Biology subjects

Herman, A. B.

Publications and source records attributed to Herman, A. B..

12 recordsLinked to original sources

bioRxiv2026

A novel multicellular model of the adult mouse sinoatrial node retains spontaneous electrical activity and enables live investigation of the S100B-associated cell population

Approximately half of the adult sinoatrial node (SAN) consists of non-myocyte populations, indicating that cardiac pacemaking depends on interactions within a multicellular tissue rather than on pacemaker cardiomyocytes alone. Among these, an S100B-associated cell population has been implicated in pacemaker function, yet its identity and physiological roles remain poorly understood. These cells are rare and dispersed throughout the small, structurally complex SAN, making them difficult to observe repeatedly while preserving the native multicellular environment. Here, we established a dissociated multicellular culture of adult mouse SAN tissue on soft collagen-gelatin hydrogels that retains spontaneous electrical activity and permits longitudinal live imaging of S100B-associated cells. Using an S100B-EGFP+ reporter, we identified at least six reproducible morphological and behavioral phenotypes, including migration, proliferation, phagocytic behavior, and spontaneous self-organization into three-dimensional clusters. Cultures remained spontaneously electrically active for more than 10 days in vitro, with peak activity around day 10. This multicellular culture model bridges the gap between intact SAN preparations and isolated-cell cultures, allowing repeated observation of rare S100B-associated cells within a spontaneously active multicellular environment. HighlightsO_LIThe platform enables longitudinal live imaging of rare S100B-associated cells within an diverse multicellular SAN culture. C_LIO_LILive imaging reveals at least six reproducible morphological and behavioral phenotypes of S100B-associated cells. C_LIO_LIDissociated multicellular SAN cultures remain spontaneously electrically active for more than 10 days in vitro. C_LI

cell biology↗

bioRxiv2026

SenCat: Cataloging human cell senescence through multiomic profiling of multiple senescent primary cell types

There is an urgent need to comprehensively catalog senescence markers across cell types in an organism in order to characterize senotypes and senescent cell heterogeneity. Here, we profiled the transcriptomes and proteomes in 14 different primary human cell types undergoing over 30 senescence paradigms to create a senescence catalog we termed SenCat. We found that, while senescent cells from all primary tissue types did not share a single unique marker, they did activate shared specific metabolic and damage-response pathways implicated in tissue repair. Machine learning analysis of the SenCat transcriptomic and proteomic datasets successfully identified independent sets of senescent human cells, and senescent-like cells in mouse lung and kidney. In sum, SenCat represents a much-needed resource to identify senescent cells across tissues in the body. HIGHLIGHTSO_LIIdentifying senescent cells in organisms in vivo remains a challenge C_LIO_LIWe created SenCat: a catalog transcriptomes and proteomes of senescent primary cells C_LIO_LIMachine learning (ML) analysis of SenCat identified robust senescence scores C_LIO_LIML-derived senescence scores uncovered senescent-like cell dynamics in vivo C_LI

molecular biology↗

bioRxiv2025

Senolytic treatment with fisetin reverses age-related endothelial dysfunction partially mediated by SASP factor CXCL12

BackgroundAdvancing age is the strongest risk factor for cardiovascular diseases (CVDs), primarily due to progressive vascular endothelial dysfunction. Cellular senescence and the senescence-associated secretory phenotype (SASP) contribute to age-related endothelial dysfunction by promoting mitochondrial oxidative stress and inflammation, which reduce nitric oxide (NO) bioavailability. However, the molecular changes in senescent endothelial cells and their role in endothelial dysfunction with aging remain incompletely unclear. As such, in this study we sought to identify the endothelial cell senescence-related signalling pathways, endothelial-derived SASP factors, and their impact on endothelial function with aging. MethodsSingle-cell transcriptomics was performed on aortas from young (6 months) and old (27 months) mice with and without in vivo senolytic treatment with fisetin (100 mg/kg/day administered in an intermittent dosing paradigm) to characterize endothelial cell senescence and transcript expression changes. Circulating levels of SASP factors were measured to validate transcriptional changes. Plasma exposure and protein addition and inhibiton experiments were conducted in isolated mouse arteries and cultured human endothelial cells to determine the causal role of the circulating SASP milieu and specific SASP factors in mediating endothelial dysfunction and underlying mechanisms-of-action. ResultsSenescent endothelial cells exhibited elevated expression of SASP factors, particularly Cxcl12, which was reversed by fisetin supplementation, with responses also reflected in circulating CXCL12 concentrations. Plasma from old mice impaired endothelial function by inducing vascular cell senescence, reducing NO, increasing mitochondrial oxidative stress, and promoting endothelial-to-mesenchymal transition--effects partially driven by CXCL12 and prevented by fisetin. ConclusionsThese results identify the SASP and CXCL12 as drivers of age-related endothelial dysfunction and establish mechanisms of senolytic intervention with fisetin supplementation. NOVELTY AND SIGNFICANCEO_ST_ABSWhat is known?C_ST_ABSO_LIAdvancing age is the primary risk factor for cardiovascular disease, in part due to progressive endothelial dysfunction. C_LIO_LICellular senescence contributes to age-related endothelial impairment through the secretion of a pro-inflammatory milieu known as the senescence-associated secretory phenotype (SASP), which can affect neighboring cells and tissue function. C_LIO_LISenolytic compounds selectively eliminate senescent cells and improve vascular function in preclinical models of aging. C_LI What new information does this article contribute?O_LIEndothelial cells are highly susceptible to senescence with aging in vivo and are selectively cleared by senolytic treatment with the natural compound fisetin. C_LIO_LISingle-cell transcriptomic profiling identifies CXCL12 as the most highly upregulated SASP factor in senescent endothelial cells and in circulation with aging, both of which are reversed by senolytic treatment with fisetin. C_LIO_LIWe identify a specific circulating SASP factor, CXCL12, as a partial mediator of endothelial dysfunction by inducing mitochondrial oxidative stress, impairing nitric oxide bioavailability, and promoting endothelial-to-mesenchymal transition (Endo, which is restored by senolytic treatment with fisetin. C_LI SummaryThis study provides novel mechanistic insight into how senescent endothelial cells and their secretory products--particularily CXCL12--contribute to age-related endothelial dysfunction. It further demonstrates that senolytic treatment with fisetin reverses these effects, highlighting a promising translational strategy for targeting vascular aging and preserving endothelial health. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=68 SRC="FIGDIR/small/670216v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@b49a8org.highwire.dtl.DTLVardef@110da92org.highwire.dtl.DTLVardef@17dbd54org.highwire.dtl.DTLVardef@1906c6_HPS_FORMAT_FIGEXP M_FIG C_FIG

physiology↗

bioRxiv2025

A Foraging-Theory Based Model Captures The Full Spectrum of Human Behavioral Diversity in a Classic RL Task

Decision-making tasks involving multiple, simultaneously presented options are mainstays of cognitive neuroscience and psychology and are increasingly important to the emerging field of computational psychiatry. Modeling approaches to these tasks overwhelmingly assume that participants make choices based on explicitly comparing the values of the presented options. Contrary to this long-held assumption, we found instead that humans employ a compare-to-threshold decision process, similar to theories of foraging, when making sequential decisions about concurrently available options. We confirmed this result in a large (1000 participant) dataset with multiple converging lines of evidence comparing both model fits and model generative performance. Value-comparison models were restricted to a reduced area of the potential space of single-trial outcome-dependent behavior, demonstrating an intrinsic limitation in the ability to reproduce strategy diversity. Furthermore, we found that using even the best-fit value-comparison model led to a substantial, systematic bias and a compression of individual differences in reconstructed behavior compared to the foraging-based model, leading to weaker predictions of behavioral health measures. Our results imply that studies using value-comparison models to link behavior with neural activity or psychiatric symptoms may be less sensitive to individual differences than a simple alternative based on ethological foraging.

neuroscience↗

bioRxiv2025

Nonlinear modulation of human exploration by distinct sources of uncertainty

Decision-making in uncertain environments requires balancing exploration and exploitation, with exploration typically assumed to increase monotonically with uncertainty. Challenging this prevailing assumption, we demonstrate a more complex relationship by decomposing environmental uncertainty into volatility (systematic change in reward contingencies, learnable) and stochasticity (random noise in observations, unlearnable). Across two behavioral experiments (N=1001, N=747) using a probabilistic reward task, we find a robust U-shaped relationship between the volatility-to-stochasticity (v/s) ratio and exploratory behavior, with participants exploring more when either stochasticity or volatility dominates. Remarkably, this pattern extends to real-world financial behavior, as demonstrated through analysis of five years of S&P 500 stock market data, where portfolio diversity (a proxy for exploration) shows the same U-shaped relationship with market volatility (systematic price movements driven by fundamental factors, e.g., economic shifts) relative to trading noise (random fluctuations from trading activity unrelated to fundamentals). These findings reveal how humans adaptively modulate exploration strategies based on the qualitative composition of uncertainty, with optimal performance occurring at intermediate uncertainty ratios. This nonlinear relationship has important implications for understanding decision-making across domains where uncertainty arises from multiple sources.

neuroscience↗

bioRxiv2025

Metric Validation for Detection of Delayed and Directed Coupling

BackgroundThe brain functions as a complex network of billions of interconnected neurons, coordinating processes from basic reflexes to high-level cognition. Dysfunction in these networks contribute to neurological and psychiatric disorders, including epilepsy, depression, and Parkinsons disease. Understanding these network alterations is essential for developing effective therapies. However, reconstructing network topology from human electrophysiology data is challenging due to sparse sampling, measurement noise, and variable time delays in interregional communication. Effective connectivity (EC) metrics have been developed to infer directed neural interactions, but their accuracy and robustness under real-world data constraints remain unclear. This study empirically evaluates common EC metrics to determine which most accurately reconstruct network topology in the presence of data limitations. MethodsWe generated Erd[o]s-Renyi networks and simulated time series using a time-delayed vector autoregressive (VAR) model. We systematically varied network size, data length, measurement noise, and network coverage. Variations of four commonly used EC metrics, cross-correlation, Granger causality, mutual information, and transfer entropy, were evaluated for reconstruction accuracy using cosine distance to compare estimated and true connectivity matrices. ResultsMultivariate transfer entropy demonstrated the highest accuracy across various conditions but required significantly longer computation times. For small networks (<30 nodes), mutual information and Granger causality rapidly and accurately reconstructed networks. For larger networks, partial cross-correlation performed well with good computational efficiency. Notably, zero-lag metrics perform no better than chance in nearly all conditions. ConclusionThe choice of an EC metric should consider specific data constraints.While multivariate transfer entropy is the most reliable across conditions, its long runtime limits its practical application. For large networks, partial cross-correlation offers a faster and reasonably accurate alternative. Granger causality and mutual information are effective for small networks. Critically, time-lagged metrics are essential for accurate network reconstructions, as failing to account for time delays leads to reconstructions no more accurate than random network models.

bioinformatics↗

bioRxiv2024

Persistent Decision-Making in Mice, Monkeys, and Humans

Humans have the capacity to persist in behavioural policies, even in challenging environments that lack immediate reward. Persistence is the scaffold on which many higher executive functions are built. However, it remains unclear whether humans are uniquely persistent or, instead, if this capacity is widely conserved across species. To address this question, we compared humans with mice and monkeys in harmonised versions of a dynamic decision-making task. The task encouraged all species to strike a balance between persistently exploiting one policy and exploring alternative policies that could become better at any moment. Although all three species had similar strategies, we found that both primate species--humans and monkeys--were able to persist in exploitation for longer than the mice. The similarities in persistence patterns in humans and monkeys, as opposed to mice, may be related to the various ecological, neurobiological, or cognitive factors that differ systematically between these species.

animal behavior and cognition↗

bioRxiv2023

Flexible Multi-Step Hypothesis Testing of Human ECoG Data using Cluster-based Permutation Tests with GLMEs

BackgroundTime series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.g. variability across subjects). Here, we propose a flexible multi-step hypothesis testing strategy using CBPT with Linear Mixed Effects Models (LMEs) and Generalized Linear Mixed Effects Models (GLMEs) that can be applied to a wide range of experimental designs and data types. MethodsWe first evaluate the statistical robustness of LMEs and GLMEs using simulated data distributions. Second, we apply a multi-step hypothesis testing strategy to analyze ERPs and broadband power signals extracted from human ECoG recordings collected during a simple image viewing experiment with image category and novelty as fixed effects. Third, we assess the statistical power differences between analyzing signals with CBPT using LMEs compared to CBPT using separate t-tests run on each fixed effect through simulations that emulate broadband power signals. Finally, we apply CBPT using GLMEs to high-gamma burst data to demonstrate the extension of the proposed method to the analysis of nonlinear data. ResultsFirst, we found that LMEs and GLMEs are robust statistical models. In simple simulations LMEs produced highly congruent results with other appropriately applied linear statistical models, but LMEs outperformed many linear statistical models in the analysis of "suboptimal" data and maintained power better than analyzing individual fixed effects with separate t-tests. GLMEs also performed similarly to other nonlinear statistical models. Second, in real world human ECoG data, LMEs performed at least as well as separate t-tests when applied to predefined time windows or when used in conjunction with CBPT. Additionally, fixed effects time courses extracted with CBPT using LMEs from group-level models of pseudo-populations replicated latency effects found in individual category-selective channels. Third, analysis of simulated broadband power signals demonstrated that CBPT using LMEs was superior to CBPT using separate t-tests in identifying time windows with significant fixed effects especially for small effect sizes. Lastly, the analysis of high-gamma burst data using CBPT with GLMEs produced results consistent with CBPT using LMEs applied to broadband power data. ConclusionsWe propose a general approach for statistical analysis of electrophysiological data using CBPT in conjunction with LMEs and GLMEs. We demonstrate that this method is robust for experiments with multiple fixed effects and applicable to the analysis of linear and nonlinear data. Our methodology maximizes the statistical power available in a dataset across multiple experimental variables while accounting for hierarchical random effects and controlling FWER across fixed effects. This approach substantially improves power and accuracy leading to better reproducibility. Additionally, CBPT using LMEs and GLMEs can be used to analyze individual channels or pseudo-population data for the comparison of functional or anatomical groups of data. Highlights[bullet] Combining CBPT with GLMEs allows statistical analysis to match experimental design. [bullet]CBPT with GLMEs accounts for subject variability and hierarchical random effects. [bullet]The proposed method maintains control of type I error, type II error, and FWER. [bullet]CBPT with GLMEs can be applied to individual channels and pseudo-population data.

neuroscience↗

bioRxiv2022

The YAP-TEAD complex promotes senescent cell survival by lowering endoplasmic reticulum stress

Sublethal cell damage can trigger a complex adaptive program known as senescence, characterized by growth arrest, resistance to apoptosis, and a senescence-associated secretory phenotype (SASP). As senescent cells accumulating in aging organs are linked to many age-associated diseases, senotherapeutic strategies are actively sought to eliminate them. Here, a whole-genome CRISPR knockout screen revealed that proteins in the YAP-TEAD pathway influenced senescent cell viability. Accordingly, treating senescent cells with a drug that inhibited this pathway, Verteporfin (VPF), selectively triggered apoptotic cell death and derepressed DDIT4, in turn inhibiting mTOR. Reducing mTOR function in senescent cells diminished endoplasmic reticulum (ER) biogenesis, causing ER stress and apoptosis due to high demands on ER function by the SASP. Importantly, VPF treatment decreased senescent cell numbers in the organs of old mice and mice exhibiting doxorubicin-induced senescence. We present a novel senolytic strategy that eliminates senescent cells by hindering ER activity required for SASP production.

cell biology↗

bioRxiv2022

Pleiotropic effects of BAFF on the senescence-associated secretome and growth arrest

Senescent cells release a variety of cytokines, proteases, and growth factors collectively known as the senescence-associated secretory phenotype (SASP). Sustained SASP contributes to a pattern of chronic inflammation associated with aging and implicated in many age-related diseases. Here, we investigated the expression and function of the immunomodulatory cytokine BAFF (B-cell activating factor), a SASP protein, in multiple senescence models. We first characterized BAFF production across different senescence models, including senescent human diploid fibroblasts (WI-38, IMR-90) and monocytic leukemia cells (THP-1), and tissues of mice induced to undergo senescence. We then identified IRF1 (interferon response factor 1) as a transcription factor required for promoting BAFF mRNA transcription in senescence. We discovered that suppressing BAFF production decreased the senescent phenotype of both fibroblasts and monocyte-derived THP-1 cells, overall reducing IL6 secretion, SA-{beta}-Gal staining, and {gamma}-H2AX accumulation. Importantly, however, the influence of BAFF on the senescence program was cell type-specific: in monocytes, BAFF promoted the early activation of NF-{kappa}B and general SASP secretion, while in fibroblasts, BAFF contributed to the production and function of TP53 (p53). We propose that BAFF is elevated across senescence models and is a potential target for senotherapy.

cell biology↗

bioRxiv2021

Pre-trial predictors of conflict response efficacy in human dorsolateral prefrontal cortex

The ability to perform motor actions depends, in part, on the brains initial state, that is, the ensemble firing rate pattern prior to the initiation of action. We hypothesized that the same principle would apply to cognitive functions as well. To test this idea, we examined a unique set of single unit data collected in human dorsolateral prefrontal (dlPFC) cortex. Data were collected in a conflict task that interleaves Simon (motor-type) and Eriksen (flanker-type) conflict trials. In dlPFC, variability in pre-trial firing rate predicted the ability to resolve conflict, as inferred from reaction times. Ensemble patterns that predicted faster Simon reaction times overlapped slightly with those predicting Erikson performance, indicating that the two conflict types are associated with near-orthogonal initial states, and suggesting that there is a weak abstract or amodal conflict preparatory state in this region. These codes became fully orthogonalized in the response state. We interpret these results in light of the initial state hypothesis, arguing that the firing patterns in dlPFC immediately preceding the start of a task predispose it for the efficient implementation of cognitive action.

neuroscience↗

bioRxiv2019

Unraveling Why We Sleep: Quantitative Analysis Reveals Abrupt Transition from Neural Reorganization to Repair in Early Development

Sleep serves disparate functions, most notably neural repair, metabolite clearance and circuit reorganization, yet the relative importance of these functions remains hotly debated. Here, we create a novel mechanistic framework for understanding and predicting how sleep changes during ontogeny (why babies sleep twice as long as adults) and across phylogeny (why mice sleep roughly five times that of whales). We use this theory to quantitatively distinguish between sleep used for neural reorganization versus repair. We conduct a comprehensive, quantitative analysis of human sleep using total sleep time, cerebral metabolic rate, brain size, synaptic density, and REM sleep (used here to also refer to Active Sleep in infants and children). Our findings reveal an abrupt transition, between 2 and 3 years of age in humans. Specifically, our results show that differences in sleep across phylogeny and during late ontogeny (after 2 or 3 years in humans) are primarily due to sleep functioning for repair or clearance, while changes in sleep during early ontogeny (before 2 - 3 years in humans) primarily support neural reorganization and learning. Moreover, our analysis shows that neuroplastic reorganization occurs primarily in REM sleep but not in NREM. In accordance with the developmental role of neuroplasticity, the percent of time spent in REM sleep is independent of brain size across species but decreases dramatically as brain size grows through development. Furthermore, the ratio of NREM sleep time to awake time emerges as a new invariant across development. This developmental transition and fundamental shift across ontogeny and phylogeny suggests a complex interplay between developmental and evolutionary constraints on sleep.

neuroscience↗