Editing Digital microfluidics (section)

=== Laboratory automation ===
In research fields such as [[synthetic biology]], where highly iterative experimentation is common, considerable efforts have been made to automate workflows.<ref>{{cite journal | vauthors = Sparkes A, Aubrey W, Byrne E, Clare A, Khan MN, Liakata M, Markham M, Rowland J, Soldatova LN, Whelan KE, Young M, King RD | display-authors = 6 | title = Towards Robot Scientists for autonomous scientific discovery | journal = Automated Experimentation | volume = 2 | issue = 1 | pages = 1 | date = January 2010 | pmid = 20119518 | pmc = 2813846 | doi = 10.1186/1759-4499-2-1 | doi-access = free }}</ref><ref>{{cite journal | vauthors = Meng F, Ellis T | title = The second decade of synthetic biology: 2010-2020 | journal = Nature Communications | volume = 11 | issue = 1 | pages = 5174 | date = October 2020 | pmid = 33057059 | pmc = 7560693 | doi = 10.1038/s41467-020-19092-2 | bibcode = 2020NatCo..11.5174M }}</ref><ref>{{cite journal | vauthors = Carbonell P, Radivojevic T, García Martín H | title = Opportunities at the Intersection of Synthetic Biology, Machine Learning, and Automation | journal = ACS Synthetic Biology | volume = 8 | issue = 7 | pages = 1474–1477 | date = July 2019 | pmid = 31319671 | doi = 10.1021/acssynbio.8b00540 | hdl = 20.500.11824/998 | s2cid = 197664634 | doi-access = free | hdl-access = free }}</ref> Digital microfluidics is often touted as a laboratory automation solution, with a number of advantages over alternative solutions such as [[Liquid handling robot|pipetting robots]] and [[Droplet-based microfluidics|droplet microfluidics]].<ref name="Kothamachu_2020">{{cite journal | vauthors = Kothamachu VB, Zaini S, Muffatto F | title = Role of Digital Microfluidics in Enabling Access to Laboratory Automation and Making Biology Programmable | language = English | journal = SLAS Technology | volume = 25 | issue = 5 | pages = 411–426 | date = October 2020 | pmid = 32584152 | doi = 10.1177/2472630320931794 | s2cid = 220062017 | doi-access = free }}</ref><ref name="Husser_2018">{{cite journal | vauthors = Husser MC, Vo PQ, Sinha H, Ahmadi F, Shih SC | title = An Automated Induction Microfluidics System for Synthetic Biology | journal = ACS Synthetic Biology | volume = 7 | issue = 3 | pages = 933–944 | date = March 2018 | pmid = 29516725 | doi = 10.1021/acssynbio.8b00025 }}</ref><ref name="Ruan_2020">{{cite journal | vauthors = Ruan Q, Ruan W, Lin X, Wang Y, Zou F, Zhou L, Zhu Z, Yang C | display-authors = 6 | title = Digital-WGS: Automated, highly efficient whole-genome sequencing of single cells by digital microfluidics | journal = Science Advances | volume = 6 | issue = 50 | pages = eabd6454 | date = December 2020 | pmid = 33298451 | pmc = 7725457 | doi = 10.1126/sciadv.abd6454 | bibcode = 2020SciA....6.6454R }}</ref> 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.<ref name="Kothamachu_2020" /><ref name="Husser_2018" /><ref name="Ruan_2020" /><ref>{{cite journal | vauthors = Liu D, Yang Z, Zhang L, Wei M, Lu Y | title = Cell-free biology using remote-controlled digital microfluidics for individual droplet control | journal = RSC Advances | volume = 10 | issue = 45 | pages = 26972–26981 | date = July 2020 | pmid = 35515808 | pmc = 9055536 | doi = 10.1039/d0ra04588h | bibcode = 2020RSCAd..1026972L }}</ref>

==== 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|capillary electrophoresis (CE)]] module with the purpose of automating the process of [[Next-generation sequencing|next generation sequencing (NGS)]] library characterization. Compared to an [[Agilent Technologies|Agilent]] BioAnalyzer (an instrument commonly used to measure sequencing library size distribution), the DMF-CE system consumed ten-fold less sample volume.<ref>{{cite journal | vauthors = Thaitrong N, Kim H, Renzi RF, Bartsch MS, Meagher RJ, Patel KD | title = Quality control of next-generation sequencing library through an integrative digital microfluidic platform | journal = Electrophoresis | volume = 33 | issue = 23 | pages = 3506–3513 | date = December 2012 | pmid = 23135807 | doi = 10.1002/elps.201200441 | s2cid = 205802837 }}</ref> 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.<ref name="Ruan_2020" /> 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.<ref name="Ruan_2020" />

==== Improved reproducibility ====
Overcoming issues of [[reproducibility]] has become a topic of growing concern across scientific disciplines.<ref>{{Cite journal | vauthors = Baker M |date=2016-05-01 |title=1,500 scientists lift the lid on reproducibility |journal=Nature |language=en |volume=533 |issue=7604 |pages=452–454 |doi=10.1038/533452a |pmid=27225100 |bibcode=2016Natur.533..452B |s2cid=4460617 |issn=1476-4687|doi-access=free }}</ref> Reproducibility can be especially salient when multiple iterations of the same experimental protocol need to be repeated.<ref>{{cite journal | vauthors = Jessop-Fabre MM, Sonnenschein N | title = Improving Reproducibility in Synthetic Biology | journal = Frontiers in Bioengineering and Biotechnology | volume = 7 | pages = 18 | date = 2019 | pmid = 30805337 | pmc = 6378554 | doi = 10.3389/fbioe.2019.00018 | doi-access = free }}</ref> 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 gene editing|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 [[Gene knockout|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.<ref>{{cite journal | vauthors = Sinha H, Quach AB, Vo PQ, Shih SC | title = An automated microfluidic gene-editing platform for deciphering cancer genes | journal = Lab on a Chip | volume = 18 | issue = 15 | pages = 2300–2312 | date = July 2018 | pmid = 29989627 | doi = 10.1039/C8LC00470F }}</ref>

==== 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.<ref name="Digital Microfluidic Cell Culture">{{cite journal | vauthors = Ng AH, Li BB, Chamberlain MD, Wheeler AR | title = Digital Microfluidic Cell Culture | journal = Annual Review of Biomedical Engineering | volume = 17 | issue = 1 | pages = 91–112 | date = 2015-12-07 | pmid = 26643019 | doi = 10.1146/annurev-bioeng-071114-040808 }}</ref>

==== Individual droplet addressability ====
DMF allows for droplet level addressability, meaning individual droplets can be treated as spatially distinct [[microreactor]]s.<ref name="Kothamachu_2020" /> 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 system|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 synthesis|cell-free protein expression]] on an OpenDrop chip.<ref name="Liu_2020">{{cite journal | vauthors = Liu D, Yang Z, Zhang L, Wei M, Lu Y | title = Cell-free biology using remote-controlled digital microfluidics for individual droplet control | journal = RSC Advances | volume = 10 | issue = 45 | pages = 26972–26981 | date = July 2020 | pmid = 35515808 | pmc = 9055536 | doi = 10.1039/D0RA04588H | bibcode = 2020RSCAd..1026972L }}</ref>

==== 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.<ref name="Kothamachu_2020" /><ref name="Liu_2020" /> In theory, real-time and end-point data can be used in conjunction with [[machine learning]] methods to automate the process of parameter optimization.