Mother Machine Research Articles

Live Imaging of Fission Yeast Single-Cell Lineages Using a Microfluidic Device.

Mother machine (MM) is a microfluidic device originally developed for long-term live imaging of Escherichia coli bacterial cells under a microscope. The simple yet sophisticated design has enabled microbiologists to track multiple single-cell lineages cultured under highly controlled external environments. Here, I describe how to fabricate a fission yeast version of MM with photolithography and soft lithography. Procedures for setting up the microfluidic device for long-term live microscopy are also explained.

‘Are you pregnant? If not, why not?’: artificial reproductive technology and the trauma of infertility

The article scrutinises Rohini S. Rajagopal’s work, what’s a lemon squeezer doing in my vagina (2021), to illustrate the escalating medicalisation of infertile bodies. In a cultural context where reproductive...

Envirotyping helps in better understanding the root cause of success and limitations of rainfed production systems

The current diagnostic agronomy study of the Bankura region of West Bengal, India, examined the variations in crop yields through a socio-ecological analysis of multiple production system components. Envirotyping for root cause analysis was employed to delve into the variables that affect the performance of rainfed production systems. Mother Earth, man, machine, management, and materials (5Ms concept) were the five indicators under which the variables were grouped. Findings demonstrated the fragility of the region’s soils due to its undulating terrain, unpredictable rainfall patterns, and frequent drought scenarios. The LULC’s NDVI showed that the agricultural area is about 60% and 43% of the total geographical area in the Hirbandh and Ranibandh blocks, respectively. Soils are acidic and diagnosed with deficiency of both macro and micronutrients (phosphorous, sulfur, and boron) having poor water holding capacity (35 to 55 mm for a 50 cm soil depth). The sand and soil organic carbon contents ranged between 43.04%–82.32% and 0.17%–1.01%, respectively with a low bacterial population. These factors are the root cause for low cropping intensity (106%) and low paddy productivity (3,021 kg/ha). Overall, the study contributes to designing and scaling-up of sustainable landscape management practices that could ensure higher cropping intensity and system productivity in similar agro-ecologies with limited evidence.

Single-cell level LasR-mediated quorum sensing response of Pseudomonas aeruginosa to pulses of signal molecules

Quorum sensing (QS) is a communication form between bacteria via small signal molecules that enables global gene regulation as a function of cell density. We applied a microfluidic mother machine to study the kinetics of the QS response of Pseudomonas aeruginosa bacteria to additions and withdrawals of signal molecules. We traced the fast buildup and the subsequent considerably slower decay of a population-level and single-cell-level QS response. We applied a mathematical model to explain the results quantitatively. We found significant heterogeneity in QS on the single-cell level, which may result from variations in quorum-controlled gene expression and protein degradation. Heterogeneity correlates with cell lineage history, too. We used single-cell data to define and quantitatively characterize the population-level quorum state. We found that the population-level QS response is well-defined. The buildup of the quorum is fast upon signal molecule addition. At the same time, its decay is much slower following signal withdrawal, and the quorum may be maintained for several hours in the absence of the signal. Furthermore, the quorum sensing response of the population was largely repeatable in subsequent pulses of signal molecules.

Feedback between stochastic gene networks and population dynamics enables cellular decision-making.

Phenotypic selection occurs when genetically identical cells are subject to different reproductive abilities due to cellular noise. Such noise arises from fluctuations in reactions synthesizing proteins and plays a crucial role in how cells make decisions and respond to stress or drugs. We propose a general stochastic agent-based model for growing populations capturing the feedback between gene expression and cell division dynamics. We devise a finite state projection approach to analyze gene expression and division distributions and infer selection from single-cell data in mother machines and lineage trees. We use the theory to quantify selection in multi-stable gene expression networks and elucidate that the trade-off between phenotypic switching and selection enables robust decision-making essential for synthetic circuits and developmental lineage decisions. Using live-cell data, we demonstrate that combining theory and inference provides quantitative insights into bet-hedging-like response to DNA damage and adaptation during antibiotic exposure in Escherichia coli.

What drives equipment manufacturing enterprises green technology innovation in the Yangtze River Economic Belt? A configuration analysis based on DEA-fsQCA model

Under the dual backdrop of the “dual-carbon” goal and the strategic goal of "Made in China 2025", the equipment manufacturing industry, as the "industrial mother machine", has to undergo a green, low-carbon transformation. Green technology innovation is an essential component of achieving green transformation. Therefore, their green technology innovation efficiency (GTIE) deserves in-depth examination to achieve the "double carbon" goal. The aim of this study is to reveal the possible paths and models for equipment manufacturing enterprises to obtain high GTIE from a configuration perspective. Based on 94 listed equipment manufacturing companies' green technology innovation data in the Yangtze River Economic Belt (YREB) from 2018 to 2021, this paper explores the GTIE and its combination improvement path with a DEA-fsQCA model. The results indicate that: (1) The average efficiency score of the sample was 0.506, only 16 enterprises reached the production frontier, and the overall GTIE in equipment manufacturing industries as a whole showed a "first drop and then a steady upward" trend from 2018 to 2021; (2) The results of heterogeneity analysis revealed that the level of GTIE showed a characteristic of the upstream region > downstream region > midstream region, foreign-invested enterprises > private-owned enterprises> state-owned enterprises, and there is significant industrial heterogeneity, with a GTIE difference of up to 0.478 between the industries with the strongest green technologys innovation ability and those with the weakest. (3) A single conditional variable cannot constitute the necessary condition for high GTIE. There are four combination paths that can produce high GTIE: PCW+ MP-dual driven, PCW+ Fin-dual driven, COCEN-oriented, and COCEN+MP-dual driven. Further analysis shows that there is a substitution effect between the absence of the financing constraint and the existence of market power. This study provides a scientific basis for facilitating the improvement of YREB's equipment manufacturing industry's green technology innovation capabilities.

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) – that is, 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.

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) - that is, 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.

Scaling of stochastic growth and division dynamics: A comparative study of individual rod-shaped cells in the Mother Machine and SChemostat platforms.

Microfluidic platforms enable long-term quantification of stochastic behaviors of individual bacterial cells under precisely controlled growth conditions. Yet, quantitative comparisons of physiological parameters and cell behaviors of different microorganisms in different experimental and device modalities is not available due to experiment-specific details affecting cell physiology. To rigorously assess the effects of mechanical confinement, we designed, engineered, and performed side-by-side experiments under otherwise identical conditions in the Mother Machine (with confinement) and the SChemostat (without confinement), using the latter as the ideal comparator. We established a protocol to cultivate a suitably engineered rod-shaped mutant of Caulobacter crescentus in the Mother Machine and benchmarked the differences in stochastic growth and division dynamics with respect to the SChemostat. While the single-cell growth rate distributions are remarkably similar, the mechanically confined cells in the Mother Machine experience a substantial increase in interdivision times. However, we find that the division ratio distribution precisely compensates for this increase, which in turn reflects identical emergent simplicities governing stochastic intergenerational homeostasis of cell sizes across device and experimental configurations, provided the cell sizes are appropriately mean-rescaled in each condition. Our results provide insights into the nature of the robustness of the bacterial growth and division machinery.

Analytical cell size distribution: lineage-population bias and parameter inference.

We derive analytical steady-state cell size distributions for size-controlled cells in single-lineage experiments, such as the mother machine, which are fundamentally different from batch cultures where populations of cells grow freely. For exponential single-cell growth, characterizing most bacteria, the lineage-population bias is obtained explicitly. In addition, if volume is evenly split between the daughter cells at division, we show that cells are on average smaller in populations than in lineages. For more general power-law growth rates and deterministic volume partitioning, both symmetric and asymmetric, we derive the exact lineage distribution. This solution is in good agreement with Escherichia coli mother machine data and can be used to infer cell cycle parameters such as the strength of the size control and the asymmetry of the division. When introducing stochastic volume partitioning, we derive the large-size and small-size tails of the lineage distribution and show that the lineage-population bias only depends on the single-cell growth rate. These asymptotic behaviours are extended to the adder model of cell size control. When considering noisy single-cell growth rate, we derive the large-size lineage and population distributions. Finally, we show that introducing noise, either on the volume partitioning or on the single-cell growth rate, can cancel the lineage-population bias.

Microfluidics for long-term single-cell time-lapse microscopy: Advances and applications.

Cells are inherently dynamic, whether they are responding to environmental conditions or simply at equilibrium, with biomolecules constantly being made and destroyed. Due to their small volumes, the chemical reactions inside cells are stochastic, such that genetically identical cells display heterogeneous behaviors and gene expression profiles. Studying these dynamic processes is challenging, but the development of microfluidic methods enabling the tracking of individual prokaryotic cells with microscopy over long time periods under controlled growth conditions has led to many discoveries. This review focuses on the recent developments of one such microfluidic device nicknamed the mother machine. We overview the original device design, experimental setup, and challenges associated with this platform. We then describe recent methods for analyzing experiments using automated image segmentation and tracking. We further discuss modifications to the experimental setup that allow for time-varying environmental control, replicating batch culture conditions, cell screening based on their dynamic behaviors, and to accommodate a variety of microbial species. Finally, this review highlights the discoveries enabled by this technology in diverse fields, such as cell-size control, genetic mutations, cellular aging, and synthetic biology.

DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics.

Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze images of single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats as inputs and the introduction of a Google Colab notebook so users can try the software without installing the code on their local machine. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data.

Master equation approach to the stochastic accumulation dynamics of bacterial cell cycle

The mechanism of bacterial cell size control has been a mystery for decades, which involves the well-coordinated growth and division in the cell cycle. The revolutionary modern techniques of microfluidics and the advanced live imaging analysis techniques allow long term observations and high-throughput analysis of bacterial growth on single cell level, promoting a new wave of quantitative investigations on this puzzle. Taking the opportunity, this theoretical study aims to clarify the stochastic nature of bacterial cell size control under the assumption of the accumulation mechanism, which is favoured by recent experiments on species of bacteria. Via the master equation approach with properly chosen boundary conditions, the distributions concerned in cell size control are estimated and are confirmed by experiments. In this analysis, the inter-generation Green’s function is analytically evaluated as the key to bridge two kinds of statistics used in batch-culture and mother machine experiments. This framework allows us to quantify the noise level in growth and accumulation according to experimental data. As a consequence of non-Gaussian noises of the added sizes, the non-equilibrium nature of bacterial cell size homeostasis is predicted, of which the biological meaning requires further investigation.

Intrinsic growth heterogeneity of mouse leukemia cells underlies differential susceptibility to a growth-inhibiting anticancer drug.

Cancer cell populations consist of phenotypically heterogeneous cells. Growing evidence suggests that pre-existing phenotypic differences among cancer cells correlate with differential susceptibility to anticancer drugs and eventually lead to a relapse. Such phenotypic differences can arise not only externally driven by the environmental heterogeneity around individual cells but also internally by the intrinsic fluctuation of cells. However, the quantitative characteristics of intrinsic phenotypic heterogeneity emerging even under constant environments and their relevance to drug susceptibility remain elusive. Here we employed a microfluidic device, mammalian mother machine, for studying the intrinsic heterogeneity of growth dynamics of mouse lymphocytic leukemia cells (L1210) across tens of generations. The generation time of this cancer cell line had a distribution with a long tail and a heritability across generations. We determined that a minority of cell lineages exist in a slow-cycling state for multiple generations. These slow-cycling cell lineages had a higher chance of survival than the fast-cycling lineages under continuous exposure to the anticancer drug Mitomycin C. This result suggests that heritable heterogeneity in cancer cells’ growth in a population influences their susceptibility to anticancer drugs.

Exploration and practice of talent training mode of Teacher Education Specialty under the background of “New Normal” construction

Teacher education is the mother machine of education and the power source to improve the quality of education. It is found that the teacher education talents cultivated by the current normal education can not fully adapt to the new situation, new journey and new mission. Therefore, we should innovate the teacher education model and build a high-quality, professional and innovative teacher team. “New Normal” is a new goal and new stage of the development of Teacher Education in China. It aims to cultivate higher quality normal students and lead the reform of the training mode of professional talents in teacher education. In view of the problems existing in the training of teachers' education professionals, this paper puts forward the following countermeasures：Build a cross-border training model, integrated training mode, collaborative and shared training mode, as well as the training mode based on practice, research and research enrichment mode; Deepen teaching reform, change students' learning methods, and highlight “student-centered”; Innovative talent evaluation mechanism.

Development of New Type of Worm Gear Pair Mother Machine Based on the Integrated Process of Machining and Measurement

Development of New Type of Worm Gear Pair Mother Machine Based on the Integrated Process of Machining and Measurement

Fluctuation relations and fitness landscapes of growing cell populations

We construct a pathwise formulation of a growing population of cells, based on two different samplings of lineages within the population, namely the forward and backward samplings. We show that a general symmetry relation, called fluctuation relation relates these two samplings, independently of the model used to generate divisions and growth in the cell population. These relations lead to estimators of the population growth rate, which can be very efficient as we demonstrate by an analysis of a set of mother machine data. These fluctuation relations lead to general and important inequalities between the mean number of divisions and the doubling time of the population. We also study the fitness landscape, a concept based on the two samplings mentioned above, which quantifies the correlations between a phenotypic trait of interest and the number of divisions. We obtain explicit results when the trait is the age or the size, for age and size-controlled models.

A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin

Abstract As a typical manufacturing equipment, CNC machine tool (CNCMT) is the mother machine of industry. Fault of CNCMT might cause the loss of precision and affect the production if troubleshooting is not timely. Therefore, the reliability of CNCMT has a big significance. Predictive maintenance is an effective method to avoid faults and casualties. Due to less consideration of the status variety and consistency of CNCMT in its life cycle, current methods cannot achieve accurate, timely and intelligent results. To realize reliable predictive maintenance of CNCMT, a hybrid approach driven by Digital Twin (DT) is studied. This approach is DT model-based and DT data-driven hybrid. With the proposed framework, a hybrid predictive maintenance algorithm based on DT model and DT data is researched. At last, a case study on cutting tool life prediction is conducted. The result shows that the proposed method is feasible and more accurate than single approach.

Bacterial Division: Journey to the Center of the Cell.

Most bacteria divide by corralling the tubulin-like FtsZ protein to mid-cell, where it assembles into a ring of treadmilling membrane-tethered oligomers. A study in this issue reveals new details about how FtsZ finds its way to the ring.

Replication Initiation Control in E. coli

Replication initiation is a key cellular event that couples DNA replication to cellular growth. Previous results showed that the cell mass at initiation is invariant regardless of the growth condition or inhibition. We explain this invariance by a simple threshold model built on the initiator molecule DnaA: 1) the initiator protein accumulates to a fixed threshold per origin and trigger initiation; 2) they show balanced biosynthesis such that their concentration remains nearly constant during cell elongation; 3) the proteome fraction of initiator proteins is fixed across different growth conditions so that initiation mass is invariant. Our current study provides quantitative measurement that support this model. Average concentration of DnaA was shown to be constant across growth conditions. The dynamics of dnaA gene expression was found constant throughout the division cycle by measurement in single-cell microfluidics (mother machine). To further test our model, we titrated DnaA concentration systematically in a physiological range around the endogenous dnaA expression level and observed a constancy of initiator numbers at initiation while the initiation mass and DnaA concentration changed in opposite direction as predicted. In addition, we conclude the initiation process is tightly regulated as manifested by the narrow distribution of initiation mass. Stochastic simulation and single-cell perturbation experiments showed that the source of control rooted in both the regulation of dnaA gene expression and the binding of DnaA to its targets. This study points to a simple inverse relationship between the initiator DnaA and initiation mass and provides a framework that connects the phenomenology of initiation mass invariance to a simple molecularly-based quantitative model.