Microfluidic Biological Sample Analysis with Gallery Data Organization
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Solution Overview
Problem
Biological assays on microfluidic devices generate extremely large datasets with unstructured data elements, making it difficult for researchers to visualize and analyze processes, especially over time, and derive meaningful information from multiple micro-objects simultaneously, which hinders efficient therapeutic antibody discovery and rapid drug screening.
Innovation Solution
A method and system for analyzing biological samples by identifying regions of interest in a microfluidic device, correlating temporal data with a workflow, and rendering associated data in a user interface, using gallery structures and interactive objects to visualize and manipulate data effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If biological assays are performed on microfluidic devices to achieve high throughput, then the quantity of biological samples that can be analyzed is improved, but the size of datasets becomes extremely large and unstructured, making visualization and analysis difficult
Solution Approach 1:
The patent segments the complex dataset into structured components by organizing data into galleries with specific categories (e.g., by region of interest, by time point, by sample type). Each gallery acts as an independent organizational unit that can be separately managed and visualized, transforming the unstructured data mass into manageable segments while preserving the high throughput capability.
Solution Approach 2:
The patent introduces temporal dimensionality by organizing data across multiple time points in a structured timeline framework. This adds a new dimension to data organization beyond simple spatial arrangement, enabling researchers to visualize and analyze changes over time while maintaining the ability to handle large volumes of data through structured categorization.
2Quantity of substance
If large datasets from multiple biological samples are generated, then the quantity of information is improved, but the time required to visualize and analyze the data increases
Solution Approach 1:
The patent performs preliminary organization of data into structured galleries and timelines during or immediately after data collection. By pre-organizing data into categorical structures before analysis begins, the system eliminates the need for time-consuming post-collection sorting and organizing, allowing researchers to directly analyze structured data while maintaining access to large quantities of information.
Solution Approach 2:
The patent creates virtual copies of data organized in multiple galleries and views that can be independently accessed and manipulated. Instead of working with the entire massive dataset at once, researchers can create and switch between specialized views (e.g., by sample type, by time point, by region), effectively copying the data into manageable representations that reduce analysis time while preserving access to the full quantity of information.
3Loss of information
If unstructured data elements are collected from biological assays, then the completeness of data is improved, but the ease of manipulating and synthesizing information deteriorates
Solution Approach 1:
The patent segments unstructured data into structured categories within galleries, organizing data elements by type, source, and temporal relationship. This segmentation process maintains the completeness of the original data while transforming it into manipulable structures that can be easily queried, filtered, and synthesized through the organized framework.
Solution Approach 2:
The patent introduces structured data organizations (galleries, timelines, views) as intermediary layers between the raw unstructured data and the researcher's analysis operations. These intermediaries serve as mediators that preserve the completeness of the original data while providing structured access points and manipulation interfaces, making the data both complete and easy to work with.
4Measurement precision
If researchers manually analyze individual micro-objects, then the precision of analysis is improved, but the productivity and throughput are reduced
Solution Approach 1:
The patent creates a universal data organization framework that can handle multiple types of micro-objects and analysis operations within a single system. The gallery and timeline structures serve as multi-functional containers that can organize different sample types, time points, and analysis parameters simultaneously, enabling precise analysis of individual micro-objects while maintaining high throughput through automated data management and structured access.
Data Source
AI summary
Disclosed are methods, systems, and articles of manufacture for performing a process on biological samples. An analysis of biological samples in multiple regions of interest in a microfluidic device and a timeline correlated with the analysis may be identified. One or more region-of-interest types for the multiple regions of interest may be determined; and multiple characteristics may be determined for the biological samples based at least in part upon the one or more region-of-interest types. Associated data that respectively correspond to the multiple regions of interest in a user interface for at least a portion of the biological samples in the user interface based at least in part upon the multiple identifiers and the timeline. A count of the biological samples in a region of interest may be determined based at least in part upon a class or type of data using a convolutional neural network (CNN).