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Register a new userStochastic Neural Networks for Automatic Cell Tracking in Microscopy Image Sequences of Bacterial Colonies
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Added by
Andreas Mang
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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We describe an automated analysis method to quantify the detailed growth dynamics of a population of bacilliform bacteria. We propose an innovative approach to frame-sequence tracking of deformable-cell motion by the automated minimization of a new, specific cost functional. This minimization is implemented by dedicated Boltzmann machines (stochastic recurrent neural networks). Automated detection of cell divisions is handled similarly by successive minimizations of two cost functions, alternating the identification of children pairs and parent identification. We validate this automatic cell tracking algorithm using recordings of simulated cell colonies that closely mimic the growth dynamics of emph{E. coli} in microfluidic traps. On a batch of 1100 image frames, cell registration accuracies per frame ranged from 94.5% to 100%, with a high average. Our initial tests using experimental image sequences of emph{E. coli} colonies also yield convincing results, with a registration accuracy ranging from 90% to 100%.
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Cell tracking enables data extraction from time-lapse cell movies and promotes modeling biological processes at the single-cell level. We introduce a new fully automated computational strategy to track accurately cells across frames in time-lapse movies. Our method is based on a dynamic neighborhoods formation and matching approach, inspired by motion estimation algorithms for video compression. Moreover, it exploits divide and conquer opportunities to solve effectively the challenging cells tracking problem in overcrowded bacterial colonies. Using cell movies generated by different labs we demonstrate that the accuracy of the proposed method remains very high (exceeds 97%) even when analyzing large overcrowded microbial colonies.
Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest. Although such objects are generally easy to identify, the process of manually annotating cells is sometimes subject to arbitrariness due to the operators interpretation of the borderline cases. We propose a Machine Learning approach that exploits a fully-convolutional network in a binary segmentation fashion to localize the objects of interest. Counts are then retrieved as the number of detected items. Specifically, we adopt a UNet-like architecture leveraging residual units and an extended bottleneck for enlarging the field-of-view. In addition, we make use of weighted maps that penalize the errors on cells boundaries increasingly with overcrowding. These changes provide more context and force the model to focus on relevant features during pixel-wise classification. As a result, the model performance is enhanced, especially in presence of clumping cells, artifacts and confounding biological structures. Posterior assessment of the results with domain experts confirms that the model detects cells of interest correctly. The model demonstrates a human-level ability inasmuch even erroneous predictions seem to fall within the limits of operator interpretation. This qualitative assessment is also corroborated by quantitative metrics as an ${F_1}$ score of 0.87. Despite some difficulties in interpretation, results are also satisfactory with respect to the counting task, as testified by mean and median absolute error of, respectively, 0.8 and 1.
Vascular tracking of angiographic image sequences is one of the most clinically important tasks in the diagnostic assessment and interventional guidance of cardiac disease. However, this task can be challenging to accomplish because of unsatisfactory angiography image quality and complex vascular structures. Thus, this study proposed a new greedy graph search-based method for vascular tracking. Each vascular branch is separated from the vasculature and is tracked independently. Then, all branches are combined using topology optimization, thereby resulting in complete vasculature tracking. A gray-based image registration method was applied to determine the tracking range, and the deformation field between two consecutive frames was calculated. The vascular branch was described using a vascular centerline extraction method with multi-probability fusion-based topology optimization. We introduce an undirected acyclic graph establishment technique. A greedy search method was proposed to acquire all possible paths in the graph that might match the tracked vascular branch. The final tracking result was selected by branch matching using dynamic time warping with a DAISY descriptor. The solution to the problem reflected both the spatial and textural information between successive frames. Experimental results demonstrated that the proposed method was effective and robust for vascular tracking, attaining a F1 score of 0.89 on a single branch dataset and 0.88 on a vessel tree dataset. This approach provided a universal solution to address the problem of filamentary structure tracking.
Colonies of bacterial cells endowed with a pili-based self-propulsion machinery represent an ideal model system for studying how active adhesion forces affect structure and dynamics of many-particle systems. As a novel computational tool, we describe here a highly parallel molecular dynamics simulation package for modeling of textit{Neisseria gonorrhoeae} colonies. Simulations are employed to investigate growth of bacterial colonies and the dependence of the colony structure on cell-cell interactions. In agreement with experimental data, active pilus retraction is found to enhance local ordering. For mixed colonies consisting of different types of cell types, the simulations show a segregation of cell types depending on the pili-mediated interactions, as seen in experiments. Using a simulated experimental setup, we study the power-spectral density of colony-shape fluctuations and the associated fluctuation-response relation. The simulations predict a strong violation of the equilibrium fluctuation-response relation across the measurable frequency range. Lastly, we illustrate the essential role of active force generation for colony dynamics by showing that pilus-mediated activity drives the spreading of colonies on surfaces and the invasion of narrow channels.
Genetic competence is a phenotypic state of a bacterial cell in which it is capable of importing DNA, presumably to hasten its exploration of alternate genes in its quest for survival under stress. Recently, it was proposed that this transition is uncorrelated among different cells in the colony. Motivated by several discovered signaling mechanisms which create colony-level responses, we present a model for the influence of quorum-sensing signals on a colony of B. Subtilis cells during the transition to genetic competence. Coupling to the external signal creates an effective inhibitory mechanism, which results in anti-correlation between the cycles of adjacent cells. We show that this scenario is consistent with the specific experimental measurement, which fails to detect some underlying collective signaling mechanisms. Rather, we suggest other parameters that should be used to verify the role of a quorum-sensing signal. We also study the conditions under which phenotypic spatial patterns may emerge.
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