Cell Reports Methods最新文献

An accurate description of protein-protein interaction (PPI) networks is key to understanding the molecular mechanisms underlying cellular systems. Here, we constructed genome-wide libraries of yeast strains to systematically probe protein-protein interactions using NanoLuc Binary Technology (NanoBiT), a quantitative protein-fragment complementation assay (PCA) based on the NanoLuc luciferase. By investigating an array of well-documented PPIs as well as the interactome of four proteins with varying levels of characterization-including the well-studied nonsense-mediated mRNA decay (NMD) regulator Upf1 and the SCF complex subunits Cdc53 and Met30-we demonstrate that ratiometric NanoBiT measurements enable highly precise and sensitive mapping of PPIs. This work provides a foundation for employing NanoBiT in the assembly of more comprehensive and accurate protein interaction maps as well as in their functional investigation.

The tumor microenvironment (TME) is increasingly appreciated to play a decisive role in cancer development and response to therapy in all solid tumors. Hypoxia, acidosis, high interstitial pressure, nutrient-poor conditions, and high cellular heterogeneity of the TME arise from interactions between cancer cells and their environment. These properties, in turn, play key roles in the aggressiveness and therapy resistance of the disease, through complex reciprocal interactions between the cancer cell genotype and phenotype, and the physicochemical and cellular environment. Understanding this complexity requires the combination of sophisticated cancer models and high-resolution analysis tools. Models must allow both control and analysis of cellular and acellular TME properties, and analyses must be able to capture the complexity at high depth and spatial resolution. Here, we review the advantages and limitations of key models and methods in order to guide further TME research and outline future challenges.

The fragmentation patterns of cell-free DNA (cfDNA) in plasma can potentially be utilized as diagnostic biomarkers in liquid biopsy. However, our knowledge of this biological process and the information encoded in fragmentation patterns remains preliminary. Here, we investigated the cfDNA fragmentomic characteristics against nucleosome positioning patterns in hematopoietic cells. cfDNA molecules with ends located within nucleosomes were relatively shorter with altered end motif patterns, demonstrating the feasibility of enriching tumor-derived cfDNA in patients with cancer through the selection of molecules possessing such ends. We then developed three cfDNA fragmentomic metrics after end selection, which showed significant alterations in patients with cancer and enabled cancer diagnosis. By incorporating machine learning, we further built high-performance diagnostic models, which achieved an overall area under the curve of 0.95 and 85.1% sensitivity at 95% specificity. Hence, our investigations explored the end characteristics of cfDNA fragmentomics and their merits in building accurate and sensitive cancer diagnostic models.

Polyphosphate (polyP) is a ubiquitous polyanion present throughout the tree of life. While polyP's widely varied functions have been interrogated in single-celled organisms, little is known about the cellular distribution and function of polyP in multicellular organisms. To study polyP in metazoans, we developed the nematode Caenorhabditis elegans as a model system. We designed a high-throughput, longitudinal-orientation cryosectioning method that allowed us to scrutinize the intracellular localization of polyP in fixed C. elegans using fluorescent polyP probes and co-immunostaining targeting appropriate marker proteins. We discovered that the vast majority of polyP is localized within the endo-lysosomal compartments of the intestinal cells and is highly sensitive toward the disruption of endo-lysosomal compartment generation and food availability. This study lays the groundwork for further mechanistic research of polyPs in multicellular organisms and provides a reliable method for immunostaining hundreds of fixed worms in a single experiment.

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.

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.