Introduction
T cells play a critical role in the immune system, relying on diverse T-cell receptors (TCRs) to detect pathogens, cancer, or autoimmune issues. Unfortunately, sequencing the TCR repertoire is challenging due to the complex recombination processes that generate this diversity, especially in the highly variable complementarity determining region 3 (CDR3). Current TCR sequencing methods using genomic DNA or RNA struggle with degraded samples like those from formalin-fixed paraffin-embedded (FFPE) tissues, limiting their effectiveness in certain studies.
To tackle these challenges, Baker et al. introduced FUME-TCRseq as a low-cost, efficient TCR sequencing method compatible with degraded RNA. It uses multiplex PCR and unique molecular identifiers (UMIs) to reduce amplification bias and sequencing errors, enhancing TCR analysis accuracy. With a shorter amplicon size, FUME-TCRseq is well-suited for degraded RNA, making it accessible to most labs. Approximately 10-fold cheaper than commercial alternatives, it is a cost-effective option, offering broader utility in TCR profiling research.
FUME-TCRseq Workflow and Validation: A Comprehensive Approach to TCR Profiling
FUME-TCRseq protocol combines the use of UMIs and multiplex PCR to target the hypervariable CDR3 region of the TCRβ gene, which is critical for antigen specificity. RNA is extracted from sources such as blood or tissue and reverse-transcribed with primers containing UMIs—12 random base pairs designed to trace sequencing reads back to the original cDNA strand. This approach corrects PCR and sequencing errors, which is essential for accurately distinguishing between true T-cell rearrangements and errors caused by amplification. The use of UMIs also corrects for PCR bias, enabling more precise quantification of T-cell clones. The CDR3 region is amplified using a pool of 38 V gene primers, developed for the monitoring of lymphoid malignancies. After amplification, the libraries are purified and sequenced on Illumina platforms, providing a cost-effective and sensitive method for profiling TCR repertoires, even from degraded RNA samples like FFPE tissues.
In the first validation of FUME-TCRseq, the method was compared with a well-known 5′-RACE approach using RNA from a whole blood sample. FUME-TCRseq outperformed 5′-RACE, with a much higher percentage of reads mapping to the TCR (98% vs. 61.4%). It also identified seven times more unique T-cell clonotypes and had a lower rate of nonfunctional sequences. The efficiency of FUME-TCRseq is likely due to better reverse transcription and less duplication during amplification. While both methods showed similar patterns of gene usage, FUME-TCRseq identified more clonotypes, though the most common ones were detected by both methods at similar frequencies.
To further test FUME-TCRseq, the method was applied to a biopsy from a patient with inflammatory bowel disease. The results were highly reproducible, as duplicate library preparations from the same RNA pool detected a similar number of unique T-cell sequences. While the overlap of clonotypes between replicates was moderate overall (34.7%), almost all of the most abundant clonotypes were consistently identified (98.6%), showing that FUME-TCRseq reliably captures the most significant T-cell expansions. This high level of reproducibility supports FUME-TCRseq as an accurate and efficient method for TCR sequencing, even in complex samples.
In a comparison with commercial methods, FUME-TCRseq demonstrated its strengths. When compared to the Immunoverse assay, FUME-TCRseq detected more unique clonotypes and had a higher percentage of on-target reads, underscoring its superior ability to capture TCR diversity. While both methods identified many of the same expanded clones, FUME-TCRseq consistently found more clonotypes overall. Additionally, when compared to immunoSEQ, which uses DNA instead of RNA, FUME-TCRseq showed significant overlap with clonotypes, particularly the most expanded ones. However, immunoSEQ tended to capture more non-functional sequences due to its DNA-based approach. These findings emphasize the potential of FUME-TCRseq to become a superior approach for immune repertoire analysis in both research and clinical settings.
Exploring Immune Landscape Shifts in Tumor Progression with FUME-TCRseq
Authors further used FUME-TCRseq in subclonal mutations . In colorectal tumors with subclonal mutations, FUME-TCRseq identified T-cell clonotypes in both wild-type (WT) and mutant regions. Even from highly degraded FFPE samples, the analysis revealed that both WT and PIK3CA-mutant subclones had a similar number of unique clonotypes. However, three clonotypes expanded exclusively in the mutant region, suggesting an immune response to a subclone-specific antigen. A particular clonotype, representing a substantial portion of TCR reads in the mutant, appeared repeatedly with different nucleotide sequences, highlighting a possible convergent T-cell response.
The study also examined a KRAS G12C mutant subclone, which had fewer unique T-cell clonotypes and lower diversity compared to the WT subclone. Despite adjusting for differences in sequencing reads, the two regions exhibited little overlap in their T-cell repertoires. This suggests a more restricted immune response in the KRAS mutant subclone, though technical factors like RNA yield may have contributed to the low clonotype overlap.
Next author analyzed the transition from benign colorectal adenoma to invasive carcinoma, where FUME-TCRseq was used to profile T-cell repertoires in both regions. Despite the adenoma and carcinoma having similar numbers of unique clonotypes, TCR diversity was lower in the carcinoma, indicating a more focused immune response in the invasive cancer. Minimal overlap in clonotypes between the regions suggests a distinct immune response as the tumor progresses, reflecting how the immune environment shifts during cancer development.
Conclusion
FUME-TCRseq is a groundbreaking method for profiling T-cell repertoires, particularly in degraded samples like those from archival FFPE tissue, which were previously challenging to analyze. By incorporating unique molecular identifiers (UMIs) and avoiding inefficient steps, FUME-TCRseq provides accurate and sensitive TCR analysis. Its FFPE compatibility represents a major advancement over existing methods, and validation studies have shown it performs better than both RACE-based and commercial TCRseq kits. While focused on the CDR3 region of the β chain, FUME-TCRseq has demonstrated impressive success in highly degraded samples, revealing new insights into immune responses in tumor subclones. This method holds significant potential for tracking immune dynamics in disease progression and treatment, offering new opportunities for biomarker discovery and therapeutic development.
FUME-TCRseq’s ability to analyze archived clinical samples opens up new opportunities for studying immune responses over time, offering valuable insights into how the immune system evolves or responds to treatment. This could help drive advances in personalized therapies and immunotherapies. With its low cost and ease of use, FUME-TCRseq is poised to accelerate discoveries in cancer research, autoimmune diseases, and more, making it a practical tool for both clinical and research applications.
Outsourcing Bioinformatics Analysis: How Bridge Informatics Can Help
As you explore the potential of FUME-TCRseq for your research, consider how it can transform your approach to studying immune responses over time. To learn more about integrating FUME-TCRseq into your work and to discuss your specific needs, click here to schedule a free introductory call with a member of our team.
Shahrzad Ghazisaeidi, PhD, Data Scientist, Bridge Informatics
Shahrzad specializes in high-throughput sequencing, data pre-processing, and downstream analysis of transcriptomic and epigenetic landscapes. She is particularly passionate about developing innovative tools for drug repurposing.
Prior to joining Bridge Informatics, Shahrzad served as a Postdoctoral Associate at the Hospital for Sick Children in Toronto, Canada where she researched the epigenetics of peripheral nerve injury models.
Shahrzad earned her Ph.D. in Physiology and Neuroscience from the University of Toronto. Her studies focused on the sex-dependent and independent gene regulation of peripheral nerve injury. Currently based in Toronto, Shahrzad balances her professional pursuits with personal interests by making time for yoga, martial arts, and poetry.
