Biology subjects

Le Treut, G.

Publications and source records attributed to Le Treut, G..

7 recordsLinked to original sources

bioRxiv2024

Challenges and Progress in RNA Velocity: Comparative Analysis Across Multiple Biological Contexts

Single-cell RNA sequencing is revolutionizing our understanding of cell state dynamics, allowing researchers to observe the progression of individual cells transcriptomic profiles over time. Among the computational techniques used to predict future cellular states, RNA velocity has emerged as a predominant tool for modeling transcriptional dynamics. RNA velocity leverages the mRNA maturation process to generate velocity vectors that predict the likely future state of a cell, offering insights into cellular differentiation, aging, and disease progression. Although this technique has shown promise across biological fields, the performance accuracy varies depending on the RNA velocity method and dataset. We established a comparative pipeline and analyzed the performance of five RNA velocity methods on three datasets based on local consistency, method agreement, identification of driver genes, and robustness to sequencing depth. This benchmark provides a resource for scientists to understand the strengths and limitations of different RNA velocity methods.

bioinformatics↗

bioRxiv2023

Tools and methods for high-throughput single-cell imaging with the mother machine

Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely-used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning based segmentation, "what you put is what you get" (WYPIWYG) - i.e., pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother-machine-based high-throughput imaging and analysis methods in their research.

systems biology↗

bioRxiv2021

Quantitative examination of five stochastic cell-cycle and cell-size control models for Escherichia coli and Bacillus subtilis

We examine five quantitative models of the cell-cycle and cell-size control in Escherichia coli and Bacillus subtilis that have been proposed over the last decade to explain single-cell experimental data generated with high-throughput methods. After presenting the statistical properties of these models, we test their predictions against experimental data. Based on simple calculations of the defining correlations in each model, we first dismiss the stochastic Helmstetter-Cooper model and the Initiation Adder model, and show that both the Replication Double Adder and the Independent Double Adder model are more consistent with the data than the other models. We then apply a recently proposed statistical analysis method and obtain that the Independent Double Adder model is the most likely model of the cell cycle. By showing that the Replication Double Adder model is fundamentally inconsistent with size convergence by the adder principle, we conclude that the Independent Double Adder model is most consistent with the data and the biology of bacterial cell-cycle and cell-size control. Mechanistically, the Independent Adder Model is equivalent to two biological principles: (i) balanced biosynthesis of the cell-cycle proteins, and (ii) their accumulation to a respective threshold number to trigger initiation and division.

systems biology↗

bioRxiv2020

Single-cell data and correlation analysis support the independent double adder model in both Escherichia coli and Bacillus subtilis

The reference point for cell-size control in the cell cycle is a fundamental biological question. We previously reported that we were unable to reproduce the conclusions of Witz et al.s eLife paper (Witz, van Nimwegen, and Julou 2019) entitled, "Initiation of chromosome replication controls both division and replication cycles in E. coli through a double-adder mechanism", despite extensive efforts. In this replication double adder (RDA) model, both replication and division cycles are determined via replication initiation as the sole implementation point of size control. Witz et al. justified the RDA model using a type of correlation analysis (the "I-value analysis") that they developed. By contrast, we previously showed that, in both Escherichia coli and Bacillus subtilis, replication initiation and cell division are determined by balanced biosynthesis of key cell cycle proteins (e.g., DnaA for initiation and FtsZ for cell division) and their accumulation to their respective threshold numbers, which Witz et al. coined the independent double adder (IDA) model. The adder phenotype is a natural quantitative consequence of these mechanistic principles. In a recent bioRxiv response to our report, Witz and colleagues explicitly confirmed two important limitations of the I-value analysis: (1) it is only applicable to non-overlapping cell cycles, wherein E. coli is known to deviate from the adder principle, and (2) it is only applicable to select biological models and, for example, cannot evaluate the IDA model. These limitations of the I-value analysis were not explained in the original eLife paper and were overlooked during the review process. In this report, we show using data analysis, mathematical modeling, and experiments why the I-value analysis - in its current implementation - cannot compare different biological models. Furthermore, the RDA model is incompatible with the adder principle and is not broadly supported by experimental data. For completeness, we also provide a detailed point-by-point response to Witz et al.s response (Witz, Julou, and van Nimwegen 2020) in the Supplemental Information.

systems biology↗

bioRxiv2020

Comment on 'Initiation of chromosome replication controls both division and replication cycles in E. coli through a double-adder mechanism'

Witz et al. recently performed single-cell mother machine experiments to track growth and the replication cycle in E. coli. They analyzed the correlation structure of selected parameters using both their data and published data, and concluded that E. coli cell-size control is implemented at replication initiation, which challenged the newly emerged division-centric mechanism of cell-size control in bacteria. We repeated Witz et al.s analysis, and performed additional experiments and analytical calculations. These results explain Witz et al.s observation and in fact support the division-centric model.

systems biology↗

bioRxiv2019

Mother machine image analysis with MM3

The mother machine is a microfluidic device for high-throughput time-lapse imaging of microbes. Here, we present MM3, a complete and modular image analysis pipeline. MM3 turns raw mother machine images, both phase contrast and fluorescence, into a data structure containing cells with their measured features. MM3 employs machine learning and non-learning algorithms, and is implemented in Python. MM3 is easy to run as a command line tool with the occasional graphical user interface on a PC or Mac. A typical mother machine experiment can be analyzed within one day. It has been extensively tested, is well documented and publicly available via Github.

systems biology↗

bioRxiv2018

Mechanistic origin of cell-size control and homeostasis in bacteria

Evolutionarily divergent bacteria share a common phenomenological strategy for cell-size homeostasis under steady-state conditions. In the presence of inherent physiological stochasticity, cells following this \"adder\" principle gradually return to their steady-state size by adding a constant volume between birth and division regardless of their size at birth. However, the mechanism of the adder has been unknown despite intense efforts. In this work, we show that the adder is a direct consequence of two general processes in biology: (1) threshold -- accumulation of initiators and precursors required for cell division to a respective fixed number, and (2) balanced biosynthesis -- maintenance of their production proportional to volume growth. This mechanism is naturally robust to static growth inhibition, but also allows us to \"reprogram\" cell-size homeostasis in a quantitatively predictive manner in both Gram-negative Escherichia coli and Gram-positive Bacillus subtilis. By generating dynamic oscillations in the concentration of the division protein FtsZ, we were able to oscillate cell size at division and systematically break the adder. In contrast, periodic induction of replication initiator protein DnaA caused oscillations in cell size at initiation, but did not alter division size or the adder. Finally, we were able to restore the adder phenotype in slow growing E. coli, the only known steady-state growth condition wherein E. coli significantly deviates from the adder, by repressing active degradation of division proteins. Together these results show that division and replication are independently controlled, and that division processes exclusively drive cell-size homeostasis in bacteria.

systems biology↗