Biology subjects

Schultheis, H.

Publications and source records attributed to Schultheis, H..

7 recordsLinked to original sources

bioRxiv2026

Single-Cell Mapping of tRNA Expression Dynamics Across Human Hematopoiesis

How transfer RNA (tRNA) expression changes as hematopoietic stem cells are specified into human blood cell types remains unknown. tRNAs translate the 64-codon genetic code into the 21-amino acid protein code, and their relative abundance can influence proteomic output. Here, we introduce simultaneous single-cell tRNA and mRNA sequencing (sc-STM-seq), a scalable, high-throughput approach for profiling tRNAs alongside mRNA transcriptomes in individual cells. Applying sc-STM-seq to human bone marrow, we map tRNA expression and splicing across hematopoietic differentiation trajectories and identify tRNAs associated with stemness, differentiation, and specific cell lineages. Our study provides an atlas of the hematopoietic tRNA landscape and establishes tRNA expression as a previously underappreciated layer of cellular heterogeneity during human blood cell development.

cell biology↗

bioRxiv2025

SC-Framework: A robust and FAIR semi-interactive environment for single-cell resolution datasets

The accelerated development of single-cell technologies has profoundly impacted the field of biological research, facilitating unparalleled insights into cellular heterogeneity. However, this progress has also produced new computational challenges in the field of bioinformatics: single-cell datasets are increasingly high-dimensional, multimodal, and large-scale, while analysis workflows often remain fragmented, data type specific, ad hoc, and difficult to reproduce. The prevailing methodologies are dependent on a combination of public tools, which hinders the reproducibility of results, limits scalability, and complicates the efforts to establish benchmarks. The necessity for a higher-level, unified framework for single-cell data analysis is paramount to address these inherent limitations. Here, we introduce the SC-Framework, providing the integration of standardized data structures, declarative workflows and standardized computational backends in a containerized environment, enabling analysts to focus on biological interpretation rather than technical overhead. SC-Framework is available at GitHub (https://github.com/loosolab/SC-Framework).

bioinformatics↗

bioRxiv2024

TNF Superfamily Member 14 Drives Post-Influenza Depletion of Alveolar Macrophages Enabling Secondary Pneumococcal Pneumonia

Secondary bacterial infection, often caused by Streptococcus pneumoniae (Spn), is one of the most frequent and severe complications of influenza A virus (IAV)-induced pneumonia. Phenotyping of the pulmonary innate immune landscape after IAV infection revealed a significant depletion of the tissue-resident alveolar macrophage (TR-AM) population at day 7, which was associated with increased susceptibility to Spn outgrowth. To elucidate the molecular mechanisms underlying TR-AM depletion, and to define putative targets for treatment, we combined single-cell transcriptomics and cell-specific PCR profiling in an unbiased manner, using in vivo models of IAV infection and IAV/Spn co-infection. The TNF superfamily 14 (TNFSF14) ligand-receptor axis was revealed as the driving force behind post-influenza TR-AM death during the early infection phase, enabling the transition to pneumococcal pneumonia, while intrapulmonary transfer of genetically modified TR-AMs and antibody-mediated neutralization of specific pathway components alleviated disease severity. With a mainly neutrophilic expression and a high abundance in the bronchoalveolar fluid (BALF) of patients with severe virus-induced ARDS, TNFSF14 emerged as a novel determinant of virus-driven lung injury. Targeting the TNFSF14-mediated intercellular communication network in the virus-infected lung can, therefore, improve host defense, minimizing the risk of subsequent bacterial pneumonia, and ameliorating disease outcome.

molecular biology↗

bioRxiv2023

Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data

Transcription factors (TFs) are crucial epigenetic regulators, which enable cells to dynamically adjust gene expression in response to environmental signals. Computational procedures like digital genomic footprinting on chromatin accessibility assays such as ATACseq can be used to identify bound TFs in a genome-wide scale. This method utilizes short regions of low accessibility signals due to steric hindrance of DNA bound proteins, called footprints (FPs), which are combined with motif databases for TF identification. However, while over 1600 TFs have been described in the human genome, only [~]700 of these have a known binding motif. Thus, a substantial number of FPs without overlap to a known DNA motif are normally discarded from FP analysis. In addition, the FP method is restricted to organisms with a substantial number of known TF motifs. Here we present DENIS (DE Novo motIf diScovery), a framework to generate and systematically investigate the potential of de novo TF motif discovery from FPs. DENIS includes functionality i) to isolate FPs without binding motifs, ii) to perform de novo motif generation and iii) to characterize novel motifs. Here, we show that the framework rediscovers artificially removed TF motifs, quantifies de novo motif usage during an early embryonic development example dataset, and is able to analyze and uncover TF activity in organisms lacking canonical motifs. The latter task is exemplified by an investigation of a scATAC-seq dataset in zebrafish which covers different cell types during hematopoiesis.

bioinformatics↗

bioRxiv2022

TF-COMB - discovering grammar of transcription factor binding sites

Cooperativity between transcription factors is important to regulate target gene expression. In particular, the binding grammar of TFs in relation to each other, as well as in the context of other genomic elements, is crucial for TF functionality. However, tools to easily uncover co-occurrence between DNA-binding proteins, and investigate the regulatory modules of TFs, are limited. Here we present TF-COMB (Transcription Factor Co-Occurrence using Market Basket analysis) - a tool to investigate co-occurring TFs and binding grammar within regulatory regions. We found that TF-COMB can accurately identify known co-occurring TFs from ChIP-seq data, as well as uncover preferential localization to other genomic elements. With the use of ATAC-seq footprinting and TF motif locations, we found that TFs exhibit both preferred orientation and distance in relation to each other, and that these are biologically significant. Finally, we extended the analysis to not only investigate individual TF pairs, but also TF pairs in the context of networks, which enabled the investigation of TF complexes and TF hubs. In conclusion, TF-COMB is a flexible tool to investigate various aspects of TF binding grammar. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/496953v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@e37cccorg.highwire.dtl.DTLVardef@1165241org.highwire.dtl.DTLVardef@725a2corg.highwire.dtl.DTLVardef@17e1893_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2019

Beyond accessibility: ATAC-seq footprinting unravels kinetics of transcription factor binding during zygotic genome activation

While footprinting analysis of ATAC-seq data can theoretically enable investigation of transcription factor (TF) binding, the lack of a computational tool able to conduct different levels of footprinting analysis has so-far hindered the widespread application of this method. Here we present TOBIAS, a comprehensive, accurate, and fast footprinting framework enabling genome-wide investigation of TF binding dynamics for hundreds of TFs simultaneously. As a proof-of-concept, we illustrate how TOBIAS can unveil complex TF dynamics during zygotic genome activation (ZGA) in both humans and mice, and explore how zygotic Dux activates cascades of TFs, binds to repeat elements and induces expression of novel genetic elements. TOBIAS is freely available at: https://github.com/loosolab/TOBIAS.

bioinformatics↗

bioRxiv2018

UROPA GUI: A web platform for genomic region annotation

The annotation of genomic ranges such as peaks resulting from ChIP-seq/ATAC-seq or other techniques represents a fundamental task of bioinformatics analysis with considerable impact on many downstream analyses. In our previous work, we introduced the Universal Robust Peak Annotator (UROPA), a flexible command line based tool which improves upon the functionality of existing annotation software. In order to reduce the complexity for biologists and clinicians, we have implemented an intuitive web-based graphical user interface (GUI) and fully functional service platform for UROPA. This extension will empower all users to generate annotations for regions of interest interactively.\n\nAvailability and ImplementationThe open source UROPA GUI server was implemented in R Shiny and Python and is available from http://loosolab.mpi-bn.mpg.de. The source code of our App can be downloaded at https://github.molgen.mpg.de/loosolab/UROPA_GUI under the MIT license.

bioinformatics↗