Development of a flow cell based Raman spectroscopy technique to overcome photodegradation in human blood

Abstract

Raman spectroscopy of blood offers significant potential for label-free diagnostics of disease. However, current techniques are limited by the use of low laser power to avoid photodegradation of blood; this translates to a low signal to noise ratio in the Raman spectra. We developed a novel flow cell based Raman spectroscopy technique that provides reproducible Raman spectra with a high signal to noise ratio and low data acquisition time while ensuring a short dwell time in the laser spot to avoid photodamage in blood lysates. We show that our novel setup is capable of detecting minute changes in blood lysate spectral features from natural aging. Moreover, we demonstrate that by rigorously controlling the experimental conditions, the aging effect due to natural oxidation does not confound the Raman spectral measurements and that blood treated with hydrogen peroxide to induce oxidative stress can be discriminated from normal blood with a high accuracy of greater than 90% demonstrating potential for use in a clinical setting.

Fig. 1

(a) Schematic of the Raman spectroscopy probe consisting of Lenses L1 – L5; BPF: 785 nm bandpass filter; EF1 and EF2: > 800 nm edge filters to transmit the Raman light only, DM1 and DM2 are dichroic mirrors (b) Close-up photograph of the water-immersion objective to excite and collect the laser and Raman-scattered light respectively from the blood sample in the flow cell.

Fig. 2

(a) Raman spectra of blood at varying depths inside the flow cell. (b) Parabolic velocity distribution of 5 µm polystyrene beads in water flowing through the quartz capillary tube, obtained by increasing the depth of the laser focal spot into the tube.

Fig. 3

Comparison of the Raman spectra from the first and the last time series separated by 160 minutes. Mean intensity along with a 95% confidence interval of the Raman spectra in the first (red) and last (blue) time series for (a) static blood and (b) flowed blood. Background-subtracted Raman spectra for the first and last time series separated by 160 minutes for (c) static blood and (d) flowed blood. Difference spectrum between the first and last time series for (e) static blood and (f) flowed blood.

Fig. 4

Plot of the mean intensity and along with a 95% confidence interval of the first 10 (red), middle 10 (blue) and last 10 (green) Raman spectra in the first time series for (a) static blood and (b) flowed blood. (c) and (d) Background-corrected Raman spectra showing the mean intensity and standard deviation of the first 10 (red) and last 10 (blue) spectra of all 14 time series for (c) static blood and (d) flowed blood. The mean of (n = 14 time series) difference spectra between the mean of the first 10 spectra and the last 10 spectra for each time series, along with 95% confidence interval, for static (e) and flowed blood (f).

Fig. 5

(a) Mean of the Raman spectra along with the 95% confidence interval corresponding to control blood (n = 4455) and blood treated with 20 mM (n = 1485) and 100 mM concentration (n = 1485) of hydrogen peroxide. (b) Tukey style box plots showing the variation in the ratio of the intensity of Raman bands at: 1212 cm⁻¹ and 1224 cm⁻¹; 1340 cm⁻¹ and 1375 cm⁻¹; 1549 and 1582 cm⁻¹; and 1604 cm⁻¹ and 1637 cm⁻¹ for each dose group. Black boxes consist of all spectra for a given dose, while grey boxes are all spectra for a given day for a given dose. Outliers have been left out for clarity and * indicates statistically significant differences (p < 0.05) between the median ratios for the dosed group relative to the control group using all spectra. (c) PCA scatter plot for a Raman data set of three control samples (Control-1,2,3) and the two dosed samples (Dose-1 and Dose-2 correspond to 20 mM and 100mM concentration of hydrogen peroxide). The order of the plot legend reflects the order of consecutive measurements.

Fig. 6

Raman spectra of flowed blood at high (165 kWcm⁻²), medium (83 kWcm⁻²), and low (20 kWcm⁻²) laser power densities.