Human Stem Cells for Ophthalmology: Recent Advances in Diagnostic Image Analysis and Computational Modelling.

Purpose of Review: To explore the advances and future research directions in image analysis and computational modelling of human stem cells (hSCs) for ophthalmological applications. Recent Findings: hSCs hold great potential in ocular regenerative medicine due to their application in cell-based therapies and in disease modelling and drug discovery using state-of-the-art 2D and 3D organoid models. However, a deeper characterisation of their complex, multi-scale properties is required to optimise their translation to clinical practice. Image analysis combined with computational modelling is a powerful tool to explore mechanisms of hSC behaviour and aid clinical diagnosis and therapy. Summary: Many computational models draw on a variety of techniques, often blending continuum and discrete approaches, and have been used to describe cell differentiation and self-organisation. Machine learning tools are having a significant impact in model development and improving image classification processes for clinical diagnosis and treatment and will be the focus of much future research.

A mathematical modelling framework for the regulation of intra-cellular OCT4 in human pluripotent stem cells.

Human pluripotent stem cells (hPSCs) have the potential to differentiate into all cell types, a property known as pluripotency. A deeper understanding of how pluripotency is regulated is required to assist in controlling pluripotency and differentiation trajectories experimentally. Mathematical modelling provides a non-invasive tool through which to explore, characterise and replicate the regulation of pluripotency and the consequences on cell fate. Here we use experimental data of the expression of the pluripotency transcription factor OCT4 in a growing hPSC colony to develop and evaluate mathematical models for temporal pluripotency regulation. We consider fractional Brownian motion and the stochastic logistic equation and explore the effects of both additive and multiplicative noise. We illustrate the use of time-dependent carrying capacities and the introduction of Allee effects to the stochastic logistic equation to describe cell differentiation. We conclude both methods adequately capture the decline in OCT4 upon differentiation, but the Allee effect model has the advantage of allowing differentiation to occur stochastically in a sub-set of cells. This mathematical framework for describing intra-cellular OCT4 regulation can be extended to other transcription factors and developed into predictive models.

OCT4 expression in human embryonic stem cells: spatio-temporal dynamics and fate transitions.

The improved in vitro regulation of human embryonic stem cell (hESC) pluripotency and differentiation trajectories is required for their promising clinical applications. The temporal and spatial quantification of the molecular interactions controlling pluripotency is also necessary for the development of successful mathematical and computational models. Here we use time-lapse experimental data of OCT4-mCherry fluorescence intensity to quantify the temporal and spatial dynamics of the pluripotency transcription factor OCT4 in a growing hESC colony in the presence and absence of BMP4. We characterise the internal self-regulation of OCT4 using the Hurst exponent and autocorrelation analysis, quantify the intra-cellular fluctuations and consider the diffusive nature of OCT4 evolution for individual cells and pairs of their descendants. We find that OCT4 abundance in the daughter cells fluctuates sub-diffusively, showing anti-persistent self-regulation. We obtain the stationary probability distributions governing hESC transitions amongst the different cell states and establish the times at which pro-fate cells (which later give rise to pluripotent or differentiated cells) cluster in the colony. By quantifying the similarities between the OCT4 expression amongst neighbouring cells, we show that hESCs express similar OCT4 to cells within their local neighbourhood within the first two days of the experiment and before BMP4 treatment. Our framework allows us to quantify the relevant properties of proliferating hESC colonies and the procedure is widely applicable to other transcription factors and cell populations.

The recent advances in the mathematical modelling of human pluripotent stem cells.

Human pluripotent stem cells hold great promise for developments in regenerative medicine and drug design. The mathematical modelling of stem cells and their properties is necessary to understand and quantify key behaviours and develop non-invasive prognostic modelling tools to assist in the optimisation of laboratory experiments. Here, the recent advances in the mathematical modelling of hPSCs are discussed, including cell kinematics, cell proliferation and colony formation, and pluripotency and differentiation.

Seeding hESCs to achieve optimal colony clonality.

Human embryonic stem cells (hESCs) and induced pluripotent stem cells (iPSCs) have promising clinical applications which often rely on clonally-homogeneous cell populations. To achieve this, it is important to ensure that each colony originates from a single founding cell and to avoid subsequent merging of colonies during their growth. Clonal homogeneity can be obtained with low seeding densities; however, this leads to low yield and viability. It is therefore important to quantitatively assess how seeding density affects clonality loss so that experimental protocols can be optimised to meet the required standards. Here we develop a quantitative framework for modelling the growth of hESC colonies from a given seeding density based on stochastic exponential growth. This allows us to identify the timescales for colony merges and over which colony size no longer predicts the number of founding cells. We demonstrate the success of our model by applying it to our own experiments of hESC colony growth; while this is based on a particular experimental set-up, the model can be applied more generally to other cell lines and experimental conditions to predict these important timescales.

Correlated random walks of human embryonic stem cells in vitro.

We perform a detailed analysis of the migratory motion of human embryonic stem cells in two-dimensions, both when isolated and in close proximity to another cell, recorded with time-lapse microscopic imaging. We show that isolated cells tend to perform an unusual locally anisotropic walk, moving backwards and forwards along a preferred local direction correlated over a timescale of around 50 min and aligned with the axis of the cell elongation. Increasing elongation of the cell shape is associated with increased instantaneous migration speed. We also show that two cells in close proximity tend to move in the same direction, with the average separation of [Formula: see text]m or less and the correlation length of around 25 µm, a typical cell diameter. These results can be used as a basis for the mathematical modelling of the formation of clonal hESC colonies.

Dynamics of single human embryonic stem cells and their pairs: a quantitative analysis.

Numerous biological approaches are available to characterise the mechanisms which govern the formation of human embryonic stem cell (hESC) colonies. To understand how the kinematics of single and pairs of hESCs impact colony formation, we study their mobility characteristics using time-lapse imaging. We perform a detailed statistical analysis of their speed, survival, directionality, distance travelled and diffusivity. We confirm that single and pairs of cells migrate as a diffusive random walk for at least 7 hours of evolution. We show that the presence of Cell Tracer significantly reduces hESC mobility. Our results open the path to employ the theoretical framework of the diffusive random walk for the prognostic modelling and optimisation of the growth of hESC colonies. Indeed, we employ this random walk model to estimate the seeding density required to minimise the occurrence of hESC colonies arising from more than one founder cell and the minimal cell number needed for successful colony formation. Our prognostic model can be extended to investigate the kinematic behaviour of somatic cells emerging from hESC differentiation and to enable its wide application in phenotyping of pluripotent stem cells for large scale stem cell culture expansion and differentiation platforms.

[Clinical and laboratory characteristics of cryptosporidiosis in Turkmenistan]. / Kliniko-laboratornye osobennosti kriptosporidioza v Turkmenistane.

AIM: The study of clinical symptoms of gastrointestinal lesions in subjects invaded with cryptosporidia. MATERIALS AND METHODS: From 1994 to 1997 383 patients with monocryptosporidiasis were observed. 75.7% of them were children. Cryptosporidia oocysts were identified in fecalia using Fulleborn technique. The specimens were stained according to Cill-Nilsson. RESULTS: Clinically, the invasion was characterized by acute onset, severe course in children, involvement of the whole gastrointestinal and respiratory tracts. CONCLUSION: Monocryptosporidiasis runs in Turkmenistan a more severe course compared to countries with moderately hot climate.