A groundbreaking study has unveiled a powerful approach to precisely dissect the effects of genetic variants at an unprecedented resolution, leveraging CRISPR base editing combined with single-cell transcriptomics. This method, termed CRAFTseq, convincingly demonstrates how subtle regulatory genetic variants can be functionally validated and quantified within complex cellular environments, overcoming limitations of bulk analyses that often obscure such nuanced gene expression changes. The research focuses on key genetic loci implicated in immune cell biology, providing the scientific community with a refined toolkit for elucidating disease-associated variants.
Central to the investigation was the fine-mapping and genomic editing of a single nucleotide polymorphism, rs2954658, located in the RPL8 locus. This variant, a T-to-C transition, has previously been associated with elevated gene expression in B cells through extensive expression quantitative trait locus (eQTL) studies and sophisticated statistical fine-mapping analyses. The challenge was to confirm whether this variant exerts a causal regulatory effect on RPL8 expression in a controlled experimental setting, something that had remained elusive due to methodological limitations.
Leveraging the power of adenine base editors (ABE8e), researchers precisely induced the rs2954658 variant within Daudi B cells. Single-cell RNA sequencing (scRNA-seq) was performed on 969 cells that passed rigorous quality control filters, revealing a highly significant effect of the T allele on RPL8 expression, with a p-value smaller than 10^-9. This equated to a subtle but reproducible 0.9-fold change in expression, a level of modulation often masked in bulk assays. Crucially, no other individual genes exhibited a significant genotype effect after adjusting for multiple comparisons, underscoring the specificity of the variant’s influence.
The power of CRAFTseq becomes even more apparent when compared against traditional bulk RNA sequencing methods. When the same genomic edits were introduced into RPL8 and PTPRC loci and analyzed at the population level, changes in gene expression failed to reach statistical significance or biological relevance, respectively. The bulk assays were confounded by complex cellular interactions, including secretion of inflammatory cytokines and heterogeneous cell populations, which dilute genotype-specific outcomes. In contrast, the CRAFTseq approach allows direct attribution of transcriptional changes to the edited genotype within individual cells, directly controlling for environmental and cell-state variables.
Extending beyond the B cell context, the team also applied base editing to manipulate the autoimmune-associated variant rs61839660 in primary human naive CD4+ T cells. These cells were cultured under T helper 1 (TH1) or regulatory T cell (Treg) polarizing conditions to assess cell-state-specific effects. Using BE4-NG base editors, the alternative T allele was introduced in cells derived from genotyped non-autoimmune donors. Single-cell analyses discerned three distinct clusters representing varying cell states, with residual expression of IL2RA (encoding CD25) and protein surface expression changes corresponding to genotype and polarization state.
The integration of CRISPR base editing, single-cell genomics, and phenotypic profiling in this work represents a significant advance in functional genomics. By enabling cell-type and cell-state-specific resolution of variant effects, CRAFTseq addresses a major bottleneck in the field: the inability to confidently assign molecular consequences to regulatory disease variants. This precision genome editing coupled with transcriptome-wide resolution holds transformative potential for understanding the genetic basis of complex traits and diseases, including autoimmune disorders and cancer.
The technical sophistication of the study is notable. Employing different base editor variants optimized for the targeted nucleotide changes, alongside multiplexed single-cell transcriptomics and indexed flow cytometry, the researchers crafted a multifaceted experimental framework. This elaborate design allowed dissection of the consequences of single-nucleotide polymorphisms not only at the RNA expression level but also by correlating expression with cell surface protein markers. The use of linear regression and likelihood ratio tests provided rigorous statistical evaluation of genotype-expression associations at single-cell resolution, a methodological triumph.
Beyond the immediate findings, the implications of this work resonate broadly across genetics and immunology. The capacity to distinguish subtle regulatory effects in discrete immune cell subsets opens avenues for linking genotype to phenotype in a manner previously achievable only in bulk or correlative studies. The observed specificity of the rs2954658 variant effect for RPL8 alone, without off-target transcriptomic perturbations, validates the precision of the editing and the reliability of CRAFTseq in uncovering true causal relationships.
Moreover, the demonstration that bulk RNA assays can overlook or misinterpret the functional impact of genetic variants emphasizes the necessity of single-cell approaches in functional variant characterization. Cellular heterogeneity, paracrine signaling, and mix of edited and unedited cells in traditional CRISPR experiments impose confounding variables that mask direct genetic effects. CRAFTseq circumvents these pitfalls, allowing researchers to parse genetic influence from cellular context comprehensively.
This study’s methodology also sets a precedent for future investigations aiming to validate disease-associated variants identified through genome-wide association studies (GWAS) or population sequencing efforts. By coupling targeted base editing with single-cell multi-omic readouts, scientists can robustly interrogate putative causal variants in relevant primary cell types and states, bridging the gap from association to mechanism with unprecedented fidelity.
The versatility of the approach is highlighted by its successful application to both immortalized cell lines and primary human immune cells under physiologically relevant culture conditions. Polarization into TH1 and Treg states allowed assessment of variant impact within immune differentiation trajectories, a critical aspect for autoimmune disease modeling. Residual analyses linking genotype with IL2RA transcript and protein expression illustrate the power to map genotype-to-phenotype relationships across molecular layers within complex cell populations.
In closing, this pioneering work published in Nature charts a new path for precisely defining the effects of disease-associated variants directly in their cellular context. By drawing on advances in genome editing, single-cell sequencing, and statistical modeling, the authors have showcased a robust platform that promises to accelerate functional genomics and the understanding of gene regulation in health and disease. The integration of mutation induction with detailed phenotypic dissection at the single-cell level heralds a transformative era for variant interpretation, personalized medicine, and therapeutic target discovery.
Subject of Research: Functional characterization of disease-associated regulatory genetic variants through CRISPR base editing and single-cell transcriptomics.
Article Title: Precisely defining disease variant effects in CRISPR-edited single cells.
Article References:
Baglaenko, Y., Mu, Z., Curtis, M. et al. Precisely defining disease variant effects in CRISPR-edited single cells. Nature (2025). https://doi.org/10.1038/s41586-025-09313-3
Image Credits: AI Generated
