We present a microfluidic chip for protein labeling in the human serum -based matrix . 10 Serum is a complex sample matrix that contains a variety of proteins, and a matrix used in many 11 clinical tests. In this work, the device performance was tested using commercial serum samples 12 from healthy donors (commercial) spiked with the following targe t proteins: cellular fibronectin (c - 13 Fn) and matrix metallopeptidase 9 (MMP9). The microfluidic molds were fabricated using micro 14 milling on acrylic and using stereolithography (SLA) three -dimensional (3D) printing for an alter- 15 native method and comparison. Simple quality control was performed for both fabrication mold 16 methods to inspect the channel height of the chip that plays a critical role in the labeling process. 17 The fabricated microfluidic chip shows good reproducibility and repeatability of the perform ance 18 for the optimized channel height of 150 µm. The spiked protein of cellular fibronectin (c -Fn) and 19 matrix metallopeptidase 9 (MMP9) in the human serum -based matrix were successfully labeled by 20 functionalized magnetic nanoparticles (MNPs). The biomarker labeling occurring in serum was 21 compared using a simple matrix sample : phosphate buffer. The measured signals obtained by using 22 a magnetoresistive (MR) biochip platform showed that the labeling using the proposed microfluidic 23 chip is in good agreement for both matrixes, i.e., the analytical performance (sensitivity) obtained 24 with serum, near the relevant cutoff values, is within the uncertainty of the measurements obtained 25 with a simple and more controlled matrix: phosphate buffer. This finding is promisin g for stroke 26 patient stratification where these biomarkers are found at high concentrations in serum. 27 Keywords: microfluidic chip ; cellular fibronectin ; MMP9 ; serum ; magnetic nanoparticle ; magneto- 28 resistive ; biosensor 29 30 1. Introduction 31 Detecting specific biomarkers in physiological body fluids, such as blood, serum, 32 plasma, sweat, and urine is a challenge in the development of biosensors [1 –4]. Even more, 33 highlighted when the sample preparation takes a considerable amount of time and de- 34 creases the diagnostic tool's effectiveness. Analytical parameters such as selectivity and 35 specificity are a further challenge for low concentrations of biomarkers. For example, in 36 emergency medicine, the stratification of ischemic stroke patients should oc cur in the 37 shortest time possible, because of the narrow time window for thrombolytic therapy [5 – 38 8]. Nevertheless, human blood is a complex sample matrix that can difficult access to rel- 39 evant stroke biomarkers, such as cellular fibronectin (c -Fn) and matri x metallopeptidase 40 9 (MMP9) [6,9,10] – strongly associated with ischemic stroke screening in clinical studies 41 for more than five decades [11,12]. When the obstacles to sample matrix complexity can- 42 not be overcome, a sample preparation method preceding the d etection is needed [3,13]. 43 Citation: Prabowo, BA.; Sousa, C.; Cardoso, S.; Freitas, P.; Fernandes, E, Labeling on a chip for human serum protein biomarkers Micromachines 2022 , x, x. https://doi.org/10.3390/xxxxx Academic Editor: Firstname Last- name Received: date Accepted: date Published: date Publisher’s Note: MDPI stays neu- tral with regard to jurisdictional claims in published maps and institu- tional affiliations. Copyright: © 2022 by the authors. Submitted for possible open access publication under the terms and con- ditions of the Creative Commons At- tribution (CC BY) license (https://cre- ativecommons.org/licenses/by/4.0/). Micromachines 2022 , 13, x FOR PEER REVIEW 2 of 11 Microfluidics sparks much attention from scientists in sample pre -treatment, such as 44 sample preparation devices for complex biological matrices, particle sorters, labeling pro- 45 cesses, mixers, and flow manipulation from liquid medi ums [1 3–20]. The conventional 46 fabrication of microfluidic devices uses the casting polymer method on a mold as a master 47 pattern that is fabricated using a costly lithography process[2]. Low -cost fabrication molds 48 were introduced using micro -milling to engr ave acrylic materials, simplifying the labori- 49 ous lithography process and significantly cutting the fabrication cost [ 21,22]. Besides the 50 straightforward fabrication process that can be done outside the cleanroom facilities, 51 acrylic is an incredibly cheap m aterial, transparent and robust for the master pattern of 52 microfluidics, with a resolution down to 100 µm[ 21,23]. Nevertheless, reproducibility and 53 low throughput yield are still the main issues of the micro -milling fabrication method. 54 The 3D printing process offers better reproducibility and high throughput produc- 55 tion of the microstructure for microfluidics[2 4,25]. Several methods for 3D printing tech- 56 niques, such as selective laser sintering, extrusion method, inkjet printing, and stereo- 57 lithography (SLA), offer a broad spectrum in terms of cost, resolution, roughness, stiff- 58 ness, and additive materials[2 6–28]. 59 Here in, we demonstrate and compare the reproducibility and repeatability of a mi- 60 crofluidic chip for magnetic labeling of protein biomarkers using molds fabricated using 61 micro -milling [29] and 3D printing with simple quality control. For that, we used two 62 relevant stroke biomarkers, c -Fn, and MMP9. According to the literature, the simultane- 63 ous detection of these biomarkers can provide 87% prediction spec ificity for HT risk (the 64 main side -effect of thrombolytic treatment in acute cases of stroke) [30]. To our 65 knowledge, the development of a microfluidic chip for magnetic labeling of relevant 66 stroke biomarkers using a human serum -based matrix is firstly pre sented. Moreover, the 67 performance is compared when labeling occurs in a simple matrix: phosphate buffer. For 68 the quantification of biomarkers was used a magnetoresistive (MR) biochip platform that 69 validates the performance of the target labeling of c -Fn an d MMP9 occurring on the MF 70 chip. 71 72 Micromachines 2022 , 13, x FOR PEER REVIEW 3 of 11 Figure 1. (A) Simulated micro -milling fabrication of the microfluidic mold in acrylic. (B) 3D model 73 of the mold with 45° printing orientation using raft support. Fabricated mold using (C) micro -mill- 74 ing and (D) SLA 3D pri nting. (E) Fabricated microfluidic chip for test flow. The dimension of the 75 microfluidic chip: total length = 75 mm; width = 25 mm; diameter of inlet = 7.5 mm, diameter of 76 concentrated chamber and outlet = 10 mm; height of inlet and outlet = 3 mm; height o f channel and 77 concentrated chamber = 250 µm; width of serpentine channel = 250 µm; total length of serpentine 78 channel = 88 mm. 79 80 2. Materials and Methods 81 2.1. Microfluidics mold fabrications 82 Microfluidic designs were drawn using AutoCAD 2013 (Autodesk Inc. , USA). Next, 83 the design will be fabricated both using micro -milling and SLA 3D printing. The mold 84 fabrication using micro -milling computer numerical control (CNC) is simulated using 85 ArtCAM software for mapping the sequence of engraving and cutting (Fig. 1A), the pro- 86 cess is described in detail in [ 29]. 87 For the micro -milli ng fabrication, were used two approaches: optimized and non - 88 optimized micro -milling . The optimizations were set up in ArtCam software. The opti- 89 mized method used the parameters of the milling step equal to half of the tool diameter. 90 In this study, the milli ng tool of 0.8 mm size was applied, therefore the milling step of 0.4 91 mm was used. While for the non -optimized micro -milling , the milling step was 0.8 mm. 92 The optimized fabrication creates a smoother structure due to the low resolution of mill- 93 ing with a to tal fabrication process of more than 12 hrs. On the other hand, the not -opti- 94 mized micro -milling resulted in a rough structure with a faster fabrication process around 95 6 hrs. The limitation of the long fabrication process for a single mold structure in micr o- 96 milling can be overcome by 3D printing, with comparable fabrication time to produce 97 several structures simultaneously (up to six molds). 98 The 3D design from AutoCAD was exported into .stl files and printed using an SLA 99 3D printer (Formlabs, UK) with the smallest resolution setting of 25 µm. The SLA 3D 100 printer was using a 1 L cartridge of resin (grey v4, Formlabs, UK). The printing orientation 101 of the mold was 45° with full raft support (Fig. 1B). After the printing process, the mold 102 structure was immersed in isopropyl alcohol (IPA) under an ultrasonic bath (Formlabs, 103 UK) for 10 min, followed by resin curing (Formlabs, UK) at 65 °C, 1 hr under the ultravi- 104 olet light illumination. Next, the printed structures were stored in an oven at 65 °C for 8 105 hr. The fabri cated molds both from micro -milling and 3D printing (Fig. 1 C and D) were 106 then washed using DI water and IPA before the PDMS casting. 2.2. PDMS casting and 107 microfluidic inspection 108 Polydimethylsiloxane (PDMS) elastomer (Dowsil ™ 184 Silicone Elastomer, Dow 109 Chemical, US) with a cured ratio solution of 10:1 were stirred and mixed (3 min, room 110 temperature/R.T.). Next, the elastomer solution was degassed in the vacuum to remove 111 the bubbles until the transparent color was achieved. Subsequently, the elastomer was 112 cast into the mold, followed by baking in the oven (2 hrs, 65 °C). The hardened elastomer 113 peeled off from the mold. A microscope slide with a size of 75 mm × 25 mm × 1 mm (Ep- 114 redia™ Microscope Slides, Thermo Scientific, US) was cleaned in DI water and isopr opyl 115 alcohol (IPA) (Merck, USA) as a substrate. Both patterned elastomer surface and micro- 116 scope slides were exposed under plasma O 2 (Expanded Plasma Cleaner PDC -002, Harrick 117 Plasma, US) for 40 seconds. The patterned elastomer was flipped to the glass subst rate 118 and bonded with uniform pressure to all the surfaces. The microfluidic channel was func- 119 tionalized using poly(dimethylsiloxane -b-ethylene oxide) (PDMS -b-PEO) as described in 120 detail in [27]. A permanent magnet with a disc shape (d=15 mm, supermagnet, We bcraft 121 GmbH, Germany) was positioned under the concentrated chamber to collect the labeled 122 biomarkers before the MR sensor measurement. 123 Micromachines 2022 , 13, x FOR PEER REVIEW 4 of 11 For the flow test (Fig. 1E), 100 µL (VT) of phosphate buffer (PB) was mixed with 2 µL 124 of magnetic nanoparticles (MNPs) ( d= 250 µm, Nanomag, Micromod, Germany) .. The 125 timer (t) is counted from the first drop and stops until the liquid reaches the outlet. The 126 rest of the liquid in the inlet was pipetted and calculated for the rest of the volume (Vi). 127 The flow rate (FR) of the channel can be estimated by the following formula: 128 𝐹𝑅=𝑉𝑇−𝑉𝑖 𝑡 (1) 129 For channel inspection and quality control (Fig. 2), the freshly peeled elastomer was 130 sliced across the channel into thin pieces. Next, the sliced chips were attached to the mi- 131 crosc ope slide and inspected under the microscope (Wide -Field Upright, Nikon –Ni-E, Ja- 132 pan). Fig 2 depicted the channel heights from the casted elastomer from fabricated several 133 molds. 134 135 Figure 2. Inspection of the microfluidic channel from casted elastomer under a wide -field upright 136 microscope . (A) Microfluidic chip from micro -milling mold (not optimized). It shows the channel 137 height around 276 µm, which is not passed for quality control. Microflui dic chip, which passes qual- 138 ity control with a channel height ~150 µm; fabricated from the mold using (B) optimized micro - 139 milling and (C) SLA 3D printing. 140 2.3 MNPs functionalization 141 2 μL of streptavidin -conjugated MNPs (d=250, concentration = 4.9×1011 mL−1, Nano- 142 mag, Micromod) were incubated with 50 μg/mL of biotinylated polyclonal antibodies (Im- 143 munostep, Spain) of c -Fn or MMP9 during 1 hr. Next, the bovine serum albumin (BSA) 144 (Sigma -Aldrich, Merck KGaA, Germany) 5% in phosphate buffer (PB) was used for MN P 145 surface blocking in 1 h of incubation. Then, the MNPs for labeling c -Fn were diluted four 146 times (1:4) and used undiluted (1:1) for MMP9. MNPs were mixed with serum -based ma- 147 trices spiked with the interested biomarkers in the inlet microfluidic chip area i n a total 148 volume of 100 μL. The detail of MNP functionalization is reported elsewhere [29]. 149 2.4. MR sensor and its functionalization 150 The MR biochip is composed of an array of 30 U -shaped spin -valve (SV) sensors and was 151 microfabricated as reported previousl y [31–33]. The gold thin layer (12.9 × 35.4 μm2) above the 152 SV sensors was treated with a heterobifunctional surface linker: 1 mg/mL of sulfosuccinimidyl 6 - 153 [3'-(2-pyridyldithio)propionamido] hexanoate (sulfo -LC-SPDP, Thermo Scientific, USA) in PB 154 during 20 min. Then, 1 µL of monoclonal antibodies (Abcam, Cambridge, UK) at 250 µl/mL 155 contacted with the sensor surface of 15 SV sensors for 2 h at room temperature (RT), followed by 156 a blocking step with 1% of BSA. The remaining 15 SV sensors were used as n egative control 157 sensors and contacted with 5% of BSA. 158 Micromachines 2022 , 13, x FOR PEER REVIEW 5 of 11 2.5. Spiked biomarkers in human serum -based matrix 159 Human serum from male AB purchased from Sigma Aldrich (Merck, USA) was used 160 untreated. Next, target proteins of c -Fn and MMP 9 were prepared at differe nt concentra- 161 tions around the clinical cutoff value of 3.4 µg/mL and 300 ng/mL, for c -Fn and MMP9, 162 respectively (in a total volume of 100 uL of serum). Therefore, c -Fn was prepared in sev- 163 eral concentrations of 1, 4, and 10 µg/mL; while MMP9 was prepared in concentrations of 164 100, 300, and 1000 ng/mL. In this work, it was used MNPs at a dilution ratio of 1:4 for c - 165 Fn and undiluted for MMP9. Human serum samples spiked with the respective bi- 166 omarkers were loaded into the microfluidic chip together with functional ized MNPs and 167 followed the protocol described in [29]. . The result of the labeling processing: MNP -la- 168 beled specific biomarkers were quantified at the MR biochip platform. The mechanism of 169 the signal acquisition from the MR sensor is described in Fig. 3 (A and B). Briefly, a meas- 170 urement is composed of three steps: (1) The baseline: is acquired by passing PB for 5 min 171 over the MR sensor surfaces, which are immobilized with monoclonal antibodies (2) The 172 saturation: MNPs complexes, i.e. MNP plus specific target captured by the microfluidic 173 chip are inserted into the biosensor flowcell. After 5 mins, the magnetic focusing is applied 174 to attract MNPs to the top of the MR sensor surface to distribute the MNPs uniformly and 175 allow opportunities for monoclonal antibodi es to capture the MNPs complexes. (3) The 176 washing: after the saturation level of up to 25 min, the sensor surfaces are washed with 177 PB-T (phosphate buffer with 0.05% Tween®20) until the signal is stable. In this step, most 178 of the non -specific binding can be released from the sensor surface, remaining only spe- 179 cific binding. Finally, the differential signal between the baseline and saturation level after 180 wash was recorded as the binding signal. 181 182 Figure 3. (A) An example of a binding signal measurement obtaine d for c -Fn using a portable MR 183 biochip as a sensing element. (B) the sandwich assay strategy occurring at the gold surface of a MR 184 sensor. 185 Micromachines 2022 , 13, x FOR PEER REVIEW 6 of 11 186 2.6. Statistical analysis 187 The MR composed of an array of 30 U -shaped SV sensors, where 15 were used for 188 target binding, and the other 15 for negative control. The signals were collected from at 189 least 10 sensors for target and negative control, respectively. 190 The measured voltage si gnal in each sensing area was normalized using the follow- 191 ing formula: 192 193 VNORM = ∆V/V SENSOR (2) 194 195 Where V NORM is the normalized signal value and unitless, ∆V is the delta between the 196 average of the baseline and binding signal, and V SENSOR is the average of the baseline (sen- 197 sor output). 198 The average signals of active sensors in a measurement batch were calculated using 199 the propagation of errors approach, where each average and standard deviation contrib- 200 ute to the calculated signal value. The curve fittings wer e obtained using the Hillsslope 201 model, with a 95% confidence level of the calibration curve, using 1000 data points, 202 ANOVA using a maximum of 400 iterations. The Signal level of detection limit (Y LOD) was 203 calculated using the formula from [35]: 204 205 YLOD = 1.645 SD C0 + 1.645 SD C1 (3) 206 Where SD C0 and SD C1 are the standard deviations from blank measu rement and 207 smallest concentration, respectively. 208 3. Results and Discussion 209 3.1. Repeatability of the microfluidics 210 Molds fabricated by CNC and 3D printer were compared to a reference mold previ- 211 ously fabricated in other published work by CNC [29]. The parameters checked for the 212 quality control (QC) of microfluidic chips produced from those different fabricated molds 213 are summarized in Table 1. The molds with acceptable QC were used for PDMS casting 214 to obtain the final microfluidic chips, which were tested against the interesting targets (c - 215 Fn and MMP9). After the labeling on a chip, the levels of protein were quantified using 216 an MR biochip platform to demonstrate the reproducibility of the molds, and respective 217 repeatability of the measurements (Fig .4). The magnitude of the MR sensor signals is in a 218 comparable range to the reference signal (light yellow region) obtained from our previous 219 study[2 9]. In contrast, the microfluidic chips with higher channel thickness show that the 220 signals were significantly lower than those that pass the QC. 221 Table 1. Inspection and quality control test: microfluidics chips from different fabricated molds. 222 REFERENCE MOLD CNC OPTIMIZED CNC 3D PRINTER Volume (serpentine + chamber) 25 µL 46 µL 25 µL 25 µL Channel height 150 µm 276 µm 150 µm 150 µm Quality Control - Failed Passed Passed 223 We provide in Fig.5 an illustration to explain the hypothesis behind the obtained re- 224 sults. The microfluidic with optimum channel height (Fig. 5A) shows a filtration mecha- 225 nism by the MNPs cluster in the concentrated chamber. The MNPs clustered concentrated 226 by the magnetic field from the permanent magnet capture the biomarkers th at flow with 227 Micromachines 2022 , 13, x FOR PEER REVIEW 7 of 11 the sample medium. On the other hand, microfluidic chips with a higher channel height 228 decreases the filtration efficiency of functionalized MNP clusters. The MNPs tend to be 229 clustered in the substrate due to the magnetic field from the magnet, while the channel 230 gap above potentially passes the biomarkers to the sponge. Moreover, the high channel 231 heights enhanced the sponge absorption due to the increased surface contact with the me- 232 dium. Although the idea of making the channel height as small as possible can lead to 233 effective filtration, the longest absorption time is due to the light contact of the sponge 234 surface with the medium. 235 Besides the channel height, we believe that other variables can also influence the ef- 236 fectiveness of the MNP filtratio n for the proposed microfluidic chip. First, the MNPs con- 237 centration is crucial to the dynamic range that can be tuned to fit the clinical cutoff 238 value[27,28]. It is essential to get enough MNPs to cluster in the reaction chamber. Second 239 is the preference o f the magnet, i.e., the shape and the strength of the permanent magnet 240 contribute to the filtration of the biomarkers. Weak magnetic fields may allow some func- 241 tionalized MNPs to be absorbed into the sponge. 242 243 Figure 4. Reproducibility 244 of the molds fabricated by micro -milling computer numerical control (CNC) and 3D printer (3DP), 245 and respective repeatability of the measurements up to 19 days. The reference signal (yellow color) 246 was referred from our previous work, and used here for comparison – such MF was obtained from 247 a mold fabricated using CNC, and used for labeling c -Fn (10 µg/mL) in buffer [29]. 248 249 Figure 5. The critical role of the channe l height of the microfluidic chip is to capture the target pro- 250 tein. (A) adequate protein labeling in an optimized channel. (B) A higher channel reduces the pro- 251 tein labeling efficiency and increases the contact surface of the medium with the sponges. 252 Micromachines 2022 , 13, x FOR PEER REVIEW 8 of 11 3.2. Protein labeling using human serum -based matrix 253 Stroke biomarkers were spiked in human serum -based matrices at the following con- 254 centrations: c -Fn at concentrations of 1, 4, and 10 µg/mL and MMP9 at concentrations of 255 100, 300, and 1000 ng/mL. These concentra tions were prepared to understand if the tech- 256 nology (microfluidic chips and MR biochip platform) can be used to detect the biomarkers 257 near the relevant clinical cutoff values defined for stroke patient stratification 3.4 µg/mL 258 and 140ng/mL for c -Fn and MMP 9, respectively[ 12,29]. To obtain the dynamic range that 259 fit the clinical cutoff value, undiluted (1:1) and diluted (1:4) MNPs were used for the la- 260 beling of c -Fn and MMP9, respectively[ 29]. 261 The quantification of c -Fn and MMP9 spiked in human serum is presented in Fig. 6 262 (A and B). The measurements obtained using a complex sample matrix (serum) were 263 cross -validated against the measurements obtained in the proof -of-concept of the micro- 264 fluidic d evice using biomarkers spiked in PB. The results showed that measurements per- 265 formed using a complex sample matrix were in good agreement with the calibration 266 curves of c -Fn and MMP9 labeled in the buffer. The standard deviation of the measure- 267 ments obtained in serum, however, was slightly higher. The estimated limit -of-detection 268 (LOD) were 205.14 ng/mL and 37.5 ng/mL for c -Fn and MMP9 in human serum -based 269 matrix, respectively. These values are higher compared to the measurements obtained in 270 a simple matrix: buffer, that were 54.6 ng/mL and 11.5 ng/mL for c -Fn and MMP9, respec- 271 tively. This can be explained by the complex matrix of serum, derived from whole blood 272 that contains several proteins that can increase the noise of biosensors and le ad to non - 273 specific bi nding events. It was reported that in human serum there are around 325 distinct 274 proteins [35]. Albumin or immunoglobulins G are major proteins that can difficult the in- 275 teraction between the specific antibodies attached to the MNPs and the interested bi- 276 omarkers. Nevertheless, using the proposed microfluidic chip, the filtration mechanism 277 described above (Fig. 5) by the MNPs occurs gradually, and it is sorted in a sp otted area. 278 It is utterly different from a conventional labeling method occurring in an Eppendorf ™ 279 tube that takes up to 1 hr (because of static incubation). 280 281 282 Figure 6. Quantification of c -Fn (A) and MMP9 (B) in human serum -based matrix (blue) near the 283 relevant clinical cutoff regions. Measurements were cross -validated with the calibration curve ob- 284 tained using biomarkers spiked in phosphate buffer (red). The green dash is the clinical cutoff value 285 for each biomarker. 286 5. Conclusions 287 We have demonstrated a functional microfluidic chip for protein labeling in human 288 serum. The microfluidic molds were fabricated using the micro -milling method and 3D 289 printer, with optimized reproducibility for a channel height of 150 µm. The repeatability 290 Micromachines 2022 , 13, x FOR PEER REVIEW 9 of 11 of the microfluidic chips was successfully demonstrated by performing measurements us- 291 ing a n MR biochip platform for several days – a proof -of-concept for quality control of the 292 microfluidic chip production. The channel height plays the most crucial role in the label- 293 ing performance because of the magnetic field that exists from the bottom of the chip. The 294 optimization of channel shapes and different pattern of magnetic fluxes are potentially 295 explored for future work. Finally, the MNPs labeling of c -Fn and MM P9 in human serum 296 can be performed in the proposed devices, with comparable performance for labeling the 297 biomarkers in the buffer. The proposed method is feasible for the performance of clinical 298 studies involving the tested biomarkers. 299 Author Contribution s: Conceptualization, Briliant Adhi Prabowo; Elisabete Fernandes, Paulo 300 Freitas; Data curation, Briliant Adhi Prabowo; Formal analysis, Briliant Adhi Prabowo and Elisabete 301 Fernandes; Investigation, Briliant Adhi Prabowo and Elisabete Fernandes; Methodology, Briliant 302 Adhi Prabowo and Elisabete Fernandes; Project administration, Elisabete Fernandes; Resources, 303 Paulo Freitas; Supervision, Elisabete Fernandes, and Paulo Freitas; Validation, Carole Sousa Visu- 304 alization, Briliant Adhi Prabowo; Writing – original dr aft, Briliant Adhi Prabowo Writing – review 305 & editing, Carole Sousa and Elisabete Fernandes 306 Funding: This work was supported by FCT funding through project FIM4Stroke (reference 307 PTDC/MEC -URG/29561/2017) Acknowledgments: The authors thank the micro and nano fabrica- 308 tion facilities of INL for the CNC and SLA 3D printer, and thanks to INESC -MN, Lisbon to provide 309 the magnetoresistive sensors and readout platform. 310 Conflicts of Interest: The authors declare no conflict of interest. 311