Cellular & Molecular Imaging

Manuscript abstract: Malaria transmission relies on sporozoite formation in the mosquito midgut and subsequent salivary gland invasion. Despite their importance, the cell biology of these processes remains poorly understood. We apply Mosquito Tissue Ultrastructure Expansion Microscopy (MoTissU-ExM), which physically expands infected mosquito tissues while preserving host and parasite ultrastructure. MoTissU-ExM reveals parasite structures and organelles, including features previously seen only by electron microscopy and novel structures not observed before. We use MoTissU-ExM to investigate sporozoite formation and salivary gland invasion, focusing on rhoptries - secretory organelles critical for host cell invasion. We establish a timeline for rhoptry biogenesis, show that two rhoptries are consumed during salivary gland invasion, and provide the first evidence that rhoptry pairs are specialized for different invasion events. We further characterize RON11 as the first protein involved in sporozoite rhoptry biogenesis; its disruption produces sporozoites that specifically fail to invade salivary gland epithelial cells, blocking parasite transmission. Dataset description: This dataset contains all microscopy data associated with the linked publication "Unlocking new understanding of Plasmodium sporozoite biology with expansion microscopy". All samples were prepared by ultrastructure-expansion microscopy (U-ExM). All samples were imaged on either a Zeiss LSM900 or LSM980 microscope, using either Airyscan-SR or Airyscan-MPLX modes. File names will include the magnification of the objective lens used as follows: 5x = EC Plan-Neofluar 5x/0.16NA Air 10x = Ziess Plan-Apochromat 10x/0.45NA air 20x = Ziess Plan-Apochromat 20x/0.8NA air 40x = Zeiss C-Apochromat 40x/1.2NA water-immersion autocorr M27 63x = Zeiss Plan-Apochromat 63x/1.4NA oil-immersion M27 Images are of mosquito tissues, or isolated parasites, from three Plasmodium species - berghei (Pb), falciparum (Pf), and yoelii (Py). Images are sorted and named as follows (folder name, file name) Plasmodium species > Tissue type/site of isolation > Parasite strain > Species abbreviation, MG/SG, Harvest day(dpi), Dye/Fluorophores (405nm -> 647nm), Objective, Image number (1->X), as (airyscan) For example, the third image taken of a P. berghei oocyst with the RON11iKD parasite line, that was harvested on Day 14 post infection, stained with NHS Ester AF405, BODIPY-FL, anti-Tubulin AF555, and Sytox Red, and imaged on the 40x-objective would be listed as follows: Plasmodium berghei > Infected midguts > RON11iKD > RON11KD MG 14dpi NHSBFlTub-SytR 40x 1 as The majority of images in this dataset are z-stacked images, but for many oocysts a single-slice image of the whole oocyst was taken. When this is the case, the single-slice image will be indicated with "SNAP". A list of the acronyms and abbreviations used in file names are as follows MG = Midgut SG = Salivary gland Spz = Sporozoite HC = Haemocoel dpi = Days post infection NHS = NHS Ester Alexa Fluor 405 BFl = Bodipy-FL-Ceramide BTRc = Bodipy-TR-Ceramide SytR = Sytox Deep Red Tub = anti-tubulin antibody CSP = anti-circumsporozoite protein antibody RAP1 = anti-rhoptry associated protein 1 antibody iKD = Inducible knockdown Ctrl = Control KD = Knockdown RON4 = anti-rhoptry neck protein 4 antibody GFP = anti-green fluorescent protein antibody WGA = Wheat germ aglutinnin BIP = anti-BiP antibody ERD2 = anti-ERD2 antibody

This dataset comprises raw, 16-bit monochrome microscopy images of human red blood cells infected with malaria at various degrees of parasitemia. The microscope used to caputure the images is a Leica DMi8 inverted brightfield microscope, using a 40x/1.3 oil immersion apochromatic objective. The cells are imaged at either one wavelength (at 405 nm) or three simultaneous wavelengths (365 nm, 405 nm, and broadband lamp). Each condition contains many fields of view for a single time point. The directory structure is organized into four date-stamped folders. Three folders contain experiments used for training and validation data collection, including two folders with images of infected cells ('SCP-2019-10-24 Malaria' and 'SCP-2019-11-12 Malaria'), and one folder containing a healthy control dataset ('SCP-2020-01-08 Healthy RBC conditions'). 'SCP-2020-06-20 Titration' is an experiment whereby a high parasitemia malaria culture was diluted serially into healthy red blood cells. Dilution points are contained within subfolders labelled by the dilution point.

"This dataset comprises processed images and class labels of UV microscopy images of human red blood cells infected with malaria at various degrees of parasitemia. The microscope used to caputure the images is a custom-built UV microscope employing a quartz Zeiss Ultrafluar 100x/0.85 finite conjugate objective. The cells are imaged at either one wavelength in deep UV (285 nm) or three simultaneous wavelengths (285 nm, 365 nm, 565 nm). Each condition contains many fields of view, extensive z-stacks, and a single time point. The directory structure is organized first by category: 'Training and validation', or 'Titration 2020-06-20'. Training and Validation is a collection of time-stamped data collection sessions acquired during development of the method. 'Titration 2020-06-20' is an experiment whereby a high parasitemia malaria culture was diluted serially into healthy red blood cells. Each dilution point was imaged and resides in a time-stamped directory."

OME-NGFF converted study from idr0090. Drug resistance threatens the effective prevention and treatment of an ever-increasing range of human infections. This highlights an urgent need for new and improved drugs with novel mechanisms of action to avoid cross-resistance. Current cell-based drug screens are, however, restricted to binary live/dead readouts with no provision for mechanism of action prediction. Machine learning methods are increasingly being used to improve information extraction from imaging data. Such methods, however, work poorly with heterogeneous cellular phenotypes and generally require time-consuming human-led training. We have developed a semi-supervised machine learning approach, combining human- and machine-labelled training data from mixed human malaria parasite cultures. Designed for high-throughput and high-resolution screening, our semi-supervised approach is robust to natural parasite morphological heterogeneity and correctly orders parasite developmental stages. Our approach also reproducibly detects and clusters drug-induced morphological outliers by mechanism of action, demonstrating the potential power of machine learning for accelerating cell-based drug discovery.

All symptoms of malaria disease are associated with the asexual blood stages of development, involving cycles of red blood cell (RBC) invasion and egress by the Plasmodium spp. merozoite. Merozoite invasion is rapid and is actively powered by a parasite actomyosin motor. The current accepted model for actomyosin force generation envisages arrays of parasite myosins, pushing against short actin filaments connected to the external milieu that drive the merozoite forwards into the RBC. In Plasmodium falciparum, the most virulent human malaria species, Myosin A (PfMyoA) is critical for parasite replication. However, the precise function of PfMyoA in invasion, its regulation, the role of other myosins and overall energetics of invasion remain unclear. Here, we developed a conditional mutagenesis strategy combined with live video microscopy to probe PfMyoA function and that of the auxiliary motor PfMyoB in invasion. By imaging conditional mutants with increasing defects in force production, based on disruption to a key PfMyoA phospho-regulation site, the absence of the PfMyoA essential light chain, or complete motor absence, we define three distinct stages of incomplete RBC invasion. These three defects reveal three energetic barriers to successful entry: RBC deformation (pre-entry), mid-invasion initiation, and completion of internalisation, each requiring an active parasite motor. In defining distinct energetic barriers to invasion, these data illuminate the mechanical challenges faced in this remarkable process of protozoan parasitism, highlighting distinct myosin functions and identifying potential targets for preventing malaria pathogenesis.

protein localization

Imaging dataset for PMID: 39314434

protein localization

in situ sequencing

in situ sequencing