High-throughput Droplet Barcoding and Automated Image Analysis in Microfluidic Droplet Trapping Array

Molecularly-targeted therapeutics and personalized medicine have dramatically increased the median survival rate of patients suffering from cancer. However, cellular heterogeneity and the personalized nature of cancer have resulted in the limited success of single drug treatments which has led to the use of multiple therapeutic combinations. This has required the development of new analytical methods capable of multiplexed high-throughput screening (HTS) technologies necessary to identify is single or multi-agent therapies are effective in ex vivo samples like liquid biopsies. Droplet microfluidic devices have garnered significant interest to facilitate high-throughput, single cell analysis of heterogeneous populations. However, these devices are still limited in their ability to assess multiple input conditions such as combinations of multiple drugs or different doses of the same drug. Moreover, HTS approaches need to be coupled with automated image analysis metrics capable of rapidly processing raw data and quantifying it in an efficient manner. The goal of this work is to address these two areas of need by developing a new method to track different inputs in a droplet microfluidic trapping array coupled with automated image analysis of single cell behavior. The first part of this study highlights the use of rare-earth (RE)-doped luminescent nanoparticles (NP) as novel method to track input conditions in droplets in a microfluidic device. The second part of the work deals with the development of an algorithm called FluoroCellTrack to efficiently analyze single cell data from high-throughput experiments in the droplet microfluidic trapping array. The β-hexagonal NaYF4 nanoparticles used for droplet tracking were doped with a rare-earth emitter with unique spectral properties that do not overlap with established fluorophores like GFP and Rhodamine. In this study, we employed europium as the dopants which has a luminescence emission spectrum in the red region upon UV excitation. We demonstrated that the RE-doped nanoparticles are biologically inert and spectrally independent with common fluorophores and fluorescent stains. This work provided a foundation for future applications using the combination of NPs and microfluidics for multiplexed droplet tracking to quantify tumor heterogeneity and assess the effectiveness of combinatorial therapies. To perform HTS of single cells, a Python algorithm (FluoroCellTrack) was developed to: (i) automatically distinguish droplets from cells, (ii) count cells in each droplet, (iii) quantify cell viability, and (iv) identify input conditions using the RE-doped nanoparticles. The performance of FluoroCellTrack was compared to manual image analysis with a difference in intracellular quantification of ~2% coupled with a decrease in analysis time ofquantification, droplet barcoding and biomarker detection

Automated liquid-handling operations for robust, resilient, and efficient bio-based laboratory practices

Increase in the adoption of liquid handling devices (LHD) can facilitate experimental activities. Initially adopted by businesses and industry-based laboratories, the practice has also moved to academic environments, where a wide range of non-standard/non-typical experiments can be performed. Current protocols or laboratory analyses require researchers to transfer liquids for the purpose of dilution, mixing, or inoculation, among other operations. LHD can render laboratories more efficient by performing more experiments per unit of time, by making operations robust and resilient against external factors and unforeseen events such as the COVID-19 pandemic, and by remote operation. The present work reviews literature that reported the adoption and utilisation of LHD available in the market and presents examples of their practical use. Applications demonstrate the critical role of automation in research development and its ability to reduce human intervention in the experimental workflow. Ultimately, this work will provide guidance to academic researchers to determine which LHD can fulfil their needs and how to exploit their use in both conventional and non-conventional applications. Furthermore, the breadth of applications and the scarcity of academic institutions involved in research and development that utilise these devices highlights an important area of opportunity for shift in technology to maximize research outcomes

Design and Optimization Methods for Pin-Limited and Cyberphysical Digital Microfluidic Biochips

<p>Microfluidic biochips have now come of age, with applications to biomolecular recognition for high-throughput DNA sequencing, immunoassays, and point-of-care clinical diagnostics. In particular, digital microfluidic biochips, which use electrowetting-on-dielectric to manipulate discrete droplets (or "packets of biochemical payload") of picoliter volumes under clock control, are especially promising. The potential applications of biochips include real-time analysis for biochemical reagents, clinical diagnostics, flash chemistry, and on-chip DNA sequencing. The ease of reconfigurability and software-based control in digital microfluidics has motivated research on various aspects of automated chip design and optimization.</p><p>This thesis research is focused on facilitating advances in on-chip bioassays, enhancing the automated use of digital microfluidic biochips, and developing an "intelligent" microfluidic system that has the capability of making on-line re-synthesis while a bioassay is being executed. This thesis includes the concept of a "cyberphysical microfluidic biochip" based on the digital microfluidics hardware platform and on-chip sensing technique. In such a biochip, the control software, on-chip sensing, and the microfluidic operations are tightly coupled. The status of the droplets is dynamically monitored by on-chip sensors. If an error is detected, the control software performs dynamic re-synthesis procedure and error recovery.</p><p>In order to minimize the size and cost of the system, a hardware-assisted error-recovery method, which relies on an error dictionary for rapid error recovery, is also presented. The error-recovery procedure is controlled by a finite-state-machine implemented on a field-programmable gate array (FPGA) instead of a software running on a separate computer. Each state of the FSM represents a possible error that may occur on the biochip; for each of these errors, the corresponding sequence of error-recovery signals is stored inside the memory of the FPGA before the bioassay is conducted. When an error occurs, the FSM transitions from one state to another, and the corresponding control signals are updated. Therefore, by using inexpensive FPGA, a portable cyberphysical system can be implemented.</p><p>In addition to errors in fluid-handling operations, bioassay outcomes can also be erroneous due the uncertainty in the completion time for fluidic operations. Due to the inherent randomness of biochemical reactions, the time required to complete each step of the bioassay is a random variable. To address this issue, a new "operation-interdependence-aware" synthesis algorithm is proposed in this thesis. The start and stop time of each operation are dynamically determined based on feedback from the on-chip sensors. Unlike previous synthesis algorithms that execute bioassays based on pre-determined start and end times of each operation, the proposed method facilitates "self-adaptive" bioassays on cyberphysical microfluidic biochips.</p><p>Another design problem addressed in this thesis is the development of a layout-design algorithm that can minimize the interference between devices on a biochip. A probabilistic model for the polymerase chain reaction (PCR) has been developed; based on the model, the control software can make on-line decisions regarding the number of thermal cycles that must be performed during PCR. Therefore, PCR can be controlled more precisely using cyberphysical integration.</p><p>To reduce the fabrication cost of biochips, yet maintain application flexibility, the concept of a "general-purpose pin-limited biochip" is proposed. Using a graph model for pin-assignment, we develop the theoretical basis and a heuristic algorithm to generate optimized pin-assignment configurations. The associated scheduling algorithm for on-chip biochemistry synthesis has also been developed. Based on the theoretical framework, a complete design flow for pin-limited cyberphysical microfluidic biochips is presented.</p><p>In summary, this thesis research has led to an algorithmic infrastructure and optimization tools for cyberphysical system design and technology demonstrations. The results of this thesis research are expected to enable the hardware/software co-design of a new class of digital microfluidic biochips with tight coupling between microfluidics, sensors, and control software.</p>Dissertatio