AI-driven TCR Single-Cell Screening Service for Accelerated Drug Development

Are extended timelines in therapeutic development delaying the delivery of essential medicines, compounded by persistent hurdles in antibody engineering such as suboptimal target specificity and binding efficiency? Concurrently, do multifaceted clinical trial demands, including prolonged participant enrollment and labor-intensive data interpretation, further hinder progress? Our AI-driven TCR single-cell screening service addresses these challenges by accelerating drug discovery through an integrated high-throughput platform. This technology enables simultaneous single-cell transcriptomic profiling and functional evaluation, streamlining the identification of viable candidates with precision.

Introduction

The precise identification and functional investigation of T-cell receptors (TCRs) that bind disease-linked antigens will be absolutely essential for the development of powerful immunotherapies. Although fundamental, conventional methods for TCR discovery are generally time-consuming and resource-intensive. Artificial intelligence (AI) and single-cell sequencing systems have recently advanced by means of their convergence to enable high-throughput detection of TCR-antigen interactions with improved resolution. This combined strategy not only avoids inefficiencies inherent in traditional methods but also simplifies the implementation of TCR-based therapy options into clinical settings.

Fig.1 Single-cell technology is widely applied in drug development.^1,3

AI-driven TCR Single-Cell Screening Service for Accelerated Drug Development at Creative Biolabs

Creative Biolabs has engineered an AI-integrated TCR single-cell screening platform designed to address key challenges in immunotherapy development. This system employs machine learning algorithms to analyze high-dimensional single-cell datasets, enabling rapid identification of antigen-specific TCR sequences with therapeutic potential. By integrating multi-omic profiling that combines V(D)J sequencing with functional readouts, the platform delivers mechanistic insights into TCR-pMHC interactions and clonal behavior. The modular architecture supports tailored experimental configurations for both novel target discovery and therapeutic optimization pipelines, enhancing research outcomes across diverse immunotherapy applications. This methodology streamlines the transition from TCR characterization to clinical candidate selection through standardized analytical frameworks.

Our Workflow

We initiate the process by obtaining relevant biological samples, such as patient tumor biopsies, PBMCs, or other T-cell-rich tissues, depending on the therapeutic target and disease.

After that, we lyse the T cells to extract RNA, and transcript counting during reverse transcription, boost TCR alpha/beta (and occasionally other) chains, usually do whole transcriptome amplification alongside TCR sequencing, and then get the resulting cDNA ready for NGS library building.

Following library preparations, we match TCR sequences to reference genes to identify CDR3 regions, demultiplex the data to connect reads to specific cells via barcodes, and align any entire transcriptome data to the relevant reference.

Using artificial intelligence, we group cells with matching TCR CDR3 sequences, predict their antigen specificity through sequence analysis, combine this with gene expression data to grasp function, and rank interesting TCRs depending on target prevalence in pertinent cell populations.

Using viral transduction, we manufacture interesting TCR candidates and present them into T cells. We next evaluate their antigen specificity, cytotoxicity, cytokine generation, and other effector activities against target cells by running these modified T cells through in vitro functional tests. Promising in vitro activity candidates could subsequently be tested in preclinical in vivo models to evaluate their pharmacokinetics, safety, and effectiveness.

Attractive Advantages

• High Throughput: Analyze thousands of single cells simultaneously.
• Enhanced Specificity: Identify TCRs with high affinity and specificity.
• Accelerated Discovery: Significantly reduce the time required for TCR identification.
• Cost-Effectiveness: Streamlined workflow reduces overall project costs.

Published Data

Summary: A large-scale analysis of over 4.7 billion TCR sequences from COVID-19 patients and healthy donors reveals key signatures of antiviral immunity. This study identifies convergent CDR3 gene usages, specificity groups, and sequence patterns associated with COVID-19 severity. The research demonstrates that T cell clonal expansion correlates with increased effector function and signaling pathways, and that machine learning can accurately predict COVID-19 infection based on TCR sequence features, providing a systems-level understanding of T cell responses to the virus.

Fig.2 TCR repertoires from COVID-19 patients and healthy donors show patterns in CDR3 gene utilization and diversity.^2,3

FAQs

Work with Creative Biolabs

Creative Biolabs is committed to providing you with the highest quality TCR discovery services. Our AI-driven TCR Single-Cell Screening Service offers a powerful solution for accelerating your immunotherapy research and drug development. If you are interested in our AI-driven TCR single-cell screening service, please feel free to reach out to us.

References

1. Zhang, Anzhuo, et al. "Single-cell technology for drug discovery and development." Frontiers in Drug Discovery 4 (2024): 1459962.
2. Park, Jonathan J., et al. "Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity." Communications Biology 6.1 (2023): 76.
3. Distributed under Open Access License CC BY 4.0, without modification.