Multi-dimensional Flow Cytometry

Multi-Dimensional Flow Cytometry

Flow cytometry can generate numbers on virtually any single cell population from pollen to sperm to amoeba to leukemic megakaryoblasts. The significance of these numbers, however, has enormous potential to be misinterpreted, misleading, or meaningless in tissues as complex as bone marrow, peripheral blood, and lymph nodes.

When flow cytometry is reported as “percent positive,” a single number is produced when unvarying positive and negative thresholds are setup for all types of specimens. The instrument controls are set up correctly, the systems are highly automated, the number generated in multiple decimal places, and presented in orderly tables for perusal by a clinician or pathologist attempting to interpret the data. However, often arbitrary “cut-offs,” e.g., 20%, are used to determine the presence or absence of a particular cell surface or cytoplasmic antibody. This approach, while potentially useful when cell populations can be discretely separated, such as in normal CD4 positive lymphocytes, is inadequate for the study of complex cell mixtures and neoplastic processes when cells do not obey normal genetic regulatory processes, or for discriminating between immature cells from their older siblings in normal developmental processes.

Normal development of hematopoietic tissues is a beautifully coordinated and choreographed genetic phenomenon that has been characterized by its cell surface antigen expression.  But in malignant cells, genes may fire off at the wrong times, too often or not enough, or not at all. The products of these genes are observable as cell surface antigens. Each particular cell has a myriad of antigens on its surface that identify it within a given tissue. An approach that details the patterns of expression of these gene products can be useful when cells are not easily separated from each other, in neoplasia, and in characterizing normal developmental patterns.

Traveling in n-Dimensional Space

Over a century ago, Edwin A. Abbott’s classic Flatlanddescribed a world with only two dimensions, length and width. Upon discovering the existence of a third dimension, A. Square, a Flatland citizen, has big adventures in higher dimensional worlds. Square has great difficulties when he attempts to explain Upward as opposed to Northward to fellow Flatlanders. Likewise, those of us who live in 3D “Spaceland” have difficulty conceptualizing or diagramming >3 dimensions (“other than the enumeration of imaginary sides and angles”). Fortunately, computer programs exist (not quite like Deep Thought) that can ease multidimensional data into meaningful information.


Multidimensional flow cytometry is a term used when combining four, five (or more) characteristics simultaneously to create a data space where each measured cell parameter becomes a dimension in the multidimensional space. Current commercial flow cytometers are capable of assessing three, four and more monoclonal antibodies simultaneously on a single cell (*see 30 second flow lecture below). Multidimensional flow cytometry combines all information from all characteristics to create a multidimensional data space in which each parameter is a separate dimension protocols. This approach preserves the intensity of the relationships between each of the parameters. Each cell is plotted as a dot in the data space with cells having similar characteristics clustering together as a group of similar dots. Computer programs allow the identification of these clusters by displaying multiple projections of the data and by coloring different clusters of dots so they can be observed from different perspectives. This is in distinction to multiparameter analyses, in which each parameter is treated separately and the data are usually expressed as percent positive. Multidimensional analysis requires specialized software to permit viewing data in a manner that preserves the relationships of the dimensions.

Studies of normal hematopoiesis have been facilitated by the use of multiple cellular markers simultaneously to distinguish one type of cell as distinctly different from another. This approach was made possible by the development of multiple chromophores with sufficient spectral separation resolved by routine flow cytometers ref. The use of three colors of immunofluorescence simultaneously, in combination with forward and right angle light scattering (measuring cell size and granularity, respectively) is the minimum number of characteristics needed to distinguish developmental differences of all lineages in bone marrow. As each cell passes the laser beam, five signals are generated corresponding to the intensity of the fluorescence staining or light scatter generated by that cell. These five signals are stored in a computer to be used to distinguish one cell from another.

When using colors >4, the analysis in multidimensional space becomes difficult,if not impossible. The rush to add more colors to the flow cytometer, referred to as polychromatic flow or color inflation, may simplify operations by reducing the number of tubes to analyze but makes the analysis so complex that intensity relationships between antigens is no longer available.  Often these analyses become nothing more than two parameter data sets or even a series of one color analyses unless very small populations of interest are being analyzed.  This type of analysis usually requires a prior knowledge of the cell phenotype.  For example, minimal residual disease in a case of acute myeloid leukemia post chemotherapy could be patterned on the known diagnostic specimen, provided that the phenotype has not evolved during therapy.  This approach is not useful when the phenotype of the original disease is unknown, as in primary diagnostics, and a 3-4 color analysis is more appropriate in these settings.

Just Say No to Flat Flow

From the beginning, Hematologics has demonstrated and validated the importance of multidimensional flow cytometric techniques in rapidly diagnosing difficult cases and in detecting small levels of neoplastic cells. This advance in the use of the technology was the result of close collaborative interactions between the treating clinicians, pathologists and Hematologics staff, evolving the integration of information.

Our goals are to demonstrate how multidimensional flow cytometry can be used, in a routine clinical laboratory setting, to establish the correct diagnosis of hematologic abnormalities and to monitor treatment. In other words, we are attempting to illustrate how flow cytometry can be used over and above the simple phenotyping of leukemia and lymphoma as diagnosed by single or dual parameter flow cytometry. We decided to create Hematologics as a small business using revenue from multiple sources including small business grants, to support our clinical and basic science research. Our stated purpose is to improve the diagnostic accuracy of difficult cases in order to improve patient outcome by improved diagnostics.

Asking the Right Question

The techniques we have developed to detect neoplastic cells do not require having access to the diagnostic specimen, a unique approach in the field of flow cytometry. This approach was validated in a bone marrow transplant setting at the Fred Hutchinson Cancer Research Center where diagnostic specimens were unavailable and difference from normal is the only method to distinguish neoplastic cells from regenerating bone marrow after transplant. Our assays have been predicated on rapid processing of the specimen with high sensitivity and specificity. We have validated these procedures by publishing the results in direct comparison to standard techniques and with correlation to patient outcome.

These patterns of expression among normal cells are invariant with identical relationships between antigens from fetal life to the elderly, and in marrow reconstitution following chemotherapy or stem cell transplant. The analysis of leukemic cells has demonstrated that they express essentially the same antigens as their normal counterparts, but the neoplastic cells in most cases exhibit abnormal relationships between the antigens. In both acute lymphoblastic and acute myeloid leukemia, the patterns of expression do not match the relationships observed in normal development. The abnormalities can be classified into different categories:

  • Lineage duplicity, such as appearance of lymphoid antigens on myeloid lineage cells or the converse
  • Antigenic absence, antigens that should normally be expressed are not detectable on neoplastic cells
  • Abnormalities of intensity, antigens are expressed on neoplastic cells at higher or lower levels than are observed in normal

Multidimensional analysis shows that leukemia cells are not only different from normal, but the detailed phenotypes may be different from each other. This variability of antigen expression makes classification of hematopoietic malignancies based on phenotype difficult since there is a spectrum of patterns with few defining characteristics that permit grouping into logical classes as with the FAB classification system. However, there are some phenotypes that can be associated with specific cytogenetic abnormalities such as t(15;17) or t(8,21). Such phenotypic patterns facilitate the detection of cytogenetic abnormalities by fluorescence in situ hybridization (FISH) because flow cytometric analysis can be performed quickly and can often give enough information to suggest specific FISH probes.

We are able to provide turn around time often within the same day and have provided results in a STAT manner as quickly as 30 minutes. We can summarize our preliminary results immediately in an e-mail message if desired. We actively consult with attending physicians, fellows, pathologists and other interested clinicians regarding patient status and the relevance of the results. We are often the first to discover unusual findings or important changes in patients, and we alert the clinicians and pathologists about the results so immediate action can be taken.

The greatest utility of multidimensional flow cytometry is in monitoring response to therapy. The variation from normal can be used as a tumor specific marker to detect residual disease in regenerating bone marrow following chemotherapy or bone marrow transplant. Several studies have shown that detection of residual tumor following treatment places a patient into a higher risk category for relapse. For example, detection of residual disease in acute lymphoblastic leukemia by multidimensional flow found a significantly higher risk for relapse.

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