Biology subjects

Larsson, A. J. M.

Publications and source records attributed to Larsson, A. J. M..

6 recordsLinked to original sources

bioRxiv2025

RNA BaseCode: Long-Read RNA Sequences with Short-Read Accuracy

RNA sequencing has revolutionized the life sciences by enabling comprehensive profiling of gene expression across tissues and individual cells. Most mammalian genes produce multiple isoforms with distinct functional consequences, yet their accurate detection and quantification remain challenging. Here we present RNA BaseCode, a synthetic long-read RNA sequencing method that combines the high accuracy and quantitative precision of short-read sequencing with internal random barcodes to reconstruct full-length RNA sequences. We applied RNA BaseCode to purified RNA to benchmark its performance against short-read and long-read RNA sequencing methods. RNA BaseCode demonstrated superior basecalling accuracy, higher quantitative precision, and an improved ability to reconstruct full-length transcripts compared to long-read sequencing. This method enables comprehensive characterization of RNA isoform expression at scale, providing a powerful and broadly applicable tool for investigating isoform regulation across biological contexts as well as the roles of these isoforms in health and disease.

genomics↗

bioRxiv2019

Clonally distinct differentiation trajectories shape CD8+ memory T cell heterogeneity after acute viral infections in humans

CD8+ T cells play essential roles in immunity to viral and bacterial infections, and to guard against malignant cells. The CD8+ T cell response to an antigen is composed of many T cell clones with unique T cell receptors, together forming a heterogenous repertoire of phenotypically and functionally distinct effector and memory cells1, 2. How individual T cell clones contribute to this heterogeneity during an immune response is key to understand immunity but remains largely unknown. Here, we longitudinally tracked CD8+ T cell clones expanding in response to yellow fever virus vaccination at the single cell level in humans. We show that only a fraction of the clones detected in the acute response persists as circulating memory T cells, indicative of clonal selection. Clones persisting in the memory phase displayed biased differentiation trajectories along a gradient of stem cell memory (SCM) towards terminally differentiated effector memory (EMRA) fates. Reactivation of single memory CD8+ T cells revealed that they were poised to recapitulate skewed differentiation trajectories in secondary responses, and this was generalizable across individuals for both yellow fever and influenza virus. Together, we show that the sum of distinct clonal differentiation repertoires results in the multifaceted T cell response to acute viral infections in humans.

immunology↗

bioRxiv2019

Single-cell RNA counting at allele- and isoform-resolution using Smart-seq3

Large-scale sequencing of RNAs from individual cells can reveal patterns of gene, isoform and allelic expression across cell types and states1. However, current single-cell RNA-sequencing (scRNA-seq) methods have limited ability to count RNAs at allele- and isoform resolution, and long-read sequencing techniques lack the depth required for large-scale applications across cells2,3. Here, we introduce Smart-seq3 that combines full-length transcriptome coverage with a 5 unique molecular identifier (UMI) RNA counting strategy that enabled in silico reconstruction of thousands of RNA molecules per cell. Importantly, a large portion of counted and reconstructed RNA molecules could be directly assigned to specific isoforms and allelic origin, and we identified significant transcript isoform regulation in mouse strains and human cell types. Moreover, Smart-seq3 showed a dramatic increase in sensitivity and typically detected thousands more genes per cell than Smart-seq2. Altogether, we developed a short-read sequencing strategy for single-cell RNA counting at isoform and allele-resolution applicable to large-scale characterization of cell types and states across tissues and organisms.

genomics↗

bioRxiv2019

Transcriptional bursts explain autosomal random monoallelic expression and affect allelic imbalance

Transcriptional bursts render substantial biological noise in cellular transcriptomes. Here, we investigated the theoretical extent of monoallelic expression resulting from transcriptional bursting and how it compared to the amounts of monoallelic expression of autosomal genes observed in single-cell RNA-sequencing (scRNA-seq) data. We found that transcriptional bursting can explain frequent observations of autosomal monoallelic gene expression in cells. Importantly, the burst frequency largely determined the fraction of cells with monoallelic expression, whereas both burst frequency and size contributed to allelic imbalance. Allelic observations deviate from the expected when analysed across heterogeneous groups of cells, suggesting that allelic modelling can provide an unbiased assessment of heterogeneity within cells. Finally, large numbers of cells are required for analyses of allelic imbalance to avoid confounding observations from transcriptional bursting. Altogether, our results shed light on the implications of transcriptional burst kinetics on allelic expression patterns and phenotypic variation between cells.

genomics↗

bioRxiv2019

Transcriptional kinetics of X-chromosome upregulation

Ohnos hypothesis postulates that X-chromosome upregulation rectifies X-dose imbalance relative to autosomal genes, present in two active copies per cell. Here we dissected X-upregulation into kinetics of transcription, inferred from allele-specific single-cell RNA-sequencing (scRNAseq) data from somatic mouse cells. We confirmed increased X-chromosome expression, and remarkably found that the X-chromosome achieved upregulation by elevated burst frequencies. This provides mechanistic insights into X-chromosome upregulation.

molecular biology↗

bioRxiv2017

Computational correction of cross-contamination due to exclusion amplification barcode spreading

Recent advances in sequencing technology have considerably increased the throughput and decreased the cost of short-read sequencing by using high-density patterned flow cells. However, high rates of cross-contamination between multiplexed libraries have been observed on data from these machines, likely due to index-switching during exclusion amplification1,2. Here, we demonstrate that a computational correction procedure based on the Sylvester equation removed 80-90% of the false positive expression signal and eliminated spurious clustering. The computational correction procedure can therefore be used to rescue aspects of affected sequence data so that researchers can take advantage of cost-effective sequencing on patterned flow cells.

genomics↗