Computational approaches identify a transcriptomic fingerprint of drug-induced structural cardiotoxicity.

Structural cardiotoxicity (SCT) presents a high-impact risk that is poorly tolerated in drug discovery unless significant benefit is anticipated. Therefore, we aimed to improve the mechanistic understanding of SCT. First, we combined machine learning methods with a modified calcium transient assay in human-induced pluripotent stem cell-derived cardiomyocytes to identify nine parameters that could predict SCT. Next, we applied transcriptomic profiling to human cardiac microtissues exposed to structural and non-structural cardiotoxins. Fifty-two genes expressed across the three main cell types in the heart (cardiomyocytes, endothelial cells, and fibroblasts) were prioritised in differential expression and network clustering analyses and could be linked to known mechanisms of SCT. This transcriptomic fingerprint may prove useful for generating strategies to mitigate SCT risk in early drug discovery.

CLANet: A comprehensive framework for cross-batch cell line identification using brightfield images.

Cell line authentication plays a crucial role in the biomedical field, ensuring researchers work with accurately identified cells. Supervised deep learning has made remarkable strides in cell line identification by studying cell morphological features through cell imaging. However, biological batch (bio-batch) effects, a significant issue stemming from the different times at which data is generated, lead to substantial shifts in the underlying data distribution, thus complicating reliable differentiation between cell lines from distinct batch cultures. To address this challenge, we introduce CLANet, a pioneering framework for cross-batch cell line identification using brightfield images, specifically designed to tackle three distinct bio-batch effects. We propose a cell cluster-level selection method to efficiently capture cell density variations, and a self-supervised learning strategy to manage image quality variations, thus producing reliable patch representations. Additionally, we adopt multiple instance learning(MIL) for effective aggregation of instance-level features for cell line identification. Our innovative time-series segment sampling module further enhances MIL's feature-learning capabilities, mitigating biases from varying incubation times across batches. We validate CLANet using data from 32 cell lines across 93 experimental bio-batches from the AstraZeneca Global Cell Bank. Our results show that CLANet outperforms related approaches (e.g. domain adaptation, MIL), demonstrating its effectiveness in addressing bio-batch effects in cell line identification.

Impact of the physical-chemical properties of poly(lactic acid)-poly(ethylene glycol) polymeric nanoparticles on biodistribution.

Nanoparticle (NP) formulations are inherently polydisperse making their structural characterization and justification of specifications complex. It is essential, however, to gain an understanding of the physico-chemical properties that drive performance in vivo. To elucidate these properties, drug-containing poly(lactic acid) (PLA)-poly(ethylene glycol) (PEG) block polymeric NP formulations (or PNPs) were sub-divided into discrete size fractions and analyzed using a combination of advanced techniques, namely cryogenic transmission electron microscopy, small-angle neutron and X-ray scattering, nuclear magnetic resonance, and hard-energy X-ray photoelectron spectroscopy. Together, these techniques revealed a uniquely detailed picture of PNP size, surface structure, internal molecular architecture and the preferred site(s) of incorporation of the hydrophobic drug, AZD5991, properties which cannot be accessed via conventional characterization methodologies. Within the PNP size distribution, it was shown that the smallest PNPs contained significantly less drug than their larger sized counterparts, reducing overall drug loading, while PNP molecular architecture was critical in understanding the nature of in vitro drug release. The effect of PNP size and structure on drug biodistribution was determined by administrating selected PNP size fractions to mice, with the smaller sized NP fractions increasing the total drug-plasma concentration area under the curve and reducing drug concentrations in liver and spleen, due to greater avoidance of the reticuloendothelial system. In contrast, administration of unfractionated PNPs, containing a large population of NPs with extremely low drug load, did not significantly impact the drug's pharmacokinetic behavior - a significant result for nanomedicine development where a uniform formulation is usually an important driver. We also demonstrate how, in this study, it is not practicable to validate the bioanalytical methodology for drug released in vivo due to the NP formulation properties, a process which is applicable for most small molecule-releasing nanomedicines. In conclusion, this work details a strategy for determining the effect of formulation variability on in vivo performance, thereby informing the translation of PNPs, and other NPs, from the laboratory to the clinic.

Physiological Replication of the Human Glomerulus Using a Triple Culture Microphysiological System.

The function of the glomerulus depends on the complex cell-cell/matrix interactions and replication of this in vitro would aid biological understanding in both health and disease. Previous models do not fully reflect all cell types and interactions present as they overlook mesangial cells within their 3D matrix. Herein, the development of a microphysiological system that contains all resident renal cell types in an anatomically relevant manner is presented. A detailed transcriptomic analysis of the contributing biology of each cell type, as well as functionally appropriate albumin retention in the system, is demonstrated. The important role of mesangial cells is shown in promoting the health and maturity of the other cell types. Additionally, a comparison of the incremental advances that each individual cell type brings to the phenotype of the others demonstrates that glomerular cells in simple 2D culture exhibit a state more reflective of the dysfunction observed in human disease than previously recognized. This in vitro model will expand the capability to investigate glomerular biology in a more translatable manner by the inclusion of the important mesangial cell compartment.

Nuclear fascin regulates cancer cell survival.

Fascin is an important regulator of F-actin bundling leading to enhanced filopodia assembly. Fascin is also overexpressed in most solid tumours where it supports invasion through control of F-actin structures at the periphery and nuclear envelope. Recently, fascin has been identified in the nucleus of a broad range of cell types but the contributions of nuclear fascin to cancer cell behaviour remain unknown. Here, we demonstrate that fascin bundles F-actin within the nucleus to support chromatin organisation and efficient DDR. Fascin associates directly with phosphorylated Histone H3 leading to regulated levels of nuclear fascin to support these phenotypes. Forcing nuclear fascin accumulation through the expression of nuclear-targeted fascin-specific nanobodies or inhibition of Histone H3 kinases results in enhanced and sustained nuclear F-actin bundling leading to reduced invasion, viability, and nuclear fascin-specific/driven apoptosis. These findings represent an additional important route through which fascin can support tumourigenesis and provide insight into potential pathways for targeted fascin-dependent cancer cell killing.

An automated cell line authentication method for AstraZeneca global cell bank using deep neural networks on brightfield images.

Cell line authentication is important in the biomedical field to ensure that researchers are not working with misidentified cells. Short tandem repeat is the gold standard method, but has its own limitations, including being expensive and time-consuming. Deep neural networks achieve great success in the analysis of cellular images in a cost-effective way. However, because of the lack of centralized available datasets, whether or not cell line authentication can be replaced or supported by cell image classification is still a question. Moreover, the relationship between the incubation times and cellular images has not been explored in previous studies. In this study, we automated the process of the cell line authentication by using deep learning analysis of brightfield cell line images. We proposed a novel multi-task framework to identify cell lines from cell images and predict the duration of how long cell lines have been incubated simultaneously. Using thirty cell lines' data from the AstraZeneca Cell Bank, we demonstrated that our proposed method can accurately identify cell lines from brightfield images with a 99.8% accuracy and predicts the incubation durations for cell images with the coefficient of determination score of 0.927. Considering that new cell lines are continually added to the AstraZeneca Cell Bank, we integrated the transfer learning technique with the proposed system to deal with data from new cell lines not included in the pre-trained model. Our method achieved excellent performance with a precision of 97.7% and recall of 95.8% in the detection of 14 new cell lines. These results demonstrated that our proposed framework can effectively identify cell lines using brightfield images.

Modelling human liver fibrosis in the context of non-alcoholic steatohepatitis using a microphysiological system.

Non-alcoholic steatohepatitis (NASH) is a common form of chronic liver disease characterised by lipid accumulation, infiltration of immune cells, hepatocellular ballooning, collagen deposition and liver fibrosis. There is a high unmet need to develop treatments for NASH. We have investigated how liver fibrosis and features of advanced clinical disease can be modelled using an in vitro microphysiological system (MPS). The NASH MPS model comprises a co-culture of primary human liver cells, which were cultured in a variety of conditions including+/- excess sugar, fat, exogenous TGFß or LPS. The transcriptomic, inflammatory and fibrotic phenotype of the model was characterised and compared using a system biology approach to identify conditions that mimic more advanced clinical disease. The transcriptomic profile of the model was shown to closely correlate with the profile of patient samples and the model displayed a quantifiable fibrotic phenotype. The effects of Obeticholic acid and Elafibranor, were evaluated in the model, as wells as the effects of dietary intervention, with all able to significantly reduce inflammatory and fibrosis markers. Overall, we demonstrate how the MPS NASH model can be used to model different aspects of clinical NASH but importantly demonstrate its ability to model advanced disease with a quantifiable fibrosis phenotype.

Delivering Robust Candidates to the Drug Pipeline through Computational Analysis of Arrayed CRISPR Screens.

Genome-wide arrayed CRISPR screening is a powerful method for drug target identification as it enables exploration of the effect of individual gene perturbations using diverse highly multiplexed functional and phenotypic assays. Using high-content imaging, we can measure changes in biomarker expression, intracellular localization, and cell morphology. Here we present the computational pipeline we have developed to support the analysis and interpretation of arrayed CRISPR screens. This includes evaluating the quality of guide RNA libraries, performing image analysis, evaluating assay results quality, data processing, hit identification, ranking, visualization, and biological interpretation.

A High-Throughput Cellular Screening Assay for Small-Molecule Inhibitors and Activators of Cytoplasmic Dynein-1-Based Cargo Transport.

Cytoplasmic dynein-1 (hereafter dynein) is a six-subunit motor complex that transports a variety of cellular components and pathogens along microtubules. Dynein's cellular functions are only partially understood, and potent and specific small-molecule inhibitors and activators of this motor would be valuable for addressing this issue. It has also been hypothesized that an inhibitor of dynein-based transport could be used in antiviral or antimitotic therapy, whereas an activator could alleviate age-related neurodegenerative diseases by enhancing microtubule-based transport in axons. Here, we present the first high-throughput screening (HTS) assay capable of identifying both activators and inhibitors of dynein-based transport. This project is also the first collaborative screening report from the Medical Research Council and AstraZeneca agreement to form the UK Centre for Lead Discovery. A cellular imaging assay was used, involving chemically controlled recruitment of activated dynein complexes to peroxisomes. Such a system has the potential to identify molecules that affect multiple aspects of dynein biology in vivo. Following optimization of key parameters, the assay was developed in a 384-well format with semiautomated liquid handling and image acquisition. Testing of more than 500,000 compounds identified both inhibitors and activators of dynein-based transport in multiple chemical series. Additional analysis indicated that many of the identified compounds do not affect the integrity of the microtubule cytoskeleton and are therefore candidates to directly target the transport machinery.

IA-Lab: A MATLAB framework for efficient microscopy image analysis development, applied to quantifying intracellular transport of internalized peptide-drug conjugate.

This work presents a MATLAB-based software package for high-throughput microscopy image analysis development, making such development more accessible for a large user community. The toolbox provides a GUI and a number of analysis workflows, and can serve as a general framework designed to allow for easy extension. For a new application, only a minor part of the object-oriented code needs to be replaced by new components, making development efficient. This makes it possible to quickly develop solutions for analysis not available in existing tools. We show its use in making a tool for quantifying intracellular transport of internalized peptide-drug conjugates. The code is freely available as open source on GitHub (https://github.com/amcorrigan/ia-lab).

A flexible high content imaging assay for profiling macrophage efferocytosis.

Macrophages are a diverse population of cells originating from the myeloid lineage, which form an important component of the innate immune system, helping to regulate immune response through secretion of pro/anti-inflammatory cytokines. However they also have an important homeostatic role - helping to remove cellular debris and apoptotic cells from the body (a phagocytic process known as efferocytosis). Here we describe a robust 384 well microplate based imaging assay, using apoptotic target cells for the specific quantification of efferocytosis in human primary monocyte derived macrophages. The methodology described allows for the assay to run in either fixed end-point or live-cell format (the former offering multiple morphological and intensity-based readouts, whilst the latter opens the possibility for future expansion of the methodology to encompass kinetic profiling). Within the methodology described we couple high content image acquisition (on the Cell Voyager 7000S) with multi-parametric image analysis - using Perkin Elmer Columbus combined with GeneData Screener.

Introducing an automated high content confocal imaging approach for Organs-on-Chips.

Organ-Chips are micro-engineered systems that aim to recapitulate the organ microenvironment. Implementation of Organ-Chips within the pharmaceutical industry aims to improve the probability of success of drugs reaching late stage clinical trial by generating models for drug discovery that are of human origin and have disease relevance. We are adopting the use of Organ-Chips for enhancing pre-clinical efficacy and toxicity evaluation and prediction. Whilst capturing cellular phenotype via imaging in response to drug exposure is a useful readout in these models, application has been limited due to difficulties in imaging the chips at scale. Here we created an end-to-end, automated workflow to capture and analyse confocal images of multicellular Organ-Chips to assess detailed cellular phenotype across large batches of chips. By automating this process, we not only reduced acquisition time, but we also minimised process variability and user bias. This enabled us to establish, for the first time, a framework of statistical best practice for Organ-Chip imaging, creating the capability of using Organ-Chips and imaging for routine testing in drug discovery applications that rely on quantitative image data for decision making. We tested our approach using benzbromarone, whose mechanism of toxicity has been linked to mitochondrial damage with subsequent induction of apoptosis and necrosis, and staurosporine, a tool inducer of apoptosis. We also applied this workflow to assess the hepatotoxic effect of an active AstraZeneca drug candidate illustrating its applicability in drug safety assessment beyond testing tool compounds. Finally, we have demonstrated that this approach could be adapted to Organ-Chips of different shapes and sizes through application to a Kidney-Chip.

Promoter-mediated diversification of transcriptional bursting dynamics following gene duplication.

During the evolution of gene families, functional diversification of proteins often follows gene duplication. However, many gene families expand while preserving protein sequence. Why do cells maintain multiple copies of the same gene? Here we have addressed this question for an actin family with 17 genes encoding an identical protein. The genes have divergent flanking regions and are scattered throughout the genome. Surprisingly, almost the entire family showed similar developmental expression profiles, with their expression also strongly coupled in single cells. Using live cell imaging, we show that differences in gene expression were apparent over shorter timescales, with family members displaying different transcriptional bursting dynamics. Strong "bursty" behaviors contrasted steady, more continuous activity, indicating different regulatory inputs to individual actin genes. To determine the sources of these different dynamic behaviors, we reciprocally exchanged the upstream regulatory regions of gene family members. This revealed that dynamic transcriptional behavior is directly instructed by upstream sequence, rather than features specific to genomic context. A residual minor contribution of genomic context modulates the gene OFF rate. Our data suggest promoter diversification following gene duplication could expand the range of stimuli that regulate the expression of essential genes. These observations contextualize the significance of transcriptional bursting.

Two-step interphase microtubule disassembly aids spindle morphogenesis.

BACKGROUND: Entry into mitosis triggers profound changes in cell shape and cytoskeletal organisation. Here, by studying microtubule remodelling in human flat mitotic cells, we identify a two-step process of interphase microtubule disassembly. RESULTS: First, a microtubule-stabilising protein, Ensconsin/MAP7, is inactivated in prophase as a consequence of its phosphorylation downstream of Cdk1/cyclin B. This leads to a reduction in interphase microtubule stability that may help to fuel the growth of centrosomally nucleated microtubules. The peripheral interphase microtubules that remain are then rapidly lost as the concentration of tubulin heterodimers falls following dissolution of the nuclear compartment boundary. Finally, we show that a failure to destabilise microtubules in prophase leads to the formation of microtubule clumps, which interfere with spindle assembly. CONCLUSIONS: This analysis highlights the importance of the step-wise remodelling of the microtubule cytoskeleton and the significance of permeabilisation of the nuclear envelope in coordinating the changes in cellular organisation and biochemistry that accompany mitotic entry.

Generation of Single-Cell Transcript Variability by Repression.

Gene expression levels vary greatly within similar cells, even within clonal cell populations [1]. These spontaneous expression differences underlie cell fate diversity in both differentiation and disease [2]. The mechanisms responsible for generating expression variability are poorly understood. Using single-cell transcriptomics, we show that transcript variability emerging during Dictyostelium differentiation is driven predominantly by repression rather than activation. The increased variability of repressed genes was observed over a broad range of expression levels, indicating that variability is actively imposed and not a passive statistical effect of the reduced numbers of molecules accompanying repression. These findings can be explained by a simple model of transcript production, with expression controlled by the frequency, rather than the magnitude, of transcriptional firing events. Our study reveals that the generation of differences between cells can be a direct consequence of the basic mechanisms of transcriptional regulation.

A continuum model of transcriptional bursting.

Transcription occurs in stochastic bursts. Early models based upon RNA hybridisation studies suggest bursting dynamics arise from alternating inactive and permissive states. Here we investigate bursting mechanism in live cells by quantitative imaging of actin gene transcription, combined with molecular genetics, stochastic simulation and probabilistic modelling. In contrast to early models, our data indicate a continuum of transcriptional states, with a slowly fluctuating initiation rate converting the gene between different levels of activity, interspersed with extended periods of inactivity. We place an upper limit of 40 s on the lifetime of fluctuations in elongation rate, with initiation rate variations persisting an order of magnitude longer. TATA mutations reduce the accessibility of high activity states, leaving the lifetime of on- and off-states unchanged. A continuum or spectrum of gene states potentially enables a wide dynamic range for cell responses to stimuli.

Modeling of Noisy Spindle Dynamics Reveals Separable Contributions to Achieving Correct Orientation.

The mechanisms by which the mammalian mitotic spindle is guided to a predefined orientation through microtubule-cortex interactions have recently received considerable interest, but there has been no dynamic model that describes spindle movements toward the preferred axis in human cells. Here, we develop a dynamic model based on stochastic activity of cues anisotropically positioned around the cortex of the mitotic cell and we show that the mitotic spindle does not reach equilibrium before chromosome segregation. Our model successfully captures the characteristic experimental behavior of noisy spindle rotation dynamics in human epithelial cells, including a weak underlying bias in the direction of rotation, suppression of motion close to the alignment axis, and the effect of the aspect ratio of the interphase cell shape in defining the final alignment axis. We predict that the force exerted per cue has a value that minimizes the deviation of the spindle from the predefined axis. The model has allowed us to systematically explore the parameter space around experimentally relevant configurations, and predict the mechanistic function of a number of established regulators of spindle orientation, highlighting how physical modeling of a noisy system can lead to functional biological understanding. We provide key insights into measurable parameters in live cells that can help distinguish between mechanisms of microtubule and cortical-cue interactions that jointly control the final orientation of the spindle.

Multiple cell and population-level interactions with mouse embryonic stem cell heterogeneity.

Much of development and disease concerns the generation of gene expression differences between related cells sharing similar niches. However, most analyses of gene expression only assess population and time-averaged levels of steady-state transcription. The mechanisms driving differentiation are buried within snapshots of the average cell, lacking dynamic information and the diverse regulatory history experienced by individual cells. Here, we use a quantitative imaging platform with large time series data sets to determine the regulation of developmental gene expression by cell cycle, lineage, motility and environment. We apply this technology to the regulation of the pluripotency gene Nanog in mouse embryonic stem cells. Our data reveal the diversity of cell and population-level interactions with Nanog dynamics and heterogeneity, and how this regulation responds to triggers of pluripotency. Cell cycles are highly heterogeneous and cycle time increases with Nanog reporter expression, with longer, more variable cycle times as cells approach ground-state pluripotency. Nanog reporter expression is highly stable over multiple cell generations, with fluctuations within cycles confined by an attractor state. Modelling reveals an environmental component to expression stability, in addition to any cell-autonomous behaviour, and we identify interactions of cell density with both cycle behaviour and Nanog. Rex1 expression dynamics showed shared and distinct regulatory effects. Overall, our observations of multiple partially overlapping dynamic heterogeneities imply complex cell and environmental regulation of pluripotent cell behaviour, and suggest simple deterministic views of stem cell states are inappropriate.

Quantitative measurement of transcription dynamics in living cells.

In a wide range of organisms the kinetics of transcription have been found to be noisy, with "bursts" or "pulses" of transcription interspersed with irregular periods of inactivity. The in vivo analysis of transcription dynamics can be most directly monitored using RNA stem loop motifs derived from MS2 and other bacteriophages. Here we describe the implementation of the MS2 RNA detection system and the steps required for precise measurement of transcription dynamics in highly motile cells. Automated image processing techniques are used to track large numbers of cells and measure transcription in a systematic and unbiased manner. We discuss popular methods for automatic image segmentation and frame-to-frame tracking of cells, and the considerations required to make measurements as quantitatively as possible.

Regulation of transcriptional bursting by a naturally oscillating signal.

Transcription is highly stochastic, occurring in irregular bursts. For temporal and spatial precision of gene expression, cells must somehow deal with this noisy behavior. To address how this is achieved, we investigated how transcriptional bursting is entrained by a naturally oscillating signal, by direct measurement of transcription together with signal dynamics in living cells. We identify a Dictyostelium gene showing rapid transcriptional oscillations with the same period as extracellular cAMP signaling waves. Bursting approaches antiphase to cAMP waves, with accelerating transcription cycles during differentiation. Although coupling between signal and transcription oscillations was clear at the population level, single-cell transcriptional bursts retained considerable heterogeneity, indicating that transcription is not governed solely by signaling frequency. Previous studies implied that burst heterogeneity reflects distinct chromatin states. Here we show that heterogeneity is determined by multiple intrinsic and extrinsic cues and is maintained by a transcriptional persistence. Unusually for a persistent transcriptional behavior, the lifetime was only 20 min, with rapid randomization of transcriptional state by the response to oscillatory signaling. Linking transcription to rapid signaling oscillations allows reduction of gene expression heterogeneity by temporal averaging, providing a mechanism to generate precision in cell choices during development.