Team:Technion Israel/Measurement

S.tar, by iGEM Technion 2016

S.Tar, by iGEM Technion 2016


Although there is an abundant number of chemotaxis assays available today, most of them were designed 40 to 50 years ago and almost none provide a real time measurement without the use of fluorescence labeling (1).
The use of Porous Si (PSi) and oxidized PSi (PSiO2) matrices for biological sensing is on the rise. So far various analytes such as DNA, proteins and bacteria have been proven to be detectable on such matrices. The common method to monitor the interaction of said analytes within the porous films is reflective interferometric Fourier transform spectroscopy (RIFTS), as it allows a real time measurement and output for the user (2,3).

Fig. 1: Trap and track chip illustration (2)

Here we present the results of an early experiment for the detection of chemotactic activity on the porous silicon films initially developed for bacterial detection.


RIFTS- Reflective Interferometric Fourier Transform Spectroscopy, is a rather simple method for monitoring molecular interactions in porous Silicon films. The measurement begins by shining a beam of white light on the film. The porous Silicon acts as a grating that scatters the light and creates a set of diffraction orders at different angles, depending on the periodicity of the pores. The reflected light beam is measured by a spectrometer. Detection is based on changes in the spectral interference pattern which results from the reflection of the beam.

Fig. 1: Light reflection off a porus silicon(2).

Molecules that penetrate into the silicon pores are observed as a change in the refractive index of the zero-order diffraction - the reflected light which is normal to the pores’ surface.

The intensity of the reflected light is given by the expression:

Equation 1: Intensity of zero-order reflected light.

With the frequency of the cosine being:

Equation 2: Frequency of zero-order reflected light.

I is the intensity of the light, Ψ is the phase delay between the source beam and the reflected one, λ is the free space optical wavelength, L is the depth of the pores (a physical property of the Silicon film) and n0 is the refractive index of the medium filling the pores.

The term 2n0L is thus the optical path - the path the light travels from the source to the sensor. The factor of two is due to the reflection which means the light travels the length of the pores twice.
This term is named the Effective Optical Thickness - EOT and is the size we are interested in measuring. A fast Fourier transform (FFT) of the reflectivity spectrum provides us with the frequency of the peaks and is a way to directly measure the EOT. Once bacteria enter the pores they induce measurable changes in the EOT since the light has to travel through the bacteria which have a different refraction index than the clear medium.

An example for typical interference patterns measured in a full experiment can be seen in figure 2. The graph presents over a 1000 interference patterns that are overlaid, giving the illusion of a smear, however that is not the case. The changes in the refractive index during the experiment are translated to changes in the frequency of the intensity which is why the patterns do not line up with one another.

Fig. 2: Example of experimental interference patterns.

Experiment General Scheme

The system used in this assay including all of its components, from the Nano-porous chip up to the laptop, was built and provided to us by Prof. Segal’s team. It relies on the same principle of detection explained above, FFT. Moreover, its ability to trap and detect bacteria has been proven beforehand.

Fig. 3: Microscope images demonstrating bacteria cells confined in the pores(2)

In their experiment, both sensors (light beam and CCD spectrometer) were fixed on a single spot of the MPSiAS that was placed in a custom-made flow cell, this was to assure that the samples reflectivity is recorded at the same spot during the entire measurement. Through that flow cell, bacterial suspension was continuously delivered while the samples reflectivity was recorded and analyzed by RIFTS.
It is clear from their results presented in figure 4, that the introduction of bacteria leads to a rise in the EOT which is attributed to the increase of the refractive index caused due to the entrapment of the bacteria in the pores.

Fig. 4: Optical thickness vs. time (in the inset: zoom of the EOT shift (2)).

As our project focuses on the chemotactic movement of bacteria, a different approach was implemented using their system. The focus of said approach was to study the effects of chemo-effectors, both attractants and repellents, on the bacteria and if it can be measured by RIFTS.

The steps taken in this assay to fit our approach were the same as mentioned above with a slight change. These changes were as follows:
1. Wash the chip with a buffer.
2. Wash the chip with bacteria to fill the pores to full capacity.
3. Wash the chip with clean medium to remove the cells that have not settled into the pores.
4. Insert a chemo-effector into the system to measure the bacterial chemotactic response.


The EOT results of most tests that were conducted with the system following our approach were similar to the one presented in figure 1. The figure represents the EOT vs time of one experience and all 4 steps mentioned can be clearly seen. The main focus was on the 4th step as the first three steps were as predicted.

Fig. 1: Example EOT vs Time graph, each of the steps is marked with an arrow. Black arrow – initial washing. Green arrow – addition of the bacteria. Orange arrow – second washing. Yellow arrow – addition of the chemo-effector.

In four experiments a clear change was seen in the EOT upon the addition of the chemo-effectors (step 4 – yellow arrow). The initial sharp jump, up for the repellent or down for the attractant, was due to the change of the solution to one containing the chemo-effector, that has a different refractive index. Nevertheless, following the sharp jump a continuous trend was observed, again going up for the repellent or down for the attractant, which we believe is the chemotactic response of the bacteria

In order to verify the assumption, further analysis was conducted, for all of the four experiments with promising results. Two normalization steps were done to verify the changes and compare between the different experiments and the results are presented below – figure 2. These normalizations were:

1. Time: this helps in comparing the responses of the different strains over time, the normalization was done according to the following equation:
      Time = Timen - Time0
Time - The normalized time that passed since the introduction of chemo-effector.
Time0 - The time elapsed since the start till the addition of the chemo-effector.
Timen - The time that elapsed since the start of the experiment.

2. OT: this allows to measure the change in the EOT while phasing out the initial jump caused by the solution change. The normalization was done as follows:
      EOT = EOTn - EOT0
EOT - The EOT measurement relative to EOT0.
EOT0 - The EOT measurement following the introduction of chemo-effector and after the initial jump.
EOTn - EOT recorded in Timen.

Fig. 2: Digital chemotaxis measurements – Each of the four lines represents a different experiment done with a different chemo-effector. Light blue – response of positive control (ΔZras) to 50 mM sodium benzoate (repellent). Dark blue – response of positive control (ΔZras) to 50 mM sodium benzoate (repellent). Yellow– response of negative control (UU1250) to 50 mM sodium benzoate (repellent). Red – Dark blue – response of our clone Tar1 to 2mM aspartic acid (attractant). The dashed lines represent digitized ±1.

Discussion & Conclusion - Digital Chemotaxis

The main hypotheses that might explain the above phenomena is when a chemo-effector is introduced to the system, the bacteria senses the gradient in the Z-axis (height) formed by the diffusion of the substance into the pores. This leads to two different responses:

On one hand, if the substance was an attractant, the bacteria will travel up the concentration gradient, which means leaving the pores, to the higher concentration above them, leading to a drop in the EOT, as seen in Results tab-figure 2, red line.

On the other hand, if the substance was a repellent, the bacteria will travel down the gradient, fleeing from the high concentration, deeper into the pores. And thus any bacteria which is not confined will enter the pores as well and will lead to rise in the EOT, Results tab-figure 2 blue lines.

In conclusion, we report here a promising and novel method for the detection of bacterial chemotaxis that provides the means to digitally quantify chemotaxis – in other words, a clear distinction between an attractant response (negative trend), repellent response (positive trend) and no response (zero trend) can be seen and proven with the help of this assay.


We would like to express our gratitude to Prof. Segal and her team whom provided the necessary equipment, guidance and help and without it, this work would not have been accomplished.

1. BERG, Howard C. E. coli in Motion. Springer Science & Business Media, 2008.‏

2. Massad-Ivanir, N., Mirsky, Y., Nahor, A., Edrei, E., Bonanno-Young, L.M., Dov, N.B., Sa'ar, A. and Segal, E., 2014. Trap and track: designing self-reporting porous Si photonic crystals for rapid bacteria detection. Analyst, 139(16), pp.3885-3894.

3. MIRSKY, Y., et al. Optical biosensing of bacteria and cells using porous silicon based, photonic lamellar gratings. Applied Physics Letters, 2013, 103.3: 033702.‏‬

S.tar, by iGEM Technion 2016


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