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1School of Molecular Bioscience, University of Sydney, 2Department of Surgery, Royal Prince Alfred Hospital, 3Department of Anatomical Pathology, Department of Anatomical Pathology, 4Department of Medicine, Concord Repatriation General Hospital
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We described a procedure for the disaggregation of colorectal cancer (CRC) to produce viable single cells, which are then captured on customized antibody microarrays recognizing surface antigens (DotScan CRC microarray). Sub-populations of cells bound to the microarray can be profiled by fluorescence multiplexing using monoclonal antibodies tagged with fluorescent dyes.
Zhou, J., Belov, L., Solomon, M. J., Chan, C., Clarke, S. J., Christopherson, R. I. Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing. J. Vis. Exp. (55), e3322, doi:10.3791/3322 (2011).
The current prognosis and classification of CRC relies on staging systems that integrate histopathologic and clinical findings. However, in the majority of CRC cases, cell dysfunction is the result of numerous mutations that modify protein expression and post-translational modification1.
A number of cell surface antigens, including cluster of differentiation (CD) antigens, have been identified as potential prognostic or metastatic biomarkers in CRC. These antigens make ideal biomarkers as their expression often changes with tumour progression or interactions with other cell types, such as tumour-infiltrating lymphocytes (TILs) and tumour-associated macrophages (TAMs).
The use of immunohistochemistry (IHC) for cancer sub-classification and prognostication is well established for some tumour types2,3. However, no single ‘marker’ has shown prognostic significance greater than clinico-pathological staging or gained wide acceptance for use in routine pathology reporting of all CRC cases.
A more recent approach to prognostic stratification of disease phenotypes relies on surface protein profiles using multiple 'markers'. While expression profiling of tumours using proteomic techniques such as iTRAQ is a powerful tool for the discovery of biomarkers4, it is not optimal for routine use in diagnostic laboratories and cannot distinguish different cell types in a mixed population. In addition, large amounts of tumour tissue are required for the profiling of purified plasma membrane glycoproteins by these methods.
In this video we described a simple method for surface proteome profiling of viable cells from disaggregated CRC samples using a DotScan CRC antibody microarray The 122-antibody microarray consists of a standard 82-antibody region recognizing a range of lineage-specific leukocyte markers, adhesion molecules, receptors and markers of inflammation and immune response5, together with a satellite region for detection of 40 potentially prognostic markers for CRC. Cells are captured only on antibodies for which they express the corresponding antigen. The cell density per dot, determined by optical scanning, reflects the proportion of cells expressing that antigen, the level of expression of the antigen and affinity of the antibody6.
For CRC tissue or normal intestinal mucosa, optical scans reflect the immunophenotype of mixed populations of cells. Fluorescence multiplexing can then be used to profile selected sub-populations of cells of interest captured on the array. For example, Alexa 647-anti-epithelial cell adhesion molecule (EpCAM; CD326), is a pan-epithelial differentiation antigen that was used to detect CRC cells and also epithelial cells of normal intestinal mucosa, while Phycoerythrin-anti-CD3, was used to detect infiltrating T-cells7. The DotScan CRC microarray should be the prototype for a diagnostic alternative to the anatomically-based CRC staging system.
Figure 1. Work flow for preparation of a suspension of live cells from a surgical sample of CRC.
1. Clinical sample disaggregation
All samples were collected from the Royal Prince Alfred Hospital (Camperdown, NSW, Australia) and Concord Repatriation Hospital (Concord West, NSW, Australia) with informed consent under Protocol No. X08-164.
2. Sample preparation for cell capture
3. Antibody microarray cell capture
4. Fluorescence multiplexing
5. Representative Results:
Results from the DotScan microarray should show consistent cell binding patterns between duplicate arrays. Strong alignment dot binding (CD44/CD29) enables a grid to be placed over the array area. Figure 2 shows an example of optimal cell capture and multiplexing. Figure 3 shows some common problems encountered during cell capture and the possible solutions
The microarray cell binding results can be quantified by measuring dot intensities expressed on a greyness scale ranging from 1 to 256. Figure 4 shows numerical data from 58 surgical CRC samples, stained with EpCAM-Alexa 647 antibody, as a heatmap with hierarchical clustering. Even though the number of samples is limited, CRCs of the same stage tend to cluster in the same group.
Figure 2. Cell binding pattern of clinical colorectal cancer tumour (Australian Clinic-Pathological Staging, ACP stage B1). (a) DotScan antibody key showing locations of antibodies for the left half of the duplicate microarray (outlined). The top section contains the original 82 antibodies of the DotScan leukemia microarray. An additional 40 antibodies, corresponding to specific surface antigens found to be up-regulated in the literature, were added as a CRC ‘satellite’ microarray. The bottom section consists of isotype control antibodies (b) Optical image of CRC cells binding to the microarray. (c) CD3 fluorescence image showing T-cells. (d) EpCAM fluorescence image showing CRC cells.
Figure 3. Examples of poor DotScan results and possible solutions. (a) Low cell binding; solution: make sure at least 4x106 viable cells are on the array (b) Isotype control binding and non-specific cell binding; solution: add heat-inactivated human AB serum to sample before incubation on microarray to minimise isotype control binding. Occasionally, a small amount of non-specific binding of cells to the nitrocellulose occurs with CRC samples and does not significantly affect the results. (c) Nitrocellulose drying out during incubation; solution: ensure sample covers the whole nitrocellulose section and microarray is incubated on a flat surface. (d) High background artifacts; solution: ensure the microarray is thoroughly washed following incubation.
Figure 4. DotScan analysis software generated bar charts representing cell binding densities on a greyness scale ranging from 1 to 256. Numbers on the axis refer to CD antigens. Other abbreviations are TCR, T-cell receptor; κ, λ, immunoglobulin light chains; sIg, surface immunoglobulin; DCC, deleted in colorectal cancer protein; EGFR, epidermal growth factor receptor; FAP, fibroblast activation protein; HLA-A,B,C HLA-DR, human leukocyte antigens DR and A,B,C respectively; MICA, MHC class I chain-related protein A; MMP-14, matrix metallopeptidase 14; PIGR, polymeric immunoglobulin receptor; TSP-1, thrombospondin-1; Mabthera, humanised anti-CD20. Click here to view larger image.
In this video, we demonstrate how the DotScan antibody microarray can be used in a simple, semi-quantitative way to study surface antigen profiles for cell populations from CRC tissue.
Obtaining a viable single cell suspension from tissue is critical to the success of the experiment, because energy-dependent processes (eg., antigen capping and/or pseudopodia formation) appear to be required for firm binding of whole cells to antibody dots during incubation, while dead cells are subsequently washed off. Type 1 collagenase was initially employed for tissue disaggregation8, but was replaced by collagenase 4 which causes less damage to cell membranes, as it contains fewer protease contaminants. This change did not affect cell yield, viability or binding patterns. Mucus from some tumours and control samples reduced cell yield and interfered with cell capture. This stickiness of the mucus was minimized by storage of disaggregated cell suspension in 10% DMSO/FCS at -80°C, presumably due to changes in mucus properties after freezing and thawing9. Subsequently, all samples were stored frozen in 10% DMSO/FCS after disaggregation and were rapidly thawed to produce viable cell suspensions, with consistent binding patterns. Samples with <50% viability or showing poor binding on alignment/housekeeping dots (CD44/CD29 ) were omitted from the analysis.
A few antibody clones showed reduced affinity when bound to the nitrocellulose possibly due to a change in conformation, e.g., the prominent CRC marker CD15s had very little or no cell binding. Antibodies that exhibited consistently negative results should be replaced with different hybridoma clones. Another possible cause of poor activity of some antibodies was interference to binding by bovine serum albumin (BSA). BSA-free antibodies should be used on the microarray where possible.
Although large patient cohorts are required for statistical analysis of microarray data, hierarchical clustering of our results (58 clinical samples) has been encouraging. Normalization of data using approaches described by Yang10 should provide improved statistical significance.
While the DotScan antibody microarray enables determination of new patterns of expression of known CD antigens, it is most effective when used in combination with proteomic discovery techniques, such as 2-dimensional gel electrophoresis and LC-iTRAQ-MS, to identify novel differentially abundant proteins. Such novel proteins are potential markers; corresponding antibodies could be added to the array, and validated with clinical CRC samples.
The use of fluorescently-labeled antibodies to profile sub-sets of captured cells provides a powerful DotScan platform for analysis of mixed populations of cells.
No conflicts of interest declared.
We thank staff at the Anatomical Pathology Laboratories of the Royal Prince Alfred and Concord Repatriation Hospitals for collecting fresh samples of CRC and normal intestinal mucosa. The work was funded by a Cancer Institute New South Wales Translational Program Grant.
|Hanks’ balanced salt solution||Sigma-Aldrich||H6136-10X1L||Buffered with 25 mM Hepes (Sigma #H3375)|
|Airpure biological safety cabinet class II||Westinghouse||1687-2340/612|
|Surgical blades||Livingstone||090609||Pack of 100|
|RPMI 1640 with 2 mM Hepes||Sigma-Aldrich||R4130-10X1L|
|Collagenase type 4||Worthington Biochemical||4188|
|Terumo Syringe (10 mL)||Terumo Medical Corp.||SS+10L||Box of 100|
|Filcon filter (200 μm)||BD Biosciences||340615|
|Filcon filter (50 μm)||Filcon filter (50 µm)||Filcon filter (50 µm) Filcon filter (50 µm) 340603|
|Fetal calf serum||GIBCO, by Life Technologies||10099-141|
|Centrifuge 5810 R||Eppendorf||7017|
|Hemocymeter Technocolor Neubar||Hirschmann||not available|
|Light microscope||Nikon Instruments||Nikon TMS|
|Cyrovial tubes||Greiner Bio-One||121278|
|Cryo freezing contrainer||Nalge Nunc international||5100-0001|
|DotScan antibody microarray kit||Medsaic||not available|
|DotScan microarray wash tray||Medsaic||not available|
|Bovine serum albumin||Sigma-Aldrich||A9418-10G|
|Heat-inactivated AB serum 2%||Invitrogen||34005100|
|Phyc–rythrin-conjugated CD3||Beckman Coulter Inc.||ET386|
|Typhoon FLA 9000||GE Healthcare||28-9558-08||532 nm laser, 580 BP30 emission filter for PE. 633 nm laser and 670 BP30 emission filter for Alexa647|
|MultiExperiment Viewer v4.4||TM4 Microarray Software Suite||Open – source software (Ref 11)|
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