Microscopy in Infectious Disease Research-Imaging Across Scales

Microbiome and infectious disease: diagnostics to therapeutics

Over 300 years of research on the microbial world has revealed their importance in human health and disease. This review explores the impact and potential of microbial-based detection methods and therapeutic interventions, integrating research of early microbiologists, current findings, and future perspectives.

Fast detection of slender bodies in high density microscopy data

Computer-aided analysis of biological microscopy data has seen a massive improvement with the utilization of general-purpose deep learning techniques. Yet, in microscopy studies of multi-organism systems, the problem of collision and overlap remains challenging. This is particularly true for systems composed of slender bodies such as swimming nematodes, swimming spermatozoa, or the beating of eukaryotic or prokaryotic flagella. Here, we develop a end-to-end deep learning approach to extract precise shape trajectories of generally motile and overlapping slender bodies. Our method works in low resolution settings where feature keypoints are hard to define and detect. Detection is fast and we demonstrate the ability to track thousands of overlapping organisms simultaneously. While our approach is agnostic to area of application, we present it in the setting of and exemplify its usability on dense experiments of swimming Caenorhabditis elegans. The model training is achieved purely on synthetic data, utilizing a physics-based model for nematode motility, and we demonstrate the model's ability to generalize from simulations to experimental videos.

Infectious and environmental placental insults: from underlying biological pathways to diagnostics and treatments

Because the placenta is bathed in maternal blood, it is exposed to infectious agents and chemicals that may be present in the mother's circulation. Such exposures, which do not necessarily equate with transmission to the fetus, may primarily cause placental injury, thereby impairing placental function. Recent research has improved our understanding of the mechanisms by which some infectious agents are transmitted to the fetus, as well as the mechanisms underlying their impact on fetal outcomes. However, less is known about the impact of placental infection on placental structure and function, or the mechanisms underlying infection-driven placental pathogenesis. Moreover, recent studies indicate that noninfectious environmental agents accumulate in the placenta, but their impacts on placental function and fetal outcomes are unknown. Critically, diagnosing placental insults during pregnancy is very difficult and currently, this is possible only through postpartum placental examination. Here, with emphasis on humans, we discuss what is known about the impact of infectious and chemical agents on placental physiology and function, particularly in the absence of maternal-fetal transmission, and highlight knowledge gaps with potential implications for diagnosis and intervention against placental pathologies.

Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review

Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases.

Advanced microscopy technologies enable rapid response to SARS-CoV-2 pandemic

The ongoing SARS‐CoV‐2 pandemic with over 80 million infections and more than a million deaths worldwide represents the worst global health crisis of the 21th century. Beyond the health crisis, the disruptions caused by the COVID‐19 pandemic have serious global socio‐economic consequences. It has also placed a significant pressure on the scientific community to understand the virus and its pathophysiology and rapidly provide anti‐viral treatments and procedures in order to help the society and stop the virus spread. Here, we outline how advanced microscopy technologies such as high‐throughput microscopy and electron microscopy played a major role in rapid response against SARS‐CoV‐2. General applicability of developed microscopy technologies makes them uniquely positioned to act as the first line of defence against any emerging infection in the future.

ERGO: Efficient Recurrent Graph Optimized Emitter Density Estimation in Single Molecule Localization Microscopy

Single molecule localization microscopy (SMLM) allows unprecedented insight into the three-dimensional organization of proteins at the nanometer scale. The combination of minimal invasive cell imaging with high resolution positions SMLM at the forefront of scientific discovery in cancer, infectious, and degenerative diseases. By stochastic temporal and spatial separation of light emissions from fluorescent labelled proteins, SMLM is capable of nanometer scale reconstruction of cellular structures. Precise localization of proteins in 3D astigmatic SMLM is dependent on parameter sensitive preprocessing steps to select regions of interest. With SMLM acquisition highly variable over time, it is non-trivial to find an optimal static parameter configuration. The high emitter density required for reconstruction of complex protein structures can compromise accuracy and introduce artifacts. To address these problems, we introduce two modular auto-tuning pre-processing methods: adaptive signal detection and learned recurrent signal density estimation that can leverage the information stored in the sequence of frames that compose the SMLM acquisition process. We show empirically that our contributions improve accuracy, precision and recall with respect to the state of the art. Both modules auto-tune their hyper-parameters to reduce the parameter space for practitioners, improve robustness and reproducibility, and are validated on a reference in silico dataset. Adaptive signal detection and density prediction can offer a practitioner, in addition to informed localization, a tool to tune acquisition parameters ensuring improved reconstruction of the underlying protein complex. We illustrate the challenges faced by practitioners in applying SMLM algorithms on real world data markedly different from the data used in development and show how ERGO can be run on new datasets without retraining while motivating the need for robust transfer learning in SMLM.

Machine learning for clinical decision support in infectious diseases: a narrative review of current applications

BACKGROUND

Machine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID).

OBJECTIVES

We aim to inform clinicians about the use of ML for diagnosis, classification, outcome prediction and antimicrobial management in ID.

SOURCES

References for this review were identified through searches of MEDLINE/PubMed, EMBASE, Google Scholar, biorXiv, ACM Digital Library, arXiV and IEEE Xplore Digital Library up to July 2019.

CONTENT

We found 60 unique ML-clinical decision support systems (ML-CDSS) aiming to assist ID clinicians. Overall, 37 (62%) focused on bacterial infections, 10 (17%) on viral infections, nine (15%) on tuberculosis and four (7%) on any kind of infection. Among them, 20 (33%) addressed the diagnosis of infection, 18 (30%) the prediction, early detection or stratification of sepsis, 13 (22%) the prediction of treatment response, four (7%) the prediction of antibiotic resistance, three (5%) the choice of antibiotic regimen and two (3%) the choice of a combination antiretroviral therapy. The ML-CDSS were developed for intensive care units (n = 24, 40%), ID consultation (n = 15, 25%), medical or surgical wards (n = 13, 20%), emergency department (n = 4, 7%), primary care (n = 3, 5%) and antimicrobial stewardship (n = 1, 2%). Fifty-three ML-CDSS (88%) were developed using data from high-income countries and seven (12%) with data from low- and middle-income countries (LMIC). The evaluation of ML-CDSS was limited to measures of performance (e.g. sensitivity, specificity) for 57 ML-CDSS (95%) and included data in clinical practice for three (5%).

IMPLICATIONS

Considering comprehensive patient data from socioeconomically diverse healthcare settings, including primary care and LMICs, may improve the ability of ML-CDSS to suggest decisions adapted to various clinical contexts. Currents gaps identified in the evaluation of ML-CDSS must also be addressed in order to know the potential impact of such tools for clinicians and patients.

Super-resolution microscopy reveals significant impact of M2e-specific monoclonal antibodies on influenza A virus filament formation at the host cell surface

Influenza A virions are highly pleomorphic, exhibiting either spherical or filamentous morphology. The influenza A virus strain A/Udorn/72 (H3N2) produces copious amounts of long filaments on the surface of infected cells where matrix protein 1 (M1) and 2 (M2) play a key role in virus filament formation. Previously, it was shown that an anti-M2 ectodomain (M2e) antibody could inhibit A/Udorn/72 virus filament formation. However, the study of these structures is limited by their small size and complex structure. Here, we show that M2e-specific IgG1 and IgG2a mouse monoclonal antibodies can reduce influenza A/Udorn/72 virus plaque growth and infectivity in vitro. Using Immuno-staining combined with super-resolution microscopy that allows us to study structures beyond the diffraction limit, we report that M2 is localized at the base of viral filaments that emerge from the membrane of infected cells. Filament formation was inhibited by treatment of A/Udorn/72 infected cells with M2e-specific IgG2a and IgG1 monoclonal antibodies and resulted in fragmentation of pre-existing filaments. We conclude that M2e-specific IgGs can reduce filamentous influenza A virus replication in vitro and suggest that in vitro inhibition of A/Udorn/72 virus replication by M2e-specific antibodies correlates with the inhibition of filament formation on the surface of infected cells.