Spatiotemporal Mapping of Neuronal Dynamics and Transcriptional Profiles
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Solution Overview
Problem
Current methods for analyzing neuronal network dynamics lack the capability to simultaneously capture and integrate molecular and functional information across multiple spatiotemporal scales within intact brain tissue samples, limiting our understanding of causal neuronal plasticity and connectivity dynamics.
Innovation Solution
A method that combines high-density electrical biosensors, spatial transcriptomics, optical imaging, and advanced computational strategies to record and analyze spatiotemporal electrophysiological dynamics and transcriptional profiles of functional neuronal cell assemblies, allowing for the integration of molecular and functional information across multiple scales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If patch-seq method is used for single-cell transcriptomics and morphologic reconstruction, then molecular and electrophysiological data can be integrated at single-cell level, but throughput remains very low and inability to resolve neuronal networks in medium to large spatial contexts
Solution Approach 1:
The patent segments the brain tissue into multiple sections that are processed in parallel. Each section is mounted on a separate Visium slide, allowing simultaneous transcriptomic analysis of multiple regions. This segmentation approach maintains single-cell resolution while dramatically increasing throughput by processing many samples concurrently rather than sequentially
Solution Approach 2:
The patent transitions from analyzing single isolated cells to analyzing spatially-resolved tissue sections. By preserving the spatial dimension and analyzing multiple sections simultaneously on separate slides, the method adds a spatial dimension to the analysis, enabling network-level insights while maintaining cellular resolution through the integration of multiple sectional views
2Measurement precision
If high-density electrical biosensors are used to record electrophysiological dynamics, then spatial and temporal resolution can be improved, but device complexity and data integration difficulty increase
Solution Approach 1:
The patent introduces a computational framework as an intermediary layer between the high-density electrical biosensors and the final analysis. This framework includes algorithms for data preprocessing, feature extraction, and integration with transcriptomic data. The intermediary computational layer simplifies the complex sensor data into meaningful patterns and correlations, making the overall system more manageable despite the high sensor density
Solution Approach 2:
The patent develops a multi-functional computational framework that handles multiple types of data (electrophysiological signals, transcriptomic profiles, spatial coordinates) using unified algorithms. This universal approach reduces device complexity by using the same computational infrastructure for diverse data types rather than requiring separate specialized systems for each measurement modality
3Measurement precision
If spatial transcriptomics is used to map gene expression, then spatial distribution of cell types can be resolved, but temporal resolution is limited providing only single snapshots
Solution Approach 1:
The patent implements continuous longitudinal sampling of the same neuronal assemblies over time. By repeatedly performing spatial transcriptomics on the same tissue sections at different time points and maintaining continuous electrophysiological monitoring, the method transforms discrete snapshots into a continuous temporal record. This allows tracking of dynamic changes in gene expression and neuronal activity while preserving spatial resolution throughout the time course
Solution Approach 2:
The patent performs preliminary electrophysiological characterization and baseline transcriptomic profiling before initiating experimental manipulations. This preliminary action establishes reference states that enable detection of temporal changes. By having baseline data ready, the system can immediately detect and quantify temporal dynamics without losing critical early changes, effectively extending the useful temporal resolution
Data Source
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AI summary
The present invention relates to an ex vivo method for mapping the spatiotemporal electrophysiological dynamics and spatial transcriptional profiles of cells in a functional neuronal cell assembly, comprising simultaneous recording and analysis of spatial data of molecular and electrical network activity down to the level of individual cells using high-density electrical biosensors, spatial transcriptomics, optical imaging, and advanced computational strategies. The invention further relates to methods for identifying the composition of a functional neuronal assembly, for monitoring the spatiotemporal electrophysiological dynamics and transcriptional profiles of cells in a functional neuronal cell assembly, for monitoring the cellular composition of a functional neuronal assembly, or for identifying a compound having an effect on the spatial electrophysiological and transcriptional dynamics and/or for identifying the composition of cells in a functional neuronal cell assembly based on the dynamics as detected, as well as devices and uses thereof. The invention allows for a better understanding of disease mechanisms, and to find therapeutic targets and to develop new drugs and treatments.