Instrument-Free Protein Microarray Fabrication for Accurate Affinity

biosensors

Article Instrument-Free Protein Microarray Fabrication for Accurate Aﬃnity Measurements

Iris Celebi 1,* , Matthew T. Geib 1, Elisa Chiodi 1 , Nese Lortlar Ünlü 2, Fulya Ekiz Kanik 1 and Selim Ünlü 1,2,*

1 Department of Electrical and Computer Engineering, Boston University, 8st Mary’s Street, Boston, MA 02215, USA; [email protected] (M.T.G.); [email protected] (E.C.); [email protected] (F.E.K.) 2 Department of Biomedical Engineering, Boston University, 8st Mary’s Street, Boston, MA 02215, USA; [email protected] * Correspondence: [email protected] (I.C.); [email protected] (S.Ü.)

Received: 13 October 2020; Accepted: 27 October 2020; Published: 29 October 2020

Abstract: Protein microarrays have gained popularity as an attractive tool for various fields, including drug and biomarker development, and diagnostics. Thus, multiplexed binding affinity measurements in microarray format has become crucial. The preparation of microarray-based protein assays relies on precise dispensing of probe solutions to achieve efficient immobilization onto an active surface. The prohibitively high cost of equipment and the need for trained personnel to operate high complexity robotic spotters for microarray fabrication are significant detriments for researchers, especially for small laboratories with limited resources. Here, we present a low-cost, instrument-free dispensing technique by which users who are familiar with micropipetting can manually create multiplexed protein assays that show improved capture efficiency and noise level in comparison to that of the robotically spotted assays. In this study, we compare the efficiency of manually and robotically dispensed α-lactalbumin probe spots by analyzing the binding kinetics obtained from the interaction with anti-α-lactalbumin antibodies, using the interferometric reflectance imaging sensor platform. We show that the protein arrays prepared by micropipette manual spotting meet and exceed the performance of those prepared by state-of-the-art robotic spotters. These instrument-free protein assays have a higher binding signal (~4-fold improvement) and a ~3-fold better signal-to-noise ratio (SNR) in binding curves, when compared to the data acquired by averaging 75 robotic spots corresponding to the same effective sensor surface area. We demonstrate the potential of determining antigen-antibody binding coefficients in a 24-multiplexed chip format with less than 5% measurement error.

Keywords: label-free biosensor; binding kinetics; aﬃnity measurements; protein microarray

1. Introduction Understanding the true nature of the interaction between ligands and analytes requires accurate analysis of the underlying binding kinetics, necessitating label-free detection (LFD) techniques to study biomolecular interactions [1]. In addition to measuring the binding affinity,it is often desirable to quantify the specificity of the interaction and cross-reactivity of the analyte to similar ligands, especially for proteins. Thus, multiplexed LFD methods have emerged as essential laboratory techniques. Solid-phase assays for LFD, especially in optical imaging modality, can interrogate arrayed sensor surfaces, and thus, beneﬁt from advances in protein microarray fabrication. Both quantitative and functional proteomics have been exploiting the advantages of microarrays for a well-controlled study of proteins [2,3]. Protein microarrays oﬀer multiplexing and low sample volume consumption, drastically reducing the time and resources required for sample analysis with respect to other methods [4].

Biosensors 2020, 10, 158; doi:10.3390/bios10110158 www.mdpi.com/journal/biosensors Biosensors 2020, 10, 158 2 of 11

To fully exploit the advantages of dense protein microarrays, binding kinetics analysis platforms, such as Surface Plasmon Resonance (SPR), utilize external custom microarray spotting services or in-house robotic microspotters (SPRi-Continuous Flow Microspotter, SPRi-Arrayer, Arrayjet, M2 instrumentSIX). These arraying instruments offer precise sample dispensing of extremely low solution volumes, and, can therefore, be utilized to create arrays with hundreds to thousands of probe spots; however, they are often bulky, complex, and expensive. Therefore, the multiplexed affinity measurements for proteins remain elusive for laboratories with limited infrastructure and resources. For those applications, for which this level of precision and multiplexing may be unnecessary, a manual (instrument-free) arraying capability along with a compatible LFD technique is highly desirable. The Interferometric Reflectance Imaging Sensor (IRIS) platform is a thin-film interference-based technology for dynamic mass accumulation measurements for label-free binding kinetics analyses. The IRIS system is a simple common path interferometer utilizing multi-color LED illumination and a CMOS camera for imaging a large (>30 mm2) sensor surface. The IRIS sensors are Si chips with a thin layer of thermally-grown oxide on top, allowing for scalable fabrication. The instrumentation and chip consumables are low-cost and accessible for smaller research labs without significant capital investment. However, there is a bottleneck regarding surface immobilization of molecular probes. Traditionally, robotic microarray spotters have been used to create high-density (hundreds of spots) multiplexed sensor surfaces with homogenous surface morphology. For high-sensitivity measurements, especially for low molecular weight (MW) target molecules, spatial averaging was employed on replicate spots for maintaining statistical significance and achieving a low noise level. While robotic printing produces molecular spots with excellent surface morphology, it introduces a high-cost infrastructure element into the affinity measurement workflow, and a low-cost alternative would be highly desirable. Here, we present a method by which a researcher familiar with dispensing samples through a micropipette can rapidly create a multiplexed protein array with as many as 24 different probes on an IRIS chip that maintains excellent sensitivity and capture efficiency. An SNR analysis of label-free binding kinetics assays is performed, and the results obtained by instrument-free arraying are compared favorably to those obtained for a microarray fabricated through an automated robotic spotter. We finally demonstrate that this instrument-free microarray fabrication method significantly reduces the cost and resources necessary for producing a multiplexed affinity measurement workflow, combined with high-sensitivity and small molecule measurement capability offered by IRIS technology. Thus, accurate multiplexed affinity measurements, once limited to high-resource laboratories, will be accessible for individual researchers and small research laboratories.

2. Materials and Methods

2.1. The IRIS Platform The principles that drive the IRIS system and the advantages it offers have been described extensively [4–6]. Briefly, the IRIS system (Figure1) works on the principle of thin-film (110 nm SiO 2/Si) interference and label-free detection of biomass accumulation with a CMOS camera. In this common path interferometer configuration, biomass accumulation on the sensor surface causes an effective increase of the top transparent layer (oxide). The change in optical path difference (OPD) results in a spectral shift in reflectance, affecting the recorded intensity of the image (Figure2). To convert intensities to mass accumulation, four images are taken before the experiment at 457 nm, 518 nm, 595 nm, and 632 nm with narrowband LEDs, and a lookup table is created for each sensor surface [7]. During experimentation, the sensor is illuminated with a single 457 nm LED. Biosensors 2020, 10, x FOR PEER REVIEW 3 of 11 Biosensors 2020, 10, 158 3 of 11 Biosensors 2020, 10, x FOR PEER REVIEW 3 of 11

(a) (b) (a) (b) Figure 1. (a) The Reflectance Imaging Sensor (IRIS) fluidic cartridge; (b) the IRIS optical setup. FigureFigure 1.1. ((aa)) TheThe ReﬂectanceReflectance ImagingImaging SensorSensor (IRIS)(IRIS) ﬂuidicfluidic cartridge;cartridge; ((bb)) thethe IRISIRIS opticaloptical setup. setup.

(a) (b) (a) (b) FigureFigure 2.2. IRISIRIS workingworking principle; principle; (a ()a demonstration) demonstration of of biomass biomass accumulation accumulation on theon sensorthe sensor surface surface and (andFigureb) the (b) resulting 2.the IRIS resulting working change change inprinciple; reflectance in reflectance (a) ofdemonstration the of film. the film .of biomass accumulation on the sensor surface and (b) the resulting change in reflectance of the film. 2.2. ImageThe full Acquisition assembly and of Analysis the microfluidic cartridge consists of placing a 50 µm imaging spacer (Grace2.2. Image Bio-Labs, Acquisition Bend, and OR) Analysis between the SiO /Si IRIS chip and an anti-reflection (AR)-coated glass slide The real-time videos were captured with2 the CMOS camera FLIR BlackFly BFS-U3-70S7M-C. To with inlet/outlet through holes, creating a sealed fluidic chamber. maximizeThe real the- timesensitivity videos of were the IRIScaptured platform, with the CMOSdominant camera noise FLIR factors BlackFly should BFS be- U3minimized.-70S7M-C. In To a 2.2.brightmaximize Image field Acquisitionthe imaging sensitivity setup and Analysisof, suchthe IRIS as IRIS platform,, the predominant the dominant noise noise contrib factorsutor should is shot be noise. minimized. Shot noise In a orbright Poisson field noise imaging originates setup, fromsuch theas IRIS discrete, the naturepredominant of electrons, noise contriband canutor be modeledis shot noise. with Shota Poisson noise distribution.or PoissonThe real-time noise Since originates the videos variance were from of captured the a Poisson discrete with distribution nature the CMOS of electrons, is equal camera to and the FLIR can number BlackFly be modeled of events, BFS-U3-70S7M-C. with in athis Poisson case, Toelectrons,distribution. maximize in atheSince shot sensitivity the noise variance limited of the of system,a IRIS Poisson platform, the distribution SNR the increases dominant is equal with noiseto thethe factorssquarenumber shouldroot of events, of bethe minimized.innumber this case of, Ingeneratedelectrons, a bright fieldelectrons.in a s imaginghot noiseThe setup, number limited such ofsystem, as electrons IRIS, the the accumulatedSNR predominant increases on noise with a single contributor the squarepixel is root shotphysically of noise. the numberlimited Shot noise by of oritsgenerated Poissonfull well noiseelectrons. capacity, originates The and number therefore from the of, discreteelectronsSNR improvement nature accumulated of electrons, by merelyon a andsingle maximizing can pixel be modeled is physically the accumulation with limited a Poisson byis distribution.insufficientits full well. Incapacity, Since the theearlier and variance work, therefore of we a Poisson,have SNR demonstrated improvement distribution isSNRby equal merely improvement to the maximizing number, due of tothe events, averaging accumulation in this nearly case, is electrons,identicalinsufficient robotically in. aIn shot the earlier noise spotted limitedwork, capture we system, haveprobes demonstrated the and SNR temporal increases SNR averaging withimprovement the [8 square]. In ,this due root study, to of averaging the we number start nearly with of generatedtheidentical same roboticallypremise electrons., and Thespotted we number demonstrate capture of electrons probes that andnoise accumulated temporal is further averaging on decreased a single [8 pixel].with In this isincrease physically study, spot we limited startarea. withThe by itsregionsthe full same well of premise interests capacity,, andinclude and we therefore, demonstratethe spatially SNR thataveraged improvement noise spotis further area by merely anddecreased its maximizing neighboring with increase the spatially accumulation spot averagedarea. The is insubackgroundregionsfficient. of interests Inarea. the Analyses earlier include work, ofthe binding wespatially have characteristics demonstratedaveraged spot and SNRarea SNR improvement,and levels its neighboringare performed due to spatially averaging in MATLAB. averaged nearly identicalbackground robotically area. Analyses spotted captureof binding probes characteristics and temporal and averaging SNR levels [8]. are In thisperformed study, we in startMATLAB. with the same2.3. Sensor premise, Chip and Surface we demonstrate Functionalization thatnoise is further decreased with increase spot area. The regions of2.3. interests Sensor includeChip Surface the spatially Functionalization averaged spot area and its neighboring spatially averaged background Sensor functionalization acts to capture and covalently bind probes to the sensor surface without area. Analyses of binding characteristics and SNR levels are performed in MATLAB. hinderingSensor their functionalization activity. We prepared acts to capture our chips and covalentlywith a DMA bind-NAS probes-MAPS to the-based sensor poly surfacemeric withoutcoating 2.3.(commerciallyhindering Sensor Chiptheir known Surfaceactivity. as Functionalization WeMCP prepared-2, Lucidant our Polymerschips with LLC) a DMA [9]. -BeforeNAS-MAPS coating,-based the chipspolymeric were plasmacoating cleaned(commercially with strong known air as plasma MCP- 2,for Lucidant 10 min Polymersto activate LLC) the silicon [9]. Before dioxide coating, surface. the chipsThe cleaned were plasma chips Sensor functionalization acts to capture and covalently bind probes to the sensor surface without werecleaned submerged with strong in a air 1% plasma w/v MCP for- 210 polymer min to activatein a 10% the saturated silicon ammoniumdioxide surface. sulfate The solution. cleaned These chips hindering their activity. We prepared our chips with a DMA-NAS-MAPS-based polymeric coating chipswere submergedwere then incubated in a 1% w/v on MCPa shaker-2 polymer plate for in 30a 10% min ,saturated washed byammonium swirling insulfate 1 L of solution. MilliQ water, These (commercially known as MCP-2, Lucidant Polymers LLC) [9]. Before coating, the chips were plasma driedchips underwere then a nitrogen incubated stream, on aand shaker placed plate into for a 30vacuum min, washed drying ovenby swirling at 80 °C in for 1 L15 of mi MilliQn. The water, chips cleaned with strong air plasma for 10 min to activate the silicon dioxide surface. The cleaned chips were aredried then under placed a nitrogen into a vacuum stream, desiccator and placed for into at leasta vacuum 1 h prior drying to use. oven at 80 °C for 15 min. The chips submerged in a 1% w/v MCP-2 polymer in a 10% saturated ammonium sulfate solution. These chips are then placed into a vacuum desiccator for at least 1 h prior to use. Biosensors 2020, 10, 158 4 of 11

Biosensorswere then 2020 incubated, 10, x FOR onPEER ashaker REVIEW plate for 30 min, washed by swirling in 1 L of MilliQ water, dried4 under of 11 a nitrogen stream, and placed into a vacuum drying oven at 80 ◦C for 15 min. The chips are then placed 2.4.into Probe a vacuum Preparation desiccator and Spotting for at least 1 h prior to use.

2.4.Before Probe Preparation spotting, all and coated Spotting chips were placed in a humidity chamber set to 57% humidity for 10 min. BothBefore BSA spotting, and α-l allactalbumin coated chips proteins were were placed diluted in a humidityto a concentration chamber of set 1mg/mL to 57% with humidity 1X-PBS for (2210 minµm.-filtered Both BSA before and α use).-lactalbumin As seen proteins in Figure were 3a, diluted a partial to a concentration array of α-lactalbumin of 1 mg/mL was with spotted 1X-PBS manually(22 µm-filtered on one before side use).of the As sensor seen in chip Figure using3a, aa partialmicropipette array of setα-lactalbumin to 0.25 uL (spot was spottedsize ~1000 manually µm), whileon one the side array of theof spots sensor with chip sizes using ranging a micropipette from 130µm set to to 0.25 1000µm uL (spot was size created ~1000 withµm), roboticwhile spotting, the array byof varying spots with the sizes number ranging of 120pL from droplets 130 µm to dispensed 1000 µm wasby M2 created iTWO™ with-300P robotic Microarray spotting, Spotter by varying at 57% the humidity,number of on 120 both pL dropletsMCP-2 coated dispensed chips. by For M2 iTWOmore ™accurately-300P Microarray spaced arrays, Spotter a atrudimentary 57% humidity, X-Y on stage both canMCP-2 be used coated for manual chips. For spotting. more accuratelyThe spotted spaced chips were arrays, then a rudimentary incubated at X-Y67% stagehumidity can beovernight. used for Uponmanual retrieving spotting. these The spotted chips from chips the were humidity then incubated chamber, at 67% MCP humidity-2 coated overnight. chips were Upon blocked retrieving in a solutionthese chips of 150 from mM the ethanolamine humidity chamber, and 100mM MCP-2 Trcoatedis at chips pH 9 were for on blockede hour. in All a solution experiments of 150 were mM performedethanolamine with and both 100 freshly mM Tris prepared at pH 9 forand one month hour.-old All batches experiments; we did were not performed observe any with degradation both freshly orprepared difference and in month-old capture efficiency. batches; we did not observe any degradation or difference in capture efficiency.

(a) (b)

FigureFigure 3. 3. SpottedSpotted IRIS IRIS chips; chips; (a)( froma) from left left to right, to right, one onecolumn column of micropipette of micropipette spots spots followed followed by five by columnsﬁve columns of robotically of robotically deposited deposited spots spots in decreasing in decreasing number number of of droplets droplets per per spot; spot; (b (b) ) identical identical roboticallyrobotically dispensed dispensed single single droplet droplet spots spots (30 (30 × 4400 array). array). × AsAs seen seen in in Figure Figure 3b,3b, a arobotic robotic spotting spotting scheme scheme was was implemented implemented for direct for direct comparison. comparison. This schemeThis scheme consisted consisted of α- oflactalbuminα-lactalbumin at 1 atmg/mL 1 mg/ mLand and 0.5 0.5mg mg/mL/mL concentration, concentration, as as well well as as negative negative controlcontrol columns columns consisting consisting of of BSA BSA at at 1mg/mL 1 mg/mL concentration. concentration. Although Although the the lower lower concentration concentration spots spots werewere used used for for binding binding consistency consistency validation, validation, thi thiss study study focuses focuses on on the the higher higher concentration concentration spots. spots. TheseThese chips chips were producedproduced usingusing the the M2 M2 iTWO iTWO™™—300P—300P robotic robotic spotter, spotter, at57% at 57% humidity, humidity, in the in same the samerun as run the as chips the chips mentioned mentioned above above to maintain to maintain consistency. consistency.

2.5.2.5. Chemical Chemical and and Biological Biological M Materialsaterials AllAll reagents andand bu buffersﬀers were were purchased purchased from from Sigma Sigma Aldrich Aldrich (St. Louis, (St. Louis, MO, USA).MO, USA).Bovine Bovine serum serumalbumin albumin (BSA) (BSA) and αand-lactalbumin α-lactalbumin were were purchased purchased from from Sigma Sigma Aldrich Aldrich (St. (St. Louis, MO, MO, USA). USA). TheThe a anti-nti-αα--lactalbuminlactalbumin antibody antibody was was purchased purchased from from Abcam Abcam (Cambridge, (Cambridge, UK). UK). All All protein protein and and antibodyantibody samples were were diluted diluted by by 1X-PBS. 1X-PBS. MCP-2 MCP was-2 was purchased purchased from from Lucidant Lucidant LLC. P-dopedLLC. P-doped silicon siliconwafers withwafers thermally with thermally grown silicon grown dioxide silicon were dioxide purchased were from purchased Silicon Valley from Microelectronics Silicon Valley Microelectronics(Santa Clara, CA, (Santa USA). Clara, CA, USA).

3. Results

3.1. SNR Analysis of a-Lactalbumin—Anti-a-Lactalbumin Binding Curve: Dense Array, Single Droplet Spots To calculate the SNR of the binding curves, a definition of the system’s signal is needed, followed by the extraction of the noise level. Here we define binding as the mass accumulation on the spot area, calculated from the thickness difference between the spot and its corresponding background, Biosensors 2020, 10, 158 5 of 11

3. Results

3.1. SNR Analysis of α-Lactalbumin—Anti-α-Lactalbumin Binding Curve: Dense Array, Single Droplet Spots To calculate the SNR of the binding curves, a definition of the system’s signal is needed, followed by Biosensorsthe extraction 2020, 10 of, x FOR the noisePEER REVIEW level. Here we define binding as the mass accumulation on the spot5 area, of 11 calculated from the thickness difference between the spot and its corresponding background, hence, hence, the difference in intensity readings between the spot and the background. The binding signal the difference in intensity readings between the spot and the background. The binding signal is defined is defined as the differential intensity, converted to an accumulated number of electrons on the as the differential intensity, converted to an accumulated number of electrons on the camera sensor. camera sensor. We know that in a shot noise limited system, the SNR level will be proportional to the We know that in a shot noise limited system, the SNR level will be proportional to the square root square root of the total number of electrons (N) accumulated on the CMOS camera sensor, since the of the total number of electrons (N) accumulated on the CMOS camera sensor, since the signal level signal level is proportional to N and the dominating noise (shot noise) will be inversely proportional is proportional to N and the dominating noise (shot noise) will be inversely proportional to √N. to √푁. The primary goal in binding experiments is extracting the binding coefficients of antigen- The primary goal in binding experiments is extracting the binding coefficients of antigen-antibody antibody complexes. Hence, our definition of the signal is slightly different from the classic idea of complexes. Hence, our definition of the signal is slightly different from the classic idea of absolute absolute intensity signal measured on the CMOS sensor; we aim to demonstrate the sufficient SNR intensity signal measured on the CMOS sensor; we aim to demonstrate the sufficient SNR level in an level in an experiment for accurate estimation of binding characteristics. For this reason, after experiment for accurate estimation of binding characteristics. For this reason, after recording the signal recording the signal level, we apply a Savitzky—Golay filter to binding-debinding events to estimate level, we apply a Savitzky—Golay filter to binding-debinding events to estimate what we define as what we define as the “noiseless” curve for each averaging step. Then, we extract the noise by the “noiseless” curve for each averaging step. Then, we extract the noise by calculating the standard calculating the standard deviation of the difference between raw data and the estimated noiseless deviation of the difference between raw data and the estimated noiseless curve. Lastly, the SNR is curve. Lastly, the SNR is calculated as the level of the binding signal divided by the level of extracted calculated as the level of the binding signal divided by the level of extracted noise. The binding noise. The binding experiment for a dense array of single 120 pL spots of probe solution, as seen in experiment for a dense array of single 120 pL spots of probe solution, as seen in Figure3b, was run Figure 3b, was run for the fabricated chips. Figure 4a illustrates the binding curves for a single spot, for the fabricated chips. Figure4a illustrates the binding curves for a single spot, and also a random and also a random selection of 5, 50, and 500 spots curves averaged together; Figure 4b shows the selection of 5, 50, and 500 spots curves averaged together; Figure4b shows the filtered curves and the filtered curves and the calculated SNR for each curve. calculated SNR for each curve.

(a) (b)

Figure 4. ((aa)) Example Example binding binding curves curves from from MCP MCP-2-2 functionalized functionalized chip chip with with single single drop drop 1 mg 1 mg/mL/mL α- lαactalbumin-lactalbumin spots spots showing showing the the effect eﬀect of of spatial spatial averaging. averaging. (b (b) )Binding Binding curves curves with with Savitzky Savitzky–Golay–Golay filteringﬁltering and their calculated SNR level.

The SNR analysesanalyses forfor bindingbinding curves curves extracted extracted from from spatially spatially averaged averaged spots spots on on diff differenterent chips chips are areshown shown in Figure in Figure5. Here, 5. Here, for each for each number number of averaged of averaged spots spots (n) (on (n) each (on step each increasing step increasing n), we n) apply, we applythe explained the explained SNR calculationSNR calculation on multiple on multiple (>100) (>100) random random selections selections of n of spots. n spots The. The median median of the of thecalculated calculated SNR SNR values values for each for each n is fitted n is tofitted a square to a square root and root power and curve,power and curve, the medianand the absolutemedian absolutedeviation deviation (MAD = median (MAD = ( |x medianmedian(x) (| x –| ))median(x)|)) for each n is for illustrated each n iswith illustrated error bars. with Medians error bars. and − MediansMADs are and plotted MADs with are respectplotted towith the respect corresponding to the corresponding total number total of electrons number averaged. of electrons The averaged. applied Thecurve applied fitting curve reveals fitting that SNRreveals increases that SNR in increases a square rootin a square dependence root dependence on the accumulated on the accumulated number of numberelectrons of with electrons an R2 withof 0.99. an R2 of 0.99. BiosensorsBiosensors2020 20,2010, ,10 158, x FOR PEER REVIEW 6 of6 of 11 11

FigureFigure 5. 5.SNR SNR dependence dependence on on a a number number of of total total electrons, electrons captured, captured from from averaging averaging a growinga growing number number ofof identical identical spots spots on on MCP-2MCP-2 functionalized chips; chips; 1 1mg/m mg/mLL ligand ligand (α (-αlactalbumin)-lactalbumin) concentration. concentration. The Thedata data points points are are fitted ﬁtted with with a square a square root root law law,, and and the the R2 R value2 value is isshown. shown.

3.2.3.2. SNR SNR Analysis Analysis Comparison Comparison of of Spots Spots with with Varying Varying Areas Areas and and Micropipette Micropipette Spots Spots InIn the the previous previous section, section, an an analysis analysis of of accumulated accumulated electron electron dependence dependence of of SNR SNR was was demonstrateddemonstrated for forα α-lactalbumin—anti--lactalbumin—antiα-α-lactalbumin-lactalbumin bindingbinding experiments with with the the dense dense array array of ofrobotic robotic spots. spots. Single Single droplet droplet (120pL) (120 pL) s spotspots were were averaged to increaseincrease thethe areaarea of of spatial spatial averaging, averaging, effeffectivelyectively increasing increasing the the number number of of electrons. electrons. A A similar similar SNR SNR analysis analysis was was performed performed on on chips chips roboticallyrobotically spotted spotted with with a a varying varying number number of of droplets droplets per per spot, spot, hence, hence varying, varying areas; areas; and and manually manually spottedspotted with with large large surface surface areas areas by by micropipetting micropipetting (Figure (Figure5). 5). In In Figure Figure6, the6, the calculated calculated SNRs SNRs of of didifferentfferent sized sized spots, spots, the the variations variations within within the the same same spot spot size, size and, and previously previously obtained obtained SNR SNR behavior behavior ofof dense dense array array single single drop drop spots spots are are plotted plotted together. together. The The SNR SNR levels levels of of spots spots with with larger larger areas areas are are observedobserved to to be be higher higher than than that that of of the the multiple multiple single single droplets droplet averagings averaging with with the the same same total total electron electron yield.yield. The The SNR SNR dependence dependence on on electrons electrons this this time time was was shown shown for for the the robotically-created robotically-created bigger bigger spots, spots, 2 withwith a squarea square root root curve curve fitting fitting having having an an R Rof2 of 0.97. 0.97. TheThe improvement improvement in in the the SNR SNR levels levels results results from from the the higher higher signal signal levels levels observed observed in in spots spots havinghaving a a larger larger area. area. Figure Figure7a 7a demonstrates demonstrates the the increasing increasing signal signal level level with with increasing increasing spot spot size. size. Clearly,Clearly, the the amount amounoft of mass mass accumulation accumulation on on the the surface surface depends depends on on the the probe probe density density and and e ffiefficiencyciency ofof each each spot, spot, and and increasing increasing the the dispensed dispensed volume volume on on the the surface surface is is expected expected to to increase increase the the number number ofof probes probes per per unit unit area, area, until until saturation saturation of of the the surface surface [10 [10].] Figure. Figure7b 7 showsb shows the the initial initial thicknesses thicknesses of of spotsspots with with varying varying sizes; sizes hence,; hence the immobilized, the immobilized probe densities, probe densities and the, mass and accumulationthe mass accumulation obtained fromobtained the experiment. from the experiment. Although measuring immobilized probe density demonstrates the capture capability of the microarray spot, it is not adequate to assess the performance. This is precisely the reason for our comparison using kinetic binding curves and error in fitting binding coefficients. Biosensors 2020, 10, 158 7 of 11 Biosensors 2020, 10, x FOR PEER REVIEW 7 of 11 Biosensors 2020, 10, x FOR PEER REVIEW 7 of 11

Figure 6. Comparison of the SNR levels obtained from MCP-2 functionalized chips. Averaged FigureFigure 6. ComparisonComparison of of the the SNR SNR levels levels obtained obtained from from MCP-2 MCP functionalized-2 functionalized chips. Averaged chips. Averaged identical identical robotic spots (blue—previously shown), robotic spots of increasing size, and micropipette identicalroboticspots robotic (blue—previously spots (blue—previously shown), shown), robotic robotic spots of spots increasing of increasing size, and size, micropipette and micropipette spots, spots, with respect to the total number of electrons collected from the spots. The SNR behavior of spots,with respectwith respect to the to total the number total number of electrons of electrons collected collected from thefrom spots. the spots. The SNR The behaviorSNR behavior of larger of larger robotic spots is also fitted to a square law curve (green). largerrobotic robotic spots isspots also is ﬁtted also tofitted a square to a square law curve law (green).curve (green).

(a) (b) (a) (b) Figure 7. (a) Binding curves from spots created with dispensing 1–100 droplets and micropipette FigureFigure 7. ( (a)a) Binding curves curves from from spots spots created created with with dispensing dispensing 1–100 1–100 droplets droplets and micropipette and micropipette spots. spots. Each curve is obtained from a single spot within its duplicates, having the median accumulation spots.Each curveEach curve is obtained is obtained from afrom single a single spot withinspot within its duplicates, its duplicates having, having the median the median accumulation accumulation level. level. (b) The calculated thickness of spots and the corresponding volumes dispensed. level.(b) The (b) calculated The calculated thickness thickness of spots of spo andts the and corresponding the corresponding volumes volumes dispensed. dispensed. Although measuring immobilized probe density demonstrates the capture capability of the 3.3. VarianceAlthough and measuring Percent Error immobilized in Extracted probe Binding density Coeffi cients demonstrates with Respect the to capture SNR Levels capability of the microarray spot, it is not adequate to assess the performance. This is precisely the reason for our microarrayAs we spot, mentioned it is not before, adequate the primary to assess goal the of performance. many binding This experiments is precisely is the the correct reason estimation for our comparison using kinetic binding curves and error in fitting binding coefficients. comparisonof binding coeusingfficients kinetic between binding the curves analyte and anderror the in ligand.fitting binding A bivalent coefficients. antibody may bind to two 3.3.separate Variance antigens, and Percent and the Error binding in Extracted of antigen Binding to one Coefficients binding with site mayRespect directly to SNR impact Levels the efficiency of 3.3.binding Variance to the and other Percent binding Error site in Extracted on the same Binding antibody. Coefficients This Bivalent with Respect model to is SNR described Levels as follows [11]: As we mentioned before, the primary goal of many binding experiments is the correct estimation As we mentioned before, the primary goal of many binding experiments is the correct estimation of binding coefficients between the analytek on1 and the ligand.kon2 A bivalent antibody may bind to two of binding coefficients between the[L] + analyte2[R] and[LR the] + ligand.[R] A[LRR bivalent] antibody may bind to two(1) separate antigens, and the binding of antigenk↔ otoff1 one bindingk↔o ffsite2 may directly impact the efficiency of separate antigens, and the binding of antigen to one binding site may directly impact the efficiency of binding to the other binding site on the same antibody. This Bivalent model is described as follows [11]: binding to the other binding site on the same antibody. This Bivalent model is described as follows [11]: kon1 kon2 [퐿] + 2[푅] ↔kon1 [퐿푅] + [푅] ↔kon2 [퐿푅푅] (1) [퐿] + 2[푅] k↔ [퐿푅] + [푅] k↔ [퐿푅푅] (1) koff1 koff2 off1 off2

Biosensors 2020, 10, 158 8 of 11 Biosensors 2020, 10, x FOR PEER REVIEW 8 of 11 where k and k represent the first association and dissociation rates, and similarly, k and k where kon1 and koff1off1 represent the first association and dissociation rates, and similarly, kon2on2 and kooff2ff2 are the secondary association and dissociation rates. T Too understand understand the the binding kinetics of these interactions,interactions, the more computationally eefficientfficient LangmuirLangmuir model can also bebe usedused [[1212]:]:

kkonon [퐿][L+] +[푅[R] ]↔ [[LR퐿푅]] (2)(2) k koff↔oﬀ

where k on andand k koffo ffrepresentrepresent the the association association and and dissociation dissociation rates rates for for the the single single-valent-valent interaction interaction of twoof two molecules. molecules. This This single single-valent-valent model model offers offers a simpler a simpler analytic analytic solution solution and and is frequently is frequently used used to modelto model the the antigen antigen-antibody-antibody binding binding characteristics, characteristics, since since the the secondary secondary association association and and primary dissociation rates rates (k (kon2on2 andand koff1 ko)ff 1can) can usually usually be beneglected neglected—for—for high high-a-affinityffinity reactions reactions—in—in favor favor of the of primarythe primary association association and andsecondary secondary dissociation dissociation rates rates (kon1 (k andon1 kandoff2). k Tooff2 demonstrate). To demonstrate the effect the eofffect SNR of improvementsSNR improvements on these on these estimations, estimations, we wefirst first applied applied the the Langmuir Langmuir fitting fitting to to extract extract the the binding binding coefficientscoefficients and simulate a theoretical curve curve,, and we simulated different different “noisy” curves by adding a random noise component having a Poisson probability distribution (Figure8 8).).

(a) (b)

2 Figure 8. ((aa)) Example Example raw raw data data from from obtained is is shown with a Bivalent and Langmuir fit, ﬁt, with R2 values of 0.998 and 0.993,0.993, respectively. ( (bb)) The The theoretical theoretical curves curves were created from the extracted coefficientscoeﬃcients with an additive Poisson noise.

We then then averaged averaged a growing a growing number number of curves of curves (n), each (n), step each having step ahaving hundred a hundredrandom selections random selectionsof n different of n curves different to mimiccurves theto mimic improvement the improvement of SNR by of spatial SNR by averaging. spatial averaging. For every For averaged every averagedcurve, the curve, Langmuir the Langmuir fitting was fitting performed, was performed, and the k onandand the k koffonrates and k wereoff rates recorded were recorded for each for random each randomselection. selection. Figure9 Figureshows 9a the shows histograms the histograms of the di ff oferent the different kon coeffi kcientson coefficients extracted extracted from each from random each randomselection selection of n averaged of n averaged curves. curves. The histogram in Figure 9a displays that increasing SNR enables greater precision in predicting kon values for α-lactalbumin and anti-α-lactalbumin association coefficient. The deviation of estimated binding coefficients from expected values drastically decreases with increased SNR of the curves; for kon, koff, and kd, a maximum percent error of 129, 553, and 1524 were observed for initial curves (SNR~10), and 2.3, 54, and 53 were observed after averaging 150 curves (SNR~123), respectively. Figure 9b demonstrates the decrease in percent error with averaging (with the increase of SNR); we also show the estimated percent error (~3%) from micropipette spots as performance comparison. With a rudimentary XY stage, an array of 6x4 spots (~1000um diameter) can be easily placed on the sensor surface, yielding a multiplexing capacity of 24.

Biosensors 2020, 10, 158 9 of 11 Biosensors 2020, 10, x FOR PEER REVIEW 9 of 11

(a)

(b)

FigureFigure 9.9. (a) Histograms forfor thethe extractedextracted kkonon valuesvalues with a corresponding corresponding number of curves averaged andand thethe yieldingyielding SNRSNR valuesvalues (SNR(SNR (a(a)) 10–87, 10-87, ((bb)) 87–125).87-125). The red-dottedred-dotted line representsrepresents thethe actualactual 5 calculatedcalculated kkonon value (1.33 × 10 ).). ( (bb)) The The highest highest percent percent error was observed from each group ofof konkon × estimationsestimations from from averaging averaging steps. steps. The The marked marked spot spot on the on plot the (green) plot (green) represents represents the projected the projected percent errorpercent from error micropipette from micropipette spots on spots the 24-multiplexing on the 24-multiplexing line. line.

4. DiscussionThe histogram in Figure9a displays that increasing SNR enables greater precision in predicting k values for α-lactalbumin and anti-α-lactalbumin association coefficient. The deviation of estimated on In this study, we have compared the efficiency of robotically spotted microarrays and binding coefficients from expected values drastically decreases with increased SNR of the curves; instrument-free created spots for the α-lactalbumin protein and anti-α-lactalbumin antibody binding for k , k and k , a maximum percent error of 129, 553, and 1524 were observed for initial curves experiment.on off, Givend that the IRIS platform is a shot noise limited system, we expect to have an (SNR~10), and 2.3, 54, and 53 were observed after averaging 150 curves (SNR~123), respectively. increased SNR, therefore, increasing accuracy and sensitivity with spatial averaging. The performed Figure9b demonstrates the decrease in percent error with averaging (with the increase of SNR); we also SNR analyses revealed that averaging performed on the single drop assays follows the √푁 rule, show the estimated percent error (~3%) from micropipette spots as performance comparison. With a showing the improvement of increasing the effective area/total electron accumulation. Four hundred rudimentary XY stage, an array of 6 4 spots (~1000 µm diameter) can be easily placed on the sensor fifty replicate spots averaged showed× an SNR of 260. Based on this result, the spots with larger surface surface, yielding a multiplexing capacity of 24. areas (hence, the increased number of electrons) were analyzed and compared with the same method. 4.We Discussion observed an improved signal level for the larger spots (robotic and instrument-free), with respect to averaging duplicate single drop spots with the same total electron yield for both surface coatings. MicropipetteIn this study, spots having we have the compared same area/electron the efficiency yield of averaging robotically 50 spotted–75 single microarrays drop duplicate and α α instrument-freedrops (SNR level created ~ 70– spots110) for showed the -lactalbumin SNR levels ofprotein 210–298. and anti- As expected-lactalbumin, the increased antibody binding volume dispensed (proportional to radius cubed) on the surface resulted in an increase in probe density within the spot area (proportional to radius squared). Probe density, due to the saturation of the spot Biosensors 2020, 10, 158 10 of 11 experiment. Given that the IRIS platform is a shot noise limited system, we expect to have an increased SNR, therefore, increasing accuracy and sensitivity with spatial averaging. The performed SNR analyses revealed that averaging performed on the single drop assays follows the √N rule, showing the improvement of increasing the effective area/total electron accumulation. Four hundred fifty replicate spots averaged showed an SNR of 260. Based on this result, the spots with larger surface areas (hence, the increased number of electrons) were analyzed and compared with the same method. We observed an improved signal level for the larger spots (robotic and instrument-free), with respect to averaging duplicate single drop spots with the same total electron yield for both surface coatings. Micropipette spots having the same area/electron yield of averaging 50–75 single drop duplicate drops (SNR level ~ 70–110) showed SNR levels of 210–298. As expected, the increased volume dispensed (proportional to radius cubed) on the surface resulted in an increase in probe density within the spot area (proportional to radius squared). Probe density, due to the saturation of the spot surface, remained the same after 10 nL. We then demonstrated the effect of noise level on the accurate extraction of binding coefficients by first fitting a binding curve obtained from the kinetic 5 5 measurements (kon = 1.33 10 k = 1.53 10 ), and then simulating noisy curves with the same × off × − parameters and additive Poisson noise. The variance of the estimated parameters decreased with an increasing number of curves averaged (therefore increased SNR) and the percent error of the values decreased by 55, 10, and 28-fold for kon, koff, and Kd, respectively. In conclusion, we demonstrated the excellent performance of the instrument-free assay fabrication method in comparison to robotic spotting. Micropipette spots achieved higher signal levels (2.8–4.3 fold) and higher SNR levels (~3 fold) than the averaging of robotic spots with the same area yield (~75 spots). Coupled with the IRIS platform, this method enables accessibility of high-sensitivity affinity measurements in a multiplexed manner for small research laboratories.

Author Contributions: Conceptualization, I.C., E.C. and S.Ü.; Data curation, I.C., M.T.G. and E.C.; Formal analysis, I.C.; Methodology, I.C., M.T.G., N.L.Ü. and F.E.K.; Project administration, S.Ü.; Software, I.C.; Supervision, S.Ü.; Validation, I.C.; Writing—original draft, I.C.; Writing—review & editing, I.C., E.C. and S.Ü. All authors have read and agreed to the published version of the manuscript. Funding: This work was partially funded by the Boston University Ignition Program and by the National Science Foundation (NSF-CORPS Award No. 2027109 and NSF-TT PFI Award No. 1941195). Conflicts of Interest: The authors declare the following competing financial interest(s): Selim Ünlü is the principal investigator of the technology translation grants listed in the funding information. He is the founder of a startup company (iRiS Kinetics, Inc.) for the commercialization of the IRIS multiplexed affinity technique.

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