The DriverMap Adaptive Immune Receptor (AIR) Spike-in controls are a set of synthetic T-Cell Receptor (TCR) and B-Cell Receptor (BCR) constructs with unique CDR3 sequences in RNA format. These controls contain nearly full-length V(D)JC structures (Fig. 1), which represent all seven TCR (TRB, TRA, TRG, TRD) and BCR (IGH, IGK, and IGL) chains. They are designed as universal RNA positive controls for validating, calibrating, and standardizing commercial and home-brewed AIR sequencing technologies based on multiplex RT-PCR or 5’-RACE (SMART) PCR assays. Please refer to Appendix (A and B) for the sequences of all immune receptor constructs, the concentration of template molecules, and the ratio between different constructs provided in the AIR Control Mix and Isoform Pools.

Fig 1. mRNA structure for TCR (alpha, beta, gamma, delta) and BCR (heavy, kappa, lambda) chains, and positions of forward and reverse PCR primers to amplify the CDR3 regions or CDR1-CDR2-CDR3 regions in AIR RNA assay.

As reviewed by the AIRR consortium Truck et al. (doi: 10.7554/eLife.66274), there is a general need for controls to standardize Adaptive Immune Receptor Repertoire Profiling assays. The DriverMap AIR Synthetic Spike-in controls help address this need by providing quantified UMI-labeled synthetic TCR and BCR isoforms that can be added to any PCR-based AIR assay to measure the following:

  • Errors in TCR or BCR sequences, introduced at reverse transcription, amplification, or NGS steps.
  • Sensitivity, linearity, and specificity in detecting AIR clonotypes affected by enzymatic inefficacy, off-target activity, low sequencing depth, and non-sufficient or excessive template sample size.
  • Cross-contamination between samples due to sample mixing, PCR contaminations, or index hopping/jumping in the NGS step.
  • Recombination between clonotypes with similar structures, generated at amplification or NGS steps.
  • Data processing errors introduced at filtering, UMI error correction, and calculation of consensus sequences.
  • Sample quality control due to potential degradation (e.g., FFPE), loss or unbalanced composition of RNA templates, or presence of inhibiting impurities.
  • Batch effects and reproducibility in overall data quality generated by differences introduced at multiple workflow stages.

Please read the entire user manual before proceeding with your experiment.

 


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Last modified: 12 January 2024

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