Optical Density

Description

Based on previous work26, optical density measurements in a bioreactor can be measured with a simple 900 nm infrared (IR) LED and photodiode pair. There are two practical benefits of using 900 nm scattered light instead of the classic OD600. First, at 900 nm, turbidity/optical density measurements are less dependent on the absorbance spectrum of the media, meaning calibration is required less frequently before each experiment. Second, wavelengths in the visible range are preserved for light induction and colorimetric assays. To maximize scattering, the LED-diode pair is offset at a 135° angle. The 3D printed part is designed to house the LEDNature Biotechnology: doi:10.1038/nbt.4151 16 diode pair slightly above the height of the stir bar, at the correct angular offset. The part can be easily customized and printed to the users required specifications with any 3D printer. In the eVOLVER configuration used in this study, the IR LED and photodiode pair (4 leads) are each connected to the CMB via screw terminals in SA slots 4 and 5, respectively (Supplementary Fig. 7). In SA slot 4, a 16-channel PWM board amplifies a 3.3V signal from the Arduino microcontroller to a 5V signal to power the IR LED. A resistor is placed on the CMB to limit current and prevent the LED from burning out. SA slot 5 contains the 16-channel ADC board, responsible for analog filtering and demultiplexing the signal from the photodiodes. The ADC board reads the sensor by measuring the voltage difference across a 1M Ohm resistor, located on the Motherboard. Both slots are managed by Arduino 3 in the system developed in this manuscript. Briefly, the Arduino code interprets serial inputs from the Raspberry Pi, flashes ON the IR LEDs to measure turbidity, and responds with the current measurements. In the present system, optical density can be measured every 30 seconds, limited by the time taken for the Arduino to average diode readings (to minimize noise). For convenience, density readings from the 900 nm LED-diode pair were calibrated to OD600 measurements from a spectrophotometer, and the calibration curve fit with a sigmoidal function (Supplementary Fig. 8). Spectrophotometer readings were performed on a Spectramax M5 using 300 uL of media in a 96-well flat bottom plate; users may substitute density calibration data from measurements used in their labs. The optical density measurements in all experiments are calculated based on the calibration curve fit for each Smart Sleeve (Supplementary Fig. 8). For our experiments, calibration was performed using a dilution series of yeast cells suspended in distilled water, but in theory any cell type and/or solution of interest (such as evaporated milk) could be used. A custom MATLAB script was developed to facilitate the density calibration process, particularly important for bringing new eVOLVER units on line. Following calibration, the system was used to compare growth of S. cerevisiae (FL100) cells in eVOLVER vials to that in 250 mL flasks with 50 mL of media shaken at 300 rpm (Supplementary Fig. 8). Finally, to quantify the variance in growth across eVOLVER vials, 96 cultures across six 16-vial eVOLVER units were grown in parallel and aligned (Supplementary Fig. 8). These results demonstrate that eVOLVER cultures are repeatable, and exhibit comparable growth rates to cultures in shaken flasks. As previously mentioned, varying temperature induces a shift in the optical density readings (Supplementary Fig. 6). In measurements performed on yeast cells, we observed the largest shift near the center of the optical density calibration curve, while at low or high OD, the shift due to temperature was minimized. This information was used to select a density range for experiments in which temperature was controlled dynamically (see Fig. 4). As cells may shift in size in response to heating, we also quantified temperature-induced offset in optical density readings using evaporated milk.

IR LED-photodiode pair integrated in each Smart Sleeve enables individual monitoring of optical density. (a) CAD drawing and photographs of a 3D printed part for housing optical parts. Designed on CAD software, printed parts housing the IR LED and photodiode are customized for 135° offset to maximize scattered light (left). Completed part printed from CAD file (center). CMB assembled with mounted LED and photodiode via screw terminals (right). (b) Schematic of system design for eVOLVER optical density module. The IR LED (SA slot 4) and photodiode (SA slot 5) are integrated into the Smart Sleeve (left). A resistor is placed on the Smart Sleeve to limit current through the LED. A turbidity measurement is triggered by a serial command from the Raspberry Pi, and consequently, the Arduino responds with the current optical density measurements (right). The Arduino coordinates the timing when the LED flashes ON and the photodiode starts collecting measurements. Nature Biotechnology:

Optical density calibration and growth characterization. (a) Optical density calibration curves. Optical density is measured by a 900 nm LED-diode pair (see Supplementary Fig. 7) and calibrated to an OD600 measurement performed on a Spectramax M5 using 300 uL of media in a 96- well flat bottom plate. The calibration curve is fitted with a sigmoidal function. All optical density measurements in the experiments are calculated based on the fitted calibration curve for each Smart Sleeve. Sensitivity of OD measurements can be tuned by swapping the photodiode resistor. Top: A larger photodiode resistance at a lower LED intensity (2125 a.u.) gives a larger dynamic range, robust after 4 months of use. Bottom: A smaller photodiode resistor at a higher LED power gives a smaller dynamic range, but with more precision. This setting is also robust over time (1 year of use). Both traces are representative of a typical Smart Sleeve. (b) Comparison of cell growth in flask vs Smart Sleeve. Comparison of yeast cells grown in flasks in a shaking incubator with cells grown in SDC in 18 different Smart Sleeves across 6 different eVOLVER systems (left). (c) Comparison of cell growth across Smart Sleeves. We characterized variability of yeast growth across 96 Smart Sleeves (6 different eVOLVER platforms). Traces were aligned at 0.2 OD before plotting in order to normalize for different lag phases.