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Development of microfluidic dilution networkbased system for lab-on-a-chip microalgal bioassays Guoxia Zheng, Ling Lu, Yusuo Yang, Junfeng Wei, Bingxu Han, Qian Zhang, and Yunhua Wang Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b02597 • Publication Date (Web): 22 Oct 2018 Downloaded from http://pubs.acs.org on October 23, 2018

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Analytical Chemistry

Development of microfluidic dilution network-based system for labon-a-chip microalgal bioassays Guoxia Zheng,†,§ Ling Lu,‡,§Yusuo Yang,†,§Junfeng Wei,†,§Bingxu Han,†,§Qian Zhang,†,§Yunhua Wang*,‡,§ †Chemical and environmental engineering institute, Dalian University, 11662, Dalian, China. ‡ Medical school, Dalian University, 11662, Dalian, China. §Environmental Micro Total Analysis lab, Dalian University, 11662,

Dalian, China.

Corresponding Author *E-mail: [email protected]. Phone (work): 86 - 411- 87402358 Fax: 86 - 411 - 87402059

ACS Paragon Plus Environment

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ABSTRACT Due to the crucial ecological significance of microalgae, Microalgal bioassays have become one of the most demanding tests from all classic aquatic toxicity tests in regulatory frameworks. However, conventional algal tests tend to be lab-intensive, time and space consuming and have not been utilized to their full potential for routine toxicity assessments. Microfluidics should be a user-friendly alternative. Particularly, dilution to generate gradients appropriate for screening experiments can be precisely attained by microfluidic network in a simple and cost/time/space saving way. Here, we demonstrate a microfluidics series towards routine microalgal bioassays, including pre-test, single and joint toxicity test. The chip mainly consists of upstream dilution network (single serial dilution module (logarithmic/linear gradient generator) or multiple (binary/ternary/quaternary) mixing serial dilution module) and downstream diffusible culturing module. It allows the processes of chemical liquid dilution and diffusion, micro-scale microalgal culture, cell stimulation and on-lined screening to be integrated into a single device. Electric theorems with the aid of EDA (electronic design automation) simulation were innovatively introduced to minimize design effort for such systems. Using the device, microalgae were successfully cultured and stressed on-chip. The simple assay provides multi-biological trait assessments of cell division rate, autofluorescence, esterase activity and mobile capacity. This work showed promising in developing a high-throughput microfluidic platform for microalgal bioassays as well as lab-on-a-chip screening experiments in the cell-based quantitative assessment of environmental health risks.

INTRODUCTION

Anthropogenic pollutants in aquatic ecosystems, such as pesticides, metals, and pharmaceuticals have significantly increased over the past decades. Their toxicity on aquatic organism has attracted more and more concern for developing ecological risk assessments and environmental protection strategies.1-4 Microalgae are primary producers in the aquatic environment, providing energy for invertebrates and fish. The toxicities of pollutants on microalgae is significant not only for the


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organisms themselves, but also for other links in the food chain that generally respond on longer timescales.5,6 Due to their ecological importance, Microalgae bioassays have become one of the most demanding tests from all conventional aquatic toxicity tests used in regulatory frameworks (e.g. notification or hazard classification, environmental risk or health assessment).6-8 In addition, compared with bioassays using higher organisms, such as invertebrates and fish, microalgal assays are inexpensive and do not involve animal-ethics issues.9,10 However, conventional algal tests have not been utilized to full potential due to the their complexities. In fact，many operators are still encountering labor, time, and space-consuming problems for performing the tests.11 Currently, it is important to develop a cost-effective and user-friendly alternative to standard algal assays for routine testing. Microfluidics, in which dilution/gradient generator, mixing module, and culturing modules could be incorporated, may be a promising solution. These microfluidics could be used to mix and/or dilute different hazard factors/reagents and let the single or joint toxicity of these reagents could be tested by a high-throughput way. Recently, microfluidic technologies have enabled the development of various lab-on-a-chip devices with cell culturing functionalities (mammalian cells, microbe, algal, parasite) as well as conducting high-throughput, combinatorial, and highly parallel processing.12-16 Particularly, dilution to generate gradients appropriate for screening experiments can be precisely attained by a microfluidic network in a simple, reagentreduced, and time-saving way. Serial mixing, parallel mixing, combinatorial mixing, logarithmic diluter, and dilution gradients have been proposed.17-22 To precisely control the fluid for these applications, the electric circuit method has been used for developing a pressure-driven microfluidic network. This method are proved to be an efficient practical design strategy.23,24 It is based on the well-known hydraulic-electric circuit analogy with correlations of pressure to voltage, volumetric flow rate to current, and hydraulic to electric resistance. It can be straight forwardly used to prescribe the flow/pressure relation in complex microfluidic networks.23,24 With the aid of OrCAD, a suite of products for EDA, 25any linear electric circuit can be simply solved to deduce its equivalent flow circuit.26 Furthermore, new methods allowing calculation and validation to be accomplished automatically were established by Grimmer



et al.27 These linear design methods have gained more success in microfluidics in recent years. However, when the calculated network is too complex, the calculation engine may get stuck in an unacceptable position. In other words, the iterative procedure can converge very slow or even can not converge in some cases.28 These are usually the cases for microalgae assays, where the single and joint toxicity of multiple pollutants with concentration gradients are usually needed. Often, this “mixing, dilution, and culturing” mode will make the channel network too complicated to be calculated. Herein, the equivalent circuit theorem and circuit partition were innovatively introduced in our collection tools for a further minimizing of design effort. Based on the Thévenin’s circuit and Substitution circuit, a linear flow network can be split into interconnection of two subnets through the connecting terminals. Then the two sub-parts can be integrated after their separately calculated using OrCAD software. This reduction and integration method can dramatically decrease the complexity of calculation and accelerate the iterative process. Thus, a complicated network can be simulated and designed in a most efficient way with a minimized error. In this paper, we developed a microfluidics serial towards routine microalgal bioassays, including pre-test, single and joint toxicity tests. The chip mainly consists of an upstream dilution network and a downstream diffusible culturing module. The dilution network could be either a single serial dilution module (logarithmic/linear gradient generator) or multiple (binary/ternary/quaternary) mixing/serial-dilution module. The chip allows the processes of chemical liquid dilution and diffusion, micro-scale microalgal culture, cell stimulation and online screening, to be performed in a single device. We critically discussed design strategies and evaluated the microfluidic performance by colorimetric analysis. To examine whether these microfluidic chips can be used for toxicity test on microalgae, biocompatibility and culturing capability of microalgal cells were investigated. Also, a single serial concentration gradient of heavy metal (Cu) and substituted benzenes (benzene, chlorobenzene, nitrobenzene, and methylbenzene),

as well as combining serial concentration

gradients of the above-mentioned benzenes, were generated. Their toxicity effects on multibiological traits of cell division rate, autofluorescence, esterase activity, and mobile capacity of microalgae were examined online. In all cases, a dose-related toxic response was successfully


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detected. These initial results will provide a route for deployment of the microfluidic dilution network-based devices, especially complex ones for the study and quantification of aquatic toxicity on microalgae, while providing a high-throughput platform for lab-on-a-chip based screening experiments in the cell-based quantitative assessment of environmental health risks. EXPERIMENTAL SECTION

Design principle and procedure.

Electricity was understood as a kind of fluid and vice

versa.29,30 Ohm’s law (V=IRE) corresponds to Hagen–Poiseuille’s law ( Δp= Q RH), where the pressure drop (Δp) is analogous to the voltage drop (V). The volumetric flow rate (Q) to the current (I).The hydraulic resistance (RH) to the electric resistance (RE). Therefore, the relative values of current and resistance in electrical circuits can be used as the volumetric flow rate and channel length unit in the equivalent microfluidic circuits. The rough draft of a serial dilution network is portrayed in the supporting information. It includes N cascaded-mixing stages that mix two solutions to each output port with the desired flow rate and output concentration gradients in a stepwise manner (Figure s1). We draw the configuration-like electrical circuit using the OrCAD Capture. The schematic capture exports netlist data to the simulator. The simulation program models the behavior of the circuit and output the data of the current and electric components (e.g. resistance) which can be used to test and refine the design before implementing on hardware. As the network complexity increases, equivalent circuit theorem and circuit partition were innovatively introduced for a further minimizing of design effort. Based on the Thévenin’s circuit and Substitution circuit, a linear flow network can be split into interconnection of two subnets through the connecting terminals.31,32 And then we can integrate the two sub-parts after their separately calculating using OrCAD software. The Design principles and working procedures are described in details in the Supporting information (Figure s2 and s3). Microfabrication and verification of the microfluidics. The devices consisted of several PDMS layers with the microfluidic circuits were fabricated by soft lithography. The detailed fabricating procedure was described in the Supporting information. The microfluidic performance was evaluated the by colorimetric analysis. All the measurement procedures and characterization results (Figure s4 and s5) were also available in the Supporting information.



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Microalgal culture on a chip. To examine whether the microfluidics can be used for toxicity test on microalgae, culturing capability of microalgal cells was investigated as previously described 33.

Microalgae in a logarithmic phase (>104 cells ml-1) were incubated and cultured in a microfluidic

device. Three different strains of marine unicellular microalgae, including a green microalgae Platymonas subcordiformis (chl-6, KLEMB, IOCAS), a diatom Phaeodactylum tricornutum (bac-2, KLEMB, IOCAS), and a red microalga Porphyridium cruentum (rho, KLEMB, IOCAS) ( Reserved by

Key laboratory of experimental marine biology, the Institute of Oceanology, Chines Academy

of Sciences) were used in this research. The images of the mircoalga were captured by CCD camera under Olympus IX-71 inverted fluorescence microscope.

Cell numbers and fluorescence intensity

were measured by Image-pro plus version 6.0. The bulk cultured microalgal cells in glass flasks were included as a control. The growth of microalgal in microfluidic and in flask were modeled in the Richards function as equation: 1

( ) 𝑦 = 𝑎[1 + (𝑑 - 1)e -𝑘 𝑥 - 𝑥𝑐 ]1 - 𝑑

Where y is the concentration of cells at day x (104 cells ml-1) and a, k, d, and xc are mathematical parameters. The parameter xc is inflection point of the curve, a is an asymptotic phase (104 cells ml1)

while d is a shape-related parameter. In biological terms, xc represents duration(d) of lag phase, k

is the maximum intrinsic rate of the population (1/d), and a is the environment carrying capacity. Microagal toxicity test on a chip. P. subcordiformis and P. cruentum were chosen as test species. The F/2 medium supplemented with copper and four substituted benzenes were used as tested pollutants. The detail information of tested chemicals is listed in the Table S1 (see in the Supporting information) Single toxicity test. 2-fold logarithmic gradient devices were used to perform pre-test to make sure of the minimum concentration of a compound to obtain 100% of its effect, i.e.EC100. P. subcordiformis and P. cruentumin logarithmic phase with densities of >105 cells ml-1 were seeded into culture chambers of the microfluidic device by pipetting. The single effects of copper and benzenes were determined on a chip. The test traits for copper were cell mobile capacity (2h treatment), growth inhibition (72h treatment), cell autofluorescence (72h treatment), and esterase activity (72h treatment). The test trait for benzenes was cell mobile capacity (4h treatment). Chemicals were added with various concentrations (Copper 23.2μmol l-1, Benzene 114mmol l-1,


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Methylbenzene 150mmol l-1, Nitrobenzene 11.4mmol l-1, and chlorobenzene11.4 mmoll-1, respectively) to test the EC100. The group of test concentrations and EC100 values were shown in Table s2 (in the Supporting information). Linear gradient devices were used to perform formal single toxicity test to make sure of the concentration of a compound to obtain 50% of its effect, i.e. EC50. P. subcordiformis and P. cruentumin at logarithmic phase (>105cells ml-1) were inoculated into culture chambers of device. Copper, Benzene, Methylbenzene, Nitrobenzene, and Chlorobenzene were added with the concentration of 5.8μmol l-1, 57 mmol l-1, 35 mmol l-1, 5.7 mmol l-1, and 5.7 mmol l-1, respectively. The groups of test concentrations were shown in Table s2. The EC50 values were calculated from the Hill model using the percentage data of motility inhibition, relative apparent biomass increase rate (Va), chlorophyll a fluorescence and FDA fluorescence: 𝑓(𝑥) = 𝑓0 - (𝑓𝑖𝑛𝑓 - 𝑓0)

𝑥𝐻 𝑥𝐻 + 𝐸𝐶𝐻 50

where f(x) represents the relative biological effect; f0 and finf are the boundaries for 0 and infinite chemical concentration (μmol l-1). The Hill number H and EC50 are characteristics of the relative biological effect f(x). Toxicity of chemical was expressed by EC50 , the concentrations of chemical (x) which reduce relative motility, biomass, chlorophyll a fluorescence, and FDA fluorescence by 50%, when compared to the control. Data are presented as mean ± SD. Joint toxicity test. The multiple mixing serial dilution systems were used to perform joint toxicity test to test the EC50 values of the mixture. P. subcordiformis in a logarithmic phase with densities of >105cells ml-1 were seeded into culture chambers of the microfluidic device by pipetting. Equitoxic mixtures of benzenes (toxicity ratio of 1:1, 1:1:1, and 1:1:1:1) were prepared according to the toxic unit (TU) concept, using previously determined EC50 values using mobile inhibition as an ecological trait. These were calculated according to Making and Dawson (1975) as followed and the groups of test concentrations of the binary/ternary/quaternary mixture were shown in Table s3 (see in the Supporting information). 34



TUi =

TR =

Ci EC50,i C1

,

C2

EC50,1 EC50,2

… ,

Cn EC50,n

n

TU =

∑TU

i

i=1

Where TR is the toxicity ratio, Ci is the exposure concentration of the chemical (mmol l-1) in the mixture, and EC50,i is the toxic concentration for the respective component chemicals of the mixture, from 1 to n. To determine the effect of any given chemical mixture, the sum of toxic contributions (M) was calculated as, n

M=

[Ci]

∑EC

i=1

50, i

Where [Ci ] is the concentration of each chemical (mmol l-1) in the mixture causing 50% effect. The joint toxicity can then be predicted by M, additive Index (AI), and mixture toxicity index (MTI). When M occurs at TU values =1, 1, the mixture is exhibiting simple addition, synergism, and antagonism. The AI was extracted as described by Marking (1977).35 When M value was=1, 1, AI value was = M-1, (1/M)-1, or M(-1)+1. AI< 0 represents antagonism, AI=0 simple addition and AI>0 indicates synergism. The MTI is determined using methodology originally described by Könemann (1981), according to the equation, MTI = 1 – log M/log M0, where M0 = TU/TUmax (TUmax is the largest TUi value in the mixture).36 Simple addition is characterized by MTI = 1. MTI < 0 represents antagonism, 0 < MTI < 1partial addition, MTI > 1 synergism and MTI = 0 indicates independent. Test methods of ecological traits. Cell mobility test. A 2-4 hour treatment time was applied to this trial. We inspect the cell mobility on chip directly with the bright-field microscope. Thirtysecond video clips (25 frames per second) of motile microalgae were captured using NikonD5100 camera at 100× magnification. Percentage of motile cells (MOT%) was calculated as the number of motile algae divided by the total number of algae in the field of view. Movement tracking was


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performed

using

the

manual

tracking

function

of

Image-J

(Daniel

Marsh,

http://rsb.info.nih.gov/ij/plugins/avi-reader.html). The parameter calculation of curvilinear velocity (VCL), average path velocity (VAP), and straight line velocity (VSL) was conducted according to the algorithm introduced by Wilson-Leedy and Ingermann. 37 Growth inhibition.

Microalge were treated by chemical for 72 hour. Images were captured

using a bright-field microscope (100×) coupled Nikon D5100 camera. The cell numbers were counted by Image-pro plus. For each toxicity test, growth inhibition was set as the endpoint and calculated as an apparent biomass increase rate: Va =

X 2 - X1 Δt

where Va represents the apparent biomass increase rate (104cells ml-1d-1), X1 and X2 are the biomass at the beginning and at the end, respectively (104cells ml-1), and Δt the duration of the test(d). Cell viability test. Microalge were treated by chemical for 72 hour in these trials. Cell viability of microalge treated by chemical was assessed by applying two indicators (esterase activity and cell autofluorescence). Cell autofluorescence derived from Chlorophyll red fluorescence was collected in P. subcordiformis (Ex : 510–550 nm, Em:>590 nm). P. cruentum were stained using fluorescein diacetate (FDA, Sigma-aldrich ) and then fluorescein derived FDA fluorescence were collected (Ex : 470–495 nm, Em :510–550 nm). To stain the cells, FDA solution of 20μg ml-1 was injected into growth chambers using syringe pump and incubated for 15 min prior to imaging. All imaging were captured with Olympus IX-71 inverted fluorescence microscope interfaced with a CCD camera. The fluorescence intensities were measured by Image-pro plus. RESULTS AND DISCUSSION

Chip design. By incorporation of dilution networks and micro-scale diffusible culture elements into a single microfluidic chip, it enables large scale of dose-response experiments quantified with multi-biological response measurements. Thus, high-throughput toxicity bioassay can be performed in a simple way. Electric circuit method with the aid of OrCAD software was used to design the microfluidics which can generate chemical gradients listed in Table s2 and s3. These chips



manipulate small volumes fluid precisely in pressure-driven microfluidic networks to achieve microalgal assay procedures including pre-test, single toxicity test or joint (e.g. binary and multiple mixtures) toxicity screening. Their design patterns with the database for manufacturing configured by OrCAD software were shown in Figure 1-3, where the voltage, current and electric resistance can be used to prescribe the relative values of pressure, volumetric flow rate, and hydraulic resistance (channel length) in microfluidic networks. Figure 1. The equivalent electrical circuits with the database for manufacturing (a) 2-fold log and (b) linear that is used as a guide for designing the microfluidic serial dilution networks (c) 2-fold log and (d) linear, in which each electrical resistor and current is represented as a relative value of channel length and volumetric flow rate. The directions of all currents flowing through every electric resistance were in accordance with those of fluids traveling in each channel. Figure 2. The equivalent electrical circuits (a) with the database for manufacturing of (b) binary mixing dilution network, in which each electrical resistor and current is represented as a relative value of channel length and volumetric flow rate. The directions of all currents flowing through every electric resistance were in accordance with those of fluids traveling in each channel (c) A binary mixing unit forms three combinational fluid sources for further dilution with buffer, A+B, A+C and A+D for four samples A, B, C, and D. Figure 3. The equivalent electrical circuits (a) with the database for manufacturing of (b) multiple mixing dilution network, in which each electrical resistor and current is represented as a relative value of channel length and volumetric flow rate. The directions of all currents flowing through every electric resistance were in accordance with those of fluids traveling in each channel. (c) A multiple mixing unit with five combinational fluid sources for further dilution with buffer, A+B+C+D, A+B+C, A+B+D, A+C+D, and B+C+D for four samples A, B, C, and D.

Using hydraulic–electric circuit analogy is extremely advantageous in designing complex microfluidic networks with large numbers of one dimensional (1D) long and straight microchannels.23,24 However, OrCAD electric circuit simulator only gives the relative values or dimensions rather than absolute values of flow rate and channel length. To achieve the design goal, we performed an analysis of the effect of input flow rates on mixing. Figure s6 shows the results of CFD-ACE simulations for the 2-fold and linear gradients


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In the serial dilution, concentration errors in each stage propagate to the next level so that full mixing in each stage is essential. The minimum length of the mixing channels required to achieve the full diffusional mixing was roughly estimated by LD=Q/Dd for a straight square channel, where Q is volumetric flow rate (μl min-1) in the mixing channel and diffusion coefficient of molecules (m2s-1).17 As the input flow rates increased, more length of LD were needed. When flow rate was lower than 0.0034m s-1, 10mm of length was sufficient. However, when flow rate increased to 0.0051m s-1, the 10mm length was not enough (FigureS6a, b, and c). We employed the mixing channel of 10mm for aesthetic and layout requirements. We adopt 0.0025m s-1 (1.5μl min-1 with a cross-sectional area of 100 μm×100 μm), a flow rate of lower than 0.0034m s-1, for full mixing. Though those effects are not usually significant in most microfluidic devices, as long as the hydraulic pressure drop across a channel is simply a function of a flow rate and a channel length.38 Figure s6f shows the simulation results for the linear and 2-fold log concentration gradients. The generators produced concentration gradients precisely for two types of gradients. In our final design, we use the 10 mm mixing channel with a maximum flow rate of 1.5 μl min-1 in the mixing channel (signed with black square frame, Figure s6e) which was corresponding to the maximum current 1.75A, 3.5A, 2.5A and 2.5A flow through the Rm in the equivalent circuit of 2-fold, linear, binary subsequent dilution circuit and multiple subsequent dilution circuit, respectively. (Figure 1-3). Thus, the mixing channel with 10mm length was sufficient for total full mixing on the chip. According to the length and flow rate of the maximum mixing channel, the values of all other channel length and flow rates in the dilution networks have been deduced. Microfluidic culture. When testing microalgae toxicity on the microfluidic chip, microalgae growth is important,

since the maintenance of cultures under laboratory conditions must be

accurately standardized to make sure tests qualities , especially in ecotoxicological studies.39 We studied three microalgae in this research. They are common species used to understand the behavior of various microalgae species (Chlorophyta, Bacillariophyta, and Rhodophyta) and different biological features (i.e. mobile, relatively easy to adherent and suspended). The microalgae cells cultured in glass flasks were used as the control. The growth of the microfluidic cultured microalgae



were monitored. The representative images were presented in Figure s7a-c (see Supporting information). Figure s7 a, b, and c, illustrated that microalgae cells’ significant augment from the start to the endpoint of the test. The isolated cell format was maintained well in microfluidic device. It is critical for microalgae growth and uptake of nutrients. The growth pattern of the microalgae cultured in the microfluidic device and Erlenmeyer glass flasks were studied using the Richards model. Both showed a sigmoidal curve and have good agreement between experimental data and predicted values simulated by Richards model. The values of parameters calculated by Richards model, such as environment carrying capacity a, maximum biomass growth rate k, lag phase time and shape parameter xc and d, from microfluidic culture were very close to that from conventional culture. The R2 values approximately equal to 1. These results implied that both microfluidic growth pattern of the microalgae colony and the micro-scale environmental conditions constructed in the device were comparable to those of conventional format. Toxicity test on a chip. To provide an integrated device towards high-throughput routine bioassays with microalgae, toxicity tests of typical aquatic pollutants of heavy metal Cu and benzenes were demonstrated in the microfluidics. Also, quantitative analyses of the 2-4h mobile viability, 72h microalgal growth inhibition, chlorophyll a fluorescence and cell viability were performed. Single toxicity test. 2-fold logarithmic gradient devices were used to perform pre-test to make sure of EC100. Linear gradient devices were used to perform a formal single toxicity test. The chemical concentrations were tested according to EC100 values. Swimming patterns of P. subcordiformis, tetra-flagellate green algae, versus chemical concentrations representatively appeared at Figure4a. Each black path corresponds to a single trajectory. Platymonas cells ran with a speed of (180.75±10.24) μmsec-1 and responded to a range of pollutants by swimming ability and tuning frequencies.40 The degrees of motility inhibition versus toxicant concentrations were well fitted by the Hill equation with R2 values ranging from 0.917 to 0.992 (Figure 4b). The impacts of copper on microalgal apparent biomass increase rate, cell autofluorescing, and esterase activity in


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the microfluidic device were shown in Figure 5. When P. subcordiformis were exposed to increasing Cu concentrations for 72h, the relative values of Va and chlorophyll a fluorescence showed a significant decrease (Figure 5a1 and a2). This indicates that increasing Cu metal concentrations affected microalgal cell growth and division. The decrease in chlorophyll fluorescence should be induced by the inhibition of the electron flow in the PSII reaction centers.41,42 Cell esterase activity was measured for P. cruentum after 72h copper exposure (Figure 5a3). FDA fluorescence decreased in a concentration-dependent manner (see in Figure 5b3) due to the inhibition of the esterase activity.43 Figure 4. Motility inhibition test on chip (a) Representative images of swimming patterns and (b) The degrees of motility inhibition: MOT%, VCL%, VAP%, VSL% of P. subcordiformis versus (1) copper concentrations (μmol1)

, (2) Benzene concentrations, (3) Methylbenzene concentrations, (4) Nitrobenzene concentrations, and (5)

Chlorobenzene concentrations. The curves indicate the results of modelized analysis using the Hill model. The EC50 values were calculated from the fitted equation. Figure 5. Single toxicity test on a chip. (a) Representative images and (b) the relative value of biological response: (1) relative apparent biomass increase rate (Va), (2) Chlorophyll a fluorescence in P. subcordiformisand (3) FDA fluorescence in P. cruentum versus copper concentration (μmol l-1)after 72 h copper exposure. The curves indicate the results of modelized analysis using the Hill model. The EC50 values were calculated from the fitted equation.

The EC50 values of test chemicals calculated from the fitted Hill model (Figure 4b and Figure 5b) based on varies ecological traits are summarized in Table 1. According to the EC50 values, similar sensitivity was found for motility inhibition (2.90-3.52 μmol l-1) and Va (3.21μmol l-1) of P. subcordiformis to copper. Their sensitivities were slightly higher than that calculated from chlorophyll a fluorescence (4.13 μmol l-1). The toxicity order of benzenes calculated form mobility inhibition is nitrobenzene ≈ chlorobenzene > methylbenzene > benzene. This order was also reported by many other researchers using different ecological trait.44-46 These results showed that atomic H of benzene substituted by different groups resulted in different toxicity, because benzene ring structures changed by different groups produced different toxic mechanisms. The toxicities of groups are in decreasing order of -NO2>-Cl > -CH3 > -H. It is well known that non-specific toxicities of



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chemicals can be described by two kinds of actions, non-polar narcosis, and polar narcosis. Nonpolar narcotic chemicals are considered baseline toxicants. Their toxicities are proportional to their concentrations at the site of action and are caused solely by membrane perturbation.According to 4 h EC50 values and poisoning symptoms, benzene, methylbenzene, and chlorobenzene were divided into non-polar narcotic chemicals. The octanol-water-partitioning coefficient (log Kow) for them were 2.15, 2.73 and 2.84, respectively, which verified the toxicity order of these three pollutants. Nitrobenzene has polar anesthetic toxicity, exhibiting toxic potency higher than that estimate by their hydrophobicity (log Kow=1.86) due to the existence of polar substituent in the molecules. The polar substituent on benzene ring produces toxicity through lip soluble action during transportation and interaction with receptors, and can further increase toxicity.Taking into account all the motility parameters, they can be grouped into two categories, percentage of motile (MOT) and movement velocity (VCL, VAP, and VSL). The movement velocity seems to provide more sensitive EC50 results, which could be an important aspect of environmental toxicity measurement.

Table 1. Single toxicity of typical pollutants (copper and benzenes) on various biological response parameters in microalgae using the microfluidic device Chemicals

EC50 Calculated

Organism/Test used

Biological trait

3.52a, 3.22b, 3.10c, 2.90d (μmol l-1)

P. subcordiformis

Motility inhibition

3.21(μmol l-1)

P. subcordiformis

Va

4.13(μmol l-1)

P. subcordiformis

Chla fluorescence

3.93(μmol l-1)

P. cruentum

FDA fluorescence

Benzene

19.34a, 15.20b, 14.76c, 15.31d (mmol l-1)

P. subcordiformis

Motility inhibition

Methylbenzene

12.44a, 9.41b, 9.58c, 7.9d (mmol l-1)

P. subcordiformis

Motility inhibition

Nitrobenzene

3.19a,

l-1)

P. subcordiformis

Motility inhibition

Chlorobenzene

2.94a, 2.08b, 1.98c, 2.77d (mmol l-1)

P. subcordiformis

Motility inhibition

Copper

aBased

on MOT;

bBased

1.94b,

on VCL;

2.29c,

cBased

2.32d

on VAP;

(mmol

dBased

on VSL

Joint toxicity test. Contaminants rarely occur in the environment as single compounds but rather as mixtures of different substances. As the potential for multiple chemical exposure increase, the environmental effects of mixed chemicals need to be carefully considered. In this study, on-chip joint toxic effects of substituted benzenes were analyzed and qualified using TU, AI and MTI based


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on the EC50 values calculated form motility inhibition (VCL%). (Table2).The simple addition of single toxicity could be determined from M=1 and AI=0; however, it is an ideal situation and not easy to reach. Broderius et. al (1990) concluded that joint toxic effects can be defined as the simple addition of single toxicity with M equal to 1±0.2.47 Form Table 2, the M and AI are ranging from 0.87 to 1.02 and -0.02 to 0.15, respectively, which indicated that all the tested binary and multiply substituted benzenes presented the simple addition of single toxicity. MTI results with MTI ranging from 0.97 to 1.2 were close to simply additive toxicity which was generally the same as M and AI results indicating. The tested chemicals are structurally similar, especially benzene, methylbenzene and chlorobenzene have a common site of action and same toxicity mechanism. Consequently, each component in the mixtures acts like a dilution of the other and can be replaced by the other without changing the overall toxicity. 48Given this, additivity between these chemicals is reasonable. This combining result was also reported by many other researchers using various indicator species and physiological effects.44-46 Table 2. Joint toxicity effects of benzenes on microalgal motility (VCL%) Substitute benzene Single toxicity test A (benzene) B (methylbenzene) C (nitrobenzene) D (chlorobenzene) Binary mixing test I A+B A+C A+D Binary mixing test II D+A D+B D+C Multiple mixing test A+B+C+D A+B+C A+B+D A+C+D

EC50(mmol l-1)

M

AI

MTI

1.02 0.89 0.93

-0.02 0.12 0.07

0.97 1.16 1.10

0.93 0.93 0.97

0.07 0.07 0.03

1.10 1.10 1.04

0.99 0.97 0.90 0.87

0.01 0.03 0.11 0.15

1.01 1.03 1.10 1.13

15.20 9.41 1.94 2.08



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B+C+D

0.85

0.18

1.14

CONCLUSION

This paper describes the development and implementation of a microfluidics series for microalgal biotests. The chip mainly consists of upstream dilution network, which could be either single

serial

dilution

module

(logarithmic/linear

gradient

generator)

or

multiple

(binary/ternary/quaternary) mixing serial dilution module, and downstream diffusible culturing module. It allows the multi-processes, namely chemical liquid dilution, diffusion, micro-scale microalgal culture, cell stimulation, and online screening, to be integrated into a single device. The equivalent circuit theorem and circuit partition, with the aid of EDA simulation,

were used

to

rapid design of microfluidic serial dilution network. This reduction and integration method can dramatically decrease the complexity of calculation and accelerate the iterative process. Thus, a complicated network can be simulated and designed in a most efficient way with a minimized error. Together with the fast, simple and inexpensive nature of the microchip fabrication, our devices can become truly disposable microfluidic platforms. Using these devices, pre-test, conventional single toxicity test, binary and multiple toxicity tests were all successfully performed. The design and application of our devices represent a solid step towards the development of a promising highthought alternative for routine microalgae bioassays and they can be applied to other cell-based screening experiments in the quantitative assessment of environmental health risks.

ASSOCIATED CONTENT

Supporting Information  The Supporting Information is available free of charge on the ACS Publications website at DOI: XXXXXXXX


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Microfluidics design principle and procedure; Microfabrication and verification (including fluorescence measurement of dilution network, Fluorescence measurement of diffusion chamber, fluorescence measurement results). (PDF)

AUTHOR INFORMATION Corresponding Author *E-mail: [email protected]. Phone (work): 86 - 411- 87402358 Fax: 86 - 411 – 87402059

Notes The authors declare no competing financial interest.

ACKNOWLEDGMENTS We thank Dr. Xingcai Zhang from Harvard University for his proof reading and discussion on the manuscript. This work was supported by National Natural Science Foundation of China (No. 41476085, No. 81471807), Scientific Research Project of Liaoning Education Department (LJQ2015005) and Dalian InnovationProject forTalents (2015R087).

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For TOC only




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Figure 1. The equivalent electrical circuits with the database for manufacturing (a) 2-fold log and (b) linear that is used as a guide for designing the microfluidic serial dilution networks (c) 2-fold log and (d) linear, in which each electrical resistor and current is represented as a relative value of channel length and volumetric flow rate. The directions of all currents flowing through every electric resistance were in accordance with those of fluids traveling in each channel. 167x220mm (300 x 300 DPI)



Figure 2. The equivalent electrical circuits (a) with the database for manufacturing of (b) binary mixing dilution network, in which each electrical resistor and current is represented as a relative value of channel length and volumetric flow rate. The directions of all currents flowing through every electric resistance were in accordance with those of fluids traveling in each channel (c) A binary mixing unit forms three combinational fluid sources for further dilution with buffer, A+B, A+C and A+D for four samples A, B, C and D. 367x409mm (300 x 300 DPI)


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Figure 3. The equivalent electrical circuits (a) with the database for manufacturing of (b) multiple mixing dilution network, in which each electrical resistor and current is represented as a relative value of channel length and volumetric flow rate. The directions of all currents flowing through every electric resistance were in accordance with those of fluids traveling in each channel (c) A multiple mixing unit forms five combinational fluid sources for further dilution with buffer, A+B+C+D, A+B+C, A+B+D, A+C+D and B+C+D for four samples A, B, C and D. 398x523mm (300 x 300 DPI)



Figure 4. Motility inhibition test on chip (a) Representative images of swimming patterns and (b) The degrees of motility inhibition: MOT%, VCL%, VAP%, VSL% of P. subcordiformis versus (1) copper concentrations (μmol-1) , (2) Benzene concentrations, (3) Methylbenzene concentrations, (4) Nitrobenzene concentrations and (5) Chlorobenzene concentrations. The curves indicate the results of modelised analysis using Hill model. The EC50 values were calculated from the fitted equation. 516x314mm (300 x 300 DPI)


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Figure 5. Single toxicity test on chip. (a) Representative images and (b) the relative value of biological response: (1) relative apparent biomass increase rate (Va), (2) Chlorophyll a fluorescence in P. subcordiformis and (3) FDA fluorescence in P. cruentum versus copper concentration (μmol l-1) after 72 h copper exposure. The curves indicate the results of modelised analysis using Hill model. The EC50 values were calculated from the fitted equation. 268x172mm (300 x 300 DPI)


Development of Microfluidic Dilution Network ... - ACS Publications

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