Opportunities presented by zebrafish larval models to study neutrophil function in tissues

Automated, high-throughput quantification of EGFP-expressing neutrophils in zebrafish by machine learning and a highly-parallelized microscope

Normal development of the immune system is essential for overall health and disease resistance. Bony fish, such as the zebrafish (Danio rerio), possess all the major immune cell lineages as mammals and can be employed to model human host response to immune challenge. Zebrafish neutrophils, for example, are present in the transparent larvae as early as 48 hours post fertilization and have been examined in numerous infection and immunotoxicology reports. One significant advantage of the zebrafish model is the ability to affordably generate high numbers of individual larvae that can be arrayed in multi-well plates for high throughput genetic and chemical exposure screens. However, traditional workflows for imaging individual larvae have been limited to low-throughput studies using traditional microscopes and manual analyses. Using a newly developed, parallelized microscope, the Multi-Camera Array Microscope (MCAM™), we have optimized a rapid, high-resolution algorithmic method to count fluorescently labeled cells in zebrafish larvae in vivo. Using transgenic zebrafish larvae, in which neutrophils express EGFP, we captured 18 gigapixels of images across a full 96-well plate, in 75 seconds, and processed the resulting datastream, counting individual fluorescent neutrophils in all individual larvae in 5 minutes. This automation is facilitated by a machine learning segmentation algorithm that defines the most in-focus view of each larva in each well after which pixel intensity thresholding and blob detection are employed to locate and count fluorescent cells. We validated this method by comparing algorithmic neutrophil counts to manual counts in larvae subjected to changes in neutrophil numbers, demonstrating the utility of this approach for high-throughput genetic and chemical screens where a change in neutrophil number is an endpoint metric. Using the MCAM™ we have been able to, within minutes, acquire both enough data to create an automated algorithm and execute a biological experiment with statistical significance. Finally, we present this open-source software package which allows the user to train and evaluate a custom machine learning segmentation model and use it to localize zebrafish and analyze cell counts within the segmented region of interest. This software can be modified as needed for studies involving other zebrafish cell lineages using different transgenic reporter lines and can also be adapted for studies using other amenable model species.

Neutrophil nucleus: shaping the past and the future

Neutrophils are innate immune cells that are key to protecting the host against infection and maintaining body homeostasis. However, if dysregulated, they can contribute to disease, such as in cancer or chronic autoinflammatory disorders. Recent studies have highlighted the heterogeneity in the neutrophil compartment and identified the presence of immature neutrophils and their precursors in these pathologies. Therefore, understanding neutrophil maturity and the mechanisms through which they contribute to disease is critical. Neutrophils were first characterized morphologically by Ehrlich in 1879 using microscopy, and since then, different technologies have been used to assess neutrophil maturity. The advances in the imaging field, including state-of-the-art microscopy and machine learning algorithms for image analysis, reinforce the use of neutrophil nuclear morphology as a fundamental marker of maturity, applicable for objective classification in clinical diagnostics. New emerging approaches, such as the capture of changes in chromatin topology, will provide mechanistic links between the nuclear shape, chromatin organization, and transcriptional regulation during neutrophil maturation.

Automated, high-throughput quantification of EGFP-expressing neutrophils in zebrafish by machine learning and a highly-parallelized microscope

Normal development of the immune system is essential for overall health and disease resistance. Bony fish, such as the zebrafish (Danio rerio), possess all the major immune cell lineages as mammals and can be employed to model human host response to immune challenge. Zebrafish neutrophils, for example, are present in the transparent larvae as early as 48 hours post fertilization and have been examined in numerous infection and immunotoxicology reports. One significant advantage of the zebrafish model is the ability to affordably generate high numbers of individual larvae that can be arrayed in multi-well plates for high throughput genetic and chemical exposure screens. However, traditional workflows for imaging individual larvae have been limited to low-throughput studies using traditional microscopes and manual analyses. Using a newly developed, parallelized microscope, the Multi-Camera Array Microscope (MCAM™), we have optimized a rapid, high-resolution algorithmic method to count fluorescently labeled cells in zebrafish larvae in vivo. Using transgenic zebrafish larvae, in which neutrophils express EGFP, we captured 18 gigapixels of images across a full 96-well plate, in 75 seconds, and processed the resulting datastream, counting individual fluorescent neutrophils in all individual larvae in 5 minutes. This automation is facilitated by a machine learning segmentation algorithm that defines the most in-focus view of each larva in each well after which pixel intensity thresholding and blob detection are employed to locate and count fluorescent cells. We validated this method by comparing algorithmic neutrophil counts to manual counts in larvae subjected to changes in neutrophil numbers, demonstrating the utility of this approach for high-throughput genetic and chemical screens where a change in neutrophil number is an endpoint metric. Using the MCAM™ we have been able to, within minutes, acquire both enough data to create an automated algorithm and execute a biological experiment with statistical significance. Finally, we present this open-source software package which allows the user to train and evaluate a custom machine learning segmentation model and use it to localize zebrafish and analyze cell counts within the segmented region of interest. This software can be modified as needed for studies involving other zebrafish cell lineages using different transgenic reporter lines and can also be adapted for studies using other amenable model species.

Zebrafish: A Relevant Genetic Model for Human Primary Immunodeficiency (PID) Disorders?

Primary immunodeficiency (PID) disorders, also commonly referred to as inborn errors of immunity, are a heterogenous group of human genetic diseases characterized by defects in immune cell development and/or function. Since these disorders are generally uncommon and occur on a variable background profile of potential genetic and environmental modifiers, animal models are critical to provide mechanistic insights as well as to create platforms to underpin therapeutic development. This review aims to review the relevance of zebrafish as an alternative genetic model for PIDs. It provides an overview of the conservation of the zebrafish immune system and details specific examples of zebrafish models for a multitude of specific human PIDs across a range of distinct categories, including severe combined immunodeficiency (SCID), combined immunodeficiency (CID), multi-system immunodeficiency, autoinflammatory disorders, neutropenia and defects in leucocyte mobility and respiratory burst. It also describes some of the diverse applications of these models, particularly in the fields of microbiology, immunology, regenerative biology and oncology.