Chiroptical Spectroscopy of Biofluids

CHAPTER 10

Chiroptical Spectroscopy of Biofluids Vladimír Setnicˇka, Lucie Habartová

University of Chemistry and Technology Prague, Prague, Czech Republic

10.1  INTRODUCTION Chirality, an omnipresent unique feature, is regarded as the signature of life. Starting with DNA double helix and l-amino acids as essential building blocks of the human body, over β-sheet structures in neurodegenerative amyloid plaques, to rare left-handed snail shells and the noncoincident left and right human hands, chiral elements shape up the world. Although chiroptical techniques have revealed a large amount of significant information about many biomolecules [1–12], the analyses have been focused primarily on pure substances or molecular systems in model environments and performed under controlled conditions. The determination of absolute configuration became a gold standard in pharmaceutical research [13–17]; the structure of essential biomolecules has also been resolved [12,18,19]. Detailed studies on interactions between human serum albumin (HSA) and other proteins with bile pigments [20,21], drugs [22], cell membranes [23,24], etc. have been conducted, and protein folding/ misfolding under various experimental conditions has been thoroughly investigated [25–27]. However, the behavior and structure of a molecule may be significantly affected by its natural environment and surrounding moieties present therein.Thus, the analysis of essential biomolecules in biological fluids holds great potential for the understanding of many biological processes.The so far conducted chiroptical studies have been limited to single molecules or simple mixtures, so that the researchers would not have to overcome the obstacles presented by the complexity of biofluids. Despite low concentrations of clinically significant molecules, high level of fluorescence and various undesirable interferences overlapping the signals of interest, numerous efforts have been made to introduce chiroptical methods into biofluid analysis [28–36]. The key studies clarifying the abilities and limitations of chiroptical spectroscopy as a probe for real biological samples are discussed in this chapter. Chiral Analysis. http://dx.doi.org/10.1016/B978-0-444-64027-7.00010-2 Copyright © 2018 Elsevier B.V. All rights reserved.

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10.2  BLOOD AND BLOOD-BASED DERIVATIVES Blood, an indispensable biofluid comprising more than 4000 components of diverse features and functions, is formed as a suspension of blood cells in liquid blood plasma [37]. Blood plasma accounts for ∼55% of total blood volume and its primary function is the maintenance of homeostasis provided by the transport of blood gasses, various nutrients, metabolites, hormones, and waste products throughout the body. This yellow opalescent aqueous solution of proteins, inorganic salts, and small organic molecules is obtained from anticoagulant-treated blood via sedimentation or, more effectively, centrifugation of the cellular part of the blood represented by red and white blood cells and platelets. If aiming for serum, no anticoagulants are used when the blood is drawn from the patient, allowing it to coagulate naturally and, subsequently, the clot formed is removed. On top of lacking blood cells and platelets as in plasma, the resulting serum is free from fibrinogen and other coagulation factors [37,38]. Blood plasma contains about 500 different proteins, whose biological function is determined by their structural arrangement, that is, conformation and/or folding [38]. It was proved that some diseases may induce changes in both the concentration and the structure of plasmatic proteins and that some structural forms of a protein are even disease-specific. For example, in the case of neurodegenerative protein misfolding diseases, plaques comprising fibrils of amyloid-β protein or neurofibrillary tangles consisting of helical tau-protein filaments are formed in the brain [39,40] and a portion thereof passes the blood–brain barrier into the blood stream [41]. Unfortunately, the levels of many commonly used protein markers are rather low in blood. Thus, for a simplified monitoring facilitating the diagnostic process, it would be convenient to identify new molecules, whose concentration would be higher and structural changes more specific.

10.2.1  Electronic circular dichroism The first chiroptical analyses of blood-based samples were performed as early as 1977, when Jung et al. [28] monitored serum levels of O-(βhydroxyethyl)-rutosides in patients administering a flavone glycoside-based vascular therapeutic agent Venoruton. As their molecules contain chromophores, the flavone glycosides are optically active and, thus, detectable by electronic circular dichroism (ECD) [42]. The interaction of the aglycone part with the covalently attached sugar moiety results in different extinction coefficients, which may be useful in investigating not only the conformation,

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but also the composition of such compounds. Moreover, ECD is suitable also for the kinetic studies of flavone glycosides, specifically hydroxyethylated rutosides. The metabolic cleavage of the rutoside molecule usually results in the complete disappearance of the ECD signal, whereas the interaction with blood biomolecules (proteins) increases chiroptical properties. As hydroxyethyl-rutosides bind to serum albumin and globulins [43], it was essential to measure serum samples without any preprocessing or modification, such as protein precipitation, which might have caused partial removal of the studied compounds. In addition to eliminating conformational changes and sample degradation, the authors recommended analyses at a strictly controlled temperature (4 °C). It was found that the ECD signals of serum or plasma had positive amplitudes, whereas the bands located at the same wavelength (∼345 nm) showed negative values for the active ingredients (tri- and tetrahydroxyethyl-rutoside) (Fig. 10.1). Although the ECD spectra of hydroxyethyl-rutosides contain multiple bands, these were not considered for the study because they are either located in the range of a fluorescence band (390 nm), less intense (312, 292, and 269 nm), or may be influenced by protein interference (<230 nm). Thus, focusing on the specific band at ∼345 nm, the pharmacokinetics of Venoruton after intravenous administration was studied. A calibration curve (Fig. 10.2) was also established for a direct quantitation. An exponential decrease followed the considerably high serum levels of flavone glycosides shortly after administration and the drug was almost

Figure 10.1  ECD spectra of human serum (—), human serum with trihydroxyethylrutoside (– + – + ), and human serum with tetrahydroxyethyl-rutoside (–o–o) measured using a path length of 1 cm at 4 °C, with 0.1 mL of aqueous hydroxyethyl-rutoside solution added to 5 mL of human serum. Reprinted from ref. [28] with permission of Springer International Publishing AG. Abbreviation: ECD, electronic circular dichroism.

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Figure 10.2  Calibration curves for the quantitation of Venoruton in two serum samples measured by ECD in a 1-cm optical cell at 4 °C. The dichroic amplitudes were determined at 338 nm. Reprinted from ref. [28] with permission of Springer International Publishing AG. Abbreviation: ECD, electronic circular dichroism.

completely eliminated from the serum within 2 h. The obtained results confirmed the usability of ECD not only for the qualitative analysis, but also for kinetic studies of flavone glycosides. The lowest detectable concentration reached 1 µg of trihydroxyethyl-rutosides per 1 mL of serum and the determination was possible even when the active compound formed a complex with serum proteins. In 1991, Purdie and Murphy [44] suggested the use of ECD as an improvement in the so far inaccurate determination of total serum cholesterol and its fractions. Based on their previous experience with the analytical selectivity associated with chiroptical spectroscopy [45], the authors aimed for a simultaneous discrimination between serum lipoproteins, while avoiding the commonly employed precipitation step, for example, the Allain–Trinder reaction scheme [46,47]. This precipitation is used to be a necessary part of a visible absorption measurement, one of the clinically approved methods for serum cholesterol assessment. However, not only did such approach lead to inaccuracies, but also the method could not have been considered for ECD, because the end-product of the reaction is optically inactive.

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Cholesterol itself may be assessed directly without any preprocessing, indeed, as its ECD spectra show a distinct band at ∼210 nm [48,49]. Nevertheless, this band may be disturbed by other strongly UV-absorbing moieties in the serum, the complete removal of which is next to impossible. Therefore, the derivatization of cholesterol is necessary to induce the most convenient ECD bands in the visible region. The selected Chugaev reagent [50] facilitated a chromogenic reaction yielding an ECD-active unsaturated steroid end-product. Despite the reaction with the Chugaev reagent, other steroids do not interfere with cholesterol because of their low serum levels. Moreover, in a case of a higher steroid level, they are still recognizable according to their own specific ECD patterns [51]. Thus, a set of 150 native and Chugaev-treated serum samples was analyzed in a 1-cm cuvette in a spectral range of 325–625 nm. During the measurements, high selectivity of ECD was observed, allowing the simultaneous identification of highdensity lipoprotein (HDL) fraction along with the combined low- and very low-density lipoproteins (LDL + VLDL) in a single experiment that did not include any precipitation/derivatization. Regression models were calculated using band intensities at 525 nm for LDL + VLDL and at 390 and 490 nm for HDL fractions. It was found that ECD was able to quantitate particular cholesterol fractions with excellent precision and, thus, outperformed the routine visible absorption assay. In fact, the achieved results were so convincing that a patent [52] was granted to Purdie 2 years later. Another chiroptical analysis of serum, specifically the direct monitoring of β-lactam antibiotics, was attempted by Gortázar et al. in 1998 [31]. Recording the spectra of serum samples of healthy individuals (Fig. 10.3),

Figure 10.3  ECD spectra of serum samples (1000-fold dilution) of a healthy volunteer (solid line) and control serum (dashed line) showing a characteristic albumin pattern. Reprinted from ref. [31] with permission of John Wiley & Sons, Inc. Abbreviation: ECD, electronic circular dichroism.

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it was possible to observe pronounced signals around 209 and 220 nm reflecting primarily α-helices of serum albumin [30,49]. To avoid detector saturation leading to inaccurate ellipticity values/spectral artefacts within the 400–200 nm region, a 1000-fold dilution of the samples with distilled water was used. In the serum samples of patients on β-lactam antibiotics, characteristic bands were expected to arise above 250 nm. However, regardless of the dilution factor, the authors struggled with strong absorption of proteins and other serum components that resulted in the ECD spectra of low signalto-noise ratio (SNR) and, thus, prevented the accurate direct quantitation of the antibiotics.To eliminate such strong interference, the removal of proteins was suggested using acetonitrile as a common deproteinization agent. Although 3 mL of acetonitrile was sufficient for maximum deproteinization of 1 mL of serum/plasma, an unbearable noise level was still present below 250 nm.This was probably caused by endogenous serum substances, such as creatinine and uric acid, both of which are soluble in acetonitrile and, thus, not removed during sample deproteinization. In such cases, the authors recommend either a complete removal of endogenous moieties or measurement above 250 nm, which is suitable for selected members of the β-lactam family. This was confirmed in a validation study performed on protein-free serum samples containing cefoxitin, cefuroxime, and cefotaxime as model drugs (Fig. 10.4). The observed spectral patterns were identical to those of aqueous solutions of the individual antibiotics. In the same study [31], Gortázar et al. also used a separation approach, in which the serum is modified, and the studied drugs are recovered by

Figure 10.4  ECD spectra of cefoxitin (solid line), cefuroxime (dashed line), and cefotaxime (dotted line) standard solutions (10 µg of the antibiotic per 1 mL of protein-free serum). Reprinted from ref. [31] with permission of John Wiley & Sons, Inc. Abbreviation: ECD, electronic circular dichroism.

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extraction into low-polarity organic solvents. In this particular case, cefoxitin was extracted from acid-treated serum into a chloroform/1-butanol mixture (3:1) and subsequently reextracted into an aqueous phase (pH 7). Such procedure resulted in serum samples completely lacking proteins and endogenous substances (creatinine, uric acid), and the measurement of the drug-containing aqueous phase yielded pronounced characteristic ECD bands of cefoxitin throughout the whole spectral range (200–400 nm). Using both sample preparation methods, the authors confirmed the suitability of ECD to detect β-lactam antibiotics in serum at therapeutic concentrations (20–40 µg/mL), which is useful for pharmacokinetic studies. Despite the advantage of no endogenous interference, the extraction approach is very complex and time consuming and, thus, may be considered only as an alternative to protein precipitation in cases of drugs that do not absorb above 250 nm. Over a decade later, the applications of chiroptical spectroscopy to blood-based samples were revisited by Tatarkovicˇ et al. [32,33] and Synytsya et al. [34]. This time, the analyses were conducted using blood plasma with the aim to find specific signature of plasmatic biomolecules that may provide information about the health or pathology of the individuals tested (Section 10.6). To eliminate any interference from residual particulates and larger molecules of plasma,Tatarkovicˇ et al. [32] tested various cut-off weight filters. Although the UV absorption (calculated from the detector high tension (HT) voltage) was significantly improved in comparison to the unfiltered plasma, the cut-off filters were not used for the author’s further studies. Some of the cut-off filters retained a large portion of plasmatic proteins, which would be convenient in the case of analyzing separate protein fractions with respect to their molecular weight. However, Tatarkovicˇ et al. also intended to analyze the plasma containing all of its low- and high-molecular-weight components. The research team, therefore, used centrifugation of plasma samples through a polyvinylidenedifluoride (PVDF) membrane filter with a porosity of 0.45 µm [33,53,54], which was effective enough to remove any possible particulates (protein aggregates, residual blood cells), but the content of plasmatic signaling biomolecules remained unaltered. The ECD experiments of raw plasma revealed a distinct spectral pattern, which was mostly consistent with that of HSA. Two characteristic, partially overlapping negative bands were observed at ∼210 and ∼220 nm as a result of π–π* and n–π* transitions, respectively [55]. Due to strong absorption, the positive π–π* band arising at ∼190 nm was not reliably detectable in

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Figure 10.5  ECD spectra of human blood plasma (dotted line) and hen egg white (dash-dotted line), and of aqueous solutions (40  g/L) of their most abundant protein components human serum albumin (solid line) and ovalbumin (dashed line). Reprinted from ref. [34] with permission of Springer International Publishing AG. Abbreviation: ECD, electronic circular dichroism.

the 10-µm Suprasil quartz optical cell until after diluting the plasma with sterile phosphate buffer (blood plasma-identical pH of 7.4; NaCl 137 mM, KCl 2.7 mM, KH2PO4 1.5 mM, Na2HPO4 8 mM) using a volume ratio of 1:3. The obtained ECD spectra were compared to those of pure HSA (Fig. 10.5), and the relative content of secondary structures (Fig. 10.6) was evaluated by circular dichroism analysis using neural networks (CDNN; version 2.1), a software developed by Böhm et al. [56]. The assessment confirmed a high content of α-helices in blood plasma originating mostly from HSA (∼64%), accompanied by a certain amount of proteins with β-sheet/ turn (∼19%) and unordered structures (∼17%).

10.2.2  Vibrational circular dichroism Even though analyses based on vibrational circular dichroism (VCD) do not need to be limited to molecules containing chiral chromophores as in

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Figure 10.6  Relative contents (%) of particular secondary structures of proteins elucidated from the ECD spectra by CDNN program, version 2.1. Reprinted from ref. [34] with permission of Springer International Publishing AG. Abbreviations: ECD, electronic circular dichroism; CDNN, circular dichroism analysis using neural networks.

ECD, the experiments are more complex, including strong absorption of water in the amide I region [12,57]. The undesirably strong water absorption is usually eliminated using a shorter pathlength (∼6 µm) or deuterated solvents, the latter of which is not very convenient for studying biomolecules in their natural biofluid environment. Despite the obvious difficulties, Synytsya et al. [34] conducted a VCD analysis of human blood plasma and compared the obtained spectra to those of HSA, the main constituent of blood plasma. To preserve the natural state of the biofluid, the samples were only subjected to centrifugation through a 0.45-µm PVDF filter to remove possible insoluble particles that would cause nonspecific scattering and linear birefringence, the latter of which alters the beam polarization state and results in baseline artefacts [58]. A demountable cell consisting of two CaF2 windows separated by 6- and 23-µm spacers was used for the analysis of HSA solutions in H2O and D2O, respectively. The obtained VCD spectra of human blood plasma (Fig. 10.7) were dominated by a positive amide I couplet originating mainly from the C=O stretching of the peptide bond [12,59]. A pronounced negative band arose at 1662 cm−1 and a weaker positive band was shifted to 1643 cm−1 if compared with that of HSA (1637 cm−1). The observed features along with the amide II band at 1518 cm−1 (N–H bending in combination with C–N stretching of the peptide bond) were consistent with the predominant αhelical structure of HSA [60].

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Figure 10.7  VCD (top) and IR absorption (bottom) spectra of human blood plasma (dotted line) and hen egg white (dash-dotted line), and of H2O (A, C) and D2O (B, D) solutions of their most abundant protein components HSA (solid line), ovalbumin (dashed line). Reprinted from ref. [34] with permission of Springer International Publishing AG. Abbreviations: VCD, vibrational circular dichroism; IR, infrared; HSA, human serum albumin.

10.2.3  Fluorescence-detected circular dichroism To cover a wider range of chiroptical methods, Tatarkovicˇ et al. [32] also inspected the potential of fluorescence-detected circular dichroism (FDCD) for the measurement of blood plasma for the very first time. Using a 380-nm long

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pass filter, a dominant band at ∼335 nm was observed in the total fluorescence spectra of unfiltered plasma. This band corresponds to aromatic amino acid residues, specifically tryptophan and tyrosine [61]. Using again the cutoff filters, the band intensity at ∼335 nm was significantly reduced, whereas a previously unobserved band appeared at ∼268 nm, reflecting primarily the fluorescence of phenylalanine residues in low-molecular plasmatic components. Thus, after removing the high-molecular substances that absorbed the emitted light from the unfiltered sample (immunoglobulins, cholesterol in lipoprotein aggregates, fibrinogen, and the major portion of HSA), the respective 335-nm band decreased in intensity. Moreover, its maximum shifted to ∼324 nm as a result of the lack of HSA, whose fluorescence band was observed at ∼343 nm (aqueous solution). Despite similar tendency, the ultrafiltration did not yield satisfactory results in the case of FDCD. The observed FDCD bands were of very low intensity, whereas the noise level increased.

10.2.4  Raman optical activity As Raman optical activity (ROA) is usually measured simultaneously with Raman, the major issue in the analysis of blood plasma is strong background fluorescence, which is a competitive phenomenon to Raman scattering. The effect most likely originates from hematoporphyrin (contribution of Q-bands in the visible region) and residual blood cells, the latter of which may also be a source of unspecific scattering. In principle, background fluorescence may be reduced by several approaches, not all of which are suitable to being applied on biofluids and current ROA instrumentation [62,63]. Instrumental modifications include the use of different excitation wavelengths (1064 nm or below 260 nm) [64], resonance or surface enhancement of the signal [65,66], measurement in the anti-Stokes region [67], or spatial offset between excitation and detection positions [68]. Physical and/ or chemical reduction may be achieved by removing fluorescing impurities with activated charcoal [26], illuminating the sample for a certain period of time (photobleaching) [69,70] or adding various fluorescence quenchers (sodium iodide, acrylamide, molecules with dansyl group, and others) [71–73]. Another way of solving the fluorescence issue is represented by a mathematical or mathematico-physical correction, when polynomial spline or frequency filters (finite impulse response, fast Fourier transform—FFT) are applied to the acquired spectra [74]; the spectrometer is equipped with two linear polarization filters [75], or excitation at two wavelengths is used (shifted-excitation Raman difference spectroscopy) [76]. Considering the benefits and limitations of the aforementioned procedures, a reasonable

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method for fluorescence reduction in blood plasma may be the use of photobleaching, fluorescence quenchers, different excitation wavelength, mathematical correction, or a combination thereof. Unfortunately, the currently only commercially available ROA spectrometer ChiralRAMAN-2X (BioTools, Inc., USA) operates with an excitation wavelength of 532 nm, which may even enhance the undesirable fluorescence phenomenon. Therefore, Tatarkovicˇ et al. [54] attempted to establish a fluorescence quenching procedure to allow for a smooth blood plasma analysis by ROA using the excitation in the visible region. To avoid saturation of the CCD detector due to high level of fluorescence, short illumination periods were used, which inevitably lead to excessive time requirements for the analyses. Assuming a 24-h spectra acquisition, the measurement with an illumination period of 0.29–0.54 s (depending on individual samples) would have taken 100–74 h of real time (including a 0.93-s delay between laser pulses, which is required for CCD readout, halfwave plates exchange, and setting liquid crystals to different polarity). For biological samples, such long-time measurement is unacceptable because of a high probability of degradation (spontaneous or laser-induced) and the impracticability to apply the approach onto large sample sets.Thus, to minimize the probability of degradation, sample temperature was maintained at 15 °C (controlled by a home-made Peltier cell holder). The research team discovered a positive effect of filtration through a PVDF membrane on background fluorescence and experimentally established the ideal filter pore size. The decrease in fluorescence of human plasma was proportional to the pore size, but in the case of too small pores (0.22  µm), the ROA/Raman signals decreased significantly [32]. Thus, PVDF filters with a pore size of 0.45 µm were selected because of their ability to retain only particles with sizes comparable to cells or larger (insoluble protein aggregates, residual blood cells). In spite of evident improvement, the fluorescence still overlapped the ROA/Raman signals of interest and the baseline remained distorted (Fig. 10.8A and D). Therefore, in the next step, kinetic fluorescence quenchers, sodium iodide, and acrylamide were tested. To expedite the quenching process, photobleaching was applied and the time dependence of the fluorescence level was evaluated. Sodium iodide reduced the background fluorescence more effectively than acrylamide at approximately the same molar concentration (10 mg of NaI or 5 mg of acrylamide per 100 µL of blood plasma) leading to 91% and 80% background decrease after 12 h of photobleaching, respectively (Figs. 10.8B and E and 10.9). Moreover, acrylamide contributed to the resulting Raman spectra with several bands [77], many of which interfered

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Figure 10.8  Raman (top) and ROA (bottom) spectra of blood plasma: raw blood plasma (A, D), plasma with 10 mg of sodium iodide per 100 µL after 12 h of photobleaching (B, E), and plasma with 10 mg of sodium iodide per 100 µL after baseline correction and FFT filtering (C + its inset, and F), 24 h of acquisition time. Reprinted from ref. [54] with permission of Springer International Publishing AG. Abbreviations: ROA, Raman optical activity; FFT, fast Fourier transform.

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with those of blood plasma components, such as 1635 and 1673 cm−1 in the amide I region of proteins [78]. Due to higher efficiency, no toxicity, and the lack of considerable spectral interference, sodium iodide proved to be a more convenient fluorescence quencher for biofluid experiments in comparison with acrylamide. Using the lowest amount of NaI with the highest quenching efficiency (10 mg per 100 µL of plasma), it was also confirmed that the addition of a foreign substance does not have any adverse effects on the plasma (pH change, protein precipitation, sample degradation). After verifying the performance on a set of 46 real human blood plasma samples, the final methodology was established as follows: filtration of blood plasma through a PVDF membrane (pore size of 0.45 µm, 13000 × g, 10 min), addition of fluorescence quencher (10 mg of solid NaI per 100 µL of plasma), photobleaching (12 h, 280 mW laser power on the sample), spectra acquisition (24 h in total, illumination period of 1–2 s according to the optimal working range of the CCD detector, 250 mW), and spectral processing (residual baseline distortion corrected by FFT filtering; Fig. 10.8C and F).

Figure 10.9  The effect of kinetic quenchers on the reduction of fluorescence background in the Raman spectra during photobleaching: raw blood plasma (A), plasma with 5 mg of acrylamide (B), and 10 mg of sodium iodide (C) per 100 µL; the inset shows the average signal decrease with its standard deviation (the initial levels were normalized to 100% for clarity). Reprinted from ref. [54] with permission of Springer International Publishing AG.

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This approach shortened the real time of the measurement from 70–100 to 44–48 h (depending on the sample) and, in some cases, even allowed the analysis in general. A substantial improvement of baseline distortion and SNR was also observed in the ROA spectra (c. Fig. 10.8D–F), the SNR being enhanced by an average factor of 3.3. In the obtained ROA/Raman spectra, blood plasma components were reflected by specific features (Fig. 10.10 and Table 10.1), including

Figure 10.10  An example of final Raman (A) and ROA (B) spectra of two different human blood plasma samples. Reprinted from ref. [54] with permission of Springer International Publishing AG. Abbreviation: ROA, Raman optical activity.

HSA Raman

Blood Plasma ROA

Raman

ROA

1655

+1680sh 1655

+1666 −1638

1605sh 1560sh

+1609 −1559

1465sh 1451

−1444

1341 1321 1274 1246 1208 1178

+1343 +1308 −1256 −1234sh −1213

1420sh 1401sh 1360sh 1340 1321 1270 1244 1239sh 1208 1175sh 1157

Raman

ROA

+1675 −1648 −1637

1662

+1675 −1646 −1646

+1595 −1575

1633sh 1605sh 1558

+1590 −1567

−1454 −1458

+1343 +1308 −1246 −1224sh +1186

1465sh 1452 1420 1387 1387 1345 1321 1275sh 1244 1233sh 1209 1175

−1458 −1432

+1338 +1304 −1246 −1224 +1189

Egg White Raman

ROA

Assignment [6,78–83]

+1673

Amide I—β-sheet, unordered

1662

−1639

Amide I—α-helix

1620sh 1606sh 1558

+1593sh −1559

1464sh 1452

−1454

1423sh 1402sh 1356sh 1344 1320 1275sh 1247 1232sh 1209 1174

+1338 +1311 −1246sh −1224 +1186

Amide I—β-sheet, unordered ν(C=C)—Tyr, Trp, Phe Trp (indole ring) ν(C=C)—carotenoids δ(CH2) δas(CH3), δ(CH2) νs(COO−)—Asp, Glu νs(COO−)—Asp, Glu δs(CH3) τ(CH2), δ(CH)—unhydrated α-helix δ(CH)—hydrated α-helix Amide III—α-helix Amide III—unhydrated β-sheet Amide III (hydrated β-sheet) δ(CH), Trp, Phe δ(CH), Tyr ν(CC)—carotenoids

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1421 1405

1628sh 1607sh 1559 1524 1465sh 1451

Ova

444

Table 10.1  Assignments of Characteristic Raman/ROA Bands to Raw Biofluids and Their Most Abundant Protein Components Wavenumber (cm−1)*

+1124 −1098 −1077 +1056sh +1041 +1006 +931 +888

−830

+502

1129 1103sh 1083 1054sh 1035 1004 961sh 942 904 880sh 852 830 790 758 744sh 721 701 667 647 624 528sh 508sh

+1124 −1091

1155 1127 1103 1083

−830

1158 +1127 1104 1081 1065 1033 1004 948 934 902 880sh 853 831

−528 +508

761 748sh 719 702 679sh 645 622 523 508sh

+1127 −1098 −1078

+1056

+945 +933 +896

−830

1033 1004 957 937 901 880sh 852 827

+1035 +1006 +961 +927

760 747 719 702

+508

644 621 531 508sh

+1127 −1093

+1035 +1006 +931 +896

−838

+508

σ(NH3+), δ(CH)ring—Phe, Tyr ν(CC), ν(CN) ν(CC), ν(CN), δ(CH) ν(CC), ν(CN) ν(CC), ν(CN) δ(CH)—Phe δ(CH)ring—Phe ν(CC); ν(CN) ν(CC), ν(CN)—α-helix ν(CC), ν(CN) Trp, Arg δ(CH)ring—Tyr δ(CH)ring—Tyr ρ(NH3+) δ(CH)ring—Trp Trp ν(CS)—Met ν(CS)—Met ν(CS)—Cys τ(CC)—Tyr τ(CC)—Phe ν(SS)—Cys ggt ν(SS)—Cys ggg

445

*symbols +/- indicate positive/negative ROA bands; sh – shoulder. Adapted from ref. [34] with permission of Springer International Publishing AG. Abbreviations: ROA, Raman optical activity; HSA, human serum albumin.

Chiroptical Spectroscopy of Biofluids

1159 1127 1103 1083 1057 1033 1004 961 943 901 880sh 853 828 792sh 756 747 718sh 701sh 670 645 622 528sh 505

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pronounced bands of carotenoids (∼1007, 1158, and 1519 cm−1) enhanced by the visible excitation at 532 nm [34,69], structure-sensitive bands within amide regions of proteins (∼1285 and 1657 cm−1), bands associated with aromatic amino acids (∼1196 and 1588 cm−1), carbohydrates (∼961 cm−1), and aliphatic side chains (1450 cm−1) [55,78].

10.3  HEN EGG WHITE Hen egg white, a clear liquid surrounding the yolk in an egg, is a protein-rich biofluid with various nutritive and immunologic functions. It protects the yolk mechanically, represents an antimicrobial barrier, and acts as a source of water and nutrients for the growing chicken embryo. The main protein components of egg white involve ovalbumin, lysozyme, ovomucoid, ovotransferrin, ovomucin, and avidin [84,85]. Synytsya et al. [34] studied the spectral response of hen egg white and compared the obtained spectra to those of ovalbumin (Ova), the most abundant protein (∼55%) in this biofluid. The ECD spectra (Fig. 10.5) were dominated by one positive (∼190 nm) and two negative bands (209 and 222 nm) forming a pattern typical for a prevailing α-helical conformation [86]. The overall spectral pattern and relative intensity of the positive and negative bands at 190 and 209 nm, respectively, suggested the presence of rather shorter α-helices and a contribution of β-sheets/turns and unordered structures [87].These findings were supported by a deconvolution analysis of the ECD spectra using neural networks [56] (Fig. 10.6). While Ova exhibited ∼33% of α-helices, accompanied by ∼47% and ∼20% of β-sheets/turns and unordered structures, respectively, hen egg white contained more α-helical (∼38%) and unordered (∼32%), but less β-sheet/turn structures (∼31%). Similarly, the VCD analyses (Fig. 10.7) confirmed a certain content of α-helices (positive amide I couplet at (−)1672 and (+)1645 cm−1 and amide II band at (−)1516 cm−1) accompanied by β-sheet and unordered structures (1622 cm−1) [59]. Although the intensities of these bands were significantly higher for egg white than for Ova, the overall spectral characteristics indicated a mixture of proteins with the predominance of α-helices or β-sheet structures [88,89]. Although the Raman spectra of hen egg white and Ova exhibited a high degree of similarity, position and pattern differed for a few ROA bands (Table 10.1), which might be caused by the complexity of hen egg white. The marker bands of α-helical and β-sheet conformations located primarily

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in the amide I and amide III regions confirmed a higher content of unordered structures and less β-sheets in hen egg white than in Ova [78,79].The decrease in the contribution of α-helix to the overall structure of hen egg white was not pronounced because of a possible overlap of the bands with those of ovomucoid glucose moieties [90].

10.4  VITREOUS HUMOR Vitreous humor is an avascular, acellular tissue filling the eyeballs. Due to transparency and homogeneity, it ensures the uninterrupted transmission of light to the retina with minimal scattering.The vitreous consists primarily of sodium hyaluronate, collagen, and noncollagenous protein, which generate the gel-like structure of this biofluid. Small optically active molecules, such as tryptophan and ascorbic acid, are also present [37]. As a structural change within the aforementioned molecules is believed to be the cause of common age- or disease-related liquefaction of the vitreous, the group of Chakrabarti [35,36] used ECD to inspect this phenomenon. The analysis of intact vitreous humor yielded ECD spectra with broad overlapping bands.The band structure was elucidated using Gaussian analysis, which resulted in four distinct bands: three negative at 196, 206, and 222 nm and a positive one at ∼253 nm (Fig. 10.11) [36]. The observed spectral pattern and intensities suggested a high content of ascorbic acid, whereas the level of free tryptophan molecules (not included in protein side chains) appeared low. The spectra were also compared with those of a collagen model sample, and the contributions of the macromolecular components were calculated. The comparison of intact gel structure and liquefied vitreous showed structural dissimilarities within hyaluronate molecules [35]. Despite theoretical assumptions, the following study [36] discovered that noncollagenous protein contributed to the ECD spectra to a greater extent than hyaluronate or collagen. The authors, therefore, conclude that a significant amount of noncollagenous protein is present in the vitreous and may have a key role in the formation and stabilization of its gel-like structure.

10.5  URINE In most cases, the completely noninvasive sample acquisition makes urine a great information source reflecting the metabolic status of the organism as well as the functioning of organs, especially the kidneys. The

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Figure 10.11  Average ECD spectrum of intact vitreous (solid line) with standard deviations resolved into component bands by Gaussian analysis, calculated sum of individual component spectra (dash-dotted line) overlaps the experimental spectrum. Reprinted from ref. [36] with permission of Elsevier B.V. Abbreviation: ECD, electronic circular dichroism.

composition of human urine is very complex and depends on several factors, such as sex, age, nutrition, liquid intake, health condition, or even environment [37,91]. From the ∼150 components of this aqueous solution, the most abundant species include urea, creatinine, uric acid, products of hemoglobin metabolism (bilirubin), and hormone metabolites. Urine may also contain toxins and endogenous substances either produced by the body (e.g. via intestinal fermentation) or ingested (drugs, pesticides, etc.) [91]. As urinary concentration of physiological optically active molecules is rather low, the chiroptical analyses have focused on endogenous substances, that is, primarily on the detection and quantitation of various chiral drugs and the evaluation of their pharmacokinetic profiles. The experiments have been based solely on ECD [29,30].The aqueous nature of urine along with low levels of chiral analytes extensively limit the use of VCD. ROA, on the other hand, is well suited for measurements of water-containing samples [1,62], yet sample concentration would still prevent the gain of signals of sufficient intensity.

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Back in 1977, the very first urinalysis by ECD was reported by Jung et al. [28], who used the achieved results as a support for the study of serum drug levels. The first systematic analysis of urine was performed by Bowen and Purdie later in 1982 [29].Their study was focused on the direct analysis of tetracycline in the urine of a patient administering a 1-g daily dose of this antibiotic and the comparison of the spectral response to that of control volunteers without any medication. Except a 50-fold dilution with distilled water, no further sample preprocessing was necessary for the measurements in the 220–350 nm range. Dissolved saccharides, proteins, or glucuronide derivatives of metabolites present in urine do not absorb in this spectral region, hence they did not cause any undesirable interference. Although the UV absorption spectra (Fig. 10.12) confirmed differences between urine with and without tetracycline, the absorption change at ∼270 nm was insufficient for any quantitation, not to mention that even the qualitative identification was inconclusive. On the contrary, ECD allowed not only an unequivocal identification of tetracycline, but also the quantitation thereof. The resulting ECD spectrum of control urine showed only mild baseline deviations at ∼280 and ∼310 nm, whereas tetracycline was

Figure 10.12  UV absorption spectra of urine (A) and urine with tetracycline (B), samples diluted 50 times. Reprinted from ref. [29] with permission of Elsevier B.V. Abbreviation: UV, ultraviolet.

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Figure 10.13  ECD spectra of control urine (A) and urine with tetracycline (B), samples diluted 50 times, and 2 × 10−5 M aqueous solution of tetracycline (C). Reprinted from ref. [29] with permission of Elsevier B.V. Abbreviation: ECD, electronic circular dichroism.

represented by a pronounced positive band at 295 nm accompanied by two negative bands of lower intensity at 290 and 323 nm (Fig. 10.13). Correlating the experimentally obtained ellipticity of the prominent 295-nm band with the molar concentration of tetracycline aqueous solution, the authors were able to determine the limit of detection for tetracycline at ∼1.8 µg/mL. Later on in 1995, a more extensive urinalysis via ECD was performed by Gortázar et al. [30]. Studying more than 200 urine samples from 61 individuals, the research team aimed for the direct determination of optically active β-lactam antibiotics therein. Some of the 51 patients treated for urinary tract infection received multiple drug treatment, which comprised different combinations of β-lactam antibiotics (ampicillin, cefoxitin, cephalexin), antimicrobial agents (gentamycin, norfloxacin, cotrimoxazole), analgesics and nonsteroidal anti-inflammatory agents (acetaminophen, acetylsalicylic acid), and others.This contributed to the complexity of urine and, thus, set a challenge for a sensitive discrimination between individual drugs in a single urine sample.The acquired ECD spectra reflected only β-lactam antibiotics, which suggests either lack of optical activity of other administered drugs or signals too low to be detected. In fact, the samples were diluted 250 times, which prevented any possible interference from metabolites or endogenous substances present in urine and, thus,

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Figure 10.14  ECD spectra of urine (250-fold dilution) from a patient under cefoxitin therapy (a dose of 1 g, intravenous application) showing a gradual signal decrease during a pharmacokinetic study (6-h monitoring). Reprinted from ref. [30] with permission of Elsevier B.V. Abbreviation: ECD, electronic circular dichroism.

allowed a smooth quantitation of the studied β-lactams (LOQ = 5 µg/mL). Due to the sensitive and in many cases selective detection of β-lactams, ECD was used for monitoring of pharmacokinetics, in which a gradual decrease in antibiotic urinary levels was observed over a 6-h period (Fig. 10.14). In a number of cases, the studied patients also showed obvious signs of severe proteinuria (150–3000 mg/L, confirmed by a routine clinical urinalysis). Unfortunately, due to the overlap of the spectral responses of βlactams and proteins, the resulting ECD spectra may show modified spectral patterns and band intensities mainly below 250 nm (Fig. 10.15). The extent of such modification is strongly affected by the concentration of the drug compared to urinary proteins, and it also depends on the proximity of the respective overlapping bands. As many representatives of the β-lactam family exhibit unaltered Cotton effects at longer wavelengths regardless of their environment (i.e. protein concentration), it is possible to focus only on the specific bands above 250 nm [92]. To simultaneously quantitate both the drug and total urinary proteins, the authors identified the β-lactams based on their specific bands above 250 nm and subtracted their ECD spectra from those of the complex

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Figure 10.15  ECD spectra of urine (250-fold dilution) from three patients under cefoxitin therapy: protein-free urine (solid line, 23.25 µg/mL of cefoxitin), urine with 11.19 µg/ mL of proteins (dashed line, 15.29 µg/mL of cefoxitin), and with 17.16 µg/mL of proteins (dotted line, 7.48 µg/mL of cefoxitin). Reprinted from ref. [30] with permission of Elsevier B.V. Abbreviation: ECD, electronic circular dichroism.

spectrum of the urine sample. Although the subtraction gave excellent results even for high drug levels, it was useless for β-lactams that do not absorb above 250 nm [92]. In such cases, the drug–protein interaction resulted not only in intensity changes, but also in the shift of the resulting spectral bands. According to the authors, the complexity of such ECD spectra may be simplified by a regression fit using the spectrum of the drug and albumin (assuming albumin as the main urinary protein if proteinuria is present). Doing so, it was possible to rapidly quantitate the drug and proteins simultaneously without the need for derivatization or chromatographic separation. Thus, ECD again confirmed its potential as a reliable sensitive tool for the detection and quantitation of optically active moieties in complex biofluids.

10.6  CHIROCLINICS—CHIROPTICAL METHODS AS DIAGNOSTIC TOOLS The main effort of today’s diagnostic procedures is to provide reliable results in a short time with the emphasis on minimal burden for the examined individuals. Many biofluids (urine, blood, plasma) are easy to obtain with minimal

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invasiveness and represent a rich source of information about numerous processes ongoing in the human body. As such, they form a basis in clinical diagnostics, blood being the most extensively utilized biofluid for such purposes [37]. Several studies have confirmed the reliability of molecular spectroscopy in the analysis of biological samples [93–97]. Outperforming the currently used inaccurate, often invasive clinical procedures in all aspects, spectroscopic methods hold the utmost potential for precise clinical diagnostics. As many biomolecules in biofluids, specifically blood plasma, exhibit optical activity, chiroptical spectroscopy may be suited to monitoring any disease-related alterations in their 3D structure if the biomolecule concentration is sufficient [11,12,78,98]. As stated by Polavarapu [99], the simultaneous use of multiple chiroptical methods is essential for a reliable determination of the structure of a chiral molecule. Adhering to this principle, the research group at the University of Chemistry and Technology Prague has been systematically applying both ECD and ROA as powerful tools for the monitoring of disease-induced structural changes in plasmatic biomolecules. In addition, to acquire the maximal amount of information, the chiroptical methods were supplemented by nonpolarized techniques, specifically Raman and infrared absorption spectroscopy [33,53,100–102]. The following sections briefly summarize the achievements.

10.6.1  Cancer and degenerative diseases Promising results were obtained in a pilot study focused on the search for a specific spectral pattern of colon cancer [33]. Blood plasma of patients suffering from colon cancer (different stages and differentiation grades) was examined and the spectral response was compared to that of healthy controls. As expected, the ECD spectra (Fig. 10.16) reflected proteins with prevailing αhelical conformation, which was illustrated by the albumin-type pattern [55]. Although the albumin-to-total-protein ratio was only slightly decreased in patients in comparison with healthy controls (0.59 vs. 0.66), the spectral differences regarding proteins were much more pronounced. The authors suggested a contribution thereto by not only a change in protein level, but also a higher content of less ordered structures formed during cancer development. Similarly to ECD, the overall pattern of the ROA spectra (Fig. 10.17) was typical for predominantly α-helical proteins [2]. Intensity changes and relative intensity differences were observed within structure-sensitive bands in the amide I (a couplet consisting of bands at (−)1645 and (+)1674 cm−1) and extended amide III (positive bands at 1295, 1311 and 1345 cm−1) regions [3,78]. The presence of positive bands in the 870–960 cm−1 region

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Figure 10.16  Average ECD spectra of human blood plasma; colon cancer patients (solid line) and healthy controls (dotted line). Reprinted from ref. [33] with permission of the Royal Society of Chemistry. Abbreviation: ECD, electronic circular dichroism.

Figure 10.17  Average ROA spectra of human blood plasma; colon cancer patients (solid line) and healthy controls (dotted line). Reprinted from ref. [33] with permission of the Royal Society of Chemistry. Abbreviation: ROA, Raman optical activity.

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supported the observations from ECD and, thus, confirmed the correlation between the spectral pattern and prevailing α-helical conformation of plasmatic proteins, whose levels were decreased in patients. In addition, variations were also observed at 1248 cm−1. The band shape thereof indicates a certain contribution of β-structures [78,103] suggesting again diseaseinduced conformational changes primarily of plasmatic proteins. As the spectral differences are often too small to be observed by the naked eye, the use of statistics is convenient. Thus, to discriminate patients and healthy controls, spectroscopic data were processed by multivariate statistical methods. In the first step, the spectra were subjected to principal component analysis, which yielded the most significant bands for a further evaluation with linear discriminant analysis (LDA). Classification models were created for each individual spectroscopic method, that is, ECD, ROA, FTIR, and Raman (Fig. 10.18), and the reliability of the established models

Figure 10.18 Graphical representation of the results of LDA for individual spectral methods; ECD (A), Raman (B), ROA (C), FTIR (D); (▲) colon cancer patients; () healthy controls. Reprinted from ref. [33] with permission of the Royal Society of Chemistry. Abbreviations: LDA, linear discriminant analysis; ECD, electronic circular dichroism; ROA, Raman optical activity; FTIR, Fourier-transform infrared spectroscopy.

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Table 10.2  Spectral Bands Selected for the Linear Discriminant Analysis Model Combining Chiroptical and Conventional Vibrational Spectroscopies to Identify Colon Cancer [33] Method Spectral Bands

ECD (mm) ROA (cm−1) FTIR (cm−1) Raman (cm−1)

192, 209, 222 833, 956, 1264, 1295, 1301, 1311, 1345, 1442, 1604, 1645, 1665, 1674 1244, 1400, 1547, 1639 1270, 1285, 1341, 1357, 1391, 1450, 1517, 1586

Abbreviations: ECD, electronic circular dichroism; ROA, Raman optical activity; FTIR, Fourier-transform infrared spectroscopy

was verified by leave-one-out cross-validation (LOOCV). Despite satisfactory values of overall accuracy reached in the individual models (81%–87%) [33], the patient and control groups overlapped, generating dissimilar misclassified samples for each spectroscopic method. With the highest sensitivity (79%) and specificity (89%) after LOOCV, ECD exhibited better ability to properly classify the samples than ROA. However, the achieved classification accuracy was still insufficient for clinical applications. As each of the utilized spectroscopic methods is sensitive to different molecular features, the obtained information may be regarded as mutually supporting or even complementary. Therefore, the discrimination accuracy improved significantly after Tatarkovicˇ et al. combined selected spectral bands from chiroptical and conventional spectroscopies (Table 10.2) into one LDA classification model. The classification insufficiencies were thereby compensated, which lead to a complete separation of both studied groups (Fig. 10.19). After LOOCV, sensitivity and specificity reached 93% and 81%, respectively, which confirmed an excellent quality of this complex statistical model. With the same strategy, the authors were also able to successfully identify specific structural changes in the case of pancreatic cancer and, subsequently, discriminate the patient group from healthy controls with an overall accuracy of ∼91% after LOOCV [100,101]. Furthermore, in the study of Alzheimer’s disease, it was possible not only to differentiate between patients and nondemented elderly individuals with a high level of accuracy, but also to distinguish the stage of dementia [102,104].

10.6.2  Metabolic disorders The established methodology [54] and statistical evaluation was also used to study type 1 diabetes mellitus [53]. This metabolic disease is

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Figure 10.19  Graphical representation of the results of LDA for the combination of ROA, ECD, Raman, and FTIR spectroscopic data; (▲) colon cancer patients; () healthy controls. Reprinted from ref. [33] with permission of the Royal Society of Chemistry. Abbreviations: LDA, linear discriminant analysis; ROA, Raman optical activity; ECD, electronic circular dichroism; FTIR, Fourier-transform infrared spectroscopy.

characterized by an autoimmune destruction of insulin-producing β-cells of the pancreas, which leads to a permanently elevated level of blood glucose (hyperglycemia) and a complete metabolic disruption. It is believed that the autoimmune reaction is initiated by virus-affected pancreatic βcells producing protein antigens with a structure different from “normal” proteins [105,106]. In addition, the resulting hyperglycemia causes excessive glycation and, thus, conformational changes in plasmatic proteins. The identification of such structurally altered proteins may be of great value for understanding the pathophysiology of type 1 diabetes and improving its early diagnosis. Št’ovícˇková et al. [53], therefore, aimed at the identification of diabetes-specific spectral markers applying the combination of chiroptical (ROA, ECD) and vibrational spectroscopy (Raman, FTIR) to human blood plasma. The observed spectral intensities and patterns varied, especially in spectral regions reflecting proteins, and the statistical evaluation by LDA classified diabetic patients and healthy controls with a high sensitivity and specificity of 92% and 100% (after LOOCV), respectively. The detection and quantitation of proteins not only in blood plasma, but also in urine might bring a new insight into the diagnostics and monitoring of diabetes. The urine of a healthy individual should contain only a minimum of proteins, for example, the physiological urinary level of albumin being <30 mg/24 h. In a case that a person is not on a protein-rich diet (including excessive consumption of protein supplements), the increased excretion of proteins in urine may indicate for the most part the presence

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of a pathological process [37,107]. During blood filtration and urine formation, blood proteins do not usually cross the glomerular membrane in the kidneys, unless it is damaged. In diabetes, such disruption results from glycation of the membrane proteins caused by long-term hyperglycemia [105,108]. Consequently, plasmatic proteins, primarily albumin, are released into urine. Even a small amount of urinary albumin (microalbuminuria; 30–300 mg/24 h, meaning ∼20–200 mg of HSA per 1 L of urine) [109] is clinically significant, because it is an indicator of diabetes compensation and a strong predictor of possible kidney failure. Unfortunately, routine diagnostics of microalbuminuria based on immunochemistry or liquid chromatography with tandem mass spectrometry are insufficient, or have high time and instrumentation requirements, respectively [110]. As HSA is chiral and exhibits a specific spectral pattern, the ECD analysis of urine comes into consideration. A preliminary study on urine samples of healthy individuals and patients with type 1 diabetes provided impressive results [111]. Notwithstanding that any microalbuminuria and/ or proteinuria was clinically excluded in the studied individuals, the ECD spectrum of one patient showed obvious signs of proteins (Fig. 10.20). The

Figure 10.20  ECD spectra of urine of diabetic patients with suspected microalbuminuria (solid line), without microalbuminuria (dotted line), and of a healthy control (dashed line). Abbreviation: ECD, electronic circular dichroism.

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authenticity of the observed bands was verified by measuring demineralized water in the same optical cell. The bands at ∼209 and 222 nm and the overall spectral pattern indicated the presence of prevailing α-helical structures of albumin [86]. In this case, ECD outclassed the routine clinical approach and again confirmed its great potential as a supportive diagnostic tool.

10.7  CONCLUDING REMARKS It has been almost 40 years since the first chiroptical analysis of biofluids was conducted. The first studies were primarily focused on the identification and kinetic studies of drugs in blood plasma/serum and urine, the biofluids being regarded only as a matrix. However, blood plasma and urine are complex mixtures of many biomolecules, some of which reflect the physiological state of human organism. Therefore, it would be very convenient to aim the attention at the analysis of molecules naturally occurring in these biofluids, as is common for methods of metabolomic and proteomic screening. For a systematic analysis of human blood plasma, it is necessary to reduce the experimental limitations represented by the complexity of this biofluid. Therefore, methodologies were developed allowing the analysis of blood plasma by the majority of chiroptical methods (ECD, VCD, ROA, and FDCD). Other biofluids, such as cerebrospinal or amniotic fluid, sperm, cervical mucus, and saliva, may also provide clinically useful information. However, they have not yet been subjected to a chiroptical analysis and, thus, remain a challenge for future research. Although seeing chiroptical spectroscopy as an established diagnostic method would be a little exaggerated at this time, it is necessary to point out that, in some cases, these advanced techniques outperformed the conventional clinical approaches and, thus, may be at least regarded as useful supportive tools facilitating a timely and reliable diagnosis.

ACKNOWLEDGMENTS The authors thank the Ministry of Health of the Czech Republic (project No. 16-31028A) and the Czech Science Foundation (project No. 17-05292S) for financial support. Partial support was provided by the “Operational Program Prague—Competitiveness” (projects No. CZ.2.16/3.1.00/21537 and /24503) and the “National Program of Sustainability I”—NPU I (LO1601 No. MSMT-43760/2015).

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