Imaging the competition between growth and production of self-assembled lipid droplets at the single-cell level

Abstract

© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. Several biotechnologies are currently available to quantify how cells allocate resources between growth and carbon storage, such as mass spectrometry. However, such biotechnologies require considerable amounts of cellular biomass to achieve adequate signal-to-noise ratio. In this way, existing biotechnologies inevitably operate in a 'population averaging' mode and, as such, they cannot unmask how cells allocate resources between growth and storage in a high-throughput fashion with single-cell, or subcellular resolution. This methodological limitation inhibits our fundamental understanding of the mechanisms underlying resource allocations between different cellular metabolic objectives. In turn, this knowledge gap also pertains to systems biology effects, such as cellular noise and the resulting cell-to-cell phenotypic heterogeneity, which could potentially lead to the emergence of distinct cellular subpopulations even in clonal cultures exposed to identical growth conditions. To address this knowledge gap, we applied a high-throughput quantitative phase imaging strategy. Using this strategy, we quantified the optical-phase of light transmitted through the cell cytosol and a specific cytosolic organelle, namely the lipid droplet (LD). With the aid of correlative secondary ion mass spectrometry (NanoSIMS) and transmission electron microscopy (TEM), we determined the protein content of different cytosolic organelles, thus enabling the conversion of the optical phase signal to the corresponding dry density and dry mass. The high-throughput imaging approach required only 2 μL of culture, yielding more than 1,000 single, live cell observations per tested experimental condition, with no further processing requirements, such as staining or chemical fixation.