Practical machine learning for disease diagnosis

Deep learning neural networks are a powerful tool in the analytical toolbox of modern microscopy, but they come with an exacting requirement for accurately annotated, ground truth cell images. Otesteanu et al. (2021) elegantly streamline this process, implementing network training by using patient-level rather than cell-level disease classification

Spatial statistics is a comprehensive tool for quantifying cell neighbor relationships and biological processes via tissue image analysis

Automated microscopy and computational image analysis has transformed cell biology, providing quantitative, spatially resolved information on cells and their constituent molecules from the sub-micron to the whole-organ scale. Here we explore the application of spatial statistics to the cellular relationships within tissue microscopy data and discuss how spatial statistics offers cytometry a powerful yet underused mathematical tool set for which the required data are readily captured using standard protocols and microscopy equipment. We also highlight the often-overlooked need to carefully consider the structural heterogeneity of tissues in terms of the applicability of different statistical measures and their accuracy and demonstrate how spatial analyses offer a great deal more than just basic quantification of biological variance. Ultimately, we highlight how statistical modeling can help reveal the hierarchical spatial processes that connect the properties of individual cells to the establishment of biological function

Multiscale benchmarking of drug delivery vectors

Cross-system comparisons of drug delivery vectors are essential to ensure optimal design. An in-vitro experimental protocol is presented that separates the role of the delivery vector from that of its cargo in determining the cell response, thus allowing quantitative comparison of different systems. The technique is validated through benchmarking of the dose–response of human fibroblast cells exposed to the cationic molecule, polyethylene imine (PEI); delivered as a free molecule and as a cargo on the surface of CdSe nanoparticles and Silica microparticles. The exposure metrics are converted to a delivered dose with the transport properties of the different scale systems characterized by a delivery time, τ. The benchmarking highlights an agglomeration of the free PEI molecules into micron sized clusters and identifies the metric determining cell death as the total number of PEI molecules presented to cells, determined by the delivery vector dose and the surface density of the cargo

Profiling Movement Quality Characteristics of Children (9-11y) During Recess

Introduction. Frequency spectrum characteristics derived from raw accelerometry, such as spectral purity, have the potential to reveal detailed information about children’s movement quality, but remain unexplored in children’s physical activity. The aim of this study was to investigate and profile children’s recess physical activity and movement quality using a novel analytical approach. Materials and Methods. A powered sample of twenty-four children (18 boys) (10.5±0.6y, 1.44±0.09m, 39.6±9.5kg, body mass index; 18.8±3.1 kg.m2) wore an ankle-mounted accelerometer during school recess, for one school-week. Hierarchical clustering, Spearman’s rho and the Mann-Whitney U test were used to assess relationships between characteristics, and to assess inter-day differences. Results. There were no significant inter-day differences found for overall activity (P>0.05), yet significant differences were found for spectral purity derived movement quality (P<0.001). Overall activity was hierarchically clustered, and positively correlated, with spectral purity (P<0.05). Discussion. This is the first study to report spectral purity derived movement quality of children’s physical activity in an uncontrolled setting and our results highlight potential for future research

Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D

Unlocking and quantifying fundamental biological processes through tissue microscopy requires accurate, in situ segmentation of all cells imaged. Currently, achieving this is complex and requires exogenous fluorescent labels that occupy significant spectral bandwidth, increasing the duration and complexity of imaging experiments while limiting the number of channels remaining to address the study’s objectives. We demonstrate that the excitation light reflected during routine confocal microscopy contains sufficient information to achieve accurate, label-free cell segmentation in 2D and 3D. This is achieved using a simple convolutional neural network trained to predict the probability that reflected light pixels belong to either nucleus, cytoskeleton, or background classifications. We demonstrate the approach across diverse lymphoid tissues and provide video tutorials demonstrating deployment in Python and MATLAB or via standalone software for Windows

Analysis of the Influence of Cell Heterogeneity on Nanoparticle Dose Response

Understanding the effect of variability in the interaction of individual cells with nanoparticles on the overall response of the cell population to a nanoagent is a fundamental challenge in bionanotechnology. Here, we show that the technique of time-resolved, high-throughput microscopy can be used in this endeavor. Mass measurement with single-cell resolution provides statistically robust assessments of cell heterogeneity, while the addition of a temporal element allows assessment of separate processes leading to deconvolution of the effects of particle supply and biological response. We provide a specific demonstration of the approach, in vitro, through time-resolved measurement of fibroblast cell (HFF-1) death caused by exposure to cationic nanoparticles. The results show that heterogeneity in cell area is the major source of variability with area-dependent nanoparticle capture rates determining the time of cell death and hence the form of the exposure–response characteristic. Moreover, due to the particulate nature of the nanoparticle suspension, there is a reduction in the particle concentration over the course of the experiment, eventually causing saturation in the level of measured biological outcome. A generalized mathematical description of the system is proposed, based on a simple model of particle depletion from a finite supply reservoir. This captures the essential aspects of the nanoparticle–cell interaction dynamics and accurately predicts the population exposure–response curves from individual cell heterogeneity distributions

FORCE-TIME CURVE ALIGNMENT FOR FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS IN VERTICAL JUMPING

Functional principal component analysis (FPCA) can be used to extract key features from time series data for use in statistical models. This study evaluated time normalisation in combination with curve registration prior to performing FPCA. Using vertical ground reaction force data from countermovement jumps, evaluation was based on linear regression for predicting peak power and jump height, and logistic regression for classifying jump type (arm swing or not). Datasets not subject to time normalisation generally produced better results with the highest accuracy being achieved when using registration with peak power as a landmark (peak power R2 = 99.3%, jump height R2 = 94.9%). Classification of jump type benefited in some cases from registration (87.0% to 91.2%). These techniques could be applied to data from wearable sensors to improve prediction and classification

Empirical comparison of genotoxic potency estimations: the in vitro DNA-damage ToxTracker endpoints versus the in vivo micronucleus assay

Genetic toxicology is an essential component of compound safety assessment. In the face of a barrage of new compounds, higher throughput, less ethically divisive in vitro approaches capable of effective, human-relevant hazard identification and prioritisation are increasingly important. One such approach is the ToxTracker assay, which utilises murine stem cell lines equipped with green fluorescent protein (GFP)-reporter gene constructs that each inform on distinct aspects of cellular perturbation. Encouragingly, ToxTracker has shown improved sensitivity and specificity for the detection of known in vivo genotoxicants when compared to existing ‘standard battery’ in vitro tests. At the current time however, quantitative genotoxic potency correlations between ToxTracker and well-recognised in vivo tests are not yet available. Here we use dose–response data from the three DNA-damage-focused ToxTracker endpoints and from the in vivo micronucleus assay to carry out quantitative, genotoxic potency estimations for a range of aromatic amine and alkylating agents using the benchmark dose (BMD) approach. This strategy, using both the exponential and the Hill BMD model families, was found to produce robust, visually intuitive and similarly ordered genotoxic potency rankings for 17 compounds across the BSCL2-GFP, RTKN-GFP and BTG2-GFP ToxTracker endpoints. Eleven compounds were similarly assessed using data from the in vivo micronucleus assay. Cross-systems genotoxic potency correlations for the eight matched compounds demonstrated in vitro–in vivo correlation, albeit with marked scatter across compounds. No evidence for distinct differences in the sensitivity of the three ToxTracker endpoints was found. The presented analyses show that quantitative potency determinations from in vitro data enable more than just qualitative screening and hazard identification in genetic toxicology

Spatially-resolved profiling of carbon nanotube uptake across cell lines

The internalisation and intra-cellular distribution of carbon nanotubes (CNT) has been quantitatively assessed using imaging flow cytometry. Spatial analysis of the bright field images indicates the presence of a small sub-population (5% of cells) in which the internalised CNTs are packed into pronounced clusters, visible as dark spots due to strong optical scattering by the nanotubes. The area of these spots can be used as a label-free metric of CNT dose and we assess the relative uptake of charge-neutral CNTs, over a 24 hours exposure period across four cell types: J774 mouse macrophage cells, A549 and Calu-6 human lung cancer cells, and MCF-7 human breast cells. The relative dose as indicated by the spot-area metric closely correlates to results using the same CNT preparation, conjugated to a FITC-label and shows pronounced uptake by the J774 cells leading to a mean dose that is >60% higher than for the other cell types. Spatial evaluation of dosing clusters is also used to quantify differences in uptake by J774 cells of CNTs with different surface functionalisation. While the percentage of CNT-cluster positive cells increases from 5% to 19% when switching from charge-neutral CNTs to poly-cationic, dendron functionalised CNTs, the single cell level analysis of internalised clusters indicates a lower dose per cell of poly-cationic CNTs relative to the charge-neutral CNTs. We concluded that there is dose homeostasis i.e., the population-averaged cellular dose of CNTs remained unchanged