Browsing publications of theresearch group Single Cell Analysis ([HIRI] SIGA) by Authors
Eleven grand challenges in single-cell data science.Lähnemann, David; Köster, Johannes; Szczurek, Ewa; McCarthy, Davis J; Hicks, Stephanie C; Robinson, Mark D; Vallejos, Catalina A; Campbell, Kieran R; Beerenwinkel, Niko; Mahfouz, Ahmed; et al. (BMC, 2020-02-07)The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.
scSLAM-seq reveals core features of transcription dynamics in single cells.Erhard, Florian; Baptista, Marisa A P; Krammer, Tobias; Hennig, Thomas; Lange, Marius; Arampatzi, Panagiota; Jürges, Christopher S; Theis, Fabian J; Saliba, Antoine-Emmanuel; Dölken, Lars; et al. (Springer-Nature, 2019-01-01)Single-cell RNA sequencing (scRNA-seq) has highlighted the important role of intercellular heterogeneity in phenotype variability in both health and disease1. However, current scRNA-seq approaches provide only a snapshot of gene expression and convey little information on the true temporal dynamics and stochastic nature of transcription. A further key limitation of scRNA-seq analysis is that the RNA profile of each individual cell can be analysed only once. Here we introduce single-cell, thiol-(SH)-linked alkylation of RNA for metabolic labelling sequencing (scSLAM-seq), which integrates metabolic RNA labelling2, biochemical nucleoside conversion3 and scRNA-seq to record transcriptional activity directly by differentiating between new and old RNA for thousands of genes per single cell. We use scSLAM-seq to study the onset of infection with lytic cytomegalovirus in single mouse fibroblasts. The cell-cycle state and dose of infection deduced from old RNA enable dose-response analysis based on new RNA. scSLAM-seq thereby both visualizes and explains differences in transcriptional activity at the single-cell level. Furthermore, it depicts 'on-off' switches and transcriptional burst kinetics in host gene expression with extensive gene-specific differences that correlate with promoter-intrinsic features (TBP-TATA-box interactions and DNA methylation). Thus, gene-specific, and not cell-specific, features explain the heterogeneity in transcriptomes between individual cells and the transcriptional response to perturbations.