Biology subjects

Huth, M.

Publications and source records attributed to Huth, M..

6 recordsLinked to original sources

bioRxiv2026

Nonlinear mixed-effect models and tailored parametrization schemes enables integration of single cell and bulk data

Experimental methods for characterizing single cells and cell populations have improved tremendously over the past decades. This progress has enabled the development of quantitative, mechanistic models for cellular processes based on either single cell or bulk data. However, coherent statistical frameworks for the model-based integration of different data types at the single-cell and population levels are still missing. In this work, we present a mathematical modeling approach for integrating single-cell time-lapse, single-cell snapshot, single-cell time-to-event and population-average data. Utilizing a formulation based on nonlinear mixed-effect modeling, we enable the description of multiple data types, with and without single-cell resolution, and we propose a tailored parameter estimation method. Furthermore, we propose a tailored parameter estimation scheme that facilitates the assessment of underlying process parameters. Our study demonstrates that the proposed approach can reliably integrate diverse data types, thereby improving parameter identifiability and prediction accuracy. Applying this framework of extrinsic apoptosis reveals that simultaneously considering multiple data types can be essential, particularly when experimental constraints limit data availability. The proposed approach is broadly applicable and may significantly advance our understanding of complex biological processes.

systems biology↗

bioRxiv2026

WT1 splice isoforms configure lineage bias during formative pluripotency

How transcriptional asymmetry first arises in the epiblast prior to gastrulation remains a central question in mammalian development. During the transition from naive to formative pluripotency, epiblast cells acquire responsiveness to lineage-inducing cues while already expressing lineage-associated transcriptional programmes. The molecular basis of these asymmetries and whether they reflect stochastic variation or regulated lineage-associated bias remain unclear. Here, using a targeted CRISPRa screen, we identify Wt1 (Wilms tumor 1) as a previously unrecognized regulator of lineage-associated transcriptional programmes during formative pluripotency. Wt1 is transiently induced during formative pluripotency in vitro and in the pre-gastrulation epiblast in vivo, with peak expression coinciding with the emergence of lineage-associated transcriptional biases. Precocious Wt1 induction overrides the naive transcriptional network and advances cells toward a post-implantation epiblast identity, whereas Wt1-deficient cells remain capable of naive exit but show impaired acquisition of formative and lineage-associated transcriptional programmes. Genome-wide binding analyses during formative state acquisition show that WT1 engages active regulatory elements of the emerging post-implantation gene regulatory network together with core formative transcription factors, including FOXO1, OTX2 and OCT4. Direct comparison of all four major Wt1 splice isoforms identifies KTS splice status as the major determinant of divergent anterior/neuroectodermal and posterior/mesodermal transcriptional programmes in vitro. In the E5.5 epiblast, Wt1 expression and splice composition align with lineage-biased transcriptional states, linking isoform usage to anterior transcriptional biases in vivo. Isoform-dependent gene expression modules are conserved in human pluripotent cells, indicating that this regulatory logic is preserved across species. Together, our findings indicate that lineage-associated transcriptional programmes diverge during formative pluripotency prior to gastrulation and identify alternative splicing as a mechanism that tunes lineage-associated transcriptional bias.

developmental biology↗

bioRxiv2023

Privacy-preserving impact evaluation using Differences-in-Differences

Difference-in-Differences (DID) is a widely used tool for causal impact evaluation but is constrained by data privacy regulations when applied to sensitive personal information, such as individual-level performance records or healthcare data, that must not be shared with data analysts. Obtaining consent can reduce sample sizes or exclude treated/untreated groups, diminishing statistical power or making estimation impossible. Federated Learning, which shares aggregated statistics to ensure privacy, can address these concerns, but advanced federated DID software packages remain scarce. We derived and developed a federated version of the Callaway and SantAnna DID, implemented within the DataSHIELD platform. Our package adheres to DataSHIELDs security measures and adds extra protections, enhancing data privacy and confidentiality. It reproduces point estimates, asymptotic standard errors, and bootstrapped standard errors equivalent to the non-federated implementation. We demonstrate this functionality on simulated data and real-world data from a malaria intervention in Mozambique. By leveraging federated estimates, we increase effective sample sizes leading to reduced estimation uncertainty, and enable estimation when single data owners cannot share the data but only have access to the treated or untreated group.

bioinformatics↗

bioRxiv2022

Accessibility of covariance information creates vulnerability in Federated Learning frameworks

Federated Learning (FL) is gaining traction in various fields as it enables integrative data analysis without sharing sensitive data, such as in healthcare. However, the risk of data leakage caused by malicious attacks must be considered. In this study, we introduce a novel attack algorithm that relies on being able to compute sample means, sample covariances, and construct known linearly independent vectors on the data owner side. We show that these basic functionalities, which are available in several established FL frameworks, are sufficient to reconstruct privacy-protected data. Additionally, the attack algorithm is robust to defense strategies that involve adding random noise. We demonstrate the limitations of existing frameworks and propose potential defense strategies analyzing the implications of using differential privacy. The novel insights presented in this study will aid in the improvement of FL frameworks.

bioinformatics↗

bioRxiv2020

NMD is required for timely cell fate transitions by fine-tuning gene expression and controlling translation

Cell fate transitions depend on balanced rewiring of transcription and translation programmes to mediate ordered developmental progression. Here we identify a feedback loop between nonsense-mediated mRNA decay (NMD) and translation initiation. We show that NMD controls the translation initiation factor Eif4a2 and its premature termination codon encoding isoform (Eif4a2PTC). NMD deficiency leads to translation of a specific truncated Eif4a2 protein, which elicits increased translation rates and is causative for significant delays in mouse embryonic stem cell (ESC) differentiation. Our results show identical mRNA targets for Smg5, Smg6 and Smg7, but illustrate a clear hierarchy between KOs in amplitude of target deregulation and differentiation phenotype (Smg5 > Smg6 > Smg7). This hierarchy highlights heterodimer independent functions for Smg5 and Smg7. Together, our findings expose an intricate link between mRNA stability and translation initiation control, that must be maintained for normal dynamics of cell state transitions.Competing Interest StatementThe authors have declared no competing interest.View Full Text

cell biology↗

bioRxiv2020

Cooperative molecular networks drive a mammalian cell state transition

In the mammalian embryo, epiblast cells must exit their naive state and acquire formative pluripotency. This cell state transition is recapitulated by mouse embryonic stem cells (ESCs), which undergo pluripotency progression in defined conditions in vitro. However, our understanding of the molecular cascades and gene-networks involved in the exit from naive pluripotency remains fragmented. Here we employed a combination of genetic screens in haploid ESCs, CRISPR/Cas9 gene disruption, large-scale transcriptomics and computational systems-biology to delineate the regulatory circuits governing naive state exit. Transcriptome profiles for 73 knockout ESC lines predominantly manifest delays on the trajectory from naive to formative epiblast. We find that gene networks operative in ESCs are active during transition from pre- to post-implantation epiblast in utero. We identified 374 naive-associated genes tightly connected to epiblast state and largely conserved in human ESCs and primate embryos. Integrated analysis of mutant transcriptomes revealed funneling of multiple gene activities into discrete regulatory modules. Finally, we delineate how intersections with signaling pathways direct this pivotal mammalian cell state transition.

systems biology↗