Toxic effect of metal doping on diatoms as probed by Broadband Terahertz Time-Domain Spectroscopy

doi:10.21203/rs.3.rs-1616674/v1

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Toxic effect of metal doping on diatoms as probed by Broadband Terahertz Time-Domain Spectroscopy

https://doi.org/10.21203/rs.3.rs-1616674/v1

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The global marine environment is increasingly affected by human activities, causing climate change, eutrophication, and pollution. These factors influence the metabolic mechanisms of phytoplankton species, such as diatoms. Among the other pollutant agents, heavy metals can have dramatic effects on diatom wellbeing. Therefore, a detailed knowledge of the interaction of diatoms with metals is essential from both a fundamental and applicative point of view.

The existing gap of knowledge on metal-diatom interactions may be bridged by applying complementary spectroscopic techniques. To this aim we assess Time-Domain Terahertz Spectroscopy as a tool for sensing diatoms in their natural environment: seawater. Despite the strong absorption of terahertz radiation in water, we show that diatoms can be sensed by probing the water absorption enhancement in the terahertz range caused by the water-diatom interaction. We reveal that the addition of metal dopants affects this absorption enhancement, thus enabling the monitoring of the toxic effects of metals on diatoms by means of Terahertz Spectroscopy. In particular, we demonstrate that this technique is able to detect the detrimental effects of heavy metals earlier than conventional methods as microscopy, enzymatic assays and molecular analyses aimed at assessing the overespression of genes involved in heavy metal-stress response.

Broadband THz-TDS spectroscopy

diatoms

heavy metal pollution

marine ecotoxicology

bioremediation

solvating water

The global marine environment is increasingly affected by human activities that cause climate change, eutrophication, and pollution¹. The latter has a strong impact on phytoplankton species that are key players in the global carbon cycle. Among different marine phytoplankton, diatoms represent the most diverse group of algae, comprising more than 100,000 different species². Diatoms are unicellular eukaryotic microalgae possessing a porous silica cell wall, known as frustule. Silicon metabolism in diatoms is strongly connected to many physiological processes such as respiration and photosynthesis^3,4. The silicon metabolism may be strongly altered by the presence of metals⁵. This toxic mechanism is rather complex with many factors which can interfere, such as for instance variations in nutrient stoichiometry. Despite its importance, the interaction between metals and diatoms is still poorly understood. This is also due to the lack of spectroscopic techniques suitable to investigate in detail this interaction on a microscopic level. On the other hand, a better comprehension of the adsorption of metals on diatoms may help in designing more efficient biosensors for water pollution monitoring.^6–9 As biosensors for metal pollution, standard methodologies are based on the identification of diatoms with specific shapes and frustule ornaments^10,11. However, these techniques are time consuming and usually do not take into due account the specific distribution of species in different regions. To solve these open issues new spectroscopic tools may provide with complementary key information, and faster and more precise screening capabilities.

In this regard, optical vibrational spectroscopies, as Raman and Infrared Absorption Spectroscopy (IAS), have proven to be valuable tools for the study of algal microorganisms since they are label-free and can also be used in-situ¹². Raman and IAS spectroscopies are complementary instruments because they probe different molecular vibrational modes, excited by one- or two-photon processes in the case of IAS and Raman spectroscopy, respectively. Raman spectroscopy holds an important drawback: the generally low signal can be obscured by diatom autofluorescence; then, the high intensity of the pump visible light can dramatically affect diatoms. This issue is partially absent in IAS, but there are other limitations that innovative techniques may circumvent. For instance, an intriguing effect that has been investigated by IAS is a structural reorganization of the biomass induced by the water-mediated diatom-diatom interactions (the so called ‘matrix’ effect)¹³. Incidentally, this effect biases the IAS spectra with detriment of an accurate determination of the biomass content. This phenomenon may be related to a structural reorganization of the hydrogen-bond network in which diatoms are immersed. IAS spectroscopy can provide only indirect information on this global reorganization since it only probes specific ‘local’ bond vibrations.

Terahertz spectroscopy may be considered an extension of IAS to the extreme range of THz frequencies. This frequency interval has long been known as the ‘THz gap’ due to the difficulty of having efficient sources and detectors for this elusive radiation. However, thanks to advances in laser technology over the past two decades, it is now possible to have THz table-top spectrometers pumped by intense and broadband THz pulses^14,15. The use of intense beams allows a significant transmission of THz light through aqueous samples over a wide range of THz frequencies¹⁶. Terahertz spectroscopy based on the use of such THz pulses is universally known as THz Time Domain Spectroscopy (THz-TDS)¹⁷.

In recent years, THz-TDS has proven being an invaluable complementary vibrational probe for investigating in-vivo biosystems since, given the low energy of THz photons, it is not harmful for living cells. THz-TDS has been used to probe global vibrations and structural deformations in biomolecules, or the collective vibrations of the hydrogen-bond (HB) network of water molecules¹⁸. These excitations do not lay in the infrared spectral range and hence they cannot be directly probed by Raman spectroscopy or IAS. Therefore, THz-TDS displays two fundamental advantages: i) compared to Raman spectroscopy, it is much less harmful for in-vivo diatoms; ii) compared to both Raman and IAS, it can directly probe the global structural reorganization of the HB network hosting diatoms and the macromolecular vibrational modes of the diatoms themselves. The latter feature is of particular interest for investigating the above mentioned ‘matrix’ effect.

Figure 1 – Principle of THz-TDS: a sub-picosecond THz pulse passes through the sample, here formed of diatoms in water. The transmitted pulse is attenuated and delayed because of the absorption coefficient and the refractive index of the sample. Therefore, it carries information on these two parameters at all the frequencies composing the pulse bandwidth.

Therefore, to assess THz-TDS as a complementary spectroscopic tool for investigating diatoms we applied this technique to the diatom species Skeletonema pseudocostatum. We measured THz absorption spectra of the latter in seawater and at different concentrations of copper, as heavy metal pollutant. Skeletonema belongs to the morphological category of the centric diatoms and is mainly found in shallow waters¹⁹. These diatoms are thus suitable as pollution indicators of coastal environments. As explained in more detail in the ‘Methods’ section, we used a self-assembled THz spectrometer in which fast and intense single-cycle THz pulses are generated by means of two-color four-wave mixing in air²⁰. Notwithstanding the technological challenge, the concept behind THz-TDS is straightforward and it is schematically shown in Fig. 1. A THz pulse with a duration of hundreds of femtoseconds, hence carrying a wide range of THz frequencies, passes through a sample (diatoms in water in the figure). The transmitted THz pulse is then measured as described in the section ‘Methods’. We note that in THz-TDS experiments the optical THz field in the time (amplitude and phase) is measured, not only its intensity. Therefore, the measured pulse carries information on both the refractive index, n(ν), whereνis the frequency, and the absorption coefficient, α(ν), of the sample. This information can be extracted with a suitable numerical procedure based on a Fast Fourier Transform, thus providing the full α(ν) and n(ν) spectra of the material under investigation over the range of frequencies composing the pulse bandwidth, without approximated numerical methods such as Kramers-Kronig relationships often used in IAS.²¹

In the main panel of Fig. 2 the THz absorption spectra of seawater (blue curve) and seawater with diatoms (red curve) are reported. We indicate these samples as ‘baseline’ and ‘control’ sample, respectively. In the inset of the figure the corresponding refractive indices are shown too. The faded areas represent the measurement statistical errors at a confidence level of 66% (one σ). The derivation of these errors is better explained in the section ‘Methods’. It is worth noting that curves become more oscillating and shaded areas larger at the highest frequencies due to an overall decrease in the signal-to-noise ratio. This reduction results from the combined effect of an increase in sample absorption and a decrease in signal detection efficiency. These oscillations may introduce peaked structures in the absorption spectra that are unphysical for liquid samples. Therefore, in the highest frequency portion of the spectra we applied a spatially variant moving average filter, analogous to the one proposed by Pupeza and co-workers²².

Figure 2. THz absorption spectra of seawater (blue curve) and diatoms in seawater (red curve). In the inset the corresponding refractive indexes are shown. The shaded areas indicate the measurement statistical error with a confidence level of 66% (one σ). Up to 3.5 THz the shaded areas overlaps with the marker size. On the upper x-axis we report the light wavenumber in cm^-1.

In the sample ‘diatoms + seawater’ we observe an overall enhancement of the absorption coefficient (absorption ‘excess’) as compared to that of seawater over the entire frequency range, with a notable exception around 4.2 THz, where the two graphs overlaps. Above 5 THz the two curves display strong fluctuations and wider shaded areas. Therefore, we focus our attention on the spectra from 0.5 to 5 THz only.

The corresponding refractive index spectra almost overlap all over the frequency range with small deviations that are nevertheless within the error bars. These results clearly show that the THz absorption spectra are sensitive to the presence of diatoms in seawater, while the refractive index spectra are not. This difference of behavior between absorption and refractive index spectra is not surprising and it has been observed for other aqueous solutions²³. The results reported in Fig. 2 unambiguously shows that THz absorption spectroscopy can be used for probing diatoms in their natural environment, i. e., seawater. Therefore, in the following we will focus our attention mainly on the absorption spectra.

Now, let us turn our attention to the variation of the absorption spectra when samples are doped with pentahydrate copper sulfate (CuSO₄). In this study we used two different concentrations of copper: 10 and 100 µM. To compare the different samples, we introduce a new parameter that measures the absorption variation compared to the seawater spectrum, i. e., $\varDelta \alpha = {\alpha }_{sample}-{\alpha }_{baseline}$, where $sample$ stands for ‘control’, ‘10 µM ‘or ‘100 µM’. A sample displays an absorption ‘excess’ or ‘defect’ when $\varDelta \alpha$ is positive or negative, respectively. The absorption variation $\varDelta \alpha$ for the three samples is shown in Fig. 3. Coherently with the previous observation, the ‘control’ sample displays an absorption ‘excess’ almost over all the frequency Fig.3 – THz absorption variation as compared to seawater for diatom in seawater, undoped (red line) and doped (green and purple lines). The dot-dashed blue line represents the zero level.

range. Although in a range where fluctuations become stronger, we observe a significant peak of $\varDelta \alpha$ around 5 THz. In both samples doped with copper there is an evident reduction of $\varDelta \alpha$ as compared to the undoped sample over the full frequency range, except for the region around 4.2 THz where the three curves almost overlap with the zero level (blue dashed line). The graphs of $\varDelta \alpha$ for the two concentrations strongly overlap, indicating that starting from a concentration of 10 $\mu$M the effect of the metal doping on the reduction of the absorption ‘excess’ has already saturated. In a next work, it will be interesting to use lower concentrations to verify the existence of a trend of the reduction of $\varDelta \alpha$ as a function of concentration.

The first result of this work to highlight is the capacity of THz-TDS of sensing the presence of diatoms in seawater. This was not granted from the beginning since it is usually very difficult to extract information on solutes in aqueous solutions because of the strong absorption of water in the THz range. To have a quantitative idea of this it is sufficient to consider that the absorption coefficient of most dried biomolecules is ∼10 cm^− 1 at 1 THz against ∼200 cm^− 1 of that of liquid water at the same frequency. Therefore, an interesting question arises here: what is the origin of the absorption ‘excess’ observed in Fig. 2?

If water and its solute do not interact, the THz spectrum of the solution simply retraces that of pure water with an overall reduction of the absorption coefficient due to the replacement of water volume by the much less absorptive solute (‘volume-displacement’ effect). This absorption ‘defect’ becomes larger as solute concentration increases. In contrast with this simple picture, much research on proteins in water has instead observed an ‘excess’ of absorption in the THz range^24,25.

This effect has been ascribed to the formation of an ‘hydration shell’ around the solute that may extend for several molecular layers (see Fig. 1). The water molecules in this ‘hydration shell’ behave differently from those in the bulk, displaying a dynamic that may be both faster and slower than that in the bulk. The ‘hydration shell’ is dynamic since there is a continuous exchange between water molecules in the shell and bulk water, with a residence time in the shell that may be again faster or slower than that in the bulk. All these times strongly depend on the nature of the solute-water interaction and hence, under some circumstances, the ‘hydration shell’ can become an indirect probe of the solute behavior²⁵.

In our case, things are much more complex since our unicellular microorganisms are orders of magnitude bigger than single proteins. In particular, diatoms have intracellular organelles and compartments where water molecules can be strongly bound or confined. These water molecules also behave differently from bulk water similarly to the molecules residing in the hydration shell^26–28.

Finally, we must consider the aforementioned ‘matrix’ effect. In this case, the diatom-diatom interactions mediated by water molecules can lead to a structural reorganization of the hydrogen-bond network with a consequent enhancement of specific collective vibrational modes. The latter may reflect in the THz absorption spectra, thus explaining the observed absorption ‘excess’.

In literature, all these forms of water that in biosystems behave differently from bulk water are named as ‘Biological Water’²⁸.

At this stage we are not able to weigh the specific contribution of all these effects, i.e., hydration-shell, confined water, or ‘matrix’ water. However, it is clear that only the presence in our system of ‘biological water’ can explain the observed absorption ‘excess’. The latter is strongly influenced by the water-diatom and diatom-diatom interactions. Therefore, our THz absorption spectra carry information on these interactions and hence on the wellbeing state of diatoms.

When we add copper to our solutions, we observe a strong reduction of the absorption excess over all the frequency range, with a remarkable residual enhancement of absorption around 4 THz. First, we note that the addition of copper sulfate cannot directly affect the spectra of seawater in terms of volume-displacement, given the low concentrations used here, the small ion volume, and the copper molar attenuation that amounts to few hundreds of M^− 1cm^{− 1 29}. On the other hand, strong effects of metal salts on the dielectric relaxation of water have been reported in the past³⁰. However, the ion concentrations used in Tielrooji et al.³⁰ are orders of magnitude higher than those used in the present work and even the maximum variation of the dielectric constant observed there could not explain the large reduction of absorption measured in our experiments.

All these considerations lead to conclude that the observed reduction of $\varDelta \alpha$ in doped samples must be ascribed to the effect of metal doping on diatoms with a consequent disturbance of ‘biological water’ in the doped samples. Therefore, few hours after doping our samples with copper, we can already measure a dramatic modification of the wellbeing state of diatoms. It is interesting to compare this result with the estimate of the number of living diatoms as measured by the Bürker chamber method³¹. In the first hours after the THz-TDS experiment till the day after, no significant variation in the number of living diatoms can be measured. Therefore, our results clearly demonstrate the ability of THz-TDS in detecting structural changes that occur already in diatoms that are still alive but whose functionality starts to be affected by the poisoning action of metals. Despite the large statistical fluctuations at higher frequencies, we observe that in the portion of the spectrum above 4.5 THz $\varDelta \alpha$ is almost zero, thus here the spectra of doped samples perfectly overlap with that of seawater. This must be compared with the peak of $\varDelta \alpha$ observed in the ‘control’ sample. We highlight that in pure water this part of the spectrum is largely influenced by the hydrogen-bond stretching modes³². Therefore, our results seem to suggest that the diatoms poisoned by the metal are no more able to maintain the hydrogen-bond structure specific of biological water with a consequence loss of spectral weight associated to these vibrational modes. This point deserves to be explored more in the future from both an experimental point of view, by increasing the signal-to-noise ratio at the highest frequencies, and from a theoretical point of view to single out the contributions to the THz spectrum of specific collective vibrational modes.

In conclusion, we have demonstrated the ability of THz-TDS in sensing diatoms in seawater. Due to the strong absorption of water in the THz range the spectral signatures of diatoms cannot be detected directly, but we find their footprints in the water absorption variation due to water-diatom interactions. We observe an ‘excess’ of absorption over the entire interval of the THz frequencies. This result allows to widen the set of spectroscopic tools for investigating marine organisms with instruments that provide complementary key information. In this respect, compared to Raman and IAS spectroscopies, THz-TDS may probe the large-scale vibrations of diatoms and water hydrogen-bond network and hence provide with information on their global structural reorganization.

When metal dopants are added the ‘absorption’ excess is strongly reduced over the entire THz frequency range. This points to a strong structural change of the ‘biological water’ acting in the doped samples. These absorption variations can be used for probing the biocide effect of metals on diatoms. We note that these changes are significant already few hours after the doping, when the presence of dead diatoms is very low or completely absent. This demonstrates that THz-TDS is suitable to monitor the poisoning effect at the earliest stage when the diatoms are still alive, although they start being unhealthy.

Sample preparation

The marine diatoms Skeletonema pseudocostatum are isolated with the capillary pipette method³³ from the Sarno River mouth (40.72875N, 14.466432E, Naples, Italy), and identified by 18s and 28s rDNA genes. Microalgae (initial concentration: 900000 cells/mL) are maintained in a f/2 medium³⁴ prepared with sterile filtered (0.22 µm) natural seawater collected from oligotrophic areas of the Gulf of Naples. Diatoms are exposed to 0 (‘control’ sample), 10 or 100 µM of copper. In the latter cases, copper concentration is obtained by dissolving appropriate amounts of copper sulfate pentahydrate to the culture medium.

The concentration of living diatoms in all samples is estimated by cell counting with a Bürker chamber³². Each experiment is performed in triplicate. 10 ml of each sample are taken immediately after adding metals in the culture medium for measuring the THz absorption spectrum as explained in the next subsections. A 10 ml aliquot of the culture medium is employed as baseline for THz measurements.

Tds-thz Experiment

The light pulses used for THz generation are provided by a regeneratively amplified femtosecond laser (Coherent Legend), which delivers optical pulses of 35 fs duration. The central wavelength of the laser is ∼800 nm and the bandwidth (full width at half-maximum) is ∼80 nm. The main part of the laser beam passes through the beam-splitter shown in Fig. 4a, where a diagram of our THz spectrometer is shown, and is focused by a lens. After the lens the beam passes through a β-barium borate nonlinear crystal that converts part of the fundamental beam in its second harmonic. In the focal point these intense beams generate a plasma formed by ionized air. Here, broadband and ultrashort THz pulses are generated via four-wave-mixing of the fundamental and second-harmonic photons³⁵. The THz bandwidth is limited only by the laser bandwidth and therefore the highest generated frequency is 40 THz. The much less intense beam reflected by the beam-splitter is sent to an adjustable delay line and used for probing in time the generated THz pulse by means of the electro-optic effect in a suitable nonlinear crystal²¹. The latter technique allows a coherent detection of the THz electric field (amplitude and phase). Despite our very large THz bandwidth, the actual bandwidth available for the experiment is limited by the choice of the detection crystal. In the present work, we use a nonstandard crystal to reach efficient detection up to 10 THz: LAPC³⁶. The spectrometer is closed in a chamber filled with high-purity (99.999%) nitrogen to strongly reduce the signal absorption by humid air. In Fig. 4b an example of power spectrum of a THz pulse propagating in nitrogen, transmitted through the most absorbing sample, and detected by electro-optic sampling in LAPC, is reported in logarithm scale. In Fig. 4c the corresponding time waveform of the optical field is shown. Despite the bandwidth of our detection crystal, the sample absorption drastically reduces the transmitted signal, so that the signal-to-noise ratio for the experiments is good only up to about 5 THz in the most absorbing samples. Yet, this bandwidth is not usual for THz-TDS applied to aqueous solutions. Liquid samples are placed in a self-assembled variable-path cell with silicon access windows. Silicon is used because it has a good and flat transmission up to 20 THz. The cell path may be continuously varied from 1 mm down to few microns with an average step of about 14 $\mu$m. For technical reasons, the ruler ticks are not equally spaced. However, each step is finely calibrated by measuring the temporal delay from the main pulse of the THz pulse replica transmitted after reflections from the two air-silicon interfaces internal to the empty cell. Then, we measure the THz pulses transmitted through the liquid samples for different thicknesses. For all measurements we use the following thicknesses: 22, 29, 47, 64, 79, and 94 $\mu$m.

Absorption coefficient and refractive index extraction

The THz fields $E\left(t\right)$ measured for different thicknesses are transformed in the frequency domain by Fast Fourier Transform to obtain the complex field $\tilde{E}\left(\nu \right)=E\left(\nu \right){e}^{i\varphi \left(\nu \right)}$, where $\nu$ represents the frequency, $E\left(\nu \right)$ and $\varphi \left(\nu \right)$ represent the module and the phase of the complex field, respectively. As we will see in the following, these two quantities are directly linked to the complex refractive index, $\tilde{n}\left(\nu \right)=n\left(\nu \right)+ik\left(\nu \right)$, of the sample. Here, $n$ is the refractive index and $k$ is the extinction coefficient. The latter is related to the absorption coefficient by $\alpha =4\pi \nu k/c$, with $c$ the speed of light in vacuum. In principle, the ratio of the Fourier transforms of only two THz temporal traces with and without the sample, with the second trace used as reference, provides the frequency-dependent complex refractive index $\tilde{n}\left(\nu \right)$. However, the extraction of the complex dielectric function of liquid samples is complicated by the presence of the container, whose windows affect the THz signal as well. In this case, the complex refractive index is more reliably extracted by isolating the sample response from the window response through measurements at varying sample thickness $d$. With this approach, the reference is the sample itself and the contribution of the cell is directly taken into account. In detail, the complex refractive index may be obtained by using the following Beer-Lambert generalized relationships:²⁶

$$n\left(\nu \right)=c \frac{\varphi \left(\nu ,d+\varDelta d\right)-\varphi (\nu ,d)}{2\pi \nu \varDelta d}=\frac{\text{c}{\Delta }\varphi \left(\nu \right)}{2\pi \nu \varDelta d}$$

$$k\left(\nu \right)=c \frac{\text{ln}E\left(\nu ,d\right)-\text{ln}E(\nu ,d+\varDelta d)}{2\pi \nu \varDelta d}=\frac{c{\Delta }\text{ln}E\left(\nu \right)}{2\pi \nu \varDelta d}$$

For all our measurements d = 22 $\mu$m, i. e., the thinnest sample, while $\varDelta d$ is the change in thickness of all the other samples. As it is apparent from Eqs. 1 and 2, the phase change ${\Delta }\varphi \left(\nu \right)$ and the change of the natural logarithm of the electric field module ${\Delta }\text{ln}E\left(\nu \right)$ depend linearly on $\varDelta d$ for a given frequency. Therefore, at each frequency, a linear model can be used for fitting the experimental results to extract $n\left(\nu \right)$ and $k\left(\nu \right)$, which become the slopes of these linear models. To fit the latter we use a standard ‘chi-square’ minimization with the axis intercept of the linear model constrained to zero³⁷.

If we assume that the statistical errors are the same for all the data for a given frequency, then the best estimates for the slope, $m$, and its error, ${\sigma }_{m}$, are given by the following relationships:

$$m=\frac{\sum _{i}^{N}{{\varDelta d}_{i}{\Delta }\text{ln}E\left(\nu \right)}_{i}}{\sum _{i}^{N}{\left({\varDelta d}_{i}\right)}^{2}}$$

$${\sigma }_{m}=\sqrt{\frac{\sum _{i}^{N}{\left({{m\varDelta d}_{i}-{\Delta }\text{ln}E\left(\nu \right)}_{i}\right)}^{2}}{(N-1)\sum _{i}^{N}\varDelta {d}_{i}^{2}}}$$

where $N$ is the number of data. The statistical errors given by Eq. 4 are represented in Figs. 2 and 3 as shaded areas (66% confidence level). From Eq. 4 it is evident that the statistical error depends on the frequency $\nu$. This dependence may lead to the strong modulations of the shaded areas graphed in Figs. 2 and 3.

In conclusion, we note that equations 1 and 2 are valid only if multiple reflections in the sample thin layer are neglected (those occurring in the silicon windows are not considered since they appear out of the measurement temporal window). In case of thin samples this is possible only for very absorbing samples, as in our case, since the pulse is strongly attenuated during the back-and-forth trip so that the echoes of the main pulse become negligible.

Author Contributions

DP, RK, MP and AS conceived the experiment. AS and IO prepared the samples. AR built up the THz spectrometer. DP, AR, RK, YQ performed the THz experiments. DP analyzed the data and wrote the paper. All the authors discussed the results and reviewed the paper.

ADDITIONAL INFORMATION

No competing interests are present.

Acknowledgments

We acknowledge funding from the Italian Government, PRIN-TWEET (grant code 2017YCTB59). We thank Mr. F. Cassese (responsible of the mechanical workshop of the Physics Department) for the realization of the variable-path liquid cell. We thank also Mr. M. Perna (Marine Biotechnology Department, SZN) and Mr. G. Lanzotti (Research Infrastructures for marine biological resources Department, Advanced Microscopy Center, SZN) for technical support.

DATA AVAILABILITY STATEMENT

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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No competing interests reported.

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Toxic effect of metal doping on diatoms as probed by Broadband Terahertz Time-Domain Spectroscopy

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