Linking zygosity and allelic balance to phenotype with programmable heterozygous editing and allele-specific single-cell RNA sequencing

Introduction/Rationale: Heterozygous single-nucleotide variants cause many inborn errors of immunity (IEI), yet most functional genomics tools generate homozygous edits, limiting insight into dominant-negative (DN) and dosage-dependent variant effects. Expression imbalance of the mutant and wild-type alleles can further complicate genotype–phenotype relationships, and has been nearly impossible to interrogate experimentally. To overcome this, we built a scalable platform to engineer heterozygous variants in primary immune cells and directly link allelic expression bias to phenotype.

Methods: We harnessed an innovative CRISPR-based editing system, helicase-assisted continuous editing (HACE), in primary human T cells to engineer heterozygous variants across CARD11, a key regulator of immune signaling, in which heterozygous variants cause diverse IEI with variable penetrance. In parallel, we developed single-cell Allele-Integrated Multi-omics sequencing (sc-AIMseq), which integrates transcriptomics, surface proteomics, and quantification of mutant and wild-type allele expression at single cell resolution.

Results: HACE screens recovered known pathogenic DN variants in CARD11 and revealed novel DN and haploinsufficient variants not evident in homozygous models. sc-AIMseq of HACE-edited T cells uncovered marked cell-to-cell variability in CARD11 allelic expression bias, which dramatically influenced T cell phenotype. We also performed sc-AIMseq on PBMCs from multiple patients with DN CARD11 mutations, which revealed that CARD11 allelic expression bias is cell-type dependent, providing a mechanistic basis for variable penetrance.

Conclusion: This work establishes a scalable strategy to model heterozygous genetic disorders in primary and patient-derived immune cells, and dissect their variable penetrance. By directly coupling heterozygous variant engineering, allelic expression bias, and phenotype at single-cell resolution, our approach enables unprecedented functional variant interpretation and advances precision immunology.