Form follows function: Nuclear morphology as a quantifiable predictor of cellular senescence.

Enlarged or irregularly shaped nuclei are frequently observed in tissue cells undergoing senescence. However, it remained unclear whether this peculiar morphology is a cause or a consequence of senescence and how informative it is in distinguishing between proliferative and senescent cells. Recent research reveals that nuclear morphology can act as a predictive biomarker of senescence, suggesting an active role for the nucleus in driving senescence phenotypes. By employing deep learning algorithms to analyze nuclear morphology, accurate classification of cells as proliferative or senescent is achievable across various cell types and species both in vitro and in vivo. This quantitative imaging-based approach can be employed to establish links between senescence burden and clinical data, aiding in the understanding of age-related diseases, as well as assisting in disease prognosis and treatment response.

Heterogeneous anomalous transport in cellular and molecular biology.

It is well established that a wide variety of phenomena in cellular and molecular biology involve anomalous transport e.g. the statistics for the motility of cells and molecules are fractional and do not conform to the archetypes of simple diffusion or ballistic transport. Recent research demonstrates that anomalous transport is in many cases heterogeneous in both time and space. Thus single anomalous exponents and single generalised diffusion coefficients are unable to satisfactorily describe many crucial phenomena in cellular and molecular biology. We consider advances in the field ofheterogeneous anomalous transport(HAT) highlighting: experimental techniques (single molecule methods, microscopy, image analysis, fluorescence correlation spectroscopy, inelastic neutron scattering, and nuclear magnetic resonance), theoretical tools for data analysis (robust statistical methods such as first passage probabilities, survival analysis, different varieties of mean square displacements, etc), analytic theory and generative theoretical models based on simulations. Special emphasis is made on high throughput analysis techniques based on machine learning and neural networks. Furthermore, we consider anomalous transport in the context of microrheology and the heterogeneous viscoelasticity of complex fluids. HAT in the wavefronts of reaction-diffusion systems is also considered since it plays an important role in morphogenesis and signalling. In addition, we present specific examples from cellular biology including embryonic cells, leucocytes, cancer cells, bacterial cells, bacterial biofilms, and eukaryotic microorganisms. Case studies from molecular biology include DNA, membranes, endosomal transport, endoplasmic reticula, mucins, globular proteins, and amyloids.

Strain-dependent elastography of cancer cells reveals heterogeneity and stiffening due to attachment.

Because cells vary in thickness and in biomechanical properties, the use of a constant force trigger during atomic force microscopy (AFM) stiffness mapping produces a varied nominal strain that can obfuscate the comparison of local material properties. In this study, we measured the biomechanical spatial heterogeneity of ovarian and breast cancer cells by using an indentation-dependent pointwise Hertzian method. Force curves and surface topography were used together to determine cell stiffness as a function of nominal strain. By recording stiffness at a particular strain, it may be possible to improve comparison of the material properties of cells and produce higher contrast representations of cell mechanical properties. Defining a linear region of elasticity that corresponds to a modest nominal strain, we were able to clearly distinguish the mechanics of the perinuclear region of cells. We observed that, relative to the lamelopodial stiffness, the perinuclear region was softer for metastatic cancer cells than their nonmetastatic counterparts. Moreover, contrast in the strain-dependent elastography in comparison to conventional force mapping with Hertzian model analysis revealed a significant stiffening phenomenon in the thin lamellipodial region in which the modulus scales inversely and exponentially with cell thickness. The observed exponential stiffening is not affected by relaxation of cytoskeletal tension, but finite element modeling indicates it is affected by substrate adhesion. The novel cell mapping technique explores cancer cell mechanical nonlinearity that results from regional heterogeneity, which could help explain how metastatic cancer cells can show soft phenotypes while simultaneously increasing force generation and invasiveness.

A quantitative biophysical principle to explain the 3D cellular connectivity in curved epithelia.

Epithelial cell organization and the mechanical stability of tissues are closely related. In this context, it has been recently shown that packing optimization in bended or folded epithelia is achieved by an energy minimization mechanism that leads to a complex cellular shape: the "scutoid". Here, we focus on the relationship between this shape and the connectivity between cells. We use a combination of computational, experimental, and biophysical approaches to examine how energy drivers affect the three-dimensional (3D) packing of tubular epithelia. We propose an energy-based stochastic model that explains the 3D cellular connectivity. Then, we challenge it by experimentally reducing the cell adhesion. As a result, we observed an increment in the appearance of scutoids that correlated with a decrease in the energy barrier necessary to connect with new cells. We conclude that tubular epithelia satisfy a quantitative biophysical principle that links tissue geometry and energetics with the average cellular connectivity.

On the adhesion-velocity relation and length adaptation of motile cells on stepped fibronectin lanes.

The biphasic adhesion-velocity relation is a universal observation in mesenchymal cell motility. It has been explained by adhesion-promoted forces pushing the front and resisting motion at the rear. Yet, there is little quantitative understanding of how these forces control cell velocity. We study motion of MDA-MB-231 cells on microlanes with fields of alternating Fibronectin densities to address this topic and derive a mathematical model from the leading-edge force balance and the force-dependent polymerization rate. It reproduces quantitatively our measured adhesion-velocity relation and results with keratocytes, PtK1 cells, and CHO cells. Our results confirm that the force pushing the leading-edge membrane drives lamellipodial retrograde flow. Forces resisting motion originate along the whole cell length. All motion-related forces are controlled by adhesion and velocity, which allows motion, even with higher Fibronectin density at the rear than at the front. We find the pathway from Fibronectin density to adhesion structures to involve strong positive feedbacks. Suppressing myosin activity reduces the positive feedback. At transitions between different Fibronectin densities, steady motion is perturbed and leads to changes of cell length and front and rear velocity. Cells exhibit an intrinsic length set by adhesion strength, which, together with the length dynamics, suggests a spring-like front-rear interaction force. We provide a quantitative mechanistic picture of the adhesion-velocity relation and cell response to adhesion changes integrating force-dependent polymerization, retrograde flow, positive feedback from integrin to adhesion structures, and spring-like front-rear interaction.

Optimising protein synthesis in cell-free systems, a review.

Over the last decades, cell-free systems have been extensively used for in vitro protein expression. A vast range of protocols and cellular sources varying from prokaryotes and eukaryotes are now available for cell-free technology. However, exploiting the maximum capacity of cell free systems is not achieved by using traditional protocols. Here, what are the strategies and choices one can apply to optimise cell-free protein synthesis have been reviewed. These strategies provide robust and informative improvements regarding transcription, translation and protein folding which can later be used for the establishment of individual best cell-free reactions per lysate batch.

Recent advances of integrated microfluidic suspension cell culture system.

Microfluidic devices with superior microscale fluid manipulation ability and large integration flexibility offer great advantages of high throughput, parallelisation and multifunctional automation. Such features have been extensively utilised to facilitate cell culture processes such as cell capturing and culturing under controllable and monitored conditions for cell-based assays. Incorporating functional components and microfabricated configurations offered different levels of fluid control and cell manipulation strategies to meet diverse culture demands. This review will discuss the advances of single-phase flow and droplet-based integrated microfluidic suspension cell culture systems and their applications for accelerated bioprocess development, high-throughput cell selection, drug screening and scientific research to insight cell biology. Challenges and future prospects for this dynamically developing field are also highlighted.

Stimulus waveform design for decreasing charge and increasing stimulation selectivity in retinal prostheses.

Retinal degenerative diseases, such as retinitis pigmentosa, begin with damage to the photoreceptor layer of the retina. In the absence of presynaptic input from photoreceptors, networks of electrically coupled AII amacrine and cone bipolar cells have been observed to exhibit oscillatory behaviour and result in spontaneous firing of ganglion cells. This ganglion cell activity could interfere with external stimuli provided by retinal prosthetic devices and potentially degrade their performance. In this work, the authors computationally investigate stimulus waveform designs, which can improve the performance of retinal prostheses by suppressing undesired spontaneous firing of ganglion cells and generating precise temporal spiking patterns. They utilise a multi-scale computational model for electrical stimulation of degenerated retina based on the admittance method and NEURON simulation environments. They present a class of asymmetric biphasic pulses that can generate precise ganglion cell firing patterns with up to 55% lower current requirements compared to traditional symmetric biphasic pulses. This lower current results in activation of only proximal ganglion cells, provides more focused stimulation and lowers the risk of tissue damage.

In Vitro and Cellular Probes to Study PARP Enzyme Target Engagement.

Poly(ADP-ribose) polymerase (PARP) enzymes use nicotinamide adenine dinucleotide (NAD+) to modify up to seven different amino acids with a single mono(ADP-ribose) unit (MARylation deposited by PARP monoenzymes) or branched poly(ADP-ribose) polymers (PARylation deposited by PARP polyenzymes). To enable the development of tool compounds for PARP monoenzymes and polyenzymes, we have developed active site probes for use in in vitro and cellular biophysical assays to characterize active site-directed inhibitors that compete for NAD+ binding. These assays are agnostic of the protein substrate for each PARP, overcoming a general lack of knowledge around the substrates for these enzymes. The in vitro assays use less enzyme than previously described activity assays, enabling discrimination of inhibitor potencies in the single-digit nanomolar range, and the cell-based assays can differentiate compounds with sub-nanomolar potencies and measure inhibitor residence time in live cells.

Synthetic biology, engineering biology, market expectation.

'Engineering biology' is being increasingly adopted as a term by organisations that seek to deliver benefits from 'synthetic biology'. However, are 'engineering biology' and 'synthetic biology' different words with the same meaning or do they signal important differences? By observing how these two terms are currently being described and applied in practice, it is possible to differentiate the two whilst also acknowledging significant overlaps and complementarity. Increasing adoption of the term 'engineering biology' reflects the maturing of synthetic biology since the early years of this century from a research concept to a technological platform that is facilitating the delivery of commercial products and services. The term 'synthetic biology' retains a strong association with its original goal to help make biology engineerable, a challenge that will inevitably continue to stimulate research for decades to come as ever more complex and demanding systems are tackled. In comparison, the term 'engineering biology' relates more commonly to the utilisation of the synthetic biology platform alongside other related technologies to deliver effective solutions in response to increasing market challenges and expectations.

Gene modification strategies using AO-mediated exon skipping and CRISPR/Cas9 as potential therapies for Duchenne muscular dystrophy patients.

Duchenne muscular dystrophy (DMD) is an X-linked genetic disease affecting 1 in 5000 young males worldwide annually. Patients experience muscle weakness and loss of ambulation at an early age, with ∼75% reduced life expectancy. Recently developed genetic editing strategies aim to convert severe DMD phenotypes to a milder disease course. Among these, the antisense oligonucleotide (AO)-mediated exon skipping and the adeno-associated viral-delivered clustered regularly interspaced short palindromic repeat (CRISPR) associated protein 9 (adeno-associated viral (AAV)-delivered CRISPR/Cas9) gene editing have shown promising results in restoring dystrophin protein expression and functionality in skeletal and heart muscle in both animals and human cells in vivo and in vitro. However, therapeutic benefits currently remain unclear. The aim of this review is to compare the potential therapeutic benefits, efficacy, safety, and clinical progress of AO-mediated exon skipping and CRISPR/Cas9 gene-editing strategies. Both techniques have demonstrated therapeutic benefit and long-term efficacy in clinical trials. AAV-delivery of CRISPR/Cas9 may potentially correct disease-causing mutations following a single treatment compared to the required continuous AO/PMO-delivery of exon skipping drugs. The latter has the potential to increase the dystrophin expression in skeletal/heart muscle with sustained effects. However, therapeutic challenges including the need for optimised delivery must be overcome in to advance current clinical data.

Mechanistic modelling of tyrosine recombination reveals key parameters determining the performance of a CAR T cell switching circuit.

Inducible genetic switches based on tyrosine recombinase-based DNA excision are a promising platform for the regulation and control of chimeric antigen receptor (CAR) T cell activity in cancer immunotherapy. These switches exploit the increased stability of DNA excision in tyrosine recombinases through an inversion-excision circuit design. Here, the authors develop the first mechanistic mathematical model of switching dynamics in tyrosine recombinases and validate it against experimental data through both global optimisation and statistical approximation approaches. Analysis of this model provides guidelines regarding which system parameters are best suited to experimental tuning in order to establish optimal switch performance in vivo. In particular, they find that the switching response can be made significantly faster by increasing the concentration of the inducer drug 4-OHT and/or by using promoters generating higher expression levels of the FlpO recombinase.

Machine learning approach of automatic identification and counting of blood cells.

A complete blood cell count is an important test in medical diagnosis to evaluate overall health condition. Traditionally blood cells are counted manually using haemocytometer along with other laboratory equipment's and chemical compounds, which is a time-consuming and tedious task. In this work, the authors present a machine learning approach for automatic identification and counting of three types of blood cells using 'you only look once' (YOLO) object detection and classification algorithm. YOLO framework has been trained with a modified configuration BCCD Dataset of blood smear images to automatically identify and count red blood cells, white blood cells, and platelets. Moreover, this study with other convolutional neural network architectures considering architecture complexity, reported accuracy, and running time with this framework and compare the accuracy of the models for blood cells detection. They also tested the trained model on smear images from a different dataset and found that the learned models are generalised. Overall the computer-aided system of detection and counting enables us to count blood cells from smear images in less than a second, which is useful for practical applications.

Cell-line characterization by infrared-induced pyroelectric effect.

Evaluation of cellular thermodynamics has recently received a high interest because of its implication in many mechanisms related with function, structure and health of cells. Recent literature reported significant efforts to provide affordable intracellular thermal components of absorption, such as thermal conductivity, to overcome the lack of experimental data. Herein, we provide lines of evidence towards the fabrication of an electronic system, using a rapid thermoelectric technique based on infrared-induced pyroelectric effect for in-vitro cell model characterization. Results demonstrated that the assessment of the average single cell thermal conductivity, sample concentration, and information on cell viability is possible over a wide concentration range. The proposed electronic system establishes a different analysis paradigm if compared to those reported in the literature, with consistent results, demonstrating that the adopted technique can provide cell-specific information and knowledge, closely linked to cell viability and its vital functions.

Characterization of cytoplasmic viscosity of hundreds of single tumour cells based on micropipette aspiration.

Cytoplasmic viscosity (µ c) is a key biomechanical parameter for evaluating the status of cellular cytoskeletons. Previous studies focused on white blood cells, but the data of cytoplasmic viscosity for tumour cells were missing. Tumour cells (H1299, A549 and drug-treated H1299 with compromised cytoskeletons) were aspirated continuously through a micropipette at a pressure of -10 or -5 kPa where aspiration lengths as a function of time were obtained and translated to cytoplasmic viscosity based on a theoretical Newtonian fluid model. Quartile coefficients of dispersion were quantified to evaluate the distributions of cytoplasmic viscosity within the same cell type while neural network-based pattern recognitions were used to classify different cell types based on cytoplasmic viscosity. The single-cell cytoplasmic viscosity with three quartiles and the quartile coefficient of dispersion were quantified as 16.7 Pa s, 42.1 Pa s, 110.3 Pa s and 74% for H1299 cells at -10 kPa (n cell = 652); 144.8 Pa s, 489.8 Pa s, 1390.7 Pa s, and 81% for A549 cells at -10 kPa (n cell = 785); 7.1 Pa s, 13.7 Pa s, 31.5 Pa s, and 63% for CD-treated H1299 cells at -10 kPa (n cell = 651); and 16.9 Pa s, 48.2 Pa s, 150.2 Pa s, and 80% for H1299 cells at -5 kPa (n cell = 600), respectively. Neural network-based pattern recognition produced successful classification rates of 76.7% for H1299 versus A549, 67.0% for H1299 versus drug-treated H1299 and 50.3% for H1299 at -5 and -10 kPa. Variations of cytoplasmic viscosity were observed within the same cell type and among different cell types, suggesting the potential role of cytoplasmic viscosity in cell status evaluation and cell type classification.

Prediction of putative small molecules for manipulation of enriched signalling pathways in hESC-derived early cardiovascular progenitors by bioinformatics analysis.

Human pluripotent stem cell-derived cardiovascular progenitor cells (CPCs) are considered as powerful tools for cardiac regenerative medicine and developmental study. Mesoderm posterior1+ (MESP1+ ) cells are identified as the earliest CPCs from which almost all cardiac cell types are generated. Molecular insights to the transcriptional regulatory factors of early CPCs are required to control cell fate decisions. Herein, the microarray data set of human embryonic stem cells (hESCs)-derived MESP1+ cells was analysed and differentially expressed genes (DEGs) were identified in comparison to undifferentiated hESCs and MESP1-negative cells. Then, gene ontology and pathway enrichment analysis of DEGs were carried out with the subsequent prediction of putative regulatory small molecules for modulation of CPC fate. Some key signalling cascades of cardiogenesis including Hippo, Wnt, transforming growth factor-ß, and PI3K/Akt were highlighted in MESP1+ cells. The transcriptional regulatory network of MESP1+ cells were visualised through interaction networks of DEGs. Additionally, 35 promising chemicals were predicted based on correlations with gene expression signature of MESP1+ cells for effective in vitro CPC manipulation. Studying the transcriptional profile of MESP1+ cells resulted into the identification of important signalling pathways and chemicals which could be introduced as powerful tools to manage proliferation and differentiation of hESC-derived CPCs more efficiently.

Adaptive fractional-order blood glucose regulator based on high-order sliding mode observer.

Type I diabetes is described by the destruction of the insulin-producing beta-cells in the pancreas. Hence, exogenous insulin administration is necessary for Type I diabetes patients. In this study, to estimate the states that are not directly available from the Bergman minimal model a high-order sliding mode observer is proposed. Then fractional calculus is combined with sliding mode control (SMC) for blood glucose regulation to create more robustness performance and make more degree of freedom and flexibility for the proposed method. Then an adaptive fractional-order SMC is proposed. The adaptive SMC protect controller against disturbance and uncertainties while the fractional calculus provides robust performance. Numerical simulation verifies that the proposed controllers have better performance in the presence of disturbance and uncertainties without chattering.

Two-dimensional polynomial type canonical relaxation oscillator model for p53 dynamics.

p53 network, which is responsible for DNA damage response of cells, exhibits three distinct qualitative behaviours; low state, oscillation and high state, which are associated with normal cell cycle progression, cell cycle arrest and apoptosis, respectively. The experimental studies demonstrate that these dynamics of p53 are due to the ATM and Wip1 interaction. This paper proposes a simple two-dimensional canonical relaxation oscillator model based on the identified topological structure of ATM and Wip1 interaction underlying these qualitative behaviours of p53 network. The model includes only polynomial terms that have the interpretability of known ATM and Wip1 interaction. The introduced model is useful for understanding relaxation oscillations in gene regulatory networks. Through mathematical analysis, we investigate the roles of ATM and Wip1 in forming of these three essential behaviours, and show that ATM and Wip1 constitute the core mechanism of p53 dynamics. In agreement with biological findings, we show that Wip1 degradation term is a highly sensitive parameter, possibly related to mutations. By perturbing the corresponding parameters, our model characterizes some mutations such as ATM deficiency and Wip1 overexpression. Finally, we provide intervention strategies considering our observation that Wip1 seems to be an important target to conduct therapies for these mutations.

Hybrid CME-ODE method for efficient simulation of the galactose switch in yeast.

It is well known that stochasticity in gene expression is an important source of noise that can have profound effects on the fate of a living cell. In the galactose genetic switch in yeast, the unbinding of a transcription repressor is induced by high concentrations of sugar particles activating gene expression of sugar transporters. This response results in high propensity for all reactions involving interactions with the metabolite. The reactions for gene expression, feedback loops and transport are typically described by chemical master equations (CME). Sampling the CME using the stochastic simulation algorithm (SSA) results in large computational costs as each reaction event is evaluated explicitly. To improve the computational efficiency of cell simulations involving high particle number systems, the authors have implemented a hybrid stochastic-deterministic (CME-ODE) method into the publically available, GPU-based lattice microbes (LM) software suite and its python interface pyLM. LM and pyLM provide a convenient way to simulate complex cellular systems and interface with high-performance RDME/CME/ODE solvers. As a test of the implementation, the authors apply the hybrid CME-ODE method to the galactose switch in Saccharomyces cerevisiae, gaining a 10-50× speedup while yielding protein distributions and species traces similar to the pure SSA CME.

Effective implicit finite-difference method for sensitivity analysis of stiff stochastic discrete biochemical systems.

Simulation of cellular processes is achieved through a range of mathematical modelling approaches. Deterministic differential equation models are a commonly used first strategy. However, because many biochemical processes are inherently probabilistic, stochastic models are often called for to capture the random fluctuations observed in these systems. In that context, the Chemical Master Equation (CME) is a widely used stochastic model of biochemical kinetics. Use of these models relies on estimates of kinetic parameters, which are often poorly constrained by experimental observations. Consequently, sensitivity analysis, which quantifies the dependence of systems dynamics on model parameters, is a valuable tool for model analysis and assessment. A number of approaches to sensitivity analysis of biochemical models have been developed. In this study, the authors present a novel method for estimation of sensitivity coefficients for CME models of biochemical reaction systems that span a wide range of time-scales. They make use of finite-difference approximations and adaptive implicit tau-leaping strategies to estimate sensitivities for these stiff models, resulting in significant computational efficiencies in comparison with previously published approaches of similar accuracy, as evidenced by illustrative applications.