High-content synaptic phenotyping in human cellular models reveals a role for BET proteins in synapse assembly.

Resolving fundamental molecular and functional processes underlying human synaptic development is crucial for understanding normal brain function as well as dysfunction in disease. Based upon increasing evidence of species-divergent features of brain cell types, coupled with emerging studies of complex human disease genetics, we developed the first automated and quantitative high-content synaptic phenotyping platform using human neurons and astrocytes. To establish the robustness of our platform, we screened the effects of 376 small molecules on presynaptic density, neurite outgrowth, and cell viability, validating six small molecules that specifically enhanced human presynaptic density in vitro. Astrocytes were essential for mediating the effects of all six small molecules, underscoring the relevance of non-cell-autonomous factors in synapse assembly and their importance in synaptic screening applications. Bromodomain and extraterminal (BET) inhibitors emerged as the most prominent hit class and global transcriptional analyses using multiple BET inhibitors confirmed upregulation of synaptic gene expression. Through these analyses, we demonstrate the robustness of our automated screening platform for identifying potent synaptic modulators, which can be further leveraged for scaled analyses of human synaptic mechanisms and drug discovery efforts.

Morphology and gene expression profiling provide complementary information for mapping cell state.

Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb human A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses, providing a data resource including dose-response data from both assays. The two assays capture both shared and complementary information for mapping cell state. Cell Painting profiles from compound perturbations are more reproducible and show more diversity but measure fewer distinct groups of features. Applying unsupervised and supervised methods to predict compound mechanisms of action (MOAs) and gene targets, we find that the two assays not only provide a partially shared but also a complementary view of drug mechanisms. Given the numerous applications of profiling in biology, our analyses provide guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations.

A multiparametric activity profiling platform for neuron disease phenotyping and drug screening.

Patient stem cell-derived models enable imaging of complex disease phenotypes and the development of scalable drug discovery platforms. Current preclinical methods for assessing cellular activity do not, however, capture the full intricacies of disease-induced disturbances and instead typically focus on a single parameter, which impairs both the understanding of disease and the discovery of effective therapeutics. Here, we describe a cloud-based image processing and analysis platform that captures the intricate activity profile revealed by GCaMP fluorescence recordings of intracellular calcium changes and enables the discovery of molecules that correct 153 parameters that define the amyotrophic lateral sclerosis motor neuron disease phenotype. In a high-throughput screen, we identified compounds that revert the multiparametric disease profile to that found in healthy cells, a novel and robust measure of therapeutic potential quite distinct from unidimensional screening. This platform can guide the development of therapeutics that counteract the multifaceted pathological features of diseased cellular activity.

A Switch in p53 Dynamics Marks Cells That Escape from DSB-Induced Cell Cycle Arrest.

Cellular responses to stimuli can evolve over time, resulting in distinct early and late phases in response to a single signal. DNA damage induces a complex response that is largely orchestrated by the transcription factor p53, whose dynamics influence whether a damaged cell will arrest and repair the damage or will initiate cell death. How p53 responses and cellular outcomes evolve in the presence of continuous DNA damage remains unknown. Here, we have found that a subset of cells switches from oscillating to sustained p53 dynamics several days after undergoing damage. The switch results from cell cycle progression in the presence of damaged DNA, which activates the caspase-2-PIDDosome, a complex that stabilizes p53 by inactivating its negative regulator MDM2. This work defines a molecular pathway that is activated if the canonical checkpoints fail to halt mitosis in the presence of damaged DNA.

BIN1 protein isoforms are differentially expressed in astrocytes, neurons, and microglia: neuronal and astrocyte BIN1 are implicated in tau pathology.

BACKGROUND: Identified as an Alzheimer's disease (AD) susceptibility gene by genome wide-association studies, BIN1 has 10 isoforms that are expressed in the Central Nervous System (CNS). The distribution of these isoforms in different cell types, as well as their role in AD pathology still remains unclear. METHODS: Utilizing antibodies targeting specific BIN1 epitopes in human post-mortem tissue and analyzing mRNA expression data from purified microglia, we identified three isoforms expressed in neurons and astrocytes (isoforms 1, 2 and 3) and four isoforms expressed in microglia (isoforms 6, 9, 10 and 12). The abundance of selected peptides, which correspond to groups of BIN1 protein isoforms, was measured in dorsolateral prefrontal cortex, and their relation to neuropathological features of AD was assessed. RESULTS: Peptides contained in exon 7 of BIN1's N-BAR domain were found to be significantly associated with AD-related traits and, particularly, tau tangles. Decreased expression of BIN1 isoforms containing exon 7 is associated with greater accumulation of tangles and subsequent cognitive decline, with astrocytic rather than neuronal BIN1 being the more likely culprit. These effects are independent of the BIN1 AD risk variant. CONCLUSIONS: Exploring the molecular mechanisms of specific BIN1 isoforms expressed by astrocytes may open new avenues for modulating the accumulation of Tau pathology in AD.

High-content, label-free analysis of proplatelet production from megakaryocytes.

BACKGROUND: The mechanisms that regulate platelet biogenesis remain unclear; factors that trigger megakaryocytes (MKs) to initiate platelet production are poorly understood. Platelet formation begins with proplatelets, which are cellular extensions originating from the MK cell body. OBJECTIVES: Proplatelet formation is an asynchronous and dynamic process that poses unique challenges for researchers to accurately capture and analyze. We have designed an open-source, high-content, high-throughput, label-free analysis platform. METHODS: Phase-contrast images of live, primary MKs are captured over a 24-hour period. Pixel-based machine-learning classification done by ilastik generates probability maps of key cellular features (circular MKs and branching proplatelets), which are processed by a customized CellProfiler pipeline to identify and filter structures of interest based on morphology. A subsequent reinforcement classification, by CellProfiler Analyst, improves the detection of cellular structures. RESULTS: This workflow yields the percent of proplatelet production, area, count of proplatelets and MKs, and other statistics including skeletonization information for measuring proplatelet branching and length. We propose using a combination of these analyzed metrics, in particular the area measurements of MKs and proplatelets, when assessing in vitro proplatelet production. Accuracy was validated against manually counted images and an existing algorithm. We then used the new platform to test compounds known to cause thrombocytopenia, including bromodomain inhibitors, and uncovered previously unrecognized effects of drugs on proplatelet formation, thus demonstrating the utility of our analysis platform. CONCLUSION: This advance in creating unbiased data analysis will increase the scale and scope of proplatelet production studies and potentially serve as a valuable resource for investigating molecular mechanisms of thrombocytopenia.

Publisher Correction: Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Identification and local delivery of vasodilators for the reduction of ureteral contractions.

Kidney stones and ureteral stents can cause ureteral colic and pain. By decreasing contractions in the ureter, clinically prescribed oral vasodilators may improve spontaneous stone passage rates and reduce the pain caused by ureteral stenting. We hypothesized that ureteral relaxation can be improved via the local administration of vasodilators and other smooth muscle relaxants. Here, by examining 18 candidate small molecules in an automated screening assay to determine the extent of ureteral relaxation, we show that the calcium channel blocker nifedipine and the Rho-kinase inhibitor ROCKi significantly relax human ureteral smooth muscle cells. We also show, by using ex vivo porcine ureter segments and sedated pigs that, with respect to the administration of a placebo, the local delivery of a clinically deployable formulation of the two drugs reduced ureteral contraction amplitude and frequency by 90% and 50%, respectively. Finally, we show that standard oral vasodilator therapy reduced contraction amplitude by only 50% and had a minimal effect on contraction frequency. Locally delivered ureteral relaxants therefore may improve ureter-related conditions.

Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl.

Segmenting the nuclei of cells in microscopy images is often the first step in the quantitative analysis of imaging data for biological and biomedical applications. Many bioimage analysis tools can segment nuclei in images but need to be selected and configured for every experiment. The 2018 Data Science Bowl attracted 3,891 teams worldwide to make the first attempt to build a segmentation method that could be applied to any two-dimensional light microscopy image of stained nuclei across experiments, with no human interaction. Top participants in the challenge succeeded in this task, developing deep-learning-based models that identified cell nuclei across many image types and experimental conditions without the need to manually adjust segmentation parameters. This represents an important step toward configuration-free bioimage analysis software tools.

A High-Content Screen Identifies TPP1 and Aurora B as Regulators of Axonal Mitochondrial Transport.

Dysregulated axonal trafficking of mitochondria is linked to neurodegenerative disorders. We report a high-content screen for small-molecule regulators of the axonal transport of mitochondria. Six compounds enhanced mitochondrial transport in the sub-micromolar range, acting via three cellular targets: F-actin, Tripeptidyl peptidase 1 (TPP1), or Aurora Kinase B (AurKB). Pharmacological inhibition or small hairpin RNA (shRNA) knockdown of each target promotes mitochondrial axonal transport in rat hippocampal neurons and induced pluripotent stem cell (iPSC)-derived human cortical neurons and enhances mitochondrial transport in iPSC-derived motor neurons from an amyotrophic lateral sclerosis (ALS) patient bearing one copy of SOD1A4V mutation. Our work identifies druggable regulators of axonal transport of mitochondria, provides broadly applicable methods for similar image-based screens, and suggests that restoration of proper axonal trafficking of mitochondria can be achieved in human ALS neurons.

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images.

Identifying nuclei is often a critical first step in analyzing microscopy images of cells and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluation metrics for nucleus segmentation do not satisfactorily capture error modes that are relevant in cellular images. We present an evaluation framework to measure accuracy, types of errors, and computational efficiency; and use it to compare deep learning strategies and classical approaches. We publicly release a set of 23,165 manually annotated nuclei and source code to reproduce experiments and run the proposed evaluation methodology. Our evaluation framework shows that deep learning improves accuracy and can reduce the number of biologically relevant errors by half. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Fluctuations in p53 Signaling Allow Escape from Cell-Cycle Arrest.

Biological signals need to be robust and filter small fluctuations yet maintain sensitivity to signals across a wide range of magnitudes. Here, we studied how fluctuations in DNA damage signaling relate to maintenance of long-term cell-cycle arrest. Using live-cell imaging, we quantified division profiles of individual human cells in the course of 1 week after irradiation. We found a subset of cells that initially establish cell-cycle arrest and then sporadically escape and divide. Using fluorescent reporters and mathematical modeling, we determined that fluctuations in the oscillatory pattern of the tumor suppressor p53 trigger a sharp switch between p21 and CDK2, leading to escape from arrest. Transient perturbation of p53 stability mimicked the noise in individual cells and was sufficient to trigger escape from arrest. Our results show that the self-reinforcing circuitry that mediates cell-cycle transitions can translate small fluctuations in p53 signaling into large phenotypic changes.

CellProfiler 3.0: Next-generation image processing for biology.

CellProfiler has enabled the scientific research community to create flexible, modular image analysis pipelines since its release in 2005. Here, we describe CellProfiler 3.0, a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional (3D) image stacks, increasingly common in biomedical research. CellProfiler's infrastructure is greatly improved, and we provide a protocol for cloud-based, large-scale image processing. New plugins enable running pretrained deep learning models on images. Designed by and for biologists, CellProfiler equips researchers with powerful computational tools via a well-documented user interface, empowering biologists in all fields to create quantitative, reproducible image analysis workflows.

p53 dynamics control cell fate.

Cells transmit information through molecular signals that often show complex dynamical patterns. The dynamic behavior of the tumor suppressor p53 varies depending on the stimulus; in response to double-strand DNA breaks, it shows a series of repeated pulses. Using a computational model, we identified a sequence of precisely timed drug additions that alter p53 pulses to instead produce a sustained p53 response. This leads to the expression of a different set of downstream genes and also alters cell fate: Cells that experience p53 pulses recover from DNA damage, whereas cells exposed to sustained p53 signaling frequently undergo senescence. Our results show that protein dynamics can be an important part of a signal, directly influencing cellular fate decisions.