Flow-Based Raman Spectroscopy: A Novel Approach for High SNR Blood Analysis with Enhanced Viability

Abstract

Raman Spectroscopy has been appointed as an entrenched technique for blood analysis. But, most of the current approaches involve compromisation with the requirement of high signal to noise ratio. This is caused due to using low laser signals to manage the photodegrability of blood cells. Obtaining sufficient Raman signal from the blood sample is crucial but keeping the cells viable is one of major challenges. A novel technique utilizing flow-based blood samples in Raman spectroscopy has been demonstrated by the authors which involve the manifestation of high SNR and low data acquisition time by operating the system with a short dwell time.

This deteriorates the photodamage in the cells and also provide more informative data for disease indication in the blood sample. The authors have designed the experiment to detect impurities/contamination in the cells without losing its viability using hydrogen peroxide [1]. This paper is a critical review analysis which highlights the advantages as well as the limitations of this novel technique [1].

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Introduction

Raman Spectroscopy is one of the majorly used vibrational spectroscopic technique that is used to perform informative and non-degenerative analysis of chemical compounds and molecular interactions. Label-free diagnosis and real-time estimation adds up to the advantageous approach of Raman Spectroscopy. Raman Spectroscopy features the principles of Raman effect and provides inelastic scattering of light due to different energy changes within the vibrational modes of an electron.

Different molecules consist of different vibrational energy levels and thereby produce different Raman signals according to their polymorphy.

The basic principle of Raman effect is based on the changes in vibrational energy states of an electron. When an electron is exposed to a high intensity laser light, it absorbs energy and attains a virtual energy state. Using the Jablonski diagram (Fig 1), it can be understood that when this electron comes back to its normal vibrational level, there are an observed change in absorbed and released energy sometimes. This is due to the electron absorbing energy or releasing energy of its own. However, the energy change causes a shift in the intensity and wavelength thus producing a photon. This photon is then used to dictate the Ramanspectrograph. This process is termed as Raman effect. Polarizability of the molecule has direct correlation with the magnitude of the Raman effect. Moreover, a molecule will only exhibit Raman effect if there is modification in the electric dipole-electric dipole polarizability between the electron and the virtual vibrational state.

The results obtained by Raman spectrum talks about the structural identity, intermolecular forces such as intrinsic stress/strain over the molecule and most importantly the presence of any contamination present in the analysed sample. This property enhances its application to assess biofluid samples such as blood, urine, saliva etc. Raman spectroscopy has proved to be a highly efficient bioanalytical tool to detect unique disease related malformations in the body cells. The typical Raman spectrum describes the distinct properties of the molecule, moreover the concentration of the molecule can be calibrated using the intensity of the spectrum demonstrating qualitative and quantitative analysis respectively. Biofluids, specifically blood has proved to be a huge source of effective data as it consists of molecules that promotes the photonic concepts of scattering and absorption which helps in detecting optical deflections. Furthermore, many of the biomarkers of various diseases reside in the blood as a reproductive source which makes it easier for the diagnosis of any abnormality present.

The composition of blood consists of RBCs, WBCs (Fig. 2) and other clotting factors. In-vivo analysis of blood and glucose concentration is a major research area [2,3] but in-vitro is referred due to the easily accessible and minimally invasive sample extraction techniques. Raman spectroscopic assays have successfully designed and proven to detect variations between the normal and oral cancer cells from the plasma of blood [4]. Highly sensitive (above 75%) demonstration of healthy cells being discriminated from the prostate cancer cells [5]. Haemoglobin sample can also provide information about the causes of heart failure, inherited blood disorders and chronic diabetes mellitus [6].

Until now, optical techniques like LTRS (Laser TweezerRaman Spectroscopy) [6,7] and SERS (Surface-enhanced Raman Spectroscopy) [8,9] have been engaged towards in-vitro blood analysis. But these techniques have probable limitations such as blood sample extractions and purification for RBCs or plasma, the possibility of inhomogeneous sample properties leading to non-accurate results and majorly the longer time for data acquisition leading to longer exposure with laser light [10,11]. Due to this increase in exposure, the risk of photodegradability of blood cells poses a big challenge. Moreover, the haemoglobin present in the blood absorbs the laser light and produces fluorescence which increases the background fluorescence for the Raman spectra which is not feasible. To inhibit this fluorescence generation laser light is utilized in near IR regions but the Raman

strength is not enough in this case to get reflected in the spectra. To get enough Raman signal laser intensity is increased leading to cell death. Alongside, studies have also witnessed that lower lasers (power density ~ 2.5 MW cm-2) reflected irreparable damage to the haemoglobin compound due to light [12]. With all these issues, Raman Spectroscopy is not considered to be the best option for blood analysis as these limitations have a negative effect on signal-to-noise ratio.

Ben and Christian along with their supervisor Sangeeta [1] aimed to beat the challenges appearing in the Raman Spectroscopy of blood. With this motivation, they introduced a novel technique for blood analysis using flow-based Raman spectroscopy. With their experiment with blood lysate sample, they exposed it with 785 nm wavelength and measured the Raman shift against the intensity of the laser diode. The advantage of using blood lysate is to avoid the sample preparation of blood by purifying the RBCs and maintain them for the experiment.

Moreover, they demonstrated the use of flowing sample analysis with shorter laser dwell time with an appropriate power density to deny the photodamage of the cells. They observed that even with these variations, the signal-to-noise ratio was obtained to be high.

This employed novel technological construct involved effortless sample management using quartz tubes, optical optimization of the laser excitation and extraction of the scattered Raman signal and homogeneous lysate formation from the blood. All these advantages evidently overcome to the limitations caused by the already existing Ramaninstrumentations. The study also included, detection of impurities (added) in the blood sample and maintained the extremities in the sample environment as well.

Materials and Methods

Samples

Blood was drawn via periphery venipuncture from healthy volunteers By following the approval procedure of Health Canada’s Research Ethics Committee. Blood was drawn in EDTAvacutainer tubes (2 x 10 ml). 3 ml of blood was taken from one donor aliquoted in the tube of 5ml volumes (BDBiosciences, San Jose, CA, USA), then The blood was shaken at room temperature for three hours and afterward promptly solidified at -80 °C to actuate hemolysis. Solidified blood tests were defrosted medium-term in a 4 °C cooler. Defrosted blood lysate tests mixed gently and drawn inside a 1 mL syringe for estimations with the stream cell-based RS arrangement as explained in Section B. This technique repeated with the same donor. The estimations were gained utilizing a blood lysate that was streaming as explained in part B or in a static structure. For this situation, the blood lysate pumped into the stream cell and at that point held static in the stream cell for estimation with the syringe pumped off.

Oxidative pressure was incited in new blood by treatment with hydrogen peroxide, a source of responsive oxygen species like bacteria, fungi . New blood tests were aliquoted into 3 ml and put into stream tubes. Hydrogen peroxide was added drop-by-drop to the blood. Sample made up of two non-peroxide and two having 20 mM and 100 mM utilizing a 30% hydrogen peroxide concentration (Sigma-Aldrich, Canada, Oakville, ON, Canada). Following treatment with hydrogen peroxide, the examples were kept on a rocker for three hours and afterward promptly solidified at -80°C until Raman acquisitions.

Construction of the flow cell-based Raman spectroscopy setup

Raman spectroscopy and follow cell arrangement was explained in Figure 3. Raman excitation is given by a variable power, multi-mode 785 nm diode laser with a most extreme intensity of 500 mW. lens of 40 mm focal length used for this experiment and 1.1 NA water immersion magnifying lens objective (Olympus Canada Inc., Richmond Hill, ON, Canada). For the removal of unwanted noise like fluorescence of fiber by using 785nm band pass-filters. The utilization of a water immersion objective in spite of objective to concentrate the laser on the blood lysate test empowers better coordinating of the refractive index over the blood/quartz and water interface and along these lines brings about higher Raman signal assortment. The multimode laser beam was focused on the spot having 12 μm and 25 μm diameter and depth. The diameter of the spot estimated from the bright field and depth of focus estimated by various heights of Raman spectra. Laser density various from 0-165 kWcm-2.

EF1 and EF2 were the edge filters filtered the excitation of laser and DM1 and DM2 helped in differentiate between the reflected and transmitted components and the scattering Raman light was collected by the objective. This is coupled to a 300 μm center multimode fiber (Thorlabs, Newton, NJ, U.S.A.) which conveys the Raman scattering light to a Tornado HyperFlux U1 spectrometer (Tornado Ghostly Systems, Toronto, ON, Canada)[1]. The arrangement likewise incorporates a brilliant field imaging capacity using a white LED source and CCD camera (Thorlabs) in the back-reflection setup; this subsystem is valuable for setting a suitable stream rate inside the stream cell as depicted beneath in Section D [1].

The Raman stream cell comprises of two syringe pumps and a quartz capillary. The thin-walled (0.01 mm) capillary is made out of boron-rich quartz with diameter of 1.5 mm. This is installed inside a the long way channel processed on a strong (25 mm x 50 mm) aluminum slide and held set up from either end utilizing channel tape.

For maintaining the steady flow through the sample Pumps arranged in such a way that one pumps create negative flow pressure and one pump create positive flow pressure at last the direction of measurement flow was reversed so that the sample came back into first syringe. In all the steps system was flushing with 100% sodium hypochlorite for purification of the capillary tube. followed by multiple times flushing with refined water.

Characterization of the flow cell

Stream properties of the framework were described by axial velocity estimations of 5 μm polystyrene beads in water moving through the framework. An arrangement of splendid field images of the streaming beads at a given depth inside the cylinder was investigated in Image, to decide the stream speed at that depth. This technique repeated at various depths inside the tube

Estimation of the Raman signal for streamed(flowed) and static blood

For the comparison between both flowed and static blood. There was 14 statics and 14 flow time was collected for three distinct samples of the same donor. Each time arrangement was made out of 100 back to back 2s Raman spectra. The laser was at first shuttered and the blood was static. At that point, the blood was streamed with a dwell time of 0.4s, for a 30s term to arrive at flow condition. The laser was un-shuttered and a 200 s time arrangement of the streamed blood was procured.

After the FTS, the laser shuttered and the stream proceeded for 30 s to acquire a fresh sample. The stream was halted and the framework was permitted to settle to a static state (zero flow) more than 60 s. At that point, the laser was un-shuttered again and a solitary volume of blood was presented to the laser for 200 s comparing to a solitary STS. At long last, the laser was re-shuttered and a similar strategy rehashed until a sum of 14 STS and 14 FTS were procured [1].

Raman spectral estimations of ordinary and hydrogen peroxide treated blood

This examination was performed on blood streaming in the quartz tube at 2.2 uL/min flow rate with an exposure dwell time of 0.22s and 120 kW cm-2 power density. The aggregate estimation time Tm, for one blood test, including the ideal opportunity for stacking the blood test into the stream cell, the ideal opportunity for getting 500 Raman spectra of flowed blood, trailed by the time for cleaning of the stream cell, was observed. A convention was grown so that each control or peroxide treated blood test was presented to surrounding oxygen for the equivalent measure of time Tm. This guaranteed each blood test had been oxidized/matured by the equivalent amount. The average Tm was 50 minutes and estimations were performed on every dose group [1].

Results and Discussion

The experiments and data analysis were designed to determine the optimum clinical conditions for the Raman blood spectrum.

Raman blood spectrum at different densities of laser power

Raman spectra are collected at three separate laser power densities from streaming and stationary blood. The Raman spectra are controlled as predicted by hemoglobin's vibrational modes that were established by previous researches. The preliminary assignments of molecular bands are provided in the appendix in Table 1 from previous research papers and are consistent with previous reports of whole blood and RBC Raman spectra [10,14]. The absence of peaks at specific Raman shifts in the study which were evident in previous papers even at higher density (165 kWcm-2) employed suggest that there was no photodegradation of blood samples at various densities and time period.

Detection of aging effects in the Raman spectra of blood

Due to auto-oxidation, the flow cell-based RS system is highly sensitive to detect changes in blood as it ages. Figure 6 provides a comparison of the first and last time-series’ mean Raman spectra separated for the fixed blood and flowed blood by 160 minutes. The background fluorescence reduces as a function of time in Fig. 4(a) and Fig. 4(b) for both static and flowing blood. The mean of the first and last time series of the background-subtracted Raman spectra for static and flowing blood is shown in Fig. 4(c) and 4(d) in each case. The difference spectra for static blood and flowed blood between the first and last time series is shown in Fig. 4(e) and Fig. 4(f).

Similar changes are noticeable for the stable and circulating blood in the intensities of Raman peaks. This is anticipated as both stationary and circulating blood will be influenced by the blood aging process. In particular, the intensity of the Raman peaks at 570, 1224, 1375, 1398, 1582 and 1635 cm-1 is significantly increased and the intensities corresponding to the Raman peaks of 788, 1211, 1545 and 1604 cm-1 are reduced in the mean spectrum corresponding to the last time series. In Fig .4(c) and 4(f), these bands of Raman are illustrated. These peaks are associated with the FeO2 stretching of the central iron atom of the globin unit and asymmetric C-C stretching mode of the porphyrin skeleton in the hemoglobin molecule.

Both levels are responsive to hemoglobin molecule’s oxygenation state and rise in oxygen concentration, these small changes affect the Raman shift. The vibration modes correlated with the half-ring range of the pyrrole are thought to be the central iron atom's oxidation state markers within the porphyrinmacrocycle. The rise in peak strength, as seen in the mean range of the last time series, indicates the transition from the ferrous (Fe+2) to the more oxidized ferric (Fe+3) state. This is consistent with earlier reports of the Raman aged blood spectra [ 10,15,16]. Therefore, this study indicates variations in the Raman spectra attributed to auto-oxidation that are associated with the natural aging of blood.

Comparison of the Raman spectra of flowed and static blood

Figures 5(a) and 5(b) show the mean intensity of the first 10 (red), middle 10 (blue) and last 10 (green) Raman spectra in the first STS (a) and FTS (b) over 200 s for one set of data with 95% confidence interval. In the stationary blood, but not in the circulating blood, the residual fluorescence reduces over time. This refers to the well-known effect of photobleaching in blood. The authors then set out to decide if there was a significant difference as a function of time in the Raman spectral maximum intensities for a single time series relating to stable and moving blood. Raman spectra mean intensity and standard deviation after background subtraction relating to the first 10 (red) and last 10 (blue) spectra for 14 time series of one data set are plotted for stable blood (c) and flowed blood (d) respectively. After performing Welch’s test to determine changes in first and last spectra for static and flowed blood samples they found out that there are major differences in Raman spectra as a feature of constant blood, these discrepancies are shown by * in the graphs compares to flowed blood similar intensities observed in first and spectra.

As shown in previous reports [14,17-19], photoinduced oxidation in the static blood might be provoked by the extended exposure period of 200s. In contrast, at the 100 μm depth inside the quartz flow cell, each flowing blood volume is exposed to the focused laser spot for only 0.4 s which is too short for causing any distinguishable changes in the Raman spectra. Thus, more empirical evidence is generated in flowed blood.

Discrimination between normal and hydrogen peroxide treated blood

The RS methodology focused on flow cells was examined to evaluate if biochemical variations between the treatment of blood with different doses of hydrogen peroxide could be observed given the impact of blood auto-oxidation. Under strictly controlled conditions, flowed blood tests are carried out. Figure 6(a) shows the mean Raman spectra and the 95 percent confidence interval for the mean Raman spectral measurement data from three separate sets for each control blood and dosed blood.

It should be mentioned that blood samples are from a single donor and tests are conducted using 3 technical replicates over 3 days. On each data set, PCA was conducted consisting of three control samples and two dosed samples (n= 480 per test, after 2 percent of the total frequency range was extracted from top and bottom). As a feature of the hydrogen peroxide dosage, a scatter plot of scores for the first two PCs in one Raman data set (Fig. 6(b)) indicates outstanding clustering of individual Raman spectra [1].