Use of an Automated Hematology Analyzer and Flow Cytometry to Assess Bone Marrow Cellularity and Differential Cell Count

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1 Annals of Clinical & Laboratory Science, vol. 34, no. 3, Use of an Automated Hematology Analyzer and Flow Cytometry to Assess Bone Marrow Cellularity and Differential Cell Count Myungshin Kim, Jayoung Kim, Jihyang Lim, Yonggoo Kim, Kyungja Han, and Chang Suk Kang Department of Clinical Pathology, College of Medicine,The Catholic University of Korea, Seoul, Korea Abstract. The automated analysis of bone marrow aspirates was performed to estimate the bone marrow cellularity and differential cell count. Total nucleated cell count (TNC) was measured using an automated hematology analyzer. TNC of the bone marrow correlated well with the bone marrow cellularity estimated by microscopic examination (r = 0.590, p ). The bone marrow cellularity was readily confirmed as <30%, from 30 to 70%, or >70% according to TNC. Differential count of bone marrow cells was done with the combination of CD45 monoclonal antibody and propidium iodide using flow cytometry. Excellent correlations were obtained for the population distributions of normoblasts, eosinophils, lymphocytes, and blasts between the results of flow cytometry and manual differential counts. The percentage of mature granulocytes in flow cytometry showed good correlation with the manual percentage of metamyelocytes + neutrophils. The percentage of immature granulocytes by flow cytometry showed good correlation with the manual percentage of blasts + promyelocytes + myelocytes. These results demonstrate that analysis of bone marrow aspirates using an automated hematology analyzer is valuable in the determination of bone marrow cellularity. Moreover, as flow cytometry provides objective findings for percentages of major cell populations, it can serve as a method to automate bone marrow differentials. (received 8 March 2004; accepted 11 March 2004) Keywords: bone marrow, differential count, hematology analyzer, flow cytometry, CD45, propidium iodide Introduction Microscopic examination of bone marrow has long been a major method for diagnostic and therapeutic evaluation of hematologic disease [1]. When pathologists observe bone marrow, they judge the marrow cellularity by histologic sections of a biopsy and ascertain the distribution of cells by a manual differential count. Though the biopsy is the gold standard for determining cellularity, determination of bone marrow cellularity on an aspirate is often helpful because preparing histologic sections takes more time than an aspirate. Moreover, as the cellularity may differ according to the site, it is better to examine many sites than only one site. Address correspondence to Kyungja Han, M.D., Department of Clinical Pathology, Catholic University Medical College, St. Mary s Hospital, Youngdeungpo-gu, Youido-dong 62, Seoul, Korea (South) ; tel ; fax ; hankja@catholic.ac.kr. The manual differential cell count is an essential part of this work in order to have an objective record. However, such microscopic examination does not produce an objective differential count because of inter-observer differences [2]. The manual differential counting method is imprecise due to the small number of cells counted, typically not more than 500 nucleated elements. It is also laborintensive and time-consuming [3]. Attempts to analyze bone marrow aspirates using automated hematology analyzers or flow cytometry have been performed for years, but such applications have remained limited since bone marrow is a heterogenous tissue containing multiple maturation stages of all lineages of hematopoietic cells [4,5]. The availability of fluorochromeconjugated monoclonal antibodies to various lineage-specific and lineage-associated cell surface antigens provides more objective and quantitative identification of the individual cell types found in /04/ $ by the Association of Clinical Scientists, Inc.

2 308 Annals of Clinical & Laboratory Science bone marrow [3,6]. However, these approaches are not easily adapted to routine use in clinical laboratories because of the complex analysis software that is required for multidimensional analysis [7]. The use of various monoclonal antibodies to identify the individual cell populations is so expensive that it has limited routine use. The method is more useful in routine diagnostic hematopathology when it is simple to perform. In this study, we estimate the bone marrow cellularity using an automated hematology analyzer. In addition, we use a flow cytometric method for differential counting of bone marrow cells using CD45 monoclonal antibody and propidium iodide (PI). As a outcome of this study, we are able to provide objective data including cellularity and differential count of bone marrow aspirates prior to morphologic assessment. Materials and Methods Patients. We studied 369 samples of bone marrow from patients who had been admitted to the Hematology Department of St. Mary s Hospital. The samples (2 ml) of EDTA-anticoagulated bone marrow were collected and analyzed within 2 hr of collection. The patients hematological disorders were acute myeloid leukemia (AML, n = 39), acute lymphoblastic leukemia (ALL, n = 22), chronic myelogenous leukemia (CML, n = 42), aplastic anemia (AA, n = 43), post-stem cell transplantation state (BMT, n = 35), incomplete remission state after chemotherapy (IR, n = 48), and controls (n = 140). The controls consisted of patients in complete remission state after chemotherapy and clinical specimens being evaluated for the bone marrow involvement of lymphoma. They were restricted to bone marrow aspirates with <5% pathological cells on microscopic examination. Microscopic examination of bone marrow. Bone marrow smears were prepared on glass slides, and, after staining with Wright stain, were observed under a microscope. Differential counts by microscopic examination were used as a reference [8]. The marrow cellularity was determined by both bone marrow aspiration and biopsy section. The biopsy cellularity was used when there was discordance between the aspirate and biopsy cellularity. Analysis by automated hematology analyzer. We analyzed the bone marrow aspirates using a Coulter GEN S (GEN S) automated hematology analyzer (Coulter Corp, Miami, FL). EDTA-anticoagulated bone marrow was diluted 10-fold with normal saline. After the diluted sample was filtered through 53 µmpore sized nylon mesh, the total nucleated cell count (TNC) was measured. Flow cytometry for differential count of bone marrow cells. 1. Sample preparation. Differential count of bone marrow samples using flow cytometry was performed as follows: Bone marrow (100 µl) at a concentration of x 10 6 cells was mixed with 10 µl of CD45, fluorescein isothiocyanate (FITC) (BD Biosciences, San Jose, CA) and incubated for 20 min in the dark at room temperature. Two ml of FACS lysing solution (BD Biosciences) was added and the mixture was incubated for 10 min. After washing twice with normal saline, the sample was incubated with 10 µl of PI (BD Biosciences) for 10 min. The final sample volume was adjusted to 500 µl using normal saline. 2. Data acquisition. All flow cytometric measurements were carried out within 60 min of sample preparation. Flow cytometric analysis was performed on a FACSCalibur (Becton-Dickinson Immunocytometry System (BDIS), San Jose, CA). Forward angle light scatter (FSC), side angle light scatter (SSC), and two-color fluorescence signals (green and orange fluorescence at 530 and 585 nm, respectively) were determined for each cell and stored in list mode data files. In toto, 20,000 events were recorded for each specimen. 3. Data analysis. List mode data files were analyzed using CellQuest Software (BDIS). As a first step, four groups of cells were gated in the CD45-FITC and PI dot-plotted according to the intensity of CD45 and PI (R1: CD45 negative, R2: CD45 weakly positive & high PI, R3: CD45 weakly positive & low PI, R4: CD45 strongly positive). Then, each group of cells was dot-plotted again by

3 Automated analysis of bone marrow aspirates 309 FSC and SSC combination. The cell populations were further identified by their FSC and SSC intensity. In the cases of AML, ALL, and IR, we differentiated the cells into 2 groups, L1 and L2, with L1 defined as CD45 weakly positive or negative cells and L2 as CD45 strong positive cells. Next, each group of cells was dot-plotted by FSC and SSC combination and identified by the clustering of populations. Statistical analysis. Numerical values were expressed as mean ± SD; t tests were conducted and Pearson s correlation coefficients determined for analysis of data. The flow cytometry results and manual microscopic results were examined and compared by regression analysis. The correlation coefficient was determined by SPSS version 9.0. Differences were considered significant at p Results Cellularity. TNC obtained by the automated hematology analyzer was 97.4 ± x 10 9 /L. TNC of the bone marrow correlated well with the bone marrow cellularity estimated by microscopic examination (r = 0.590, p = 0.000). When the cellularity was categorized as <30%, from 30 to 70%, and >70%, there was significant difference of TNC among the different cellularity groups (33.8 ± 30.0 x 10 9 /L vs 83.8 ± 75.8 x 10 9 /L vs ± x 10 9 /L, p ) (Fig. 1). Clustering of bone marrow populations by CD45- FITC and PI. The nucleated elements were separated from debris by CD45-FITC and PI dot plot. The patterns of normal bone marrow were highly reproducible with 4 separate groups of cells, all easily recognizable by combined CD45-FITC and PI (Fig. 2A). The positions of these cell clusters were so consistent that the boundaries could be defined using one set of data and required little or no change for the analysis of other sets of data even if they were collected on different days. The cell populations that were secondarily gated by FSC and SSC intensity after first gating were identified as follows: R1, normoblasts and immature granulocytes; R2, normoblasts and immature granulocytes; R3, Fig. 1. Comparison of total nucleated cell counts (TNC) among the different cellularity groups. normoblasts, monocytes, eosinophils, and mature granulocytes; and R4, lymphocytes, monocytes, and mature granulocytes (Fig. 2B,C). As a result, populations of normoblasts, lymphocytes, monocytes, eosinophils, immature granulocytes, and mature granulocytes were classified. The cell populations of the CML, AA, and BMT cases were well separated using the same gating sets that were used in controls. On the other hand, in cases of AML, ALL and IR, the 4 groups of cells were not easily separated by CD45-FITC and PI dot-plot and the subpopulations of each group of cells could not be correctly identified by the presence of a lot of blasts. Therefore, we analyzed those cases using the following modified method: Instead of grouping the cells into four categories by CD45- FITC and PI dot plot, we differentiated them into two groups, L1 and L2, according to their CD45 intensity. The secondarily gated cell populations were identified as follows: L1, normoblasts, blasts, and total granulocytes; and L2, lymphocytes, blasts, and total granulocytes (Fig. 3). As a result, populations of normoblasts, lymphocytes, total granulocytes, and blasts were classified. In acute leukemia cases, the cells on scattergram by FSC and SSC were clustered in one population. Typically AML revealed a wider distribution of SSC

4 310 Annals of Clinical & Laboratory Science Fig. 2. Flow cytometric analysis of bone marrow aspirate from a patient in the control group after staining with CD45-FITC and PI. In the first step, 4 groups of cells were gated (panel A). In the second step, each group of cells was reanalyzed into separate cell clusters: N, normoblasts; IG, immature granulocytes; MG, mature granulocytes; Mono, monocytes; Eos, eosinophils; and Lym, lymphocytes (panel B). A case with marked eosinophilia shows eosinophils that are easily separated from MG (panel C) Table 1. Comparison of subpopulation percentages (mean ± SD) obtained by manual differential count and by the flow cytometric method in bone marrow aspirates from the total group of 369 cases. Cell types Manual differential (%, n = 369) Flow cytometry (%, n = 369) correlation coefficient (r) p vs manual differential normoblasts lymphocytes eosinophils mature granulocytes immature granulocytes monocytes blasts total granulocytes 19.8 ± ± ± ± 17.0* 11.8 ± ± ± ± ± ± ± ± ± ± ± ± * metamyelocytes + myelocytes blasts + promyelocytes + myelocytes counted only in acute myeloid leukemia, acute lymphoblastic leukemia, and incomplete remission cases promyelocytes + myelocytes + metamyelocytes + neutrophils

5 Automated analysis of bone marrow aspirates 311 Fig. 3. Flow cytometric analysis of two patients with acute leukemia. In the first step, two groups of cells were gated (panel A). In the second step, each group of cells was reanalyzed into separate cell clusters: N, normoblasts; Bl, blasts; Lym, lymphocytes; G, total granulocytes. A case of acute lymphoblastic leukemia (panel B). A case of acute myeloid leukemia (panel C). Patient (C) shows a higher SSC level than patient (B). than ALL did. To differentiate AML from ALL, the geometric mean value of SSC was useful, being significantly greater in AML than in ALL (198.4 ± 77.1 vs 96.7 ± 22.6, p ) (Fig. 4). Fig. 4. Comparison of side angle scattergram geometric mean values (SSC GEOM) between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). The bold line represents the median value of SSC GEOM, the box represents the ± SD range of SSC GEOM, and the vertical line represents the range of individual SSC GEOMs. Differential counts of bone marrow cells. Table 1 presents the correlation data between flow cytometric differential data and manual differential measurements. Excellent correlations between the 2 methods were obtained in normoblasts, lymphocytes and eosinophils. Although the correlation for monocytes was low, it was significant (r = 0.463, p ). The percentages of normoblasts differed between the 2 methods, with microscopic values being about 5% higher than the flow cytometry ones. The percentage of mature granulocytes in flow cytometry had a better correlation with the manual percentage of metamyelocytes + neutrophils than with the manual percentage of myelocytes + metamyelocytes + neutrophils (r = vs r = 0.568). The percentage of immature granulocytes

6 312 Annals of Clinical & Laboratory Science by flow cytometry had the best correlation with the manual percentage of blasts + promyelocytes + myelocytes (r = 0.624, p ). In the cases of AML, ALL, and IR, the blast count correlated well between the two methods (r = 0.911, p ). The percentage of total granulocytes by flow cytometry correlated well with the sum of promyelocytes, myelocytes, metamyelocytes, and neutrophils (r = 0.926, p ). Discussion In this study, we analyzed the diluted bone marrow aspiration sample by an automated hematology analyzer on the basis that counts of total nucleated elements using the automated hematology analyzer correlate significantly with manual counts [2,9]. TNC of the bone marrow correlated well with the bone marrow cellularity estimated by microscopic examination (r = 0.590, p ). By this method, we can easily confirm the bone marrow cellularity as either <30%, from 30 to 70%, or >70% according to TNC. The estimated bone marrow cellularity by the automated hematology analyzer was different from that by microscopic examination in a few cases. Such discrepancy was most prominent in bone marrow with >70% cellularity, which does not reveal any adequate particles on the smeared specimen because of bone marrow fibrosis or bone marrow packed with leukemic cells. The bone marrow aspiration sample itself was unsatisfactory not only for the evaluation of bone marrow cellularity but also for the diagnosis of bone marrow condition because of the admixing of bone marrow with sinusoidal blood [10]. Except for such cases, the automated hematology analyzer can be useful for a rough estimate of bone marrow cellularity. In this paper, we evaluated a simple and inexpensive flow cytometric method to classify the lineage and maturity of bone marrow cells. This method does not require specialized handling or processing techniques. By combining the CD45 expression of a cell with its PI intensity, we effectively separated the basic groups of cells for bone marrow differentials. CD45 has different surface expressions based on the lineage and maturity of the cell. CD45 expression is highest in mature lymphocytes and monocytes, and erythroid precursors lack the CD45 antigen [11]. PI is used to separate the nucleated elements of bone marrow from red blood cell (RBC) ghosts, debris, lyse-resistant RBC, and platelets [12]. We found that the immature granulocytes showed higher PI intensity than normoblasts and mature granulocytes. It has been known that the PI intensity was affected by the permeability, chromatin structure, and histone content of various cell types [13,14]. We suspect that most of the S- and G2/Mphase cells consist of immature granulocytes and that their chromatin pattern is less compact. In the cases of AML, ALL, and IR, the 4 groups of cells were not easily gated by CD45-FITC and PI dot-plot, and the subpopulations of each group of cells were incorrectly identified, owing to the presence of many blasts. Therefore, we analyzed these cases using the modified method described in the results. The conventional flow cytometric markers of FSC and SSC, which reflect size and granularity, provide the best visual differentiation for more detailed bone marrow differential counts [2,3]. We found that results obtained by this technique show strong correlation with the manual bone marrow differential counts over a wide range of values. Excellent correlations between the two methods were obtained for the population distributions of normoblasts, eosinophils, and lymphocytes. Although it remained significant, the correlation for monocytes was low because the percentage of bone marrow monocytes was low and there was no case with a high percentage of the monocyte fraction. The percentages of normoblasts differed between the two methods, with microscopic values being higher than the flow cytometry ones. It is assumed that some of these cells were lost by hemolytic agents. The percentages of mature granulocytes and immature granulocytes obtained by flow cytometry gave good correlations with the manual percentages of metamyelocytes + neutrophils and of blasts + promyelocytes + myelocytes, respectively. In the cases of AML, ALL, and IR, the blast count correlated well between the two methods. The percentage of total granulocytes by flow cytometry correlated well with the sum of promyelocytes, myelocytes, metamyelocytes, and neutrophils and was considered to be a normal

7 Automated analysis of bone marrow aspirates 313 granulocytic precursor. Thus, we are able to estimate the proportions of normal erythroid and granulocytic precursors and lymphocytes in the cases of AML, ALL, and IR. As noted, we found that the cells on scattergram by FSC and SSC were clustered in one population in acute leukemia cases. The geometric mean value of SSC, being significantly greater in AML than in ALL, was useful to differentiate AML from ALL. Thus this simple technique has potential application in clinical flow cytometry laboratories for bone marrow immunophenotyping and analysis. Our results show that analysis of bone marrow aspirates using an automated hematology analyzer provides objective data about the number of nucleated elements, and that it is valuable in the determination of bone marrow cellularity. This CD45/PI gating system is superior to the traditional CD45/SS gating because the debris are effectively separated and the cell clusters are more easily distinguished as rectangular, not round, or irregularly shaped. The secondary gating by FSC/SSC can compensate for the minor error in the CD45/PI gating. This system also can be applied to leukemia cases. Therefore, we are able to estimate the percentages of blasts and normal hematopoietic precursors. It provides a method that allows any clinical flow cytometry laboratory to automate bone marrow differentials and it can also be used to assist with the diagnosis of bone marrow pathology, including acute leukemias. References 1. Terstappen LW, Safford M, Loken MR. Flow cytometric analysis of human bone marrow III. Neutrophil maturation. Leukemia 1990;4: Sakamoto C, Yamane T, Ohta K, Hino M, Tsuda I, Tatsumi N. Automated enumeration of cellular composition in bone marrow aspirate with the CELL-DYN4000 automated hematology analyzer. Acta Haematol 1999; 101: Fujimoto H, Sakata T, Hamaguchi Y, Shiga S, Tohyama K, Ichiyama S, Wang F, Houwen B. Flow cytometric method for enumeration and classification of reactive immature granulocyte population. Cytometry 2000;42: Poon AO, McClure PD, Atkinson JB, Ing G. Diagnostic value of the Hemalog D-90 in classifying acute childhood leukaemias. Clin Lab Haemat 1981;3: den Ottolander GJ, Baelde HA, Huibregtsen L, Paauwe JL, van der Burgh JF. The H1 automated differential counter in determination of bone marrow remission in acute leukemia. Am J Clin Pathol 1995;103: Stelzer GT, Shults KE, Loken MR. CD45 gating for routine flow cytometric analysis of human bone marrow specimens. Ann N Y Acad Sci 1993;677: Knapp JC, Russel KTR, Ricordi C. An automated method for flow-cytometric analysis of human vertebral body marrow. Transplant proc 1997;29, Davey FR, Hutchison RE. Hematopoiesis. In: Clinical Diagnosis and Management by Laboratory Methods, 20th ed (Henry JB, Ed), Saunders, Philadelphia, 2001; pp Tatsumi J, Tatsumi Y, Tatsumi N. Counting and differential of bone marrow cells by an electronic method. Am J Clin Pathol 1986;86: Ozkaynak MF, Scribano P, Gomperts E, Lzadi P, Millin R, Isaacs H Jr, Ettinger LJ. Comparative evaluation of the bone marrow by the volumetric method, particle smears, and biopsies in pediatric disorders. Am J Hematol 1988;29: Rainer RO, Hodges L, Seltzer GT. CD45 gating correlates with bone marrow differential. Cytometry 1995;22: Tsuji T, Sakata T, Hamaguchi Y, Wang F, Houwen B. New rapid flow cytometric method for the enumeration of nucleated red blood cells. Cytometry 1999;37: Hodgetts J, Hoy JG, Jacobs A. Assessment of DNA content and cell cycle distribution of erythoid and myeloid cells from bone marrow. J Clin Pathol 1988;41: Darznykiewicz Z, Traganos F, Kapuscinski J, Staiano- Coico L, Melamed MR. Accessibility of DNA in situ to various fluorochromes: relationship to chromatin changes during erythoid differentiation of Friend leukaemia cells. Cytometry 1984;5: