Machine Learning Classifier for T Cell Receptor Identification
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Solution Overview
Problem
The challenge lies in identifying target-specific T cells and their receptor sequences from a vast number of T cell receptor (TCR) sequences, as existing methods are inefficient in finding disease-specific TCR sequences due to the immense diversity and complexity of TCRs and their antigen interactions.
Innovation Solution
The method involves deriving single cell T cell data using technologies like TargetScape and TCR Antigen Profiling (TAP), which provide high-dimensional T cell data including antigen specificity, cell phenotype, and TCR sequences. Machine learning classifiers are then trained on these datasets to classify target-specific T cells based on their profiles, allowing for the identification of putative disease-specific TCR sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional antigen prediction algorithms and empirical testing methods are used to identify disease-specific TCR sequences, then the process is straightforward and easy to implement, but the identification efficiency is extremely low and the time required is excessive due to the vast number of potential TCR sequences (exceeding 10^10)
Solution Approach 1:
The patent creates a computational copy of the immune system's recognition process by training machine learning models on multiomics datasets. Instead of physically testing each TCR against antigens, the system learns from existing data patterns and applies this knowledge to predict antigen specificity of new TCR sequences, dramatically accelerating the identification process while maintaining accuracy
Solution Approach 2:
The patent transforms the identification problem by changing from direct physical testing to computational prediction based on multiple parameters simultaneously. The machine learning models analyze combinations of TCR sequence features, protein marker expressions, and gene expression patterns to predict antigen specificity, effectively navigating the vast TCR space without exhaustive testing
2Measurement precision
If the search space is limited to a few hundred empirically tested antigens, then the testing process becomes feasible, but the measurement precision and completeness of disease-specific TCR identification is insufficient due to the limited antigen panel and imperfect prediction algorithms
Solution Approach 1:
The patent develops a universal machine learning framework that can handle multiple types of biological data (TCR sequences, protein markers, gene expression) and apply them to identify antigen-specific T cells across different diseases and antigen types. The system is designed to be adaptable to various disease contexts while maintaining a consistent analytical approach
Solution Approach 2:
The patent introduces machine learning models as intermediary systems that bridge the gap between raw multiomics data and meaningful biological insights. These models process and integrate complex datasets, extracting patterns that directly indicate antigen specificity without requiring exhaustive experimental validation of each interaction
3Measurement precision
If comprehensive multiomics datasets with high-dimensional single cell data are collected to improve identification accuracy, then the measurement precision increases, but the device complexity and data processing requirements increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-processing and normalizing multiomics datasets before feeding them to machine learning models. The system prepares reference datasets in advance, extracting and standardizing features from TCR sequences, protein expressions, and gene expression data, which simplifies subsequent classification tasks and reduces computational complexity during actual identification
Solution Approach 2:
The patent segments the complex identification task into distinct processing stages: data collection and normalization, feature extraction from different omics layers, model training on segmented datasets, and final classification. This segmentation allows each component to be optimized independently, managing overall system complexity while maintaining high identification accuracy
Data Source
AI summary
The present application describes a computer-implemented method for identifying target-specific T cells and their T cell Receptor (TCR) sequences. The method includes deriving single cell T cell data from a sample. The data comprises T cell profile and T cell TCR sequence. The method also includes selecting candidate T cells and their TCR sequences from the single cell T cell data using a machine learning classifier that is trained to classify T cells based on their profiles. The method may also include aggregating results over clonotypes, adding T cells with similar TCR sequences and ranking the list of candidates.