Toponome Imaging System: In Situ Protein Network Mapping in Normal and Cancerous Colon from the Same Patient Reveals More than Five-Thousand Cancer Specific Protein Clusters and Their Subcellular Annotation by Using a Three Symbol Code

Abstract

In a proof of principle study, we have applied an automated fluorescence toponome imaging system (TIS) to examine whether TIS can find protein network structures, distinguishing cancerous from normal colon tissue present in a surgical sample from the same patient. By using a three symbol code and a power of combinatorial molecular discrimination (PCMD) of 2(21) per subcellular data point in one single tissue section, we demonstrate an in situ protein network structure, visualized as a mosaic of 6813 protein clusters (combinatorial molecular phenotype or CMPs), in the cancerous part of the colon. By contrast, in the histologically normal colon, TIS identifies nearly 5 times the number of protein clusters as compared to the cancerous part (32 009). By subcellular visualization procedures, we found that many cell surface membrane molecules were closely associated with the cell cytoskeleton as unique CMPs in the normal part of the colon, while the same molecules were disassembled in the cancerous part, suggesting the presence of dysfunctional cytoskeleton-membrane complexes. As expected, glandular and stromal cell signatures were found, but interestingly also found were potentially TIS signatures identifying a very restricted subset of cells expressing several putative stem cell markers, all restricted to the cancerous tissue. The detection of these signatures is based on the extreme searching depth, high degree of dimensionality, and subcellular resolution capacity of TIS. These findings provide the technological rationale for the feasibility of a complete colon cancer toponome to be established by massive parallel high throughput/high content TIS mapping.

Toponome imaging system: multiplex biomarkers in oncology

“…This approach has yielded interesting results: 5708 cancer-specific CMPs have been identified in CRC, of 6813 CMPs identified in total, compared with normal colorectal tissue that, strikingly, contained 32 009 unique CMPs. These results were obtained using tissue from the same patient and a panel of only 21 affinity reagents [47]. The $5-fold reduction in unique CMPs seen in this cancerous tissue is probably testament to the dedifferentiation inherent to advanced malignancy.…”

“…Modern IFHC is, however, a reliable method that enhances Glossary Absent/anti-colocated protein (A): a protein that is not found, by definition, in any CMPs that comprise a CMP motif within a sample [49]. Biomarker: a measurable cellular component or other substance within an organism used as an indicator of a biological state (e.g., Ki67 in rapidly proliferating cells [47]). See below for more information on multiplex biomarkers.…”

“…Protein expression signals observed at each subcellular data point (e.g., a single pixel or voxel) can then be expressed as a binary code (after a threshold for each signal is either automatically generated or validated by an expert biologist), representing a combinatorial molecular phenotype (CMP). The CMP code consists of either 'tag present' (=1) or 'tag absent' (=0) for each data point [2,44,45,47,48]. This binarization of the system gives a theoretical power of combinatorial molecular discrimination (PCMD) of >2 100 (where 100 antibodies are used) within each subcellular data point and generates a series of several thousand unique CMPs, or protein clusters, found in the target tissue.…”

“…Proteins that are found in all CMP motifs are designated as 'lead' proteins (L), those which are not identified in any are designated as 'absent/ anti-colocated' (A), and those which are variably present/ absent across motifs are designated as wild-card (W) proteins. As an example, in a TIS study of one patient with CRC, Ki67 was identified as a lead protein, CD133 as a wild-card protein, and CD36 as an absent protein [47]. The LAW code represents the higher order functional organization of the toponome within any given cell, and a further example is provided in Box 1 [42,44].…”

Analyzing multi-tag bioimages with BioIMAX colocation mining tools

“…One tissue sample was selected from cancerous tissue, the other sample was selected from healthy colon tissue from the same patient. An antibody library of 22 tags (see [8] for details) was applied to record a stack of 22 fluorescence images from manually selected two visual fields in each sample, leading to four TIS data sets. After image registration has been applied [9,10], a set of N = 8 channels, i. e. proteins were selected for a deeper analysis.…”