Biology subjects

Zehn, D.

Publications and source records attributed to Zehn, D..

10 recordsLinked to original sources

bioRxiv2026

Concurrent de novo inference of cell types and gene regulatory networks with NetMap

Existing methods for GRN inference based on single-cell RNA-sequencing (scRNA-seq) data typically require prior cell type calling and then infer GRNs at cell type resolution. This risks propagation of errors from cell annotation to GRN inference. Moreover, it makes existing methods blind to regulatory programs that are shared between different cell types, as well as to subtle differences in regulatory activity between cells of the same type. To address these limitations, we present NetMap, a pipeline that jointly infers cell-level GRNs and clusters cells based on shared regulatory programs, combining state-of-the-art single-cell autoencoders and explainable artificial intelligence methods. NetMap outperforms existing methods on synthetic data with a known ground-truth GRN and is able to reliably detect biologically plausible regulons on real data. Moreover, requiring only a small set of user-provided marker genes, NetMap can be used for GRN structure-informed probabilistic cell type assignment. We demonstrate the utility of this approach using PBMC data, where we achieve near perfect clustering and cell type recovery performance compared with manually created cell type labels. Finally, we applied NetMap to a data set with naive, activated and exhausted T-cells, and were able to verify that NetMap retrieves relevant T-cell state signatures from the data, with two approaches. NetMap can be installed as a python package from https://github.com/bionetslab/netmap and is compatible with the scverse system. Scripts to reproduce our results can be found at https://github.com/bionetslab/netmap-evaluation.

bioinformatics↗

bioRxiv2025

Identifying transcription factors driving cell differentiation

Cellular differentiation is a fundamental biological process at the core of development and growth in multicellular organisms. Understanding it at the level of transcriptomic regulation opens the possibility for novel biological insights, for steering cell development for therapeutic purposes, and for developing targeted therapies for diseases. While many methods exist that infer developmental trajectories from single-cell RNA sequencing data, only few can effectively determine which biological mechanisms drive differentiation along such trajectories. To close this gap, we developed SwitchTFI (switch transcription factor identification), a method to identify differentiation-driving regulatory mechanisms and the transcription factors (TFs) that play a key role in them. SwitchTFI infers a cell state transition-specific gene regulatory network (GRN) from a user-provided baseline GRN via decision stump learning and permutation-based significance tests. Key TFs are then extracted by ranking them according to their centralities in the transition GRN. We show that SwitchTFI can identify TFs known to be involved in pancreatic endocrinogenesis and in erythrocyte differentiation and that it outperforms competitor methods with respect to the functional coherence of the predicted driver TFs. SwitchTFI is available as a Python package at https://github.com/bionetslab/SwitchTFI.

bioinformatics↗

bioRxiv2022

Absence of microbiome in chronic infections impairs viral control without affecting CD8 T cell function

Prolonged antigen exposure in chronic viral infections reduces the effector capacity of cytotoxic T cells - a phenomenon known as T cell exhaustion. Development of T cell exhaustion is driven by high viral titers, strong TCR stimulation, and high antigen concentrations associated with strong inflammatory signals. A largely unexplored factor has been the influence of the microbiome in these processes. Here, we report that T cell exhaustion progresses independently of the presence or absence of a microbiome in chronic lymphocytic choriomeningitis virus (LCMV) infections. Virus-specific CD8 T cells in germ-free mice showed high expression of the inhibitory receptor PD-1 and decreased cytokine production. Moreover, their global gene expression patterns, as determined by single-cell sequencing, were similar to those of cells in specific pathogen-free mice. In line with this, we observed similar pathogen loads with and without a microbiome. Thus, our study demonstrates that the microbiome is dispensable for the induction of T cell exhaustion and for the limited virus control seen in chronic LCMV infections.

immunology↗

bioRxiv2021

T cell receptor and IL-2 signaling strength control memory CD8+ T cell functional fitness via chromatin remodeling

Cognate antigen signal controls CD8+ T cell priming, expansion size and effector versus memory cell fates, but it is not known if and how it modulates the functional features of memory CD8+ T cells. Here we show that the strength of T cell receptor (TCR) signaling determines the requirement for interleukin-2 (IL-2) signals to form a pool of memory CD8+ T cells that competitively re-expand upon secondary antigen encounter. Combining strong TCR and intact IL-2 signaling synergistically induces genome-wide chromatin accessibility in regions targeting a wide breadth of biological processes, consistent with their greater functional fitness. Chromatin accessibility in promoters of genes encoding for stem cell, cell cycle and calcium-related proteins correlated with faster intracellular calcium accumulation, initiation of cell cycle and more robust expansion. High-dimensional flow-cytometry analysis also highlights higher subset diversity and phenotypes. These results formally establish that epitope selection in vaccine design strongly impacts memory CD8+ T cell epigenetic programming and functions. One Sentence SummaryThe strength of antigenic and interleukin 2 signals received by CD8+ T cells during vaccination epigenetically programs their ability to form functional memory.

immunology↗

bioRxiv2021

Inflammatory signals are sufficient to elicit TOX expression in mouse and human CD8 T cells

T cell receptor (TCR) stimulation leads to expression of the transcription factor TOX. Prolonged TCR signaling, such as encountered during chronic infections or in tumors, leads to sustained TOX expression, which induces a state of exhaustion or dysfunction. While CD8 memory T cells (Tmem) in specific pathogen-free laboratory mice typically do not express TOX, functional human Tmem show heterogeneous TOX expression levels. Whether TCR-independent mechanisms can alter TOX expression in human and murine Tmem has not been defined. We report that human and mouse Tmem increase TOX expression following stimulation with inflammatory cytokines IL-12, IL-15, and IL-18. TOX and PD-1 expression patterns often appear to be directly correlated, however, we found that TOX is not necessary for cytokine-driven expression of PD-1. Together, these observations highlight that inflammation is sufficient to alter TOX and PD-1 expression and that the signals regulating TOX expression appear well conserved in human and murine Tmem.

immunology↗

bioRxiv2020

PySCNet: A tool for reconstructing and analyzing gene regulatory network from single-cell RNA-Seq data

SummaryThe Advanced capacities of high throughput single cell technologies have facilitated a great understanding of complex biological systems, ranging from cell heterogeneity to molecular expression kinetics. Several pipelines have been introduced to standardize the scRNA-seq analysis workflow. These include cell population identification, cell marker detection and cell trajectory reconstruction. Yet, establishing a systematized pipeline to capture regulatory relationships among transcription factors (TFs) and genes at the cellular level still remains challenging. Here we present PySCNet, a python toolkit that enables reconstructing and analyzing gene regulatory networks (GRNs) from single cell transcriptomic data. PySCNet integrates competitive gene regulatory construction methodologies for cell specific or trajectory specific GRNs and allows for gene co-expression module detection and gene importance evaluation. Moreover, PySCNet offers a user-friendly dashboard website, where GRNs can be customized in an intuitive way. AvailabilitySource code and documentation are available: https://github.com/MingBit/PySCNet Contactming.wu@tum.de

bioinformatics↗

bioRxiv2020

Comparative single-cell trajectory network enrichment identifies pseudo-temporal systems biology patterns in hematopoiesis and CD8 T-cell development

Single cell transcriptomics (scRNA-seq) technologies allow for investigating cellular processes on an unprecedented resolution. While software packages for scRNA-seq raw data analysis exist, no method for the extraction of systems biology signatures that drive different pseudo-time trajectories exists. Hence, pseudo-temporal molecular sub-network expression profiles remain undetermined, thus, hampering our understanding of the molecular control of cellular development on a single cell resolution. We have developed Scellnetor, the first network-constraint time-series clustering algorithm implemented as interactive webtool to identify modules of genes connected in a molecular interaction network that show differentiating temporal expression patterns. Scellnetor allows selecting two differentiation courses or two developmental trajectories for comparison on a systems biology level. Scellnetor identifies mechanisms driving hematopoiesis in mouse and mechanistically interpretable subnetworks driving dysfunctional CD8 T-cell development in chronic infections. Scellnetor is the first method to allow for single cell trajectory network enrichment for systems level hypotheses generation, thus lifting scRNA-seq data analysis to a systems biology level. It is available as an interactive online tool at https://exbio.wzw.tum.de/scellnetor/.

bioinformatics↗

bioRxiv2019

Progressive differentiation of memory CD8 T cells in the cervicovaginal tissue

Tissue-resident memory CD8 T cells (CD8 TRM) are critical for maintaining barrier immunity. CD8 TRM have been mainly studied in the skin and gut with recent studies suggesting that the signals that control tissue-residence and phenotype are highly tissue-dependent. We examined the T cell compartment in healthy human cervicovaginal tissue (CVT) and found that most CD8 T cells were granzyme B+ and TCF-1-. To address if this phenotype is driven by CVT tissue-residence, we used a mouse model to control for environmental factors. Using localized and systemic infection models, we found that CD8 TRM in the mouse CVT gradually acquired a granzyme B+, TCF-1- phenotype as seen in human CVT. In contrast to CD8 TRM in the gut, these CD8 TRM were not stably maintained regardless of the initial infection route, which led to reductions in local immunity. Our data show that residence in the CVT is sufficient to progressively shape the size and function of its CD8 TRM compartment. SummaryThe tissue-resident memory (TRM) CD8 T cell compartment in human and mouse cervicovaginal tissue (CVT) is remarkably similar. The CVT TRM compartment is maintained autonomously and does not reach phenoypical or numerical equilibrium. The numerical decline leads to impaired viral control in a secondary challenge.

immunology↗

bioRxiv2018

Attenuation of chronic antiviral T cell responses through constitutive COX2-dependent prostanoid synthesis by lymph node fibroblasts

Fibroblastic reticular cells (FRC) of lymphoid T zones actively promote T cell trafficking, homeostasis and expansion, but can also attenuate excessive T cell responses via inducible nitric oxide and constitutive prostanoid release. It has remained unclear under which conditions these FRC-derived mediators can dampen T cell responses and whether this occurs in vivo. Here we confirm that murine lymph node FRC produce prostaglandin E2 (PGE2) in a cyclooxygenase-2 (COX2)-dependent and inflammation-independent fashion. We show that this COX2/PGE2 pathway is active during both strong and weak T cell responses, in contrast to nitric oxide which only comes into play during strong T cell responses. In chronic infections in vivo, PGE2-receptor signaling in virus-specific CD8 T cells was shown by others to suppress T cell survival and function. Using CCL19cre x COX2flox/flox mice we now identify CCL19cre+ FRC as the critical source of this COX2-dependent suppressive factor, suggesting PGE2-expressing FRC within lymphoid tissues are an interesting therapeutic target to improve T cell mediated pathogen control during chronic infection.

immunology↗

bioRxiv2017

Strong homeostatic TCR signals induce formation of self-tolerant virtual memory CD8 T cells

Virtual memory T cells are foreign antigen-inexperienced T cells that have acquired memory-like phenotype and constitute for 10-20% of all peripheral CD8+ T cells in mice. Their origin, biological roles, and relationship to naive and foreign antigen-experienced memory T cells are incompletely understood. By analyzing TCR repertoires and using retrogenic monoclonal T-cell populations, we show that virtual memory T cells originate exclusively from strongly self-reactive T cells. Moreover, we show that the stoichiometry of the CD8 interaction with Lck regulates the size of the virtual memory T-cell compartment via modulating the self-reactivity of individual T-cell clones. We propose a so far unappreciated peripheral T-cell fate decision checkpoint that eventually leads to the differentiation of highly self-reactive T cells into virtual memory T cells. This underlines the importance of the variable level of self-reactivity in polyclonal T cells for the generation of functional T-cell diversity. Although virtual memory T cells descend from the highly self-reactive clones and acquire a partial memory program, they do not show higher capacity to induce autoimmune diabetes than naive T cells. Thus, virtual memory T cells are not generally more responsive than naive T cells, because their activity highly depends on the immunological context.\n\nSummaryWe conclude that virtual memory T cells are formed from self-reactive CD8+ T cells in a process regulated by CD8-Lck stoichiometry. Despite their self-reactivity and partial memory differentiation program, virtual memory T cells did not show a strong autoimmune potential.

immunology↗