3D-IBEX: Achieving multiplex 3-dimensional imaging for deep phenotyping of cells in tissues

2D multiplexed antibody-based imaging provides a framework to study cell-cell interactions in thin tissue sections but lacks the ability to interrogate spatial relationships in larger 3D anatomical structures. Several methods for 3D imaging of optically cleared tissue exist, but current approaches do not provide the marker depth afforded by high content 2D imaging. To overcome this constraint, we combined a fast hydrophilic tissue clearing technique, clearing-enhanced 3D (Ce3D), with the recently developed Iterative Bleaching Extends Multiplexity (IBEX) technique, and created 3D-IBEX. 3D-IBEX offers a high-resolution multiplex 3D imaging method. It has been used to analyze murine lung, lymph nodes, retina, and cornea as well as human retina and jejunum, yielding seamless views of large-scale tissue structures while obtaining single-cell resolution of multiple markers, permitting the identification of discrete cell subsets and structures. To date, up to 15 markers have been visualized in a single sample using 3 iterative staining and imaging cycles. This represents a major advance in the emerging field of multiplexed high-resolution 3D imaging but requires further refinements to address issues such as slow antibody penetration, the need for new computational algorithms to process large datasets, and implementation of methods for optimal registration of iterative images. When fully realized, 3D-IBEX will open new avenues for characterization of rare cell subsets missed in thin tissue sections, visualization of structures like nerves and vessels whose trajectories in a tissue are disrupted by sectioning, and delineation of mesoscale tissue domains that contribute to homeostasis and are often the substrates for pathology.

Intramural NIAID Research Opportunities (INRO) program.

IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues

High-content imaging is needed to catalog the variety of cellular phenotypes and multicellular ecosystems present in metazoan tissues. We recently developed iterative bleaching extends multiplexity (IBEX), an iterative immunolabeling and chemical bleaching method that enables multiplexed imaging (>65 parameters) in diverse tissues, including human organs relevant for international consortia efforts. IBEX is compatible with >250 commercially available antibodies and 16 unique fluorophores, and can be easily adopted to different imaging platforms using slides and nonproprietary imaging chambers. The overall protocol consists of iterative cycles of antibody labeling, imaging and chemical bleaching that can be completed at relatively low cost in 2-5 d by biologists with basic laboratory skills. To support widespread adoption, we provide extensive details on tissue processing, curated lists of validated antibodies and tissue-specific panels for multiplex imaging. Furthermore, instructions are included on how to automate the method using competitively priced instruments and reagents. Finally, we present a software solution for image alignment that can be executed by individuals without programming experience using open-source software and freeware. In summary, IBEX is a noncommercial method that can be readily implemented by academic laboratories and scaled to achieve high-content mapping of diverse tissues in support of a Human Reference Atlas or other such applications.

Image Segmentation, Registration and Characterization in R with SimpleITK

Many types of medical and scientific experiments acquire raw data in the form of images. Various forms of image processing and image analysis are used to transform the raw image data into quantitative measures that are the basis of subsequent statistical analysis. In this article we describe the SimpleITK R package. SimpleITK is a simplified interface to the insight segmentation and registration toolkit (ITK). ITK is an open source C++ toolkit that has been actively developed over the past 18 years and is widely used by the medical image analysis community. SimpleITK provides packages for many interpreter environments, including R. Currently, it includes several hundred classes for image analysis including a wide range of image input and output, filtering operations, and higher level components for segmentation and registration. Using SimpleITK, development of complex combinations of image and statistical analysis procedures is feasible. This article includes several examples of computational image analysis tasks implemented using SimpleITK, including spherical marker localization, multi-modal image registration, segmentation evaluation, and cell image analysis.