A single-cell transcriptome atlas reveals paradoxical monoallelic expression of RNA editing and heterozygous SNPs

Linking zygosity and allelic balance to phenotype with programmable heterozygous editing and allele-specific single-cell RNA sequencing 2307550

An innovative CRISPR-based editing system in primary human T cells is harnessed to engineer heterozygous variants across CARD11, a key regulator of immune signaling, in which heterozygous variants cause diverse IEI with variable penetrance.

Reconstruction of cell diversity and cell lineages from somatic mutations in single-cell transcriptomic data

Abstract Single-cell RNA sequencing (scRNA-seq) enables high-resolution profiling of individual cells. However, inferring temporal relationships among cells remains a challenge. Here, we present real-time course analysis (RTCA), a simple direct phylogenetic signal framework that reconstructs cell lineages from somatic variants detected across cells using variants derived from nuclear-encoded transcripts in scRNA-seq data. Our simulations demonstrate that only a modest increase in informative variant sites is sufficient to maintain accurate tree reconstruction, even with a tenfold increase in the number of cells. This scalability makes RTCA applicable to datasets generated by recent single-cell sequencing technologies and is also suitable for reanalyzing existing datasets. We also performed a comparative analysis between our method and a representative genotype-mediated inference framework, PhylinSic, which shares certain conceptual similarities with RTCA. The simulation results show that RTCA is more robust to sparse mutation signals and dropout-induced missing data than PhylinSic. In an application to the datasets from 2 healthy human placental samples, RTCA successfully reconstructed bifurcating phylogenetic trees. By mapping expression-based cell type clusters onto the trees, we evaluated the degree of monophyly within lineages and found our results consistent with known placental differentiation pathways. The identified cell lineages also aligned with classifications based on gene expression and pseudotime analysis. Compared to gene expression-based and pseudotime analysis, RTCA provides a temporally based model of cell trajectories, integrating lineage and expression information in a biologically meaningful manner. In summary, RTCA offers a scalable, cost-effective solution for reconstructing developmental processes in complex normal tissues.

Exploring Heteroplasmic Variants in mtDNA: Insights from Single-Cell Transcriptomics

Mitochondrial DNA (mtDNA) heteroplasmy, which is the coexistence of wild-type and mutant mtDNA variants within the same cell, plays a critical role in modulating cellular phenotype as well as disease severity and penetrance. Bulk RNA sequencing is not able to detect cell-to-cell variability in heteroplasmy, limiting our understanding of mitochondrial pathological mechanisms. In this study, we leverage single-cell RNA sequencing (scRNA-seq) combined with a robust bioinformatics pipeline to characterize mtDNA heteroplasmy. We employed four fibroblast lines from patients harboring heteroplasmic mtDNA pathogenic variants in genes encoding respiratory complex I subunits. While RNA heteroplasmy corresponds to DNA-based measurements at the bulk-level, single-cell analysis uncovers a diverged distribution: most cells have near-homoplasmic (wild-type or mutant) mtDNA, with few cells showing intermediate levels. Furthermore, we find that high mutation levels correlate with transcriptional profile changes, though these responses are highly sample-specific, suggesting that nuclear background and cellular context critically influence mitochondrial dysfunction and compensatory mechanisms. Our findings highlight the power of single-cell technologies to better understand the complex link between mtDNA genetic diversity and mitochondrial phenotypic variability, and to study crucial aspects in mitochondrial biology and pathology, such as clonal dynamics, at single-cell resolution.

Cross-chemistry single-nucleus RNA-seq identifies gene length and CpG-island promoters as determinants of transcriptional noise

Cell-to-cell transcriptional heterogeneity, or noise, is an intrinsic property of the transcriptome with implications for development, disease progression, and aging. Bulk RNA-seq masks this variability by averaging gene expression across cells, whereas single-cell RNA sequencing (scRNA-seq) resolves it. Nevertheless, separating biological noise from technical variance remains challenging, particularly across platforms with different chemistries. We benchmarked two widely adopted technologies, Evercode WT (SPLiT-seq, Parse Biosciences) and Chromium (10x Genomics), on human lymphoblastoid nuclei. Evercode WT achieved targeted sequencing depth and nuclei number far more reliably, and its random-hexamer priming yielded more intronic reads and non-coding RNA genes; Chromium recovered more cells and detected polyadenylated transcripts and cell-line markers more sensitively. Despite these opposing biases, the platforms showed comparable gene detection and strongly correlated expression profiles. Using datasets from both platforms, we defined a noise metric detrended from mean expression and showed that per-gene estimates were reproducible across chemistries. Noise was lower in G2M than in G1 and was most strongly associated with gene length rather than exonic length. Expression of genes with CpG-island promoters was less variable than that of those without. This study establishes a platform-independent basis for quantifying transcriptional noise and a framework for selecting an appropriate scRNA-seq platform. Highlights Parse Evercode WT demonstrates superior predictability in targeted cell recovery and sequencing depth estimations compared to 10x Chromium. Distinct biases: Parse captures intronic sequences; 10x targets polyadenylated mRNA. Detrended transcriptional noise is reproducible across both barcoding chemistries. Noise scales with gene length, not exonic length, and is lower at CGI promoters.

Encoded and non-genetic protein variants expand human functional proteome.

Bioinformatics for human long-read whole-genome sequencing