Samuel C.
Saccomano
a and
Kevin J.
Cash
*ab
aChemical and Biological Engineering Department, Colorado School of Mines, Golden, CO, USA. E-mail: kcash@mines.edu
bQuantitative Biosciences and Engineering, Colorado School of Mines, Golden, CO, USA
First published on 25th November 2021
We developed a ratiometric oxygen-sensitive nanosensor and demonstrated application in monitoring metabolic oxygen consumption in microbial samples over time. Based on a near-infrared (NIR) emitting oxygen-quenched luminophore, platinum(II) octaethylporphine ketone (PtOEPK), along with a stable dioctadecyl dicarbocyanine reference dye (DiD), this nanosensor system provides an advantageous approach for overcoming imaging issues in biological systems, such as autofluorescence and optical scattering in the visible wavelength region. The dyes are encapsulated within a polymer-based nanoparticle matrix to maintain them at a constant ratio in biological samples, precluding the need for complex synthetic approaches. With this constant ratio of the two dyes, the nanosensor response can be measured as a ratio of their two signals, accounting for nanosensor concentration artifacts in measurements. The nanosensors are reversible, which enabled us to temporally monitor systems in which dissolved oxygen concentrations both increase and decrease. These sensors were applied for the monitoring of oxygen in samples of Saccharomyces cerevisiae (brewing yeast) in a 96-well optical fluorescence plate reader format over 60 h. By mixing the nanosensors directly into the sample well with the yeast, we were able to dynamically track metabolic activity changes over time due to varying cell concentration and exposure to an antimicrobial agent. This system could be a potential platform for high-throughput screening of various species or variants of microbes with unknown metabolic rates in response to external stimuli (antimicrobials, metabolites, etc.).
Chemically responsive nanoparticle-based sensors (nanosensors) are an emerging technology which aims to overcome many of the common challenges in making biological measurements. These nanoparticles consist of a highly plasticized polymer matrix which encapsulates luminescent indicator dyes and is permeable to molecular oxygen.15 Many hydrophobic dyes are known to have poor solubility in aqueous environments and can be difficult to apply in a biological settings.16 By encapsulating these dyes in a polymer matrix, we can better control the sensor response which allows for sensing in a wider variety of samples.17 Additionally, these nanoparticles are stable enough to measure temporal dynamics over several hours to several days and can be evenly dispersed throughout the sample for measuring 3-dimensional gradients18 when paired with the necessary optical instrumentation. Applications which have used oxygen-sensitive nanosensors include, intracellular oxygen measurements in cancer cells,19–22 confocal imaging of various cell types,23–25 oxygen dynamics in biofilms,18,26,51 oxygen sensing in microfluidics27 and in vivo monitoring.28
Many optical probes still face the challenge of overcoming the background signal produced from biomolecules present in biological tissues.29 In thin films and surface tissues the background is relatively low, however in deeper tissue samples there can be significant absorbance or emission signals in the visible light range (400–700 nm). Probes that emit photons in the near-infrared (NIR) wavelength range (700 nm–3000 nm) can be much easier to detect for these types of biological measurements.30 NIR probes have been used for measuring many analytes in different applications. For example, Jo et al. used a fluorescent NIR tracer to measure glucose uptake in human cervical cancer cells.31 Barone et al. demonstrated the applicability of carbon nanotubes as near-infrared sensors which respond to changes in dielectric properties of the material in the presence of glucose.32 Cash et al. used fluorescent nanosensors for in vivo imaging and measurement of lithium in mice.33 Hirayama et al. measured copper ion regulation in mice using NIR copper sensors due to its link to progression of degenerative diseases such as Alzheimer's and Wilson's disease.34 A more extensive review of near-IR probes for bioimaging applications was written by Li et al.30
Platinum(II) octaethylporphine ketone (PtOEPK) is a luminescent dye which emits in the NIR range at approximately 760 nm when excited, but is reversibly quenched in the presence of oxygen, thus making it a good candidate for optical measurements of oxygen in biological samples.35 PtOEPK is a metalloporphyrin dye which has a longer emission wavelength and phosphorescent lifetime compared to many other oxygen-sensitive dyes.
Mechanistically, molecular oxygen quenches PtOEPK luminescence through a process called collisional quenching.36 As the luminophore is excited, the triplet state oxygen collides with the chelated platinum in the center of the molecule quenching the excited energy state of the dye and dispersing the additional energy through non-radiative decay.11
Emission wavelengths for metalloporphyrins start at around 650 nm and range up to as high as 950 nm (PtOEPK ∼760 nm) with large Stokes’ shifts (PtOEPK ∼360 nm) and relatively high quantum yields (PtOEPK ∼12%).16 Additionally, metalloporphyrin phosphorescence lifetimes are very long ranging from 10 microseconds to 1 millisecond16 (PtOEPK ∼60 μs (ref. 35)). Other factors such as brightness and photostability can vary greatly depending on the exact dye that is used.37
In this study, we developed a ratiometric nanosensor to measure oxygen. We used PtOEPK as the oxygen-sensitive component and 1,1′-dioctadecyl-3,3,3′,3′-tetramethylindodicarbocyanine (DiD) as a reference dye which does not respond to oxygen. The two dyes are both in the same nanoparticle matrix upon fabrication meaning that the ratio of the dye signals can be used to measure oxygen even when there are variations in signal due to nanoparticle concentration or some changing optical conditions of the samples.38 A diagram of the nanosensor and the oxygen quenching mechanism can be seen in Fig. S1.† In this work we apply these oxygen sensors to a high-throughput assay for measuring oxygen in biological systems, demonstrated here with oxygen consumption in different strains of brewing yeast.
A calibration curve was generated by measuring the ratio of luminescence at 757 nm (oxygen-sensitive dye) to 675 nm (reference dye) at each of the oxygen concentrations. A pseudo-Stern–Volmer equation is used to plot these values as a ratio of the intensity in the absence of the quenching species (0 mg L−1 O2) to the intensity at another fixed concentration. The Stern–Volmer equation is described by eqn (1) in the Results section. The pseudo-Stern–Volmer constant, or KpSV was found with a linear regression of the plot.
Reversibility was tested by cycling between pure nitrogen (deoxygenation) and air (21% oxygenation) to see if the signal would change as a function of cycle number. Each bubbling step was run for 40 min between measurements, and they were tested for 5 cycles.
Temperature response was tested by performing nitrogen and air bubbling steps while the cuvette was partially submerged in a heated water bath (Fisher Scientific, Waltham, MA, USA) followed by a rapid measurement with the system outlined above before being returned to the bath. pH response was tested by carefully diluting the sensors 1:
1 in a mixture of potassium phosphate dibasic and potassium phosphate monobasic buffers. Nanosensor concentration was tested by diluting the stock concentration in PBS to either 50%, 20%, 10% or 5% of its original concentration.
The response time of the nanosensor was estimated using oxygen purging by glucose and glucose oxidase (GOx) to artificially drive the oxygen concentration down in a well plate. 160 μL of sensor and 20 μL of 100 mM glucose were placed into 3 wells of a 96-well optical bottom plate. The wells received either 20 μL of PBS, 20 μL of 20 units per mL GOx, or 20 μL of 100 units per mL GOx. Immediately after addition of the final reagents the plate was loaded into a microplate reader and fluorescence was measured every 5 seconds at 590 nm excitation with 675 nm and 760 nm emissions for the two dyes. The delay between reagent addition and the first read was measured at 15 s. Size and charge of the nanoparticles were measured using a Malvern ZEN360 ZetaSizer (Southborough, MA, USA).
For quenching based approaches like this, oxygen concentration in relation to signal intensity can be described by the Stern–Volmer relationship. The Stern–Volmer equation for collisional quenching of luminophores is given by eqn (1).
![]() | (1) |
I 0 and I refer to the intensity of the luminescence signal in the absence and presence of oxygen at a given concentration, respectively. The quenching constant (kq) and the decay lifetime (τ0) combined to form the Stern–Volmer constant or KSV. We created a pseudo-Stern–Volmer plot (Fig. 2) using the ratio of the signal of the two dyes (PtOEPK and DiD) instead of the oxygen-quenched dye alone so any variation in dye concentration could be accounted for. To generate a curve, the ratiometric signal in the absence of oxygen is divided by the ratiometric signal at the individual oxygen concentrations to obtain a linear correlation with respect to the dissolved oxygen concentration. The pseudo-Stern Volmer constant (KpSV) is obtained from the slope of the linear regression of this plot demonstrating the relationship between the increasing concentration of the quenching molecule and the decrease in signal. While the pseudo-Stern–Volmer correlation can vary based on the instrument setup, a single instrument setup can be used to determine the consistency in the performance of the sensors over several samples or batches. For our air and nitrogen bubbling setup (with n = 3), we found the KpSV to be 1.74 ± 0.29 when plotted from 0 to 6.65 mg L−1. The result of this value is that the difference in signal between atmospheric oxygen levels and anoxic conditions is roughly 10-fold. The R2 value of the fit was 0.93 indicating some level of non-linearity. The degree of linearity is usually determined by the extent of dynamic quenching vs. static quenching and whether the quenching step is limited by diffusion of oxygen to the dye,39 however, when compared to the Stern Volmer of just the oxygen dye (see Fig. S6†) which shows a high degree of linearity, it indicates the reference dye could be the source of the minor non-linearity. Sensitivity of the sensor was also tested specifically for lower concentrations of oxygen at 0, 0.40, 0.79, 1.19 and 1.58 mg L−1 consecutively (see Fig. S7†). The sensors responded linearly in this range suggesting it is well suited for measuring low oxygen concentrations.
To monitor oxygen concentrations in biological systems, the sensors must be reversible to measure both increases and decreases in oxygen concentrations. We tested reversibility by alternating the composition of the bubbling solution between pure nitrogen and air streams. The signal was measured over the course of 5 cycles between the two conditions as seen in Fig. 3. After a slight increase in the ratiometric signal from cycle 1 to cycle 2, the signal was consistent across the remaining 4 cycles. The maximum variance was 8% between cycle 2 and 5 for the deoxygenated sample. Error bars indicate that no data points had a standard deviation greater than 11% for the deoxygenated sample (cycle 3) and 14% for the oxygenated sample (cycle 5) over n = 3 replicate sensor batches. The oxygen dye alone showed very similar results demonstrating the dye's excellent reversibility (see Fig. S8†).
Temperature, pH and concentration dependence were also tested at oxygenated and deoxygenated levels. As expected from similar dyes, the signal decreased significantly as the temperature of the sensors was increased from 25 °C to 40 °C. Phosphorescence has been shown to be sensitive to temperature in the past due to thermal quenching which decreases the luminescence lifetime of dye.40 However, both dyes were impacted similarly by temperature resulting in a consistent ratiometric signal and showing that oxygen could be measured across biologically relevant temperatures (see Fig. S9†). We tested the pH response of the nanosensors from 5.8 to 8.2 (see Fig. S10†). The sensors showed the highest luminescence at pH 7.6 and 8.2 for the PtOEPK dye and pH 7.0 for the DiD dye though both showed easily measurable signal across the tested range. We determined the concentration dependence of the nanosensors (see Fig. S11†) by diluting a batch of nanosensors down to 5% of the original nanosensor concentration through a series of dilutions. While, as expected, luminescence is not a linear function of concentration, the ratiometric signal between the oxygen dye and the reference dye stayed constant in both oxygenated and deoxygenated samples showing the value of ratiometric measurements to correct for nanosensor concentration. Response time was determined by measuring the ratiometric signal of the sensor as the oxygen in solution was consumed using glucose and GOx (see Fig. S12†). The data showed that response was limited by the speed of oxygen depletion (at low enzyme concentrations) or the delay time of instrument (∼15 s). The actual response time of the sensors is likely much faster as similar fluorescent nanosensors have millisecond scale response times.41 The diameter of the particles was 161.4 ± 1.7 nm, polydispersity was 0.15 ± 0.02 and zeta potential was −25.9 ± 2.3.
We tested the nanosensor function in biological systems by measuring oxygen consumption of Saccharomyces cerevisiae (the yeast commonly used in brewing). Molecular oxygen is a critical component to cellular respiration, which produces many of the precursor molecules essential to the fermentation processes in brewing yeast that lead to alcohol production. Various lipids and growth factors require the high energy yield of oxygen metabolism for production, and are essential components to the yeast in their anaerobic metabolic state.42 We used nanosensors mixed with yeast samples in a microwell plate to assess yeast metabolism. Over the course of ∼60 hours the ratiometric signals of the nanosensors were monitored to track the aerobic respiration process (oxygen consumption) of yeast cells when exposed to fresh aerated wort. Two different strains of yeast were tested: the Kolsch I and Kveik: Oslo strains (Propagate Lab, Golden, CO). Fig. 4A shows a set of ratiometric curves for 3 different concentrations of the Kolsch yeast diluted from stock solutions containing ∼200 billion cells (according to manufacturer) in approximately 200 mL of growth media. At the initial phase of the growth profile, the low ratiometric signal is an indication of the high oxygen concentration, which we would presume to be close to atmospheric levels due to low consumption by the yeast. Samples with lower amounts of initial yeast, saw a greater lag time before a rapid increase in signal was observed at the initial stages of growth. Conversely, the higher initial yeast concentrations saw an increase in the ratiometric signal within the first few hours. Thus, our sensor can distinguish between different oxygen concentrations resulting from different consumption rates based on the number of cells metabolizing oxygen. Eventually each sample reached an equilibrium where the ratiometric curve flattens. This does not indicate where the oxygen level has reached zero, but merely where oxygen consumption and oxygen diffusion from the atmosphere reach a steady state. Larger initial amounts of yeast also correlated to higher ratiometric signals at which the equilibrated state is reached. In Fig. S14,† we see how the importance of the ratiometric signal comes into play as well. The individual dye channels show unexpected behaviour, especially as cell concentration is changed, but when the ratio is taken, the artifacts are removed, and the oxygen concentration profile seems to show a clearer trend. Fig. 4B shows a faster change in oxygen concentrations from the Kveik strain at the same dilution as the Kolsch, though this is likely due to a higher initial cell count from package listed concentrations (Kolsch: 1.82 ± 0.08 billion cells per mL; Kveik 2.79 ± 0.09 billion cells per mL). Similar trends are observed at higher and lower cell dilutions as well (see Fig. S15†). The Kveik showed a similar trend in oxygen metabolism with respect to cell concentration as the Kolsch – with increasing cell density causing a more rapid oxygen concentration change and lower steady state oxygen concentration (see Fig. S16†). We also tested the metabolism of live cells after the addition of an antimicrobial agent. Fig. 4C shows the ratiometric signal of cells spiked with potassium metabisulfite (PMB) at t = 42 h as compared to those with no PMB added. In the Kolsch strain there is clearly a detectable difference between the 2 conditions. The cells that were given PMB in the middle of the run saw a very rapid decrease in signal back to atmospheric conditions as compared to the no PMB samples that continued to hold consistent signal at the equilibrium oxygen concentration. An additional condition was run in which the PMB was added at the start of the run for each yeast dilution (see Fig. S17†). The PMB did not initially slow down the consumption of oxygen, but only at 15 h did the antimicrobial take effect and the oxygen concentration came back to atmospheric levels. The oxygen response in the Kveik: Oslo strain did not respond as dramatically to the addition of PMB (Fig. S18†). In samples where PMB was added at t = 0 h oxygen consumption was slowed for a period of time before recovering to near equilibrium concentrations. Cells exposed to PMB after 42 hours did see an increase in oxygen levels which would occur with diminished metabolism, but they did so much more slowly than the Kolsch strain.
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Fig. 4 Oxygen nanosensors in yeast respond to changes in oxygen metabolism as yeast are grown over time. A. Yeast are diluted at 1![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
The controls for this experiment showed the expected behaviour when the yeast or the nanosensor were removed from the well (see Fig. S19†). In the absence of yeast, no oxygen is metabolized and the ratiometric signal remains constant. When the nanosensors were removed, background fluorescence from the yeast and wort was minimal. To test if the nanosensors were toxic towards yeast we performed a metabolic assay to assess the impact of the sensors (Fig. S20†). The assay showed that sensors had minimal effect on cell growth and viability as compared to samples grown in its absence, similar to previous reports on similar nanosensors.43 Luminescence spectra for yeast (see Fig. S21†) showed that the longer excitation and emission wavelengths of the nanosensors was beneficial as compared to a shorter wavelength dye combination (such as PtTFPP and DiA)18 where greater background fluorescence was seen at high concentrations of yeast and wort.
Our calibration data shows that the oxygen-responsive dye is sensitive to changes in oxygen that occur between atmospheric conditions (21% gas, 6.65 mg L−1 in solution) and anoxic conditions (0%, 0 mg L−1). This allows us to identify changes in dissolved oxygen levels with respect to spatial and temporal gradients, and the NIR wavelengths allow use in samples with high background fluorescence in visible wavelengths. Reversibility is also an important aspect of these sensors – the luminescent properties of the nanosensor do not change after an oxygen quenching event. Additionally, PtOEPK and DiD are part of dye families which are known for good photostability. Papkovsky et al. showed that PtOEPK was stable for at least 18 h under continuous illumination.44 While DiD specifically has not been tested, carbocyanines have been shown to demonstrate good photostability as well.45,46 This reversibility and stability are what enables the sensors to be a powerful tool for monitoring both increases and decreases in oxygen concentration as a given sample changes over time as one would see in metabolic monitoring. Also, while the sensors have not been tested under extreme pH or temperature conditions the sensors have shown to work well in the most common physiological and biological conditions. In other biological samples, it is possible that chemicals besides oxygen can affect the luminescent property of the sensor, although the ratiometric nature of the readout does help to mitigate some potential artifacts (e.g. sensor concentration, degradation, and distribution) as both dyes are likely to be impacted similarly.
The application of these sensors in monitoring oxygen in yeast samples shows that the sensors function in a biological setting. In this particular case, we look at how the metabolic activity of yeast can be monitored by tracking the oxygen levels in solution throughout the lifetime of the microorganism. The sensors showed suitable sensitivity in real time to distinguish the relative amount of oxygen being consumed based on the number of yeast cells present as well as the activity level of the strain of yeast cell. Therefore, there is the potential to study subtle differences in the metabolic activity of randomized or specifically tuned yeast samples, or even other types of bacterial or tissue samples, in a high throughput manner. The oxygen sensor method was also able to pick up on distinct response characteristic in the presence of an antimicrobial agent. While most antimicrobial susceptibility tests simply determine whether the microorganism is or isn't susceptible to the agent, our sensors can pinpoint when metabolic activity is activated or slowed based on the changes in oxygen concentration. If the rate of atmospheric oxygen diffusion is known or controlled (to e.g. ∼0 mg cm−2 s−1) then the rate of oxygen consumption could be measured based on the slope of the ratiometric signal at given time points. Even without controlling the oxygen diffusion into the solution, it is likely that the oxygen concentrations were near zero based on the ratiometric signal. As we increase the yeast concentration in the samples, the higher yeast concentrations can more efficiently draw down the oxygen concentration to a lower steady state given the same oxygen transport rate into the solution for all yeast concentrations. This may also explain the “overshoot” in the temporal data at the highest yeast concentrations, where the actively metabolizing yeast are able to bring oxygen below the steady state value before transitioning to fermentative metabolism. This effect is more pronounced at higher yeast density as expected from their increased total oxygen consumption.
Potassium metabisulfite affects yeast viability as sulfur dioxide is formed upon dissolution into aqueous media. The sulfur dioxide and other by-products kill the yeast causing metabolic activities to halt. The PMB experiment highlights the need for a reversible sensor, as without it the sensors would not be able to detect the increase in oxygen concentrations after the cessation of metabolism. Interestingly, the PMB added at different time points (0 h and 42 h) had two very different effects on metabolism. A lag period was observed in samples with PMB added at 0 h where they initially metabolized oxygen but would later die. There are two potential mechanisms which could account for that. The first is that sulfur dioxide converts into sulfurous acid under low pH conditions further increasing the toxicity of the agent to the cells. Yeast are known to decrease the pH in their surrounding environment over time and thus the yeast could be dying as a result of increasing sulfurous acid concentrations over time.47 The second possibility is that sulfur dioxide is bound by glucose and other carbonyl containing sugars, therefore reducing its efficacy. As yeast metabolize the glucose in the surrounding medium, more sulfur dioxide is present to poison the cells.47
The different response to PMB from the two yeast strains used may result from the different lineage of these yeasts, with Kveik yeasts being genetically distinct from traditional brewing yeasts48 and showing improved stress tolerance.49 Resistance to sulfur dioxide in yeast is something that can vary greatly from strain to strain due mostly to genetic factors. Some variants have adapted to survive sulfur dioxide through mechanisms such as (1) entering a dormant, but “protected” state, (2) removal of sulfur dioxide through efflux pumps (3) reduction of sulfur dioxide through sulfur-based metabolic pathways and (4) production of biomolecules which bind and inactivate sulfur dioxide.50
The ratiometric nature of the measurement also gives us several other advantages in biological systems. Not only does it account for well-to-well or spatial variations in nanosensor concentration, but it can also be used to account for optical artifacts which may alter the luminescent measurements when just one dye is used. In yeast, the total amount of light absorbed or scattered in the well increases as the cell density increases. Autofluorescence of microbial species is another property which could change optical aspects of the assay. The advantage of these sensors is that the excitation source will be affected equally by these artifacts given that they use the same excitation wavelength. While there could be optical differences in the emission signals, absorption concerns are less common at near-IR wavelengths. Another aspect of these sensors that we observe is their ability to work effectively in a 200 μL to 2 mL volume range (and presumably both smaller and larger) useful for experiments that require low volumes or high throughput automated screening assays.
In applications such as what we have demonstrated in this paper, ratiometric nanosensors have several advantages over previously developed sensors. In other methods such as electrochemical and optode-based methods there remain several factors which make oxygen-sensing difficult such as disruption of the system, consumption of the analyte and limited spatial information. Microelectrodes generally suffer from all three as the needle must be in direct proximity of the microbial system and will consume the analyte due to the reduction of oxygen needed for the sensing mechanism.9 Our nanosensors overcome these issues as we show that they are non-toxic and reversible without consuming the analyte being measured thus portraying a more accurate depiction of the system under investigation. Both electrochemical and optode-based methods suffer from limited spatial resolution due to them only being able to sense in one (microelectrodes) or two (optodes) dimensions.9,12 In applications, where spatial information is needed, nanosensors are a more suitable method of measurement.
The stability of the reference dye is an aspect of the sensors that needs to be further characterized to fully validate the accuracy of our ratiometric readout. The advantage of the ratiometric approach is that it allows us to account for factors which affect both dyes equally within the sensor. If the DiD dye is less stable to certain factors than the PtOEPK dye, then the ratiometric approach does not work as well under those conditions. Due to the nature of the oxygen-quenching mechanism, platinum porphyrin dyes are known to have poor sensitivity at oxygen levels above atmospheric conditions meaning they would only be suitable for monitoring organisms that consume oxygen and likely not ones that produce oxygen as a part of their metabolic function or only minimally impact oxygen concentrations.
There are a variety of ways that future work could help to overcome these issues. The dye combination can be modified to improve consistency of the ratiometric approach across a wider variety of settings. Ideally, a reference dye that is more stable and further shifted into the NIR emission range should help to overcome factors such as dye susceptibility and sample autofluorescence. Exploring oxygen-sensitive dyes that emit further into the NIR-I (700–1000 nm) or even into the NIR-II (1000–3000 nm) range would open up the possibility of measuring oxygen in tissue samples with high levels of light absorption. The sensitivity and nanometer-scale of our sensors allows scaling down volumes and scaling up throughput to 384 and 1536 well plates that can screen a larger number of samples at once in an automated setting. A similar approach could also be applied to other analytes critical to cellular processes.
In live samples, the sensitivity of the sensors allows for trends in oxygen metabolism to be tracked for up to 60 hours in yeast samples. We distinguished oxygen consumption differences between samples with varying cell density, cell strain and exposure to antimicrobial agents. The ratiometric aspect of the sensors allows us to overcome optical artifacts such as light absorption and scattering characteristics that change over time due to cell proliferation.
Overall, these sensors are a favorable platform for the testing and screening of biological samples to measure oxygen consumption in high throughput assays. These same principles could potentially be applied to bacterial, tissue, cell, and other yeast cultures in the future.
Footnote |
† Electronic supplementary information (ESI) available. See DOI: 10.1039/d1an01855h |
This journal is © The Royal Society of Chemistry 2022 |