Tertiary lymphoid structures (TLS) identification and density assessment on H&E-stained digital slides of lung cancer

Tertiary lymphoid structures (TLS) are ectopic aggregates of lymphoid cells in inflamed, infected, or tumoral tissues that are easily recognized on an H&E histology slide as discrete entities, distinct from lymphocytes. TLS are associated with improved cancer prognosis but there is no standardised method available to quantify their presence. Previous studies have used immunohistochemistry to determine the presence of specific cells as a marker of the TLS. This has now been proven to be an underestimate of the true number of TLS. Thus, we propose a methodology for the automated identification and quantification of TLS, based on H&E slides. We subsequently determined the mathematical criteria defining a TLS. TLS regions were identified through a deep convolutional neural network and segmentation of lymphocytes was performed through an ellipsoidal model. This methodology had a 92.87% specificity at 95% sensitivity, 88.79% specificity at 98% sensitivity and 84.32% specificity at 99% sensitivity level based on 144 TLS annotated H&E slides implying that the automated approach was able to reproduce the histopathologists’ assessment with great accuracy. We showed that the minimum number of lymphocytes within TLS is 45 and the minimum TLS area is 6,245μm2. Furthermore, we have shown that the density of the lymphocytes is more than 3 times those outside of the TLS. The mean density and standard deviation of lymphocytes within a TLS area are 0.0128/μm2 and 0.0026/μm2 respectively compared to 0.004/μm2 and 0.001/μm2 in non-TLS regions. The proposed methodology shows great potential for automated identification and quantification of the TLS density on digital H&E slides

Electron tomography unravels new insights into fiber cell wall nanostructure; exploring 3Dma cromolecular biopolymeric nano‑architecture of spruce fiber secondary walls

Lignocellulose biomass has a tremendous potential as renewable biomaterials for fostering the “bio-based society” and circular bioeconomy paradigm. It requires efficient use and breakdown of fiber cell walls containing mainly cellulose, hemicellulose and lignin biopolymers. Despite their great importance, there is an extensive debate on the true structure of fiber walls and knowledge on the macromolecular nano-organization is limited and remains elusive in 3D. We employed dual-axis electron tomography that allows visualization of previously unseen 3D macromolecular organization/biopolymeric nano-architecture of the secondary S2 layer of Norway spruce fiber wall. Unprecedented 3D nano-structural details with novel insights into cellulose microfibrils (~ 2 nm diameter), macrofibrils, nano-pore network and cell wall chemistry (volume %) across the S2 were explored and quantified including simulation of structure related permeability. Matrix polymer association with cellulose varied between microfibrils and macrofibrils with lignin directly associated with MFs. Simulated bio-nano-mechanical properties revealed stress distribution within the S2 and showed similar properties between the idealized 3D model and the native S2 (actual tomogram). Present work has great potential for significant advancements in lignocellulose research on nano-scale understanding of cell wall assembly/disassembly processes leading to more efficient industrial processes of functionalization, valorization and target modification technologies

A structure-function based approach to floc hierarchy and evidence for the non-fractal nature of natural sediment flocs

Abstract Natural sediment flocs are fragile, highly irregular, loosely bound aggregates comprising minerogenic and organic material. They contribute a major component of suspended sediment load and are critical for the fate and flux of sediment, carbon and pollutants in aquatic environments. Understanding their behaviour is essential to the sustainable management of waterways, fisheries and marine industries. For several decades, modelling approaches have utilised fractal mathematics and observations of two dimensional (2D) floc size distributions to infer levels of aggregation and predict their behaviour. Whilst this is a computationally simple solution, it is highly unlikely to reflect the complexity of natural sediment flocs and current models predicting fine sediment hydrodynamics are not efficient. Here, we show how new observations of fragile floc structures in three dimensions (3D) demonstrate unequivocally that natural flocs are non-fractal. We propose that floc hierarchy is based on observations of 3D structure and function rather than 2D size distribution. In contrast to fractal theory, our data indicate that flocs possess characteristics of emergent systems including non-linearity and scale-dependent feedbacks. These concepts and new data to quantify floc structures offer the opportunity to explore new emergence-based floc frameworks which better represent natural floc behaviour and could advance our predictive capacity

Frozen-hydrated chromatin from metaphase chromosomes has an interdigitated multilayer structure

Cryo-electron tomography and small-angle X-ray scattering were used to investigate the chromatin folding in metaphase chromosomes. The tomographic 3D reconstructions show that frozen-hydrated chromatin emanated from chromosomes is planar and forms multilayered plates. The layer thickness was measured accounting for the contrast transfer function fringes at the plate edges, yielding a width of similar to 7.5 nm, which is compatible with the dimensions of a monolayer of nucleosomes slightly tilted with respect to the layer surface. Individual nucleosomes are visible decorating distorted plates, but typical plates are very dense and nucleosomes are not identifiable as individual units, indicating that they are tightly packed. Two layers in contact are similar to 13 nm thick, which is thinner than the sum of two independent layers, suggesting that nucleosomes in the layers interdigitate. X-ray scattering of whole chromosomes shows a main scattering peak at similar to 6 nm, which can be correlated with the distance between layers and between interdigitating nucleosomes interacting through their faces. These observations support a model where compact chromosomes are composed of many chromatin layers stacked along the chromosome axis

Towards Deep Cellular Phenotyping in Placental Histology

The placenta is a complex organ, playing multiple roles during fetal development. Very little is known about the association between placental morphological abnormalities and fetal physiology. In this work, we present an open sourced, computationally tractable deep learning pipeline to analyse placenta histology at the level of the cell. By utilising two deep Convolutional Neural Network architectures and transfer learning, we can robustly localise and classify placental cells within five classes with an accuracy of 89%. Furthermore, we learn deep embeddings encoding phenotypic knowledge that is capable of both stratifying five distinct cell populations and learn intraclass phenotypic variance. We envisage that the automation of this pipeline to population scale studies of placenta histology has the potential to improve our understanding of basic cellular placental biology and its variations, particularly its role in predicting adverse birth outcomes.Comment: Updated MRC funding material. Corrected typo that suggested ensembling and Inception accuracy were the same (updated to reflect the fact the ensemble model is 1% better than previously reported

A theoretical and computational study of cavity formation in biological systems

In this thesis, I present my work on the emergence of self-organised structure within cellular systems, with a particular emphasis on the formation of fluid filled cavities. Self-organisation is a striking hallmark of living systems, and plays a particularly import role in developmental biology. To study such systems, I develop a novel hydrodynamic theory of cells in a background fluid of water and solutes. The solutes and water can move passively across the membrane of the cells. Furthermore, solutes can be actively transported in or out of the cell both isotropi- cally and along a polar axis. Within this theory I demonstrate the existence of two potential mechanisms for cavity formation: spinodal phase separation driven by cell-cell adhesions, and an instability driven by active pumping of solutes into defects in the polarity field. This theory is general in scope, i.e. it is a framework to describe a variety of behaviours of any system consisting of adhering cells that can polarise and actively pump fluid. I also present a study of a specific experimental system: mouse embryonic stem cell (mESC) aggregates. When grown from wild type cells, these aggregates form a spherical structure with cells polarised towards the centre. Fluid is pumped into the centre and a cavity opens. Such aggregates are the simplest example of mESC organoids that recapitulate key in vivo developmental processes in vitro. In order to quantify the growth of mESC aggregates, I develop an image segmentation and analysis pipeline. This pipeline allows me to extract meaningful, structured information from noisy 3D experimental time series data. In order to model the growth of mESC aggregates in silico, I develop a novel 2D model of polarised, deformable cells with continuous boundaries, called the Spline Model. Using the Spline Model as a prototype, I recapitulate key features of the experiments. Finally, I develop a 3D model of polarised, deformable cells. I demonstrate quantitative agreement between cell shapes produced by this model and in experiment. I study the dynamics of cell aggregates for the case where adhesion forces are coupled to apicobasal polarity, and make quantitative comparisons between these simulations and experiments. I find a positive correlation between the measured polarity of E- cadherin and predictions based on integration of extracellular matrix signalling. Furthermore, by coupling polarity to increased apical adhesion, I demonstrate the ability of extended cellular aggregates to undergo a transition to a compact state. When the coupling is removed, the transition no longer occurs. This behaviour is reminiscent of β1-KO cells, in which polarity alignment mechanisms are disrupted, that fail to form compact, organised aggregates

Label-Free Digital Holotomography Reveals Ibuprofen-Induced Morphological Changes to Red Blood Cells.

Understanding the dose-dependent effect of over-the-counter drugs on red blood cells (RBCs) is crucial for hematology and digital pathology. Yet, it is challenging to continuously record the real-time, drug-induced shape changes of RBCs in a label-free manner. Here, we demonstrate digital holotomography (DHTM)-enabled real-time, label-free concentration-dependent and time-dependent monitoring of ibuprofen on RBCs from a healthy donor. The RBCs are segmented based on three-dimensional (3D) and four-dimensional (4D) refractive index tomograms, and their morphological and chemical parameters are retrieved with their shapes classified using machine learning. We directly observed the formation and motion of spicules on the RBC membrane when aqueous solutions of ibuprofen were drop-cast on wet blood, creating rough-membraned echinocyte forms. At low concentrations of 0.25-0.50 mM, the ibuprofen-induced morphological change was transient, but at high concentrations (1-3 mM) the spiculated RBC remained over a period of up to 1.5 h. Molecular simulations confirmed that aggregates of ibuprofen molecules at high concentrations significantly disrupted the RBC membrane structural integrity and lipid order but produced negligible effect at low ibuprofen concentrations. Control experiments on the effect of urea, hydrogen peroxide, and aqueous solutions on RBCs showed zero spicule formation. Our work clarifies the dose-dependent chemical effects on RBCs using label-free microscopes that can be deployed for the rapid detection of overdosage of over-the-counter and prescribed drugs