*Adapted from a grad school thing. Enjoy 🙂
1. Introduction
Acoustic droplet ejection (ADE) is a technique wherein high-frequency sound waves are focused onto the surface of a fluid in order to eject droplets, as shown in Figure 1 [1]. The physics and technology governing this process have been active areas of research & development for nearly a century now. Motivation for ADE is driven by limitations with conventional methods when it is necessary to dispense small volumes of fluid with high fidelity. While ADE is especially effective at the milliliter and nanoliter scales, droplets as small as a picoliter may be produced with high precision and accuracy [2]. Only sound is required to transfer the liquid; this eliminates the cost of washing, replacing, and disposing of any solids that may regularly come into contact with the liquid such as pipette tips, pin tools, and ejection nozzles. Thus, due to favorable economical and operational conditions, ADE is suitable for a wide variety of applications.
In recent times, this field has made substantial contributions to the life sciences. The technology is gentle enough to transfer proteins, high molecular weight DNA, and live cells without damaging them [3]. Proteomics, cell-based assays, and drug discovery are just a few of many biomedical arenas that have benefited substantially from ADE. One application of interest is the development of tailored cancer therapy via ex vivo assessment of drug activity in patient tumor cells, which I describe later on.
2. Brief survey of relevant literature
2.1 An overview of ADE physics
The acoustic transfer process is typically rapid; hundreds of drops are ejected per second until the desired volume is effectively transferred. Such rapidness leads to dynamic surface deformations – for example, the formation of capillary waves – which adds time-dependent complexity to the process. As the fluid volume is depleted with increasing ejection repetitions, the relative effect of each force changes. Under dynamic transfer conditions, the fluid’s surface energy is closely monitored in order to gauge the acoustic energy required to eject a droplet from its surface. Higher frequencies produce smaller droplets [1].
In a simplified static model, one can consider ADE as force balance between the incoming focused acoustic energy, the viscous properties of the fluid, and the surface tension of the fluid. Viscous properties dissipate the acoustic energy while surface tension works to minimize the fluid’s surface area to volume ratio [1]. Consequently, a droplet is formed.
Once a droplet is ejected from the fluid surface, it must reach its intended destination and it must coalesce with the destination fluid. The trajectory of the droplet is determined by the electrostatic environment, the focus of acoustic signal near the fluid surface, and the corresponding velocity of the droplet among other factors. Droplet coalescence is dependent on factors such as droplet diameter, physical properties of the fluid, and electromagnetic field environment [1].
2.2 Novel technologies that enable accuracy
Dynamic Fluid Analysis (DFA) was developed by Labcyte Inc, a leading developer of acoustic-dispensing technology. DFA is an integrated system. It quantifies changing energy requirements during the course of a transfer and adjusts the acoustic power that is applied to the fluid’s surface accordingly. The system determines fluid height and properties by sending acoustic energy into the source well and then receiving echoes from three predefined interfaces [4], as depicted in Figure 2 [1]. Viscosity, for example, may be calculated from two subsequent measurements of resonant frequency and bandwidth using a software algorithm [5]. DFA is an active and autonomous process; the operator does not have to perform calibration or recalibration at any point as is required by other liquid-handling devices. Because transfer parameters are determined on a well-by-well basis, DFA can be applied on complex or inconsistent reagents [4]. The aforementioned enables fluid transfer that is fast, accurate, precise, and uninterrupted.
Secondly, high-voltage (HV) grid technology is incorporated into ADE systems, giving rise to electric power transmission. HV grid potential can be used to influence droplet velocity. In one study by Sackmann et al, it was shown that HV grid enhances accuracy, precision, and coalescence of droplets at the destination as well as overall robustness of fluid transfer. When HV grid was disabled, undesirable effects were observed; bouncing droplets were captured on side walls of the assay plate, and drop coalescence was poor [1]. Thus, DFA and HV grid are indispensable novel technologies for ADE.
3. Application in tailored cancer therapy
3.1 Background and motivation
Cancer drug development is founded on the principle that, given a particular drug, groups of similar patients will experience similar responses. However, intrinsic differences exist in the drug sensitivity of cells from patient-to-patient and from tumor-to-tumor. Drug response is considerably variable in practice; one of several alternatives with seemingly equal efficacy at the group level may, in fact, be favorable to the individual patient [6]. Next-line chemotherapy frequently works where standard first-line chemotherapy fails, therefore, but the selection process becomes vague. Any guidance for that next course of treatment becomes especially valuable.
If tumor cell drug sensitivity can be assessed readily and rapidly, drugs that are more likely to benefit the individual patient can be identified and prescribed. Ex vivo assessment is performed to predict the effect that a drug may have on tumor cells in vivo. ADE can be applied in ex vivo assessment to substantially improve workflow and to allow great flexibility in the panel of drugs being tested. Roughly 80 compounds may be tested for each patient in one replication of such methods. Furthermore, ADE allows any available set of compounds to be assayed, which has not been the case up until now. Larger testing capability increases the chance of discovering important drug interactions and improving patient outcomes. By combating certain constraints associated with conventional methods, ADE may drastically improve the ability of physicians to provide tailored cancer therapy to their patients.
3.2 Methods
First, tumor samples are collected in sterile transport medium and provided with buffer and nutrients. Several purification steps are taken, and cytospin glass staining is executed to identify as well as concentrate the tumor cells for testing. Once final conditioning measures are performed, the cells are diluted in complete culture medium and are ready for seeding into 384- well experimental plates. A cytospin glass is saved for morphological evaluation of cells used in the assessment [6].
Next, drug-response assessment is performed in the form of fluorometric microculture cytotoxicity assay (FMCA). FMCA measures the cytotoxic or cytostatic effect of various compounds by determining the amount of fluorescein diacetate that is converted into fluorescein. This correlates directly with the amount of cells with intact plasma membranes, and consequently, cell survival altogether. It is a non-clonogenic microplate-based cell viability assay, and so it is irrelevant of cell proliferation. The 384-well plate version of this assay is adaptable to robotics, lending itself to ADE [6].
Once the 384-well microtiter plate has been seeded with cells, the role of ADE is specifically in the loading of drugs into each well. Acoustic dispensation can be integrated into the laboratory information system, complete with automatically generated computations and subsequent clinical referral. A software application can be developed and utilized to generate a customized transfer scheme for the plate layout. For example, the acoustic dispensing system can be programmed to detect arbitrary concentrations from the source plates and to generate transfer instructions that compensate. It may track source-well depletion and switch to a replicate well when necessary [6]. Any number and variation of assay plates can be combined in one test run, and because a test run is specified only once, the risk of user error is minimized.
Survival index percentage (SI%) is an outcome measure of FMCA. This is the ratio of surviving cells to unexposed cells. SI% is calculated for each well in the assay according to the following equation:
SI% = 100 × [𝑓(Exposed) – 𝑓(Blank)] ÷ [𝑓(Control) – 𝑓(Blank)]
in which 𝑓(Exposed) is the fluorescent signal of the experimental well, 𝑓(Blank) is the average fluorescent signal of the blank wells, and 𝑓(Control) is the average fluorescent signal of the control wells [6]. Median SI% and standard deviation (𝜎) is obtained for a set of reference samples as well and is performed according to the patient’s diagnosis. In one method, drug response is classified as the following:
| Low Drug Resistance (LDE) | If SI% < Median |
| Intermediate Drug Resistance (IDE) | if Median ≤ SI% < Median + 1 𝜎 |
| Extreme Drug Resistance (EDE) | If SI% ≥ Median + 1 𝜎 |
The EDE classification indicates in vivo drug resistance. The IDE and LDE classifications are considered to have predictive value in terms of in vivo drug response. A clinical referral response can then be generated, and an authentic example of such is illustrated in Figure 3 [6]. A physician in this scenario might choose to prescribe a drug in the LDR class for this particular patient.
Determining the probability that the patient will respond to each drug is the ultimate goal of the analysis. This is accomplished using Bayes’ theorem, which describes the probability of an event based on conditions that might be related to the event.
The above application has a special name and place in scientific experimentation. Deemed “high-throughput screening,” the idea is for researchers to quickly conduct millions of chemical, genetic, or pharmacological tests in order to identify compounds of interest. ADE expands the implications of such, indeed, in ways that scientists will take advantage of for years to come.
Works cited
- Sackmann EK, Majlof L, Hahn-Windgassen A et al. “Technologies that enable accurate and precise nano- to milliliter-scale liquid dispensing of aqueous reagents.” JALA 2016.
- Hadimioglu B, Stearns R, Ellson R. “Moving liquids with sound.” JALA 2016.
- Grant RJ, Roberts K, Pointon C et al. “Achieving accurate compound concentration in cell-based screening.” JBS 2009.
- Labcyte. “Dynamic fluid analysis.” 2016. http://www.labcyte.com/echo-technology/dynamic-fluid-analysis
- Bujard MR, Tittmann BR. “Method of measuring the dynamic viscosity of a viscous fluid utilizing acoustic transducer.” USPTO 1989.
- Blom K, Nygren P, Alvarsson J et al. “Ex vivo assessment of drug activity in patient tumor cells.” JALA 2016.
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