Biology subjects

Schumacher, L.

Publications and source records attributed to Schumacher, L..

6 recordsLinked to original sources

bioRxiv2025

Non-decision time-informed collapsing threshold diffusion model: A joint modeling framework with identifiable time-dependent parameters

Over the past sixty years, evidence accumulation models have emerged as a dominant framework for explaining the neural and behavioral aspects of the process underlying decision making. These models have also been widely used as a measurement instrument to assess individual differences in latent cognitive constructs underlying decision making. A central assumption of most of these models is that decision makers accumulate noisy evidence until a fixed decision threshold is reached. However, both behavioral and neuroscientific findings, along with theoretical considerations related to optimality, have suggested that the decision threshold varies over time. Although time-dependent threshold models often provide a better account of empirical data, a major challenge associated with these models is the unreliable estimation of their parameters. This limitation has led researchers to emphasize model-fitting comparisons rather than interpreting parameter values or accounting for individual differences in the dynamics of the decision threshold. In this work, we address the reliability issue of parameter estimation in time-dependent threshold diffusion models by proposing a joint modeling approach that links non-decision time to external observations. Parameter recovery simulations demonstrate that informing the diffusion model with trial-level noisy measurements of non-decision time substantially improves the reliability of parameter estimation for time-dependent threshold diffusion models. Additionally, we reanalyzed the experimental data from two perceptual decision-making tasks to illustrate the feasibility of the proposed modeling approach. Non-decision time measurements were extracted from electroencephalography (EEG) recordings using the hidden multivariate pattern method. The cognitive modeling results revealed that, in addition to the reliable parameter estimation, constraining non-decision time improves the absolute fit to behavioral data.

neuroscience↗

bioRxiv2025

Clinical progression of clonal hematopoiesis is determined by a combination of mutation timing, fitness, and clonal structure

Clonal hematopoiesis (CH) is characterized by expanding blood cell clones carrying somatic mutations in healthy aged individuals and is associated with various age-related diseases and all-cause mortality. While CH mutations affect diverse genes associated with myeloid malignancies, their mechanisms of expansion and disease associations remain poorly understood. We investigate the relationship between clonal fitness and clinical outcomes by integrating data from three longitudinal aging cohorts (n=713, observations=2,341). We demonstrate pathway-specific fitness advantage and clonal composition influence clonal dynamics. Further, the timing of mutation acquisition is necessary to determine the extent of clonal expansion reached during the host individuals lifetime. We introduce MACS120, a metric combining mutation context, timing, and variant fitness to predict future clonal growth, outperforming traditional variant allele frequency measurements in predicting clinical outcomes. Our unified analytical framework enables standardized clonal dynamics inference across cohorts, advancing our ability to predict and potentially intervene in CH-related pathologies.

genetics↗

bioRxiv2024

Data-modeling the interplay between single cell shape, single cell protein expression, and tissue state

Changes in cell shape are fundamentally involved in signaling, intracellular organization, function, and intercellular interactions within tissues, in health and disease. Investigating the interplay between cell shape and protein expression was limited, until recently, by the number of proteins that can be imaged simultaneously or by population averaging. We combined spatial multiplexed single cell imaging and machine learning to systematically investigate the intricate relationships between cell shape and protein expression in the context of heterogeneous human cells in their native state in human tissue samples in situ. Our analysis established a universal bi-directional link between the cells shape and its protein expression across different cell types, diseases, and disease states in human tissues, enabling new applications. Machine learning interpretability showed that the contribution of shape features to a prediction can potentially infer new protein functions. Unbiased screening of the links between all pairs consisting of one protein and one cell type identified a subpopulation of large p53-positive tumor cells across two cancers. Ultimately, inclusion of single cell shape properties enhanced Graph Neural Network disease state prediction. Our results open the door to unraveling the intricate connections between protein expression at the single cell level, cell shape, tissue organization, and tissue state in a physiological context.

cell biology↗

bioRxiv2024

Licensing and competition of stem cells at the niche combine to regulate tissue maintenance

To maintain and regenerate adult tissues after injury, the numbers, proliferation, and differentiation rates of tissue-resident stem cells must be precisely regulated. The regulatory strategies preventing exhaustion or overgrowth of the stem cell pool, whether there is coordination between different mechanisms, and how to detect them from snapshots of the cell populations, remains un-resolved. Recent findings in the Drosophila testes show that prior to differentiation, somatic stem cells transition to a state that licenses them to differentiate upon receiving a commitment signal, but remain capable of fully regaining stem cell function. Here, we build stochastic mathematical models for the somatic stem cell population to investigate how licensing contributes to homeostasis and the variability of stem cell numbers. We find that licensing alone is sufficient regulation to maintain a stable homeostatic state and prevent stem cell extinction. Comparison with previous experimental data argues for the likely presence of regulation through competition for niche access. We show that competition for niche access contributes to the reduction of the variability of stem cell numbers but does not prevent extinction. Our results suggest that a combination of both regulation strategies, licensing and competition for niche access, is needed to reduce variability and prevent extinction simultaneously.

developmental biology↗

bioRxiv2023

Modelling the dynamics of senescence spread

Cellular senescence is a cell surveillance mechanism that arrests the cell cycle in damaged cells. The senescent phenotype can spread from cell to cell through paracrine and juxtacrine signalling, but the dynamics of this process are not well understood. Although senescent cells are important in ageing, wound healing, and cancer, it is unclear how the spread of senescence is contained in senescent lesions. In the absence of the immune system, senescence could theoretically spread infinitely from one cell to another, but this contradicts experimental evidence. To investigate this issue, we developed both a minimal mathematical model and a stochastic simulation of senescence spread. Our results suggest that differences in the number of signalling molecules secreted between subtypes of senescent cells can limit the spread of senescence. We found that dynamic, timedependent paracrine signalling prevents the uncontrolled spread of senescence and we demonstrate how model parameters can be determined using Bayesian inference in a proposed experiment.

systems biology↗

bioRxiv2021

P2Y6 receptor signaling in natural killer cells impairs insulin sensitivity in obesity

Natural killer (NK) cells contribute to the development of obesity-associated insulin resistance and have previously been shown to up-regulate the expression of the P2Y purinoreceptor 6 (P2Y6R) upon high fat diet (HFD)-induced obesity. Here, we reveal that NK cell-specific inactivation of the P2Y6R gene improves insulin sensitivity in obese mice and reduces the expression of chemokines in adipose tissue infiltrating NK-cells. Obese mice lacking P2Y6R specifically in NK cells exhibited a reduction in adipose tissue inflammation, exhibited improved insulin-stimulated suppression of lipolysis in adipose tissue and a reduction in hepatic glucose production, leading to an overall improvement of systemic insulin sensitivity. In contrast, myeloid lineage specific P2Y6R inactivation does not affect energy or glucose homeostasis in obesity. Collectively, we show that P2Y6R signaling in NK cells contributes to the development of obesity-associated insulin resistance and thus might be a future target for the treatment of obesity-associated insulin resistance and type 2 diabetes.

immunology↗