(694c) Coupled Quantitative Transcriptomic and High-Throughput Morphological Analysis Predict Biological Effects of Environmental Toxins on Human Lung Cells

Exposure to air pollution is linked to diseases of the lungs, brain, and heart and is estimated to contribute to over 7 million deaths annually by the World Health Organization. Although the link between exposure and adverse health effects has been made apparent through epidemiological studies, the role of unique air pollution components in disease pathogenesis needs to be further characterized on the cellular and molecular level. In this study, we conduct quantitative transcriptomics and epitranscriptomics in conjunction with high-throughput biochemical and microscopy analysis to predict effects of environmental toxins on human cells.

Specifically, we examine human bronchial epithelial cells (BEAS-2B cells) as a model system to determine the effects of multiple complex airborne particulate matter (PM) mixtures. We use an interdisciplinary approach involving RNA sequencing and large-scale epitranscriptomics by mass spectrometry, coupled with high-throughput microscopy and a recently developed visually-aided morpho-phenotyping recognition (VAMPIRE) tool, to catalog distinct features of cells exposed to three chemically defined PM mixtures. Using these systems and quantitative biology tools, we have developed a new integrated approach that combines large-scale biological data to understand the interplay between environmentally-induced transcriptional changes and the resulting morphological phenotypes. Collectively, these methods have allowed us to predict outcomes upon exposure to different stresses. We have also used this approach to address the challenges associated with deconvoluting the effects of specific components in PM, which has been a challenge in the field thus far.

We will discuss this novel approach and key findings including (1) distinct morphological traits of single-cell derived clones associated with transcriptomic phenotypes that predict specific cell-fates in the presence of various PM components and how these can be used as prediction factors of cell fates, (2) correlations between pathway activation in the presence of specific pollutants and chemical components, and (3) dynamic changes in RNA modification patterns across a time course of pollutant exposure that suggest an additional complex layer of stress regulation. Together, this systematic approach enables deconvolution of how PM components will uniquely affect lung cell health. We believe this approach can be broadly applicable to investigate subtle changes in cellular responses to different stimuli and pathologies.