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Team:Peking/Project/BioSensors/MulticomponentAnalysis
From 2013.igem.org
Multi-component Analysis
We have equipped our toolkit with a collection of biosensors, each capable of sensing a specific group of aromatic compounds (Fig. 1). Considering the complexity of practical analysis, we may need to analyze a multi-component sample using more than one biosensors. For example, a sample maybe consists of both 4-CISaA (an inducer of NahR) and 3-CIBzO (an inducer of XylS). If we want to quantify the concentrations of both 4-CISaA and 3-CIBzO, the biosensors NahR and XylS need to be applied to the sample separately. To make sure that the multi-component analysis is valid, we must guarantee that the presence of 3-CIBzO won’t interfere with the dose response of NahR biosensor to 4-CISaA, and vice versa.
If the presence of an inducer of XylS (not an inducer of NahR) doesn’t interfere with the dose response of NahR to any of its inducers, and vice versa, we call the biosensor NahR and XylS are "orthogonal"; namely, no synergistic/antagonistic effects happen between the inducers of NahR and XylS biosensors.
We examined the orthogonality between 4 representative biosensors (Fig.2). The orthogonality test between two biosensors, biosensor I and biosensor II, was performed in the following procedure:
1. A typical inducer A for biosensor I and a typical inducer B for biosensor II were selected.
2. The dose response of biosensor I to inducer A was measured, under the perturbation of inducer B.
3. The dose-response of biosensor II to inducer B was measured, under the perturbation of inducer A.
If biosensor I and biosensor II are orthogonal, the dose response of biosensor I to inducer A should be constant, regardless of the concentrations of inducer B; and the dose response of biosensor II to inducer B should be constant, regardless of the concentrations of inducer A. Namely, for two "orthogonal" biosensors, the perturbation of an unrelated inducer has negligible effect on the dose response of a biosensor to its related inducer (Fig. 3).
The orthogonality between XylS, NahR, HbpR and DmpR biosensors have been carefully evaluated using the assay discussed above (Fig. 2). The data were processed by linear fitting and the slopes of the fitting curves were compared with 1 (Fig. 3, Fig. 4). The closer the slope was to 1, the more orthogonal the two biosensors were. Results showed that the biosensor pairs, XylS and NahR (Fig.4a, b), XylS and HbpR (Fig.4c, d), NahR and HbpR (Fig.4e, f), XylS and DmpR (Fig.4g, h), NahR and DmpR (Fig.4i, j), and HbpR and DmpR (Fig.4k, l) are all orthogonal, as summarized in Fig. 5.
In conclusion, we have confirmed the orthogonality among inducers of different biosensors, which is one of the main features we expect for our aromatics-sensing toolkit; this allowed the combination of these biosensors to profile aromatics for the ease of practical applications.
Features of Our Biosensor Collection
We have carefully determined the detection range of each fine-tuned biosensor through the ON-OFF test of 78 diverse aromatics and those detection ranges composed the comprehensive detection profile of our biosensor toolkit. Calibration dose-response curves for typical inducers were determined. In addition, orthogality tested were carried out systematically to confirm the non-existence of synergistic or antagonistic effects between typical inducers of each of the biosensors. With these two features, our biosensor collection is feasible for the analysis of practical samples, which might contain several different kinds of aromatic compounds (Fig. 6).
Fig. 6 Features of biosensor collection we built. The collection of fine-tuned and well-characterized biosensors for various typical aromatic compounds and the orthogonality among inducers made our biosensor collection feasible for the analysis of practical samples. For more information about the two features, please visit Biosensor Introduction.
How to use the Biosensor Collection?
In a practical analysis, the water sample containing appropriate antibiotics was added to the toolkit equipped with the well-characterized biosensors, namely XylS, NahR, DmpR, HbpR, XylR and HcaR. Then the fluorescence output for each biosensor was measured respectively (Fig. 7a). Then, for each compound, a previously established calibration dose-response curve was used to obtain the measured concentration based on fluorescence output of the corresponding biosensor (Fig. 7b).
Fig. 7 Demonstration of the usage of our biosensor collection. (a) Water sample was added into the toolkit equipped with six biosensors, each sensing a specific category of aromatic compounds: XylS for benzoates, NahR for salicylic acids, DmpR for phenols, HbpR for biphenyls, XylR for xylene and HcaR for phynolpropionate. (b) The fluorescence output of the biosensor was used to retrieve the measured concentration of a specific inducer based on the previous established calibration dose-response curve.
Method Illustration and Verification
To evaluate the reliability of such method, we prepared a large collection of samples composed of one or more aromatic compounds of different concentrations. And measured concentrations of the aromatic compounds were obtained using the methods mentioned above. Then ‘Deviation Fold’ for each compound was calculated, by dividing measured concentration with real concentration:
A fold close to one indicates that the method is accurate, and vice versa. In the following practical-case-illustration of our biosensor toolkit, we prepared a large collection of samples containing four aromatics, three can be detected by corresponding sensors (XylS for 3-methyl-benzoate, NahR for 4-methyl-salicylic acid, HbpR for 2-hydroxyl-biphenyl), one (that is phenol) as interference, since no sensor was used to sense it (Fig. 8a). Three concentrations were set for each of the four compounds and there came out 81 multicomponent samples (Fig. 8b: horizontal and vertical concentration bars). The 'Deviation Fold' was calculated for biosensor XylS, NahR and HbpR respectively in all the samples (If the real concentration of a specific aromatic compound is zero, ‘Deviation Fold’ was calculated by dividing the measured normalized fluorescence intensity of biosensor with the background normalized fluorescence intensity of the biosensor). As for the interference effect, 'derivation fold' was calculated as the mean of the absolute value of the folds in logarithm scale between the normalized fluorescence intensity with or without the interference, phenol, in the testing samples of the three biosensors. Experimental results were shown in the heat map, where the lighter the color was, the closer to one was the ‘Deviation Fold’, which means the more reliable was our biosensor toolkit (Fig. 8b: color bar in the right). By statistics analysis, over 90% of the deviation folds were between 0.5 and 2, successfully verifying the reliability of our method (Fig. 8b).
Fig. 8 A practical multicomponent analysis case to illustrate the reliability of our biosensor toolkit and aromatics-sensing methods. (a) A large set of sample with four aromatics of different concentrations were prepared. Three biosensors were used to test the corresponding aromatics: XylS for 3-methylbenzoate (3-MeBzO); NahR for 4-methyl-salicylic acid (4-MeSaA); HbpR for 2-hydroxyl-biphenyl (2-HBP). Phenol existed as interference since no biosensor was used to test it in this case. (b) Test results demonstrated in heat map. Each square that represents a sample (real concentrations of the 4 aromatic compounds in the sample can be obtained from the ‘concentration bars’ in horizontal and in vertical) contains four colored mini-squares that indicate deviation folds for the four compounds.
Figure 1. The aromatics spectrum showing the aromatics-sensing profiles of our individual biosensors. Each color segment in the central spectrum represents the detection profile of a biosensor. Structural formula highlighted in color stand for the aromatic compounds that can be detected by our biosensors.
Figure 2. The orthogonality assay for the biosensor I and the biosensor II. (a) Biosensor I was added into the assay. Different inducer mixtures were added into lane 1, 2, and 3, respectively. Effect of inducer B (detected by biosensor II) upon the dose-response curve of inducer A (detected by biosensor I) was tested by comparing the fluorescence intensity of biosensor I among lane 1 ,2, and 3. (b) Biosensor II was added into the assay. Different mixtures of inducers were added into lane 1, 2, and 3, respectively. Effect of inducer A upon the dose-response curve of inducer B was tested by comparing the fluorescence intensity of biosensor II among lane 1 ,2, and 3.
Figure 3.Correlation of the perturbation of inducer B with the dose response of biosensor I to its inducer A, and vice versa. (a) The schematics for the orthogonality assay as in Fig. 2. (b) Horizontal axis represents the fluorescence when there is no inducer B perturbation and the vertical axis represents the fluorescence when the cell is exposed to the perturbation of inducer B in non-zero concentrations. If the dose response of biosensor I is constant, regardless of the concentrations of inducer B, the slope of the line should be close to 1, if not absolutely equal.
Figure 4. Linear fitting of the data obtained from the orthogonality assay showing that the orthogonality between the 4 representative biosensors. The experiments and data processing were performed as described in Fig. 2 and Fig. 3.The black dashed line denotes slope=1 as the reference line. These fittings showed the orthogonality between biosensors, (a, b) XylS and NahR; (c, d) XylS and HbpR; (e, f) NahR and HbpR, (g, h) XylS and DmpR, (i, j) NahR and DmpR, and (k, l) HbpR and DmpR. The experiment data, linear fitting curves of biosensor, and cognate inducers are in different colors: XylS in red, NahR in green, HbpR in orange and DmpR in dark cyan.
Figure 5. Summary of the orthogonality assay to evaluate the synergistic/antagonistic effects between the inducers of 4 representative biosensors. No synergistic or antagonistic effects between the inducers of 4 representative biosensors (XylS, NahR, HbpR, and DmpR) were observed. For instance, although the sensing profiles of NahR and XylS overlap to some extent, the NahR-specific and XylS-specific inducers proved to be really orthogonal.
