TCRβ CDR3 Sequence Analysis for COVID-19 Diagnosis
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Solution Overview
Problem
Current diagnostic and therapeutic tools for COVID-19 lack effectiveness in accurately predicting exposure and severity of the disease, particularly in identifying antigen-specific cellular immune responses.
Innovation Solution
The development of methods to assess T cell receptor β chain complementary determining region 3 (TCRβ CDR3) sequences, which involve identifying specific TCRβ CDR3 sequences associated with COVID-19 infection through machine learning models and using these sequences to diagnose and prognosticate COVID-19.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diagnostic tools are used for COVID-19, then the diagnostic process is simple and quick, but the accuracy in predicting exposure and disease severity is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/lab-based diagnostic methods with a computational approach using machine learning models that analyze TCRβ CDR3 sequences. The system uses sequence data input directly into a computational model to generate diagnostic predictions, eliminating the need for complex laboratory infrastructure while achieving high accuracy in predicting COVID-19 exposure and severity.
Solution Approach 2:
The patent introduces TCRβ CDR3 sequences as an intermediary biomarker between the virus infection and diagnostic detection. Instead of directly detecting the virus or requiring complex imaging, the system uses these immune response sequences as a mediator that can be easily extracted from blood samples and analyzed computationally to infer infection status and severity.
2Reliability
If TCRβ CDR3 sequence analysis is performed, then prediction of COVID-19 presence and severity is improved, but the complexity of the assessment method increases
Solution Approach 1:
The patent transforms the complex biological process of immune response into a simplified computational parameter analysis. By focusing on specific TCRβ CDR3 sequence parameters and feeding them into a trained machine learning model, the system converts biological complexity into a manageable data processing task that achieves reliable predictions without requiring complex assessment infrastructure.
Solution Approach 2:
The patent creates a computational copy of the complex immune response system through machine learning models. Instead of directly analyzing the full complexity of immune system interactions, the system creates a simplified digital representation that can process TCRβ CDR3 sequences and generate predictions, effectively copying the essential predictive capability while reducing operational complexity.
3Loss of information
If conventional diagnostic methods are used, then the testing process is straightforward, but the ability to identify antigen-specific cellular immune responses is limited
Solution Approach 1:
The patent extracts the critical information needed for diagnosis from the complex immune response by focusing on TCRβ CDR3 sequences. These sequences contain the essential signatures of antigen-specific cellular immune responses, and by extracting and analyzing only these specific sequence features, the system preserves crucial immune response information while simplifying the testing operation to a straightforward sequence analysis process.
Data Source
AI summary
Provided are methods of assessing a biological sample obtained from an individual for the presence of a T cell that expresses a T cell receptor (TCR) comprising a TCRβ CDR3 sequence set forth in Table 1. Such methods further comprise identifying the individual as having COVID-19 when the presence of the TCR is detected. The presence of one or more of these TCRs may be of use in prognosticating severity of COVID-19 in the individual and the individual may be treated based on the expected severity of COVID-19. Treatment methods may include administering to the individual a T cell engineered to express the TCR. Such engineered T cells are also disclosed. Also provided are compositions and multimers that find use, e.g., in practicing the methods of the present disclosure.