Expression Patterns of Energy-Related Genes in Single Cells Uncover Key Isoforms and Enzymes That Gain Priority Under Nanoparticle-Induced Stress

Abstract

Cellular responses to nanoparticles (NPs) have been largely studied in cell populations, providing averaged values that often misrepresent the true molecular processes that occur in the individual cell. To understand how a cell redistributes limited molecular resources to achieve optimal response and survival requires single-cell analysis. Here we applied multiplex single molecule-based fluorescence in situ hybridization (fliFISH) to quantify the expression of 10 genes simultaneously in individual intact cells, including glycolysis and glucose transporter genes, which are critical for restoring and maintaining energy balance. We focused on individual gill epithelial cell responses to lithium cobalt oxide (LCO) NPs, which are actively pursued as cathode materials in lithium-ion batteries, raising concerns about their impact on the environment and human health. We found large variabilities in the expression levels of all genes between neighboring cells under the same exposure conditions, from only a few transcripts to over 100 copies in individual cells. Gene expression ratios among the 10 genes in each cell uncovered shifts in favor of genes that play key roles in restoring and maintaining energy balance. Among these genes are isoforms that can secure and increase glycolysis rates more efficiently, as well as genes with multiple cellular functions, in addition to glycolysis, including DNA repair, regulation of gene expression, cell cycle progression, and proliferation. Our study uncovered prioritization of gene expression in individual cells for restoring energy balance under LCO NP exposures. Broadly, our study gained insight into single-cell strategies for redistributing limited resources to achieve optimal response and survival under stress.

Super-Resolution Imaging of Plant Receptor-Like Kinases Uncovers Their Colocalization and Coordination with Nanometer Resolution

“…Image processing is described in detail in previous work, using in-house MATLAB scripts that are available upon request [ 46 , 47 ]. Centroids for blinking fluorescent objects were identified and used to generate a blinking density map, where the density of the map was constructed to indicate the number of blinking events within 20 nm (the error of fluorescence localization within cells).…”