Microfluidic Platform for Studying Chemotaxis of Adhesive Cells

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A microfluidic platform for studying chemotaxis of adhesive cells revealed a gradient-dependent migration and acceleration of cancer stem cells Heng ZOU, Wanqing Yue, Wai-Kin YU, Dandan LIU, Chi-Chun Fong, Jianlong Zhao, and Mengsu Yang Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.5b00873 • Publication Date (Web): 19 Jun 2015 Downloaded from http://pubs.acs.org on July 6, 2015

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Analytical Chemistry

A microfluidic platform for studying chemotaxis of adhesive cells revealed a gradient-dependent migration and acceleration of cancer stem cells Heng Zou†,1,2, Wanqing Yue†,1,2, Wai-Kin Yu1, Dandan Liu1, Chi-Chun Fong1,2, Jianlong Zhao3, Mengsu Yang1,2,*

1

Department of Biomedical Sciences, City University of Hong Kong, 83 Tat Chee

Avenue, Kowloon, Hong Kong SAR, People’s Republic of China 2

Key Laboratory of Biochip Technology, Biotech and Health Centre, Shenzhen

Research Institutes of City University of Hong Kong, Shenzhen, People’s Republic of China 3

State Key Lab of Transducer Technology, Shanghai Institute of Microsystem and

Information Technology, Chinese Academy of Sciences, Shanghai 200050, China.

† These authors contributed equally to this work.

* Corresponding author: Prof. Mengsu Yang, Department of Biomedical Science, City University of Hong Kong, Telephone: (852) 3442 7797, Fax: (852) 2788 7406, Email: [email protected]

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Abstract

Recent studies reveal that solid tumors consist of heterogeneous cells with distinct phenotypes and functions. However, it's unclear how different subtypes of cancer cells migrate under chemotaxis. Here, we developed a microfluidic device capable of generating multiple stable gradients, culturing cells on-chip, and monitoring single cell migratory behavior. The microfluidic platform was used to study gradient-induced chemotaxis of lung cancer stem cell (LCSC) and differentiated LCSC (dLCSC) in real time. Our results showed the dynamic and differential response of both LCSC and dLCSC to chemotaxis, which was regulated by β-catenin dependent Wnt signaling pathway. The microfluidic analysis showed that LCSC and dLCSC from the same origin behaved differently in the same external stimuli, suggesting the importance of cancer cell heterogeneity. We also observed for the first time the acceleration of both LCSC and dLCSC during chemotaxis caused by increasing local concentration in different gradients, which could only be realized through the microfluidic approach. The capability to analyze single cell chemotaxis under spatially controlled conditions provides a novel analytical platform for the study of cellular microenvironments and cancer cell metastasis.


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Chemotaxis is an important component during tumor progression and metastasis. Cancer cell chemotaxis is induced by cytokines and growth factors in the tumor microenvironment and sensed by cancer cells via receptors located on the cell membrane.1 The highly complex and dynamic micro-cellular environment, as the results of co-existing multiple cell types, different extracellular matrices (ECM), and mechanical stress, generated different biochemical gradients.2,3 Biochemical gradients affect cancer cell chemotaxis through change of cellular morphology, modulation of migration rate, and regulation of signaling cascades and gene expression. Recent research has identified cancer stem cells, which play a critical role in tumorigenic process, and provide one explanation for the phenotypic and functional heterogeneity among cancer cells in tumors.4 The model suggests that some cancers are organized into a hierarchy of subpopulations of tumorigenic cancer stem cells and their nontumorigenic progeny.5 In these cases, cancer stem cells are thought to be the key culprit in tumorigenesis with chemo and radio-therapy resistant and metastatic properties. The lack of therapeutic advances is partly due to the incomplete understanding of the roles of cancer stem cells in cancer cell metastasis and invasion.6 For example, it is known that Wnt signaling pathway is involved in the regulation of cancer cell adhesion, migration and differentiation,7-9 but how the Wnt signaling pathway regulates the chemotaxis of cancer cells and cancer stem cell is unclear. Therefore, quantitative study of cancer stem cell and normal cancer cell chemotaxis induced by multiple gradients will improve our understanding on the vital and initial steps of tumor metastasis in tumor microenvironment and provide insight for the development of new therapeutic interventions targeting tumor metastasis.



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In cancer research, the study of chemotaxis has been hindered by the poor gradient control and high reagent consumption using traditional methods which were established half a century ago.10-12 Microfluidics technology has been developed as an advanced

bioanalytic

manipulation,16,17

tool

which

enables

the

precise

flow13-15

and

cell

single cell stimulation and analysis,18-20 and reconstruction of

cellular microenvironment.21-23 As controllable concentration gradients can be generated inside microstructures for real-time quantitative measurements with low reagent consumptions,24-26 several microfluidic devices have been proposed for the study of cancer cell biology including chemotaxis.27-31 For example, the well-known Christmas-tree microfluidic structure can generate different concentration gradients with the stair-shaped and polynomial gradient profiles.23,32 However, the use of this structure for cell migration study is limited due to the relatively large shear force, which may have undesired effects on cell migration and morphology.33 The passive diffusion concentration generator has been used to study cell invasion under linear gradients with a low shear force,34 but the diffusion based gradient generator has the limitations of long establishment time and low gradient stability. Long term stable gradient can be achieved by using digital pump to keep the continuous flow running and avoid concentration depletion,35 but most microfluidic studies on cell chemotaxis were either conducted in a single concentration gradient or in discontinuous concentrations, which over-simplified the cancer cell migration rate into an average value.35-39 In fact, continuous and dynamic changes of the concentrations of nutrients, cytokines and chemokines are involved in the process of cancer initiation, progression and metastasis in vivo. Therefore, there is a need to develop a microfluidic platform capable of generating continuous concentration gradient chip with low shear rate and long-term stability for the real-time monitoring of cell migration.


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Herein, we report a microfluidic network in which multiple continuous and linear gradients were attainable for long-term monitoring of cell migratory behaviors. We examined the chemotactic migration of individual cancer cells, using lung cancer stem cell (LCSC) and differentiated lung cancer stem cell (dLCSC) as the model system, under multiple serum gradient profiles. We also investigated the role of β-catenin signaling, an important component of the Wnt signaling pathway, in the regulation of cancer stem cell chemotaxis. The microfluidics platform presented may be readily applicable to study cancer heterogeneity under chemotaxis in complex tumor microenvironments.

EXPERIMENTAL SECTION Cell culture. Lung cancer stem cells (LCSC, cat# 36107-34P, CELPROGEN®, USA) and differentiated lung cancer stem cells (dLCSC, 16th passages of LCSC) were cultured in DMEM/F12 (1:1, cat# 11039-021, GIBCO®, USA) in the incubator with 5% CO2 at 37 (CO2 water-jacketed incubator, Nuaire®, USA). The LCSC and dLCSC were distinctive phenotypes in term of the morphology, and the presence of specific expression of stemnessrelated surface proteins. The culture mediums were all supplementary with 10% fetal bovine serum (FBS) and 1% penicillin-Streptomycin (10000 Units/mL of Pennicillin and 10000 µg/mL of Streptomycin, Life technologies, USA).

Flow cytometry. To examine the distinct stemness-related surface proteins of LCSC and dLCSC, cells were detached and washed once with 0.5% BSA in phosphate buffered saline (PBS), and incubated with primary anti-human Oct4 FITC conjugated (Invitrogen), anti-



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human ABCG APC conjugated (Invitrogen) and anti-human N-cadherin antibody FITC conjugated (BD) for 60 min at room temperature. And then measure by flow cytometry.

Tumor sphere formation. LCSC and dLCSC were washed with PBS, then suspended in the tumor sphere culture medium, which contains serum-free DMEM/F12 supplemented with 1% penicillin-Streptomycin, 20 ng/ml human recombinant epidermal growth factor (EGF), 10 ng/ml human recombinant basic fibroblast growth factor (bFGF), 2% B-27 supplement without vitamin A, 1% N2 supplement (Invitrogen, Carlsbad, CA, USA). The cells were subsequently cultured in non-surface treatment 6-well plates (Corning, NY, USA) at a density of 4000 cells/well for 20 days in the tumor sphere culture medium.

Non-scratching wound healing assay. Non-scratching wound healing assay was performed in the Ibidi culture insert (Ibidi®, Munich, Germany), which consists of two cell culture chambers separated by an interval with the width of 500 µm. 70 µL of cell suspension prelabeled with fluorescent cell tracker (CM-DiI cell labeling solution, Vybrant®, life technologies, USA) at a density of 5 × 105 cells/min 10% serum medium were seeded in each chamber. After cell adhesion and growth for 24 hours, cells were treated with 1 µM XAV-939 (Selleck® USA), a β-catenin inhibitor, in 10% serum medium for 12 hours followed by removal of the insert. In normal condition, fresh medium containing 0%, 2%, 5%, 8% and 10% were prepared, while in drug condition, all the different serum concentration medium were containing 1 µM XAV-939. Cell migration during the chemotaxis was recorded by imaging under the fluorescence microscope coupled with digital camera (Coolpix4500, Nikon®, Japan) using 5× objective after insert removal (0 h) and continuously culturing with 6 hours interval for 24 h. The gap filling percentage at different time points were calculated using the following equation, G cell = At / A0 × 100% , where Gcell is the gap filling percentage, while At and A0 are the gap areas at time t and 0 respectively.(Figure 1D-E) The average cell migration rates were calculated in Table 2. 6 ACS Paragon Plus Environment

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Microfluidic chip fabrication. The microfluidic device was designed based on a combination of the V-shaped geometry26 and microchannel networks27, and fabricated according to the protocol we published previously using soft lithography technique by molding poly(dimethylsiloxane) (PDMS) (10:1 silicone elastomer with curing agent, Sylgard 184, Dow Corning®, Midland, MI.) against a master made of printed circuit board (PCB,75×125×1.6mm,Kinsten® Pty. Ltd.).26 (Supplementary Figure S1) The microfluidic device consisted of two layers molded from the PCB masters etched by ferric trichloride solution (1.56×10-3 mol/ml) for different times: the top layer was etched for 45 minutes for the generation of master with the height of 21 µm, and the bottom layer was etched for 13 minutes such that the height of the microchannels was about 9 µm, respectively. Inlets and outlets on the PDMS replica were drilled by circular holes puncher with the diameter of 1.22 mm. Two PDMS layers were treated by air plasma for 2 minutes (Plasma cleaner/sterilizer, PPC-3XG, Harrick®, NY, US) and bonded together with precise alignment under the microscope.

Scanning electron microscopy. A scanning electron microscopy (SEM) was used to characterize the dimensions and structures of the channels in the top and bottom layers of the microfluidic chip. Briefly, the slices of the top and bottom PDMS layers were cleaned by the ultrasonic device and then mounted on SEM stubs (Electron Microscopy Sciences, Fort, Washington, USA) using conductive adhesive tape (cat# 77817-12, Electron Microscopy Sciences, Fort, Washington, USA ). The PDMS slices were subsequently coated with a gold layer using a Cressington Sputter Coater 208 HR (BAL TEC, SCD050, California, USA). To obtain the vertical structure of the PDMS layer, a 45 degree rotation of the SEM plane was made for a better SEM imaging. SEM images were acquired using the high resolution SEM (Philips, XL30, ESEM FEG, Netherland)



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Generation of concentration gradients. The concentration gradients in the microfluidic device were characterized using Rose Bengal, a fluorescent dye (Dye content 95%, LLC., Sigma-Aldrich®, US) with diffusion coefficient of 7×10-10 m2/s, diluted by deionized water to the concentration of 0.1 mg/ml.40 Two inlets were connected to separate syringes by flexible tubes (PE60, Inner dimension: 0.76 mm, Outer dimension: 1.22 mm, Becton Dickinson®, US): inlet 1 was perfused with dye solution and inlet 2 was perfused with deionized water

(Supplementary Figure S2). The volumetric flow rate of each solution was controlled at 0.8 µl/min using a multichannel digital syringe pump (LSP04-1A, Longer Pump®, US). Series of fluorescence images were captured every 15 s for 2 hours by fluorescence microscope (MEI3000B, Leica®, US) at room temperature (Filter Cube: Cy3, Excitation Filter: BP 545/40; Dichromatic Mirror: 565; Suppression Filter: BP 610/75).

The concentration gradients in the microfluidic device were simulated by COMSOL software (COMSOL Multiphysics 4.0, Burlington, MA, USA). The 3D model was constructed according to the dimension of the microfluidic device listed in Table 1 and composed of grid patterns with 302880 nodes. Two Newtonian fluids were simulated as inlets solution with the flow rate of 1 mm/s, in accordance with the experimental condition. Standard Navier-Stokes equation was solved to simulate the streamlines and no slip condition was imposed at all the boundaries.

Cell loading and trapping. The microfluidic device also incorporated the function for cell trapping and gradient-induced migration. Cancer cells in suspension (diameter ~11 µm) were seeded at a density of 1×105 cell/ml. A 10 µl drop of medium containing the cells with densities of 0, 1×104 , 1×105 , 1×106 , 1×107 cells/mL were loaded in the inlet for cell trapping. The number of cells trapped at the gap between the main channel and the connecting channels depended on the pressure drop in the main channel and the loaded cell density.


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On-chip chemotaxis analysis. Cells were detached and re-suspended at the density of 1×105 cells/ml in fresh medium containing 10% FBS for on-chip cell culture. XAV-939 (1 µM diluted with medium) was used as Wnt/β-catenin pathway inhibitor to selectively inhibit βcatenin-mediated transcription by degrading the intercellular β-catenin levels. As the degradation of β-catenin is a reversible and transient process when treated with XAV-939, LCSC and dLCSC were exposed to the drug throughout the experiments in the drug treatment condition. Prior to cell loading, PDMS chips were pre-loaded with 70% ethanol and sterilized under the UV light for 20 minutes. After sterilization, the chips were washed by PBS twice and incubated with 10 µl of fibronectin (20 µg/ml) for 1 hour. Cells were then loaded in the microfluidic device due to the pressure drop by adding 10 µl of cell suspension in inlet 1 and 5 µl of medium in inlet 2 and the outlet, respectively. After incubation in the medium containing 10% FBS for 24 hours, cells were starved with 0.5% FBS in medium for 12 hours. Finally, serum-free medium from inlet 1 and medium with 10% FBS from inlet 2 were pumped into the microfluidic device at the volumetric flow rate of 0.8 µl/min.

The microfluidic chips were maintained in the incubator and cell images were captured under the microscope at 6, 12, 18 and 24 hours after perfusion, respectively. Cell migration rate in the chip without gradients have been measured at the same time points as control. Cell trajectories were measured from the time-lapse images by Leica Application Suite Advanced Fluorescence software (LAS AF, Leica Microsystems, 3.x, Leica®, German). The normalized gradient in each connecting channel can be simply calculated by

∇CN =

100% Lc

(1)

where ∇CN is the normalized concentration gradient, and Lc is the channel length. The average migration rate v was measured by



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vt1 =

Lt 2 − Lt1 t2 − t1

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(2)

where Lt1 and Lt2 are the cell migration distances at the time point t1 and t2, respectively.

Statistical Analysis. For both conventional assays and on-chip analysis, at least triplicated experiments were performed for each cell line with and without drug treatment. In each condition, at least 50 cells were measured and all quantitative data were presented as means ± s.d.

RESULTS AND DISCUSSION Cancer cells and their migration. Cancer stem cells are identified as a small proportion of cancer cells with self-renewal and differentiation capacity, which may serve as potential targets for cancer therapy.41 It is important to investigate the chemotactic behaviors of cancer stem cells and cancer cells in order to understand the tumor growth and metastasis in vivo. In this study, we used cancer stem cell isolated from small cell lung cancer, LCSC and its differentiated phenotype, dLCSC, as the model for chemotaxis study. Phenotypically, the morphology of LCSC has a cobblestone, epithelial-like shape growing in clusters, while that of the dLCSC has a more fibroblast-like shape growing relatively dispersedly (Figure 1A). Genotypically, LCSC and dLCSC expressed different levels of stemness-related genes and surface protein markers, including

ABCG2, Oct4, and

N-cadherin (Figure

1B).

Tumorigenically, LCSC formed tumor spheres more rapidly in vitro (Figure 1C).


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As the complex tumor microenvironment may have limited nutrients due to uncontrolled proliferation of tumor cells, the concentration gradients of serum and nutrients are the key factors to induce cancer cell migration. We first evaluated the LCSC and dLCSC migration ability by the conventional non-scratching wound healing assay with different serum concentrations (Figure 1D). The migration rates of LCSC and dLCSC at different serum concentration as measured by the non-scratching wound-healing assay were listed in Table 2. The migration rates of dLCSCs at all serum concentrations were greater than that of the LCSC. As Wnt/β-catenin signaling pathway has been implicated to regulate cell migration through the differential expression of β-catenin target genes42, we have used 1 µM XAV-939, a β-catenin inhibitor, to inhibit Wnt/β-catenin signaling pathway. The results showed the serum concentration-dependent migratory ability of LCSC and dLCSC was significantly attenuated by XAV-939 (Figure 1E). The effect of β-catenin on cancer cell migration was clearly demonstrated, showing that the inhibition of Wnt/β-catenin pathway suppressed the migration ability of both cancer cell and cancer stem cells. For the both LCSC and dlCSC with or without XAV-939 treatment, the migration rates increased with serum concentration and plateaued at around 5% serum (Table 2).

Microfluidics for generating multiple continuous gradients. Cancer cells experience different biochemical concentration gradients in vivo during chemotactic migration. The LCSC and dLCSC migration rates measured by the wound healing assay were averaged estimated of populations of cells under a constant concentration, which could not truly reflect the

behaviors of individual cells within

microenvironment including the changing concentration of surrounding factors. We have designed and fabricated a microfluidic device which integrated the functional 11 ACS Paragon Plus Environment


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modules of a well-defined multiple gradient generator and a long-term cell culturing chamber for monitoring the cellular migratory behaviors in real-time (Figure 2A). The device was made by binding two PDMS replicas patterned with channel dimension parameters (Table 1 and Figure 2B-E). The constant flow perfusion controlled by a digital syringe pump kept the concentration in two main channels stable, such that different gradients in the five connecting parallel channels were generated due to the variable diffusion lengths of the connecting channels. The stimulation

results

using

COMSOL

Multiphysics

indicated

that

different

concentration distributions could be generated in each connecting channel (Figure 3A), which also suggested that as there is a gap between the connecting channel and the main channel. The different geometry height also created gradients among different layers of the channels (Figure 3A bottom, where z = 0 µm indicated the bottom boarder of the channel).

The gradient profiles were experimentally characterized in the microfluidic device by perfusion of Rose Bengal dye and deionized water in inlet 1 and inlet 2, respectively. The continuous flow was controlled at the rate of 0.8 µl/min, such that the flow rate in the main channels was 1 mm/s. Concentration gradients in the connecting channels were generated quickly and stabilized by keeping the flow constant (Figure 3B). The simulation results also showed that the gradient generation time is independent of the channel length. For the channel with 450 µm length, the establishment of concentration was showed be to stable within 3 minutes (Figure 3C). Moreover, the normalized concentration distribution in the five parallel connecting channels was measured individually (Figure 3D, colored solid dots). Due to the linear gradient profiles, the gradient in each channel could be normalized (Table 1). In Figure 3, 12 ACS Paragon Plus Environment

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small deviations at the two ends of the channels were observed when comparing the simulated gradient profile (Figure 3D, solid line) with the experimental results (Figure 3D, colored solid dots), which was caused by the channel geometry at two ends as shown in the Figure 3A bottom. We further determined the average concentration distribution within the 11 µm plane in each connecting channel which exhibited a stable linear gradient form (Figure 3D, dash lines), since the adherent cell height was about 11 µm. Therefore, this V-shaped microfluidic networks could generate stable and continuous linear concentration gradients in multiple channels, thus providing a platform for monitoring cell chemotaxis in real-time.

Cell loading and morphological changes in multiple gradients. The microfluidic device

was used to study chemotactic migration behavior of LCSC and dLCSC in different serum gradients. After cell loading, the cancer cells can be easily located by the gap structure along the main channel (Figure 4A). Notably, the size of the LCSC and dLCSC in suspension state was larger than the channel height. Thus the cells could be trapped along the gap structure by the flow pressure. By applying the COMSOL multiphysics software, we have built the fluidic mechanic modeling inside the microfluidic chip during the cell loading. We analyzed the pressure distribution and the velocity distribution within the channels (Figure 4B-D). Another advantage of the microfluidic chip is the low shear force to the chemotaxis cells. The shear rate of the fluid during the cell chemotaxis was simulated by COMSOL and the shear force in the connecting channel was extremely low (Figure 4E) providing a stable cell migration microenvironment. The higher cell loading density, the more cells were be trapped along the connecting channel entrance.(Supplementary Figure S3) After adhesion, the cells height became lower than the height of the gap, allowing the cells 13 ACS Paragon Plus Environment


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to migrate through the gap into the microchannels. During chemotaxis, cancer cell in channel were tracked clearly and observed at different time points by using microscopy imaging for the data analysis (Figure 5A-B).

We found that both LCSC and dLCSC showed dynamic morphological plasticity responding to the different serum gradients. The aspect ratio, which was defined as the ratio of the cell length and the width of the cell towards the gradient source (Figure 5C), was found to increase with migration time (Figure 5D-E), indicating that the cells elongated during chemotaxis. In the channels, both LCSC and dLCSC gradually extended their pseudopodia to sense the chemokines and growth factors towards the higher serum concentration under the low shear force in the connecting channels. On the other hand, in a uniform local concentration, the cancer cells maintained a stable morphology state within the aspect ratio 2. (Supplementary Figure S4) In the presence of β-catenin signaling inhibitor XAV-939, the elongation of the cancer cells was not as significant as that under the normal condition (Figure 5F-G). The relationship of the cell morphology (aspect ratio) and the cell migration rate has been investigated previously,43,44 which suggested a positive relationship between aspect ratio and cell migration rate. This cell morphology changing during cancer cell chemotaxis may be related with the actin filaments and cell skeleton repolarization.45 Cell migration rate can be predicted from its aspect ratio, with more canoe-like cells (large aspect ratio) being expected to move faster.

Chemotactic migration of cancer stem cells. The controlled multiple-gradient microfluidic chip was used to investigate the migratory behavior of LCSCs and 14 ACS Paragon Plus Environment

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dLCSC in different linear serum gradients in parallel. Chemotactic rates were analyzed at single cell level in real time. Our data demonstrated that LCSC displayed a slower migration rate in each gradient than dLCSC, indicating that LCSC were less sensitive to serum gradient stimulation than dLCSC. This is consistent with previous studies that LCSC possessed the inertia properties of quiescence, resistance to apoptosis, and resistance to hypoxia and drug therapies.46,47 Recent evidences also showed that cancer cell chemotaxis under the same gradients varied due to different cancer cell types with heterogeneous expression of growth factor receptors and their ligands.48

The migration distances of individual LCSC and dLCSC were determined in the five different serum gradients under normal condition and with drug treatment after 24 h (Figure 6A-B), respectively. The cell distance on a constant serum concentration without gradient were measured as control (Figure 6C-D and supplementary Figure S5). The control experiments showed that the on-chip migration rates of LCSC and dLCSC depended on serum concentration similar to those measured off-chip (Figure 1E), and that the migration rates were independent of the length of the migration channels (Supplementary Table S1) On the other hand, the chemotactic migration distance of cancer cells was gradient-dependent. Both the LCSC and dLCSC migrated longer distance in higher serum gradient (Figure 6A-B). Under the drug treatment condition, the migration distances were decreased by 70-80% for both LCSC and dLCSC. When compared with normal condition, both LCSC and dLCSC with XAV939 treatment migrated a shorter migration distance in each gradient, which was consistent with the wound healing assay results and confirmed the involvement of βcatenin signaling in cancer chemotaxis regulation.49 15 ACS Paragon Plus Environment


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The chemotactic rates for LCSC and dLCSC of different time points of migration were calculated for each serum gradient. We noted for both cell types under all gradient conditions, the chemotactic rates increased with time in a linear dependent manner (Figure 7A-B), even in the presence of the XAV-939 (Figure 7C-D). The control experiment showed that the migration rates of both cell types remained steady in any particular serum concentration in the absence of gradient (Figure 7E-F). While the gradient in each channel remained constant through the channel length, it was expected that cells would migrated at a constant rate along the chemotaxis. To our surprise, the real-time monitoring of the cancer cell migration in different gradients showed an increasing migration rate for both LCSC and dLCSC under normal condition and drug treatment in all gradients (Figure 7A-D). The acceleration decreased markedly under the drug treatment condition to that under normal condition at each time point. To the best of our knowledge, this is the first time the acceleration was observed for cell undergoing chemotactic migration. In most of the gradients the acceleration appeared to be constant (The slopes in Figure 7A-D), suggesting that the cells migrated faster and faster along the chemotactic gradients. We also noted that the acceleration was gradient-dependent, i.e. the acceleration increased with increasing gradient (Figure 7G-H). We have also re-plotted the cell migration rates at different serum concentration in each channel (which could be derived from the gradients and the specific locations in the channels), and present in the supplementary Figure S6 compared to the control (constant serum concentration without gradient) showing the acceleration of LCSC and dLCSC during chemotaxis. The dynamics of cancer stem cell and cancer cell migration during chemotaxis remains largely unknown50, which is likely to be related to a number of factors such as cell metabolism51, energy 16 ACS Paragon Plus Environment

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consumption52 and cellular sensitization53. Further work will focus on the biological significance and the mechanisms of the acceleration during cancer chemotaxis.

CONCLUSIONS We have developed a microfluidic device capable of generating multiple stable gradients, culturing cells on-chip, and monitoring single cell migratory behavior. The microfluidic platform was used to study gradient-induced chemotaxis of LCSC and dLCSC in real time. Different continuous linear gradients and long-term cell culture were attainable in the microfluidic device, which facilitated the monitoring of cell migratory behaviors including the morphological changes and migration rates at single cell level. Multiple gradients could be generated and stably maintained in microchannels during cell culture and migration. The migratory behavior of individual cancer stem cells and cancer cells induced by serum could be observed in parallel as a function of gradient and time. Specifically, the LCSC and dLCSC from the same origin exhibited significant different migration rates in response to different gradients, indicating the dynamic sensitivity of cancer stem cell and cancer cell in chemotaxis. Such chemotactic migration was particularly regulated by β-catenin dependent Wnt signaling pathway, since the inhibition of β-catenin signaling suppressed the chemotactic migration rates. The results showed LCSC displayed slower migration rate compared to dLCSC and was less responsive to the serum gradient stimulation. dLCSC were derived from LCSC following multiple passages of in vitro culture, yet the cells from the same origin behaved differently in response to the same external stimuli, suggesting the importance of cancer cell plasticity and heterogeneity in



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regulating tumor progression and metastasis. Meanwhile, we also observed for the first time the acceleration of cancer cells during chemotactic migration, which may be caused by increasing local concentration in different serum gradients. The capability to analyze single cell chemotaxis under spatially controlled conditions provides a novel analytical platform for the study of cellular microenvironments and drug screening.

AUTHOR INFORMATION Corresponding Author * Prof. Mengsu Yang, Department of Biomedical Sciences, City University of Hong Kong, Telephone: (852) 3442 7797, Fax: (852) 2788 7406, Email: [email protected]

Author Contributions † H.Z. and W.Y. contributed equally to this work.

Notes The authors declare no competing financial interests.

ACKNOWLEDGMENTS This work was supported by grants from the National Key Scientific Research Program (973 Program No. 2012CB933302), Knowledge Innovation Program of Shenzhen Municiple Government (JCYJ20140419115507575), the Research Grants Council of the Hong Kong 18 ACS Paragon Plus Environment

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Special Administrative Region, China (CRF Project No. CityU9/CRF/13G), State Key Laboratory of Environmental and Biological Analysis and Strategic Development Fund of HKBU (SKLP_14-15_P008) and Guangdong Natural Science Foundation (2014A030310282).

Supporting Information Available. Supporting information includes Figure S1-S6 and Table S1. This information is available free of charge via the Internet at http://pubs.acs.org/.



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Tables Table 1. The dimensions and the normalized gradients of the microfluidic channels.

Table 2. The wound healing rates of LCSC and dLCSC in different serum concentration with normal condition and drug condition.



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Figures

Figure 1. The characterization of LCSC and dLCSC and wound healing assay A). Bright field of cell morphology of LCSC (Top) and dLCSC (Bottom) in petri-dish. Scale Bar: 50 µm. B). Flow cytometry measurements of cell stemness-related surface proteins expression of ABCG2, Oct4 and N-cadherin of LCSC and dLCSC. C). The LCSC and dLCSC tumor sphere generation in vitro. Scale Bar: 100 µm. D-E). The images and the migration abilities of the non-scratching wound healing assay of LCSC and dLCSC in different serum concentrations respectively at 24h in (D) normal condition and (E) drug conditions (1 μM XAV-939). The red dash rectangles indicate the gaps at 0h time point. Scale Bar: 100 µm. The gap filling percentages of both LCSC and dLCSC in each condition at different time points were showed. Error bars represent one standard deviation of triplicate experiments for each condition.


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Figure 2. 3D schematic illustration of microfluidic chip design for multiple gradients generation and the SEM characterization. A). The microfluidic chip consists of two main channels forming a 30° V-shaped structure and five parallel connecting channels with different lengths (See Table 1). Two gaps between the main channel and connecting channels enable entrapment of suspension cells at the entrance of the connecting channels and cells are able to migration after adhesion. Inlet 1 is designed for cell loading and medium perfusion, and inlet 2 is designed for chemokine perfusion. Stable and continuous gradients are generated in connecting channels, thus inducing cell chemotaxis. B-E). The microfluidic device is comprised by integration of two PDMS replicas that are face-to-face bonded. The SEM imaging of the PDMS layers of the microfluidic chip. B). The top PDMS layer of the microfluidic chip with the gap between the connecting channels and main channels (magnification of 26X). Scale bar is 1 mm. C). The 150X of the top PDMS layer. The depth of the connecting and main channels is around 21 μm. Scale bar is 200 μm. D). The bottom PDMS layer of the microfluidic chip with the 38X magnification. Scale bar is 500 μm. E). the 150X of the bottom PDMS layer. The depth of the connecting channel is around 9 μm. Scale bar is 200 μm.



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Figure 3. Characterization of the generation of multiple gradients in the microfluidic device by fluorescence images and numerical simulation. A). 3D Simulation results of the concentration distribution in the microfluidic device showed multiple gradients generated in the five connecting channels with different lengths (Top) and concentration distribution on the vertical slice of the CH1 in the y-z plane (Bottom). B). Time–lapse images of the connecting channels after perfusion of Rose Bengal (Ex: 550 μm /Em: 570 μm) for 4s, 60s, 180s, and 2h, respectively. Scale bar is 100 μm. C). The dynamic concentrations established in 450 µm channel within 3 minutes with a normalized high concentration of 1 and low concentration of 0. D). The concentration gradients in connecting channels of stimulation results (in black line), within 11 μm height (in black dash line) and experimental results (in color dots).


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Figure 4. The cell trapping in the microfluidic device and the numerical results of the flow in microfluidic device. A). The cell loading with different cell densities in microfluidic chips by drop pressure. Top: 1×105 Cell/ml loading density; Bottom: 1×106 Cell/ml cell loading density. The scale bar is 100 μm. B-C). The flow pressure distribution within the channels when cells were loaded at (B) the surface and (C) at the slice of the cell height (9µm). D). The flow velocity distribution within the channels when cells were loaded at the slice of the cell height (9µm). E). The flow shear stress distribution within the channels during the cell chemotaxis at the slice of the cell height (9µm).



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Figure 5. The dynamic cell morphology during chemotaxis. A). Image of dLCSC migration in the CH1 in normal condition. The red arrows point out the locations of the first migrated cell. The scale bar is 50 µm. B). The LCSC migration locations in channels at time point 18h. scale bar is 100 µm. C). Graphical description of aspect ratio (L/W) of the LCSC in CH1 during chemotactic migration (0 h and 24 h). The scale bar is 50 µm. D-G). Comparisons of the aspect ratio of LCSC and dLCSC during cell migration in normal condition and drug condition. All the measurements were taken at time points of 0h, 6h, 12h, 18h and 24h. In each condition, the measured number of cells n≥120.


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Figure 6. Serum induced chemotactic migration distances of LCSC and dLCSC. A-B). The migration distance of LCSC and dLCSC at 24h after chemotaxis in channels in the normal condition and drug condition. C-D). The migration distance of LCSC and dLCSC chemotaxis at different time points in different serum concentrations without gradients (channel 3). Other channels results were showed in supplementary Figure S5. In each condition, the measured number of cells n≥120.



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Figure 7. The acceleration of the LCSC and dLCSC during chemotaxis. A-B). The increasing LCSCs and dLCSCs migration rates in channels at different local serum concentrations in the gradients. C-D).The increasing LCSCs and dLCSCs migration rates in channels at different local serum concentrations in the gradients under drug condition. E-F) The LCSC and dLCSC migration rates at different time points in different serum concentration without gradients. G-H). The acceleration of LCSC and dLCSC in the normal and drug treatment condition conditions. In each condition, the measured number of cells n≥120.


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For TOC only


Microfluidic Platform for Studying Chemotaxis of Adhesive Cells

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