IEEE CONECCT2014 1569822173

Development of Biochip Arryer And Imaging System

for Making Biochip

Ratnesh Singh Sengar, A K Upadhyay, S. Mishra, R.K.Puri,D.N.Badodkar, Manjit Singh

Division of Remote Handling & Robotics Bhabha Atomic Research Centre

Mumbai, India. (e-mail: ratneshss@yahoo.com)

Abstract-A bio-chip arrayer system has been developed indigenously for producing bio-chips. The bio-chips are based on use of track etched membranes (TEM) as a novel solid support. TEM are extremely thin (10 micron) and highly microporous membranes. We have tested polycarbonate (PC) TEM by manual spotting of antibodies for the development of the antibody bio­chip for simultaneous estimation of the thyroid related hormones in sera of patients with thyroid disorders. However, manual spotting is a labor-intensive and error prone process. The arrayer has been developed using piezoelectric based pin head for non contact spotting of solution on the porous TEM. This is a computer controlled three axes robotic arm to automate and streamline different processes. It precisely and accurately dispenses few picolitre to nanolitre solution resulting in excellent spot reproducibility and gives uniform spot morphology. It uses real time pressure control and excellent circuit for voltage and pulse width for piezo-dispenser. It has been developed according to the flexible industry standard (25 X 75 mm) glass slide microarray format and can be used with any 3-D (membranes or gel pads) or 2-D (glass) substrates. Real time machine vision system has also been developed to monitor the dispenser performance. The system has been modeled using the neural networks to dispense the different viscous samples to maintain the same spot size. This system includes user friendly control software to define all critical parameters for printing operation. The performance, accuracy and repeatability of the system are evaluated by printing test patterns and quantifying various spot parameters using statistical methods.

Keywords-Arrayer; biochip; microporousmembrane; neural network; pico litre dispensing; piezoelectric; TEM

I . INTRODUCTION

Imrnunoassays are widely used to measure the concentration of a variety of the analytes in the clinical and biological samples. Multianalyte immunoassays (MAlA) use an "antibody biochip" [ I ] where miniscule spots of different antibodies, are immobilized on an inert matrix at spatially pre­determined sites . MAlA offers the possibility of quantifying several analytes simultaneously and economically. Biochip arrayer is a robotic system which picks up given solutions from a source plate and deposits them at specified locations on a substrate in small quantities (nano-litre to pi co-litre). We have used PC TEM as a novel substrate for making antibody bio-

Bharti Jain, M.G.R. Rajan Radiation Medicine Centre

Bhabha Atomic Research Centre Mumbai, India.

chip. TEM are thin ( 1 0 micron) and highly microporous membranes having pores at the density of 1 05_ 1 08 pores/cm2. TEM provides flat and smooth surface for the immobilization of the antibodies. It has been tested by manual spotting of antibodies and was found to be an excellent substrate giving uniform spot morphology, high protein binding capacity, low background, and physically and chemically robust support for MAlA. A bio-chip has been developed for the estimation of thyroid related hormones in the sera of patient having different thyroid disorders.

Manual spotting of biological samples is a labor-intensive and error prone process . A robotic arrayer can provide minute and more uniform spots resulting in good quality, repeatable and accurate spotting which in tum facilitates the subsequent evaluation of data. We have developed a bio-chip arrayer that uses piezoelectrically driven print head having 50 micron nozzle diameter to deposit antibody solution on thin microporous TEM. The system is capable of depositing thousands of extremely small droplets of antibodies, one droplet at a time, with each droplet containing a different antibody.

Piezoelectrically driven print head requires pulse excitation for proper actuation [2] [3 ] . A droplet is ej ected from the print head nozzle when the amount of energy delivered through actuation is larger than the viscous energy dissipation plus the energy needed to form droplet surface . Excess energy is converted to kinetic energy hence determine the initial velocity of ejected droplet. Bogy & Talke [4] investigated behavior of droplet formation by applying simple analysis such as propagation and reflection of acoustic waves and gives an idea of pressure profile at different instants following application of actuation pulse.

Print head control is developed for excitation of piezoelectric element to dispense picolitre to nanolitre volume of solution which is mounted on z-axis of robotic system. The amplitude and duration of actuation pulse are the parameters which are controlled for micro drop ejection from piezoelectric print head [4] . The piezo controller generates pulses having amplitude range 50-1 80V and a variable width 10 /lm to 80 /lm. The results show how amplitude and duration of actuation pulse affect volume ejecting from nozzle orifice . The pressure

control unit provides holding pressure for solution from dispensing it at mUltiple locations on the same membrane or on different membranes in a given experiment. The formation of microdrops during the piezoelectric drop-on-demand printing is investigated using CCD camera. Even with all these features, the cost of the system is very reasonable.

A. Design features

II. DESCRIPTION

The important considerations while designing this precise 3 -axes robotic system are high speed, micron level accuracy and minimum vibration. The system should also have non­contact dispenser head to dispense few pico litres of solution on the thin microporous membrane.

B. Bio-chip cassette

MAlA is a solid phase immunoassays based on highly selective array of minuscule amount of antibodies immobilized at specific sites on a suitable support. An essential pre requisite for the development of MAlA is choosing an appropriate solid­phase matrix and coupling chemistry for immobilizing antibodies . Ideally, the substrate should provide have high protein binding ability, low non-specific binding, good spot morphology, physical and chemical robustness and should be amenable to automation. Substrates used are based on functionally modified glass, gold, plastics and aluminum oxide sheets . Although glass is most commonly used substrate it requires extensive chemical pre-treatment and was found to be fragile. Despite variety of the substrates being available, none seems to meet all the demands of a substrate required for making antibody chips. A recent development is to move from planar to micro-porous supports. This has also been achieved with polyacrylamide, agarose and nitrocellulose gel pads on the glass surface providing high immobilization efficiency, wetting property and enhanced signal output. However, this kind of combination substrates are difficult to fabricate; spotted antibody solution spreads on the hydrophilic substrate; and there is difficulty in equilibrating with different buffers and washing steps during the assay procedure. Among various substrates tested, PC-TEM was found to be a suitable substrate for antibody chips. A novel bio-chip cassette was designed and developed in which PC TEM is mounted by auto-stretching technology. It provides uniform surface without any wrinkle. Each cassette has two 20 mm diameter and 10 micron thick PC TEM. Bio-chip cassette has dimensions of standard glass slide 3"xl"x 0.04"and can be used with any standard scanner as shown in Fig. l . Being highly microporous, it provide enhanced surface area for additional immobilization of antibodies and being hydrophobic give well defmed antibody spots . The thin

Fig. I : BioChip Cassette

2

Fig. 2: Biochip Arrayer and Imaging System

membrane in the cassette allows accessibility to different reagents during assay procedure and provides easy and efficient washing method by using vacuum.

C. Robotic System

The biochip making process requires very accurate and specific placement of spotting solution on the biochip surface . A Robotic System has been used and developed for the said purpose as shown in Fig.2. The robot is a critical part for precise dispensing of solution on 1 0 micron thin PC TEM. Hence the system hardware consisting of a 3 -axes robotic motion has been designed to be highly precise with a positioning accuracy of ± l0 microns. It uses servomotors, high resolution encoders, precision ground ball-screw based linear actuators, an advanced control system scheme and a well damped base table.

D. Dispenser Pin Head Control

In order to produce stable mono-disperse drops, a digital pulse generator driving a pulse power amplifier has been developed as an optimal electrical excitation source for piezoelectric dispenser. The pulse parameters needed to drive piezo electric dispenser vary depending upon geometry, size of element and fluid that is to be ejected. The used print head requires pulse excitation having an amplitude range 50V - 1 80V and variable pulse width 10 f.ls to 80 f.ls with rise and fall time less than 500ns to dispense picolitre amount of spotting solutions having viscosities 0.4 to 20 mPas . The basic block diagram of the scheme to produce required pulses and the photograph of the developed piezo controller are shown in Fig.3 and Fig.4 respectively. The digital pulse generator has been developed using 1 6 bit microcontroller board with inbuilt hardware timers [5] . To have an online control over the piezo controller, it is serially interfaced with host computer on RS232 link. The 5V pulses, having user defined width, are amplified to required voltage level using a high voltage MOSFET power operational amplifier, which has sufficient bandwidth, slew rate and drive capability required for actuating piezoelectric pin [6] . The high voltage supply required for amplifier is derived from 12V supply using DC to high voltage DC convertor module.

Microcontrollcr (Inlineon C167CR ) �V.variab1e"'idth pul�e

Fig. 3 : Block diagram of Piezo-controller

E. Software

Piezoelectric Ejector

A real-time control as well as user-friendly software has been implemented using Windows XP with high speed quad core processor. Although Windows XP is not real time operating system, using high performance high precision hardware timer in multithreading & multi-core environment make real time control possible on high speed computer. Software has been developed using object oriented paradigm.

We have developed user interface to control various parameters like pulse width, height of pulse, pressure, drop count, frequency and mode (burst or continuous) for controlling volume of spotting solution. Another block is implemented to control the parameters for dispensing head like fill time, empty time, clean time, delay time and post heating. We have also implemented a block to control motion parameters for all three axes and separate files handling for each user to facilitate storage of individual experiment. It has user friendly interface where user can configure all the control parameters from the desktop. It also gives the benefits of several distinguishable features such as online calibration of all the resources and automatic program abortion due to any malfunctions in the system.

Fig. 4: Prototype of developed Piezo-controller

3

Light Source (LED) CCD Camera

Fig. 5 : Block diagram of machine vision system

F. Machine Vision System

It is very necessary to establish a good vision system so that a very sharp and clear image is grabbed. The machine vision system consists of a CCD camera and a continuous/stroboscopic, uniform illumination light source (LED). LED can be operated in either continuous mode or stroboscopic mode. In stroboscopic mode, strobe illuminates the drop at same frequency at which drops have been generated so that an image of stationary drop can be observed. Also the time delay between the illumination strobe and the drop ejection pulse can be set for their synchronization. A CCD camera with l OX zoom lens is used to capture the falling solution drops . The Computer is fitted with 32 bit frame grabber card through PCI interface to capture and transfer images in real time to computer for further image processing. The block diagram of the experimental setup is given in Fig.5 and actual implementation of the above proposed experimental setup is given in Fig. 6 .

Fig. 6 : Experimental set-up o f machine vision system

Pressure Control Unit

+ 1 PielO Pulse Controller

Strobe Driver U n it

'L Pielo Dispenser Head

RS-232

I l Power Supply I Strobe 1i2ht ] I Camera I--Frame Grabber + V "!"

C RS-232 • ,, " Z J I Servo Drive I • Computer Controller • 0 r " �I • " M • " L-- • "

• " &. "- - - - - - . X Servo Motors

Limit Switches n & Home Sensors

I Encoder I

Flg.7 : Block dIagram of the control system BIOChlp Arrayer and Imagmg System

G. Control System

A realistic servo dynamic model of the system is constructed; various control loop compensators are used to achieve a fast response with less oscillation in the system. The position and speed loop compensators are implemented as software based lead, lag compensators and notch filters_

Depending on the bandwidth requirements, different loops may cycle at different rates and be tuned for a stable system_ The computer based control block diagram for Biochip Arrayer and Imaging System as shown in Fig _7 .

H. Dispensing Control for Solution of Different Viscosities

The uniformity of volume of dispensed solutions on the biochip is one of the critical parameter for accurate quantitative analysis of the spot One of the �equirements. of our system is to keep the same drop size for dIfferent s�ottmg solutions which will result in uniformity of spot SIze _ A machine vision and neural network based control system has been employed as shown in Fig. 5 . The pulse width and voltage amplitude are the two parameters which is used to drive the piezoelectric dispenser for dispensing the different volume of spotting solutions . A machine vision system measures the area of the falling drop. The captured image is segmented using the canny edge operator. The calculated a�ea of drop gives the indication of the real volume of the fallIng drop. The area of the falling drop varies with the properties of the sample, even if all the other parameters are kept same_ The main property of the sample is its viscosity and surface tension. The surface tension depends upon the viscosity. Therefore, only viscosity is considered here to classify the samples. .

In our arrangement, a spotting solution is dispensed WIth the current settings of the dispenser parameters (pulse width and pulse height) and the size (area) of the dispensed drop is measured using machine vision. This information is used to estimate the governed property 11 (viscosity) of the spotting solution and can be expressed as

4

/l = F(pulsewidth, pulseheight, sizeofdrop) ( 1 )

where F i s complex and nonlinear function of pulse width, pulse height and size of drop. After estimating 11, the next step is to calculate the required dispenser parameters, e .g. pulse width and pulse height to generate the drop of a desired size . This can be expressed as

(pulsewidth, pulseheight) = G(desiredsize, /l) (2)

where G denotes the nonlinear and complex relations between them. Both F and G are not easy to find out directly. A neural network is an efficient tool to model such nonlinear functions . The process of viscosity measurement [7] [8] [9] and fluid dispensing [ 1 0] are already modeled through neural network in the different literatures . For the first time in this paper, the machine vision is used to predict the property of spotting solution and is used to refine the control of the dispensing process. Each of F and G is modeled using a s�p�rate feed forward multi-layer perceptron (MLP) type of artIfiCial neural network (ANN) models . The first ANN model is used to model F and is depicted as network F in the Fig. 8. The output of the first model is connected with the second ANN model of G. The network architecture of both ANNs consists of one input layer, two hidden layer, and one output layer. .The tm:ee neurons in the input layer of the network F are assOCIated WIth the three input parameters : the current value of pulse width and pulse height, and size of the drop measured by the machine vision. The output layer consists of single neuron which shows the predicted value of the property 11 of the sample_ The two neurons in the input layer of the network G are associated with the two input parameters : the estimated value of the property 11 and the desired size of the drop. The output layer consists of two neurons which show the predicted value of the control parameters : pulse height and pulse width. Each hidden layers in both ANNs consists of 4 neurons . Each neuron as indicated in Fig .8 , receives signals from the neurons

[i----------------------------------------------------------�-----------------------------------------------------------'[

Pui s :: W idth Pul s e Width

Pul s e e i g h t

De s i red S i l e _______ --' ��----� �----�) y

'L..mnnnnnmnnnm���9_(�_�nnmnnnnnm_ .... mnmnmn_���(�_Gnnnnmnnnnnmnnnnn.J> Fig .8 Two feed forward neural networks cascaded with each other

of the previous layer weighted by the interconnect values between neurons except input layer. Neurons then produce an output signal by passing the summed signal through an activation function as given by

(3)

where Yj is the output of /h neuron of a layer, Xi is the ilh input to this neuron coming from previous layer and weighted by wij, and bj denotes the bias of t neuron. The activation function <p is a monotonic increasing sigmoid nonlinear function of the form given by

1 <peS) = 1 + e-(J8 (4) where � is a positive real number experimentally derived constant for an ANN. For network F, it is set to l .27 and for network G, it is set to 0 .92. Also the all data given to input layers are normalized between 0 and I as in Eq.5

Xoriginal - xmin xnormalized = X - X . max mm (5)

The learning of the ANN model is accomplished by the training process. Two ANNs used in this paper are trained separately, i .e . connection between the output of network F and network G is removed before training. The backpropagation method is used to train each network using the training data. The objective of training is to minimize the square error between the desired output and predicted output.

so .ov

r 1 5 .001 5 .00011 Stop i • 62.0

AmpIirude(J) : nov Widlb(3) : 30Us RiseTime{3) : 4000s FIU Time(3) : 400ns

Fig. 9: Pulse generated from Piezo-controller

5

III. EXPERIMENT RESULTS AND DATA ANALYSIS

A. Variation of Dispensed Volume Against Pulse Width for a Sample

To find relation between dispensed volume and pulse width, actuation pulses having a fixed 120V amplitude and user defmed pulse widths, are generated and given to piezoelectric dispenser. The shape of one of the pulse having 50/1s width is

given in Fig. 9 .Volume of dispensed solution on membrane substrate is found accurately from a standard commercialized instrument. Fig. l O shows how dispensed volume is affected by the pulse width of actuation pulse. It was observed that as pulse width is increased from 26/1s to 68/1s, a linear increase in dispensed volume is occurred.

The Fig. 10 shows for pulses having width less than 26/1s and amplitude l20V, liquid try to eject from the nozzle but drawn back by the negative pressure cycle of the excitation, so no liquid is actually dispensed. For larger pulse width (greater than 68/1s), multiple drops are formed simultaneously thus producing satellite drop.

B. Variation of Dispensed Volume Against Pulse Amplitude for a Sample

To fmd relation between dispensed volume and pulse amplitude, actuation pulses having fixed width (30/1s) and variable pulse amplitude, are generated and given to piezoelectric dispenser. The Fig. 1 1 shows that as pulse amplitude is increased from 90V to 1 50V, an increase in dispensed volume is observed. After 140V, volume increment is lesser for the same increment in pulse amplitude as most part of supplied energy is changed to kinetic energy of droplet and

280 240

Ii 200 Qj 160 § 120 g 80

40 o

20

�

30

.-...------

40 50 Pulse Width (IlS)

--...,....-

60

Fig. 1 0 : Dispensed volume variation against pulse width

80 - 60

I : • Ii

:::=:: Qj E 40 :::J 0 20 > 0

80 100 120 140 Applied Voltage(V)

Fig. l l : Volume variations against pulse amplitude

70

160

Table I: Volume of dispensed solution at a fixed pulse

Row no. Volume (pI) measured from scanner at Pulse width 30l1s and Pulse Amplitude 120V

I 46,52,48,48,48

2 48,46,48,52,50,48

3 46,48,48,48,50,48,48

4 50,48,46,46,50,48,48

5 46,48,46,48,50,46,46

hence its velocity increases sharply. For pulses amplitude less than 90V at 30llS pulse width, liquid is not dispensed. For larger pulse amplitude (greater than 150V), satellite drops formation occurs .

C. Repeatability of Printing Patterns

Repeatability of the piezoelectric print head is found out by carrying out experiments multiple times while keeping the parameters of actuation pulse fixed and finding out the dispensed volume from the scanner. The result of the experiments is listed in Table I .

• Mean: 47.94pl • Standard Deviation: 1 . 59767 • Coefficient of Variation (CV) (%) :

Std. Deviation * 100 = ; .332

mean

The standard deviation and %CV is quite small. It shows that almost similar volume has been dispensed for the entire biochip.

D. Error Measurement During Dispensing of Different Viscous Samples

The performance of the model was measured by connecting them together as shown in Fig .8 and then measuring the drop size after applying estimated control parameters . The mean percentage error (merr) between desired size (Sd) and obtained size (So) for N number of test samples was measured as

1 � ISd - So l merr = N L Sd x 100% (6)

The result is presented in Table II. The proposed ANN model has reduced the error significantly. The size measured with subpixel accuracy may reduce the error further.

Table II : Measured error with ANN model

Test Set With ANN Without any No. model model

I 2 . 0 1% 12 .0 1% 2 2 .83% 16 . 1 0%

3 3 . 57% 20.65%

6

IV. CONCLUSION

At the center of the bio-chip manufacturing is a creative fusion of biology and engineering. Advanced contact printing technologies, ink-jet technologies and semiconductor techniques are all used to manufacture bio-chips at present. We have developed high quality non-contact type biochip arrayer and imaging system. The positioning accuracy of the 3 -axes robotic motion is achieved with in 10 microns. A TEM based biochip cassette has been developed using autostreching technology which was shown to be a better substrate than other substrates used for making bio-chip . Efforts are on to improve the speed, accuracy and space utilization of the system besides producing better quality of spots by using non contact dispensing piezoelectric based pin head.

We have developed a volumetric control system for falling solution to generate actuation pulses with user defined parameters to dispense volume in picolitre range. The rise time and fall time of the pulses has been achieved within 450 ns . The proposed ANN based model has succeeded in getting uniform spot size for different samples having 3 -4% variation in viscosity. Error analysis of the spotting process is very critical for producing consistent and good quality spot grid and will help in minimizing erroneous results in further processes like hybridization, analysis and scanning. Low coefficient of variation ensures uniformity of spots during the whole procedure of making biochip.

The hardware and software both are designed to provide high performance automation together with the flexibility required for individual sample handling.

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