Light/fluorescence microscopy of cells & tissues, and electron/cryo-EM imaging of macromolecular structures — a metadata catalog with a durable link back to the source archive, not a hosted image gallery. These are primary research datasets (often multi-GB to multi-TB), so this platform never downloads or stores the underlying imaging data itself. Datasets already in either source archive are ingested via accession paste/CSV at /mirroring by a Continental Admin (no automated harvesting — neither source archive supports geography-filterable search); a dataset not yet in either archive can instead be submitted directly below by any node operator, for Continental Admin review. Either way, whoever submits an accession or a self-submitted dataset is the one asserting African origin — this platform does not verify it.
A machine learning approach to define antimalarial drug action from heterogeneous cell-based screens (OME-NGFF)
BioImage Archive:S-BIAD882 · Image Data Resource (IDR) (University of Dundee) · Plasmodium falciparum 309.1
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.
Phytoplankton is a minor fraction of the global biomass playing a major role in primary production and climate. Despite improved understanding of phytoplankton diversity and genomics, we lack nanoscale subcellular imaging approaches to understand their physiology and cell biology. Here, we present a complete Focused Ion Beam - Scanning Electron Microscopy (FIB-SEM) workflow (from sample preparation to image processing) to generate nanometric 3D phytoplankton models. Tomograms of entire cells, representatives of six ecologically-successful phytoplankton unicellular eukaryotes, were used for quantitative morphometric analysis. Besides lineage-specific cellular architectures, we observed common features related to cellular energy management: i) conserved cell-volume fractions occupied by the different organelles; ii) consistent plastid-mitochondria interactions, iii) constant volumetric ratios in these energy-producing organelles. We revealed detailed subcellular features related to chromatin organization and to biomineralization. Overall, this approach opens new perspectives to study phytoplankton acclimation responses to abiotic and biotic factors at a relevant biological scale.
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.
Neurophysiology and neuropathology are mediated by the interaction of neurons and glial cells, which cannot be modelled by monocultures. However, mixed cultures are difficult to use and analyse for high-throughput screening. Here, we show the utility of compound and target screening using primary neuron-glia cultures to model inflammatory neurodegeneration alongside live-cell stains and automated classification of neurons, astrocytes or microglia using open-source analysis software. Out of 227 compounds with known bioactivities, 29 protected against lipopolysaccharide-induced neuronal loss, including drugs affecting adrenergic, steroid, inflammatory and MAP kinase signalling. The screen also identified physiological compounds, such as noradrenaline and progesterone, that protected, and identified neurotoxic compounds, such as a TLR7 agonist, that induced microglial proliferation. Thus, combining automated image analysis of complex cultures with high-throughput screening of known compounds in a cellular model of disease allows identification of important biology, as well as potential targets and drugs for treatment.
Machine Learning-based Phenotypic Imaging to Characterise the Targetable Biology of Plasmodium falciparum Male Gametocytes for Transmission-Blocking Antimalarials
BioImage Archive:S-BIAD633 · Michael Delves (London School of Hygiene & Tropical Medicine) · Plasmodium falciparum 309.1
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 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 in 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 extendible to any disease model.
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
Integrative Chemical Genetics Platform Identifies Condensate Modulators Linked to Neurological Disorders
BioImage Archive:S-BIAD3024 · (Yale University) · Theileria parva
Aberrant biomolecular condensates are implicated in multiple incurable neurological disorders, including Amyotrophic Lateral Sclerosis, Frontotemporal Dementia, and DYT1 dystonia. However, the role of condensates in driving disease etiology remains incompletely understood. Here, we establish myeloid leukemia factor 2 (MLF2) as a disease-associated phase transition biomarker and develop a scalable high-content platform that identifies condensate modulators across broad chemical and genetic space. We uncover FDA-approved drugs that remodel aberrant condensate composition, validating the approach for drug discovery. A genome-wide CRISPR/Cas9 screen identifies genes linked to microcephaly and related neurodevelopmental disorders whose loss drives nuclear condensate accumulation. Machine learning resolves two phenotypic clusters: RNF26 deletion induces nuclear envelope condensates reminiscent of nuclear pore defects, whereas loss of microcephaly-associated ZNF335 drives accumulation of distinct nucleoplasmic condensates. Our study provides a scalable resource for identifying corrective modulators of aberrant condensates and establishes a link between dysregulated phase transitions and neurodevelopmental disorders.
Plasmodium parasites, the causative agents of malaria, undergo complex replication within vertebrate and insect hosts, presenting unique opportunities for therapeutic intervention. A key challenge during these replication events, i.e., schizogony in vertebrate red blood cells and sporogony in oocysts within mosquitos, is ensuring the faithful partitioning of nuclei and organelles into the numerous daughter cells that form at once from a single parent. While nuclear microtubule-organizing centers, or centriolar plaques (CPs), have been hypothesized to play a central role in this process, the molecular mediators linking the CPs and organelles remain incompletely defined. Here, we characterize two striated fiber assemblin (SFA) homologs, SFA1 and SFA2, in Plasmodium falciparum and Plasmodium berghei across two hosts. We show that these SFAs form a physical bridge between the CP and the nascent apical poles of daughter cells, facilitating high-fidelity progeny formation during schizogony and sporogony. Loss of SFA function disrupts merozoite and sporozoite formation, with profound consequences for transmission. These findings establish SFAs as essential organizers of parasite morphogenesis and highlight them as potential targets for antimalarial therapies. This submission includes the source microscopy image data for experiments performed in P. falciparum.