Abstract

Real time single cell multiplexed assay for proteolytic activity matrix analysis by using droplet microfluidics

Chen Chia-Hung, Assistant Professor, National University Of Singapore

This study reports the use of a droplet-based microfluidic platform to detect multiplexed protease activities at single cell resolution. Multiple extracellular proteases interact in a post-translationally regulated and integrated fashion to govern cancer cell behavior and disease progression; importantly, simultaneous measurement of multiple protease activities helps parse this complex enzymatic activities. Here, we modified FRET (fluorescence resonance energy transfer)-based substrates to accommodate different fluorescent pairs with distinct excitation and emission wavelengths to obtain multiple signals from droplets containing single cells. Multiple protease-substrate reactions in a single cell were measured simultaneously to obtain specific protease activities and network level information. The working principle is shown in Fig. 1. First, cells were flowed through a pinch-flow cross-junction channel and mixed with multiple FRET-substrates for cell encapsulation in water-in-oil droplets (Fig. 1a and 1b). The droplets were then sent to an observing chamber, where fluorescence signals were monitored. Four substrate-protease reactions (in UV, Blue, Green, Red fluorescence and IR channels) in a droplet were simultaneously monitored at three distinct pairs of fluorescent excitation (UV: 400nm, B: 470nm, G: 546nm, R: 635nm) and emission (B: 520nm, G: 580nm, R: 670nm) wavelengths (Fig. 1c). Microscope optical filters were used to minimize crosstalk of signals for these multiplexed measurements (Fig. 1d). To infer a quantitative profile of multiple proteolytic activities from single cells, we applied the computational method Proteolytic Activity Matrix Analysis (PrAMA). The capability to determine multiple protease activities at single cell resolution has the potential to characterize rare individual cells (circulating tumor cells) in patient samples.