Single-Cell Distribution Analysis of AR Levels by High-Throughput Microscopy in Cell Models: Application for Testing Endocrine-Disrupting Chemicals

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.

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.

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.