Gene amplification and characterization workflow

A general overview of the experimental workflow is presented first (Fig 1), followed by details on each part. The pJL1-sfGFP Plasmid (Addgene Plasmid #102634) was used as our standard expression template due to its widespread use in the CFE community as a clear functional benchmark–the encoded super folder green fluorescent protein (sfGFP) has robust translational efficiency and can be easily screened by fluorescence output. The plasmid was transfected in E. coli TOP10 cells and purified. Linear sfGFP gene fragments were designed from this plasmid, amplifying everything between the T7 promoter and termination regions; additional bases were added to allow for circularization and rolling circle amplification (S1 File). Cleaned, purified, and linearized DNA was repaired and end-prepared (poly-A tailing) for adapter ligation, attaching the motor protein for nanopore sequencing to the DNA fragments. The DNA library was then loaded and sequenced with the Oxford Nanopore minION chip. Output files were base called and aligned with the reference sequence for the sfGFP gene and custom codes were used to extract the mutation data from it. Normalized mutation (substitution, insertion, deletion) error rates were the final output of this workflow (Fig 1). Additional details for each of these steps follow.

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Fig 1. An overview of experimental workflow.

(a) DNA samples are amplified to higher concentrations using a panel of polymerases or plasmid DNA that is prepared from cells resulting in amplified products which have nicks and imperfections (b) DNA fragment nicks are repaired, then end-prepped with poly-A tails at the 3’ ends. (c) Adapters with motor proteins are ligated to the poly-A tailed end of the genes. (d) The DNA library is loaded onto minION flow cells to start sequencing. Motor proteins attach to the pore proteins on the flow cell and push one strand of DNA through the chip pore creating a unique impedance signature for each base pair. (e) Raw electrical signals are saved as Fast5 files. (f) Raw signals are base called to assign oligonucleotides to each impedance event and these are converted to FastQ. (g) Post sequencing analysis including alignment and enumeration of insertions, deletions, and substation of base pairs are performed on the nanopore base called data.

https://doi.org/10.1371/journal.pone.0305457.g001

Amplification methods

Following transformation of the pJL1-sfGFP plasmid into Top10 E. coli, colonies containing the plasmid were cultured and stored in a glycerol stock at -80°C. To initiate cell growth, a small amount of the stored cells was transferred to 10 ml of LB media and treated with Kanamycin to a final concentration of 50 μg/mL to selectively purify the cells with pJL1-sfGFP, due to the plasmid antibiotic resistance gene. The E. coli cells, containing the plasmid, were then cultured for 16 hours at 37°C with a rotation speed of 230 RPM. After the 16-hour incubation, the grown cells were collected, lysed, and the DNA was purified using the Promega Wizard Plus SV Miniprep DNA Purification System. The purified DNA was quantified using the Qubit dsDNA BR Assay, with the Qubit 4 Fluorometer, resulting in a typical yield of 250–330 ng/μL DNA concentration in 50 μL of nuclease-free water.

As described in detail in our prior works [13], all linear expression templates (DNA) were designed and optimized for expression in E.coli using IDT’s codon optimization and purchased from Integrated DNA Technologies (IDT) (Coralville, IA) as gBlock gene fragments. Sequences can be found in S1 File. LET DNAs were suspended in nuclease free water to reach a final concentration of 10 ng/μl. All PCR amplifications including OneTaq polymerase (New England BioLabs, Ipswich, MA), Phusion High-Fidelity polymerase (New England BioLabs, Ipswich, MA), and Q5 High-Fidelity polymerase (New England BioLabs, Ipswich, MA) were done as stated in the manufacturer’s protocols. Resulting, amplified DNA was cleaned, purified, and concentrated using a Zymo purification kit (D4004) and the final concentration was calculated using a Qubit dsDNA BR Assay and with the Qubit 4 Fluorometer to reach at least 50 μl of DNA with an average of 70 ng/μl final concentration.

Rolling circle amplification (RCA) was done with the same LET as ordered above. Synthesized DNA was digested by HindIII-HF restriction enzyme (New England BioLabs, Ipswich, MA) by adding 5 μl of 10x buffer and 1 μl enzyme to 25 μl of purchased DNA with a concentration of 20 ng/μL; HindIII-HF was then inactivated by raising the solution temperature to 80°C for 20 min. T7 DNA ligase (New England BioLabs) ligated the sticky ends of the last step product to make circularized templates (CT). It was performed at 25°C for 1 h with the following reagents: 25 μl digested DNA from previous step, 25 μl of T7 DNA ligase 2x buffer, and 1 μL T7 DNA ligase enzyme. The circularized DNA templates were cleaned and purified using a Zymo purification kit and eluted in 50 μL of nuclease free water to get a yield of 30 ng/μL. Rolling circle amplification was done with a TempliPhi kit (GE Healthcare, Pittsburgh, PA) which works with 10 pg/μl concentration of CTs. Therefore, previous step samples were diluted 1000x and added with the kit’s protocols (1 μL template DNA, 5 μl sample buffer, 5 μl reaction buffer, 0.2 μl enzyme) and replicated in the 4 PCR tubes to get a higher amount of RCA amplified DNA. The reaction was done with overnight incubation time (12–14 h) and at 30°C. Nuclease free water (14 μL) was added to each PCR tube to dilute the viscous sample, mixed, and then purified with the Zymo kit and diluted in 50 μL of nuclease-free water to get to 70 ng/μL final concentration of DNA.

Sequencing and DNA library preparation

100–200 fmol of amplified or not amplified DNA was prepared via the SQK-LSK114 or SQK-LSK110 sample preparation kit (ONT) to load on R10.4.1 or R9.4.1 flow cells. In short, DNAs are repaired with NEBNext FFPE module (New England BioLabs) and end-prepped with Ultra II End-prep module (New England BioLabs) at the same time. After purification and DNA quantity measurements (Qubit dsDNA BR Assay), adapter ligation and clean up was done following manufacturer’s protocols. Final DNA concentration was measured with the Qubit dsDNA BR Assay and 50 fmol of the DNA was loaded into the minION flow cells via following the ONT workflow instructions.

The principle of the device is to use the motor protein to push one strand of the DNA attached to it to protein pores and the flow cell’s sensor array. Each passing oligonucleotide causes a unique disruption of current which is the electrical signal output of the device. For each sample, around 20–40 Giga bases of data (2–6 million reads) were collected in the raw electrical format (Fast5) to be processed in the next steps.

Illumina sequencing and heterogeneous samples preparation

Various mixtures of sfGFP LET and the folding reporter GFP (frGFP) LET were prepared by combining different ratios of DNA encoding for frGFP to sfGFP, specifically in proportions of 1:10, 1:100, and 1:1000. Subsequently, these samples underwent Illumina sequencing using the end-paired 150 bp MiSeq sequencer 300-cycle kit and Oxford Nanopore sequencing using R10.4.1 flow-cells and SQK-LSK114 kit. For the Illumina sequenced samples, quality control, coverage checking, and de novo genome assembly were carried out using QIAGEN CLC Genomics Workbench 23, applying default settings to ensure robust and consistent results.

Base calling, mapping, and data analysis

Fast5 files from minION output, were base called to nucleotides in the format of fastQ files using the Guppy base caller,V 4.5.3 [20] on Ubuntu 20.04LTS (detailed codes and settings are in S1 File) with different Q-score filters and with 400bps and HAC settings for R10.4.1 and 450bps and HAC for R9.4.1. Alignment was done for both Illumina and Oxford Nanopore data with Minimap2 [21] using the preset parameters to align the fastQ and fasta files with the reference sequence. Minimap2 output (SAM format) was converted, sorted and indexed into BAM by Samtools program [22] and converted to CSV format by a custom code from our group (S1 File). The CIGAR string, which is a string that includes the data of matched, mismatched, inserted and deleted sequences and their position in the read sequence (example shown in S1 File) was analyzed to extract the error rates for each mutation by parsing the CIGAR string into the parts each operator in it is representing. The error rate for each mutation type (insertion, deletion, and substitution) was defined as the percentage of total mutations of each type normalized to the aligned length of the read data with the reference sequence (also equal to the error probability per base pair as a percentage). For the pJL1-sfGFP plasmid the maximum common sequence between the plasmid, and the linear template is solely considered as a reference sequence to map plasmid data with, which is from the T7 promoter to T7 terminator on pJL1-sfGFP.

Error prone PCR

The reaction mixture is made of 50 μl nuclease free water, 10 μl 100 mM Tris-Cl, pH 8.3 (10 mM final), 2.5 μl 2 M KCl (50 mM final), 3.5 μl 200 mM MgCl2 (7 mM final), 3.5 μl 200 mM MgCl₂ (7 mM final), 16 μl 25 mM dNTP mix (1 mM final), 2 μl 100 μM forward primer (2 μM final), 2 μl 100 μM backward primer (2 μM final), 10 μl 10 ng/μl template DNA (1 ng/μl final), 2 μl 25 mM MnCl₂ (0.5 mM final), and 1 μl 5 U/μl OneTaq DNA polymerase (0.05 U/μl final). Each thermal cycle consists of three steps of a) 95°C for 1 min, b) 50°C for 1 min, (c) 72°C for 1 min. A 10 μl of reaction mixture is collected after 10 cycles and purified and cleaned with Zymo purification kit (D4004). The resulting DNA after 10 cycles got amplified with the OneTaq polymerase (New England BioLabs, Ipswich, MA) as the manufacturers protocol to reach a final concentration about 30–70 ng/μl measured by Qubit. The same procedure was continued after 15, 20, 25, and 30 cycles of error prone amplification with the reaction mixture.

Cell free protein expression and emission spectra analysis

All cell‐free reactions were conducted at 37°C in volumes of 15 μl using the following: 3.6 μl of 30% v/v E. coli extract (produced according to our previously published work on scalable extract [23], 5 μl of PANOx-SP system as supplement mixture (260 mM potassium glutamate, 8 mM magnesium glutamate, 4 mM potassium oxalate, 1.5 mM spermidine, 15 mM potassium phosphate (pH 7.0), 1.2 mM AMP, 0.86 mM each of GMP, UMP, and CMP, and 2 mM 19 amino acids with 1 mM tyrosine due to solubility) 5 nM of DNA template, and remaining volume with nuclease-free water to 15 μl total volume. Fluorescent protein sfGFP was expressed in a 384‐well plate covered with a transparent film at 37°C for 10 h. The reactions were monitored using a SpectraMax iD3 microplate reader (Molecular Devices, USA) with a fluorescence reading every 30 min after the start of the reaction. sfGFP fluorescence was detected with an excitation/emission of 485/528 nm, a band‐pass window of ±20 nm with auto gain. Emission peak data was gathered with a fixed excitation at 475 and an emission range from 495 nm to 600 nm, a band‐pass window of ±20 nm, and auto gain. The resulting emission spectra was collected after 4 hours of reaction, the peak was detected and the fluorescence intensities after the peak were mirrored in order to make a symmetrical emission peak. Lorentzian function was fitted to the mirrored spectra and FWHM was extracted from the function (see the S1 File).

Oxford Nanopore read quality and quantity

The genes were sequenced using Oxford Nanopore Technology sequencing flow cells and library preparation kits, specifically R10.4.1 paired with the SQK-LSK114 kit, and R9.4.1 paired with the SQK-LSK110 kit, for both amplified and non-amplified genes. The quantity of sequenced data varied depending on the library quality for both methods. It was previously reported that R10.4.1 generated half the amount of data compared to R9.4.1 in whole genome sequencing [24]. However, we observed that R10.4.1 produced more data in certain samples. For instance, in the case of the Phusion polymerase amplified sample, R9.4.1 generated 91% more sequencing data than R10.4.1. Conversely, when working with plasmids, R10.4.1 generated 193% more data than R9.4.1.

In terms of read accuracy, R10.4.1 outperformed R9.4.1 by yielding a greater amount of higher accuracy data. This is evident in the distribution of Q-scores (which can be converted to read accuracy using Eq 1) for read accuracy across various samples, as depicted in S1 Fig in S1 File. When comparing the averages, each sample’s Q-score showed improvement ranging from 1.35 to 4.89. Notably, a significant observation was the ability of R10.4.1 to generate data with Q-scores exceeding 20 (Q20+), thus enabling a focus on high-quality data in subsequent analysis steps.

Eq (1)

Error rates in heterogeneous samples, Illumina vs Oxford Nanopore

Although short-read sequencing is recognized for its higher read accuracy compared to Oxford Nanopore, it has limitations in individual gene analysis. Short-read sequencing requires the assembly of longer sequences from numerous short DNA fragments. This process not only demands time and computational resources but may also lead to incomplete assembly in highly repetitive sequence regions [25]. Consequently, heterogeneity analysis is often conducted on short reads mapped to the reference genome. In this experiment, we aimed to explore the capability of NGS to detect heterogeneity within samples by mixing different ratios of the two genes, namely sfGFP and a modified folding reporter protein (frGFP) gene as the errored sequence which is designed to have has 3.741% substitution, 1.011% deletions and 1.011% insertion error rates calculated based on the reference genes mapping compared to the sfGFP.

Initially, we conducted an analysis of de novo assembled sequences in an effort to generate longer contigs from MiSeq Illumina sequencing data, as shown in S1 Table in S1 File. Even in the case of the pure samples, which exhibited the longest contig at 734 base pairs (bps) for frGFP pure sample, it was insufficient to reach the full gene sequence length of 999 bps. Notably, for samples containing known, simulated impurities, the maximum contig length decreased. Additionally, the coverage of the sequenced DNA to the sfGFP reference genome was non-uniform, with observed lower coverage at the start and end regions of the gene. Through the analysis of error positions in frGFP and sfGFP sequences, highly erroneous areas were identified from the 774th to the 834th bp, coinciding with regions of low coverage. This phenomenon suppresses the average error rate by having reduced coverage at highly erroneous regions of the gene (S2 Fig in S1 File).

The error rate analysis was conducted on both long-read and short-read sequences mapped to the sfGFP reference sequence, resulting in the calculation of average error rates for the mixtures, as shown in Fig 2. It’s worth noting that the vendor’s sfGFP gene, when mapped to the sfGFP DNA template sequence, exhibits a baseline error in both Illumina-sequenced and nanopore-sequenced data, indicative of previously reported inherent errors in the synthesized DNA from the vendor detected with the current read accuracy [25]. Another observation in Illumina is the abnormal error rates observed when frGFP was mapped to sfGFP for Illumina sequencing. These rates consistently reported smaller values for error rates across various error types. In contrast, the Oxford Nanopore sequencing method reported error rates for pure frGFP samples that were closer to the expected values (Fig 2B).

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Fig 2. Benchmarking error rates observed in short-read vs. long-read sequencing approaches with known error-introduced heterogenous samples.

(a) Error rate averages of short-read Illumina sequenced data of different mixtures of pure sfGFP and errored frGFP sequences. (b) Error rate averages of long-read Oxford Nanopore sequenced data of different mixtures of pure sfGFP and errored frGFP sequences. Error bars show standard error and red solid lines show the expected percentage based on known ratios of pure and error templates. KDE fitted plots for the distribution of (c) Substitution, (d) Deletion, and (e) Insertion errors for Oxford Nanopore sequenced samples with introduced error.

https://doi.org/10.1371/journal.pone.0305457.g002

For the mixture analysis, the Illumina data displayed a convergence with the expected trend for deletions, while divergent patterns were observed in substitution and insertion assessments. By scrutinizing the distribution of error rates in the Illumina data, it became apparent that the majority of the errored sequences in the mixtures were sequences without any errors, with a small portion of sequences contributing to the errors in the averages (see S2 Fig in S1 File). Interestingly, even when Illumina sequenced pure frGFP sequences, the majority of reads exhibited no deletions or insertions errors, prompting an inquiry into the potential artificial error suppression within the Illumina sequencing pipeline during heterogeneity analysis which was not observed in the Oxford Nanopore sequenced data (S4 Fig in S1 File).

In contrast, Oxford Nanopore sequenced error rates across all sections matched expectations with the anticipated increasing trend as the ratio of frGFP elevated in the mixture (Fig 2B). Calculating the averages of errors in the samples and comparing them with the expected error rates for each mutation type, as determined from the values observed for pure sfGFP and frGFP for each sequencing method, revealed noteworthy differences. This analysis, detailed in the S3, S4 Tables in S1 File, indicated that Illumina samples exhibited more pronounced divergence from the expected results in every section, further indicating the superior performance of long-read sequencing for heterogeneity analysis.

Kernel Density Estimation (KDE) was employed to analyze the distribution of errors of Oxford Nanopore sequencing, yielding valuable insights into each distribution. Specifically, KDE provided information about two key aspects: the peak value (mode of the fitted model) and the peak height, which signifies the sharpness of the peak and the homogeneity of the sample. As illustrated in Fig 2C–2E, when the proportion of errored sequences in the mixture increased, the mode values for the error rates also increased, while the peak height decreased as the ratio of impurity increased. Notably, in the case of the pure frGFP sample, although the mode values exceeded those of the 1:100 mixture, indicating more error rates for the mode of the distribution, but the peak height indicated a higher level of homogeneity in the frGFP pure sample, further supporting the idea that peak height can be indicative of sample homogeneity.

Error rate assessment using different read-quality filters

Oxford Nanopore data was collected with R9.4.1 and R10.4.1 flow cells to compare the effects of chip version and the read quality filter. First, we investigate the effect of different Q-score filters applied with the base caller to average error rates of different samples. It should be noted that only the linear templates could generate enough Q20 data for this analysis and therefore only non-amplified sfGFP and PCR products are analyzed in this section. RCA and plasmid samples had reduced read efficiency likely due to blocked pores caused by excess amounts of smaller DNA fragments. This would be resultant of digestion steps necessary to prepare these for Oxford Nanopore sequencing. By analyzing the read length of the sequenced data, smaller fragments were observed in the RCA and plasmid samples (S5 Fig in S1 File) which would change the expected molar weight of each template and overshooting the correct molar window suggested by the vendor for sequencing (10–20 fmol of final prepared DNA library for the R10.4.1 flow cell). Three quality score filters were applied in this study: 1) the default setting on the Guppy base caller configured at a Q-score equal to 9 which filters out sequences with a read accuracy lower than 87.4%, 2) Q-score 15 (96.8% read accuracy) and 3) Q-score 20 (99% read accuracy). As depicted in Fig 3A, while increasing the Q-score filter resulted in decreased error rate values, the relative ranking between different polymerases remained consistent in substitution error assessment. However, for deletion (Fig 3B) and insertion errors (Fig 3C), the default Q-score setting exhibited significant variations in polymerase performance, with this variation diminishing as read accuracy increased. At Q-score 20, only OneTaq enzyme demonstrated statistically significant difference compared to non-amplified DNA, Phusion, and Q5. These findings emphasize the crucial role of read accuracy in benchmarking error rates. However, it’s important to note that increasing Q-score filters lead to a reduction in data volume. For example, by applying Q-score 15 filter, 20–50% of data passed the filter and by applying Q-score 20 between 0.6% - 0.01% of data passed the required read quality (also see S1 Fig in S1 File for Q-score distribution of samples). Therefore, a trade-off between read accuracy and data size is inevitable. Consequently, for further analysis of error rate distributions among all amplification samples, a Q-score of 15 was employed.

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Fig 3. Comparing mutation error rate averages and distributions for different amplification methods.

Average substitution (a), deletion (b), and insertion (c) error rates in samples amplified by various polymerases, filtered by fit accuracy (Q score). Distributions of substitution (d), deletion (e), and insertion (f) error rates between samples amplified by different methods.

https://doi.org/10.1371/journal.pone.0305457.g003

Amplified gene analysis

The panel of polymerases (OneTaq, Phusion, and Q5) were used to amplify the linear template gene using standard PCR as well as Phi-29 polymerase with RCA. pJL1-sfGFP plasmids were also amplified using a cell-based amplification, purified, and digested to make linear pJL1-sfGFP ready for sequencing. The results from the starting synthetic gene without amplification is also included in this section as a relative baseline for the errors assessed. Kernel density estimation models (KDE) were used to fit the distributions of each error type and visualize error rate profiles in the samples. For the substitution error rate (Fig 3D), the Q5 enzyme exhibits the lowest mode at 0.302%, while among the polymerases, it displays the highest peak. This observation suggests a more uniform error rate profile compared to the other polymerases. Despite RCA having the highest peak across all samples, it also possesses the highest mode value of 0.723%. Consequently, it generates sequences with a homogeneous but elevated error rate. This could be due to errors introduced in the circularization steps. The plasmid substitution error distribution records the lowest peak, indicating greater heterogeneity in error rates among sequences produced by this amplification method compared to non-cell-based methods.

In the case of deletions (Fig 3E), all polymerases demonstrate closely clustered error rate modes, ranging from 0.169% for OneTaq to 0.199% for Q5. The peak height of Phusion suggests its status as the most homogeneous polymerase in this panel. RCA, once again, exhibits a sharp peak but with a mode value of 0.38, while plasmid amplification results in the widest peak and the most heterogeneous sequences.

In insertions (Fig 3F), all polymerases exhibit close modes (0.200–0.203%) and peak heights. RCA stands out with the sharpest peak and the largest mode for error rate (0.816%). Plasmid amplification yields a mode value of 0.440, featuring the widest and lowest peak, once again showcasing the most heterogeneous amplification.

To assess the statistical significance of the error rate distributions, a bootstrapping [26] approach was employed for this analysis. This analysis tests the selection of multiple samples, each with a specified size, from two datasets. Following this, a Mann-Whitney U test was executed on each pair of bootstrapped samples. Over multiple iterations, the average p-value from the Mann-Whitney U test was computed, serving as the representative p-value for the comparison of the two distributions (see S1 File). All amplified samples exhibited statistical significance when compared to other samples, as determined by a 95% confidence level.

Analysis of error location

Oxford Nanopore sequencing data readily allows for determining the locations of each type of mutation along the matched open reading frame (ORF) to determine if they are localized or randomly distributed. When plotted as stacked heatmaps (Fig 4A–4C) repeating patterns of mutation locations are apparent in the synthetic (not-amplified) DNA, as well as the OneTaq, Q5, RCA, and Phusion amplified DNA. This indicates that the mutation findings were not random errors from the Oxford Nanopore sequencer but rather conserved areas of high error rates in the original synthesized DNA. The plasmid, which is amplified via cellular growth, has a pattern of errors distinct from the linear templates. Only the plasmid portion common to the LET sequences, namely between the T7 promoter and terminator or from 185^th bp to 1756^th bp backward on 3’, were analyzed; this results in 915 bps of the total pJL1-sfGFP being analyzed and plotted. More isolated, distinct bands in the plasmid insertion and deletion mutation heatmaps are observed, which indicates that these plasmid mutations are isolated to a few, specific hotspots. The location of the insertion and deletion hotspots can be projected to the pJL1-sfGFP plasmid map (S1 File) and are determined to be before the final beta-barrel of the protein (GFP-11, Addgene Sequencing Result #199339) and after the sfGFP protein gene, respectively. It is not evident what the mechanism is for these localized errors, but it is clear Oxford Nanopore sequencing can identify them and help screen DNA for potential errors prior to downstream uses such as expression templates in CFE.

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Fig 4.

Location of mutations within the template and dependency of error rates to RCA product length: (a) substitution, (b) deletion, and (c) insertion on the sfGFP reference gene. The intensity of colors corresponds to the likelihood of mutation occurrence (the color axis represents the probability of mutation at each base pair or the count of observed mutations at that location, relative to the total number of read base pairs at that location), with darker shades indicating higher probability spots for mutations. (d) Substitution (e) deletion (f) and insertion error rate distributions of RCA products based on the length of the matched sequence length.

https://doi.org/10.1371/journal.pone.0305457.g004

Also, by mapping the RCA sequences onto a repeated reference sfGFP sequence, error rates normalized by the overall matched length of the sequence can be plotted to analyze the dependency of error rates observed in RCA samples to the length of RCA product. As depicted in Fig 4E and 4F, the error rate profiles for longer amplified DNAs closely follow the mode of errors observed in shorter fragments observed in the distribution plots (Fig 3D–3F). Moreover, the distribution is observed to be linear with respect to length which suggests that the Phi29 polymerase contributes little to the observed error rate, and rather duplicates errors found in the starting, circularized template (in our case, caused by initial PCR of the starting template). If the Phi29 enzyme contributed significantly to the error, it would cause more error as the template increased in length which would cause this observed peak to slant upwards in Fig 4D–4F. Therefore, RCA can be a valid CFE template generation technique if care is given to the initial quality of starting sequence (using a high-quality polymerase for initial amplification).

Secondary evidence of mutation heterogeneity from cell-free expressed sfGFP spectrums

To corroborate the sequencing results and link to CFE performance, sfGFP was expressed in cell free extracts using the panel of amplified DNA linear templates, plasmid, and synthetic DNA from vendor (no amplification). We hypothesized that the emission spectrum data, from 495 nm to 600 nm, would broaden as more mutations were made to the protein, caused by imperfections in the beta barrel structure and thus would vary the photophysical properties of this standard reporter protein. Different mutants of sfGFP have shown varied emission and excitation maximum wavelengths. For example two amino acid mutations (N149Y/Q204H) on sfGFP can result in producing pH-tdGFP which has an emission peak (Em λ) of 515 nm that is red shifted 5 nm in comparison with 510 nm Em λ of sfGFP [27]. As an example of blue shifting of Em λ, usGFP with two mutations on sfGFP original sequence (Q69L/N164Y) has shown an Em λ of 508 nm [28].Even one mutation can change sfGFP to a yellow sfYFP (T203Y), a cyan sfCFP (Y66W) or a blue sfBFP (Y66H) [29]. The intensity of the spectrum (overall fluorescence count) would not be a good measure, as this can vary between LET and plasmid simply due to the efficiency of transcription and competing degradation of linear templates.

Excitation wavelength was held constant and CFE replicates were done in 8–10 wells for each amplification method due to inherent variability of this technique [30]. The widths of the emission peaks (around 510 nm) were determined using the full width at half maximum (FWHM) of the fitted Lorentzian function to the mirrored data (Fig 5A). The reason for mirroring data after the peak is 1) emission data near to the excitation cannot be collected and 2) to construct a symmetrical peak suitable for fitting symmetric models such as Lorentzian and Gaussian. As proof of concept, sfGFP genes were first subjected to an error-prone PCR step with varying cycle numbers and then normally amplified to reach a suitable concentration for performing CFE. As shown in Fig 5B, with an increase in the cycles of error-prone PCR, the FWHM average of the samples also increases, indicating a broadening effect on the emission spectra peak. Analyzing FWHM averages for different amplification methods (Fig 5C), it is observed that RCA and OneTaq-amplified genes exhibited the widest peaks. Non-amplified genes, as well as those amplified with Q5, Phusion, and the plasmid, showed the narrowest peaks, and they were not statistically different from each other. This suggests the production of more homogenous sfGFP using Q5 and Phusion for linear amplifications, as well as using the plasmid template. Additionally, it’s important to note that non-amplified genes also exhibited small variations in FWHM values (Fig 5b and 5C). This variability can be accounted for by variability in measurement technique (data taken on different days) and some variability caused by using different batches of fresh DNA from the vendor.

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Fig 5. Emission spectra analysis.

(a) Depiction of emission peak half width measurement using mirrored emission spectral data fitted by a Lorentzian function. The green dotted line is the peak wavelength and the red dotted line is the half-maximum intensity. The purple line indicated the FWHM for the spectra. (b) A comparison between the emission spectra peak’s FWHM of different sfGFP genes with 0,10, 15, 20, 25, 30 cycles of error prone-PCR. Each bar is the average and the error bars are the standard error of the samples. (c) sfGFP emission spectra peak’s FWHM of different amplification methods.

https://doi.org/10.1371/journal.pone.0305457.g005

Conclusion

In conclusion, this study highlights the potential of Oxford Nanopore sequencing as a useful tool for assessing amplified DNA quality especially when PacBio is beyond budget or geographic access; DNA homogeneity is a critical parameter in biotechnological laboratories globally, especially for synthetic biologists that require a high-quality genetic blueprint for their engineered protein product or circuit. Through error profile analysis, the high-fidelity polymerases Phusion and Q5 are found to perform best, offering robust amplification of linear templates for applications such as cell free protein expression. RCA with Phi29 polymerase is also a viable technique if the starting circular template has low error. Oxford Nanopore sequencing data also readily allows for assessment of mutation location. In the case of the sfGFP plasmid, the majority of deletions and insertions were found to occur downstream of the GFP coding sequence, thus not affecting the fluorescence properties. Alternatively, we demonstrated how sfGFP heterogeneity could be measured using emission peak width measurement. Oxford Nanopore can be used to measure performance of new template synthesis and amplification techniques as they are discovered and provide a means for benchmarking quality prior to CFE production.