From Genes to Biomarkers: Understanding the Biology of Malaria Gametocytes and Their Detection

Each year, approximately 230 million malaria cases and 400,00 malaria deaths are reported worldwide. Malaria is a life-threatening disease caused by Plasmodium parasites that are transmitted from one individual to another through the bites of infected female Anopheles mosquitoes. Malaria parasites replicate asexually in the human host, and, in each replication cycle, a portion of the asexual stages develops into sexual gametocytes that permit transmission. The proportion of infections that carries gametocytes and the infectivity of gametocytes are indicators of human-to-mosquito transmission potential. In P. falciparum, gametocytes appear 10–14 days after infection, whereas in P. vivax gametocytes appear simultaneously with asexual schizonts. Such difference in development not only increases the length of time that an individual is infectious, but also increases the likelihood of transmission before treatment. The conversion from asexual parasites to gametocytes is also highly variable between infections. Differences in age, host immune response, parasite genetic composition, density of red blood cells, presence of co-infecting parasite strains, and antimalarial drug use could affect gametocytes production. In P. vivax, the unique ability to produce hypnozoites, a dormant liver stage of the parasite, may allow gametocytes to be produced periodically from relapse and contribute to transmission. In this chapter, we will provide an overview of the biology of Plasmodium gametocytes, existing tools for gametocyte detection, and features of gametocyte genes. The biological insights and genetic findings are essential to developing better detection biomarkers and effective strategies to reduce transmission in malaria-endemic countries

Recent Advancement in the Surface-Enhanced Raman Spectroscopy-Based Biosensors for Infectious Disease Diagnosis

Diagnosis is the key component in disease elimination to improve global health. However, there is a tremendous need for diagnostic innovation for neglected tropical diseases that largely consist of mosquito-borne infections and bacterial infections. Early diagnosis of these infectious diseases is critical but challenging because the biomarkers are present at low concentrations, demanding bioanalytical techniques that can deliver high sensitivity with ensured specificity. Owing to the plasmonic nanomaterials-enabled high detection sensitivities, even up to single molecules, surface-enhanced Raman spectroscopy (SERS) has gained attention as an optical analytical tool for early disease biomarker detection. In this mini-review, we highlight the SERS-based assay development tailored to detect key types of biomarkers for mosquito-borne and bacterial infections. We discuss in detail the variations of SERS-based techniques that have developed to afford qualitative and quantitative disease biomarker detection in a more accurate, affordable, and field-transferable manner. Current and emerging challenges in the advancement of SERS-based technologies from the proof-of-concept phase to the point-of-care phase are also briefly discussed

Optimization of Spectral and Spatial Conditions to Improve Super-Resolution Imaging of Plasmonic Nanoparticles

Interactions between fluorophores and plasmonic nanoparticles modify the fluorescence intensity, shape, and position of the observed emission pattern, thus inhibiting efforts to optically super-resolve plasmonic nanoparticles. Herein, we investigate the accuracy of localizing dye fluorescence as a function of the spectral and spatial separations between fluorophores (Alexa 647) and gold nanorods (NRs). The distance at which Alexa 647 interacts with NRs is varied by layer-by-layer polyelectrolyte deposition while the spectral separation is tuned by using NRs with varying localized surface plasmon resonance (LSPR) maxima. For resonantly coupled Alexa 647 and NRs, emission to the far field through the NR plasmon is highly prominent, resulting in underestimation of NR sizes. However, we demonstrate that it is possible to improve the accuracy of the emission localization when both the spectral and spatial separations between Alexa 647 and the LSPR are optimized