Automated Analysis and Classification of Histological Tissue Features by Multi-Dimensional Microscopic Molecular Profiling

<div><p>Characterization of the molecular attributes and spatial arrangements of cells and features within complex human tissues provides a critical basis for understanding processes involved in development and disease. Moreover, the ability to automate steps in the analysis and interpretation of histological images that currently require manual inspection by pathologists could revolutionize medical diagnostics. Toward this end, we developed a new imaging approach called multidimensional microscopic molecular profiling (MMMP) that can measure several independent molecular properties <i>in situ</i> at subcellular resolution for the same tissue specimen. MMMP involves repeated cycles of antibody or histochemical staining, imaging, and signal removal, which ultimately can generate information analogous to a multidimensional flow cytometry analysis on intact tissue sections. We performed a MMMP analysis on a tissue microarray containing a diverse set of 102 human tissues using a panel of 15 informative antibody and 5 histochemical stains plus DAPI. Large-scale unsupervised analysis of MMMP data, and visualization of the resulting classifications, identified molecular profiles that were associated with functional tissue features. We then directly annotated H&E images from this MMMP series such that canonical histological features of interest (e.g. blood vessels, epithelium, red blood cells) were individually labeled. By integrating image annotation data, we identified molecular signatures that were associated with specific histological annotations and we developed statistical models for automatically classifying these features. The classification accuracy for automated histology labeling was objectively evaluated using a cross-validation strategy, and significant accuracy (with a median per-pixel rate of 77% per feature from 15 annotated samples) for <i>de novo</i> feature prediction was obtained. These results suggest that high-dimensional profiling may advance the development of computer-based systems for automatically parsing relevant histological and cellular features from molecular imaging data of arbitrary human tissue samples, and can provide a framework and resource to spur the optimization of these technologies.</p></div

Gallery of Tissue Feature Predictions by Automated Histology.

<p>Color-coded output of <i>de novo</i> histological feature identification (performed as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128975#pone.0128975.g006" target="_blank">Fig 6</a>) is presented for six tissue sections. The average per-pixel classification accuracy across all annotated features for these samples was 72% (median = 75%). Tissue sections displayed are normal human colon (#1.6.7), normal human liver (#1.8.10), human breast with malignant carcinoma (#1.9.2), normal human fallopian tube (#1.9.6), normal human duodenum (#1.9.7), and normal terminal ileum of the small bowel (#1.10.7).</p

Overview of Multi-dimensional Microscopic Molecular Profiling (MMMP).

<p>The overall MMMP approach is depicted using an example tissue section from normal human duodenum (sample #1.9.7). (a) Slides were subjected to repeated cycles of staining and imaging with fluorescent primary antibodies and DAPI. At the end of each cycle, fluorescent signal was removed by a chemical bleaching process, and slides were again imaged, before proceeding to the next round of this iterative procedure. After the final antibody stain (#15 Sma), slides were analyzed with a series of histochemical stains. (b) A set of tiling images spanning each tissue section was initially generated by the microscope system. The tiling images were then computationally ‘stitched’ together to produce a single image per staining cycle for each sample. (c) Image registration was performed to align images from the same tissue section across cycles. Mean intensities of the DAPI signal from all immuno-fluorescence images are shown from before (Unregistered) and after (Registered) the image registration procedure was completed. (d) Following registration, signal intensities from the relevant channels for each image (columns) in the MMMP series were extracted for each pixel (rows) within the tissue section and compiled into a large data matrix of <i>in situ</i> molecular profiles.</p

Principal Components Analysis of MMMP Data.

<p>(a) The mean value of the cumulative percentage of the variance in the data explained is plotted as a function of the number of principal components for data representing 102 human tissue samples. Red error bars indicate +/–one standard deviation. (b) The cumulative percentage of variance explained by the first <i>X</i> principal components is shown as a function of <i>X</i> for an example tissue section from the terminal ileum of the small intestine (sample #1.10.7). (c) H&E (hematoxylin and eosin) stained image of sample #1.10.7. (d) Images depicting scaled intensity values for the first three principal components (PC-1, PC-2, and PC-3) of the MMMP data matrix for sample #1.10.7 are shown.</p

Automated Histology by Classification of MMMP Data.

<p>(a) The normal human colon tissue section (Sample #1.6.7) from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128975#pone.0128975.g005" target="_blank">Fig 5</a> was partitioned into two independent regions according to a checkerboard pattern (Region 1 = white squares, Region 2 = black squares). Region 1 was used as a “training set” to build a classification model for recognizing histological features based on their molecular profiles, and this model was applied to generate predictions based on data from the “test set” of Region 2. A reciprocal analysis was also performed (i.e. Region 2 = training set, Region 1 = test set) to generate automated labeling of histological categories for the entire tissue section. (b) The results from the automated histology classification are illustrated, using the appropriate color scheme for features as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0128975#pone.0128975.g005" target="_blank">Fig 5</a>. (c) The accuracy of automated feature recognition was evaluated by comparison to the original annotations. The ‘confusion matrix’ representing the frequency with which pixels manually annotated to each feature (rows) were automatically classified as belonging to any given feature (columns) is shown. The classification accuracy for each feature is shown to the right (overall median accuracy was 68%). (d) The per-feature accuracy for classification based all dimensions of the MMMP data for Sample #1.6.7 is plotted (<i>y</i>-axis) versus the corresponding classification accuracy obtained when only data from the hematoxylin and eosin channels was used (<i>x</i>-axis).</p

Molecular Analysis of Annotated Histological Features.

<p>(a) Hematoxylin and eosin image of a normal human colon tissue section (Sample #1.6.7) was used as a reference for manual annotation of relevant cellular and histological features. (b) Different types of histological features were annotated and assigned arbitrary color codes for visualization purposes. Each labeled feature is displayed using the indicated color scheme. (c) Feature-specific molecular profiles were identified by cluster analysis of MMMP data associated with each annotated histological category, and presented in heatmap format. The associated feature types are indicated by the appropriate color code on the left of the heatmap, following the color scheme used in panel (b).</p

Analysis of Molecular Profiles from Full MMMP Dataset.

<p>(a) Cluster centroid vectors from <i>k</i>-means clustering of 102 human tissue samples were compiled into a single MMMP data matrix, which was then hierarchically clustered and displayed in heatmap format. Multi-dimensional scaling was used to generate a universal color palette (shown at right) for representing cluster membership based on similarity of molecular profiles. (b) Color-based visualization of the molecular profiles within each of three tissue sections is shown using the universal color mapping function depicted above. Tissue sections were derived from normal human duodenum (#1.9.7), terminal ileum of the small bowel (#1.10.7), and colon (#1.6.7), respectively.</p

MSANTD3 immunostaining of salivary gland benign and malignant specimens.

<p>MSANTD3 immunostaining of salivary gland benign and malignant specimens.</p

MSANTD3 structural domains and phylogenetic conservation.

<p><b>(A)</b> Schematic depiction of MSANTD3 domains, showing the location of the Myb/SANT-like domain within the N-terminus. <b>(B)</b> Schematic depiction of MSANTD3 MACAW sequence alignments across species. Boxes indicate conserved blocks, while the shading indicates pair-wise scores relative to human MSANTD3 with colors indicated in the key. <i>Above</i>, the horizontal black bar indicates the location of the conserved Myb/MSANT domain found by NCBI search (Pfam 13873). <b>(C)</b> Actual MACAW alignment within the MYB/SANT region. <b>(D)</b> Phylogenetic tree based on the global alignment made by ClustalX and visualized using Treeview software.</p

Novel genes fusions in acinic cell carcinoma.

<p><b>(A)</b> Predicted structure of the <i>HTN3</i>-<i>MSANTD3</i> fusion gene. Exon 1 (non-coding) of <i>HTN3</i> is fused to the exon 2 (first coding exon) of <i>MSANTD3</i>, leading to predicted overexpression of full-length MSANTD3 protein (translation start site is indicated). Fusion junction-spanning sequence reads are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171265#pone.0171265.s001" target="_blank">S1 Fig</a>. <b>(B)</b> Predicted structure of the <i>PRB3</i>-<i>ZNF217</i> gene fusion. Here, exon 2 (coding) of <i>PRB3</i> is fused to exon 2 (first coding) of <i>ZNF217</i>, possibly leading to the overexpression (by internal initiation of translation) of full-length ZNF217 protein. Fusion junction-spanning sequence reads are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171265#pone.0171265.s002" target="_blank">S2 Fig</a>.</p