Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells, and parasite life stage categorization from unprocessed, heterogeneous smear images. Based on a pretrained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. Application of a residual neural network-50 model to infected cells also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Finally, combining our method with a regression model successfully recapitulates intraerythrocytic developmental cycle with accurate lifecycle stage categorization. Combined with a mobile-friendly web-based interface, called PlasmoCount, our method permits rapid navigation through and review of results for quality assurance. By standardizing assessment of Giemsa smears, our method markedly improves inspection reproducibility and presents a realistic route to both routine lab and future field-based automated malaria diagnosis.

In this paper, we summarize a global survey of 484 participants of the imaging community, conducted in 2020 through the NIH-funded Center for Open Bioimage Analysis (COBA). This 23-question survey covered experience with image analysis, scientific background and demographics, and views and requests from different members of the imaging community. Through open-ended questions, we asked the community to provide feedback for the open-source tool developers and tool user groups. The community’s requests for tool developers include general improvement of tool documentation and easy-to-follow tutorials. Respondents encourage tool users to follow the best practice guidelines for imaging and ask their image analysis questions on the Scientific Community Image Forum (forum.image.sc). We analyzed the community’s preferred method of learning based on level of computational proficiency and work description. In general, written step-by-step and video tutorials are preferred methods of learning by the community, followed by interactive webinars and office hours with an expert. There is also enthusiasm for a centralized online location for existing educational resources. The survey results will help the community, especially developers, trainers, and organizations like COBA, decide how to structure and prioritize their efforts.

Fluorescence microscopy is a critical tool for cell biology studies on bacterial cell division and morphogenesis. Because the analysis of fluorescence microscopy images evolved beyond initial qualitative studies, numerous images analysis tools were developed to extract quantitative parameters on cell morphology and organization. To understand cellular processes required for bacterial growth and division, it is particularly important to perform such analysis in the context of cell cycle progression. However, manual assignment of cell cycle stages is laborious and prone to user bias. Although cell elongation can be used as a proxy for cell cycle progression in rod-shaped or ovoid bacteria, that is not the case for cocci, such as Staphylococcus aureus. Here, we describe eHooke, an image analysis framework developed specifically for automated analysis of microscopy images of spherical bacterial cells. eHooke contains a trained artificial neural network to automatically classify the cell cycle phase of individual S. aureus cells. Users can then apply various functions to obtain biologically relevant information on morphological features of individual cells and cellular localization of proteins, in the context of the cell cycle.

Thick specimens, as encountered in cryo-scanning transmission electron tomography, offer special challenges to conventional reconstruction workflows. The visibility of features, including gold nanoparticles introduced as fiducial markers, varies strongly through the tilt series. As a result, tedious manual refinement may be required in order to produce a successful alignment. Information from highly tilted views must often be excluded to the detriment of axial resolution in the reconstruction. We introduce here an approach to tilt series alignment based on identification of fiducial particle clusters that transform coherently in rotation, essentially those that lie at similar depth. Clusters are identified by comparison of tilted views with a single untilted reference, rather than with adjacent tilts. The software, called ClusterAlign, proves robust to poor signal to noise ratio and varying visibility of the individual fiducials and is successful in carrying the alignment to the ends of the tilt series where other methods tend to fail. ClusterAlign may be used to generate a list of tracked fiducials, to align a tilt series, or to perform a complete 3D reconstruction. Tools to evaluate alignment error by projection matching are included. Execution involves no manual intervention, and adherence to standard file formats facilitates an interface with other software, particularly IMOD/etomo, tomo3d, and tomoalign.

Cryo-soft-X-ray tomography is being increasingly used in biological research to study the morphology of cellular compartments and how they change in response to different stimuli, such as viral infections. Segmentation of these compartments is limited by time-consuming manual tools or machine learning algorithms that require extensive time and effort to train. Here we describe Contour, a new, easy-to-use, highly automated segmentation tool that enables accelerated segmentation of tomograms to delineate distinct cellular compartments. Using Contour, cellular structures can be segmented based on their projection intensity and geometrical width by applying a threshold range to the image and excluding noise smaller in width than the cellular compartments of interest. This method is less laborious and less prone to errors from human judgement than current tools that require features to be manually traced, and it does not require training datasets as would machine-learning driven segmentation. We show that high-contrast compartments such as mitochondria, lipid droplets, and features at the cell surface can be easily segmented with this technique in the context of investigating herpes simplex virus 1 infection. Contour can extract geometric measurements from 3D segmented volumes, providing a new method to quantitate cryo-soft-X-ray tomography data. Contour can be freely downloaded at github.com/kamallouisnahas/Contour.

Spatially resolved transcriptomics (SRT) is a growing field that links gene expression to anatomical context. SRT approaches that use next-generation sequencing (NGS) combine RNA sequencing with histological or fluorescent imaging to generate spatial maps of gene expression in intact tissue sections. These technologies directly couple gene expression measurements with high-resolution histological or immunofluorescent images that contain rich morphological information about the tissue under study. While broad access to NGS-based spatial transcriptomic technology is now commercially available through the Visium platform from the vendor 10× Genomics, computational tools for extracting image-derived metrics for integration with gene expression data remain limited. We developed VistoSeg as a MATLAB pipeline to process, analyze and interactively visualize the high-resolution images generated in the Visium platform. VistoSeg outputs can be easily integrated with accompanying transcriptomic data to facilitate downstream analyses in common programing languages including R and Python. VistoSeg provides user-friendly tools for integrating image-derived metrics from histological and immunofluorescent images with spatially resolved gene expression data. Integration of this data enhances the ability to understand the transcriptional landscape within tissue architecture. VistoSeg is freely available at http://research.libd.org/VistoSeg/.

Image-processing pipelines require the design of complex workflows combining many different steps that bring the raw acquired data to a final result with biological meaning. In the image-processing domain of cryo-electron microscopy single-particle analysis (cryo-EM SPA), hundreds of steps must be performed to obtain the three-dimensional structure of a biological macromolecule by integrating data spread over thousands of micrographs containing millions of copies of allegedly the same macromolecule. The execution of such complicated workflows demands a specific tool to keep track of all these steps performed. Additionally, due to the extremely low signal-to-noise ratio (SNR), the estimation of any image parameter is heavily affected by noise resulting in a significant fraction of incorrect estimates. Although low SNR and processing millions of images by hundreds of sequential steps requiring substantial computational resources are specific to cryo-EM, these characteristics may be shared by other biological imaging domains. Here, we present Scipion, a Python generic open-source workflow engine specifically adapted for image processing. Its main characteristics are: (a) interoperability, (b) smart object model, (c) gluing operations, (d) comparison operations, (e) wide set of domain-specific operations, (f) execution in streaming, (g) smooth integration in high-performance computing environments, (h) execution with and without graphical capabilities, (i) flexible visualization, (j) user authentication and private access to private data, (k) scripting capabilities, (l) high performance, (m) traceability, (n) reproducibility, (o) self-reporting, (p) reusability, (q) extensibility, (r) software updates, and (s) non-restrictive software licensing.

High-resolution and multiplexed imaging techniques are giving us an increasingly detailed observation of a biological system. However, sharing, exploring, and customizing the visualization of large multidimensional images can be a challenge. Here, we introduce Samui, a performant and interactive image visualization tool that runs completely in the web browser. Samui is specifically designed for fast image visualization and annotation and enables users to browse through large images and their selected features within seconds of receiving a link. We demonstrate the broad utility of Samui with images generated with two platforms: Vizgen MERFISH and 10x Genomics Visium Spatial Gene Expression. Samui along with example datasets is available at https://samuibrowser.com.

Drug discovery uses high throughput screening to identify compounds that interact with a molecular target or that alter a phenotype favorably. The cautious selection of molecules used for such a screening is instrumental and is tightly related to the hit rate. In this work, we wondered if cell painting, a general-purpose image-based assay, could be used as an efficient proxy for compound selection, thus increasing the success rate of a specific assay. To this end, we considered cell painting images with 30,000 molecules treatments, and selected compounds that produced a visual effect close to the positive control of an assay, by using the Frechet Inception Distance. We then compared the hit rates of such a preselection with what was actually obtained in real screening campaigns. As a result, cell painting would have permitted a significant increase in the success rate and, even for one of the assays, would have allowed to reach 80% of the hits with 10 times fewer compounds to test. We conclude that images of a cell painting assay can be directly used for compound selection prior to screening, and we provide a simple quantitative approach in order to do so.

Detection of RNA spots in single-molecule fluorescence in-situ hybridization microscopy images remains a difficult task, especially when applied to large volumes of data. The variable intensity of RNA spots combined with the high noise level of the images often requires manual adjustment of the spot detection thresholds for each image. In this work, we introduce DeepSpot, a Deep Learning-based tool specifically designed for RNA spot enhancement that enables spot detection without the need to resort to image per image parameter tuning. We show how our method can enable downstream accurate spot detection. DeepSpot’s architecture is inspired by small object detection approaches. It incorporates dilated convolutions into a module specifically designed for context aggregation for small object and uses Residual Convolutions to propagate this information along the network. This enables DeepSpot to enhance all RNA spots to the same intensity, and thus circumvents the need for parameter tuning. We evaluated how easily spots can be detected in images enhanced with our method by testing DeepSpot on 20 simulated and 3 experimental datasets, and showed that accuracy of more than 97% is achieved. Moreover, comparison with alternative deep learning approaches for mRNA spot detection (deepBlink) indicated that DeepSpot provides more precise mRNA detection. In addition, we generated single-molecule fluorescence in-situ hybridization images of mouse fibroblasts in a wound healing assay to evaluate whether DeepSpot enhancement can enable seamless mRNA spot detection and thus streamline studies of localized mRNA expression in cells.

A common task in cryo-electron microscopy data processing is to compare three-dimensional density maps of macromolecules. In this paper, we propose an algorithm for aligning three-dimensional density maps, which exploits common lines between projection images of the maps. The algorithm is fully automatic and handles rotations, reflections (handedness), and translations between the maps. In addition, the algorithm is applicable to any type of molecular symmetry without requiring any information regarding the symmetry of the maps. We evaluate our alignment algorithm on publicly available density maps, demonstrating its accuracy and efficiency. The algorithm is available at https://github.com/ShkolniskyLab/emalign.

Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique used to probe the local environment of fluorophores. The fit-free phasor approach to FLIM data is increasingly being used due to its ease of interpretation. To date, no open-source graphical user interface (GUI) for phasor analysis of FLIM data is available in Python, thus limiting the widespread use of phasor analysis in biomedical research. Here, we present Fluorescence Lifetime Ultimate Explorer (FLUTE), a Python GUI that is designed to fill this gap. FLUTE simplifies and automates many aspects of the analysis of FLIM data acquired in the time domain, such as calibrating the FLIM data, performing interactive exploration of the phasor plot, displaying phasor plots and FLIM images with different lifetime contrasts simultaneously, and calculating the distance from known molecular species. After applying desired filters and thresholds, the final edited datasets can be exported for further user-specific analysis. FLUTE has been tested using several FLIM datasets including autofluorescence of zebrafish embryos and in vitro cells. In summary, our user-friendly GUI extends the advantages of phasor plotting by making the data visualization and analysis easy and interactive, allows for analysis of large FLIM datasets, and accelerates FLIM analysis for non-specialized labs.

Principal component analysis (PCA) plays an important role in the analysis of cryo-electron microscopy (cryo-EM) images for various tasks such as classification, denoising, compression, and ab initio modeling. We introduce a fast method for estimating a compressed representation of the 2-D covariance matrix of noisy cryo-EM projection images affected by radial point spread functions that enables fast PCA computation. Our method is based on a new algorithm for expanding images in the Fourier–Bessel basis (the harmonics on the disk), which provides a convenient way to handle the effect of the contrast transfer functions. For $ N $ images of size $ L\times L $ , our method has time complexity $ O\left({NL}^3+{L}^4\right) $ and space complexity $ O\left({NL}^2+{L}^3\right) $ . In contrast to previous work, these complexities are independent of the number of different contrast transfer functions of the images. We demonstrate our approach on synthetic and experimental data and show acceleration by factors of up to two orders of magnitude.

This paper presents a deep-learning-based workflow to detect synapses and predict their neurotransmitter type in the primitive chordate Ciona intestinalis (Ciona) electron microscopic (EM) images. Identifying synapses from EM images to build a full map of connections between neurons is a labor-intensive process and requires significant domain expertise. Automation of synapse classification would hasten the generation and analysis of connectomes. Furthermore, inferences concerning neuron type and function from synapse features are in many cases difficult to make. Finding the connection between synapse structure and function is an important step in fully understanding a connectome. Class Activation Maps derived from the convolutional neural network provide insights on important features of synapses based on cell type and function. The main contribution of this work is in the differentiation of synapses by neurotransmitter type through the structural information in their EM images. This enables the prediction of neurotransmitter types for neurons in Ciona, which were previously unknown. The prediction model with code is available on GitHub.

To overcome the physical barriers caused by light diffraction, super-resolution techniques are often applied in fluorescence microscopy. State-of-the-art approaches require specific and often demanding acquisition conditions to achieve adequate levels of both spatial and temporal resolution. Analyzing the stochastic fluctuations of the fluorescent molecules provides a solution to the aforementioned limitations, as sufficiently high spatio-temporal resolution for live-cell imaging can be achieved using common microscopes and conventional fluorescent dyes. Based on this idea, we present COL0RME, a method for covariance-based $ {\mathrm{\ell}}_0 $ super-resolution microscopy with intensity estimation, which achieves good spatio-temporal resolution by solving a sparse optimization problem in the covariance domain and discuss automatic parameter selection strategies. The method is composed of two steps: the former where both the emitters’ independence and the sparse distribution of the fluorescent molecules are exploited to provide an accurate localization; the latter where real intensity values are estimated given the computed support. The paper is furnished with several numerical results both on synthetic and real fluorescence microscopy images and several comparisons with state-of-the art approaches are provided. Our results show that COL0RME outperforms competing methods exploiting analogously temporal fluctuations; in particular, it achieves better localization, reduces background artifacts, and avoids fine parameter tuning.

Eukaryotic cells are constantly subject to DNA damage, often with detrimental consequences for the health of the organism. Cells mitigate this DNA damage through a variety of repair pathways involving a diverse and large number of different proteins. To better understand the cellular response to DNA damage, one needs accurate measurements of the accumulation, retention, and dissipation timescales of these repair proteins. Here, we describe an automated implementation of the “quantitation of fluorescence accumulation after DNA damage” method that greatly enhances the analysis and quantitation of the widely used technique known as laser microirradiation, which is used to study the recruitment of DNA repair proteins to sites of DNA damage. This open-source implementation (“qFADD.py”) is available as a stand-alone software package that can be run on laptops or computer clusters. Our implementation includes corrections for nuclear drift, an automated grid search for the model of a best fit, and the ability to model both horizontal striping and speckle experiments. To improve statistical rigor, the grid-search algorithm also includes automated simulation of replicates. As a practical example, we present and discuss the recruitment dynamics of the early responder PARP1 to DNA damage sites.

An emergent volume electron microscopy technique called cryogenic serial plasma focused ion beam milling scanning electron microscopy (pFIB/SEM) can decipher complex biological structures by building a three-dimensional picture of biological samples at mesoscale resolution. This is achieved by collecting consecutive SEM images after successive rounds of FIB milling that expose a new surface after each milling step. Due to instrumental limitations, some image processing is necessary before 3D visualization and analysis of the data is possible. SEM images are affected by noise, drift, and charging effects, that can make precise 3D reconstruction of biological features difficult. This article presents Okapi-EM, an open-source napari plugin developed to process and analyze cryogenic serial pFIB/SEM images. Okapi-EM enables automated image registration of slices, evaluation of image quality metrics specific to pFIB-SEM imaging, and mitigation of charging artifacts. Implementation of Okapi-EM within the napari framework ensures that the tools are both user- and developer-friendly, through provision of a graphical user interface and access to Python programming.

Cryogenic electron tomography (cryoET) is capable of determining in situ biological structures of molecular complexes at near-atomic resolution by averaging half a million subtomograms. While abundant complexes/particles are often clustered in arrays, precisely locating and seamlessly averaging such particles across many tomograms present major challenges. Here, we developed TomoNet, a software package with a modern graphical user interface to carry out the entire pipeline of cryoET and subtomogram averaging to achieve high resolution. TomoNet features built-in automatic particle picking and three-dimensional (3D) classification functions and integrates commonly used packages to streamline high-resolution subtomogram averaging for structures in 1D, 2D, or 3D arrays. Automatic particle picking is accomplished in two complementary ways: one based on template matching and the other using deep learning. TomoNet’s hierarchical file organization and visual display facilitate efficient data management as required for large cryoET datasets. Applications of TomoNet to three types of datasets demonstrate its capability of efficient and accurate particle picking on flexible and imperfect lattices to obtain high-resolution 3D biological structures: virus-like particles, bacterial surface layers within cellular lamellae, and membranes decorated with nuclear egress protein complexes. These results demonstrate TomoNet’s potential for broad applications to various cryoET projects targeting high-resolution in situ structures.

In this article, we propose an algorithm for aligning three-dimensional objects when represented as density maps, motivated by applications in cryogenic electron microscopy. The algorithm is based on minimizing the 1-Wasserstein distance between the density maps after a rigid transformation. The induced loss function enjoys a more benign landscape than its Euclidean counterpart and Bayesian optimization is employed for computation. Numerical experiments show improved accuracy and efficiency over existing algorithms on the alignment of real protein molecules. In the context of aligning heterogeneous pairs, we illustrate a potential need for new distance functions.

Microscopy is a widely used method in biological research to observe the morphology and structure of cells. Amongst the plethora of microscopy techniques, fluorescent labeling with dyes or antibodies is the most popular method for revealing specific cellular organelles. However, fluorescent labeling also introduces new challenges to cellular observation, as it increases the workload, and the process may result in nonspecific labeling. Recent advances in deep visual learning have shown that there are systematic relationships between fluorescent and bright-field images, thus facilitating image translation between the two. In this article, we propose the cross-attention conditional generative adversarial network (XAcGAN) model. It employs state-of-the-art GANs (GANs) to solve the image translation task. The model uses supervised learning and combines attention-based networks to explore spatial information during translation. In addition, we demonstrate the successful application of XAcGAN to infer the health state of translated nuclei from bright-field microscopy images. The results show that our approach achieves excellent performance both in terms of image translation and nuclei state inference.