Cell Sorting Using AI Embeddings for Multi-Marker Precision
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Solution Overview
Problem
Current cell sorting techniques based on nonlinear embeddings are subjective and inexact, particularly when dealing with relations between three or more cell markers, as they rely on two-dimensional gate sequences that are difficult to apply effectively.
Innovation Solution
The use of a machine learning model, such as a neural network, to learn a parametric representation of cell sorter data, allowing for real-time embedding and sorting based on embedding coordinates, which transforms high-dimensional data into lower-dimensional representations for precise cell sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional two-dimensional gate sequences are used for cell sorting, then the sorting process is simple to implement, but the sorting precision and accuracy deteriorate when dealing with relations between three or more cell markers
Solution Approach 1:
The patent transitions from two-dimensional gate sequences to high-dimensional parametric embeddings by learning coordinate transformations that map cell marker data into embedding spaces. This dimensional expansion enables simultaneous consideration of multiple cell markers (three or more) in a unified coordinate system, resolving the limitation of conventional 2D approaches while maintaining computational tractability through learned transformation functions.
Solution Approach 2:
The invention changes the parameter representation from discrete gate boundaries in 2D space to continuous embedding coordinates in high-dimensional space. By learning parametric representations that map cell data to embedding coordinates, the system achieves more precise sorting decisions that can incorporate relationships among multiple markers simultaneously, improving sorting precision without requiring exponentially more gates.
2Measurement precision
If high-dimensional cell sorter data is processed using traditional methods, then the computational load is low, but the sorting accuracy and ability to handle multiple markers deteriorates
Solution Approach 1:
The patent performs preliminary learning of embedding coordinate transformations during an offline training phase using representative cell data. This pre-computed parametric representation is then applied during actual sorting operations, allowing high-dimensional data to be processed efficiently through the learned transformation rather than requiring computationally intensive real-time analysis of all marker combinations. The preliminary learning captures complex relationships that can be rapidly applied during sorting.
Solution Approach 2:
The invention creates a learned parametric model that copies the essential structure and relationships of high-dimensional cell marker data into a compressed embedding space. This parametric representation serves as a simplified copy that retains the critical information needed for accurate sorting while requiring significantly less computational resources to process during actual sorting operations compared to analyzing raw high-dimensional data directly.
3Reliability
If subjective gate sequences are used for cell sorting, then the method is easy to implement, but the reliability and exactness of sorting deteriorates
Solution Approach 1:
The patent implements self-service through automated learning of embedding coordinate transformations from training data. The system automatically learns the optimal parametric representation and coordinate mappings without requiring subjective human intervention to define gate sequences. This automated learning process produces consistent, objective sorting decisions that improve reliability while the learned models can be applied systematically across different datasets and sorting tasks.
Solution Approach 2:
The invention incorporates feedback mechanisms during the learning phase, where the parametric model is trained on labeled cell data with known sorting outcomes. The learning algorithm adjusts the embedding coordinate transformations to minimize errors between predicted and actual sorting decisions. This feedback-driven training ensures the learned parametric representation accurately reflects the relationships needed for reliable sorting, making the method more objective and dependable than subjective gate sequences.
Data Source
AI summary
Disclosed herein are apparatuses, systems, as well as related methods, computing devices, and computer-readable media related to real time cell sorter cell sorting using embeddings. For example, in some embodiments a method may comprise receiving first cell sorter data. The first cell sorter data may include cell sorter data including microscopy data, hyperspectral imaging data, high-dimensional vector data, or one or more combinations thereof. In some embodiments, the cell sorter data may include quantitative fluorescence data expressed as one or more of antibodies bound per cell, antibody binding capacity (ABC), molecules of equivalent soluble fluorochrome (MESF), one or more other quantitative indicators of fluorescence, or one or more combinations thereof. In some embodiments, the quantitative fluorescence data includes one or more fluorescence signals from: one or more fluorescent proteins, one or more fluorescent dyes, one or more fluorescently conjugate antibodies, or one or more combinations thereof.