Capillary-assisted microfluidic biosensing platform captures single cell secretion dynamics in nanoliter compartments
Graphical abstract
Introduction
Cellular heterogeneity is playing a critical role in driving and regulating processes in human organs as in chemotherapeutic drug response (Stevens et al., 2016), neural systems behavior (Stefen et al., 2018), immune response (Satija and Shalek, 2014), organ regeneration (Hassanzadeh-Barforoushi et al., 2016) and embryonic development (Krieger and Simons, 2015). Over the past decade, single cell analysis tools have been introduced to measure a wide range of heterogeneous factors such as genomic (Shendure et al., 2017), transcriptomic (Pickrell et al., 2010), and proteomic (Wu and Singh, 2012) variations. Despite extensive efforts in obtaining data from single cells using technologies such as Fluorescence-activated cell sorting- FACS (Atkin-Smith et al., 2017), SCI-seq (Vitak et al., 2017), and Drop-seq (Macosko et al., 2015), none of those methods are able to quantify critical dynamic phenomena such as single cell protein secretion and metabolic activity. Such information is invaluable for unraveling mechanisms controlling the single cells' sensing, decision making and signaling (Konry et al., 2016).
Real-time monitoring of the single cell dynamics requires identification of the target analyte, design of an appropriate biosensor, and finally employment of a suitable signal read-out method (Jeknić et al., 2019). Single cell intracellular dynamics have been quantified by targeting biomolecules such as transcription factors (Tay et al., 2010), metabolic byproducts (Li et al., 2019) and second messengers (Colombo et al., 2017) using fluorescent fusion proteins, fluorescent bio-probes and Fluorescence Resonance Energy Transfer (FRET) probes, respectively. Measurement of these intracellular signals is relatively easy as the cell membrane keeps proteins inside the cell with the concentration in a well-detectable range (Lo et al., 2015).
Unlike intracellular events, extracellular secretion is diffused into the cell's surrounding environment, diluted by the media and therefore yields significantly lower concentrations. Measuring such events necessitates cell encapsulation to preserve the secreted molecules and requires incorporation of more sensitive biosensors. Sandwich immunoassays with capture antibody coated on microwells (Shirasaki et al., 2014) or co-encapsulated microspheres (Konry et al., 2011; Chokkalingam et al., 2013) and capture technology on cell membrane (Liu et al., 2019) have been introduced to measure pg/ml of cytokine secretion from single cells. In a new effort, the sandwich assay has also been used for multiplexed single cell secretion using antibody barcode patterning in a nanoliter microtrough array (Chen et al., 2019) and measuring secretion frequency and magnitude through quantitative microengraving technique (Han et al., 2010). While the well-known mechanism of sandwich assay makes it a desirable technique, the binding nature of the assay, its incapability to perform continuous measurement and its reliance on the antibody specificity limits the rate of the measurement and its robustness. To overcome these challenges, label-free methods based on mass spectrometry (Amantonico et al., 2010), electrochemistry (Zhou et al., 2013) and optical biosensing (Li et al., 2018) have been developed. However, the level of complexity associated with these assays as well as their low throughput compromises their widespread adoption. Methods based on one step chemical reaction of the target analytes and recognition molecules are simple, eliminate complicated surface functionalization steps, and are capable of continuous measurement of cell secretion dynamics (Cao et al., 2018; Zhang et al., 2019). For instance, protease activity of single circulating tumor cells (CTCs) (Dhar et al., 2018) and human leukemia cell lines (Haaβ et al., 2015) were quantified using peptide cleavage and uptake by the single cells.
In order to maintain single cells' chemical signature and achieve the level of sensitivity required for low concentration of extracellular secretion, microfluidic methods based on cell encapsulation in microchambers (Eyer et al., 2012; Shen et al., 2015) and pico/nanoliter droplets (J. Collins et al., 2015; Köster et al., 2008) have been proposed. However, both methods entail complex fluidic control such as multilayer valving system (Dang et al., 2019), challenging pressure adjustment for tuning droplet size and droplet immobilization (Courtney et al., 2017). To address these issues, methods for simultaneous generation and immobilization of single cell compartments in the form of semi-droplets have been introduced (Cohen et al., 2010; Hassanzadeh-Barforoushi et al., 2018; Sposito and DeVoe, 2017). Sample digitization into an array of dead-end traps was achieved using geometrical pinning of the liquid sample (Cohen et al., 2010; Sposito and DeVoe, 2017). While providing a self-filling capability, the digitization mechanism works only within a narrow range of geometrical and fluidic properties. Another concern about digitization systems is the formation of trapped air bubbles. This issue has been addressed by designing air vents (Shemesh et al., 2014) or air plugs (Hassanzadeh-Barforoushi et al., 2018) at the end of each trap. Despite operational simplicity offered by such strategy the underlying mechanism governing the air venting and the fluid mechanics of the simultaneous generation and splitting of droplet compartments in those systems remains unanswered.
In this paper, we present a detailed analysis of the engineering of a microfluidic device for the formation of an array of hundreds of bubble-free nanoliter liquid compartments. This analysis provides a deep insight for designing microfluidic systems for long-term single cell encapsulation and real-time secretion profiling. In addition, by incorporating capillary forces, the presented approach offers simplicity and reliability of performing single cell secretion assays. Once single cell encapsulation is achieved, we explore opportunities for performing single cell secretion profiling. We report the rate at which single cells of the same phenotype produce proteases and describe different modes of cell secretion dynamics.
Section snippets
Design and fabrication procedure
Channel designs were drawn in AutoCAD 2015 (Autodesk®) and printed on a high resolution glass photomask. Photolithography using a Karl Suss MA6 Mask Aligner (SUSS MicroTec, Germany) was then used to reflect the mask pattern on an nLOF2020 coated silicon on insulator (SOI) wafer (100 mm wafer diameter, 80 ± 1 μm top layer, 2 μm buried oxide layer, and 500 ± 15 μm base silicon layer). Deep Reactive Ion Etching (DRIE) using STS system was employed to make the final high aspect ratio (1:8) device.
Results and discussion
As shown in Fig. 1A, cancer cell populations inside a tumor or in the circulation system as circulating tumor cells (CTCs) show distinct stages of epithelial-mesenchymal transition (EMT) which corresponds to different levels of invasiveness for these cells. In order to decipher this invasiveness capacity, it is crucial to identify and categorize the cell population based on their heterogeneity in extracellular secretions (Gangoda et al., 2017; Ji et al., 2019); one of which is ECM-degrading
Conclusions
The ability to capture the heterogeneous MMP secretion dynamics among single cancer cells provides valuable information regarding their interaction with their surrounding tissue and the role of individual cells in tumor metastasis (Cathcart et al., 2015). Such information can be very useful in the development of therapeutic drugs targeting these biomolecules. To enable single cell level secretion measurements, we report a new capillary-assisted microfluidic biosensing approach which is based on
Author contributions
The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.
CRediT authorship contribution statement
Amin Hassanzadeh-Barforoushi: Conceptualization, Investigation, Methodology, Validation, Funding acquisition, Visualization, Data curation, Writing - original draft, Writing - review & editing. Majid Ebrahimi Warkiani: Conceptualization, Resources, Methodology, Writing - review & editing. David Gallego-Ortega: Supervision, Resources, Writing - review & editing. Guozhen Liu: Resources, Writing - review & editing. Tracie Barber: Supervision, Resources, Writing - review & editing.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
A. H–B. would like to acknowledge the funding through the postdoctoral writing fellowship scheme provided by the faculty of Engineering, University of New South Wales (UNSW). G. Liu acknowledges the funding support of the ARC Future Fellowship (FT160100039). We would like to thank Garvan Institute of Medical Research/the Kinghorn Cancer Centre for kindly providing the cancer cell lines for this research.
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