using colorimetric sensor arrays Monitoring biological processes by observing volatile chemical emissions using colorimetric sensor arrays Christina Corral Mentored by Dr. Calvin Chue, Dr. Aleksandar Miklos, and Dr. Michele Maughan Introduction Recombinant bacteria are being used in a multitude of ways to produce economically, medically, scientifically, and environmentally important materials. Examples include pharmaceuticals such as insulin, and vaccines. Having a real-time, and inexpensive way to monitor a bio process would be beneficial, and would allow researchers another method of optimizing their processes. This can be accomplished by analyzing the headspace above a bacterial culture using a colorimetric sensor array (CSA) and correlating the volatile organic chemical (VOC) profile of the CSA. The test subject is a strain of K-12 E.coli. The comparison of what stage the E.coli is in and the readings from the CSA are a strong indication of whether the CSA can detect metabolic changes in the E.coli. A CSA is a small rectangles of thin paper that has a grid of 76 dots. The dots change color according to what they are exposed to. Every organic compound has a unique colorimetric pattern (Suslick, 2004). Results The two ways to analyze CSA response are visual data and numerical RGB data. The visual data is mostly qualitative and involves looking for change in the color of the dots on the ticket. This can be hard to do with just the human eye so a difference map is created from the images from the flatbed scanner. A difference map takes the initial image of the ticket and subtracts it from an image taken of the ticket after exposure to liquid culture (see Figure 1a and b). Any dots that show up on the difference map are evidence of change in the sensor. MathWorks’s Matlab® is used to calculate the RGB values for each dot on the CSA. Principal component analysis (PCA) was performed on the average of the delta RGB values to compare them. PCA produces scores as seen in Graph 1. It transforms multidimensional data by finding a synthetic axis on which the variance of all cases is maximized. The score is the position of the case on that axis. Results (continued) Graph 1: This graph shows PCA of the CSA changes over time. PCA is a common tool used in exploratory data analysis which transforms data to capture maximal variance in fewer dimensions (two dimensions are shown in this graph). This transform produces scores which are correlated with the original data but are unitless. The origin point is the initial, unexposed state for all 6 of the cases, which then radiate away. The divergent paths taken by each case indicate that the changes observed on the CSAs can be uniquely attributed to each case. The CSAs were able to detect and distinguish between different metabolic changes. The red line for the “Plain E.coli + IPTG” test represents an outlier case which is explained by atypical OD600 readings for that experiment. This culture was not growing at the expected rate (by OD600) and this observation was reflected in the CSA changes as well. Material and Methods Six experimental conditions were tested. This consisted of three main test cases each observed with and without isopropyl β-D-1-thiogalactopyranoside (IPTG) present. The three test cases were plain Luria-Bertani broth, unmodified E.coli, and E.coli with a green fluorescent protein insert. IPTG is used in induce protein expression, and was used specifically in this experiment to stress the E.coli (see Figure 2). CSA data was taken to monitor chemical change in the E.coli. OD600 data was taken to tell what stage the E.coli was in. Set up for the experiments include culturing K-12 strain E.coli overnight in LB broth that had been mixed and autoclaved for sterility. The media was then inoculated with the different strains of E.coli, and then incubated at 37 °C and 120 rpm. The CSA was fitted to the cap of the flask. Every 30 minutes the CSAs were taken out and scanned on a flatbed scanner. A few milliliters of media were also taken for OD600 readings. Conclusions The purpose of these experiments was to confirm whether or not colorimetric sensor arrays could detect metabolic changes in bacteria. PCA was performed on the data and showed that there was a difference in the color of the dots between the beginning and the end of the trials. It is very apparent that these arrays are sensitive to metabolic changes but more data needs to collected to ensure that all the changes are confidently discriminable above the noise in the array and experimental error. Figure: 1a Figure: 1b Figure 1a and b above are of a CSA before (left) and after (right) exposure to unmodified E.coli with IPTG added mid-log phase. Red circles are placed around dots that changed on this CSA. Some of the color change is very minor. This is why a computer program is used to distinguish change. Figure 2: The two flasks on the left both have E.coli with a green fluorescent protein insert in them. The one on the left is glowing because IPTG has been added to the media. IPTG is used to triggers transcription of the lac operon, and is used to induce protein expression.  References Suslick, K., Rakow, N., & Sen, A. (2004). Colorimetric Sensor Arrays for Molecular Recognition. Tetrahedron, 60, 11133-11138.