Musodiq O. Bello

Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue

Limitations on the number of unique protein and DNA molecules that can be characterized microscopically in a single tissue specimen impede advances in understanding the biological basis of health and disease. Here we present a multiplexed fluorescence microscopy method (MxIF) for quantitative, single-cell, and subcellular characterization of multiple analytes in formalin-fixed paraffin-embedded tissue. Chemical inactivation of fluorescent dyes after each image acquisition round allows reuse of common dyes in iterative staining and imaging cycles. The mild inactivation chemistry is compatible with total and phosphoprotein detection, as well as DNA FISH. Accurate computational registration of sequential images is achieved by aligning nuclear counterstain-derived fiducial points. Individual cells, plasma membrane, cytoplasm, nucleus, tumor, and stromal regions are segmented to achieve cellular and subcellular quantification of multiplexed targets. In a comparison of pathologist scoring of diaminobenzidine staining of serial sections and automated MxIF scoring of a single section, human epidermal growth factor receptor 2, estrogen receptor, p53, and androgen receptor staining by diaminobenzidine and MxIF methods yielded similar results. Single-cell staining patterns of 61 protein antigens by MxIF in 747 colorectal cancer subjects reveals extensive tumor heterogeneity, and cluster analysis of divergent signaling through ERK1/2, S6 kinase 1, and 4E binding protein 1 provides insights into the spatial organization of mechanistic target of rapamycin and MAPK signal transduction. Our results suggest MxIF should be broadly applicable to problems in the fields of basic biological research, drug discovery and development, and clinical diagnostics.

3D volume reconstruction of a mouse brain from histological sections using warp filtering

Sectioning tissues for optical microscopy often introduces upon the resulting sections distortions that make 3D reconstruction difficult. Here we present an automatic method for producing a smooth 3D volume from distorted 2D sections in the absence of any undistorted references. The method is based on pairwise elastic image warps between successive tissue sections, which can be computed by 2D image registration. Using a Gaussian filter, an average warp is computed for each section from the pairwise warps in a group of its neighboring sections. The average warps deform each section to match its neighboring sections, thus creating a smooth volume where corresponding features on successive sections lie close to each other. The proposed method can be used with any existing 2D image registration method for 3D reconstruction. In particular, we present a novel image warping algorithm based on dynamic programming that extends Dynamic Time Warping in 1D speech recognition to compute pairwise warps between high-resolution 2D images. The warping algorithm efficiently computes a restricted class of 2D local deformations that are characteristic between successive tissue sections. Finally, a validation framework is proposed and applied to evaluate the quality of reconstruction using both real sections and a synthetic volume.

Quantitative single cell analysis of cell population dynamics during submandibular salivary gland development and differentiation

Summary Epithelial organ morphogenesis involves reciprocal interactions between epithelial and mesenchymal cell types to balance progenitor cell retention and expansion with cell differentiation for evolution of tissue architecture. Underlying submandibular salivary gland branching morphogenesis is the regulated proliferation and differentiation of perhaps several progenitor cell populations, which have not been characterized throughout development, and yet are critical for understanding organ development, regeneration, and disease. Here we applied a serial multiplexed fluorescent immunohistochemistry technology to map the progressive refinement of the epithelial and mesenchymal cell populations throughout development from embryonic day 14 through postnatal day 20. Using computational single cell analysis methods, we simultaneously mapped the evolving temporal and spatial location of epithelial cells expressing subsets of differentiation and progenitor markers throughout salivary gland development. We mapped epithelial cell differentiation markers, including aquaporin 5, PSP, SABPA, and mucin 10 (acinar cells); cytokeratin 7 (ductal cells); and smooth muscle &agr;-actin (myoepithelial cells) and epithelial progenitor cell markers, cytokeratin 5 and c-kit. We used pairwise correlation and visual mapping of the cells in multiplexed images to quantify the number of single- and double-positive cells expressing these differentiation and progenitor markers at each developmental stage. We identified smooth muscle &agr;-actin as a putative early myoepithelial progenitor marker that is expressed in cytokeratin 5-negative cells. Additionally, our results reveal dynamic expansion and redistributions of c-kit- and K5-positive progenitor cell populations throughout development and in postnatal glands. The data suggest that there are temporally and spatially discreet progenitor populations that contribute to salivary gland development and homeostasis.

Building 3D surface networks from 2D curve networks with application to anatomical modeling

Constructing 3D surfaces that interpolate 2D curves defined on parallel planes is a fundamental problem in computer graphics with wide applications including modeling anatomical structures. Typically the problem is simplified so that the 2D curves partition each plane into only two materials (e.g., air versus tissue). Here we consider the general problem where each plane is partitioned by a curve network into multiple materials (e.g., air, cortex, cerebellum, etc.). We present a novel method that automatically constructs a surface network from curve networks with arbitrary topology and partitions an arbitrary number of materials. The surface network exactly interpolates the curve network on each plane and is guaranteed to be free of gaps or self-intersections. In addition, our method provides a flexible framework for user interaction so that the surface topology can be modified conveniently when necessary. As an application, we applied the method to build a high-resolution 3D model of the mouse brain from 2D anatomical boundaries defined on 350 tissue sections. The surface network accurately models the partitioning of the brain into 17 abutting anatomical regions with complex topology.

Autofluorescence Removal by Non-Negative Matrix Factorization

This paper describes a new, physically interpretable, fully automatic algorithm for removal of tissue autofluorescence (AF) from fluorescence microscopy images, by non-negative matrix factorization. Measurement of signal intensities from the concentration of certain fluorescent reporter molecules at each location within a sample of biological tissue is confounded by fluorescence produced by the tissue itself (autofluorescence). Spectral mixing models use mixing coefficients to specify how much fluorescence from each source is present and unmixing algorithms separate the two fluorescent sources. Current spectral unmixing methods for AF removal often require a priori knowledge of mixing coefficients. Those which do not, such as principal component analysis, generate negative mixing coefficients that are not physically meaningful. Non-negative matrix factorization constrains mixing coefficients to be non-negative, and has been used for spectral unmixing, but not AF removal. This paper describes a novel non-negative matrix factorization algorithm which separates fluorescent images into true signal and AF components utilizing an estimate of the dark current. We also present a test-bed, based on fluorescent beads, to compare the performance of different AF removal algorithms. Our algorithm out-performed previous state of the art on validation images.

Multi-modal imaging of histological tissue sections

Two common imaging modalities for histological sections are brightfield and fluorescence microscopy imaging. Hematoxylin-Eosin (H&E) based brightfield microscopy has been the traditional imaging technique for imaging morphology, while an epi-fluorescent microscope is used for immunofluorescent staining of specific proteins or fluorescent in situ hybridization (FISH) for genetic based analysis of DNA. Simultaneous imaging of both microscopy modalities has been difficult due to optical and chemical effects of the H&E dyes. We present a novel sequential imaging and registration technique that enables brightfield and fluorescent imaging on the same tissue section, hence combining the traditional anatomic pathology with the newly emerging field of molecular pathology. First the tissue is labeled with fluorescent biomarkers, and imaged through a fluorescence microscope, and then the tissue is re-labeled with H&E dyes, and imaged again with traditional brightfield. Our robust registration algorithms achieve 99.8% registration success rate on tissue micro array (TMA) sections.

Learning-Based Segmentation Framework for Tissue Images Containing Gene Expression Data

Associating specific gene activity with functional locations in the brain results in a greater understanding of the role of the gene. To perform such an association for the more than 20 000 genes in the mammalian genome, reliable automated methods that characterize the distribution of gene expression in relation to a standard anatomical model are required. In this paper, we propose a new automatic method that results in the segmentation of gene expression images into distinct anatomical regions in which the expression can be quantified and compared with other images. Our contribution is a novel hybrid atlas that utilizes a statistical shape model based on a subdivision mesh, texture differentiation at region boundaries, and features of anatomical landmarks to delineate boundaries of anatomical regions in gene expression images. This atlas, which provides a common coordinate system for internal brain data, is being used to create a searchable database of gene expression patterns in the adult mouse brain. Our framework annotates the images about four times faster and has achieved a median spatial overlap of up to 0.92 compared with expert segmentation in 64 images tested. This tool is intended to help scientists interpret large-scale gene expression patterns more efficiently

Automated pipeline for atlas-based annotation of gene expression patterns: Application to postnatal day 7 mouse brain

Massive amounts of image data have been collected and continue to be generated for representing cellular gene expression throughout the mouse brain. Critical to exploiting this key effort of the post-genomic era is the ability to place these data into a common spatial reference that enables rapid interactive queries, analysis, data sharing, and visualization. In this paper, we present a set of automated protocols for generating and annotating gene expression patterns suitable for the establishment of a database. The steps include imaging tissue slices, detecting cellular gene expression levels, spatial registration with an atlas, and textual annotation. Using high-throughput in situ hybridization to generate serial sets of tissues displaying gene expression, this process was applied toward the establishment of a database representing over 200 genes in the postnatal day 7 mouse brain. These data using this protocol are now well-suited for interactive comparisons, analysis, queries, and visualization.

Landmark-Driven, Atlas-Based Segmentation of Mouse Brain Tissue Images Containing Gene Expression Data

To better understand the development and function of the mammalian brain, researchers have begun to systematically collect a large number of gene expression patterns throughout the mouse brain using technology recently developed for this task. Associating specific gene activity with specific functional locations in the brain anatomy results in a greater understanding of the role of the gene’s products. To perform such an association for a large amount of data, reliable automated methods that characterize the distribution of gene expression in relation to a standard anatomical model are required. In this paper, we present an anatomical landmark detection method that has been incorporated into an atlas-based segmentation. The addition of this technique significantly increases the accuracy of automated atlas-deformation. The resulting large-scale annotation will help scientists interpret gene expression patterns more rapidly and accurately.

Subdivision meshes for organizing spatial biomedical data

As biomedical images and volumes are being collected at an increasing speed, there is a growing demand for efficient means to organize spatial information for comparative analysis. In many scenarios, such as determining gene expression patterns by in situ hybridization, the images are collected from multiple subjects over a common anatomical region, such as the brain. A fundamental challenge in comparing spatial data from different images is how to account for the shape variations among subjects, which make direct image-to-image comparisons meaningless. In this paper, we describe subdivision meshes as a geometric means to efficiently organize 2D images and 3D volumes collected from different subjects for comparison. The key advantages of a subdivision mesh for this purpose are its light-weight geometric structure and its explicit modeling of anatomical boundaries, which enable efficient and accurate registration. The multi-resolution structure of a subdivision mesh also allows development of fast comparison algorithms among registered images and volumes.