Figure 1 – (a) Schematic depicting cell lysis by tip
sonication: (i) sonicator tip is inserted into the sonication vessel,
submerging tip in liquid. (ii) Controller for specifying pulse,
amplitude & duration iii) The tube sits in ice-bath for cooling iv)
Input energy from controller (ii) is set as a function of signal
amplitude (translates to tip power based on fluid volume and vessel),
pulse durations, and total sonication time v) Thermal energy transfers
to ice-bath from cell suspension domain vi) The sonicated liquid is
divided into circulated zone & dead zone (b) Axis-symmetric models
(right half) of common laboratory tubes: i) 1.5 mL microcentrifuge tube
ii) 5 mL microcentrifuge tube iii) 15 mL Falcon tube iv) 50 mL Falcon
tube
It is also important to note that adverse temperature effects of
sonication extend beyond this paper’s focusing example of cell lysate
preparation. For example, mass loss of graphene nano particles is caused
by temperature rise in sonication (Baig et al., 2018). In ultrasonic
extraction of corn, overexposure causes reduction of glucose release due
to denaturation of enzyme due to temperature rise. There is a reduction
in taste, vitamin content and loss of texture in foods that over
processed with ultrasonic extraction (Tobergte & Curtis, 2013). In
another example, single walled carbon nanotubes (SWCNT) bundle ends are
frayed due to temperature rise caused by ultrasonic exposure (Hassan,
Reddy, Haque, Minett, & Gomes, 2013; Strano et al., 2003). Thus, a
model for mixing and temperature rise caused by sonication will find
uses in broader biotechnology applications.
Sonication protocols are designed to mitigate this influence of
temperature rise and improve mixing; however, they currently require
much empirical testing to determine. Typically, the probe is operated in
a pulsed fashion with defined on and off cycles. Additionally, the
sonication vessel is submerged in an ice bath or chiller line. There is
a tradeoff between the number of cells lysed and the extract viability
which decreases when over-sonicated. From a physical standpoint, it is
logical that there exists a threshold temperature for each type of
extract, and corresponding sets of optimum parameters to stay below this
threshold, but in practice, finding this threshold and controlling
temperature rise experimentally can be difficult. The sonicator
parameters of input power, pulse durations, and total sonication times
are empirically optimized for a given vessel, often necessitating a
large number of experiments to cover the design space (Kwon & Jewett,
2015). Characterization tools can help direct such experiments, but
their utility for small volume lysate preparations are limited (Sutkar
& Gogate, 2009). The velocity field inside a sonoreactor can be
monitored by particle image velocimetry (PIV) techniques (G. S. B.
Lebon, Tzanakis, Pericleous, Eskin, & Grant, 2019; Donato Rubinetti,
Weiss, Wahlen, & Müller, 2016), but for opaque lysate in a small tube
such techniques cannot be used to conveniently measure the velocity
pattern. It is also difficult to routinely measure temperature in such
small volumes by thermocouple and as the vessel is seated in an ice
bath, it is also difficult to monitor the inner vessel temperature by
NIR imaging. Thus, to more efficiently design optimal sonication
protocols for thermally sensitive procedures like cell extract
preparation, we propose finite element modeling of mixing and thermal
effects.
Finite element modeling of ultrasound systems has been demonstrated in
literature, especially for larger reactors. Fluid flow in large
sonoreactor (> 500 mL) has been extensively studied (B.
Lebon, Tzanakis, Pericleous, & Eskin, 2019; D. Rubinetti & Weiss,
2018; Xu, Yasuda, & Koda, 2013). The temperature-rise in large
reactors, however, is less prevalent, likely due to temperature rise
being negligible in a large vessel when subjected to low power density
input and having a larger surface area for thermal dissipation (Trujillo
& Knoerzer, 2011). Effects of ultrasound (temperature rise and fluid
streaming) are mostly studied for medical applications of
high-intensity, focused ultrasound (Moriyama, Yoshizawa, & Umemura,
2012; Solovchuk, Sheu, Lin, Kuo, & Thiriet, 2012). Conversely, in cell
extract preparation, much smaller, laboratory scale tubes (Fig 1b) are
used and both mixing and temperature effects will need to be managed.
This can be done using a similar framework of finite element modeling.
This paper provides a computational model to predict the effects of tip
sonication parameters on mixing and temperature rise in small vessels,
commonly used for cell extract preparation. First the relationship
between fluid flow patterns and tip immersion depth is modeled; this is
used to estimate the optimum tip depth which maximizes mixing. This is
done by numerically calculating the stationary velocity field inside the
sonication tubes for different tip immersion depths and comparing the
fractional volume with regular, circulating streamlines. Next a heat
transfer model is used to predict temperature rise over time due to
sonication in lab scale tubes which is then experimentally validated.
This model is then used to estimate the optimal threshold temperature in
a common cell extract strain (BL21 DE3 star) using optimized CFPS
experiments presented in literature (Kwon & Jewett, 2015). This model
coupled to experimental data can then provide insight on the effect of
sonication temperature on cell lysis yield. A stepwise workflow is then
presented for use of these numerical models in obtaining optimized
parameters for other novel sonication set ups. Lastly, we provide
convenient master-plots for determination of optimal sonication
conditions for new temperature thresholds without need of running the
modeling code.