User:Roryjm0/sandbox

Note for reviewer
I am part of the CHEM 525 class, and I intend to include the text below under the "Applications" section of the Digital Microfluidics Wikipedia page.

Laboratory automation
In research fields such as synthetic biology, where highly iterative experimentation is common, considerable efforts have been made to automate workflows. Digital microfluidics is often touted as a laboratory automation solution, with a number of advantages over alternative solutions such as pipetting robots and droplet microfluidics. These stated advantages often include a reduction in the required volume of experimental reagents, a reduction in the likelihood of contamination and cross-contamination, potential improvements in reproducibility, increased throughput, individual droplet addressability, and the ability to integrate with sensor and detector modules to perform end-to-end or even closed loop workflow automation.

Reduced experimental footprint
One of the core advantages of digital microfluidics, and of microfluidics in general, is the use and actuation of picoliter to microliter scale volumes. Workflows adapted from the bench to a DMF system are miniaturized, meaning working volumes are reduced to fractions of what is normally required for conventional methods. For example, Thaitrong et al. developed a DMF system with a capillary electrophoresis (CE) module with the purpose of automating the process of next generation sequencing (NGS) library characterization. Compared to an Agilent BioAnalyzer (an instrument commonly used to measure sequencing library size distribution), the DMF-CE system consumed ten-fold less sample volume. Reducing volumes for a workflow can be especially beneficial if the reagents are expensive or when manipulating rare samples such as circulating tumor cells and prenatal samples. Miniaturization also means a reduction in waste product volumes.

Reduced probability of contamination
DMF-based workflows, particularly those using a closed configuration with a top-plate ground electrode, have been shown to be less susceptible to outside contamination compared to some conventional laboratory workflows. This can be attributed to minimal user interaction during automated steps, and the fact that the smaller volumes are less exposed to environmental contaminants than larger volumes which would need to be exposed to open air during mixing. Ruan et al. observed minimal contamination from exogenous nonhuman DNA and no cross-contamination between samples while using their DMF-based digital whole genome sequencing system.

Improved reproducibility
Overcoming issues of reproducibility has become a topic of growing concern across scientific disciplines. Reproducibility can be especially salient when multiple iterations of the same experimental protocol need to be repeated. Using liquid handling robots that can minimize volume loss between experimental steps are often used to reduce error rates and improve reproducibility. An automated DMF system for CRISPR-Cas9 genome editing was described by Sinha et al, and was used to culture and genetically modify H1299 lung cancer cells. The authors noted that no variation in knockout efficiencies across loci was observed when cells were cultured on the DMF device, whereas cells cultured in well-plates showed variability in upstream loci knockout efficiencies. This reduction in variability was attributed to culturing on a DMF device being more homogenous and reproducible compared with well plate methods.

Increased throughput
While DMF systems cannot match the same throughput achieved by some liquid handling pipetting robots, or by some droplet-based microfluidic systems, there are still throughput advantages when compared to conventional methods carried out manually.

Individual droplet addressability
DMF allows for droplet level addressability, meaning individual droplets can be treated as spatially distinct microreactors. This level of droplet control is important for workflows where reactions are sensitive to the order of reagent mixing and incubation times, but where the optimal values of these parameters may still need to be determined. These types of workflows are common in cell-free biology, and Liu et al. were able to demonstrate a proof-of-concept DMF-based strategy for carrying out remote-controlled cell-free protein expression on an OpenDrop chip.

Detector module integration for end-to-end and closed-loop automation
An often cited advantage DMF platforms have is their potential to integrate with on-chip sensors and off-chip detector modules. In theory, real-time and end-point data can be used in conjunction with machine learning methods to automate the process of parameter optimization.