A Humanized Monoclonal Antibody Potentiates Killing by Antibiotics of Diverse Biofilm-Forming Respiratory Tract Pathogens

New strategies to treat diseases wherein biofilms contribute significantly to pathogenesis are needed as biofilm-resident bacteria are highly recalcitrant to antibiotics due to physical biofilm architecture and a canonically quiescent metabolism, among many additional attributes. We, and others, have shown that when biofilms are dispersed or disrupted, bacteria released from biofilm residence are in a distinct physiologic state that, in part, renders these bacteria highly sensitive to killing by specific antibiotics. We sought to demonstrate the breadth of ability of a recently humanized monoclonal antibody against an essential biofilm structural element (DNABII protein) to disrupt biofilms formed by respiratory tract pathogens and potentiate antibiotic-mediated killing of bacteria released from biofilm residence. Biofilms formed by six respiratory tract pathogens were significantly disrupted by the humanized monoclonal antibody in a dose- and time-dependent manner, as corroborated by CLSM imaging. Bacteria newly released from the biofilms of 3 of 6 species were significantly more sensitive than their planktonic counterparts to killing by 2 of 3 antibiotics currently used clinically and were now also equally as sensitive to killing by the 3 rd antibiotic. The remaining 3 pathogens were significantly more susceptible to killing by all 3 antibiotics. A humanized monoclonal antibody directed against protective epitopes of a DNABII protein effectively released six diverse respiratory tract pathogens from biofilm residence in a phenotypic state that was now as, or significantly more, sensitive to killing by three antibiotics currently indicated for use clinically. These data support this targeted, combinatorial, species-agnostic therapy to mitigate chronic bacterial diseases.

Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies

Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static phenotypic readouts. Transmitted light images, on the other hand, provide direct morphological readouts and can be acquired over time to provide a rich data source for dynamic cell phenotypic state identification. Here, we describe an end-to-end deep learning platform, UPSIDE (Unsupervised Phenotypic State IDEntification), for discovering cell states and their dynamics from transmitted light movies. UPSIDE uses the variational auto-encoder architecture to learn latent cell representations, which are then clustered for state identification, decoded for feature interpretation, and linked across movie frames for transition rate inference. Using UPSIDE, we identified distinct blood cell types in a heterogeneous dataset. We then analyzed movies of patient-derived acute myeloid leukemia cells, from which we identified stem-cell associated morphological states as well as the transition rates to and from these states. UPSIDE opens up the use of transmitted light movies for systematic exploration of cell state heterogeneity and dynamics in biology and medicine.

Memory shapes microbial populations

Correct decision making is fundamental for all living organisms to thrive under environmental changes. The patterns of environmental variation and the quality of available information define the most favourable strategy among multiple options, from randomly adopting a phenotypic state to sensing and reacting to environmental cues. Cellular memory—the ability to track and condition the time to switch to a different phenotypic state—can help withstand environmental fluctuations. How does memory manifest itself in unicellular organisms? We describe the population-wide consequences of phenotypic memory in microbes through a combination of deterministic modelling and stochastic simulations. Moving beyond binary switching models, our work highlights the need to consider a broader range of switching behaviours when describing microbial adaptive strategies. We show that memory in individual cells generates patterns at the population level coherent with overshoots and non-exponential lag times distributions experimentally observed in phenotypically heterogeneous populations. We emphasise the implications of our work in understanding antibiotic tolerance and, in general, bacterial survival under fluctuating environments.

Shooting yourself in the foot: How immune cells induce antibiotic tolerance in microbial pathogens

Antibiotic treatment failure of infection is common and frequently occurs in the absence of genetically encoded antibiotic resistance mechanisms. In such scenarios, the ability of bacteria to enter a phenotypic state that renders them tolerant to the killing activity of multiple antibiotic classes is thought to contribute to antibiotic failure. Phagocytic cells, which specialize in engulfing and destroying invading pathogens, may paradoxically contribute to antibiotic tolerance and treatment failure. Macrophages act as reservoirs for some pathogens and impede penetration of certain classes of antibiotics. In addition, increasing evidence suggests that subpopulations of bacteria can survive inside these cells and are coerced into an antibiotic-tolerant state by host cell activity. Uncovering the mechanisms that drive immune-mediated antibiotic tolerance may present novel strategies to improving antibiotic therapy.

Inhibition of Tunneling Nanotubes between Cancer Cell and the Endothelium Alters the Metastatic Phenotype

The interaction of tumor cells with blood vessels is one of the key steps during cancer metastasis. Metastatic cancer cells exhibit phenotypic state changes during this interaction: (1) they form tunneling nanotubes (TNTs) with endothelial cells, which act as a conduit for intercellular communication; and (2) metastatic cancer cells change in order to acquire an elongated phenotype, instead of the classical cellular aggregates or mammosphere-like structures, which it forms in three-dimensional cultures. Here, we demonstrate mechanistically that a siRNA-based knockdown of the exocyst complex protein Sec3 inhibits TNT formation. Furthermore, a set of pharmacological inhibitors for Rho GTPase–exocyst complex-mediated cytoskeletal remodeling is introduced, which inhibits TNT formation, and induces the reversal of the more invasive phenotype of cancer cell (spindle-like) into a less invasive phenotype (cellular aggregates or mammosphere). Our results offer mechanistic insights into this nanoscale communication and shift of phenotypic state during cancer–endothelial interactions.

Estimation of three-dimensional chromatin morphology for nuclear classification and characterisation

Classification and characterisation of cellular morphological states are vital for understanding cell differentiation, development, proliferation and diverse pathological conditions. As the onset of morphological changes transpires following genetic alterations in the chromatin configuration inside the nucleus, the nuclear texture as one of the low-level properties if detected and quantified accurately has the potential to provide insights on nuclear organisation and enable early diagnosis and prognosis. This study presents a three dimensional (3D) nuclear texture description method for cell nucleus classification and variation measurement in chromatin patterns on the transition to another phenotypic state. The proposed approach includes third plane information using hyperplanes into the design of the Sorted Random Projections (SRP) texture feature and is evaluated on publicly available 3D image datasets of human fibroblast and human prostate cancer cell lines obtained from the Statistics Online Computational Resource. Results show that 3D SRP and 3D Local Binary Pattern provide better classification results than other feature descriptors. In addition, the proposed metrics based on 3D SRP validate the change in intensity and aggregation of heterochromatin on transition to another state and characterise the intermediate and ultimate phenotypic states.

Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies

SummaryIdentification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static phenotypic readouts. Transmitted light images, on the other hand, provide direct morphological readouts and can be acquired over time to provide a rich data source for dynamic cell phenotypic state identification. Here, we describe an end-to-end deep learning platform, UPSIDE (for Unsupervised Phenotypic State IDEntification), for discovering cell states and their dynamics from transmitted light movies.UPSIDE uses the variational auto-encoder architecture to learn latent cell representations, that are then clustered for state identification, decoded for feature interpretation, and linked across movie frames for transition rate inference. Using UPSIDE, we identified distinct blood cell types in a heterogeneous dataset. From acute myeloid leukemia cell movies, we then identified stem-cell associated morphological states and their inter-conversion rates. UPSIDE opens up use of transmitted light movies for systematic exploration of cell state heterogeneity and dynamics in biology and medicine.

Estimation of Three-Dimensional Chromatin Morphology for Nuclear Classification and Characterisation

Classification and characterisation of cellular morphological states are vital for understanding cell differentiation, development, proliferation and diverse pathological conditions. As the onset of morphological changes transpires following genetic alterations in the chromatin configuration inside the nucleus, the nuclear texture as one of the low-level properties if detected and quantified accurately has the potential to provide insights on nuclear organisation and enable early diagnosis and prognosis. This study presents a three dimensional (3D) nuclear texture description method for cell nucleus classification and variation measurement in chromatin patterns on the transition to another phenotypic state. The proposed approach includes third plane information using hyperplanes into the design of the Sorted Random Projections (SRP) texture feature. The significance of including third plane information for low-resolution volumetric images is investigated by comparing the performance of 3D texture descriptor with its respective pseudo 3D form that ignores the interslice intensity correlations. Following classification, changes in chromatin pattern are estimated by computing the ratio of heterochromatin and euchromatin corresponding to their respective intensities and image gradient obtained by 3D SRP. The proposed method is evaluated on two publicly available 3D image datasets of human fibroblast and human prostate cancer cell lines in two phenotypic states obtained from the public Statistics Online Computational Resource. Experimental results show that 3D SRP and 3D Local Binary Pattern provide better results than other utilised handcrafted descriptors and deep learning features extracted using a pre-trained model. The results also show the advantage of utilising 3D feature descriptor for classification over its corresponding pseudo version. In addition, the proposed method validates that as the cell passes to another phenotypic state, there is a change in intensity and aggregation of heterochromatin.Author SummaryAutomated classification and measurement of cellular phenotypic traits can significantly impact clinical decision making. Early detection of diseases requires an accurate description of low-level cellular features to detect small-scale abnormalities in the few abnormal cells in the tissue microenvironment. The challenge is the development of a computational approach for 3D textural feature description that can capture the heterogeneous information in multiple dimensions and characterise the cells in their ultimate and intermediate phenotypic states effectively. Our work has proposed the method and metrics to measure chromatin condensation pattern and classify the phenotypic states. Experimental evaluation on the 3D image set of human fibroblast and human prostate cancer cell collections validates the proposed method for the classification of cell states. Results also signify the credibility of proposed metrics to characterise the cellular phenotypic states and contributes to studies related to early diagnosis, prognosis and drug resistance.

Acclimation capacity and rate change through life in the zooplankton Daphnia

When a change in the environment occurs, organisms can maintain an optimal phenotypic state via plastic, reversible changes to their phenotypes. These adjustments, when occurring within a generation, are described as the process of acclimation. While acclimation has been studied for more than half a century, global environmental change has stimulated renewed interest in quantifying variation in the rate and capacity with which this process occurs, particularly among ectothermic organisms. Yet, despite the likely ecological importance of acclimation capacity and rate, how these traits change throughout life among members of the same species is largely unstudied. Here we investigate these relationships by measuring the acute heat tolerance of the clonally reproducing zooplankter Daphnia magna of different size/age and acclimation status. The heat tolerance of individuals completely acclimated to relatively warm (28°C) or cool (17°C) temperatures diverged during development, indicating that older, larger individuals had a greater capacity to increase heat tolerance. However, when cool acclimated individuals were briefly exposed to the warm temperature (i.e. were ‘heat-hardened'), it was younger, smaller animals with less capacity to acclimate that were able to do so more rapidly because they obtained or came closer to obtaining complete acclimation of heat tolerance. Our results illustrate that within a species, individuals can differ substantially in how rapidly and by how much they can respond to environmental change. We urge greater investigation of the intraspecific relationship between acclimation and development along with further consideration of the factors that might contribute to these enigmatic patterns of phenotypic variation.