Biology subjects

Rummel, T.

Publications and source records attributed to Rummel, T..

4 recordsLinked to original sources

bioRxiv2025

Single-cell RNA-seq using UltraMarathonRT expands theknown transcriptome

The ability to map messenger RNA (mRNA) molecules from individual cells using next-generation sequencing technologies, known as single-cell RNA-seq (scRNA-seq), is transforming biology by redefining cellular identities with unmatched detail. However, all current protocols depend on copying RNA into complementary DNA with a single reverse transcriptase (RT) derived from murine leukemia virus, which is an RT enzyme known for low processivity and limited ability to unfold complex RNA structures. Here, for the first time, we introduce a group II intron reverse transcriptase, UltraMarathonRT (uMRT), to perform scRNA-seq. We demonstrate that this enzyme reveals an unexpected transcriptomics landscape by capturing additional genes and other genomic features that conventional RTs miss. We also combined uMRT with metabolic RNA labeling, nucleoside conversion and scRNA-seq to explore genome-wide transcriptome dynamics at the single-cell level. Overall, we establish uMRT as a transformative biotechnological tool for single-cell transcriptomics.

molecular biology↗

bioRxiv2025

Exonuclease ISG20 inhibits human cytomegalovirus replication by inducing an innate immune defense signature

ISG20 is an interferon-regulated protein that exhibits RNase activity thereby inhibiting the replication of a broad spectrum of RNA viruses. By single cell RNA sequencing, we identified ISG20 as an antiviral factor for human cytomegalovirus (HCMV) as it was upregulated in a population of HCMV-resistant cells. In accordance with an antiviral role on herpesviruses, overexpression of ISG20 in primary human fibroblasts led to reduced HCMV and HSV-1 replication, while knockdown of ISG20 enhanced virus growth. In Western blot kinetics, we observed that inhibition of HCMV replication by ISG20 occurs at the early stage of infection which correlated with reduced amounts of viral early and late transcripts. However, neither the half-life of viral and cellular RNAs nor of viral DNA was decreased in ISG20-expressing cells, indicating that ISG20 does not exert its antiviral effect via a degradation of RNAs or DNA. Instead, RNA-seq analysis revealed an innate immune defense signature upon ISG20 expression that comprised the upregulation of a distinct set of interferon stimulated genes (ISGs), zinc finger proteins (ZNFs) and of transposable elements (TEs). Our data indicate that this gene signature augments both IFN production and response of the host cell. Consistently, the JAK-STAT inhibitor ruxolitinib rescued HCMV gene expression in ISG20-expressing cells. We conclude that ISG20 induces a so far unprecedented immune defense signature that serves to amplify the IFN-mediated host cell defense thus explaining its broad antiviral activity.

microbiology↗

bioRxiv2024

Identifying Modulators of Cellular Responses by Heterogeneity-sequencing

The response of individual cells to drug treatment, virus infections or other molecular stimuli is highly heterogeneous and depends on the cells initial state. Library preparation for single-cell transcriptomics is destructive, precluding a direct comparison between the initial state and the stimulus outcome. Consequently, current methods are restricted to identifying correlative associations rather than resolving causal drivers of heterogeneous outcomes. We developed Heterogeneity-seq, which combines single-cell RNA-seq with metabolic RNA labeling (scSLAM-seq) and double machine learning to overcome this limitation. By leveraging simultaneous measurements of unlabeled and labeled RNA in individual cells, Heterogeneity-seq uncovers the transition from pre-stimulated cell states to distinct stimulation outcomes across thousands of cells. These links enable the identification of factors that causally govern heterogeneous cellular responses. We used Heterogeneity-seq to identify both known and novel genes that drive responses to drug treatment, as well as pro- and antiviral host factors governing cytomegalovirus infection.

bioinformatics↗

bioRxiv2022

grandR: a comprehensive package for nucleotide conversion sequencing data analysis

Metabolic labeling of RNA is a powerful technique for studying the temporal dynamics of gene expression. Nucleotide conversion approaches greatly facilitate the generation of data but introduce challenges for their analysis. We here present grandR, a comprehensive package for quality control, differential gene expression analysis, kinetic modeling, and visualization of such data. We compare several existing methods for inference of RNA synthesis rates and half-lives using progressive labeling time courses. We demonstrate the need for recalibration of effective labeling times and introduce a Bayesian approach to study the temporal dynamics of RNA using snapshot experiments.

bioinformatics↗