Machine learning-based phenotypic imaging to characterise the targetable biology of <i>Plasmodium falciparum</i> male gametocytes for the development of transmission-blocking antimalarials

Preventing parasite transmission from humans to mosquitoes is recognised to be critical for achieving elimination and eradication of malaria. Consequently developing new antimalarial drugs with transmission-blocking properties is a priority. Large screening campaigns have identified many new transmission-blocking molecules, however little is known about how they target the mosquito-transmissible Plasmodium falciparum stage V gametocytes, or how they affect their underlying cell biology. To respond to this knowledge gap, we have developed a machine learning image analysis pipeline to characterise and compare the cellular phenotypes generated by transmission-blocking molecules during male gametogenesis. Using this approach, we studied 40 molecules, categorising their activity based upon timing of action and visual effects on the organisation of tubulin and DNA within the cell. Our data both proposes new modes of action and corroborates existing modes of action of identified transmission-blocking molecules. Furthermore, the characterised molecules provide a new armoury of tool compounds to probe gametocyte cell biology and the generated imaging dataset provides a new reference for researchers to correlate molecular target or gene deletion to specific cellular phenotype. Our analysis pipeline is not optimised for a specific organism and could be applied to any fluorescence microscopy dataset containing cells delineated by bounding boxes, and so is potentially extendible to any disease model

Health Care for Mitochondrial Disorders in Canada: A Survey of Physicians

Background: An improved understanding of diagnostic and treatment practices for patients with rare primary mitochondrial disorders can support benchmarking against guidelines and establish priorities for evaluative research. We aimed to describe physician care for patients with mitochondrial diseases in Canada, including variation in care. Methods: We conducted a cross-sectional survey of Canadian physicians involved in the diagnosis and/or ongoing care of patients with mitochondrial diseases. We used snowball sampling to identify potentially eligible participants, who were contacted by mail up to five times and invited to complete a questionnaire by mail or internet. The questionnaire addressed: personal experience in providing care for mitochondrial disorders; diagnostic and treatment practices; challenges in accessing tests or treatments; and views regarding research priorities. Results: We received 58 survey responses (52% response rate). Most respondents (83%) reported spending 20% or less of their clinical practice time caring for patients with mitochondrial disorders. We identified important variation in diagnostic care, although assessments frequently reported as diagnostically helpful (e.g., brain magnetic resonance imaging, MRI/MR spectroscopy) were also recommended in published guidelines. Approximately half (49%) of participants would recommend mitochondrial cocktails for all or most patients, but we identified variation in responses regarding specific vitamins and cofactors. A majority of physicians recommended studies on the development of effective therapies as the top research priority. Conclusions: While Canadian physicians\u27 views about diagnostic care and disease management are aligned with published recommendations, important variations in care reflect persistent areas of uncertainty and a need for empirical evidence to support and update standard protocols

The distribution of cells from individual drug treatments visualised within the entire dataset.

The distribution of cells from individual drug treatments visualised within the entire dataset.</p

Phenotypic distribution of male gametocytes treated with 45 transmission-blocking compounds.

2446 cells (augmented in eight different orientations) from 45 different treatments/controls were analysed by PhIDDLI and visualised by t-SNE and k-means clustering into nine clusters. (A) Localisation of the unactivated control samples (blue shades) and untreated control samples (red shades) within the dataset. Different shades indicate control cells from the six independent experiments comprising the entire screen. (B) The analysed dataset coloured by computed cluster identity. Images show representative cells from each cluster. The colour of the border surrounding the cell matches the cluster it represents. Scale bars = 3μm.</p

The activity of TCAMS compound identified in the Pf DGFA screen.

(A) 85 molecules were confirmed active in the Pf DGFA. 58 were previously identified in one or more transmission-blocking screens. (B) The majority of molecules displayed similar activity against male and female gametocytes, or biased/specific activity against male gametocytes. No molecule was identified with >4.7-fold greater activity against female gametocytes. Abbreviations (IC50 = concentration giving 50% inhibition, PbODA = P. berghei Ookinete Development Assay [5], PfDGFA = P. falciparum Dual Gamete Formation Assay, PfFGAA = P. falciparum Female Gametocyte Activation Assay [7], PfGCT = P. falciparum Gametocyte Viability ATP Assay [18]).</p

The PfDGFA potency of active compounds were determined and compared to cytotoxic activity against HepG2 cells (Activity: D = dual; M = male-biased; F = female-biased)

The PfDGFA potency of active compounds were determined and compared to cytotoxic activity against HepG2 cells (Activity: D = dual; M = male-biased; F = female-biased)</p

Using phenotypic imaging to cluster drug-treated cells with similar phenotypes.

(A) Raw fluorescence microscopy images were processed in ICY Bioimage Analysis to identify cells and then passed to the PhIDDLI pipeline for machine learning based clustering. (B) PhIDDLI interactive clustering output showing the distribution of cell phenotypes and navigable to view individual cells within each cluster.</p

Principle component analysis (PCA) plot of the first two principle components (representing 56.8% of the total variance) of the cluster assignments from Fig 4.

The percentage of cells from each drug treatment falling into each of the 9 identified clusters was compared by PCA. All nine computed principle components were then used to cluster each drug phenotype by k-means clustering and the elbow method which determined 5 clusters was optimal. Cluster assignment is summarised in Fig 5. Bar = 3μm. (PDF)</p

Selected cells from Cluster 6 (Fig 4).

Cluster 6 represents the convergence of many clusters and consequently there was significant diversity of cell morphology within the cluster. The majority were irregular-shaped but showed ordered microtuble organisation. Bar = 3μm. (PDF)</p