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Background Single particle tracking (SPT) is nowadays one of the most popular technique to probe spatio-temporal dynamics of proteins diffusing within the plasma membrane. Indeed membrane components of eukaryotic cells are very dynamic molecules and can diffuse according to different motion modes. Trajectories are often reconstructed frame-by-frame and dynamic properties often evaluated using mean square displacement (MSD) analysis. However, to get statistically significant results in tracking experiments, analysis of a large number of trajectories is required and new methods facilitating this analysis are still needed. Results In this study we developed a new algorithm based on back-propagation neural network (BPNN) and MSD analysis using a sliding window. The neural network was trained and cross validated with short synthetic trajectories. For simulated and experimental data, the algorithm was shown to accurately discriminate between Brownian, confined and directed diffusion modes within one trajectory, the 3 main of diffusion encountered for proteins diffusing within biological membranes. It does not require a minimum number of observed particle displacements within the trajectory to infer the presence of multiple motion states. The size of the sliding window was small enough to measure local behavior and to detect switches between different diffusion modes for segments as short as 20 frames. It also provides quantitative information from each segment of these trajectories. Besides its ability to detect switches between 3 modes of diffusion, this algorithm is able to analyze simultaneously hundreds of trajectories with a short computational time. Conclusion This new algorithm, implemented in powerful and handy software, provides a new conceptual and versatile tool, to accurately analyze the dynamic behavior of membrane components.

High-grade gliomas are lethal brain cancers whose progression is robustly regulated by neuronal activity. Activity-regulated release of growth factors promotes glioma growth, but this alone is insufficient to explain the effect that neuronal activity exerts on glioma progression. Here we show that neuron and glioma interactions include electrochemical communication through bona fide AMPA receptor-dependent neuron–glioma synapses. Neuronal activity also evokes non-synaptic activity-dependent potassium currents that are amplified by gap junction-mediated tumour interconnections, forming an electrically coupled network. Depolarization of glioma membranes assessed by in vivo optogenetics promotes proliferation, whereas pharmacologically or genetically blocking electrochemical signalling inhibits the growth of glioma xenografts and extends mouse survival. Emphasizing the positive feedback mechanisms by which gliomas increase neuronal excitability and thus activity-regulated glioma growth, human intraoperative electrocorticography demonstrates increased cortical excitability in the glioma-infiltrated brain. Together, these findings indicate that synaptic and electrical integration into neural circuits promotes glioma progression. Neurons form synapses onto glioma cells, and depolarization of glioma membranes promotes glioma growth in vivo, whereas blocking electrochemical signalling blocks tumour growth.

BackgroundDissecting complex tissue biology at high resolution requires integrated spatial multiomics, yet existing platforms often lack a unified workflow for simultaneous protein and RNA analysis from a single tissue section. We present a novel protein capability for the Xenium In Situ platform that addresses this deficiency, enabling concurrent protein and RNA analysis from the same FFPE tissue within the same, fully integrated, automated workflow. This approach offers unparalleled resolution for characterizing intricate biological processes. Our multiomics solution supports up to 27 immune oncology protein markers and is compatible with Xenium 480-plex gene expression panels across diverse tissue types, providing enhanced resolution for biological investigation.MethodsWe applied our multiomic workflow to simultaneously detect 27 protein and 477 RNA targets in FFPE clear cell renal cell carcinoma (ccRCC) samples. We performed integrated analysis of protein and RNA data to extensively characterize the tumor microenvironment (TME) in ccRCC to interrogate disease state and progression.ResultsThe ccRCC TME comprises diverse immune cell types whose interplay with tumor cells is critical for disease pathogenesis and response to therapy. Our integrated spatial proteomics and transcriptomics approach resulted in enhanced accurate immune cell typing and elucidated critical cell state information by revealing functional phenotypes. Integrative clustering analysis revealed significant spatial and molecular heterogeneity within the ccRCC TME, identifying distinct spatially-organized cellular neighborhoods. We further identified molecular signatures enriched in distinct cell populations and cellular neighborhoods, providing key insights into their biological roles in ccRCC pathogenesis and potential therapeutic vulnerabilities.ConclusionsCompared to studying protein or RNA alone, spatial multiomics, which integrates protein and transcriptomic data together, offers complementary and superior advantages for studying complex tissue biology. We present a robust, fully integrated workflow on the Xenium In Situ platform, enabling simultaneous transcriptomic and proteomic profiling within a tissue sample. Our results highlight the power of this multiomic platform in dissecting the ccRCC TME, revealing complex spatial.

Abstract Healthy gastrointestinal functions require a healthy Enteric Nervous System (ENS). ENS health is often defined by the presence of normal ENS structure. However, we currently lack a comprehensive understanding of normal ENS structure as current methodologies of manual enumeration of neurons within tissue and ganglia can only parse limited tissue regions; and are prone to error, subjective bias, and peer-to-peer discordance. Thus, there is a need to craft objective methods and robust tools to capture and quantify enteric neurons over a large area of tissue and within multiple ganglia. Here, we report on the development of an AI-driven tool COUNTEN which parses HuC/D-immunolabeled adult murine myenteric ileal plexus tissues to enumerate and classify enteric neurons into ganglia in a rapid, robust, and objective manner. COUNTEN matches trained humans in identifying, enumerating and clustering myenteric neurons into ganglia but takes a fraction of the time, thus allowing for accurate and rapid analyses of a large tissue region. Using COUNTEN, we parsed thousands of myenteric neurons and clustered them in hundreds of myenteric ganglia to compute metrics that help define the normal structure of the adult murine ileal myenteric plexus. We have made COUNTEN freely and openly available to all researchers, to facilitate reproducible, robust, and objective measures of ENS structure across mouse models, experiments, and institutions. Competing Interest Statement The authors have declared no competing interest. Footnotes * ↵* Co-first authors * ↵§ Co-senior authors * https://github.com/KLab-JHU/COUNTEN

Abstract Alpha-actinin 2 (ACTN2) anchors actin within cardiac sarcomeres. The mechanisms linking ACTN2 mutations to myocardial disease phenotypes are unknown. Here, we characterize patients with novel ACTN2 mutations to reveal insights into the physiological function of ACTN2. Patient-derived iPSC-cardiomyocytes harboring ACTN2 protein-truncating variants were hypertrophic, displayed sarcomeric structural disarray, impaired contractility, and aberrant Ca2+-signaling. In heterozygous indel cells, the truncated protein incorporates into cardiac sarcomeres, leading to aberrant Z-disc structure. In homozygous stop-gain cells, affinity-purification mass spectrometry reveals an intricate ACTN2 interactome with sarcomere and sarcolemma-associated proteins. Loss of the C-terminus of ACTN2 disrupts interaction with ACTN1 and GJA1, two sarcolemma-associated proteins, that may lead to the clinical arrhythmic and relaxation defects. The causality of the stop-gain mutation was verified using CRISPR-Cas9 gene editing. Together, these data advance our understanding of the role of ACTN2 in the human heart and establish recessive inheritance of ACTN2 truncation as causative of disease. Competing Interest Statement E.A.A. is founder at Personalis and DeepCell, Inc, and advisor for SequenceBio and Genome Medical. CC is an employee at GeneMatters. M.T.W. has ownership interest in Personalis. The other authors declare no competing interests.