, in Brachypodium and sorghum. Our analyses uncover regulators of CSLF6 and also shed light on the evolutionary conservation or divergence of gene regulatory elements among grass species. These results highlight NEEDLE's capability to provide biologically relevant TF predictions and demonstrate its value for non-model plant species with dynamic transcriptome datasets.

To restore vision in the blind, advances in visual cortical prosthetics (VCPs) have offered high-channel-count electrical interfaces. Here, we design a 100-fiber optical bundle interface apposed to known feature-specific (color, shape, motion, and depth) functional columns that populate the visual cortex in humans, primates, and cats. Based on a non-viral optical stimulation method (INS, infrared neural stimulation; 1,875 nm), it can deliver dynamic patterns of stimulation, is non-penetrating and non-damaging to tissue, and is movable and removable. In addition, its magnetic resonance (MR) compatibility (INS-fMRI) permits systematic mapping of brain-wide circuits. In the MRI, we illustrate (1) the single-point activation of functionally specific networks, (2) shifting cortical networks activated via shifting points of stimulation, and (3) "moving dot" stimulation-evoked activation of higher-order motion-selective areas. We suggest that, by mimicking patterns of columnar activation normally activated by visual stimuli, a columnar VCP opens doors for the planned activation of feature-specific circuits and their associated visual percepts.

Spatially resolved transcriptomics (ST) has revolutionized the field of biology by providing a powerful tool for analyzing gene expression in situ. However, current ST methods, particularly barcode-based methods, have limitations in reconstructing high-resolution images from barcodes sparsely distributed in slides. Here, we present SuperST, an algorithm that enables the reconstruction of dense matrices (higher-resolution and non-zero-inflated matrices) from low-resolution ST libraries. SuperST is based on deep image prior, which reconstructs spatial gene expression patterns as image matrices. Compared with previous methods, SuperST generated output images that more closely resembled immunofluorescence images for given gene expression maps. Furthermore, we demonstrated how one can combine images created by SuperST with computer vision algorithms. In this context, we proposed a method for extracting features from the images, which can aid in spatial clustering of genes. By providing a dense matrix for each gene in situ, SuperST can successfully address the resolution and zero-inflation issue.