Biology subjects

Borgsmueller, N.

Publications and source records attributed to Borgsmueller, N..

5 recordsLinked to original sources

bioRxiv2024

DemoTape: Computational demultiplexing of targeted single-cell sequencing data

BackgroundSingle-cell sequencing can provide novel insights into the understanding and treatment of diseases. In cancer, for example, intratumor heterogeneity is a major cause of treatment resistance and relapse. Although technological progress has substantially increased the throughput of sequenced cells, single-cell sequencing remains cost and labor-intensive. Multiplexing, i.e., the pooling and subsequent joint preparation and sequencing of samples, followed by a demultiplexing step, is a common practice to reduce expenses and confounding batch effects, especially in single-cell RNA sequencing. ResultsHere, we introduce demoTape, a computational demultiplexing method for targeted single-cell DNA sequencing (scDNA-seq) data based on a distance metric between individual cells at single-nucleotide polymorphisms loci. To validate demoTape, we sequence three B-cell lymphoma patients separately and multiplexed on the Tapestri platform. We find similar genotypes, clones, and evolutionary histories in all three samples when comparing the individual with the demultiplexed samples. Using the three individually sequenced samples, we simulate multiplexed ground truth data and show that demoTape outperforms state-of-the-art demultiplexing methods designed for RNA sequencing data. Additionally, we demonstrate through downsampling that the inferred clonal composition remained largely stable for samples with fewer cells despite the inevitable loss in resolution of low-frequency clones. ConclusionsMultiplexing and subsequent genotype-based demultiplexing of scDNA-seq will reduce costs and workload, eventually allowing the sequencing of more samples. This will open new possibilities and accelerate the investigation of biological questions where cellular heterogeneity on the genomic level plays a crucial role.

bioinformatics↗

bioRxiv2024

Robust and cost-efficient single-cell sequencing through combinatorial pooling

Single-cell sequencing is widely used to study molecular cell-to-cell heterogeneity. Even though the cost of sequencing has dropped throughout the last decades, single-cell assays remain expensive, because they require strategies to index molecules by cells. The high costs of indexing can be mitigated by pooling samples prior to sequencing library preparation. Computational methods have been developed to leverage molecular features that are distinct between different samples to separate the pools into distinct datasets. However, since all multiplexed samples are processed in the same way, information on the origin of each demultiplexed dataset is lost. To map datasets to their sample of origin, additional information such as molecular indexing or additional genotyping is needed. Here, we propose a class of experimental designs that allows identifying the sample of origin of each demultiplexed dataset, only relying on the genetic profiles of the samples and the composition of pools. Our approach is based on splitting and pooling samples in specific combinations. We find a most cost-efficient experimental design in this class and prove its optimality. We present a dynamic programming algorithm to iteratively simplify an optimal experimental design by breaking it into several independent designs while maintaining optimality. Furthermore, we propose a subclass of experimental designs which allow robust sample identification even under partial failure of the experiment and present a provably optimal design in this subclass. We provide an implementation for automatic sample identification under these optimal combinatorial pooling strategies and demonstrate its functionality in a simulation study.

bioinformatics↗

bioRxiv2023

V-pipe 3.0: a sustainable pipeline for within-sample viral genetic diversity estimation

The large amount and diversity of viral genomic datasets generated by next-generation sequencing technologies poses a set of challenges for computational data analysis workflows, including rigorous quality control, adaptation to higher sample coverage, and tailored steps for specific applications. Here, we present V-pipe 3.0, a computational pipeline designed for analyzing next-generation sequencing data of short viral genomes. It is developed to enable reproducible, scalable, adaptable, and transparent inference of genetic diversity of viral samples. By presenting two large-scale data analysis projects, we demonstrate the effectiveness of V-pipe 3.0 in supporting sustainable viral genomic data science.

bioinformatics↗

bioRxiv2022

Detection of isoforms and genomic alterations by high-throughput full-length single-cell RNA sequencing for personalized oncology

Understanding the complex background of cancer requires genotype-phenotype information in single-cell resolution. Here, we perform long-read single-cell RNA sequencing (scRNA-seq) on clinical samples from three ovarian cancer patients presenting with omental metastasis and increase the PacBio sequencing depth to 12,000 reads per cell. Our approach captures 152,000 isoforms, of which over 52,000 are novel. Isoform-level analysis accounting for non-coding isoforms reveals 20% overestimation of protein-coding gene expression on average. We also detect cell type-specific isoform and poly-adenylation site usage in tumor and mesothelial cells, and find that mesothelial cells transition into cancer-associated fibroblasts in the metastasis, partly through the TGF-{beta}/miR-29/Collagen axis. Furthermore, we identify gene fusions, including an experimentally validated IGF2BP2::TESPA1 fusion, which is misclassified as high TESPA1 expression in matched short-read data, and call mutations confirmed by targeted NGS cancer gene panel results. With these findings, we envision long-read scRNA-seq to become increasingly relevant in oncology and personalized medicine.

cancer biology↗

bioRxiv2020

Bayesian non-parametric clustering of single-cell mutation profiles

The high resolution of single-cell DNA sequencing (scDNA-seq) offers great potential to resolve intra-tumor heterogeneity by distinguishing clonal populations based on their mutation profiles. However, the increasing size of scDNA-seq data sets and technical limitations, such as high error rates and a large proportion of missing values, complicate this task and limit the applicability of existing methods. Here we introduce BnpC, a novel non-parametric method to cluster individual cells into clones and infer their genotypes based on their noisy mutation profiles. BnpC employs a Dirichlet process mixture model coupled with a Markov chain Monte Carlo sampling scheme, including a modified split-merge move and a novel posterior estimator to predict clones and genotypes. We benchmarked our method comprehensively against state-of-the-art methods on simulated data using various data sizes, and applied it to three cancer scDNA-seq data sets. On simulated data, BnpC compared favorably against current methods in terms of accuracy, runtime, and scalability. Its inferred genotypes were the most accurate, and it was the only method able to run and produce results on data sets with 10,000 cells. On tumor scDNA-seq data, BnpC was able to identify clonal populations missed by the original cluster analysis but supported by supplementary experimental data. With ever growing scDNA-seq data sets, scalable and accurate methods such as BnpC will become increasingly relevant, not only to resolve intra-tumor heterogeneity but also as a pre-processing step to reduce data size. BnpC is freely available under MIT license at https://github.com/cbg-ethz/BnpC.

bioinformatics↗