DRPnet: automated particle picking in cryo-electron micrographs using deep regression.

BACKGROUND: Identification and selection of protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based particle picking network to automatically detect particle centers from cryoEM micrographs. This is a challenging task due to the nature of cryoEM data, having low signal-to-noise ratios with variable particle sizes, shapes, distributions, grayscale variations as well as other undesirable artifacts. RESULTS: We propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. This approach, entitled Deep Regression Picker Network or "DRPnet", is simple but very effective in recognizing different particle sizes, shapes, distributions and grayscale patterns corresponding to 2D views of 3D particles. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined to reduce false particle detections by the second classification CNN. DRPnet's first CNN pretrained with only a single cryoEM dataset can be used to detect particles from different datasets without retraining. Compared to RELION template-based autopicking, DRPnet results in better particle picking performance with drastically reduced user interactions and processing time. DRPnet also outperforms the state-of-the-art particle picking networks in terms of the supervised detection evaluation metrics recall, precision, and F-measure. To further highlight quality of the picked particle sets, we compute and present additional performance metrics assessing the resulting 3D reconstructions such as number of 2D class averages, efficiency/angular coverage, Rosenthal-Henderson plots and local/global 3D reconstruction resolution. CONCLUSION: DRPnet shows greatly improved time-savings to generate an initial particle dataset compared to manual picking, followed by template-based autopicking. Compared to other networks, DRPnet has equivalent or better performance. DRPnet excels on cryoEM datasets that have low contrast or clumped particles. Evaluating other performance metrics, DRPnet is useful for higher resolution 3D reconstructions with decreased particle numbers or unknown symmetry, detecting particles with better angular orientation coverage.

Malaria Screener: a smartphone application for automated malaria screening.

BACKGROUND: Light microscopy is often used for malaria diagnosis in the field. However, it is time-consuming and quality of the results depends heavily on the skill of microscopists. Automating malaria light microscopy is a promising solution, but it still remains a challenge and an active area of research. Current tools are often expensive and involve sophisticated hardware components, which makes it hard to deploy them in resource-limited areas. RESULTS: We designed an Android mobile application called Malaria Screener, which makes smartphones an affordable yet effective solution for automated malaria light microscopy. The mobile app utilizes high-resolution cameras and computing power of modern smartphones to screen both thin and thick blood smear images for P. falciparum parasites. Malaria Screener combines image acquisition, smear image analysis, and result visualization in its slide screening process, and is equipped with a database to provide easy access to the acquired data. CONCLUSION: Malaria Screener makes the screening process faster, more consistent, and less dependent on human expertise. The app is modular, allowing other research groups to integrate their methods and models for image processing and machine learning, while acquiring and analyzing their data.

Vasoactive agonists exert dynamic and coordinated effects on vascular smooth muscle cell elasticity, cytoskeletal remodelling and adhesion.

In this study, we examined the ability of vasoactive agonists to induce dynamic changes in vascular smooth muscle cell (VSMC) elasticity and adhesion, and tested the hypothesis that these events are coordinated with rapid remodelling of the cortical cytoskeleton. Real-time measurement of cell elasticity was performed with atomic force microscopy (AFM) and adhesion was assessed with AFM probes coated with fibronectin (FN). Temporal data were analysed using an Eigen-decomposition method. Elasticity in VSMCs displayed temporal oscillations with three components at approximately 0.001, 0.004 and 0.07 Hz, respectively. Similarly, adhesion displayed a similar oscillatory pattern. Angiotensin II (ANG II, 10(-6) M) increased (+100%) the amplitude of the oscillations, whereas the vasodilator adenosine (ADO, 10(-4) M) reduced oscillation amplitude (-30%). To test whether the oscillatory changes were related to the architectural alterations in cortical cytoskeleton, the topography of the submembranous actin cytoskeleton (100-300 nm depth) was acquired with AFM. These data were analysed to compare cortical actin fibre distribution and orientation before and after treatment with vasoactive agonists. The results showed that ANG II increased the density of stress fibres by 23%, while ADO decreased the density of the stress fibres by 45%. AFM data were supported by Western blot and confocal microscopy. Collectively, these observations indicate that VSMC cytoskeletal structure and adhesion to the extracellular matrix are dynamically altered in response to agonist stimulation. Thus, vasoactive agonists probably invoke unique mechanisms that dynamically alter the behaviour and structure of both the VSMC cytoskeleton and focal adhesions to efficiently support the normal contractile behaviour of VSMCs.

REDESIGN: RDF-based Differential Signaling Framework for Precision Medicine Analytics.

Pathway-based analysis holds promise to be instrumental in precision and personalized medicine analytics. However, the majority of pathway-based analysis methods utilize "fixed" or "rigid" data sets that limit their ability to account for complex biological inter-dependencies. Here, we present REDESIGN: RDF-based Differential Signaling Pathway informatics framework. The distinctive feature of the REDESIGN is that it is designed to run on "flexible" ontology-enabled data sets of curated signal transduction pathway maps to uncover high explanatory differential pathway mechanisms on gene-to-gene level. The experiments on two morphoproteomic cases demonstrated REDESIGN's capability to generate actionable hypotheses in precision/personalized medicine analytics.

Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy.

Despite the remarkable progress that has been made to reduce global malaria mortality by 29% in the past 5 years, malaria is still a serious global health problem. Inadequate diagnostics is one of the major obstacles in fighting the disease. An automated system for malaria diagnosis can help to make malaria screening faster and more reliable. We present an automated system to detect and segment red blood cells (RBCs) and identify infected cells in Wright-Giemsa stained thin blood smears. Specifically, using image analysis and machine learning techniques, we process digital images of thin blood smears to determine the parasitemia in each smear. We use a cell extraction method to segment RBCs, in particular overlapping cells. We show that a combination of RGB color and texture features outperforms other features. We evaluate our method on microscopic blood smear images from human and mouse and show that it outperforms other techniques. For human cells, we measure an absolute error of 1.18% between the true and the automatic parasite counts. For mouse cells, our automatic counts correlate well with expert and flow cytometry counts. This makes our system the first one to work for both human and mouse.

PathEdEx - Uncovering High-explanatory Visual Diagnostics Heuristics Using Digital Pathology and Multiscale Gaze Data.

BACKGROUND: Visual heuristics of pathology diagnosis is a largely unexplored area where reported studies only provided a qualitative insight into the subject. Uncovering and quantifying pathology visual and nonvisual diagnostic patterns have great potential to improve clinical outcomes and avoid diagnostic pitfalls. METHODS: Here, we present PathEdEx, an informatics computational framework that incorporates whole-slide digital pathology imaging with multiscale gaze-tracking technology to create web-based interactive pathology educational atlases and to datamine visual and nonvisual diagnostic heuristics. RESULTS: We demonstrate the capabilities of PathEdEx for mining visual and nonvisual diagnostic heuristics using the first PathEdEx volume of a hematopathology atlas. We conducted a quantitative study on the time dynamics of zooming and panning operations utilized by experts and novices to come to the correct diagnosis. We then performed association rule mining to determine sets of diagnostic factors that consistently result in a correct diagnosis, and studied differences in diagnostic strategies across different levels of pathology expertise using Markov chain (MC) modeling and MC Monte Carlo simulations. To perform these studies, we translated raw gaze points to high-explanatory semantic labels that represent pathology diagnostic clues. Therefore, the outcome of these studies is readily transformed into narrative descriptors for direct use in pathology education and practice. CONCLUSION: PathEdEx framework can be used to capture best practices of pathology visual and nonvisual diagnostic heuristics that can be passed over to the next generation of pathologists and have potential to streamline implementation of precision diagnostics in precision medicine settings.

Accuracy and Reproducibility of Nuclear/Cytoplasmic Ratio Assessments in Urinary Cytology Specimens.

BACKGROUND: Evaluation of the nuclear to cytoplasmic ratio is commonly used for assessment of the presence of malignancy and for grading and typing of malignant neoplasms. Despite its widespread usage, little information exists regarding the accuracy and reproducibility of non-automated assessment. METHODS: Forty-seven cells obtained from Papanicolaou stained urine cytologies were assessed by quantitative image analysis for nuclear area and cell area. The nuclear/cytoplasmic ratio was calculated. Visual estimates of the N/C ratio were made by six pathologists. Statistical analysis was performed to determine accuracy, precision, and interrater reliability. RESULTS: True N/C ratios varied from 0.02 to 0.81. 27% of cases demonstrated a true N/C ratio between 0.5 and 0.7. Quantitative estimates of N/C ratios were less precise and less accurate at high N/C ratios. The coefficient of variation was 27%. The majority of raters demonstrated decreased accuracy and precision of estimates as N/C ratio increased. Overall classification accuracy was 73%. Accuracy of classification was 53% for cases with a true N/C ratio between 0.4 and 0.8. Absolute interrater agreement was 75%. Chance corrected agreement (kappa) was 0.54. CONCLUSIONS: Visual quantitation of N/C ratio showed only a fair correlation with actual N/C ratio with correlation decreasing with increasing N/C ratio. In the critical range, 0.5-0.7 N/C ratio both interobserver correlation and correlation with true N/C ratio may be insufficiently accurate for precise category assignment as used in the Paris System. Diagn. Cytopathol. 2017;45:107-112. © 2016 Wiley Periodicals, Inc.

RDF SKETCH MAPS - KNOWLEDGE COMPLEXITY REDUCTION FOR PRECISION MEDICINE ANALYTICS.

Realization of precision medicine ideas requires significant research effort to be able to spot subtle differences in complex diseases at the molecular level to develop personalized therapies. It is especially important in many cases of highly heterogeneous cancers. Precision diagnostics and therapeutics of such diseases demands interrogation of vast amounts of biological knowledge coupled with novel analytic methodologies. For instance, pathway-based approaches can shed light on the way tumorigenesis takes place in individual patient cases and pinpoint to novel drug targets. However, comprehensive analysis of hundreds of pathways and thousands of genes creates a combinatorial explosion, that is challenging for medical practitioners to handle at the point of care. Here we extend our previous work on mapping clinical omics data to curated Resource Description Framework (RDF) knowledge bases to derive influence diagrams of interrelationships of biomarker proteins, diseases and signal transduction pathways for personalized theranostics. We present RDF Sketch Maps - a computational method to reduce knowledge complexity for precision medicine analytics. The method of RDF Sketch Maps is inspired by the way a sketch artist conveys only important visual information and discards other unnecessary details. In our case, we compute and retain only so-called RDF Edges - places with highly important diagnostic and therapeutic information. To do this we utilize 35 maps of human signal transduction pathways by transforming 300 KEGG maps into highly processable RDF knowledge base. We have demonstrated potential clinical utility of RDF Sketch Maps in hematopoietic cancers, including analysis of pathways associated with Hairy Cell Leukemia (HCL) and Chronic Myeloid Leukemia (CML) where we achieved up to 20-fold reduction in the number of biological entities to be analyzed, while retaining most likely important entities. In experiments with pathways associated with HCL a generated RDF Sketch Map of the top 30% paths retained important information about signaling cascades leading to activation of proto-oncogene BRAF, which is usually associated with a different cancer, melanoma. Recent reports of successful treatments of HCL patients by the BRAF-targeted drug vemurafenib support the validity of the RDF Sketch Maps findings. We therefore believe that RDF Sketch Maps will be invaluable for hypothesis generation for precision diagnostics and therapeutics as well as drug repurposing studies.

Peroxiredoxin 6 (Prdx6) supports NADPH oxidase1 (Nox1)-based superoxide generation and cell migration.

Nox1 is an abundant source of reactive oxygen species (ROS) in colon epithelium recently shown to function in wound healing and epithelial homeostasis. We identified Peroxiredoxin 6 (Prdx6) as a novel binding partner of Nox activator 1 (Noxa1) in yeast two-hybrid screening experiments using the Noxa1 SH3 domain as bait. Prdx6 is a unique member of the Prdx antioxidant enzyme family exhibiting both glutathione peroxidase and phospholipase A2 activities. We confirmed this interaction in cells overexpressing both proteins, showing Prdx6 binds to and stabilizes wild type Noxa1, but not the SH3 domain mutant form, Noxa1 W436R. We demonstrated in several cell models that Prdx6 knockdown suppresses Nox1 activity, whereas enhanced Prdx6 expression supports higher Nox1-derived superoxide production. Both peroxidase- and lipase-deficient mutant forms of Prdx6 (Prdx6 C47S and S32A, respectively) failed to bind to or stabilize Nox1 components or support Nox1-mediated superoxide generation. Furthermore, the transition-state substrate analogue inhibitor of Prdx6 phospholipase A2 activity (MJ-33) was shown to suppress Nox1 activity, suggesting Nox1 activity is regulated by the phospholipase activity of Prdx6. Finally, wild type Prdx6, but not lipase or peroxidase mutant forms, supports Nox1-mediated cell migration in the HCT-116 colon epithelial cell model of wound closure. These findings highlight a novel pathway in which this antioxidant enzyme positively regulates an oxidant-generating system to support cell migration and wound healing.

Influence of membrane cholesterol and substrate elasticity on endothelial cell spreading behavior.

Interactions between implanted materials and the surrounding host cells critically affect the fate of bioengineered materials. In this study, the biomechanical response of bovine aortic endothelial cells (BAECs) with different membrane cholesterol levels to polyacrylamide (PA) gels was investigated by measuring cell adhesion and spreading behaviors at varying PA elasticity. The elasticity of gel substrates was manipulated by cross-linker content. Type I collagen (COL1) was coated on PA gel to provide a biologically functional environment for cell spreading. Precise quantitative characterization of changes in cell area and perimeter of cells across two treatments and three bioengineered substrates were determined using a customized software developed for computational image analysis. We found that the initial response of endothelial cells to changes in substrate elasticity was determined by membrane cholesterol levels, and that the extent of endothelial cell spreading increases with membrane cholesterol content. All of the BAECs with different cholesterol levels showed little growth on substrates with elasticity below 20 kPa, but increased spreading at higher substrate elasticity. Cholesterol-depleted cells were consistently smaller than control and cholesterol-enriched cells regardless of substrate elasticity. These observations indicate that membrane cholesterol plays an important role in cell spreading on soft biomimetic materials constructed with appropriate elasticity.

Segmentation and classification of cell cycle phases in fluorescence imaging.

Current chemical biology methods for studying spatiotemporal correlation between biochemical networks and cell cycle phase progression in live-cells typically use fluorescence-based imaging of fusion proteins. Stable cell lines expressing fluorescently tagged protein GFP-PCNA produce rich, dynamically varying sub-cellular foci patterns characterizing the cell cycle phases, including the progress during the S-phase. Variable fluorescence patterns, drastic changes in SNR, shape and position changes and abundance of touching cells require sophisticated algorithms for reliable automatic segmentation and cell cycle classification. We extend the recently proposed graph partitioning active contours (GPAC) for fluorescence-based nucleus segmentation using regional density functions and dramatically improve its efficiency, making it scalable for high content microscopy imaging. We utilize surface shape properties of GFP-PCNA intensity field to obtain descriptors of foci patterns and perform automated cell cycle phase classification, and give quantitative performance by comparing our results to manually labeled data.

Tracking cells in Life Cell Imaging videos using topological alignments.

BACKGROUND: With the increasing availability of live cell imaging technology, tracking cells and other moving objects in live cell videos has become a major challenge for bioimage informatics. An inherent problem for most cell tracking algorithms is over- or under-segmentation of cells - many algorithms tend to recognize one cell as several cells or vice versa. RESULTS: We propose to approach this problem through so-called topological alignments, which we apply to address the problem of linking segmentations of two consecutive frames in the video sequence. Starting from the output of a conventional segmentation procedure, we align pairs of consecutive frames through assigning sets of segments in one frame to sets of segments in the next frame. We achieve this through finding maximum weighted solutions to a generalized "bipartite matching" between two hierarchies of segments, where we derive weights from relative overlap scores of convex hulls of sets of segments. For solving the matching task, we rely on an integer linear program. CONCLUSION: Practical experiments demonstrate that the matching task can be solved efficiently in practice, and that our method is both effective and useful for tracking cells in data sets derived from a so-called Large Scale Digital Cell Analysis System (LSDCAS). AVAILABILITY: The source code of the implementation is available for download from http://www.picb.ac.cn/patterns/Software/topaln.

Multi-feature contour evolution for automatic live cell segmentation in time lapse imagery.

Cell boundary segmentation in live cell image sequences is the first step towards quantitative analysis of cell motion and behavior. The time lapse microscopy imaging produces large volumes of image sequence collections which requires fast and robust automatic segmentation of cell boundaries to utilize further automated tools such as cell tracking to quantify and classify cell behavior. This paper presents a methodology that is based on utilizing the temporal context of the cell image sequences to accurately delineate the boundaries of non-homogeneous cells. A novel flux tensor-based detection of moving cells provides initial localization that is further refined by a multi-feature level set-based method using an efficient additive operator splitting scheme. The segmentation result is processed by a watershed-based algorithm to avoid merging boundaries of neighboring cells. By utilizing robust features, the level-set algorithm produces accurate segmentation for non-homogeneous cells with concave shapes and varying intensities.