iGEM 2022 — Team exeter

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## **Design**

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# Contents

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[### rGO Binding](#slide-7)

[### Accessible Micropipette](#slide-8)

# rGO Binding

---

From a review of current literature, we found that the most promising avenue for binding graphene to our silk fibres involved using suspensions of graphene oxide (GO) particles, before reducing them *in situ* after binding to produce reduced graphene oxide (rGO) [1]. When consulting Dr Ana Neves, a senior lecturer and associate research fellow with the Nano-Engineering, Science and Technology Group at the University of Exeter, we were informed that directly binding rGO particles is a more efficient method for fibre coating with a conductive layer of graphene.

Thus, we adapted the GO binding protocol in [1]. Preliminary investigations involved cleaning native spider silk samples via heating in a water bath, after vortexing with dish soap. rGO-coated fibres and films were prepared from the silk samples via submersion in a diluted rGO suspension and ultrasonication. Multimeter testing indicated rGO coating increased electrical conductivity of the fibre and film by factors of 5.14 and 5.43 respectively over the baseline conductivity of dragline silk - 4.4 x 10-6 Sm-1 [2].

![Natural spider silk contaminated with colourful fibres](https://static.igem.wiki/teams/4339/wiki/2-1-10x-2.png)

Figure 1 - Natural spider silk following cleaning with hot water bath and dish soap. The sample is still contaiminated with colourful fibres

Examination under an optical microscope (10-400x magnification) showed spider silk samples were heavily contaminated with dust and other debris (as shown in Figure 1), limiting the validity of conclusions drawn from these investigations. To reduce contamination from dust and debris we switched to using silk samples from commercially bought silkworm cocoons. Cocoons were cut into millimetre long strips, boiled in sodium carbonate solution to remove glue-like sericin proteins and again sonicated with diluted suspensions of rGO, producing samples such as that shown in Figure 2.

![Fibroin ultrasonicated with a diluted RGO suspension](https://static.igem.wiki/teams/4339/wiki/fibroin-rgo-10x-1.png)

Figure 2 - A fibroin fibre sample ultrasonicated with an RGO suspension

Investigations showed that whilst ultrasonication caused reaggregation of silk fibres, reducing efficiency of film formation, it did increase the efficiency of the rGO coating procedure and consequently increased conductivity. This procedure yielded a film with a conductivity of 3.7 x 10-5 Sm-1, compared with around 3.5 x 10-5 Sm-1 for a film with ionic impurities resulting from exposure to calcium chloride and glycerol [3]. rGO coating was further augmented by submerging the rGO-coated film again in undiluted rGO suspension, yielding samples such as that shown in Figure 3. This yielded a sample with a conductivity of 1.7 x 10-3 Sm-1, a factor of around 50 times greater than for the uncoated fibroin film.

![Fibroin submerged in an undiluted RGO suspension](https://static.igem.wiki/teams/4339/wiki/fibroin-high-conc-10x-3.png)

Figure 3 - A twice-coated fibroin fibre sample which has been submerged in undiluted RGO suspension

Thus preliminary investigations established that coating silk with rGO rather than GO followed by an in situ reduction step, is favourable for coating our final recombinant spidroin-derived protein fibres. Moreover, the most efficient rGO coating is achieved when protein fibres are ultrasonicated with rGO suspension, dried to create a film and then submerged in undiluted rGO, as shown by conductivity results displayed in Figure 4.

![Conductivity of different RGO-bound silk sample types](https://static.igem.wiki/teams/4339/wiki/graphenebindingresults.jpg)

Figure 4 - Graph showing the conductivity of different RGO-bound silk sample types

# Accessible Micropipette

---

As part of our work for the [Inclusivity Award](https://2022.igem.wiki/exeter/inclusivity), we used 3D CAD modelling to design a modified micropipette grip to make it more accessible to people with poor motor coordination, weak grip strength, or hand tremors. This micropipette grip is designed to fit securely around the base of the pipette body, and allows for the attachment of a velcro or elastic strap at both the top and bottom of the pipette, without interfering with the function of the pipette. The attachment point has also been reinforced to ensure that it will not wear out over time. Due to the position in which the pipette grip has to be attached, the body of the brace attachment has been made longer, so that a window can be added over the volume display, allowing for the pipette grip to be used with micropipettes that have both fixed and adjustable volume.

### STL file of the micropipette brace

![Pipette brace - main body](https://static.igem.wiki/teams/4339/wiki/pipette-brace.png)

Figure 5 - Main Body Pipette Brace

To test the micropipette brace, a series of technical assessments were conducted using a P100 Satorius BioHit micropipette to transfer variable quanities of water. The results of our technical assessment showed improvements in precision and accuracy when participants used the brace compared to without, as participants more consistently transferred the correct volume of liquid when using the brace. Participants were also able to hold the micropipette in the manner recommended by manufacturers and experienced fewer tremors when using the pipette. When participants were asked to provide feedback, participant's stated that they felt more confident when using the pipette, and that they found the brace more comfortable to use compared to the unmodified pipette. However, participants also stated that the thickness of the brace occasionally made it more difficult to press the button to change the volume of the pipette, however the participants also quickly found that the brace itself could be used to gain more leverage against the button by lifting the brace up over the button and using it to depress it. Future iterations of this hardware would likely include a built-in lever that would make it easier to depress the button.

Originally, our micropipette brace was designed specifically for the Satorius BioHit pipette, which has a unique slit along the finger rest that was intended to be used with the BioHit pipette rack. However, when designing our pipette brace, we used this slit to our advantage, as its location and size meant that it could function as the perfect secondary attachment point for our strap, allowing the brace to fit securely along the hand, without interferring with the pipette. This design was incredibily successful, as shown by the data on our [**Inclusivity page**](https://2022.igem.wiki/exeter/inclusivity). However, our current design was only compatible with a specific brand of pipettes, requiring laboratories to either already own them or purchase them, suddenly making our inexpensive hardware incredibly costly.

![image of both the original brace designed for a BioHit pipette, and the new brace designed for a wider variety of pipettes](https://static.igem.wiki/teams/4339/wiki/pipette-braces.png)

Figure 6 - Comparison of original brace designed for a BioHit pipette and the original brace with the secondary attachment, designed for wider variety of pipettes.

Ensuring that the body of the current pipette brace would be compatible with a wide range of micropipettes was fairly simple, as we chose a highly flexible material, TPU filament, for the brace, which allowed it to fit on a variety of pipette bodies, regardless of their size. As we were initially using the Satorius BioHit pipette, we printed the main body brace at a scale up of about 165.42% of the overall model from its original size as found in the STL file. Depending on the pipette brand, the brace did have to be scaled up slightly for a better fit, however the fundamental design did not need to be altered. While this solved the first hurdle of universal design, we still had to design a secondary attachment point to make the brace compatible for a wide range of pipettes. The body of the pipette brace could not simply be extended with an identical attachment point near the top, as doing so would prevent access to the micropipette plunger.

To address this issue, we designed a second component that could be used in conjunction with the brace to create a second attachment point. This component had to be sleek and non-obstructive during usage, to ensure it did not interfere with the finger rest or the plunger, however it still had to allow for a hand to be afixed to the pipette in the correct position. As a result, we decided that the attachment point should be fitted so that it faced backward, which could then be used with a longer velcro strap that split part way along so that the brace could be securely attached, without obstructing the micropipette.

### STL file of the secondary attachment point

![Pipette brace - top](https://static.igem.wiki/teams/4339/wiki/top-pipette-brace.png)

Figure 7 - Accessory Top Body Pipette Brace

The accessory top brace was printed in slightly varying sizes and have been tested on a range of pipette brands that are available in our teaching labs.

In the printing process of the top brace, we didn't have immediate access to different pipette brands and only knew that the top of micropipettes are generally slightly larger than their main bodies. As a result, we printed several top braces at varying sizes, having upscaled the size by 2mm, 4mm, 6mm, and 8mm on the X-axis and Y-axis. The size-up measurements from the original size, as found in the STL files were as follow:

* - Increase of 2 mm in both directions: Inside measurements are 34.81 mm and 31.56 mm. A 175.54% scale up of the overall model.
* - Increase of 4 mm in both directions: Inside measurements are now 36.81 mm and 33.56 mm. A 189.94% scale up of the overall model.
* - Increase of 6 mm in both directions: Inside measurements are now 38.81 mm and 35.56 mm. A 195.75% scale up of the overall model.
* - Increase of 8 mm in both directions. Inside measurements are now 40.81 mm and 35.56 mm. A 205.80% scale up of the overall model.

The results were as follows: from the pipettes available in our laboratories, the top brace without any additional size increase fitted the BioHit Proline (WOLF) pipette; the brace with a 2mm size increase fitted the Axygen and Gilson pipettes, a 4mm size increase fitted the Labmate and Satoris Transfer pipettes; and a 6mm increase fitted the Alpha Laboratories pipettes. No pipettes were large enough to fit the pipette brace with an 8mm increase.

![Pipette top Biomacromolecules](https://static.igem.wiki/teams/4339/wiki/pipette-types-and-top-brace.jpg)

Figure 8 - Top Pipette Braces Tested on Different Brands

### References

1. [1] Liang B et al. Fabrication and application of flexible silk-rGO composite film electrodes decorated with spiky Pt nanospheres. *Nanoscale*. 2014;6: 4264-4274. doi: [10.1039/C3NR06057H](https://doi.org/10.1039/C3NR06057H)
2. [2] Steven E et al. Physical characterization of functionalized spider silk: electronic and sensing properties. *Sci Technol Adv Mater*. 2011;12(5): 055002. doi: [10.1088/1468-6996/12/5/055002](https://doi.org/10.1088/1468-6996/12/5/055002)
3. [3] Yadav R et al. Tailoring of electrical and optical properties of regenerated silk fibroin films with metal oxides. *Journal of Materials Science: Materials in Electronics*. 2020;31: 17784-17797. doi: [10.1007/s10854-020-04332-4](https://doi.org/10.1007/s10854-020-04332-4)

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# Contents

---

[### Silk Preparation](#slide-6)

[### CycA Characterisation](#slide-7)

[### Fibroin rGO](#slide-8)

# Preliminary Spider Silk Preparation

---

Before we began transforming our cells and conducting our synthetic silk experiments, we wanted to begin developing and testing our reduced graphene oxide (rGO) binding protocols since this binding ability is essential to the success of our project. However, since this was occurring before the production of our recombinant silk proteins, we needed to find alternative means for accessing spider silk proteins. With the exorbitant cost of natural farmed spider silk and the ethical issues surrounding it, it was not feasible for our team to purchase natural spider silk, so we turned to collecting wild spider silk around the University of Exeter’s campus.

Since our project aimed to reduce the involvement of animals in the production of our final composite, we decided to avoid collecting webs that were currently occupied by spiders, instead choosing to collect ones that had been previously vacated. However, the silk fibres had become thoroughly contaminated with dust, clothing fibres, dead insects, and other unidentified contaminants, which would likely affect the binding affinity of the fibres, and so our team had to first develop a cleaning protocol that purified the fibres while retaining their structural integrity.

An overview of our cleaning protocols have been detailed below (Table 1).

| Sample Number | Cleaning Method |
| --- | --- |
| Phase 1 | |
| 1.1 | 20 ml distilled water, vortex, centrifuge |
| 1.2 | 20 ml distilled water, centrifuge |
| 1.3 | 20 ml distilled water, 1 ml dish soap, vortex, centrifuge |
| 1.4 | 20 ml distilled water, 1 ml dish soap, centrifuge |
| 1.5 | 20 ml distilled water, 1g NaCl, vortex, centrifuge |
| 1.6 | 20 ml distilled water, 1g NaCl, centrifuge |
| 1.7 | 20 ml distilled water, 1 ml fabric conditioner, vortex, centrifuge |
| 1.8 | 20 ml distilled water, 1 ml fabric conditioner, contrifuge |
 Phase 2 | || 1.9 | 20 ml distilled water, sonication, 1 ml dish soap, vortex |
| 1.10 | 20 ml distilled water, sonication, vortex |
| 1.11 | 20 ml distilled water, hot water bath, 1 ml dish soap, vortex |
| 1.12 | 20 ml distilled water, hot water bath, vortex |
 Phase 3 | || 2.1 | 20 ml distilled water, vortex, hot water bath, 1 ml dish soap, vortex |
| 2.2 | 20 ml distilled water, vortex, hot water bath, 1 ml dish soap, vortex |

In our protocol, we have detailed our collection proceedure, the methodology we used when running preliminary cleaning comparisons on the silk samples, and the finalized cleaning protocol we developed based on the results of our preliminary experiments. The detailed protocol can be found by clicking the button below.

### Collecting and Processing Natural Spider's Silk

# CycA characterisation

---

To identify whether engineering of the alanine permease CycA into *E. coli* enables elevated net uptake of alanine from growth medium, we determined alanine uptake by BL21 DE3 pLysS *E. coli* across a 7 hour incubation period, using an alanine detection kit purchased from Merck.

To maximise likelihood of CycA inclusion yielding a detectable increase in alanine uptake, we wanted to determine the maximum concentration of extracellular alanine the growth medium can be doped with without inhibiting *E. coli* growth significantly. Therefore, we conducted an alanine tolerance preliminary, growing BL21 DE3 pLysS (engineered with either CycA in a pX1900 backbone or with just the plasmid) in alanine concentrations ranging from 5.8 mM (undoped LB) up to 100 mM. We measured OD600 and cells/ml every hour across a 7 hour period, determining that extracellular alanine concentrations up to 100 mM do not significantly impact cell growth.

We then determined alanine uptake by BL21 DE3 pLysS, engineered with either CycA in a pX1900 backbone or with just the plasmid, by incubating both strains with and without additional alanine doping of LB (adding 1M alanine solution to make up the growth medium to a final alanine concentration of 100 mM). Once again, we measured OD600 and cells/ml every hour across a 7 hour period. Additionally, we harvested pellets from each sample every hour.

Pellets from the t = 3, 5 and 7 hour samples (beginning, middle and end of log phase) were resuspended and their alanine content was determined using the Merck Alanine Assay kit. Alanine uptake is determined via addition of an enzyme which converts present alanine into pyruvate. This pyruvate is bound by a probe, initiating a detectable colour change. Therefore, initial alanine concentration is proportional to absorbance at 570 nm. We then normalised alanine uptake by cells/ml to determine alanine uptake per cell for each sample at the aforementioned timepoints, in order to deduce whether inclusion of CycA elevated rate of alanine absorption. A summary of the alanine detection process is detailed in the figure below. A detailed protocol is available for download by clicking the button below.

### CycA Alanine uptake protocol

![Schematic for the quantification of alanine uptake per cell](https://static.igem.wiki/teams/4339/wiki/alanine-detection-schematic.png)

Figure 1 - Schematic showing the process for quantifying alanine uptake per cell by independently measuring cells/ml and total alanine uptake in each culture

### Accuracy of silica microsphere OD600 calibration

Using OD600 as a measurement of population growth for *E. coli* is inherently limited by the fact that assessing optical density alone cannot distinguish between the effects of live cells and the effects of all other debris on turbidity. Additionally, OD600 cannot be used as a measure of live cell count without calibration As investigated in the Interlab Measurement study in 2018, the results of which were outlined in a Beal et al paper[1], such calibration can be performed using a suspension of silica microsphere beads, allowing OD600 to be correlated with beads/ml, in turn giving an approximate measure of cells/ml present in the sample.

Our PI, Dr Chloe Singleton, previously produced a calibration curve across a range of beads/ml concentrations from 0 to 1.5 x 1010, following the procedure used in the 2018 Interlab study, detailed in the PDF linked below. Using the Imagestream flow cytometry data recorded for normalisation of alanine uptake in CycA characterisation, we were able to directly analyse the accuracy of the estimation of cells/ml from OD measurements using the silica microsphere calibration curve across a 7 hour time course, via comparison with cells/ml data taken directly from sample image analysis. We interpolated the standard microsphere curve, using recorded OD data, to estimate cells/ml and then calculated percentage difference between actual cells/ml data and the estimated values derived from microsphere calibration for all 4 conditions of the alanine uptake analysis. The detailed protocol followed by Dr Singleton, taken from the 2018 Interlab Measurement study can be downloaded by pressing the button below.

### 2018 Interlab Protocol

# Fibroin-rGO Preliminaries

---

Constant iterations of the design, build, test, learn cycle moulded the following protocols. We have included a PDF to display the progression of our protocols as well as a separate PDF of the final method. After numerous attempts at using silkworm cocoons to make films, we concluded a pre-dispersed medium such as silk fibroin solution would be more suitable for our needs. Methods using both fibroin sources are detailed.

Cocoons:

In regards to composite formation, we carried out preliminary testing of rGO binding with silk fibroin to enable constant progress throughout our project. Fibroin is produced from silkworms rather than spiders, but it is the most available and affordable silk fibre and has previously been investigated for similar applications [2] which is why we used it. Our aim was to gain a better insight into the ease of working with rGO and begin to optimise a method for film production.

Initially, silkworm cocoons were used to make a first film as per Liang‘s protocol [2]. We purchased silkworm cocoons online which were washed by heating with sodium carbonate in a heat block then undergoing vacuum filtration before washing with MilliQ water. The rGO suspension was added to the sample and ultrasonicated before undergoing vacuum filtration and drying to yield a film.

Conductivity of resulting films (and a baseline for non-rGO combined materials) was measured by the four-pronged probe method that we were informed of by Graphenea. If the sample was not fit for four probe testing, then a multimeter was used. This gave a rough idea of whether conductivity was being affected by treatment of the cocoons.

Solution:

Following early production of films using cocoons, we found it challenging to create an evenly separated solution. We ordered a silk fibroin solution in the hopes of producing a more uniform dispersion of protein and rGO. We used a solution of 5% w/v concentration as this was easily available and similar to concentrations used by previously published literature [3].

Butanediol and PVA were later added in this protocol as we found difficulty in producing thermostable composites at room temperature without them. Both additions have good biocompatibility [4][5]. Butanediol can induce fibroin to form a crystalline structure which can reduce the dissolution rate of the film and increase its flexibility [6]. PVA has an amphiphilic structure which theoretically could aid in dispersing the graphene aqueous solution [7]. PVA also increases the young's modulus and yields stress of graphene bionano composites [8].

### Producing Films Using Fibroin Cocoons

### Producing Films Using Fibroin Solution

### References

* [1] - Beal, J., Farny, N.G., Haddock-Angelli, T. et al. Robust estimation of bacterial cell count from optical density. *Commun Biol* 200;3(512) Doi:  [10.1038/s42003-020-01127-5](https://www.nature.com/articles/s42003-020-01127-5)
* [2] - Liang B, Fang L, Hu Y, Yang G, Zhu Q, Ye X. Fabrication and application of flexible graphene silk composite film electrodes decorated with spiky Pt nanospheres. Nanoscale. 2014;6(8):4264-74.* [3] - Yusoff NI, Wahit MU, Jaafar J, Wong TW. Characterization of graphene-silk fibroin composites film. Materials Today: Proceedings. 2018 Jan 1;5(10):21853-60.* [4] Chocarro-Wrona C, de Vicente J, Antich C, Jiménez G, Martínez-Moreno D, Carrillo E, Montañez E, et al. Validation of the 1,4-butanediol thermoplastic polyurethane as a novel material for 3D bioprinting applications. Bioeng Transl Med. 2020 Oct 20; 6(1). Available at https://doi.org/10.1002/btm2.10192* [5] Khalaji S, Golshan Ebrahimi N, Hosseinkhani H. Enhancement of biocompatibility of PVA/HTCC blend polymer with collagen for skin care application. International Journal of Polymeric Materials and Polymeric Biomaterials. 2021 May 03;70(7):459-468. Available at https://doi.org/10.1080/00914037.2020.1725761* [6] Zhang H, Zhao J, Xing T, Lu S, Chen G. Fabrication of silk fibroin/graphene film with high electrical conductivity and humidity sensitivity. Polymers. 2019 Oct 28;11(11):1774.* [7] Ziv E, Attia D, Vasilyev G, Mendelson O, Zussman E, Yerushalmi-Rozen R. The role of polymer–solvent interactions in polyvinyl-alcohol dispersions of multi-wall carbon nanotubes: from coagulant to dispersant. Soft Matter. 2019;15(1):47-54.* [8] Hong X, Zou L, Zhao J, Li C, Cong L. Dry-wet spinning of PVA fiber with high strength and high Young’s modulus. InIOP Conference Series: Materials Science and Engineering 2018 Nov 1 (Vol. 439, No. 4, p. 042011). IOP Publishing.

# Results

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# Contents

---

[### CycA Characterisation](#slide-6)

[### Silica OD Calibration](#slide-7)

[### Estimating Cell Density](#slide-8)

[### Fibroin Graphene Films](#slide-9)

# CycA Characterisation

---

In order to characterise the function of the amino acid transport protein CycA, we attempted to measure the difference in alanine uptake over time by *E. coli* BL21 DE3 pLysS, engineered with either an empty pX1900 plasmid backbone (control strain), or the cycA gene present in this backbone (CycA strain). In order to calculate alanine uptake per cell, we additionally measured cell density (cells/ml) via imaging flow cytometry. Results indicated that total net alanine uptake per cell was actually reduced in the CycA strain versus the control strain, most likely suggesting CycA is being expressed (using up alanine and other metabolic resources) but is not functional, so does not contribute to elevated alanine uptake.

In performing this experiment, we acquired a set of cell density measurements with corresponding OD600 data, for both *E. coli* strains. This data, and data previously collected by our PI Dr Chloe Singleton, allowed us to assess the accuracy of estimating cell density from standard curves of OD600 against the concentration of silica microspheres, as was investigated in the iGEM 2018 Interlab study. We were also able to use this data to produce our own standard curve of OD600 against cell density measurements, which other teams will be able to use in the future to rapidly estimate cell density from OD600 data to a reasonable degree of accuracy.

Firstly, to determine the tolerance of both strains to extracellular alanine and thus, a suitable alanine concentration for the extracellular medium in our alanine uptake assay, we incubated the control and CycA strains in a range of alanine concentrations from 5.8 (blank LB) up to 100 mM. As is indicated below in figure 1, there is no obvious difference in the growth of either strain across the range of extracellular alanine concentrations studied. From this, we concluded that we could use the uppermost concentration of extracellular alanine, 100 mM, in the growth medium for our alanine assay.

![Growth curves for control and CycA strains grown in varying concentrations of extracellular alanine](https://static.igem.wiki/teams/4339/wiki/alaninetolerance.jpg)

Figure 1 - Growth curves for control and CycA strains grown in varying concentrations of extracellular alanine, showing varying [Ala] has little impact on cell growth over time

We then cultured both strains in undoped and doped LB ([Ala] = 5.8 and 100 mM respectively), measuring OD600 and cells/ml (using an Imagestream imaging flow cytometer) every hour across a 7 hour incubation period, as well as harvesting pellets for the final alanine assay. Figure 2 below shows the variation in cells/ml for each condition with time. It is apparent that the form of growth is essentially the same for the control strain at both alanine concentrations. In contrast the growth for the CycA strain seems to vary between the 2 alanine concentrations. In undoped LB, the CycA strain does not appear to reach stationary phase within the time range measured over, suggesting expression of the protein has delayed the onset of this phase of growth. Whereas, in doped LB, the CycA strain appears to reach stationary phase at an earlier time point than the other conditions and may in fact enter death phase, with the final cells/ml dropping from t=6 to t=7.

It is possible that the combination of expression of likely non-functional and misfolded CycA proteins as inclusion bodies, and the associated metabolic strain is sufficient to trigger cell death by t=7. Alternatively, the apparent decrease in cells/ml may instead result from a change in cell morphology in response to suboptimal conditions (lack of available resources, accumulation of extracellular waste etc), reducing the proportion of live cells present which are identified correctly by the Imagestream.

![Cells/ml curves for control and CycA strains grown in undoped and alanine-doped LB medium](https://static.igem.wiki/teams/4339/wiki/all-cells-ml.jpg)

Figure 2 - Graphs showing change in cells/ml over time for control and CycA strains grown in undoped and alanine-doped LB medium

In order to determine alanine concentration in the harvested pellets, we used the Merck-distributed alanine assay kit. First a standard curve was plotted of absorbance at 570 nm, which is proportional to the concentration of pyruvate (into which alanine has been enzymatically converted) bound by an alanine probe, against known concentrations of alanine standards. We then measured the absorbance of samples collected at t = 3, 5 and 7 hours after inoculation, in turn determining their relative total alanine concentrations, and normalised them by cells/ml, to calculate alanine uptake per cell shown below in figure 3.

Alanine uptake for each strain is higher for the [Ala] = 100 mM condition, as is to be expected. However, alanine uptake is greater for the control strain than the CycA strain, at both alanine concentrations. The most logical explanation for this is that CycA, whilst being expressed, is non-functional, resulting in investment of metabolic resources into the transcription and translation of the protein, without a coupled increase in alanine uptake. This would divert resources (ATP, transcriptional and translational machinery etc) away from synthesis of other porins responsible for alanine uptake. Moreover, CycA translation itself uses significant amounts of intracellular alanine, with alanine accounting for 10% of the AA sequence of the protein (47/470 residues). The simplest explanation for the non-functionality of CycA is that the protein is misfolding, resulting in the formation of inclusion bodies.

![Bar graph showing the alanine uptake per cell for the control and CycA strains in undoped and doped LB](https://static.igem.wiki/teams/4339/wiki/alanine-uptake-per-cell.jpg)

Figure 3 - Bar graph showing the alanine uptake per cell for the control and CycA strains in undoped and doped LB

The strength of the conclusions we can draw is severely limited by the amount of data we have been able to collect. Whilst we managed to culture in triplicate both strains in undoped and doped LB, we were not able to perform repeats for the alanine tolerance experiment. Moreover, without monitoring OD600 at sufficiently high concentrations of extracellular alanine to limit growth of *E. coli*, we were not able to put an actual upper bound to the alanine concentration at which our 2 strains could grow. Finally, we were significantly limited in the amount of samples we could run the alanine detection assay for, by the availability of the necessary reagents. Consequently, we could only analyse one sample per condition (out of 3), preventing us from assessing the reliability of conclusions drawn regarding alanine uptake per cell.

# Accuracy of silica microsphere OD600 calibration

---

To determine the accuracy of calibrating OD600 measurements using silica microsphere suspensions, with an aim to estimate cells/ml, we interpolated a standard curve for OD600 against microspheres/ml, produced by our PI, Dr Chloe Singleton, shown below in figure 4. A strong linear correlation is obtained between OD600 and beads/ml with an R2 value of 0.9921. This confirms the linearity of the relationship between the 2 quantities across an OD600 range from 0 to 0.8, enabling interpolation of objects/ml data within this range.

![Standard curve showing the linear relationship between OD and beads/ml across the concentration range](https://static.igem.wiki/teams/4339/wiki/calculation-of-beads-ml.jpg)

Figure 4 - Standard curve showing the linear relationship between OD and beads/ml across the concentration range measured over

We then compared plots of OD600 and interpolated microspheres/ml results against time, with plots of cells/ml (using Imagestream data from the CycA characterisation experiment), shown in figure 5 below. The form of the plots of OD600 and microsphere ‘beads’ per ml against time are identical, which is logical given the direct interpolation of beads/ml values from OD600 measurements. In contrast, the form of the plot of cells/ml against time substantially differs from the other two plots, reflecting the inability of OD600, and thus interpolated beads/ml measurements, to ignore the contribution of non-live cell objects to absorbance. Whilst the cells/ml plot corroborates the OD600 and beads/ml plots in so far as when log and stationary phases are entered, the variation in growth after stationary phase is entered (t>5 hours) between the 4 conditions measured over is not accurately captured in the beads/ml plot.

The beads/ml plot suggests growth in all 4 conditions for the final 2 hours is approximately uniform, however, actual cells/ml data shows growth was much greater for the CycA strain in undoped LB, which does not actually appear to reach stationary phase within the time period measured over, than the other 3 conditions. Moreover, as is more clearly illustrated in figure 2, cell count/ml may have actually decreased from t=6 to t=7 hours after inoculation for the CycA strain in doped LB. The decrease in cells/ml observed in the Imagestream data for this condition, and the variation in the rate of slowdown of growth for the other 3 conditions, is not accurately captured in the beads/ml approximation. This most likely results from the fact that microsphere estimates from OD600 data cannot distinguish between live cells and other factors contributing to absorbance, including aggregates of cell debris from lysed cells, which can be identified and filtered out of flow cytometry data on an Imagestream through examination of the relevant object images.

![Graphs showing the change in OD<sub>600</sub>, interpolated beads/ml and cells/ml for all 4 conditions across a single 7 hour incubation period](https://static.igem.wiki/teams/4339/wiki/comparison.jpg)

Figure 5 - Graphs showing the change in OD600, interpolated beads/ml and cells/ml for all 4 conditions across a single 7 hour incubation period

The percentage inaccuracy of the microsphere calibrated estimate of objects/ml as a function of time is plotted below in figure 6. The form of the curve for each condition is approximately the same. Percentage difference (between Imagestream cells/ml data and approximated beads/ml data) increases with time during lag phase from t=0 to a maximum at t=2-3 hours after inoculation. Percentage difference then decreases with time during log phase from t=3 to a minimum at t=5 hours. Finally, percentage difference then either plateaus, as for the control strain in doped LB and CycA strain in undoped LB, or increases in stationary phase, as for the other 2 strains. It is a point of note that the 3 phases of growth (excluding death phase) are clearly demarcated by turning points in the plots of percentage difference against time. The variation in growth between all 4 conditions from t=6 to t=7 hours is reflected in the fact that the percentage difference varies across this time period differently for the 4 conditions.

![Graph showing the percentage error in the microsphere calibrated OD-based estimation of objects/ml relative to the Imagestream derived measurement of cells/ml](https://static.igem.wiki/teams/4339/wiki/difference.jpg)

Figure 6 - Graph showing the percentage error in the microsphere calibrated OD-based estimation of objects/ml relative to the Imagestream derived measurement of cells/ml

These results illustrate clearly the limitations of estimating cell count via microsphere calibrated OD600 measurements. Measuring change in OD600 across an incubation period cannot capture changes in cell morphology in response to environmental stressors, nor cell lysis in the eventuality of the resultant debris aggregating and contributing to overall light scattering. Results also exemplify the advantages provided by use of Imagestream flow cytometry, which not only measures objects per ml by analysing light scattering, it also yields images of each identified object. These images allowed us to examine whether any changes in cell morphology over time were present, and provided reference points when gating the signal region which denotes objects of interest.

Strong conclusions cannot however be drawn from this data alone. Whilst the reliability of data obtained is verified by the collection of 3 replicates, the reproducibility of our conclusions has not been tested. We hope future iGEM teams will attempt to reproduce our results and further assess the degree of accuracy of microsphere calibrated OD600 measurements of live cell count.

# Estimating cell density from OD

---

In the process of generating data for measuring alanine uptake, we obtained OD600 measurements using a Tecan Infinite M PLEX plate reader and corresponding cells/ml measurements using an Imagestream image flow cytometer for 4 different strains of *E. coli* BL21 DE3 pLysS across a 7 hour incubation period. Linear regression of OD600 as a function of log10(cells/ml) (given OD600 is a logarithmic metric of light attenuation) identifies the former is directly proportional to the latter across an OD600 range from 0 to 0.9. As shown below in figure 7 below, the goodness of fit of the linear regression is at a maximum value when data up until t = 5 hours after inoculation is included in the statistical analysis. However, for all datasets with a final time point from 2 up until 7 hours after inoculation (the complete incubation period), R2 values above 0.93 are obtained. This indicates that across the OD600 range studied, log10(cells/ml) and thus cells/ml can be interpolated from OD600 measurements with a reasonable degree of accuracy.

![Graph showing the R^2 values for linear regression of OD as a function of cells/ml, for varying proportions of the total 7 hour dataset.](https://static.igem.wiki/teams/4339/wiki/r2.jpg)

Figure 7 - Graph showing the R2 values for linear regression of OD as a function of cells/ml, for varying proportions of the total 7 hour dataset, from the first 2 hours of data up to and including the entire 7 hour incubation period.

As indicated in figure 8 below, the goodness of fit for a linear correlation is clearly superior for the up to t=5 hours dataset than the up to t=7 dataset. However, the latter dataset covers OD600 values up to approximately 0.9, versus 0.5 for the t=5 dataset, significantly increasing the range over which cells/ml can be interpolated from the standard curve, rather than being extrapolated. Therefore, when looking to estimate cells/ml from OD600 using the standard curves obtained below, a trade-off must be struck between the superior fit of the correlation for the t=5 dataset, versus the practicality of having to produce samples with OD600 values which lie within (or in close proximity to) the narrower range measured over.

![Standard curves of OD as a function of cells/ml for a restricted 5 hour dataset and the complete 7 hour dataset](https://static.igem.wiki/teams/4339/wiki/odvscelldensity.jpg)

Figure 8 - Standard curves of OD as a function of cells/ml for a restricted 5 hour dataset and the complete 7 hour dataset, showing goodness of fit for a linear correlation is superior for the limited dataset

To assess the degree of disparity in estimates of cells/ml from the 2 standard curves, cells/ml values were interpolated from both curves above for OD600 values ranging from 0.05 to 0.9 and the percentage difference between them was calculated. As shown below in figure 9, across an OD600 range from 0.05 to 0.2, the percentage difference in cells/ml values obtained using the different standard curves is sufficiently small (less than 10%) to have little effect for experimental applications which require only a low degree of accuracy (e.g. identifying the correct volume of cell culture on which to perform a GeneJet RNA extraction which requires approximately 1 x 109 cells).

However, for OD600 values greater than 0.2, percentage differences up to -42% are yielded. This indicates that selecting the appropriate standard curve for estimating cells/ml is relatively important, and depends on whether overall goodness of fit or a desire to avoid extrapolation is prioritised. As is indicated below (with data points for which cells/ml is extrapolated rather than interpolated circled in blue for the t=5 standard curve, and orange for the t=7 curve), a greater degree of extrapolation is required when using the narrower t=5 dataset, so the t=7 standard curve may be more appropriate for analysis of higher OD600 datasets.

![Graph showing the percentage difference between cells/ml values derived from the t=5 and t=7 standard curves for a range of OD values. Datapoints for which cells/ml must be extrapolated denoted by blue circles for the t=5 standard curve and orange for the t=7 curve.](https://static.igem.wiki/teams/4339/wiki/percentagedifference.jpg)

Figure 9 - Graph showing the percentage difference between cells/ml values derived from the t=5 and t=7 standard curves for a range of OD600 values. Datapoints for which cells/ml must be extrapolated denoted by blue circles for the t=5 standard curve and orange for the t=7 curve.

Overall, the results obtained indicate that OD600 can provide a good basis for estimating cells/ml of a sample, with standard curves indicating a strong direct proportionality between them for OD600 values ranging from 0.05 to 0.85. Using the data gathered, as well as the equations for the linear correlations of the 2 standard curves plotted, future iGEM teams working with BL21 DE3 and its derivatives will be able to rapidly estimate cells/ml for samples from easily obtained OD600 values recorded using the Tecan M PLEX plate reader (specifically using black clear bottom Corning 96 well plates without the lid on). This will provide a rapid and cheap way of estimating cells/ml for a variety of wet lab applications (e.g. normalising uptake of a substrate to assess uptake per cell).

This method is preferable to microsphere based calibration because it relies on real-world image flow cytometry data of *E. coli* cells rather than silica beads approximating the optical properties of these cells. By interpolating standard curves of OD600 against cells/ml data, extraneous factors which reduce the validity of using OD600 itself as a measure of cells/ml, such as the contribution of aggregated cell debris, are accounted for. This method is also a useful alternative to image flow cytometry itself, given the expense and lack of availability of the equipment, in addition to the expertise required to operate it. We hope future iGEM teams, with access to the necessary equipment, will expand the applicability of estimating cells/ml by interpolating standard curves of OD600 against image flow cytometry data by producing similar standard curves using other strains of E. coli and species of model organism (e.g. Saccharomyces cerevisiae), as well as recording OD600 using other 96 well plates and plate readers, and recording corresponding cells/ml data using other flow cytometers.

# Fibroin-Graphene Films

---

#### Producing SF-rGO bionanocomposite films from silkworm cocoons

The majority of our preliminary experiments succeeded in forming a graphene and fibroin film, however they often remained clumpy, making it difficult to assess their conductive properties, particularly using methodologies such as the four point probe. Literature suggests this coagulation is due to sericin binding proteins that ‘glue’ the protein fibres together [1]. The silkworm cocoons were boiled in Na2CO3 in a degumming process to remove the sericin binding proteins according to previous literature [2]. Softening of the structure occurred but an evenly dispersed protein solution was not obtained. For these reasons, we did not obtain any data from these early material prototypes. This led us to use silk fibroin solution instead, as the protein is already pre-dispersed.

![](https://static.igem.wiki/teams/4339/wiki/9dc3b249-7f37-482c-8ddc-6d92aa1470b3-1-201-a.jpeg)

Figure 10 - Preliminary rGO Fibroin film using silk worm cocoons.

#### Producing SF-rGO bionanocomposite films from fibroin solution

**Scanning electron microscopy**

The surface morphology of rGO/fibroin composite was observed under a scanning electron microscope (SEM) (Zeiss Gemini 500) with an acceleration voltage of 1 kV. Before the observation, the surface of the samples were coated with a sputter coater equipped with a gold target to increase electrical conductivity of the samples.

![](https://static.igem.wiki/teams/4339/wiki/fibroin-film.jpg)

![](https://static.igem.wiki/teams/4339/wiki/sem-silk.png)

Figure 11 - rGO/Silk Fibroin biocomposite films (left) and its associated SEM image (right).

From these images it can be observed that an even distribution of graphene and fibroin is seen in the composite, as there is no visible segregation of material. This evidence shows that the method we used to homogenise our biocomposite films was successful. However, the images also show a slightly uneven surface. Further analysis would confirm the cause of this, but we speculate it could be from coagulation of protein on a microscopic scale. Potential effects of this on the composite’s biocompatibility are unknown.

**Conductivity**

Films produced from fibroin solution were visually smoother, enabling more reliable conductivity analysis. Resistance was measured using a multimeter, giving conductivity values of up to 33-45 Scm-1. Silk fibroin alone is not conductive [3], so this increase is promising. Furthermore, this falls into the semiconductor range (between 10-8-102Scm -1)[4].

In comparison to polymer scaffolds produced by previous literature, the conductivity is high. Ideal conductivity to mimic nerve conduction is around 5.6 mS cm-1

**Raman spectroscopy**

These films were later analysed using Raman spectroscopy with the aim to give information on the structure and insight of the binding of graphene and fibroin - potentially highlighting any changes between their isolated and biocomposite forms.

Raman spectra graphs obtained are detailed below. The graph uses the average of our collected data after background subtraction. Raman spectra can suffer from fluorescence interference which can be orders of magnitude more than the Raman peaks, leading to a broad background under the Raman peaks. Distorted background results can lead to incorrect integration or peak height determination, which are central to many spectral applications. Hence it is necessary to separate the Raman peaks from the background [5].

![](https://static.igem.wiki/teams/4339/wiki/raman-graphs.jpg)

Figure 12 - Raman spectra rGO/Fibroin composite films.

**Conclusions and discussion from Raman data**

The peaks for the rGO Raman spectra at 1572cm-1 and 1342cm-1 correspond to the characteristic G and D bands of graphene respectively. The G band is indicative of sp2-hybridised carbon-carbon bonds in a hexagonal lattice and the D band corresponds to disorder in the graphene structure [6].

The Raman spectra from the rGO/SF biocomposite retains the G and D bands observed in the rGO spectra, demonstrating the presence of rGO in the composite. However, in the prescence of fibroin there is a noticeable increase in the relative intensity between the two peaks. The relative increase in absorbance intensity of the G peak may be attributed to the binding of SF with the graphene, although individual fibroin peaks will be difficult to identify in the composite [6]. Alternatively, this intensity increase could be due to potential layering of the rGO [7]. The peak in the blank rGO spectra at ~1100cm-1 may be due to the presence of nanocrystalline sp3 carbons in distortions of the rGO; the disappearance of this peak in the composite spectra may suggest even dispersion of the graphene during the formation of the composite [8].

Details of the system and parameters for Raman analysis are as follows:
Raman spectra were collected using a confocal Raman spectrometer (Alpha 300R, WITec GmbH), equipped with a UHTS 300 VIS spectrometer and a thermoelectrically cooled CCD detector (down to -62 °C), with a 600 g mm-1 grating. For excitation, a 532-nm Nd:YAG laser was focused onto the sample using a Zeiss 50 x objective (NA 0.7), with a maximum power of 15mW at the sample. Between 5 - 10 single Raman spectra were recorded for each sample, each with an exposure time of 1 second and twenty accumulations. WITec Project 4.0 software was used to average the spectra and remove fluorescent background using the 'shape function'.

### References

* [1] Kunz RI, Brancalhão RM, Ribeiro LD, Natali MR. Silkworm sericin: Properties and biomedical applications. BioMed research international. 2016 Nov 14;2016.* [2] Liang B, Fang L, Hu Y, Yang G, Zhu Q, Ye X. Fabrication and application of flexible graphene silk composite film electrodes decorated with spiky Pt nanospheres. Nanoscale. 2014;6(8):4264-74.* [3] Dong X, Liu Q, Liu S, Wu R, Ma L. Silk Fibroin Based Conductive Film for Multifunctional Sensing and Energy Harvesting. Advanced Fiber Materials. 2022 Mar 23:1-9.* [4] Fielding LA, Hillier JK, Burchell MJ, Armes SP. Space science applications for conducting polymer particles: synthetic mimics for cosmic dust and micrometeorites. Chemical Communications. 2015;51(95):16886-99.* [5] H Ruan, LK Dai. Automated Background Subtraction Algorithm for Raman Spectra Based on Iterative Weighted Least Squares. Asian Journal of Chemistry. 2011 Aug 10;23(12): 5229-5234.* [6] Liang B., Fang L., Hu Y., Yang G., Zhu Q., Ye X. Fabrication and application of flexible graphene silk composite film electrodes decorated with spiky Pt nanospheres. Nanoscale (6), March 2014.* [7] Wall M., The Raman Spectroscopy of Graphene and the Determination of Layer Thickness, Thermo Fisher Scientific, 2011* [8] Paillard B., On the origin of the 1100cm-1 Raman band in amorphous and nanocrystalline sp3 carbon. EPL, 2007 Jan