Mass spectrometry imaging: Towards a lipid microscope?

Biological imaging techniques are the most efficient way to locally measure the variation of different parameters on tissue sections. These analyses are gaining increasing interest since 20 years and allow observing extremely complex biological phenomena at lower and lower time and resolution scale. Nevertheless, most of them only target very few compounds of interest, which are chosen a priori, due to their low resolution power and sensitivity. New chemical imaging technique has to be introduced in order to overcome these limitations, leading to more informative and sensitive analyses for biologists and physicians.Two major mass spectrometry methods can be efficiently used to generate the distribution of biological compounds over a tissue section. Matrix-Assisted Laser Desorption/Ionisation-Mass Spectrometry (MALDI-MS) needs the co-crystallization of the sample with a matrix before to be irradiated by a laser, whereas the analyte is directly desorbed by a primary ion bombardment for Secondary Ion Mass Spectrometry (SIMS) experiments. In both cases, energy used for desorption/ionization is locally deposited -some tens of microns for the laser and some hundreds of nanometers for the ion beam- meaning that small areas over the surface sample can be separately analyzed. Step by step analysis allows spectrum acquisitions over the tissue sections and the data are treated by modern informatics software in order to create ion density maps, i.e., the intensity plot of one specific ion versus the (x,y) position.Main advantages of SIMS and MALDI compared to other chemical imaging techniques lie in the simultaneous acquisition of a large number of biological compounds in mixture with an excellent sensitivity obtained by Time-of-Flight (ToF) mass analyzer. Moreover, data treatment is done a posteriori, due to the fact that no compound is selectively marked, and let us access to the localization of different lipid classes in only one complete acquisition.     

Open-source deep-learning software for bioimage segmentation

Microscopy images are rich in information about the dynamic relationships among biological structures. However, extracting this complex information can be challenging, especially when biological structures are closely packed, distinguished by texture rather than intensity, and/or low intensity relative to the background. By learning from large amounts of annotated data, deep learning can accomplish several previously intractable bioimage analysis tasks. Until the past few years, however, most deep-learning workflows required significant computational expertise to be applied. Here, we survey several new open-source software tools that aim to make deep-learning–based image segmentation accessible to biologists with limited computational experience. These tools take many different forms, such as web apps, plug-ins for existing imaging analysis software, and preconfigured interactive notebooks and pipelines. In addition to surveying these tools, we overview several challenges that remain in the field. We hope to expand awareness of the powerful deep-learning tools available to biologists for image analysis.     

Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans

Quantitative imaging has become a vital technique in biological discovery and clinical diagnostics; a plethora of tools have recently been developed to enable new and accelerated forms of biological investigation. Increasingly, the capacity for high-throughput experimentation provided by new imaging modalities, contrast techniques, microscopy tools, microfluidics and computer controlled systems shifts the experimental bottleneck from the level of physical manipulation and raw data collection to automated recognition and data processing. Yet, despite their broad importance, image analysis solutions to address these needs have been narrowly tailored. Here, we present a generalizable formulation for autonomous identification of specific biological structures that is applicable for many problems. The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM). These low-level functions are readily available in a large array of image processing software packages and programming languages. Our framework is thus both easy to implement at the modular level and provides specific high-level architecture to guide the solution of more complicated image-processing problems. We demonstrate the utility of the classification routine by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. Using these examples as a guide, we envision the broad utility of the framework for diverse problems across different length scales and imaging methods.     

Deep Learning and Its Applications in Biomedicine

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning.     

Visualization and correction of automated segmentation,tracking and lineaging from 5-D stem cell image sequences

Background

Neural stem cells are motile and proliferative cells that undergo mitosis, dividing to produce daughter cells and ultimately generating differentiated neurons and glia. Understanding the mechanisms controlling neural stem cell proliferation and differentiation will play a key role in the emerging fields of regenerative medicine and cancer therapeutics. Stem cell studies in vitro from 2-D image data are well established. Visualizing and analyzing large three dimensional images of intact tissue is a challenging task. It becomes more difficult as the dimensionality of the image data increases to include time and additional fluorescence channels. There is a pressing need for 5-D image analysis and visualization tools to study cellular dynamics in the intact niche and to quantify the role that environmental factors play in determining cell fate.

Results

We present an application that integrates visualization and quantitative analysis of 5-D (x,y,z,t,channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks.

Conclusions

By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. We combine unsupervised image analysis algorithms with an interactive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-328) contains supplementary material, which is available to authorized users.     

Responses of two parasitoids,the exotic Spathius agrili Yang and the native Spathius floridanus Ashmead,to volatile cues associated with the emerald ash borer,Agrilus planipennis Fairmaire

Two parasitoids, the introduced specialist Spathius agrili Yang (Braconidae), and the native generalist Spathius floridanus Ashmead, have been proposed as biological control agents of the emerald ash borer, Agrilus planipennis Fairmaire (Buprestidae). However, little is known about their host-location behaviors. We evaluated responses to their host complex, Fraxinus pennsylvanica stem tissue, F. pennsylvanica foliage, and an A. planipennis larva within a stem. Experiments were conducted in a Y-tube olfactometer, using wasps reared on A. planipennis larvae in F. pennsylvanica stems. Naïve S. agrili were attracted to the entire complex, and to leaf tissue, relative to blanks. S. agrili were also more attracted to stems containing larvae and leaf tissue together than leaf tissue alone. Naïve S. floridanus were attracted to larvae within stems, but nothing else. A further distinction is that S. agrilli moved more, in the presence of foliage. Thus, S. agrili and S. floridanus appear to employ different host-location strategies. The former is attracted to host plant cues, which then elicit increased searching, whereas the latter is only attracted to infested tissue directly. We found no evidence that oviposition influences attraction by S. agrili, suggesting other forms of experience should be evaluated for potential sources of learned cues. Further, S. agrili that declined opportunities to oviposit oriented away from host-associated cues, suggesting distinct behavioral sequences occur by females that are not reproductively ready. Further understanding of host-location behavior may improve biological control by these parasitoids, by suggesting strategies for pre-release conditioning and providing tools for assessing post-release establishment.     

BrcaSeg: A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment. Here, we propose BrcaSeg, an image analysis pipeline based on a convolutional neural network (CNN) model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin (H&E) stained histopathological images. The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas (TCGA) Program. BrcaSeg achieves a classification accuracy of 91.02%, which outperforms other state-of-the-art methods. Using this model, we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data. We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios. Gene Ontology (GO) enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes, whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues. Taken all together, our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors. BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.     

CentTracker: a trainable,machine-learning–based tool for large-scale analyses of Caenorhabditis elegans germline stem cell mitosis

Investigating the complex interactions between stem cells and their native environment requires an efficient means to image them in situ. Caenorhabditis elegans germline stem cells (GSCs) are distinctly accessible for intravital imaging; however, long-term image acquisition and analysis of dividing GSCs can be technically challenging. Here we present a systematic investigation into the technical factors impacting GSC physiology during live imaging and provide an optimized method for monitoring GSC mitosis under minimally disruptive conditions. We describe CentTracker, an automated and generalizable image analysis tool that uses machine learning to pair mitotic centrosomes and that can extract a variety of mitotic parameters rapidly from large-scale data sets. We employ CentTracker to assess a range of mitotic features in a large GSC data set. We observe spatial clustering of mitoses within the germline tissue but no evidence that subpopulations with distinct mitotic profiles exist within the stem cell pool. We further find biases in GSC spindle orientation relative to the germline’s distal–proximal axis and thus the niche. The technical and analytical tools provided herein pave the way for large-scale screening studies of multiple mitotic processes in GSCs dividing in situ, in an intact tissue, in a living animal, under seemingly physiological conditions.     

Imaging Denatured Collagen Strands In vivo and Ex vivo via Photo-triggered Hybridization of Caged Collagen Mimetic Peptides

Collagen is a major structural component of the extracellular matrix that supports tissue formation and maintenance. Although collagen remodeling is an integral part of normal tissue renewal, excessive amount of remodeling activity is involved in tumors, arthritis, and many other pathological conditions. During collagen remodeling, the triple helical structure of collagen molecules is disrupted by proteases in the extracellular environment. In addition, collagens present in many histological tissue samples are partially denatured by the fixation and preservation processes. Therefore, these denatured collagen strands can serve as effective targets for biological imaging. We previously developed a caged collagen mimetic peptide (CMP) that can be photo-triggered to hybridize with denatured collagen strands by forming triple helical structure, which is unique to collagens. The overall goals of this procedure are i) to image denatured collagen strands resulting from normal remodeling activities in vivo, and ii) to visualize collagens in ex vivo tissue sections using the photo-triggered caged CMPs. To achieve effective hybridization and successful in vivo and ex vivo imaging, fluorescently labeled caged CMPs are either photo-activated immediately before intravenous injection, or are directly activated on tissue sections. Normal skeletal collagen remolding in nude mice and collagens in prefixed mouse cornea tissue sections are imaged in this procedure. The imaging method based on the CMP-collagen hybridization technology presented here could lead to deeper understanding of the tissue remodeling process, as well as allow development of new diagnostics for diseases associated with high collagen remodeling activity.     

Nanoscale Imaging of Caveolin-1 Membrane Domains In Vivo

Light microscopy enables noninvasive imaging of fluorescent species in biological specimens, but resolution is generally limited by diffraction to ~200–250 nm. Many biological processes occur on smaller length scales, highlighting the importance of techniques that can image below the diffraction limit and provide valuable single-molecule information. In recent years, imaging techniques have been developed which can achieve resolution below the diffraction limit. Utilizing one such technique, fluorescence photoactivation localization microscopy (FPALM), we demonstrated its ability to construct super-resolution images from single molecules in a living zebrafish embryo, expanding the realm of previous super-resolution imaging to a living vertebrate organism. We imaged caveolin-1 in vivo, in living zebrafish embryos. Our results demonstrate the successful image acquisition of super-resolution images in a living vertebrate organism, opening several opportunities to answer more dynamic biological questions in vivo at the previously inaccessible nanoscale.     

detectIR: A Novel Program for Detecting Perfect and Imperfect Inverted Repeats Using Complex Numbers and Vector Calculation

Inverted repeats are present in abundance in both prokaryotic and eukaryotic genomes and can form DNA secondary structures – hairpins and cruciforms that are involved in many important biological processes. Bioinformatics tools for efficient and accurate detection of inverted repeats are desirable, because existing tools are often less accurate and time consuming, sometimes incapable of dealing with genome-scale input data. Here, we present a MATLAB-based program called detectIR for the perfect and imperfect inverted repeat detection that utilizes complex numbers and vector calculation and allows genome-scale data inputs. A novel algorithm is adopted in detectIR to convert the conventional sequence string comparison in inverted repeat detection into vector calculation of complex numbers, allowing non-complementary pairs (mismatches) in the pairing stem and a non-palindromic spacer (loop or gaps) in the middle of inverted repeats. Compared with existing popular tools, our program performs with significantly higher accuracy and efficiency. Using genome sequence data from HIV-1, Arabidopsis thaliana, Homo sapiens and Zea mays for comparison, detectIR can find lots of inverted repeats missed by existing tools whose outputs often contain many invalid cases. detectIR is open source and its source code is freely available at: https://sourceforge.net/projects/detectir.     

Credibility Analysis of Putative Disease-Causing Genes Using Bioinformatics

Background

Genetic studies are challenging in many complex diseases, particularly those with limited diagnostic certainty, low prevalence or of old age. The result is that genes may be reported as disease-causing with varying levels of evidence, and in some cases, the data may be so limited as to be indistinguishable from chance findings. When there are large numbers of such genes, an objective method for ranking the evidence is useful. Using the neurodegenerative and complex disease amyotrophic lateral sclerosis (ALS) as a model, and the disease-specific database ALSoD, the objective is to develop a method using publicly available data to generate a credibility score for putative disease-causing genes.

Methods

Genes with at least one publication suggesting involvement in adult onset familial ALS were collated following an exhaustive literature search. SQL was used to generate a score by extracting information from the publications and combined with a pathogenicity analysis using bioinformatics tools. The resulting score allowed us to rank genes in order of credibility. To validate the method, we compared the objective ranking with a rank generated by ALS genetics experts. Spearman''s Rho was used to compare rankings generated by the different methods.

Results

The automated method ranked ALS genes in the following order: SOD1, TARDBP, FUS, ANG, SPG11, NEFH, OPTN, ALS2, SETX, FIG4, VAPB, DCTN1, TAF15, VCP, DAO. This compared very well to the ranking of ALS genetics experts, with Spearman''s Rho of 0.69 (P = 0.009).

Conclusion

We have presented an automated method for scoring the level of evidence for a gene being disease-causing. In developing the method we have used the model disease ALS, but it could equally be applied to any disease in which there is genotypic uncertainty.     

Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling

PURPOSE: To evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image scaling employed by magnetic resonance imaging manufacturers. METHODS: A series of gadoteridol-doped distilled water solutions (0%, 0.5%, 1%, and 2% volume concentrations) was prepared for manual substitution into one (of three) phantom compartments to create “variable signal,” whereas the other two compartments (containing mineral oil and 0.25% gadoteriol) were held unchanged. Pseudodynamic images were acquired over multiple series using four scanners such that the histogram of pixel intensities varied enough to provoke variable image scaling from series to series. Additional diffusion-weighted images were acquired of an ice-water phantom to generate scanner-specific apparent diffusion coefficient (ADC) maps. The resulting pseudodynamic images and ADC maps were analyzed by eight centers of the Quantitative Imaging Network using 16 different SW tools to measure compartment-specific region-of-interest intensity. RESULTS: Images generated by one of the scanners appeared to have additional intensity scaling that was not accounted for by the majority of tested quantitative image analysis SW tools. Incorrect image scaling leads to intensity measurement bias near 100%, compared to nonscaled images. CONCLUSION: Corrective actions for image scaling are suggested for manufacturers and quantitative imaging community.     

KymographClear and KymographDirect: two tools for the automated quantitative analysis of molecular and cellular dynamics using kymographs

Dynamic processes are ubiquitous and essential in living cells. To properly understand these processes, it is imperative to measure them in a time-dependent way and analyze the resulting data quantitatively, preferably with automated tools. Kymographs are single images that represent the motion of dynamic processes and are widely used in live-cell imaging. Although they contain the full range of dynamics, it is not straightforward to extract this quantitative information in a reliable way. Here we present two complementary, publicly available software tools, KymographClear and KymographDirect, that have the power to reveal detailed insight in dynamic processes. KymographClear is a macro toolset for ImageJ to generate kymographs that provides automatic color coding of the different directions of movement. KymographDirect is a stand-alone tool to extract quantitative information from kymographs obtained from a wide range of dynamic processes in an automated way, with high accuracy and reliability. We discuss the concepts behind these software tools, validate them using simulated data, and test them on experimental data. We show that these tools can be used to extract motility parameters from a diverse set of cell-biological experiments in an automated and user-friendly way.     

FRAP, FLIP, FRET, BRET, FLIM, PRIM...new techniques for a colourful life

Cell and tissue imaging provides scientists with wonderful tools, thanks to a fruitful dialog between chemistry, optical, mechanical, computational sciences and biology. Confocal microscopy, videomicroscopy together with a new generation of fluorochromes (especially those derived from green fluorescent protein, GFP) and image analysis software allow to visualize life in all its dimensions (space and time). Cell imaging also allows to quantify biological processes at the cellular level, to analyse both stoechiometry and dynamics of molecular interactions involved in cell and tissue regulations. Entering the new era of post-genomics requires a better knowledge of advantages and limitations of these new approaches.     

Automated Filtering of Intrinsic Movement Artifacts during Two-Photon Intravital Microscopy

In vivo imaging using two-photon microscopy is an essential tool to explore the dynamic of physiological events deep within biological tissues for short or extended periods of time. The new capabilities offered by this technology (e.g. high tissue penetrance, low toxicity) have opened a whole new era of investigations in modern biomedical research. However, the potential of using this promising technique in tissues of living animals is greatly limited by the intrinsic irregular movements that are caused by cardiac and respiratory cycles and muscular and vascular tone. Here, we show real-time imaging of the brain, spinal cord, sciatic nerve and myenteric plexus of living mice using a new automated program, named Intravital_Microscopy_Toolbox, that removes frames corrupted with motion artifacts from time-lapse videos. Our approach involves generating a dissimilarity score against precalculated reference frames in a specific reference channel, thus allowing the gating of distorted, out-of-focus or translated frames. Since the algorithm detects the uneven peaks of image distortion caused by irregular animal movements, the macro allows a fast and efficient filtering of the image sequence. In addition, extra features have been implemented in the macro, such as XY registration, channel subtraction, extended field of view with maximum intensity projection, noise reduction with average intensity projections, and automated timestamp and scale bar overlay. Thus, the Intravital_Microscopy_Toolbox macro for ImageJ provides convenient tools for biologists who are performing in vivo two-photon imaging in tissues prone to motion artifacts.