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Single cell analysis and cell sorting has enabled the study of development, growth, differentiation, repair and maintenance of “liquid” tissues and their cancers. The application of these methods to solid tissues is equally promising, but several unique technical challenges must be addressed. This report illustrates the application of multidimensional flow cytometry to the identification of candidate stem/progenitor populations in non-small cell lung cancer and paired normal lung tissue. Seventeen paired tumor/normal lung samples were collected at the time of surgical excision and processed immediately. Tissues were mechanically and enzymatically dissociated into single cell suspension and stained with a panel of antibodies used for negative gating (CD45, CD14, CD33, glycophorin A), identification of epithelial cells (intracellular cytokeratin), and detection of stem/progenitor markers (CD44, CD90, CD117, CD133). DAPI was added to measure DNA content. Formalin fixed paraffin embedded tissue samples were stained with key markers (cytokeratin, CD117, DAPI) for immunofluorescent tissue localization of populations detected by flow cytometry. Disaggregated tumor and lung preparations contained a high proportion of events that would interfere with analysis, were they not eliminated by logical gating. We demonstrate how inclusion of doublets, events with hypodiploid DNA, and cytokeratin+ events also staining for hematopoietic markers reduces the ability to quantify epithelial cells and their precursors. Using the lung cancer/normal lung data set, we present an approach to multidimensional data analysis that consists of artifact removal, identification of classes of cells to be studied further (classifiers) and the measurement of outcome variables on these cell classes. The results of bivariate analysis show a striking similarity between the expression of stem/progenitor markers on lung tumor and adjacent tumor-free lung. © 2012 International Society for Advancement of Cytometry
Flow cytometry is an analytical technique designed to make measurements on single cells in suspension. As instrumentation, reagents and analytical tools have improved, flow cytometry has been applied brilliantly to the study of heterogeneous “liquid” tissues such as blood and bone marrow, as well as other easily dissociated solid tissues such as lymph node and spleen. Much of what has been discovered in the fields of immunology and hematology is the direct result of the ability to study such heterogeneous tissues at the level of the single cell. Classical hematologists were able to discover differentiation pathways and make inferences about the identity of hematopoietic progenitor cells by grouping cells on the basis of their morphological features and affinity for dyes (1). These techniques reached their limitations when cells of different function but identical morphology (e.g., CD4 and CD8 T lymphocytes) could not be distinguished, and when important cell populations, inferred by biological experiments, evaded detection because of their low frequency (e.g., definitive hematopoietic stem cells). These problems (2, 3) and many others, have yielded to flow cytometry because of its robust analytical capabilities, and also because it has been a preparative method since the inception of the fluorescence activated cell sorter (4, 5).
Flow cytometry has the potential to advance the study of solid tissue differentiation, maintenance and repair in the same way, but its application has been challenging since it was first used to characterize ovarian carcinoma(6), pancreas (7), and hepatocytes (8). Many of the difficulties that early investigators faced are still problematic: How to tease strongly adhered cells into single cell suspension while maintaining viability; how to quantify the selection bias that occurs when some cell types survive the process better than others; how to determine which markers and functions are perturbed by the process of disaggregation, and when such loss is irreversible; how to identify and eliminate cellular debris and other sources of artifact from the analysis; and finally, having identified discrete cell populations, how to understand this information in the context of whole tissue architecture? This report will attempt to address these problems using the detection of a panel of putative stem/progenitor markers in non-small cell lung carcinoma and normal adjacent lung tissue as an example.
The hyaluronic acid receptor CD44, a ubiquitous adhesion molecule, was early implicated in tumor metastasis (9) and is the principal marker proposed by Clarke and coworkers to identify tumorigenic breast cancer cells (10). In normal stem cell biology, CD44 has been proposed to play a role in the migration and homing of mesenchymal stem cells (11). CD90 appears to be a lineage-independent adult tissue stem cell marker and was first described on primitive human BM stem cells (12). It is also expressed on oval cells of the liver (13), and on perivascular stem cells (14) which are closely related to mesenchymal stem cells (15). We have proposed CD90 as a principal cancer stem/progenitor cell marker in a variety of epithelial cancers and demonstrated its presence on cytokeratin+/ABCG2+ cells in lung, ovarian, gastric and breast cancer (16, 17). CD45-/CD90+ cells have been detected in liver tumors and in the circulation of liver cancer patients (18). When CD44 and CD90 are coexpressed on cytokeratin+ cells, they mark highly tumorigenic cells in breast cancer. As few as 50 cells directly sorted from clinical isolates are capable of tumor formation when coinjected with irradiated tumor (16, 19) or adipose-derived stromal cells (20). Further, in liver cancer, CD44+/CD90+ cells demonstrated a more aggressive phenotype than their CD44 negative/CD90+ counterpart and form metastatic lesions in the lungs of immunodeficient mice (18). Like CD90, the type III tyrosine kinase CD117 (KIT, stem cell factor receptor) marks stem/progenitor cells in a variety of tissues, but is also present in terminally differentiated cells such as mast cells (21). Activating mutations in CD117 are implicated in gastrointestinal stromal tumors (GISTs) (22), and myeloid leukemia (23). In normal stem cell biology, CD117 was recently used to identify and sort-purify human lung stem cells capable of regenerating bronchioles, alveoli, and pulmonary vessels (24). In the bone marrow CD133 marks hematopoietic stem/progenitor cells (25). It is present on human prostate epithelial basal cells (26) and has also been implicated on putative cancer stem cells in a variety of tumors (27–29), and most recently in poor risk lung cancer (30).
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FFPE sections (Fig. 1) are critical to the interpretation of flow cytometry performed on digested tissues. These preparations provide a histologic context for key markers used in flow cytometry and provide a standard by which single cells suspensions may be evaluated for selection bias. The principal disadvantages of immunohistostaining, the small number of cells that can be evaluated, the subjective nature of analysis, and the technical difficulties associated with polychromatic staining, are easily overcome by flow cytometry. In the present data set, immunofluorescent staining provided several important cues for interpretation of flow cytometric data: 1) Cytokeratin negative CD117+ cells are mast cells; 2) Some morphologically identifiable tumor cells are cytokeratin negative; 3) The dichotomy observed by flow cytometry between patients with CD117+ and CD117- tumors was confirmed; and 4) Tissue processing for flow cytometry results in overrepresentation of hematopoietic cells, especially lymphocytes. In previous studies, combining immunohistostaining with flow cytometry allowed us to localize the cytokeratin+/CD44+/CD90+ population observed by flow cytometry to the invasive edge of breast tumors (19), and to determine the histologic location of CD45-/CD146-/CD31-/CD34+ adipose stem cells (35).
Among the first flow cytometric applications for disaggregated tumors was the study of tumor infiltrating immune cells (36). Compared to the study of epithelial tissues, which are complex vascularized structures in which cells are organized by avid adhesion to extracellular matrices and each other, tissue infiltrating immune cells are weakly associated and readily recovered as viable single cells. Tumors can be challenging to dissociate because they may be hardened by fibrosis and contain necrotic areas. However, careful observation and sequential digestion of tumor tissue will actually yield more cells per gram than normal tissue (Supporting Information Table S1).
Even the most careful tissue digestion will result in undigested tissue clumps, apoptotic cells, dead cells and subcellular debris. All of these will interfere with analysis and interpretation unless identified in the data set and removed by logical gating. The methods described here have previously been used for adipose tissue (35), normal breast and breast cancer tissues (19). Stem/progenitor populations are rare in most tissues and require special considerations for their detection and enumeration (37, 38). Most importantly, it is necessary to examine a sufficient number of “clean” events to yield an appropriate number of analyzable events. A population of 100 cells in an analytical region will give a Poisson counting coefficient of variation of 10%, as originally worked out by Student for the hemocytometer (39). To take an example from a recently published article describing multipotential adult human lung stem cells (24), which were present at a frequency of 1/24,000, a minimum of 2.4 million events (post artifact removal) must be acquired to attain a counting CV = 10%. Dealing with such large data sets requires specialized analytical software designed for parallel processing. Several packages are available for offline analysis. A hierarchical approach to data analysis, such as the artifact removal, classifier, outcome method described here helps to focus data exploration and analysis. However, the problem of more quantifiable features (i.e. analytical regions) than cases, with many variables highly correlated, is inherent in multidimensional cytometry data, and argues ultimately for an automated approach to data analysis. In its simplest form, this entails applying modern multivariate statistical techniques (40, 41) to the results of conventional gate/region type analyses such as those described here. Eventually, it may be possible to replace manual gate/region-based analysis with automated cluster-finding algorithms, but this can be a double-edged problem if attaining complete objectivity requires us to relinquish a wealth of a priori knowledge concerning the biological constraints imposed on marker expression.
In this data set, three of the four most significant distinguishing features identified by bivariate analysis involved a combination of morphology (light scatter), cytokeratin expression, and DNA content, features long used to identify tumor cells. Prior to analysis of stem/progenitor marker expression on nonhematopoietic cells, we chose to identify four classifier populations on the basis of cytokeratin expression and DNA content. In tumor samples, cytokeratin+ cells with >2N DNA are clearly tumor cells, but this does not exclude the possibility of cytokeratin negative or pseudodiploid tumor cells. Similarly normal lung airway cells have a proliferative (and therefore >2N) component (Supporting Information Fig. S3).
After subsetting the data on the basis of cytokeratin expression and DNA content, we found a striking similarity between stem/progenitor marker patterns in tumor and adjacent tumor-free lung. The conservation of expression patterns suggests that these proteins may play important functional roles in both tumor and the normal tissues (24). Similarly, we (17) and others (42–44) have demonstrated that constitutive self-protection mediated by ABC transporter activity in normal tissue stem cells can be retained or re-expressed in a subset of malignant cells. These data support the interpretation that airway stem cells and their malignant counterparts share at least some of these growth factor receptors and adhesion molecules, as has been demonstrated in colon cancer and normal colon (45). For example, CD44/CD90 expression on cytokeratin negative cells is consistent with mesenchymal stem cells in normal tissue, but in metastatic cancer, CD44/CD90 coexpression on cytokeratin positive cells (19) may signal epithelial to mesenchymal transition (46).
Taken together, our finding that tumor cells share stem/progenitor and adhesion markers with tumor-free chronically injured lung tissue is consistent with the hypothesis that the self-renewing, self-protected tumorigenic cell can take the form of a stem-progenitor hybrid in aggressive epithelial neoplasms such as lung cancer (17). Combining stem-like self-renewal and protection with high proliferative capacity, they need not be rare to exploit mechanisms employed by normal tissue stem cells for their renewal and survival.