SPACe (Swift Phenotypic Analysis of Cells): an open-source, single cell analysis of Cell Painting data.

Phenotypic profiling by high throughput microscopy has become one of the leading tools for screening large sets of perturbations in cellular models. Of the numerous methods used over the years, the flexible and economical Cell Painting (CP) assay has been central in the field, allowing for large screening campaigns leading to a vast number of data-rich images. Currently, to analyze data of this scale, available open-source software ( i.e. , CellProfiler) requires computational resources that are not available to most laboratories worldwide. In addition, the image-embedded cell-to-cell variation of responses within a population, while collected and analyzed, is usually averaged and unused. Here we introduce SPACe ( S wift P henotypic A nalysis of Ce lls), an open source, Python-based platform for the analysis of single cell image-based morphological profiles produced by CP experiments. SPACe can process a typical dataset approximately ten times faster than CellProfiler on common desktop computers without loss in mechanism of action (MOA) recognition accuracy. It also computes directional distribution-based distances (Earth Mover's Distance - EMD) of morphological features for quality control and hit calling. We highlight several advantages of SPACe analysis on CP assays, including reproducibility across multiple biological replicates, easy applicability to multiple (∼20) cell lines, sensitivity to variable cell-to-cell responses, and biological interpretability to explain image-based features. We ultimately illustrate the advantages of SPACe in a screening campaign of cell metabolism small molecule inhibitors which we performed in seven cell lines to highlight the importance of testing perturbations across models.

Characterization of flavonoids with potent and subtype-selective actions on estrogen receptors alpha and beta.

The initial step in estrogen-regulated transcription is the binding of a ligand to its cognate receptors, named estrogen receptors (ERα and ERß). Phytochemicals present in foods and environment can compete with endogenous hormones to alter physiological responses. We screened 224 flavonoids in our engineered biosensor ERα and ERß PRL-array cell lines to characterize their activity on several steps of the estrogen signaling pathway. We identified 83 and 96 flavonoids that can activate ERα or ERß, respectively. While most act on both receptors, many appear to be subtype-selective, including potent flavonoids that activate ER at sub-micromolar concentrations. We employed an orthogonal assay using a transgenic zebrafish in vivo model that validated the estrogenic potential of these compounds. To our knowledge, this is the largest study thus far on flavonoids and the ER pathway, facilitating the identification of a new set of potential endocrine disruptors acting on both ERα and ERß.

eEF1A2 promotes PTEN-GSK3ß-SCF complex-dependent degradation of Aurora kinase A and is inactivated in breast cancer.

The translation elongation factor eEF1A promotes protein synthesis. Its methylation by METTL13 increases its activity, supporting tumor growth. However, in some cancers, a high abundance of eEF1A isoforms is associated with a good prognosis. Here, we found that eEF1A2 exhibited oncogenic or tumor-suppressor functions depending on its interaction with METTL13 or the phosphatase PTEN, respectively. METTL13 and PTEN competed for interaction with eEF1A2 in the same structural domain. PTEN-bound eEF1A2 promoted the ubiquitination and degradation of the mitosis-promoting Aurora kinase A in the S and G2 phases of the cell cycle. eEF1A2 bridged the interactions between the SKP1-CUL1-FBXW7 (SCF) ubiquitin ligase complex, the kinase GSK3ß, and Aurora-A, thereby facilitating the phosphorylation of Aurora-A in a degron site that was recognized by FBXW7. Genetic ablation of Eef1a2 or Pten in mice resulted in a greater abundance of Aurora-A and increased cell cycling in mammary tumors, which was corroborated in breast cancer tissues from patients. Reactivating this pathway using fimepinostat, which relieves inhibitory signaling directed at PTEN and increases FBXW7 expression, combined with inhibiting Aurora-A with alisertib, suppressed breast cancer cell proliferation in culture and tumor growth in vivo. The findings demonstrate a therapeutically exploitable, tumor-suppressive role for eEF1A2 in breast cancer.

Pediatric human nose organoids demonstrate greater susceptibility, epithelial responses, and cytotoxicity than adults during RSV infection.

Respiratory syncytial virus (RSV) is a common cause of respiratory infections, causing significant morbidity and mortality, especially in young children. Why RSV infection in children is more severe as compared to healthy adults is not fully understood. In the present study, we infect both pediatric and adult human nose organoid-air liquid interface (HNO-ALIs) cell lines with two contemporary RSV isolates and demonstrate how they differ in virus replication, induction of the epithelial cytokine response, cell injury, and remodeling. Pediatric HNO-ALIs were more susceptible to early RSV replication, elicited a greater overall cytokine response, demonstrated enhanced mucous production, and manifested greater cellular damage compared to their adult counterparts. Adult HNO-ALIs displayed enhanced mucus production and robust cytokine response that was well controlled by superior regulatory cytokine response and possibly resulted in lower cellular damage than in pediatric lines. Taken together, our data suggest substantial differences in how pediatric and adult upper respiratory tract epithelium responds to RSV infection. These differences in epithelial cellular response can lead to poor mucociliary clearance and predispose infants to a worse respiratory outcome of RSV infection.

A novel ERß high throughput microscopy platform for testing endocrine disrupting chemicals.

In this study we present an inducible biosensor model for the Estrogen Receptor Beta (ERß), GFP-ERß:PRL-HeLa, a single-cell-based high throughput (HT) in vitro assay that allows direct visualization and measurement of GFP-tagged ERß binding to ER-specific DNA response elements (EREs), ERß-induced chromatin remodeling, and monitor transcriptional alterations via mRNA fluorescence in situ hybridization for a prolactin (PRL)-dsRED2 reporter gene. The model was used to accurately (Z' = 0.58-0.8) differentiate ERß-selective ligands from ERα ligands when treated with a panel of selective agonists and antagonists. Next, we tested an Environmental Protection Agency (EPA)-provided set of 45 estrogenic reference chemicals with known ERα in vivo activity and identified several that activated ERß as well, with varying sensitivity, including a subset that is completely novel. We then used an orthogonal ERE-containing transgenic zebrafish (ZF) model to cross validate ERß and ERα selective activities at the organism level. Using this environmentally relevant ZF assay, some compounds were confirmed to have ERß activity, validating the GFP-ERß:PRL-HeLa assay as a screening tool for potential ERß active endocrine disruptors (EDCs). These data demonstrate the value of sensitive multiplex mechanistic data gathered by the GFP-ERß:PRL-HeLa assay coupled with an orthogonal zebrafish model to rapidly identify environmentally relevant ERß EDCs and improve upon currently available screening tools for this understudied nuclear receptor.

Infant and Adult Human Intestinal Enteroids are Morphologically and Functionally Distinct.

Background & Aims: Human intestinal enteroids (HIEs) are gaining recognition as physiologically relevant models of the intestinal epithelium. While HIEs from adults are used extensively in biomedical research, few studies have used HIEs from infants. Considering the dramatic developmental changes that occur during infancy, it is important to establish models that represent infant intestinal characteristics and physiological responses. Methods: We established jejunal HIEs from infant surgical samples and performed comparisons to jejunal HIEs from adults using RNA sequencing (RNA-Seq) and morphologic analyses. We validated differences in key pathways through functional studies and determined if these cultures recapitulate known features of the infant intestinal epithelium. Results: RNA-Seq analysis showed significant differences in the transcriptome of infant and adult HIEs, including differences in genes and pathways associated with cell differentiation and proliferation, tissue development, lipid metabolism, innate immunity, and biological adhesion. Validating these results, we observed a higher abundance of cells expressing specific enterocyte, goblet cell and enteroendocrine cell markers in differentiated infant HIE monolayers, and greater numbers of proliferative cells in undifferentiated 3D cultures. Compared to adult HIEs, infant HIEs portray characteristics of an immature gastrointestinal epithelium including significantly shorter cell height, lower epithelial barrier integrity, and lower innate immune responses to infection with an oral poliovirus vaccine. Conclusions: HIEs established from infant intestinal tissues reflect characteristics of the infant gut and are distinct from adult cultures. Our data support the use of infant HIEs as an ex-vivo model to advance studies of infant-specific diseases and drug discovery for this population.

Gene transcription regulation by ER at the single cell and allele level.

In this short review we discuss the current view of how the estrogen receptor (ER), a pivotal member of the nuclear receptor superfamily of transcription factors, regulates gene transcription at the single cell and allele level, focusing on in vitro cell line models. We discuss central topics and new trends in molecular biology including phenotypic heterogeneity, single cell sequencing, nuclear phase separated condensates, single cell imaging, and image analysis methods, with particular focus on the methodologies and results that have been reported in the last few years using microscopy-based techniques. These observations augment the results from biochemical assays that lead to a much more complex and dynamic view of how ER, and arguably most transcription factors, act to regulate gene transcription.

Analysis and Modeling of Early Estradiol-induced GREB1 Single Allele Gene Transcription at the Population Level.

Single molecule fluorescence in situ hybridization (smFISH) can be used to visualize transcriptional activation at the single allele level. We and others have applied this approach to better understand the mechanisms of activation by steroid nuclear receptors. However, there is limited understanding of the interconnection between the activation of target gene alleles inside the same nucleus and within large cell populations. Using the GREB1 gene as an early estrogen receptor (ER) response target, we applied smFISH to track E2-activated GREB1 allelic transcription over early time points to evaluate potential dependencies between alleles within the same nucleus. We compared two types of experiments where we altered the initial status of GREB1 basal transcription by treating cells with and without the elongation inhibitor flavopiridol (FV). E2 stimulation changed the frequencies of active GREB1 alleles in the cell population independently of FV pre-treatment. In FV treated cells, the response time to hormone was delayed, albeit still reaching at 90 minutes the same levels as in cells not treated by FV. We show that the joint frequencies of GREB1 activated alleles observed at the cell population level imply significant dependency between pairs of alleles within the same nucleus. We identify probabilistic models of joint alleles activations by applying a principle of maximum entropy. For pairs of alleles, we have then quantified statistical dependency by computing their mutual information. We have then introduced a stochastic model compatible with allelic statistical dependencies, and we have fitted this model to our data by intensive simulations. This provided estimates of the average lifetime for degradation of GREB1 introns and of the mean time between two successive transcription rounds. Our approach informs on how to extract information on single allele regulation by ER from within a large population of cells, and should be applicable to many other genes. AUTHOR SUMMARY: After application of a gene transcription stimulus, in this case the hormone 17 ß -estradiol, on large populations of cells over a short time period, we focused on quantifying and modeling the frequencies of GREB1 single allele activations. We have established an experimental and computational pipeline to analyze large numbers of high resolution smFISH images to detect and monitor active GREB1 alleles, that can be translatable to any target gene of interest. A key result is that, at the population level, activation of individual GREB1 alleles within the same nucleus do exhibit statistically significant dependencies which we quantify by the mutual information between activation states of pairs of alleles. After noticing that frequencies of joint alleles activations observed over our large cell populations evolve smoothly in time, we have defined a population level stochastic model which we fit to the observed time course of GREB1 activation frequencies. This provided coherent estimates of the mean time between rounds of GREB1 transcription and the mean lifetime of nascent mRNAs. Our algorithmic approach and experimental methods are applicable to many other genes.

High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery.

Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.

Binary Classification of the Endocrine Disrupting Chemicals by Artificial Neural Networks.

We develop a machine learning framework that integrates high content/high throughput image analysis and artificial neural networks (ANNs) to model the separation between chemical compounds based on their estrogenic receptor activity. Natural and man-made chemicals have the potential to disrupt the endocrine system by interfering with hormone actions in people and wildlife. Although numerous studies have revealed new knowledge on the mechanism through which these compounds interfere with various hormone receptors, it is still a very challenging task to comprehensively evaluate the endocrine disrupting potential of all existing chemicals and their mixtures by pure in vitro or in vivo approaches. Machine learning offers a unique advantage in the rapid evaluation of chemical toxicity through learning the underlying patterns in the experimental biological activity data. Motivated by this, we train and test ANN classifiers for modeling the activity of estrogen receptor-α agonists and antagonists at the single-cell level by using high throughput/high content microscopy descriptors. Our framework preprocesses the experimental data by cleaning, scaling, and feature engineering where only the middle 50% of the values from each sample with detectable receptor-DNA binding is considered in the dataset. Principal component analysis is also used to minimize the effects of experimental noise in modeling where these projected features are used in classification model building. The results show that our ANN-based nonlinear data-driven framework classifies the benchmark agonist and antagonist chemicals with 98.41% accuracy.

Machine Learning Methods for Endocrine Disrupting Potential Identification Based on Single-Cell Data.

Humans are continuously exposed to a variety of toxicants and chemicals which is exacerbated during and after environmental catastrophes such as floods, earthquakes, and hurricanes. The hazardous chemical mixtures generated during these events threaten the health and safety of humans and other living organisms. This necessitates the development of rapid decision-making tools to facilitate mitigating the adverse effects of exposure on the key modulators of the endocrine system, such as the estrogen receptor alpha (ERα), for example. The mechanistic stages of the estrogenic transcriptional activity can be measured with high content/high throughput microscopy-based biosensor assays at the single-cell level, which generates millions of object-based minable data points. By combining computational modeling and experimental analysis, we built a highly accurate data-driven classification framework to assess the endocrine disrupting potential of environmental compounds. The effects of these compounds on the ERα pathway are predicted as being receptor agonists or antagonists using the principal component analysis (PCA) projections of high throughput, high content image analysis descriptors. The framework also combines rigorous preprocessing steps and nonlinear machine learning algorithms, such as the Support Vector Machines and Random Forest classifiers, to develop highly accurate mathematical representations of the separation between ERα agonists and antagonists. The results show that Support Vector Machines classify the unseen chemicals correctly with more than 96% accuracy using the proposed framework, where the preprocessing and the PCA steps play a key role in suppressing experimental noise and unraveling hidden patterns in the dataset.

Oral follicle-stimulating hormone receptor agonist affects granulosa cells differently than recombinant human FSH.

OBJECTIVE: To determine whether TOP5300, a novel oral follicle-stimulating hormone (FSH) receptor (FSHR) allosteric agonist, elicits a different cellular response than recombinant human FSH (rh-FSH) in human granulosa cells from patients undergoing in vitro fertilization. DESIGN: Basic science research with a preclinical allosteric FSHR agonist. SETTING: University hospital. PATIENT(S): Patients with infertility at a single academic fertility clinic were recruited under an Institutional Review Board-approved protocol. Primary granulosa cell cultures were established for 41 patients, of whom 8 had normal ovarian reserve (NOR), 17 were of advanced reproductive age (ARA), 12 had a diagnosis of polycystic ovary syndrome (PCOS), and 4 had a combination of diagnoses, such as ARA and PCOS. INTERVENTION(S): Primary granulosa-lutein (GL) cell cultures were treated with rh-FSH, TOP5300, or vehicle. MAIN OUTCOME MEASURE(S): Estradiol (E2) production using enzyme-linked immunosorbent assay, steroid pathway gene expression of StAR and aromatase using quantitative polymerase chain reaction, and FSHR membrane localization using immunofluorescence were measured in human GL cells. RESULT(S): TOP5300 consistently stimulated E2 production among patients with NOR, ARA, and PCOS. Recombinant FSH was the more potent ligand in GL cells from patients with NOR but was ineffective in cells from patients with ARA or PCOS. The lowest level of FSHR plasma membrane localization was seen in patients with ARA, although FSHR localization was more abundant in cells from patients with PCOS; the highest levels were present in cells from patients with NOR. The localization of FSHR was not affected by TOP5300 relative to rh-FSH in any patient group. TOP5300 stimulated greater expression of StAR and CYP19A1 across cells from all patients with NOR, ARA, and PCOS combined, although rh-FSH was unable to stimulate StAR and aromatase (CYP19A1) expression in cells from patients with PCOS. TOP5300-induced expression of StAR and CYP19A1 mRNA among patients with ARA and NOR was consistently lower than that observed in cells from patients with PCOS. CONCLUSION(S): TOP5300 appears to stimulate E2 production and steroidogenic gene expression from GL cells more than rh-FSH in PCOS, relative to patients with ARA and NOR. It does not appear that localization of FSHR at cell membranes is a limiting step for TOP5300 or rh-FSH stimulation of steroidogenic gene expression and E2 production.

Basic Image Analysis and Manipulation in ImageJ/Fiji.

Microscopy-generated images contain quantitative information that can be extracted using a variety of analysis methods and automated pipelines. In this protocol, basic aspects of image analysis are outlined, including software installation, data import, image processing functions, and analytical tools that can be used to extract information from microscopy data using the open-source software ImageJ/Fiji. Step-by-step protocols for analyzing objects (i.e., nuclei) in a fluorescence image are included, focusing on segmentation routines. © 2023 Wiley Periodicals LLC.

DNMT3A-coordinated splicing governs the stem state switch towards differentiation in embryonic and haematopoietic stem cells.

Upon stimulation by extrinsic stimuli, stem cells initiate a programme that enables differentiation or self-renewal. Disruption of the stem state exit has catastrophic consequences for embryogenesis and can lead to cancer. While some elements of this stem state switch are known, major regulatory mechanisms remain unclear. Here we show that this switch involves a global increase in splicing efficiency coordinated by DNA methyltransferase 3α (DNMT3A), an enzyme typically involved in DNA methylation. Proper activation of murine and human embryonic and haematopoietic stem cells depends on messenger RNA processing, influenced by DNMT3A in response to stimuli. DNMT3A coordinates splicing through recruitment of the core spliceosome protein SF3B1 to RNA polymerase and mRNA. Importantly, the DNA methylation function of DNMT3A is not required and loss of DNMT3A leads to impaired splicing during stem cell turnover. Finally, we identify the spliceosome as a potential therapeutic target in DNMT3A-mutated leukaemias. Together, our results reveal a modality through which DNMT3A and the spliceosome govern exit from the stem state towards differentiation.

Transcriptome, proteome, and protein synthesis within the intracellular cytomatrix.

Despite the knowledge that protein translation and various metabolic reactions that create and sustain cellular life occur in the cytoplasm, the structural organization within the cytoplasm remains unclear. Recent models indicate that cytoplasm contains viscous fluid and elastic solid phases. We separated these viscous fluid and solid elastic compartments, which we call the cytosol and cytomatrix, respectively. The distinctive composition of the cytomatrix included structural proteins, ribosomes, and metabolome enzymes. High-throughput analysis revealed unique biosynthetic pathways within the cytomatrix. Enrichment of biosynthetic pathways in the cytomatrix indicated the presence of immobilized biocatalysis. Enzymatic immobilization and segregation can surmount spatial impediments, and the local pathway segregation may form cytoplasmic organelles. Protein translation was reprogrammed within the cytomatrix under the restriction of protein synthesis by drug treatment. The cytosol and cytomatrix are an elaborately interconnected network that promotes operational flexibility in healthy cells and the survival of malignant cells.

Sensitive image-based chromatin binding assays using inducible ERα to rapidly characterize estrogenic chemicals and mixtures.

The United States Environmental Protection Agency (EPA) has been pursuing new high throughput in vitro assays to characterize endocrine disrupting chemicals (EDCs) that interact with estrogen receptor signaling. We characterize two new PRL-HeLa cell models expressing either inducible C-terminal (iGFP-ER) or N-terminal (iER-GFP) tagged estrogen receptor-α (ERα) that allows direct visualization of chromatin binding. These models are an order of magnitude more sensitive, detecting 87 - 93% of very weak estrogens tested compared to only 27% by a previous PRL-HeLa variant and compares favorably to the 73% detected by an EPA-developed computational model using in vitro data. Importantly, the chromatin binding assays distinguished agonist- and antagonist-like phenotypes without activity specific assays. Finally, analysis of complex environmentally relevant chemical mixtures demonstrated how chromatin binding data can be used in risk assessment models to predict activity. These new assays should be a useful in vitro tool to screen for estrogenic activity.

Effects of α and ß-adrenergic signaling on innate immunity and Porphyromonas gingivalis virulence in an invertebrate model.

To investigate the role of adrenergic signalling (AS) in the host immune response and Porphyromonas gingivalis virulence, we compared norepinephrine (NE) and isoproterenol (ISO) responses in Galleria mellonella. P. gingivalis infection was evaluated by survival; humoral immune responses (i.e. melanization and cecropin and gloverin mRNA expression); cellular immune responses (i.e. haemocyte count, nodulation by histology); and P. gingivalis recovery (CFU/mL). P. gingivalis was cultivated in the presence of ISO (PgISO) or NE and injected into the larvae for survival evaluation. Finally, we co-injected ISO and PgISO to evaluate the concomitant effects on the immune response and bacterial virulence. None of the ligands were toxic to the larvae; ISO increased haemocyte number, even after P. gingivalis infection, by mobilizing sessile haemocytes in a ß-adrenergic-specific manner, while NE showed the opposite effect. ISO treatment reduced larval mortality and the number of recovered bacteria, while NE increased mortality and showed no effect on bacterial recovery. ISO and NE had similar effects on melanization and decreased the expression of cecropin. Although co-cultivation with NE and ISO increased the gene expression of bacterial virulence factors in vitro, only the injection of PgISO increased larval death, which was partially reversed by circulating ISO. Therefore, α- and ß-adrenergic signalling had opposite effects after P. gingivalis infection. Ultimately, the catecholamine influence on the immune response overcame the effect of more virulent strains. The effect of AS directly on the pathogen found in vitro did not translate to the in vivo setting.

Multiple Respiratory Syncytial Virus (RSV) Strains Infecting HEp-2 and A549 Cells Reveal Cell Line-Dependent Differences in Resistance to RSV Infection.

Respiratory syncytial virus (RSV) is a leading cause of pediatric acute respiratory infection worldwide. There are currently no approved vaccines or antivirals to combat RSV disease. A few transformed cell lines and two historic strains have been extensively used to study RSV. Here, we reported a thorough molecular and cell biological characterization of HEp-2 and A549 cells infected with one of four strains of RSV representing both major subgroups as well as historic and more contemporary genotypes (RSV/A/Tracy [GA1], RSV/A/Ontario [ON], RSV/B/18537 [GB1], and RSV/B/Buenos Aires [BA]) via measurements of viral replication kinetics and viral gene expression, immunofluorescence-based imaging of gross cellular morphology and cell-associated RSV, and measurements of host response, including transcriptional changes and levels of secreted cytokines and growth factors. IMPORTANCE Infection with the respiratory syncytial virus (RSV) early in life is essentially guaranteed and can lead to severe disease. Most RSV studies have involved either of two historic RSV/A strains infecting one of two cell lines, HEp-2 or A549 cells. However, RSV contains ample variation within two evolving subgroups (A and B), and HEp-2 and A549 cell lines are genetically distinct. Here, we measured viral action and host response in both HEp-2 and A549 cells infected with four RSV strains from both subgroups and representing both historic and more contemporary strains. We discovered a subgroup-dependent difference in viral gene expression and found A549 cells were more potently antiviral and more sensitive, albeit subtly, to viral variation. Our findings revealed important differences between RSV subgroups and two widely used cell lines and provided baseline data for experiments with model systems better representative of natural RSV infection.

Imaging-Based Screening of Deubiquitinating Proteases Identifies Otubain-1 as a Stabilizer of c-MYC.

The ubiquitin-proteasome pathway precisely controls the turnover of transcription factors in the nucleus, playing an important role in maintaining appropriate quantities of these regulatory proteins. The transcription factor c-MYC is essential for normal development and is a critical cancer driver. Despite being highly expressed in several tissues and malignancies, the c-MYC protein is also continuously targeted by the ubiquitin-proteasome pathway, which can either facilitate or inhibit c-MYC degradation. Deubiquitinating proteases can remove ubiquitin chains from target proteins and rescue them from proteasomal digestion. This study sought to determine novel elements of the ubiquitin-proteasome pathway that regulate c-MYC levels. We performed an overexpression screen with 41 human proteases to identify which deubiquitinases stabilize c-MYC. We discovered that the highly expressed Otubain-1 (OTUB1) protease increases c-MYC protein levels. Confirming its role in enhancing c-MYC activity, we found that elevated OTUB1 correlates with inferior clinical outcomes in the c-MYC-dependent cancer multiple myeloma, and overexpression of OTUB1 accelerates the growth of myeloma cells. In summary, our study identifies OTUB1 as a novel amplifier of the proto-oncogene c-MYC.

Quality Control for Single Cell Imaging Analytics Using Endocrine Disruptor-Induced Changes in Estrogen Receptor Expression.

BACKGROUND: Diverse toxicants and mixtures that affect hormone responsive cells [endocrine disrupting chemicals (EDCs)] are highly pervasive in the environment and are directly linked to human disease. They often target the nuclear receptor family of transcription factors modulating their levels and activity. Many high-throughput assays have been developed to query such toxicants; however, single-cell analysis of EDC effects on endogenous receptors has been missing, in part due to the lack of quality control metrics to reproducibly measure cell-to-cell variability in responses. OBJECTIVE: We began by developing single-cell imaging and informatic workflows to query whether the single cell distribution of the estrogen receptor-α (ER), used as a model system, can be used to measure effects of EDCs in a sensitive and reproducible manner. METHODS: We used high-throughput microscopy, coupled with image analytics to measure changes in single cell ER nuclear levels on treatment with ∼100 toxicants, over a large number of biological and technical replicates. RESULTS: We developed a two-tiered quality control pipeline for single cell analysis and tested it against a large set of biological replicates, and toxicants from the EPA and Agency for Toxic Substances and Disease Registry lists. We also identified a subset of potentially novel EDCs that were active only on the endogenous ER level and activity as measured by single molecule RNA fluorescence in situ hybridization (RNA FISH). DISCUSSION: We demonstrated that the distribution of ER levels per cell, and the changes upon chemical challenges were remarkably stable features; and importantly, these features could be used for quality control and identification of endocrine disruptor toxicants with high sensitivity. When coupled with orthogonal assays, ER single cell distribution is a valuable resource for high-throughput screening of environmental toxicants. https://doi.org/10.1289/EHP9297.