Quantitative phase imaging and Raman micro-spectroscopy applied to Malaria

Background Malaria is due to parasitism of red blood cells (RBC) by protozoan parasites of the genus Plasmodium. Three main parameters have to be determined for patient treatment: parasite species, the rate of infected blood cells (parasitemia), and development stage. Even if a series of laboratory techniques are available, a suited treatment needs microscopy skills [1]. Microscopic observation needs a specialist and is time consuming (e. g. observation of hundreds fields of view at 100x immersion objective) and automating 100x slide scanning of white light imaging of thin film stained blood smears is not straightforward. Seeking for an easy to automatize alternative, we thought that combining two microscopy techniques: quantitative phase imaging for quick detection and Raman micro-spectroscopy for molecular characterization could appear to be an efficient multipurpose solution. Raman micro-spectroscopy is a good candidate to identify molecular species in microscopic. Laser light is focused in a tiny volume. Due to molecular vibrations, part of the light is non-linearly scattered to longer (Stokes) or shorter (Anti-Stokes) wavelength. The wavelength shift is directly linked to the vibration energy. The scattered light spectrum contains lines typical of the molecular binding in the focused volume. However imaging is very time consuming as each pixel needs to be acquired individually (between 1 and 10s per pixel). Quantitative phase imaging is an imaging technique that measures the optical path difference of light travelling through different part of a semi-transparent medium. If a biological tissue has local different index of refraction, we obtain an image that reflects these index changes. This kind of maps usually has a contrast more than two orders of magnitude higher than bright field microscopic images. In addition, they give information about the relative index change in the tissue. Combining a morphological technique suited for rare events detection with a molecular technique suited for local molecular signature acquisition could provide an easy way to address automation of parasite parameters acquisition.