Whole-brain tissue mapping toolkit using large-scale highly multiplexed immunofluorescence imaging and deep neural networks

Dragan Maric,Aditi Singh,Andrea Sedlock,Xiaoyang Rebecca Li,Jahandar Jahanipour,Kedar Grama,Badrinath Roysam,Aryan Mobiny,Hien Van Nguyen

doi:10.1038/s41467-021-21735-x

Abstract

Mapping biological processes in brain tissues requires piecing together numerous histological observations of multiple tissue samples. We present a direct method that generates readouts for a comprehensive panel of biomarkers from serial whole-brain slices, characterizing all major brain cell types, at scales ranging from subcellular compartments, individual cells, local multi-cellular niches, to whole-brain regions from each slice. We use iterative cycles of optimized 10-plex immunostaining with 10-color epifluorescence imaging to accumulate highly enriched image datasets from individual whole-brain slices, from which seamless signal-corrected mosaics are reconstructed. Specific fluorescent signals of interest are isolated computationally, rejecting autofluorescence, imaging noise, cross-channel bleed-through, and cross-labeling. Reliable large-scale cell detection and segmentation are achieved using deep neural networks. Cell phenotyping is performed by analyzing unique biomarker combinations over appropriate subcellular compartments. This approach can accelerate pre-clinical drug evaluation and system-level brain histology studies by simultaneously profiling multiple biological processes in their native anatomical context.

Highlights

Mapping biological processes in brain tissues requires piecing together numerous histological observations of multiple tissue samples
This requires the ability to record high-resolution images of brain tissue covering a comprehensive panel of molecular biomarkers, over a large spatial extent, e.g., whole-brain slices, and automated ability to generate quantitative readouts of biomarker expression for all cells, identifying the type/sub-type and phenotypic state of each cell based on unique combinations of biomarkers, and aggregating the resulting data at scales ranging from subcellular compartments, individual cells, local multi-cellular niches, to whole-brain regions
To address the above challenges, we present a comprehensive toolkit for achieving a major scaling of the multiplexing level and spatial extent using a conventional epifluorescence microscope optimized for high-content imaging to phenotype all major cell classes resident to the whole brain, efficiently overcoming the fluorescence signal limitations, and achieving highly enriched and high-quality source imagery for reliable automated scoring at scale

Summary

Introduction

Mapping biological processes in brain tissues requires piecing together numerous histological observations of multiple tissue samples. Our toolkit includes 3 major components: (1) optimized multiplex IHC staining and multispectral epifluorescence imaging protocols and associated computational tools; (2) robust fluorescence signal isolation algorithms; and (3) comprehensive deep learning-based methods for automated cell phenotyping at scale (Fig. 1a) This toolkit generates a comprehensive data table that can be analyzed without limitations and profiled at multiple scales ranging from individual cells and multi-cellular niches to wholebrain anatomic regions in thin sections which enables high fidelity protein expression screening and data mining due to accessibility of target proteins to applied antibodies. The readouts can be stacked to assess 3D brain immunohistology datasets from serial 2D sections (Fig. 1b), allowing comprehensive systemlevel studies of brain structure and function

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