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Register a new userTracking Single-Cells in Overcrowded Bacterial Colonies
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Added by
Athanasios Balomenos D.
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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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.
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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%.
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.
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.
This article presents a semantic tracker which simultaneously tracks a single target and recognises its category. In general, it is hard to design a tracking model suitable for all object categories, e.g., a rigid tracker for a car is not suitable for a deformable gymnast. Category-based trackers usually achieve superior tracking performance for the objects of that specific category, but have difficulties being generalised. Therefore, we propose a novel unified robust tracking framework which explicitly encodes both generic features and category-based features. The tracker consists of a shared convolutional network (NetS), which feeds into two parallel networks, NetC for classification and NetT for tracking. NetS is pre-trained on ImageNet to serve as a generic feature extractor across the different object categories for NetC and NetT. NetC utilises those features within fully connected layers to classify the object category. NetT has multiple branches, corresponding to multiple categories, to distinguish the tracked object from the background. Since each branch in NetT is trained by the videos of a specific category or groups of similar categories, NetT encodes category-based features for tracking. During online tracking, NetC and NetT jointly determine the target regions with the right category and foreground labels for target estimation. To improve the robustness and precision, NetC and NetT inter-supervise each other and trigger network adaptation when their outputs are ambiguous for the same image regions (i.e., when the category label contradicts the foreground/background classification). We have compared the performance of our tracker to other state-of-the-art trackers on a large-scale tracking benchmark (100 sequences)---the obtained results demonstrate the effectiveness of our proposed tracker as it outperformed other 38 state-of-the-art tracking algorithms.
We study intact and bulging Escherichia coli cells using atomic force microscopy to separate the contributions of the cell wall and turgor pressure to the overall cell stiffness. We find strong evidence of power-law stress-stiffening in the E. coli cell wall, with an exponent of 1.22 pm 0.12, such that the wall is significantly stiffer in intact cells (E = 23 pm 8 MPa and 49 pm 20 MPa in the axial and circumferential directions) than in unpressurized sacculi. These measurements also indicate that the turgor pressure in living cells E. coli is 29 pm 3 kPa.
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