Biology subjects

Buchler, N. E.

Publications and source records attributed to Buchler, N. E..

6 recordsLinked to original sources

bioRxiv2024

Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle

The life cycle of biomedical and agriculturally relevant eukaryotic microorganisms involves complex transitions between proliferative and non-proliferative states such as dormancy, mating, meiosis, and cell division. New drugs, pesticides, and vaccines can be created by targeting specific life cycle stages of parasites and pathogens. However, defining the structure of a microbial life cycle often relies on partial observations that are theoretically assembled in an ideal life cycle path. To create a more quantitative approach to studying complete eukaryotic life cycles, we generated a deep learning-driven imaging framework to track microorganisms across sexually reproducing generations. Our approach combines microfluidic culturing, life cycle stage-specific segmentation of microscopy images using convolutional neural networks, and a novel cell tracking algorithm, FIEST, based on enhancing the overlap of single cell masks in consecutive images through deep learning video frame interpolation. As proof of principle, we used this approach to quantitatively image and compare cell growth and cell cycle regulation across the sexual life cycle of Saccharomyces cerevisiae. We developed a fluorescent reporter system based on a fluorescently labeled Whi5 protein, the yeast analog of mammalian Rb, and a new High-Cdk1 activity sensor, LiCHI, designed to report during DNA replication, mitosis, meiotic homologous recombination, meiosis I, and meiosis II. We found that cell growth preceded the exit from non-proliferative states such as mitotic G1, pre-meiotic G1, and the G0 spore state during germination. A decrease in the total cell concentration of Whi5 characterized the exit from non-proliferative states, which is consistent with a Whi5 dilution model. The nuclear accumulation of Whi5 was developmentally regulated, being at its highest during meiotic exit and spore formation. The temporal coordination of cell division and growth was not significantly different across three sexually reproducing generations. Our framework could be used to quantitatively characterize other single-cell eukaryotic life cycles that remain incompletely described. An off-the-shelf user interface Yeastvision provides free access to our image processing and single-cell tracking algorithms.

cell biology↗

bioRxiv2022

Gene expression noise accelerates the evolution of a biological oscillator

Gene expression is a biochemical process, where stochastic binding and unbinding events naturally generate fluctuations and cell-to-cell variability in gene dynamics. These fluctuations typically have destructive consequences for proper biological dynamics and function (e.g., loss of timing and synchrony in biological oscillators). Here, we show that gene expression noise counter-intuitively accelerates the evolution of a biological oscillator and, thus, can impart a benefit to living organisms. We used computer simulations to evolve two mechanistic models of a biological oscillator at different levels of gene expression noise. We first show that gene expression noise induces oscillatory-like dynamics in regions of parameter space that cannot oscillate in the absence of noise. We then demonstrate that these noise-induced oscillations generate a fitness landscape whose gradient robustly and quickly guides evolution by mutation towards robust and self-sustaining oscillation. These results suggest that noise can help dynamical systems evolve or learn new behavior by revealing cryptic dynamic phenotypes outside the bifurcation point. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/485207v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@fdbc9forg.highwire.dtl.DTLVardef@6a8b08org.highwire.dtl.DTLVardef@cdbc7borg.highwire.dtl.DTLVardef@1116812_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2019

Genetic transformation and live-cell nuclear and actin dynamics during the life cycle of a chytrid

Chytrids are early-diverging fungi that share ancestral features of animals, including cells that crawl and swim. At later stages, chytrid cells resemble fungi with a chitin-based cell wall and hyphal-like structures known as rhizoids. Chytrids are important evolutionary transitional forms, but much remains unknown about their cell biology because we lack genetic tools for the live-cell imaging of their nuclear and cytoskeletal dynamics. Here, we generated stable transgenic lines of the soil chytrid Spizellomyces punctatus, and coupled live-cell microscopy and fluorescent tagging to measure the timing and coordination of growth, the cell cycle, and the actin cytoskeleton. We show that Spizellomyces zoospores rapidly encyst, develop rhizoids, and undergo multiple rounds of synchronous nuclear division in a sporangium, followed by cellularization, to create and release hundreds of zoospores. The life cycle is complete in less than 30 hours. We further demonstrate that crawling zoospores, akin to animal cells, display polymerized actin at the leading edge of amoeboid fronts. After encystment, polymerized actin reorganizes into fungal-like cortical patches and cables that extend into the rhizoid. Actin remains highly dynamic during sporo-genesis with the formation of actin perinuclear shells each cell cycle and the emergence of polygonal territories during cellularization. Spizellomyces is a fast-growing and genetically-tractable organism that should be useful for comparative cell biology and understanding the evolution of fungi and early eukaryotes.

cell biology↗

bioRxiv2018

Accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts

Understanding how stochastic gene expression is regulated in biological systems using snapshots of single-cell transcripts requires state-of-the-art methods of computational analysis and statistical inference. A Bayesian approach to statistical inference is the most complete method for model selection and uncertainty quantification of kinetic parameters from single-cell data. This approach is impractical because current numerical algorithms are too slow to handle typical models of gene expression. To solve this problem, we first show that time-dependent mRNA distributions of discrete-state models of gene expression are dynamic Poisson mixtures, whose mixing kernels are characterized by a piece-wise deterministic Markov process. We combined this analytical result with a kinetic Monte Carlo algorithm to create a hybrid numerical method that accelerates the calculation of time-dependent mRNA distributions by 1000-fold compared to current methods. We then integrated the hybrid algorithm into an existing Monte Carlo sampler to estimate the Bayesian posterior distribution of many different, competing models in a reasonable amount of time. We validated our method of accelerated Bayesian inference on several synthetic data sets. Our results show that kinetic parameters can be reasonably constrained for modestly sampled data sets, if the model is known a priori. If the model is unknown,the Bayesian evidence can be used to rigorously quantify the likelihood of a model relative to other models from the data. We demonstrate that Bayesian evidence selects the true model and outperforms approximate metrics, e.g., Bayesian Information Criterion (BIC) or Akaike Information Criterion (AIC), often used for model selection.

molecular biology↗

bioRxiv2018

Heterogeneous timing of asexual cycles in Plasmodium falciparum quantified by extended time-lapse microscopy

Malarial fever arises from the synchronous bursting of human red blood cells by the Plasmodium parasite. The released parasites re-infect neighboring red blood cells and undergo another asexual cycle of differentiation and proliferation for 48 hours, before again bursting synchronously. The synchrony of bursting is lost during in vitro culturing of the parasite outside the human body, presumably because the asexual cycle is no longer entrained by host-specific circadian cues. Therefore, most in vitro malaria studies have relied on the artificial synchronization of the parasite population. However, much remains unknown about the degree of timing heterogeneity of asexual cycles and how artificial synchronization may affect this timing. Here, we combined time-lapse fluorescence microscopy and long-term culturing to follow single cells and directly measure the heterogeneous timing of in vitro asexual cycles. We first demonstrate that unsynchronized laboratory cultures are not fully asynchronous and the parasites exhibit a bimodal distribution in their first burst times. We then show that synchronized and unsynchronized cultures had similar asexual cycle periods, which indicates that artificial synchronization does not fundamentally perturb asexual cycle dynamics. Last, we demonstrate that sibling parasites descended from the same schizont exhibited significant variation in asexual cycle period, although smaller than the variation between non-siblings. The additional variance between non-siblings likely arises from the variable environments and/or developmental programs experienced in different host cells.

microbiology↗

bioRxiv2017

Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells

Single-molecule RNA fluorescence in situ hybridization (smFISH) provides unparalleled resolution on the abundance and localization of nascent and mature transcripts in single cells. Gene expression dynamics are typically inferred by measuring mRNA abundance in small numbers of fixed cells sampled from a population at multiple time-points after induction. The sparse data that arise from the small number of cells obtained using smFISH present a challenge for inferring transcription dynamics. Here, we developed a computational pipeline (BayFish) to infer kinetic parameters of gene expression from smFISH data at multiple time points after induction. Given an underlying model of gene expression, BayFish uses a Monte Carlo method to estimate the Bayesian posterior probability of the model parameters and quantify the parameter uncertainty given the observed smFISH data. We tested BayFish on smFISH measurements of the neuronal activity inducible gene Npas4 in primary neurons. We showed that a 2-state promoter model can recapitulate Npas4 dynamics after induction and we inferred that the transition rate from the promoter OFF state to the ON state is increased by the stimulus.\n\nAuthor SummaryGene expression can exhibit cell-to-cell variability due to the stochastic nature of biochemical reactions. Single cell assays (e.g. smFISH) directly quantify stochastic gene expression by measuring the number of active promoters and transcripts per cell in a population of cells. The data are distributions and their shape and time-evolution contain critical information on the underlying process of gene expression. Recent work has combined models of stochastic gene expression with maximum likelihood methods to infer kinetic parameters from smFISH distributions. However, these approaches do not provide a probability distribution or likelihood of model parameters inferred from the smFISH data. This information is useful because it indicates which parameters are loosely constrained by the data and suggests follow up experiments. We developed a suite of MATLAB programs (BayFish) that estimate the Bayesian posterior probability of model parameters from smFISH data. The user specifies an underlying model of stochastic gene expression with unknown parameters ({theta}) and provides smFISH data (Y). BayFish uses a Monte Carlo algorithm to estimate the Bayesian posterior probability P({theta}|Y) of model parameters. BayFish is easily modified and can be applied to other models of stochastic gene expression and smFISH data sets.

systems biology↗