Characterising epithelial tissues using persistent entropy

In this paper, we apply persistent entropy, a novel topological statistic, for characterization of images of epithelial tissues. We have found out that persistent entropy is able to summarize topological and geometric information encoded by \alpha-complexes and persistent homology. After using some statistical tests, we can guarantee the existence of significant differences in the studied tissues.Comment: 12 pages, 7 figures, 4 table

Characterising epithelial tissues using persistent entropy

In this paper, we apply persistent entropy, a novel topological statis- tic, for characterization of images of epithelial tissues. We have found out that persistent entropy is able to summarize topological and geomet- ric information encoded by -complexes and persistent homology. After using some statistical tests, we can guarantee the existence of signi cant di erences in the studied tissues.Ministerio de Economía y Competitividad MTM2015-67072-

Persistent entropy: a scale-invariant topological statistic for analyzing cell arrangements

In this work, we develop a method for detecting differences in the topological distribution of cells forming epithelial tissues. In particular, we extract topological information from their images using persistent homology and a summary statistic called persistent entropy. This method is scale invariant, robust to noise and sensitive to global topological features of the tissue. We have found significant differences between chick neuroepithelium and epithelium of Drosophila wing discs in both, larva and prepupal stages. Besides, we have tested our method, with good results, with images of mathematical tesselations that model biological tissues

Stable topological summaries for analyzing the organization of cells in a packed tissue

We use topological data analysis tools for studying the inner organization of cells in segmented images of epithelial tissues. More specifically, for each segmented image, we compute different persistence barcodes, which codify the lifetime of homology classes (persistent homology) along different filtrations (increasing nested sequences of simplicial complexes) that are built from the regions representing the cells in the tissue. We use a complete and well-grounded set of numerical variables over those persistence barcodes, also known as topological summaries. A novel combination of normalization methods for both the set of input segmented images and the produced barcodes allows for the proven stability results for those variables with respect to small changes in the input, as well as invariance to image scale. Our study provides new insights to this problem, such as a possible novel indicator for the development of the drosophila wing disc tissue or the importance of centroids’ distribution to differentiate some tissues from their CVT-path counterpart (a mathematical model of epithelia based on Voronoi diagrams). We also show how the use of topological summaries may improve the classification accuracy of epithelial images using a Random Forest algorithm.Ministerio de Ciencia e Innovación PID2019-107339GB-I0

Emergent dynamics of confluent tissues in homeostasis and growth

Emergent dynamics in confluent tissues play an important role in many biological processes. Here, we study these dynamics through the lens of active matter physics, specifically focussing on two emergent phenomena: nematic collective behaviour in homeostasis and the dynamics of a growing tissue boundary. We first examine the emergence of extensile nematic behaviour at the tissue level, and how a collection of contractile cells can give rise to it. By constructing and analysing a linearised hydrodynamic model, we show that this extensile behaviour results from fluctuating polar forces that arise from cell-substrate interactions. We show that polar fluctuations generically lead to extensile behaviour in the absence active contractile forces, and can still generate extensile behaviour in their presence. We then confirm our results by analysing the dynamics of nematic defects in a cell-based numerical model. In order to analyse these nematic defects, one must have a reliable and efficient means of detecting them, which currently is not the case for many confluent tissues. Due to this, we then develop a machine learning model to detect nematic defects in confluent tissues that is readily implementable on experimental images of cell layers. We demonstrate that our model outperforms current detection techniques and that this manifests itself in our method requiring less data to accurately capture defect properties, improving the accuracy of experimental data interpretation. Confluent tissue dynamics are not only important in homeostasis, but also during growth, such as in wound healing. As such, we also examine the dynamics of the boundary of a growing tissue. We study this problem using a novel lattice-Boltzmann method for a growing tissue with a moving front. We find that, at small system sizes, the interface fluctuations grow with scaling in agreement with the Kardar-Parisi-Zhang universality class. However, when using a density-dependent growth regime, we find the onset of a novel instability at larger system sizes, which we develop an analytical theory to characterise. In this thesis we have developed new fundamental understanding of confluent tissue dynamics in homeostasis and the physics of growing tissue interface stability. We have also developed new defect detection methodology and simulation methods for modelling growing tissues. The tools and understanding generated here provide fruitful avenues of future research, and equip biophysicists to tackle further questions, in these important biological systems.Open Acces

The role of physics in epithelial homeostasis and development

Developing epithelial tissues are characterised by the disordered cell packing caused by ongoing cell proliferation and changes in tissue size. However, cell packing in adult epithelial tissues exhibits a high level of order, and typically, the apical tissue surface resembles a regular hexagonal lattice of planar polygons. One of the central questions in tissue development concerns the mechanisms which induce cells to repack. The change in packing may transform the tissue into a regular pattern of hexagonal cells, as seen during the refinement of Drosophila M. wing and notum tissue, or it can occur as a mechanism which drives tissue shape change, as seen during embryonal axis elongation during Drosophila convergent extension. We study cell repacking in epithelia effected by the forces that act at the interface between adjacent cells. To this end, we develop a mechanical model of epithelial tissue based on the ideas of the cellular Potts model and building on previous vertex models. Analysing expanding and fixed-size tissues, we find that steady state packing geometries depend on the regularity in the timing of cell divisions. We predict that cells in topologically active epithelia leave the tissue in response to mechanical compression and geometric anisotropy. Through a collaboration with biologists Eliana Marinari and Buzz Baum, we find that such mechanically driven cell delamination indeed occurs in the Drosophila notum. We thus identify a novel process of tissue homeostasis, whereby live cells delaminate from developing epithelium in order to limit overcrowding. Analysing the relation between stable packing geometries and the mechanical parameters, we suggest that an increase in the strength of acto-myosin contractility alone could cause tissue to repack into a regular lattice. Modifying the model to describe polarised acto-myosin localisation, we computationally reproduce cell intercalation and actin cable and rosette formation during convergent extension in Drosophila

Positron emision [i.e. emission] tomography (PET) in non-malignant chest diseases

Molecular imaging is a functional imaging that identify disease in its earliest stages and determine the exact location of metabolically active tissue such as tumours. Often before symptoms occur or abnormalities can be detected with other diagnostic tests. Two simultaneous studies to explore the potentials of Positron Emission tomography (PET) have been conducted. In the first study, the role of PET in pulmonary drug deposition has been evaluated whereas in the second study, it’s potential in monitoring disease progression and treatment response monitoring in IPF has been discovered.Gamma imaging such as planer and Single Photon Emission computed tomography (SPECT) have been used for decades in the imaging of pulmonary drug deposition, despite numerous advantage of PET very few studies were found in the literature. Two studies were conducted using in-house developed lung surrogate phantom and Andersen cascade impactor to demonstrate PET role in pulmonary drug deposition. The lung surrogate phantom study is a ‘’proof of concept’’ in which series of experiments was conducted leading to the development of a usable model. Each experimental procedure was conducted repeatedly over time to reduce the level of experimental errors. To my knowledge, this is the first phantom experiment quantifying the deposition pattern of aerosolized [18F]-FDG while mimicking human tidal breathing. In a separate experiment the Andersen cascade impactor (ACI) have been used to measure distribution of beclometasone dipropionate (BDP), formoterol fumarate dihydrate (FFA) as well as [18F]-2-fluorp-2-deoxy-D-glucose ([18F]-FDG) along the stages of ACI.The overall activity deposition within the phantom; cylinder and the extra-pulmonary section of the tube were 8.07 ± 3.51MBq. The deposition within the cylinder (lung surrogate) was 6.27 ± 2.55MBq. The average total internal dose (phantom cylinders and the extra-pulmonary section of the tube was calculated to be 0.2mSv/PET scan. These results are expected in human clinical trial under similar experimental conditions.The Aerodynamic particle size distribution (APSD) along the fractionating part of the AIM comprises of large particle mass (LPM) and small particle mass (SPM). The LPM is APSD >5μg deposited on stage 1 (representing to upper respiratory tract), whereas, the SPM comprised of the particle size 1-5 μg and < 1 μg deposited on stage 2 (representing the small airways and lung parenchyma) and an exhalation filter. In general, the deposition of the drugs and [18F]-FDG within the fractionating part of the impactor was predominantly within 1-5μg, which is a desirable fine particle fraction (FPF) of the active pharmaceutical ingredients (API) leading to pulmonary deposition.The potentials of PET imaging in pulmonary drug deposition has been demonstrated in these experiments using lung surrogate phantom and cascade impactor. [18F]-FDG PET imaging has the potentials in providing better understanding of regional distribution of pulmonary drug deposition. Standardization of these methods will enable PET imaging to be used in pulmonary drug development.In the second study, A retrospective studies using PET data was carried out to measure uptake of [18F]-FDG in the region of apparently normal lung in IPF. This was compared to normal control lung images to ascertain differences in their uptake value.HRCT is the current gold standard imaging the diagnosis of IPF. Recently there is growing interest in exploring the potentials of PET imaging in the disease progression and treatment response monitoring in IPF.Patients with IPF that had undergone PET-CT imaging for investigation of concomitant cancer diagnosis were identified retrospectively in a single interstitial lung disease (ILD) tertiary referral centre. Non IPF patients that had a PET-CT scan in the same centre for cancer diagnosis without non-malignant lung disease were identified to form two control groups: a lung cancer control group and a control group with no evidence of intra-thoracic disease (extra-thoracic malignancy controls). These two control groups were identified to allow assessment of whether the presence of thoracic malignancy effected [18F]-FDG uptake. In the event of no effect being identified, a pooled analysis comparing IPF patients and all controls was planned.No difference in standard uptake value (SUV) Maximum (Max) and SUV mean uptake was observed in the mean of 4 (Region of Interest) ROIs between lung cancer controls and extra-thoracic malignancy controls in all 3 normalizations (SUV Max body weight (BW), SUV body surface area (BSA) and SUV activity concentration (AC)) and therefore data from these groups were pooled for comparison with IPF patients. The SUV Max and SUV mean of radiologically normal lung in IPF patients was significantly higher than the normal lung in controls. However, the CT number/Hounsfield unit of the IPF patients and the control group are comparable. In addition, 20 textural features were identified in each ROI both in CT and PET data sets. Five out of the twenty CT textural features shows significant differences between the 2 controls as such, they were excluded. Fifteen were pooled together for comparison with IPF patients. Five out of the fifteen CT textural features shows significant differences when compared with IPF and all are consistent with five features that shows significant difference in PET dataset.Increase [18F]-FDG PET signal within areas of areas of apparently normal lung parenchyma has been demonstrated using SUV with 3 different normalization methods as well as using textural feature analysis. These findings have shown the heterogeneous nature of the disease process indicating the possibility of the disease activity within the apparently normal lung CT lung images. These finding may provide insight into the pathogenesis of the disease and may be helpful in monitoring the disease progression and treatment response

A design and synthesis of model membrane systems for probing lipid-polyphenol interactions

Modelling cellular membranes is crucial to understand how their structure affects function. Further, to investigate membrane-target interactions, accurate biological membrane models are essential. Understanding membrane interactions in this way has applications for in vivo processes, pharmaceutics and medicine, nutrition, and agriculture among others. This thesis presents research findings for two particular kinds of model membranes, bacterial and epithelial, and their interactions with a series of polyphenolic compounds. A distinction is made within about the nature of the “models”, where some models referred to are physical in nature, and others are computational or mathematical models. The epithelial model becomes focused on the human gastrointestinal epithelium. Here the lipid composition of this novel model epithelial membrane presented is the most complex and accurate model published to date. This work emphasises the importance of developing more complex model membranes for biological studies. This research aims to bridge the gap between their study and the ability to use surface sensitive techniques to measure membranes and their interactions. Similarly, the benefits of polyphenolic compounds are identified, and some polyphenols whose mechanism of action is relatively poorly understood are investigated in order to move towards developing structure-activity relationships. Analysis of model membranes through development and interaction with polyphenolic compounds is achieved through complementary surface sensitive techniques that allow lipid bilayer formation and interaction with polyphenols to be measured. Development of the lipid composition of the model bacterial and epithelial membranes takes place through analysis of single lipid components and investigates the effects of mixing lipids within model membranes through calorimetric and surface pressure measurements. Determination of polyphenol presence at a membrane interface, real-time measurement of mass changes for membrane formation and polyphenol interaction, and most crucially structural resolution of interactions in the nanometer regime are achieved using state of the art supported and floating lipid membranes. Mechanisms by which interaction of polyphenols with model biological lipid membranes is explored, with a comparison between bacterial and epithelial membranes highlighted to show variation in polyphenol interaction with membranes of differing phospholipid composition. The effects of lipid composition of membranes is studied using epithelial membranes of iteratively more complex and accurate composition, with membrane composition informed by a thorough meta-analysis of epithelial membrane lipid headgroup composition. We are able to show two different modes of action of polyphenols within membranes depending on the type and lipid composition of the membrane where nuances of polyphenol interaction are rationalised in terms of the molecular properties of the polyphenol under investigation. Bacterial model membranes, both physically and computationally, show strong and persistent interactions under flow with (-)-epigallocatechin gallate and Tellimagrandin-II, when characterised by neutron reflectometry. In the case of Tellimagrandin-II, apparent membrane lysis is observed with multilamellar membrane stacking occurring in the sample cell. By contrast, the epithelial membrane shows binding to the surface of, and intercalation into the tail region, of the membrane. The differences between these two modes of interaction have important implications where the dietary, pharmaceutical, and antimicrobial properties of these compounds is concerned