Hypothesis testing via integrated computer modeling and digital fluorescence microscopy

Computational modeling has the potential to add an entirely new approach to hypothesis testing in yeast cell biology. Here, we present a method for seamless integration of computational modeling with quantitative digital fluorescence microscopy. This integration is accomplished by developing computational models based on hypotheses for underlying cellular processes that may give rise to experimentally observed fluorescent protein localization patterns. Simulated fluorescence images are generated from the computational models of underlying cellular processes via a "model-convolution" process. These simulated images can then be directly compared to experimental fluorescence images in order to test the model. This method provides a framework for rigorous hypothesis testing in yeast cell biology via integrated mathematical modeling and digital fluorescence microscopy.     

A protocol to analyse cellular dynamics during plant development

In vivo microscopy generates images that contain complex information on the dynamic behaviour of three-dimensional (3D) objects. As a result, adapted mathematical and computational tools are required to help in their interpretation. Ideally, a complete software chain to study the dynamics of a complex 3D object should include: (i) the acquisition, (ii) the preprocessing and (iii) segmentation of the images, followed by (iv) a reconstruction in time and space and (v) the final quantitative analysis. Here, we have developed such a protocol to study cell dynamics at the shoot apical meristem in Arabidopsis. The protocol uses serial optical sections made with the confocal microscope. It includes specially designed algorithms to automate the identification of cell lineage and to analyse the quantitative behaviour of the meristem surface.     

Quantifying Intracellular Particle Flows by DIC Object Tracking

Label-free imaging techniques such as differential interference contrast (DIC) allow the observation of cells and large subcellular structures in their native, unperturbed states with minimal exposure to light. The development of robust computational image-analysis routines is vital to quantitative label-free imaging. The reliability of quantitative analysis of time-series microscopy data based on single-particle tracking relies on accurately detecting objects as distinct from the background, i.e., segmentation. Typical approaches to segmenting DIC images either involve converting images to those resembling phase contrast, mimicking the optics of DIC object formation, or using the morphological properties of objects. Here, we describe MATLAB based, single-particle tracking tool with a GUI for mobility analysis of objects from in vitro and in vivo DIC time-series microscopy. The tool integrates contrast enhancement with multiple modified Gaussian filters, automated threshold detection for segmentation and minimal distance-based two-dimensional single-particle tracking. We compare the relative performance of multiple filters and demonstrate the utility of the tool for DIC object tracking (DICOT). We quantify subcellular dynamics of a time series of Caenorhabditis elegans embryos in the one-celled stage by detecting birefringent yolk granules in the cytoplasm with high precision. The resulting two-dimensional map of oscillatory dynamics of granules quantifies the cytoplasmic flows driven by anaphasic spindle oscillations. The frequency of oscillations across the anterior-posterior (A-P) and transverse axes of the embryo correspond well with the reported frequency of spindle oscillations. We validate the quantitative accuracy of our method by tracking the in vitro diffusive mobility of micron-sized beads in glycerol solutions. Estimates of the diffusion coefficients of the granules are used to measure the viscosity of a dilution series of glycerol. Thus, our computational method is likely to be useful for both intracellular mobility and in vitro microrheology.     

The effect of overabundant projection directions on 3D reconstruction algorithms

The experimental process of collecting images from macromolecules in an electron microscope is such that it does not allow for prior specification of the angular distribution of the projection images. As a consequence, an uneven distribution of projection directions may occur. Concerns have been raised recently about the behavior of 3D reconstruction algorithms for the case of unevenly distributed projections. It has been illustrated on experimental data that in the case of a heavily uneven distribution of projection directions some algorithms tend to elongate the reconstructed volumes along the overloaded direction so much as to make a quantitative biological analysis impossible. In answer to these concerns we have developed a strategy for quantitative comparison and optimization of 3D reconstruction algorithms. We apply this strategy to quantitatively analyze algebraic reconstruction techniques (ART) with blobs, simultaneous iterative reconstruction techniques (SIRT) with voxels, and weighted backprojection (WBP). We show that the elongation artifacts that had been previously reported can be strongly reduced. With our specific choices for the free parameters of the three algorithms, WBP reconstructions tend to be inferior to those obtained with either SIRT or ART and the results obtained with ART are comparable to those with SIRT, but at a very small fraction of the computational cost of SIRT.     

A novel method for fast and statistically verified morphological characterization of filamentous fungi

Along with productivity and physiology, morphological growth behavior is the key parameter in bioprocess design for filamentous fungi. Despite complex interactions between fungal morphology, broth viscosity, mixing kinetics, transport characteristics and process productivity, morphology is still commonly tackled only by empirical trial-and-error techniques during strain selection and process development procedures. In fact, morphological growth characteristics are investigated by computational analysis of only a limited number of pre-selected microscopic images or via manual evaluation of images, which causes biased results and does not allow any automation or high-throughput quantification. To overcome the lack of tools for fast, reliable and quantitative morphological analysis, this work introduces a method enabling statistically verified quantification of fungal morphology in accordance with Quality by Design principles. The novel, high-throughput method presented here interlinks fully automated recording of microscopic images with a newly developed evaluation approach reducing the need for manual intervention to a minimum. Validity of results is ensured by concomitantly testing the acquired sample for representativeness by statistical inference via bootstrap analysis. The novel approach for statistical verification can be equally applied as control logic to automatically proceed with morphological analysis of a consecutive sample once user defined acceptance criteria are met. Hence, analysis time can be reduced to an absolute minimum. The quantitative potential of the developed methodology is demonstrated by characterizing the morphological growth behavior of two industrial Penicillium chrysogenum production strains in batch cultivation.     

Oufti: an integrated software package for high‐accuracy,high‐throughput quantitative microscopy analysis

With the realization that bacteria display phenotypic variability among cells and exhibit complex subcellular organization critical for cellular function and behavior, microscopy has re‐emerged as a primary tool in bacterial research during the last decade. However, the bottleneck in today's single‐cell studies is quantitative image analysis of cells and fluorescent signals. Here, we address current limitations through the development of Oufti, a stand‐alone, open‐source software package for automated measurements of microbial cells and fluorescence signals from microscopy images. Oufti provides computational solutions for tracking touching cells in confluent samples, handles various cell morphologies, offers algorithms for quantitative analysis of both diffraction and non‐diffraction‐limited fluorescence signals and is scalable for high‐throughput analysis of massive datasets, all with subpixel precision. All functionalities are integrated in a single package. The graphical user interface, which includes interactive modules for segmentation, image analysis and post‐processing analysis, makes the software broadly accessible to users irrespective of their computational skills.     

Back to the future: education for systems-level biologists

We describe a graduate course in quantitative biology that is based on original path-breaking papers in diverse areas of biology; each of these papers depends on quantitative reasoning and theory as well as experiment. Close reading and discussion of these papers allows students with backgrounds in physics, computational sciences or biology to learn essential ideas and to communicate in the languages of disciplines other than their own.     

A red line not to cross: Evaluating the limitation and properness of gel image tuning procedures

Currently, results of gel electrophoresis are commonly documented in digital formats by image acquisition instruments. In this study, gel images tuned by a common image processing software package, Photoshop, were assessed to understand the transforming algorithms and their impacts on quantitative analysis. TotalLab 100, an electrophoresis gel image analysis software package, was applied for image quantitation and evaluation. The three most frequently used image tuning functions—adjustments of the brightness, contrast, and grayscale span (level) of images—were investigated using both data generated from a standard grayscale tablet and an actual electrophoresis gel image. The influences of these procedures were analyzed for the grayscale transformation between the input and output images. Although all three procedures differentially improved the visualization of the input image, adjusting the contrast of images disrupted the quantitative information because of its nonlinear transforming algorithm. Under certain conditions, adjusting the brightness or the level of images could preserve the quantitative information because of the linear transforming algorithms. It was found that when the minimum and maximum grayscales of a gel image were recognized, using a commercial software package to maximally stretch the level may significantly improve the quality of a gel image without jeopardizing quantitative analysis.     

Reduction in required volume of imaging data and image reconstruction time for adaptive-illumination Fourier ptychographic microscopy

Fourier ptychographic microscopy (FPM) is a promising super-resolution computational imaging technology. It stitches a series of low-resolution (LR) images in the Fourier domain by an iterative method. Thus, it obtains a large field of view and high-resolution quantitative phase images. Owing to its capability to perform high-spatial bandwidth product imaging, FPM is widely used in the reconstruction of conventional static samples. However, the influence of the FPM imaging mechanism limits its application in high-speed dynamic imaging. To solve this problem, an adaptive-illumination FPM scheme using regional energy estimation is proposed. Starting with several captured real LR images, the energy distribution of all LR images is estimated, and select the measurement images with large information to perform FPM reconstruction. Simulation and experimental results show that the method produces efficient imaging performance and reduces the required volume of data to more than 65% while ensuring the quality of FPM reconstruction.     

Automated analysis of high‐content microscopy data with deep learning

Existing computational pipelines for quantitative analysis of high‐content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone‐arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open‐source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high‐content microscopy data.     

Morphology-Based Prediction of Osteogenic Differentiation Potential of Human Mesenchymal Stem Cells

Human bone marrow mesenchymal stem cells (hBMSCs) are widely used cell source for clinical bone regeneration. Achieving the greatest therapeutic effect is dependent on the osteogenic differentiation potential of the stem cells to be implanted. However, there are still no practical methods to characterize such potential non-invasively or previously. Monitoring cellular morphology is a practical and non-invasive approach for evaluating osteogenic potential. Unfortunately, such image-based approaches had been historically qualitative and requiring experienced interpretation. By combining the non-invasive attributes of microscopy with the latest technology allowing higher throughput and quantitative imaging metrics, we studied the applicability of morphometric features to quantitatively predict cellular osteogenic potential. We applied computational machine learning, combining cell morphology features with their corresponding biochemical osteogenic assay results, to develop prediction model of osteogenic differentiation. Using a dataset of 9,990 images automatically acquired by BioStation CT during osteogenic differentiation culture of hBMSCs, 666 morphometric features were extracted as parameters. Two commonly used osteogenic markers, alkaline phosphatase (ALP) activity and calcium deposition were measured experimentally, and used as the true biological differentiation status to validate the prediction accuracy. Using time-course morphological features throughout differentiation culture, the prediction results highly correlated with the experimentally defined differentiation marker values (R>0.89 for both marker predictions). The clinical applicability of our morphology-based prediction was further examined with two scenarios: one using only historical cell images and the other using both historical images together with the patient''s own cell images to predict a new patient''s cellular potential. The prediction accuracy was found to be greatly enhanced by incorporation of patients'' own cell features in the modeling, indicating the practical strategy for clinical usage. Consequently, our results provide strong evidence for the feasibility of using a quantitative time series of phase-contrast cellular morphology for non-invasive cell quality prediction in regenerative medicine.     

Exploring the structural requirements for jasmonates and related compounds as novel plant growth regulators: A current computational perspective

Jasmonates and related compounds have been highlighted recently in the field of plant physiology and plant molecular biology due to their significant regulatory roles in the signaling pathway for the diverse aspects of plant development and survival. Though a considerable amount of studies concerning their biological effects in different plants have been widely reported, the molecular details of the signaling mechanism are still poorly understood. This review sheds new light on the structural requirements for the bioactivity/property of jasmonic acid derivatives in current computational perspective, which differs from previous research that mainly focus on their biological evaluation, gene and metabolic regulation and the enzymes in their biosynthesis. The computational results may contribute to further understanding the mechanism of drug-receptor interactions in their signaling pathway and designing novel plant growth regulators as high effective ecological pesticides.Key words: jasmonates, amino acid conjugates of jasmonic acid, plant growth regulators, quantitative structure-activity relationship, quantitative structure-property relationship, a mini-review     

The Quantitative Methods Boot Camp: Teaching Quantitative Thinking and Computing Skills to Graduate Students in the Life Sciences

The past decade has seen a rapid increase in the ability of biologists to collect large amounts of data. It is therefore vital that research biologists acquire the necessary skills during their training to visualize, analyze, and interpret such data. To begin to meet this need, we have developed a “boot camp” in quantitative methods for biology graduate students at Harvard Medical School. The goal of this short, intensive course is to enable students to use computational tools to visualize and analyze data, to strengthen their computational thinking skills, and to simulate and thus extend their intuition about the behavior of complex biological systems. The boot camp teaches basic programming using biological examples from statistics, image processing, and data analysis. This integrative approach to teaching programming and quantitative reasoning motivates students’ engagement by demonstrating the relevance of these skills to their work in life science laboratories. Students also have the opportunity to analyze their own data or explore a topic of interest in more detail. The class is taught with a mixture of short lectures, Socratic discussion, and in-class exercises. Students spend approximately 40% of their class time working through both short and long problems. A high instructor-to-student ratio allows students to get assistance or additional challenges when needed, thus enhancing the experience for students at all levels of mastery. Data collected from end-of-course surveys from the last five offerings of the course (between 2012 and 2014) show that students report high learning gains and feel that the course prepares them for solving quantitative and computational problems they will encounter in their research. We outline our course here which, together with the course materials freely available online under a Creative Commons License, should help to facilitate similar efforts by others.This is part of the PLOS Computational Biology Education collection.     

Waveguide evanescent field scattering microscopy: bacterial biofilms and their sterilization response via UV irradiation

Waveguide Evanescent Field Scattering (WEFS) microscopy is introduced as a new and simple tool for label‐free, high contrast imaging of bacteria and bacteria sensors. Bacterial microcolonies and single bacteria were discriminated both by their bright field images and by their evanescent scattering intensity. By comparing bright field images with WEFS images, the proportion of planktonic: sessile (i.e., “floating”: attached) bacteria were measured. Bacteria were irradiated with UV light, which limited their biofilm forming capability. A quantitative decrease in attachment of individual, sessile bacteria and in attached, microcolony occupied areas was easily determined within the apparent biofilms with increasing UV dose. WEFS microscopy is an ideal tool for providing rapid quantitative data on biofilm formation. (© 2014 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

A large field CCD system for quantitative imaging of microarrays

We describe a charge-coupled device (CCD) imaging system for microarrays capable of acquiring quantitative, high dynamic range images of very large fields. Illumination is supplied by an arc lamp, and filters are used to define excitation and emission bands. The system is linear down to fluorochrome densities 1 molecule/µm². The ratios of the illumination intensity distributions for all excitation wavelengths have a maximum deviation ~±4% over the object field, so that images can be analyzed without computational corrections for the illumination pattern unless higher accuracy is desired. Custom designed detection optics produce achromatic images of the spectral region from ~ 450 to ~750 nm. Acquisition of a series of images of multiple fluorochromes from multiple arrays occurs under computer control. The version of the system described in detail provides images of 20 mm square areas using a 27 mm square, 2K × 2K pixel, cooled CCD chip with a well depth of ~10⁵ electrons, and provides ratio measurements accurate to a few percent over a dynamic range in intensity >1000. Resolution referred to the sample is 10 µm, sufficient for obtaining quantitative multicolor images from >30 000 array elements in an 18 mm × 18 mm square.     

An Image Skeletonization-Based Tool for Pollen Tube Morphology Analysis and Phenotyping

The mechanism underlying pollen tube growth involves diverse genes and molecular pathways. Alterations in the regulatory genes or pathways cause phenotypic changes reflected by cellular morphology, which can be captured using fluorescence microscopy. Determining and classifying pollen tube morphological phenotypes in such microscopic images is key to our understanding the involvement of genes and pathways. In this context, we propose a computational method to extract quantitative morphological features, and demonstrate that these features reflect morphological differences relevant to distinguish different defects of pollen tube growth. The corresponding software tool furthermore includes a novel semi-automated image segmentation approach, allowing to highly accurately identify the boundary of a pollen tube in a microscopic image.     

An image-based computational model of oscillatory flow in the proximal part of tracheobronchial trees

A computational model of an oscillatory laminar flow of an incompressible Newtonian fluid has been carried out in the proximal part of human tracheobronchial trees, either normal or with a strongly stenosed right main bronchus. After acquisition with a multislice spiral CT, the thoracic images are processed to reconstruct the geometry of the trachea and the first six bronchus generations and to virtually travel inside this duct network. The facetisation associated with the 3D reconstruction of the tracheobronchial tree is improved to get a computation-adapted surface triangulation, which leads to a volumic mesh composed of tetrahedra. The Navier-Stokes equations associated with the classical boundary conditions and different values of the flow dimensionless parameters are solved using the finite element method. The airways are supposed to be rigid during rest breathing. The flow distribution among the set of bronchi is determined during the respiratory cycle. Cycle reproducibility and mesh size effects on the numerical results are examined. Helpful qualitative data are provided rather than accurate quantitative results in the context of multimodelling, from image processing to numerical simulations.