Fully Automated Computational Approach for Precisely Measuring Organelle Acidification with Optical pH Sensors
Article title: Fully Automated Computational Approach for Precisely Measuring Organelle Acidification with Optical pH Sensors Authors: Anil Chandra, Saumya Prasad, Francesco Alemanno, Maria De Luca, Riccardo Rizzo, Roberta Romano, Giuseppe Gigli, Cecilia Bucci, Adriano Barra, Loretta L. del Mercato. ACS Applied Materials & Interfaces, 2022; https://pubs.acs.org/doi/10.1021/acsami.2c00389
ABSTRACT: pH balance and regulation within organelles are fundamental to cell homeostasis and proliferation. The ability to track pH in cells becomes significantly important to understand these processes in detail. Fluorescent sensors based on micro- and nanoparticles have been applied to measure intracellular pH; however, an accurate methodology to precisely monitor acidification kinetics of organelles in living cells has not been established, limiting the scope of this class of sensors. Here, silica-based fluorescent microparticles were utilized to probe the pH of intracellular organelles in MDA-MB-231 and MCF-7 breast cancer cells. In addition to the robust, ratiometric, trackable, and bioinert pH sensors, we developed a novel dimensionality reduction algorithm to automatically track and screen massive internalization events of pH sensors. We found that the mean acidification time is comparable among the two cell lines (ΔTMCF-7 = 16.3 min; ΔTMDA-MB-231 = 19.5 min); however, MCF-7 cells showed a much broader heterogeneity in comparison to MDA-MB-231 cells. The use of pH sensors and ratiometric imaging of living cells in combination with a novel computational approach allow analysis of thousands of events in a computationally inexpensive and faster way than the standard routes. The reported methodology can potentially be used to monitor pH as well as several other parameters associated with endocytosis.
KEYWORDS: ratiometric pH sensors, silica microparticles, fluorescence, pH sensing, organelle acidification, microparticle tracking, data compression, automated cluster analysis
Fundings: European Research Council (ERC-Starting Grant INTERCELLMED, N. 759959), AIRC MFAG-2019 (N. 22902), AIRC IG-2016 (N.19068), MAECI (BULBUL, F85F21006230001), PON «R&I» ARS01-00876, TecnoMed Puglia (DGR n.211, 21/11/2018).