Biology subjects

Neuert, G.

Publications and source records attributed to Neuert, G..

11 recordsLinked to original sources

bioRxiv2025

TrueProbes: Quantitative Single-Molecule RNA-FISH Probe Design Improves RNA Detection

Single-molecule RNA fluorescence in situ hybridization (smRNA-FISH) is a widely used method for visualizing and quantifying RNA molecules in cells and tissues at high spatial resolution. The technique relies on fluorescently labeled oligonucleotide probes that hybridize to target RNA. Accurate quantification depends on high probe specificity to ensure fluorescent signals reflect target RNA binding rather than off-target interactions. Numerous factors, including genome sequence complexity, secondary probe structure, hybridization conditions, and gene expression variability across cell types and lines, influence smRNA-FISH probe efficacy. Existing smRNA-FISH probe design tools have limitations, including narrow heuristics, incomplete off-target assessment, and reliance on "trial-and-error approaches. To address these challenges, we developed TrueProbes, a probe design software platform that integrates genome-wide BLAST-based binding analysis with thermodynamic modeling to generate high-specificity probe sets. TrueProbes ranks and selects probes based on predicted binding affinity, target specificity, and structural constraints. It also incorporates a thermodynamic-kinetic simulation model to provide predictive design metrics and optimize probe performance under user-defined conditions. We benchmarked TrueProbes against several widely used smRNA-FISH design tools and found that it consistently outperformed alternatives across multiple computational metrics and experimental validation assays. Probes designed with TrueProbes showed enhanced target selectivity and superior experimental performance.

systems biology↗

bioRxiv2025

TrueSpot: A robust automated tool for quantifying signal puncta in fluorescent imaging

Characterizing the movement of biomolecules in single cells quantitatively is essential to understanding fundamental biological mechanisms. RNA fluorescent in situ hybridization (RNA-FISH) is a technique for visualizing RNA in fixed cells using fluorescent probes. Automated processing of the resulting images is essential for large datasets. Here we demonstrate that our RNA-FISH image processing tool, TrueSpot, is useful for automatically detecting the locations of RNA at single molecule resolution. TrueSpot also performs well on images with immunofluorescent (IF) and GFP tagged clustered protein targets. Additionally, we show that our 3D spot detection approach substantially outperforms current 2D spot detection algorithms.

systems biology↗

bioRxiv2025

Transcriptional stochasticity reveals multiple mechanisms of long noncoding RNA regulation at the Xist - Tsix locus

Long noncoding RNAs (LncRNAs) are increasingly recognized as being involved in human physiology and diseases, but there is a lack of mechanistic understanding for the majority of lncRNAs. We comparatively tested proposed mechanisms of antisense lncRNA regulation at the X-chromosome Inactivation (XCI) locus. We find that due to stochasticity in transcription, different mechanisms based on the act of transcription regulate Xist and Tsix at different levels of nascent transcription. At medium levels, RNA polymerases transcribe Xist and Tsix on each strand at the same transcription site and deposit significant amounts of the histone mark H3K36me3, which inhibits Xist. At high levels of nascent transcription, many RNA polymerases transcribe Xist or Tsix resulting in transcriptional interference. Therefore, lncRNA expression variability is not just a quirk of transcription but an important aspect of regulation that allows multiple mechanisms to be employed by the same gene locus within the same cell population.

systems biology↗

bioRxiv2023

Phenotypic consequences of logarithmic signaling in MAPK stress response

How cells respond to dynamic environmental changes is crucial for understanding fundamental biological processes and cell physiology. In this study, we developed an experimental and quantitative analytical framework to explore how dynamic stress gradients that change over time regulate cellular volume, signaling activation, and growth phenotypes. Our findings reveal that gradual stress conditions substantially enhance cell growth compared to conventional acute stress. This growth advantage correlates with a minimal reduction in cell volume dependent on the dynamic of stress. We explain the growth phenotype with our finding of a logarithmic signal transduction mechanism in the yeast Mitogen-Activated Protein Kinase (MAPK) osmotic stress response pathway. These insights into the interplay between gradual environments, cell volume change, dynamic cell signaling, and growth, advance our understanding of fundamental cellular processes in gradual stress environments.

systems biology↗

bioRxiv2020

Diverse cell stimulation kinetics identify predictive signal transduction models

The drive to understand cell signaling responses to environmental, chemical and genetic perturbations has produced outstanding fits of computational models to increasingly intricate experiments, yet predicting quantitative responses for new biological conditions remains challenging. Overcoming this challenge depends not only on good models and detailed experimental data but perhaps more so on how well the two are integrated. Our quantitative, live single-cell fluorescence imaging datasets and computational framework to model generic signaling networks show how different changing environments (hereafter kinetic stimulations) probe and result in distinct pathway activation dynamics. Utilizing multiple diverse kinetic stimulations better constrains model parameters and enables predictions of signaling dynamics that would be impossible using traditional step-change stimulations. To demonstrate our approachs generality, we use identified models to predict signaling dynamics in normal, mutated, and drug-treated conditions upon multitudes of kinetic stimulations and quantify which proteins and reaction rates are most sensitive to which extracellular stimulations.

systems biology↗

bioRxiv2019

Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments

Modern biological experiments are becoming increasingly complex, and designing these experiments to yield the greatest possible quantitative insight is an open challenge. Increasingly, computational models of complex stochastic biological systems are being used to understand and predict biological behaviors or to infer biological parameters. Such quantitative analyses can also help to improve experiment designs for particular goals, such as to learn more about specific model mechanisms or to reduce prediction errors in certain situations. A classic approach to experiment design is to use the Fisher information matrix (FIM), which quantifies the expected information a particular experiment will reveal about model parameters. The Finite State Projection based FIM (FSP-FIM) was recently developed to compute the FIM for discrete stochastic gene regulatory systems, whose complex response distributions do not satisfy standard assumptions of Gaussian variations. In this work, we develop the FSP-FIM analysis for a stochastic model of stress response genes in S. cerevisae under time-varying MAPK induction. We verify this FSP-FIM analysis and use it to optimize the number of cells that should be quantified at particular times to learn as much as possible about the model parameters. We then extend the FSP-FIM approach to explore how different measurement times or genetic modifications help to minimize uncertainty in the sensing of extracellular environments, and we experimentally validate the FSP-FIM to rank single-cell experiments for their abilities to minimize estimation uncertainty of NaCl concentrations during yeast osmotic shock. This work demonstrates the potential of quantitative models to not only make sense of modern biological data sets, but to close the loop between quantitative modeling and experimental data collection.

systems biology↗

bioRxiv2019

Multiplex RNA single molecule FISH of inducible mRNAs in single yeast cells

Transcript levels powerfully influence cell behavior and phenotype and are carefully regulated at several steps. Recently developed single cell approaches such as RNA single molecule fluorescence in-situ hybridization (smFISH) have produced advances in our understanding of how these steps work within the cell. In comparison to single-cell sequencing, smFISH provides more accurate quantification of RNA levels. Additionally, transcript subcellular localization is directly visualized, enabling the analysis of transcription (initiation and elongation), RNA export and degradation. As part of our efforts to investigate how this type of analysis can generate improved models of gene expression, we used smFISH to quantify the kinetic expression of STL1 and CTT1 mRNAs in single Saccharomyces cerevisiae cells upon 0.2 and 0.4M NaCl osmotic stress. In this Data Descriptor, we outline our procedure along with our data in the form of raw images and processed mRNA counts. We discuss how these data can be used to develop single cell modelling approaches, to study fundamental processes in transcription regulation and develop single cell image processing approaches.

systems biology↗

bioRxiv2019

Generating kinetic environments to study dynamic cellular processes in single cells

Cells of any organism are consistently exposed to changes over time in their environment. The kinetics by which these changes occur are critical for the cellular response and fate decision. It is therefore important to control the temporal changes of extracellular stimuli precisely to understand biological mechanisms in a quantitative manner. Most current cell culture and biochemical studies focus on instant changes in the environment and therefore neglect the importance of kinetic environments. To address these shortcomings, we developed two experimental methodologies to precisely control the environment of single cells. These methodologies are compatible with standard biochemistry, molecular, cell and quantitative biology assays. We demonstrate applicability by obtaining time series and time point measurements in both live and fixed cells. We demonstrate the feasibility of the methodology in yeast and mammalian cell culture in combination with widely used assays such as flow cytometry, time-lapse microscopy and single-molecule RNA Fluorescent in-situ Hybridization. Our experimental methodologies are easy to implement in most laboratory settings and allows the study of kinetic environments in a wide range of assays and different cell culture conditions.

systems biology↗

bioRxiv2019

Automated cell boundary and 3D nuclear segmentation of cells in suspension

To characterize cell types, cellular functions and intracellular processes, an understanding of the differences between individual cells is required. Although microscopy approaches have made tremendous progress in imaging cells in different contexts, the analysis of these imaging data sets is a long-standing, unsolved problem. The few robust cell segmentation approaches that exist often rely on multiple cellular markers and complex time-consuming image analysis. Recently developed deep learning approaches can address some of these challenges, but they require tremendous amounts of data and well-curated reference data sets for algorithm training. We propose an alternative experimental and computational approach, called CellDissect, in which we first optimize specimen preparation and data acquisition prior to image processing to generate high quality images that are easier to analyze computationally. By focusing on fixed suspension and dissociated adherent cells, CellDissect relies only on widefield images to identify cell boundaries and nuclear staining to automatically segment cells in two dimensions and nuclei in three dimensions. This segmentation can be performed on a desktop computer or a computing cluster for higher throughput. We compare and evaluate the accuracy of different nuclear segmentation approaches against manual expert cell segmentation for different cell lines acquired with different imaging modalities.

systems biology↗

bioRxiv2017

Linearly changing stress environment causes cellular growth phenotype

Cells are exposed to changes in extracellular stimulus concentration that vary as a function of rate. However, the effect of stimulation rate on cell behavior and signaling remains poorly understood. Here, we examined how varying the rate of stress application alters budding yeast cell viability and mitogen-activated protein kinase (MAPK) signaling at the single-cell level. We show that cell survival and signaling depend on a rate threshold that operates in conjunction with a concentration threshold to determine the timing of MAPK signaling during rate-varying stimulus treatments. We also discovered that the stimulation rate threshold is sensitive to changes in the expression levels of the Ptp2 phosphatase, but not of another phosphatase that similarly regulates osmostress signaling during switch-like treatments. Our results demonstrate that stimulation rate is a regulated determinant of signaling output and provide a paradigm to guide the dissection of major stimulation rate-dependent mechanisms in other systems.

systems biology↗

bioRxiv2017

Predicting single-cell transcription dynamics even when the central limit theorem fails

Despite substantial experimental and computational efforts, mechanistic modeling remains more predictive in engineering than in systems biology. The reason for this discrepancy is not fully understood. Although randomness and complexity of biological systems play roles in this concern, we hypothesize that significant and overlooked challenges arise due to specific features of single-molecule events that control crucial biological responses. Here we show that modern statistical tools to disentangle complexity and stochasticity, which assume normally distributed fluctuations or enormous datasets, don't apply to the discrete, positive, and non-symmetric distributions that characterize spatiotemporal mRNA fluctuations in single-cells. We demonstrate an alternate approach that fully captures discrete, non-normal effects within finite datasets. As an example, we integrate single-molecule measurements and these advanced computational analyses to explore Mitogen Activated Protein Kinase induction of multiple stress response genes. We discover and validate quantitatively precise, reproducible, and predictive understanding of diverse transcription regulation mechanisms, including gene activation, polymerase initiation, elongation, mRNA accumulation, spatial transport, and degradation. Our model-data integration approach extends to any discrete dynamic process with rare events and realistically limited data.\n\nSignificance StatementSystems biology seeks to combine experiments with computation to predict complex biological behaviors. However, despite tremendous data and knowledge, most biological models make terrible predictions. By analyzing single-cell-single-molecule measurements of mRNA in yeast during stress response, we explore how prediction accuracy is controlled by experimental distributions shapes. We find that asymmetric data distributions, which arise in measurements of positive quantities, can cause standard modeling approaches to yield excellent fits but make meaningless predictions. We demonstrate advanced computational tools that solve this dilemma and achieve predictive understanding of many spatiotemporal mechanisms of transcription control including RNA polymerase initiation and elongation and mRNA accumulation, transport and decay. Our approach extends to any discrete dynamic process with rare events and realistically limited data.

systems biology↗