Cell Reports Methods最新文献

Designer receptors exclusively activated by designer drugs (DREADDs) are engineered G-protein-coupled receptors that afford reversible manipulation of neuronal activity in vivo. Here, we introduce size-reduced DREADD derivatives miniD_q and miniD_i, which inherit the basic receptor properties from the G_q-coupled excitatory receptor hM3D_q and the G_i-coupled inhibitory receptor hM4D_i, respectively, while being approximately 30% smaller in size. Taking advantage of the compact size of the receptors, we generated an adeno-associated virus (AAV) vector carrying both miniD_q and the other DREADD family receptor (κ-opioid receptor-based inhibitory DREADD [KORD]) within the maximum AAV capacity (4.7 kb), allowing us to modulate neuronal activity and animal behavior in both excitatory and inhibitory directions using a single viral vector. We confirmed that expressing miniD_q, but not miniD_i, allowed activation of striatum activity in the cynomolgus monkey (Macaca fascicularis). The compact DREADDs may thus widen the opportunity for multiplexed interrogation and/or intervention in neuronal regulation in mice and non-human primates.

Virtual reality (VR) has emerged as a powerful tool for investigating neural mechanisms of decision-making, spatial cognition, and navigation. In many head-fixed VRs for rodents, animals locomote on spherical treadmills that provide rotation information in two axes to calculate two-dimensional (2D) movement. On the other hand, zebrafish in a submerged head-fixed VR can move their tail to enable movement in 2D VR environment. This motivated us to create a VR system for adult zebrafish to enable 2D movement consisting of forward translation and rotations calculated from tail movement. Besides presenting the VR system, we show that zebrafish can learn a virtual Morris water maze-like (VMWM) task in which finding an invisible safe zone was necessary for the zebrafish to avoid an aversive periodic mild electric shock. Results show high potential for our VR system to be combined with optical imaging for future studies to investigate spatial learning and navigation.

Cellular energy metabolism significantly contributes to immune cell function. To further advance immunometabolic research, novel methods to study the metabolism of immune cells in complex samples are required. Here, we introduce CENCAT (cellular energetics through noncanonical amino acid tagging). This technique utilizes click labeling of alkyne-bearing noncanonical amino acids to measure protein synthesis inhibition as a proxy for metabolic activity. CENCAT successfully reproduced known metabolic signatures of lipopolysaccharide (LPS)/interferon (IFN)γ and interleukin (IL)-4 activation in human primary macrophages. Application of CENCAT in peripheral blood mononuclear cells revealed diverse metabolic rewiring upon stimulation with different activators. Finally, CENCAT was used to analyze the cellular metabolism of murine tissue-resident immune cells from various organs. Tissue-specific clustering was observed based on metabolic profiles, likely driven by microenvironmental priming. In conclusion, CENCAT offers valuable insights into immune cell metabolic responses, presenting a powerful platform for studying cellular metabolism in complex samples and tissues in both humans and mice.

Artificial intelligence (AI) and deep learning technologies hold promise for identifying effective drugs for human diseases, including pain. Here, we present an interpretable deep-learning-based ligand image- and receptor's three-dimensional (3D)-structure-aware framework to predict compound-protein interactions (LISA-CPI). LISA-CPI integrates an unsupervised deep-learning-based molecular image representation (ImageMol) of ligands and an advanced AlphaFold2-based algorithm (Evoformer). We demonstrated that LISA-CPI achieved ∼20% improvement in the average mean absolute error (MAE) compared to state-of-the-art models on experimental CPIs connecting 104,969 ligands and 33 G-protein-coupled receptors (GPCRs). Using LISA-CPI, we prioritized potential repurposable drugs (e.g., methylergometrine) and identified candidate gut-microbiota-derived metabolites (e.g., citicoline) for potential treatment of pain via specifically targeting human GPCRs. In summary, we presented that the integration of molecular image and protein 3D structural representations using a deep learning framework offers a powerful computational drug discovery tool for treating pain and other complex diseases if broadly applied.

There are myriad types of biomedical data-molecular, clinical images, and others. When a group of patients with the same underlying disease exhibits similarities across multiple types of data, this is called a subtype. Existing subtyping approaches struggle to handle diverse data types with missing information. To improve subtype discovery, we exploited changes in the correlation-structure between different data types to create iSubGen, an algorithm for integrative subtype generation. iSubGen can accommodate any feature that can be compared with a similarity metric to create subtypes versatilely. It can combine arbitrary data types for subtype discovery, such as merging genetic, transcriptomic, proteomic, and pathway data. iSubGen recapitulates known subtypes across multiple cancers even with substantial missing data and identifies subtypes with distinct clinical behaviors. It performs equally with or superior to other subtyping methods, offering greater stability and robustness to missing data and flexibility to new data types. It is available at https://cran.r-project.org/web/packages/iSubGen.

Understanding animal behavior is crucial in behavioral neuroscience, aiming to unravel the mechanisms driving these behaviors. A significant milestone in this field is the analysis of behavioral reactions during social interactions. Despite their importance in social learning, the behavioral aspects of these interaction are not well understood in detail due to the lack of appropriate tools. We introduce a high-precision, marker-based motion-capture system for analyzing behavior in songbirds, accurately tracking body location and head direction in multiple freely moving finches during social interaction. Focusing on zebra finches, our analysis revealed variations in eye use based on individuals presented. We also observed behavioral changes during virtual and live presentations and a conditioned-learning paradigm. Additionally, the system effectively analyzed social interactions among mice. This system provides an efficient tool for advanced behavioral analysis in small animals and offers an objective method to infer their focus of attention.

Two-dimensional neuronal cultures have a limited ability to recapitulate the in vivo environment of the brain. Here, we introduce a three-dimensional in vitro model for human glia-to-neuron conversion, surpassing the spatial and temporal constrains of two-dimensional cultures. Focused on direct conversion to induced dopamine neurons (iDANs) relevant to Parkinson disease, the model generates functionally mature iDANs in 2 weeks and allows long-term survival. As proof of concept, we use single-nucleus RNA sequencing and molecular lineage tracing during iDAN generation and find that all glial subtypes generate neurons and that conversion relies on the coordinated expression of three neural conversion factors. We also show the formation of mature and functional iDANs over time. The model facilitates molecular investigations of the conversion process to enhance understanding of conversion outcomes and offers a system for in vitro reprogramming studies aimed at advancing alternative therapeutic strategies in the diseased brain.

Cardiomyocyte proliferation is a challenging metric to assess. Current methodologies have limitations in detecting the generation of new cardiomyocytes and technical challenges that reduce widespread applicability. Here, we describe an improved cell suspension and imaging-based methodology that can be broadly employed to assess cardiomyocyte cell division in standard laboratories across a multitude of model organisms and experimental conditions. We highlight additional metrics that can be gathered from the same cell preparations to enable additional relevant analyses to be performed. We incorporate additional antibody stains to address potential technical concerns of miscounting. Finally, we employ this methodology with a dual-thymidine analog-labeling approach to a post-infarction murine model, which allowed us to robustly identify unique cycling events, such as cardiomyocytes undergoing multiple rounds of cell division.

Genomic diversity within species can be driven by both point mutations and larger structural variations, but so far, strain-tracking approaches have focused mostly on the former. In a recent issue of Nature Biotechnology, Ley and colleagues¹ introduce SynTracker, a tool designed for scalable strain tracking with microsynteny in low-coverage metagenomic settings.

The ongoing co-circulation of multiple severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strains necessitates advanced methods such as high-throughput multiplex pseudovirus systems for evaluating immune responses to different variants, crucial for developing updated vaccines and neutralizing antibodies (nAbs). We have developed a quadri-fluorescence (qFluo) pseudovirus platform by four fluorescent reporters with different spectra, allowing simultaneous measurement of the nAbs against four variants in a single test. qFluo shows high concordance with the classical single-reporter assay when testing monoclonal antibodies and human plasma. Utilizing qFluo, we assessed the immunogenicities of the spike of BA.5, BQ.1.1, XBB.1.5, and CH.1.1 in hamsters. An analysis of cross-neutralization against 51 variants demonstrated superior protective immunity from XBB.1.5, especially against prevalent strains such as "FLip" and JN.1, compared to BA.5. Our finding partially fills the knowledge gap concerning the immunogenic efficacy of the XBB.1.5 vaccine against current dominant variants, being instrumental in vaccine-strain decisions and insight into the evolutionary path of SARS-CoV-2.