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Leukemic B cells can be distinguished from normal B cells based on their surface staining patterns and their morphology, but the differences can be subtle and the abnormal events rare. In order to reliably differentiate normal from leukemic B cells, objective quantitation of large data sets is required. This is accomplished with the ImageStream cytometer, which automatically acquires multispectral images of thousands of individual cells in flow at very high speeds. The associated IDEAS data analysis software measures hundreds of photometric and morphometric features per cell, thereby enabling simultaneous immunophenotyping and morphology-based measurements. The features quantify individual image characteristics such as shape, size, signal strength and texture. We have leveraged these features by implementing an automated classification routine that identifies the feature combination that best separates different cell types of interest. Our automated classification routine utilizes a modification of Fisher’s Linear Discriminant criterion to measure discrimination power between two or more populations of cells. The classifier generates a composite feature consisting of a weighted linear combination of basic image features that provides maximum discrimination. Thus, our method transforms a multi-parameter classification task that can be difficult to conceptualize into a classification based on a single composite feature that can be readily conceptualized and visualized using a simple histogram output. Since the composite feature is generated from, and tested and optimized on, large numbers of cells, we obtain results that are highly objective, repeatable, statistically significant and scalable with data size. We used this classification approach to study normal and tumor cells of the hematopoietic lineage with the goal of improving diagnosis of hematological disorders. Here we show the results of using automated classification to distinguish the subtle differences between B cells of CLL patients and those of normal patients. We also used the classifier to distinguish B-cell ALL from B-cell CLL. In both cases, we used a combination of shape, size, texture and signal strength features from the SSC, brightfield and nuclear imagery generated by the ImageStream system. The data presented demonstrate the application of a novel automated cell classification method to the discrimination of normal and tumor cells within large and heterogeneous cell samples.

The ImageStream technology performs high speed acquisition of brightfield, laser scatter and up to four fluorescent images per cell for several thousands of cells in suspension, thereby enabling simultaneous immunophenotyping and morphology-based measurements. This is the only technology combining cytology and flow cytometry in one single platform. Our aim was to study normal and tumour cells of the haematopoietic lineage with this new technology in order to improve diagnosis of haematological disorders. We have defined cytomorphological criteria of normal bone marrow (n=4) and circulating blood cells (n=40). Cells were multi-colour labelled with both DRAQ5 nuclear stain and CD45 ECD-mAb, and additionally labeled with a combination of mAbs against either CD3/CD19, CD11b/CD16, CD14/CRTH2, or CD71/CD235. Results for normal cells were compared to those obtained by classical cytometry and cytology. We then applied these criteria to samples with patients with circulating leukemic cells, including 1 myelodysplatic syndrome (MDS), 1 myeloproliferative syndrome (MPS), 3 acute lymphoblastic leukaemia (ALL), 2 follicular lymphomas (FL) and 20 chronic lymphocytic lymphomas (CLL). We have created completely new quantitative cytomorphological criteria for classifying blood cells using parameters that measure cellular size and shape, nuclear to cytoplasmic area ratio, nuclear lobe count, SSC texture, the ratio between the size and the major axis of CD45, the ratio between the intensity and the compactness of SSC signal, and the intensity of DRAQ5 labelling, to name a few. Using these criteria, we have characterised normal bone marrow differentiation and normal circulating blood cells. We have obtained a perfect correlation with classical cytology and flow cytometry. Analysis of pathological samples showed that abnormal cells were recognized in all cases. We found an abnormal blast cell compartment and an abnormal monocytic differentiation branch in the case of MDS. We have also defined specific cytomorphological properties that distinguish ALL, FL and CLL tumour cells from normal cells. We also provide data that enumerates the proportion of large cells, of atypical CLL cells and of cells in the G2/M phase. Altogether, these results show that a technology combining cytology and flow cytometry in a single platform leads to the discovery of completely new and quantitative cytomorphological parameters defining each stage of normal cell and each category of abnormal cells of the haematopoietic lineage, opening completely new perspectives for the diagnosis of haematopoietic neoplasms.