Single-Cell Analysis Reveals Distinct Gene Expression and Heterogeneity in Male and Female Plasmodium falciparum Gametocytes

© 2018 Walzer et al. Sexual reproduction is an obligate step in the Plasmodium falciparum life cycle, with mature gametocytes being the only form of the parasite capable of human-to-mosquito transmission. Development of male and female gametocytes takes 9 to 12 days, and although more than 300 genes are thought to be specific to gametocytes, only a few have been postulated to be male or female specific. Because these genes are often expressed during late gametocyte stages and for some, male- or female-specific transcript expression is debated, the separation of male and female populations is technically challenging. To overcome these challenges, we have developed an unbiased single-cell approach to determine which transcripts are expressed in male versus female gametocytes. Using microfluidic technology, we isolated single mid- to late-stage gametocytes to compare the expression of 91 genes, including 87 gametocyte-specific genes, in 90 cells. Such analysis identified distinct gene clusters whose expression was associated with male, female, or all gametocytes. In addition, a small number of male gametocytes clustered separately from female gametocytes based on sex-specific expression independent of stage. Many female-enriched genes also exhibited stage-specific expression. RNA fluorescent in situ hybridization of male and female markers validated the mutually exclusive expression pattern of male and female transcripts in gametocytes. These analyses uncovered novel male and female markers that are expressed as early as stage III gametocytogenesis, providing further insight into Plasmodium sex-specific differentiation previously masked in population analyses. Our single-cell approach reveals the most robust markers for sex-specific differentiation in Plasmodium gametocytes. Such single-cell expression assays can be generalized to all eukaryotic pathogens

Automated Detection of <i>P</i>. <i>falciparum</i> Using Machine Learning Algorithms with Quantitative Phase Images of Unstained Cells

<div><p>Malaria detection through microscopic examination of stained blood smears is a diagnostic challenge that heavily relies on the expertise of trained microscopists. This paper presents an automated analysis method for detection and staging of red blood cells infected by the malaria parasite <i>Plasmodium falciparum</i> at trophozoite or schizont stage. Unlike previous efforts in this area, this study uses quantitative phase images of unstained cells. Erythrocytes are automatically segmented using thresholds of optical phase and refocused to enable quantitative comparison of phase images. Refocused images are analyzed to extract 23 morphological descriptors based on the phase information. While all individual descriptors are highly statistically different between infected and uninfected cells, each descriptor does not enable separation of populations at a level satisfactory for clinical utility. To improve the diagnostic capacity, we applied various machine learning techniques, including linear discriminant classification (LDC), logistic regression (LR), and <i>k</i>-nearest neighbor classification (NNC), to formulate algorithms that combine all of the calculated physical parameters to distinguish cells more effectively. Results show that LDC provides the highest accuracy of up to 99.7% in detecting schizont stage infected cells compared to uninfected RBCs. NNC showed slightly better accuracy (99.5%) than either LDC (99.0%) or LR (99.1%) for discriminating late trophozoites from uninfected RBCs. However, for early trophozoites, LDC produced the best accuracy of 98%. Discrimination of infection stage was less accurate, producing high specificity (99.8%) but only 45.0%-66.8% sensitivity with early trophozoites most often mistaken for late trophozoite or schizont stage and late trophozoite and schizont stage most often confused for each other. Overall, this methodology points to a significant clinical potential of using quantitative phase imaging to detect and stage malaria infection without staining or expert analysis.</p></div

OPL maps.

<p>Uninfected RBC and RBCs infected by <i>P</i>. <i>falciparum</i> in early trophozoite, late trophozoite, and schizont stages represented respectively as: (A-D) OPL maps, (E-F) OPL maps from different viewpoint (scale bars = 5μm).</p

Infection staging.

<p>Table showing performance of multinomial machine learning algorithms: Infection stages.</p

Symmetry values.

<p>Symmetry values of uninfected RBC and RBCs infected by <i>P</i>. <i>falciparum</i> in early trophozoite, late trophozoite, and schizont stages respectively as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0163045#pone.0163045.g003" target="_blank">Fig 3</a> versus angles of rotation.</p

Centroid vs. Center of mass.

<p>Binary mask used to calculate centroid and ΔOPL map used to calculate center of mass for an RBC with <i>P</i>. <i>falciparum</i> at schizont stage (scale bars = 5μm).</p

WBCs.

<p>WBCs, N = 27 (square tile = 20μm x 20μm).</p