Author summary We have created nf-core/airrflow, a workflow to help researchers study the immune system in healthy and disease states, such as infections, autoimmunity, and cancer. The adaptive immune...

The 2025 Immcantation Users Group Meeting (https://immcantation.github.io/users-meeting/2025/index.html) took place on January 30th, 2025. It served as a vir...

Bulk B- and T-cell adaptive immune receptor (BCR/TCR) repertoire sequencing (AIRRseq) enables comprehensive analysis of adaptive immune diversity, but achieving balanced sequencing depth across heterogeneous clinical samples remains a major cost driver. Standard equimolar pooling prior to sequencing disproportionately allocates reads, leading to undersequencing of libraries with high sequence abundance and oversampling of those with few sequences. Here, a quantitative pooling strategy is described in which libraries are pooled based on estimated target-specific unique molecular identifier (UMI) counts rather than library molarity, ensuring a uniform number of supporting reads per target sequence UMI across samples. Libraries were prepared from human RNA using the NEBNext Immune Sequencing (IS) kit and quantified by qPCR during the second amplification (PCR2) step. Deeply sequenced samples from a dengue 1 human infection model (DHIM1) clinical study were used to model the log-linear relationship between PCR2 cycle threshold (Ct) values and detected sequence counts. This model was then applied to a second, independent batch of samples from a dengue 3 human infection model (DHIM3) clinical study to predict UMI abundance and guide equi-depth pooling prior to sequencing. Sequencing of these equi-depth pools demonstrated a near-uniform reads-per-sequence ratio across samples, confirming that this approach achieves balanced depth without oversampling and at reduced cost. Simulations across a range of between-sample UMI-count distributions reflecting real-world sample pools showed that equi-depth pooling is expected to reduce the total number of required sequencing reads by approximately 70% compared with equimolar pooling, with savings increasing as the spread of library sizes grows. Beyond cost efficiency, equi-depth pooling eliminates the complexity-dependent sampling bias inherent to equimolar pooling, ensuring uniform sequencing depth and comparable UMI recovery across small and large libraries. For new workflows or sample types, implementation requires either an existing calibration dataset or a pilot sequencing experiment to establish the relationship between PCR2 Ct values and relative UMI abundance. The equi-depth method thus provides a robust, scalable, and cost-efficient strategy for bulk AIRRseq studies where library sizes vary widely.

Bulk B- and T-cell adaptive immune receptor (BCR/TCR) repertoire sequencing (AIRRseq) enables comprehensive analysis of adaptive immune diversity, but achieving balanced sequencing depth across heterogeneous clinical samples remains a major cost driver. Standard equimolar pooling prior to sequencing disproportionately allocates reads, leading to undersequencing of libraries with high sequence abundance and oversampling of those with few sequences. Here, a quantitative pooling strategy is described in which libraries are pooled based on estimated target-specific unique molecular identifier (UMI) counts rather than library molarity, ensuring a uniform number of supporting reads per target sequence UMI across samples. Libraries were prepared from human RNA using the NEBNext Immune Sequencing (IS) kit and quantified by qPCR during the second amplification (PCR2) step. Deeply sequenced samples from a dengue 1 human infection model (DHIM1) clinical study were used to model the log-linear relationship between PCR2 cycle threshold (Ct) values and detected sequence counts. This model was then applied to a second, independent batch of samples from a dengue 3 human infection model (DHIM3) clinical study to predict UMI abundance and guide equi-depth pooling prior to sequencing. Sequencing of these equi-depth pools demonstrated a near-uniform reads-per-sequence ratio across samples, confirming that this approach achieves balanced depth without oversampling and at reduced cost. Simulations across a range of between-sample UMI-count distributions reflecting real-world sample pools showed that equi-depth pooling is expected to reduce the total number of required sequencing reads by approximately 70% compared with equimolar pooling, with savings increasing as the spread of library sizes grows. Beyond cost efficiency, equi-depth pooling eliminates the complexity-dependent sampling bias inherent to equimolar pooling, ensuring uniform sequencing depth and comparable UMI recovery across small and large libraries. For new workflows or sample types, implementation requires either an existing calibration dataset or a pilot sequencing experiment to establish the relationship between PCR2 Ct values and relative UMI abundance. The equi-depth method thus provides a robust, scalable, and cost-efficient strategy for bulk AIRRseq studies where library sizes vary widely.

The 2026 Immcantation Users Group Meeting (https://immcantation.github.io/users-meeting/2026/index.html) took place on January 22nd, 2026. It served as a vir...