Cinquin Bertrand, Bensalem SakinaIntroduction :  Seen as microfactories, microalgaes are organisms already in use in many industries. Howewer, the molecules of interests they're making are enclosed and so far the only way of releasing is done by killing the biomass, filtering and extracting the molecule. Working on membrane porosity is challenging and could help finding a "clean way" of producing such molecules. Controlled electroporation could be a way. Previous experiments shows that there is a voltage dependency to form non permanent holes in the membrane.  This reversible damage will allow the escape of some molecules and the repair by the microalgae to take place. Howewer, measuring the effects on electroparation different parameters is challenging. 

[DOI] By Kota Miura, Perrine Paul-Gilloteaux, Sébastien Tosi, Julien Colombelli 27, Oct. 2017 NEUBIAS activities are spiraling around bioimage analysis “WORKFLOWS”. Since this term is often used in slightly different ways by each person, we clarify the definition of “WORKFLOW” used in the NEUBIAS community in the following article. While doing so we also introduce the related activities organized by NEUBIAS. Software packages such as ImageJ, MATLAB, CellProfiler or ICY are often used to analyze bioimages. These software packages are “COLLECTIONS” of image processing and analysis algorithms. Although their distribution and the way to access their resources is different since they can come with or without Graphical User Interface (GUI), as libraries such as ImgLib2, OpenCV, ITK, VTK, and Scikit-Image; we invariably refer to them as “COLLECTIONS”. To actually analyze bioimage data scientifically and address an underlying biological problem, one needs to hand pick some algorithms from these COLLECTIONS, carefully adjust their functional parameters to the problem and assemble them in a meaningful order. Such a sequence of image processing algorithms with a specified parameter set is what we call a “Workflow”. The implementations of the algorithms that are used in the WORKFLOWS are the “COMPONENTS” constituting that workflow (or “WORKFLOW COMPONENTS”). From the point of view of the expert who needs to assemble a workflow, a collection is a package bundling many different COMPONENTS. Many plugins offered for ImageJ are mostly also COLLECTIONS (e.g. Trackmate, 3D Suite, MOSAIC…), as they bundle implementations of related algorithms. Each WORKFLOW is uniquely associated with a specific biological research project because the question asked therein and the acquired image quality is often unique. This leads to a unique combination of COMPONENTS and parameter set. Some COLLECTIONS, especially those designed with GUI, offer WORKFLOW TEMPLATES. These templates are pre-assembled sequences of image processing tasks to solve a typical bioimage analysis problem; all one needs to do is to adjust the parameters of each step. For example, in the case of Trackmate plugin for ImageJ, a GUI wizard guides the user to choose an algorithm for each step among several candidates and also to adjust their parameters to achieve a successful particle tracking WORKFLOW. When these algorithms and parameters are set, the WORKFLOW is built. CellProfiler also has a helpful GUI that assists the user in building a WORKFLOW based on WORKFLOW TEMPLATES, that allows the user to easily swap the algorithms for each step and test various parameter combinations. Though such templates are available for some typical tasks, COLLECTIONS generally do not provide helpful clues to construct a WORKFLOW – how and in which order the COMPONENTS should be assembled depends on expert knowledge, empirical knowledge or testing. Since the biological questions are so diverse, the WORKFLOW often needs to be original and might not match any available WORKFLOW TEMPLATES. Building a WORKFLOW from scratch needs some solid knowledge about the COMPONENTS and the ways to combine them. It also needs an understanding of the biological problem itself. Each WORKFLOW is in essence associated with a specific biological question, and this question and the image acquisition setup affect the required precision of the analysis. In some cases, a higher precision does not imply more meaningful results, this should be carefully planned together with the statistical treatment (which also affect to some extent the choice of the COMPONENTS). Figure 1 summarizes the above explanations. [] Many biologists feel difficulty in analyzing image data, because of the existing gap between a COLLECTION of COMPONENTS and a practical WORKFLOW. A COLLECTION bundles COMPONENTS without WORKFLOWS, but it is often erroneously assumed that installing a COLLECTION is enough for solving bioimage analysis problem. The truth is that some expert has to choose COMPONENTS, adjust their parameters and build a WORKFLOW (Fig.1 red arrows), which is largely dependent on a priori knowledge. The correct assembly of COMPONENTS as an executable script is even more difficult in general, as it requires some programming skills. The use of COMPONENTS directly from library-type of COLLECTIONS, which hosts many useful COMPONENTS, also requires programming skills to access their API. Bioimage analysts are then there to fill this gap but even they, who professionally analyze image data, need to always search for the best COMPONENTS to solve problems, reaching the required accuracy or coping with huge data in a practical time. Another important aspect and difficulty is the reproducibility of WORKFLOWS. We often want to know how other people are performing image analysis to learn new bioimage analysis strategies. We then try to find WORKFLOWS addressing a similar biological problem. However, many articles do not document the WORKFLOWS they used in sufficient details to reproduce the results. Some WORKFLOWS are written as a detailed text description in Materials and Methods, but we recommend to publish them as executable scripts with documented parameter sets for clarity and reproducibility of analysis and results. As an extreme example, we found articles with their image analysis section in Materials and Methods merely documenting that ImageJ was used for the image analysis. Such a minimalism should be strictly avoided. For these reasons, we are promoting to publish bioimage analysis WORKFLOWS in a reproducible format. The best format is a version-tracked script, i.e. a computer program because it is clear and reproducible. A script embedded in a Docker image is even better for avoiding problems associated with a difference in execution environments. Many activities in NEUBIAS aim to address these difficulties, trying to make the process of choosing COMPONENTS and the construction of WORKFLOW easier, and to secure the reproducibility of published bioimage analysis WORKFLOWS. The workgroup 1 (WG1, Strategy and Scheduling) endorses the application of COST policies and moderates the strategic decisions taken in the organization of workshops and conferences. They promote communication among developers (who are implementing COMPONENTS and maintaining COLLECTIONS), bioimage analysts, microscopists and biologists, to increase the exchange of the usage information of the COLLECTIONS and WORKFLOWS to bridge their gap (Figure 1 left and right). The workgroup 2 (WG2, Training) aims at developing a multi-level training program in bioimage analysis based on the WORKFLOW-COMPONENTS CONCEPT. For beginners, basic COMPONENTS and their algorithms, as well as their useful assembly, are introduced. For intermediate level students, scripting languages are taught, to automate and to author reproducible WORKFLOWS, and at the same time to learn practical WORKFLOWS actually used in biological research projects. For advanced level students, mainly comprised of professional bioimage analysts, advanced WORKFLOWS and new COMPONENTS are introduced and studied in details for applications to a wider problem in biology and also to more efficiently author WORKFLOWS. The workgroup 7 (WG7, STSMs and Career Path) is organizing the extended and more individual aspect of such training, by supporting the travel of bioimage analysts and developers to other labs for implementing COMPONENTS and WORKFLOWS in situ during missions of extended duration. WG7 is also making an effort to pave the career path of bioimage analysts, the novel type of profession in the life sciences community mediating computational science and life sciences. The workgroup 4 (WG4, The webtool BISE) is creating a searchable database of COLLECTIONS, WORKFLOWS and COMPONENTS. General web search engines, such as Google, generally return hits of COLLECTIONS but not to the level of COMPONENTS. In addition, WORKFLOWS are in many cases hidden in biological papers and difficult to be discovered. For these reasons, the webtool is expected to become a useful tool for those conducting the bioimage analysis. The webtool is also designed to note impressions on the usability of COMPONENTS and WORKFLOWS so that individual experiences can be swiftly shared within the community. The workgroup 5 (WG5, benchmarking) is setting up a web tool enabling the interactive testing and benchmarking of some of the WORKFLOWS from BISE. From this webtool the user will be able to select some specific annotated image dataset stored in an internal database and run compatible WORKFLOWS on these images. The results from the analysis will be compared to the annotations (ground truth) to compute and display some problem specific performance metrics. The user will also be able to explore the results by interactively visualizing them overlaid on the original images. Since typically many WORKFLOWS are available to solve a specific bioimage analysis problem, benchmarking them in such a unified environment is instrumental for fair comparison. The workgroup 6 (WG6, open publication) aims at publishing the NEUBIAS teaching materials based on the WORKFLOW-COMPONENTS CONCEPT for a wider distribution outside of the NEUBIAS community, with detailed explanation on practical WORKFLOWS associated with specific biological problems, for a better and more effective bioimage analysis in the biological community. The workgroup 3 (WG3, outreach) works on communicating the outcome of all these activities towards wider scientific community, and also to promote communication among NEUBIAS working groups. Overall, various activities of NEUBIAS are consistently directed: They all are reaching toward a clearer procedure for choosing COMPONENTS and for a more efficient, explicit and reproducible authoring of WORKFLOWS for biological image analysis.

INTRODUCTION ImageJ macro is a useful tool to automate image processing and analysis. In this tutorial, we learn how to write a macro for analyzing intensity dynamics from a time lapse sequence. Analysis is done by processing a two-channel image stack, a time lapse sequence of the process of NPC protein relocalizing from cytoplasm to the nuclear membrane[1]. Two images shown in figure [fig:NucStrategy] are from the first and the last time points of the movie[2]. Compare these images carefully. You might recognize that the green signal in the periphery of nuclei (red) is stronger in the second image. We want to write a macro to compare this difference in a quantitative way. The processing involves two steps: We first segment the nucleus in a first (histone) channel. Second, we use that segmented nucleus rim as a mask to measure the intensity changes in the second channel. [1] Courtesy of Andreas Boni [2] The original 4D hyperstack file is NPC1.tif. You could load the file via CourseModule plugin.

OVERVIEW This module is divided into four separate steps. Each step has an enumerated workflow followed by an exercise. The result of each exercise is a macro. To ensure a smooth progress, the solution-macro for each exercise is provided and should be used as the starting point for consecutive exercises. Aim FISH (Fluorescent In-Situ Hybridization) is a complex gene staining technique with numerous variants where the quality of the staining depends on several physical parameters (e.g. level of DNA de-condensation). FISH aims at labeling DNA sequences specific to a certain gene (or chromosome) so that they appear as a “bright fluorescent spots” in fluorescent channels. We will write an ImageJ macro to process images from such a FISH assay: First to segment the spermatozoid nuclei from a DAPI staining, and then to classify the nuclei based on their chromosomal content (multiplicity of FISH spots in 3 different fluorescent channels). Introduction The automatic spermatozoids classification as proposed in is a powerful mean to extract statistics of chromosomal anomalies (see Fig. [fig:intro]) on large sample datasets (typically >10’000 cells). It can also be used to drive a motorized microscope to perform an “intelligent” scan : the classification is performed from a low resolution scan and a secondary scan (high resolution) is automatically triggered to only acquire the cells showing some specific anomalies. datasets The nuclei of the spermatozoids were DAPI stained and the chromosomes of interest (here X, Y and 18) were stained by FISH. The fixed sample was scanned by a motorized stage microscope to tile the whole area of the sample. Several z slices were recorded for each fluorescent channel (DAPI, aqua, orange, and green). The analysis will be performed on the z maximum intensity projection of the images (in each channel).

OVERVIEW Aim In this module we will implement a simple ImageJ macro to segment and analyze the blood vessel network of a subcutaneous tumor (see figure  [fig:bloodvessels]). The analysis is fully performed in 3D and possible strategies to extract statistics of the network geometry and interactively visualize the results are also discussed and implemented. Introduction Segmenting and extracting the geometry of the blood vessel network inside specific sub-regions of a tumor is a powerful investigation tool: The density of the vascularization and vessel branching points and the thickness of the vessels are for instance crucial age indicators to understand how the structure developed and possibly necrosed. With the help of a simple ImageJ macro these statistics can be extracted and the network 3D rendered with judicious color/transparency to provide insights on its organization. datasets The blood vessel datasets were acquired by a custom made (IRB Barcelona) macroSPIM allowing to image large (up to 1 cm), fixed and optically cleared samples (pieces of organs, tumors, whole organisms...). The preparation protocol and the imaging are similar to . For this project mice developing some specific tumors are injected a rhodamine-lectin construct to stain their blood vessels before sacrificing. IMPORTANT NOTE : Two stacks cropped from the original dataset are provided, namely ”BloodVessels_small.tif” and ”BloodVessels_med.tif”. It is HIGHLY RECOMMENDED to first work on the smaller stack as processing time is not negligible. You may test the final ImageJ macro on the larger stack.

INTRODUCTION Since the beginning of the 21st century the use of digital imaging microscopy has spread widely among life scientists and analysis of image data became increasingly important. Already long before those technologies became available, started in the 17th century, life scientists have been sketching - or imaging manually - living organisms and their structures to understand how they develop themselves and operate. Computer-based image analysis radically upgraded these traditional methods in life sciences, and opened novel approaches to measure shapes, distributions and dynamics from multi-dimensional images captured through high-end microscopes. From those data life scientists are trying to decode the essence of biological systems in their spatial and temporal context. However, digital image analysis in life sciences is a new method that only became available recently in the life science community, and many scientists are still struggling to improve their image analysis skills. We wrote this textbook aiming at such life scientists who have some experience in biological image analysis and who are trying to learn more to increase their own capability in extracting quantitative information from image data. What is Bioimage analysis? It might sound evident to you, but we would like to clarify our definition to avoid the confusion in the use of the term “image analysis” and also to be clear with the goal of this textbook. In the image processing field, “image analysis” is a way of identifying objects and patterns in images by computer. We quote a definition from a famous image processing textbook by Gonzales and Woods (Gonzalez and Woods, 1992, Digital Image Processing, Addison-Wesley Publishing): ”Image analysis is a process of discovering, identifying, and understanding patterns that are relevant to the performance of an image-based task. One of the principal goals of image analysis by computer is to endow a machine with the capability to approximate, in some sense, a similar capability in human beings.” In the light of this definition image analysis, which is also called “computer vision”, aims at mimicking the way we see the world and how we identify its visible structures. Image analysis in biology does undeniably also hold this element, but more importantly, its main goal is to MEASURE biological structures and phenomena in order to study and understand biological systems in a quantitative way. To achieve this task, we in fact do not have to be bothered with similarity to the human recognition - we have more emphasis on the objectivity of quantitative measurement, rather than how that computer-based recognition becomes in agreement with human recognition. Therefore, in biology, image analysis is a process of identifying spatial distribution of biological components in images, and measure their characteristics to study their underlying mechanisms in an unbiased way. To underline this difference in the goals of image analysis in the two fields and to distinguish them from each other we will now on refer to image analysis in biology as BIOIMAGE ANALYSIS . Scope of this Textbook The textbook starts with an overview of existing bioimage analysis tools and programming environments (chapter 2). The next two chapters (chapters 3 and 4) are dedicated to the basics of programming in ImageJ macro language and Matlab. These two software packages programming environments are arguably the most widespread tools used for bioimage analysis. If you are already acquainted with these programming languages you can skip this part. Based on the programming skills acquired in the first chapters, chapters 5 to 10 are devoted to typical biological problems. The reader is guided step by step to write increasingly challenging ImageJ and Matlab scripts addressing these problems. These scripts are powerful tools to automatically extract quantitative and statistical data from biological images. Image analysis can also be performed without writing a single line of code, but programming does offer many advantages. Firstly, repetitive tasks can be automated to decrease the user workloads. Secondly, written programs are the best practice to secure the reproducibility of a given experimental protocol down to statistical results. Thirdly, written programs can be excellent documentations of complex image analysis workflows, allowing us to inspect the details of the processing in a glance and provides a platform to further improve those methods. Finally workflows are endowed with modularity, which enables us to construct new programs by re-using some sections of existing workflows and modifying them. Many image processing and analysis tools are available and offered to life scientists, but instructions on how to efficiently combine functions of those tools, and how to design workflows matched to a specific problem have largely been missing. We think that this is because bioimage analysis problems are so diverse that standardization of bioimage analysis is barely applicable. For this reason, each chapter focuses on a specific and clearly defined biological problem. By providing information on the general approach as well as details on solving the particular task, we hope that the reader will get valuable information to customize these image analysis workflows to one’s own need, and also learn a template and good practices to write some new efficient workflows from scratch. In this book we minimized explanations on the details of each image processing steps, i.e. details of functions and algorithms. For their mathematical backgrounds and how they are implemented, many textbooks focusing on those aspects are readily available (e.g. Gonzalez and Woods). Instead, we provide more explanations on how to construct the image analysis workflows by assembling various image processing algorithms. We start from original images and measure certain biological events, in order to provide the information necessary for biologists to analyze their own image data in a quantitative way. We hope that this book will help you in solving your problems. Moreover we are looking forward that you will soon discover the joy of programming bioimage analysis workflows and then share those knowledge and the joy with your peers, to further advance this field. Acknowledgements We thank Perrine Paul-Gilloteaux (Institut Curie) and Julien Collombeli (IRB Barcelona) for their commitment in organizing European BioImage Analysis Symposium (EuBIAS) with us, the scope of which includes publishing this textbook. We thank Cornelia Monzel (University of Heidelberg & Institut Curie) for keeping us on the track to publish this textbook and also for the critical review of this introduction. We are grateful to the Course and Conference office of EMBL Heidelberg, who supported our bioimage analysis course for the past three years. All chapters in this textbook were originally from those courses. Jason Swedlow (Univ. Dundee) has been a passionate supporter of EuBIAS and we thank him for his continuous encouragements and supports. Jean Salamero (Institut Curie) has been a great adviser and supporter of our initiative. We would like to thank Rainer Pepperkok (EMBL Heidelberg) for supporting the activity of image analysts community especially to KM. Finally we would like to thank our fellow bioimage analysts - all the authors of this textbook, instructors and former student of the course - for their full commitment and enthusiasm in learning, sharing and transmitting this knowledge, to increase the speed and quality of scientific research in the field. Test Subsection Just to see how this edit would be synced in Authorea via Github. Further edited by Seb as test.