Platelet Counting

PART III

32

Clinical Tests of Platelet Function

Platelet Counting Samuel Kemble*, Carol Briggs†a and Paul Harrison* * Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom, †Department of Haematology, University College London Hospitals, London, United Kingdom

INTRODUCTION 581 MANUAL PLATELET COUNTING 582 AUTOMATED PLATELET COUNTING 582 Impedance Platelet Counting 583 Optical Platelet Counting 584 Optical Fluorescence Platelet Counting 585 Quality Control for Automated Hematology Analyzers Immunological Platelet Counting 586 Automated Immunological Counting 587 IMAGE-BASED PLATELET COUNTING 587 POC PLATELET COUNTING 589 RETICULATED PLATELETS/IMMATURE PLATELET FRACTION 589 CONCLUSIONS 590 REFERENCES 590

586

INTRODUCTION This chapter is dedicated to our co-author and colleague Carol Briggs who sadly passed away after a short illness in 2015. Carol was a co-author on previous editions of this chapter and the community not only misses her friendship but her knowledge and vast experience of hematology, instrumentation, and standardization. Carol also made many landmark contributions to this important field.1 There are now six main analytical procedures for platelet counting (1) manual counting using phase contrast microscopy, (2) impedance analysis, (3) optical light scatter/fluorescence analysis using various commercially available analyzers, (4) immunoplatelet counting by flow cytometry, (5) image-based platelet counting, and (6) point-of-care (POC) platelet counting. Although manual methods have been largely replaced by automated instrumentation, many research and nonspecialized laboratories interested in a reliable method for accurately counting platelets can still either perform manual counting or utilize small impedance analyzers if access to a larger automated blood counter is not possible. Early methods to enumerate platelets in blood were usually inaccurate and irreproducible until the mid-20th century. In 1953, Brecher et al. developed a manual phase contrast microscopy method, enabling platelets to be easily discriminated from lysed red cells within a counting chamber or hemocytometer.2 a

Deceased.

Platelets. https://doi.org/10.1016/B978-0-12-813456-6.00032-1 Copyright © 2019 Elsevier Inc. All rights reserved.

Although the development of the Coulter Principle in the 1950s (see section “Impedance platelet counting”) revolutionized blood counting, platelet counts were added to the automated full blood count only in the late 1970s. In early impedance analyzers, platelet counting could be performed only by analysis of PRP or purified platelet preparations and was thus prone to considerable error. Until the widespread availability of platelet counting as part of the full blood count, the majority of platelet counts were still performed manually via phase contrast microscopy.2 The manual phase count is still recognized as the gold-standard reference method.3 Thus, until very recently, the calibration of platelet counts on automated blood cell counters and quality control material was still routinely performed via the manual method by the majority of instrument manufacturers. However, the manual method not only is timeconsuming, subjective, and tedious, but results in high levels of imprecision with typical inter-observer coefficient of variations (CV) in the range of 10%–25%. At low platelet numbers, because fewer cells are counted, observed CVs increase proportionally. Although relatively imprecise, the manual method still offers a relatively inexpensive, simple, and viable means to enumerate platelets in the nonspecialized laboratory. The introduction of automated full blood counters using impedance technology resulted in a dramatic improvement in precision, with typical CVs of <3%, because much higher total numbers of platelets are counted. However, impedance platelet counting methods still have significant limitations, despite their widespread use. One of the major problems is that cell size analysis cannot discriminate platelets from other similarly sized particles, such as small or fragmented red cells, immune complexes, etc. These may be erroneously included in the platelet count, and in severely thrombocytopenic samples, the number of interfering particles may even exceed the number of true platelets. In addition, large or giant platelets may be excluded from the count based on their size because they cannot be resolved from red cells. There may also be significant variation in the results obtained on different analyzers with the same sample due to differences in the method of analysis, linearity over the entire measuring range, and the number of events actually counted. More recently, multiple light scatter parameters and/or fluorescence, rather than impedance sizing alone, have been introduced for platelet counting in automated hematology analyzers. This has improved the ability of automated analyzers to discriminate platelets. A new analyzer is also now available for enumerating platelets within full blood by imaging cells on slides (section “Image-based platelet counting”). POC analyzers are also becoming available for rapid testing of blood samples without the requirement of a dedicated laboratory (section “POC platelet counting”). Despite these newer methods, there are still occasional cases in which absolute accuracy of the platelet count remains a

581

PART III Clinical Tests of Platelet Function

challenge. Thus, there was renewed interest in the development of an improved reference procedure to enable optimization of automated platelet counting. This latter method utilizes specific monoclonal antibodies to platelet cell surface antigens (e.g., anti-CD41, -CD42, or -CD61) conjugated to a suitable fluorophore. Through performance of flow cytometric analysis of the ratio of fluorescent platelet events to nonfluorescent red cell events, a highly accurate and precise technique is now available for counting platelets in whole blood. This is now the reference method accepted by the International Council for Standardization in Hematology (ICSH) and the International Society for Laboratory Hematology (ISLH) (see section “Immunological platelet counting”).4,5 This relatively new approach permits the implementation of a new international reference method to calibrate cell counters, assign values to calibrators, and obtain a direct platelet count on a wide variety of pathological samples. This should lead, it is hoped, to an improved accuracy of platelet counting in thrombocytopenia and therefore facilitate further studies to establish whether current platelet transfusion thresholds (see Chapter 64) can be safely lowered without risk of bleeding. The method can also be adapted so that platelets can be quantified within purified preparations via the addition of precise numbers of fluorescent beads.4,6 The latter method has also been adapted to count platelets in mouse blood.7

MANUAL PLATELET COUNTING Despite the widespread use of automated technology, manual counting of platelets is still widely performed in underresourced laboratories and within research laboratories with no access to specialized instrumentation. It may also be occasionally necessary to use manual counting methods in the clinical laboratory if the platelet count is low or there are atypical platelets present in the sample. Until recently, the international reference method for platelet counting was still performed by the standard manual method using phase contrast microscopy and was established by the ICSH. Whole blood platelet counts are usually performed on ethylene diamine tetraacetic acid (EDTA)-anticoagulated blood obtained by standard clean venipuncture. To discriminate platelets from red cells, one usually performs manual counting by visual examination of diluted and lysed whole blood using a Neubauer counting chamber, which contains a precise volume of fluid. Purified platelet preparations can also be counted with this method. Specific methodological details of manual platelet counting are available elsewhere.3,8

AUTOMATED PLATELET COUNTING There are now several methods on commercial analyzers for counting platelets, including aperture impedance, optical scattering, fluorescence, and now imaging. Table 32.1 lists some of the currently available large hematology analyzers that incorporate platelet counting. Normal platelets give a classical lognormal volume distribution curve, which is particularly useful as the basis for determining a valid platelet count (Fig. 32.1). Other derived platelet parameters are highly dependent on the individual technology, and are influenced by the anticoagulant and delay time from sampling to analysis (e.g., EDTAinduced swelling). If mean platelet volume (MPV) is to be reliably measured, then the potential influence of anticoagulant on the MPV must be controlled for, either by using an alternative anticoagulant or standardizing the time delay between sampling and analysis.9 In impedance analyzers, the MPV and platelet distribution width (PDW) are derived from the platelet distribution curve. Although these derived platelet parameters must be interpreted carefully, there is normally

TABLE 32.1 Examples of Currently Available Large Hematology Analyzers Manufacturer

Instrument

Abbott Diagnostics

CELL-DYN 4000 CELL-DYN Sapphire Alinity H series

Beckman Coulter

HORIBA Medical Siemens Sysmex Corporation

LH 750

Principle of Platelet Count Impedance, optical, and immunological Impedance, optical, and immunological Impedance, optical, and immunological Impedance

DxH 600, 800, and 1500 ABX Pentra-Series

Impedance

Yumizen series ADVIA 120 ADVIA 2120 XE-Series

Impedance, optical Optical Optical Impedance and optical fluorescence Impedance and optical fluorescence

XN Series

PLT

Impedance

12

Relative frequency

582

2 PL

30 PDW

P-LCR

40 (fL)

PU

Fig. 32.1 Typical platelet size distribution in an automated hematology analyzer. PL, lower discrimination for platelet size distribution. PDW, platelet distribution width. P-LCR, platelets-large cell ratio. PU, upper discrimination for platelet size distribution. (Courtesy of Sysmex Corporation.)

an established inverse relationship between MPV and the platelet count—in other words, the total platelet mass that contributes to the maintenance of hemostatic function. There is also evidence that MPV is an important risk factor for acute myocardial infarction and venous thromboembolism.10–12 Recent data suggest that both MPV and platelet count are highly heritable but tightly regulated traits. Indeed, various chromosomal loci have now been associated with both MPV and platelet counts and has led to the identification of key genes that regulate thrombopoiesis (Chapters 2 and 5).13 Recent research is now beginning to address some of the important issues with standardization of MPV measurements given the importance of preanalytical and analytical variables associated with this measurement including the potential development of standards to calibrate instruments and to understand differences between different measurement technologies.9 All automated methods used for platelet counting must be demonstrated to be precise, show minimum fluctuation in repeated results on the same sample, and give linear results over the entire analytical range. At high counts, there is a growing probability of coincidence, i.e., two or more cells passing through the sensing zone at the same time, as well as possible sample carryover if a high count precedes a low count. With

Platelet Counting

thrombocytopenic samples, it is important that added counts due to spurious signals caused by electronic noise are not included within the reported result. It is also desirable that there is minimal method variation between different analyzers; results obtained with different systems on the same sample should be comparable.

Wallace Coulter first described the resistance detection method, usually referred to as the “Coulter Principle” or impedance method (Fig. 32.2).14 In this method, biological cells are regarded as completely nonconductive resistivity particles. When a blood cell passes through an aperture (sensing zone) suspended in electrolyte solution, the change in electric impedance is detected. Each individual cell gives an impedance signal, which is proportional to the volume of the cell detected, and so this method can be used to size and count individual cells. Impedance was originally used for the counting of red cells and white cells; the first Coulter platelet counter required the use of PRP to avoid counting red cells as platelets. Many research laboratories still utilize such small analyzers to count platelets in PRP or in purified platelet preparations (Table 32.2). It was not until the 1970s that improvements in technology, including coincidence correction and hydrodynamic focusing, allowed the discrimination of platelets from red cells to enable an accurate platelet count to be obtained from a whole blood sample. Ideally, if cells pass through the sensing zone one by one, the total number of detected cells is counted. However, simultaneous occupancy of the sensing zone by more than one particle occurs. This phenomenon is called “coincidence,” and the resulting count error is known as the coincidence error. The magnitude of coincidence error increases with the concentration of cells suspended. For major hematology analyzers, by measuring the results from several samples of different concentrations, the coincidence correction formula can be established. The correction formula may be integrated into the analyzer’s computer and the coincidence corrected result reported.

Vacuum

Internal electrode

Aperture current pathway External electrode

Suspension of cells

External housing (aperture bath)

Aperture

TABLE 32.2 Examples of Currently Available Compact Analyzers for Platelet Counting

Aperture housing

Fig. 32.2 Electrical impedance method or Coulter Principle. See text for explanation.

Principle of Platelet Count

Manufacturer

Instrument

Abbott Diagnostics

CELL-DYN 1200 AC%T DxH 300 Pentra 60 Micros 60 Advia 70 KX21

Impedance

XS-1000i pocH-100i

Impedance Impedance

Beckman Coulter

Impedance Platelet Counting

583

HORIBA Medical Siemens Sysmex Corporation

Impedance Impedance Impedance Impedance Impedance Impedance

In order to minimize coincidence physically, the hydrodynamic focusing method has been developed for some analyzers. If two cells pass through the sensing zone together, the count may be corrected by the coincidence correction, but a large single pulse will be generated and it is not possible to determine if this arises from one large cell or two small cells. If a cell passes through the sensing zone close to the wall, where high current density exists, an M-shaped pulse is generated; while the count result may be valid because of the coincidence correction, there is no way to correct the measurement of the cell volume. Hydrodynamic focusing resolves these problems. In hydrodynamic focusing, a steady flow of diluent is drawn through the aperture, and the cell suspension is injected into this moving body of liquid in a fine stream close to the aperture entrance (Fig. 32.3). The likelihood of two cells passing through the aperture together is dramatically decreased, and no cell goes near the wall or the entrance angle of the sensing zone where high current density exists. Hydrodynamic focusing produces a clear discrimination between red cells and platelets. In the presently available Beckman Coulter analyzers (e.g., LH 750, UniCel DxH 800), particles between 2 and 25 femtoliters (fL) in volume are counted as platelets. The DxH 800 accumulates platelet events for up to 20 s or 1800 events, whichever is sooner. Pulses are obtained from three red cell/ platelet orifices to obtain 64-channel size distribution histograms for each orifice. These histograms are smoothed, and a high point and two low points are identified in the distribution. A log-normal curve is fitted to these points. The curves have a range of 0–70 fL, and the platelet count and parameters are derived from this curve. The DxH 800 platelet count also uses information from the white blood cell histogram and Nucleated Red Blood Cell histogram to allow for correction for interfering substances such as giant platelets and platelet clumps. In the Sysmex counting systems (e.g., XE and XN series), platelets are also counted by the orifice impedance method. A platelet size distribution plot is produced using three thresholds. One is fixed at the 12 fL level, and the other two are allowed to hunt the upper and lower ends of the platelet population between certain limits. The lower platelet size threshold may move between 2 and 6 fL, and the higher between 12 and 30 fL. The purpose of these thresholds is to endeavor to distinguish platelets from small red cells or red cell fragments at the upper end of the platelet population, and debris at the lower end. Analyzers using the standard impedance measurements are able (for most samples) to provide an accurate platelet count down to 20  109/L. Below this level, impedance analyzers become less accurate, due to decreasing statistical confidence, fewer events analyzed, and the increasing influence of background and plasma nonplatelet particulate matter.

32

584

PART III Clinical Tests of Platelet Function

Fig. 32.3 Schematic diagram of sheath flow and the principle of hydrodynamic focusing. Platelets and red cells are analyzed by a hydrodynamic focusing system, which eliminates potential errors of coincidence, recirculation, and stress changes associated with traditional methods of analysis. This results in more accurate platelet and red cell counts and sizing, even when cell counts are low or high. (Reprinted with permission from the Sysmex Corporation.)

A major disadvantage of the electrical impedance method for counting platelets is the difficulty in distinguishing large platelets from extremely microcytic or fragmented red cells, even with the use of hydrodynamic focusing methods. False increases in the platelet count will occur when red cell or white cell fragments, microcytic red cells, immune complexes, bacteria, or cell debris are included in the reported platelet count. False decreases in the count will occur in the presence of large platelets, platelet clumping (as seen with pseudo-thrombocytopenia by EDTA-dependent agglutinins), or platelet satellitism.

index of effectively sphered individual platelets are simultaneously determined on a cell-by-cell basis by measuring two angles of laser light scatter at 2°–3° and at 5°–15°. The two scatter measurements are converted to volume (platelet size) and refractive index (platelet density) values using the Mie theory of light scattering for homogenous spheres. The platelet scatter cytogram map resolves volumes between 1 and 30 fL, and refractive index values between 1.35 and 1.44. Large platelets, red cell fragments, red cell ghosts, microcytes, and cellular debris are distinguished. Platelets are identified within the map on the platelet scatter cytogram based on their volume and refractive index (1.35–1.40) (Fig. 32.4). Red cell fragments and microcytes with the same volume range have a greater refractive index than platelets and fall below and to the right of the grid, and red cell ghosts with a refractive index less than platelets fall above and to the left of the grid (Fig. 32.5). Large platelets with volumes between 30 and 60 fL are identified in the large platelet area of the red cell map. The reported twodimensional platelet count is the sum of platelets and large platelets identified in the platelet and red cell scatter cytograms. Published data suggest that the two-dimensional platelet count improves the accuracy of the platelet count in thrombocytopenic samples.6,15 The CELL-DYN 4000, Sapphire, and Alinity instruments (Abbott) also routinely report an optical platelet count (as well as an impedance count) based on two light scatter parameters: intermediate light scatter (7°) and a wide-angle scatter (90°). An algorithm is used to identify platelets using these two parameters to exclude, insofar as possible, nonplatelet particles while including all platelets. This is a two-dimensional analysis in which platelets must fall within a region that defines the correlation between the two light scatter parameters (a sloping window) and between a lower threshold and an upper discriminator (Fig. 32.6). The three-discriminator lines are set dynamically; the lower threshold is fixed. A simultaneous determination of the impedance platelet count is performed and discrepancies between the two counts generate an alert flag suggesting the presence of sample interferences. The combination of twodimensional optical analysis and flow impedance counting on the CELL-DYN 4000 has made a significant contribution to improving the accuracy and precision of platelet counting.

Optical Platelet Counting More recently, optical light scatter methods have been introduced for platelet counting. In one-dimensional platelet analysis, platelets are counted and sized by a flow cytometry system in which the cells in a suitable diluent pass through a narrow beam of light (i.e., helium-neon laser). The illumination and light scatter by each cell is measured at a single angle (2°–3°). This allows assessment of the number of electrical pulses generated in proportion to the number of cells and cell volume. In these automated systems, a series of algorithms or a smoothing or fitting routine on the platelet volume histogram is used to establish validity of each platelet count. To improve discrimination of platelets accurately from nonplatelet particles, two-dimensional laser light scatter was developed. The ADVIA 120 and 2120 analyzers (Siemens) use two-dimensional platelet analysis; volume and refractive

Fig. 32.4 Identification of platelets by the ADVIA 120 hematology analyzer (Siemens). The vertical axis (B) indicates low-angle light scatter or cell volume. The horizontal axis (A) indicates high-angle light scatter or refractive index. Particles in area 1 are platelets. Particles in area 2 are red cells. (Photo courtesy of Siemens Healthineers © Siemens Healthcare Diagnostics Inc. 2019.)

Platelet Counting

Fig. 32.5 Identification of platelets by the Siemens ADVIA 120 hematology analyzer. The vertical axis indicates low-angle light scatter or cell volume. The horizontal axis indicates high-angle light scatter or refractive index. (Photo courtesy of Siemens Healthineers © Siemens Healthcare Diagnostics Inc. 2019.)

Optical Fluorescence Platelet Counting An optical fluorescence platelet count is also performed on Sysmex XE Series analyzers, in addition to the traditional

585

impedance count.16 The optical fluorescent platelet count is measured in the reticulocyte channel. A polymethine dye is used to stain the RNA/DNA of reticulated cells and platelet membranes and granules. This technology allows the simultaneous counting of the red cell reticulocytes, erythrocytes, and fluorescent platelets (Fig. 32.7). Within the flow cell, each single cell is passed through the light beam of a semiconductor diode laser. The fluorescence intensity of each cell is analyzed, which allows the separation of platelets from red cells and reticulocytes. The fluorescent staining of the platelets not only allows the exclusion of nonplatelet particles from the count, but also allows the inclusion of large or giant platelets. The optical fluorescence count is more reliable at levels below 100  109/L and may allow more appropriate clinical decisions to be made, particularly with regard to platelet transfusions. However, for samples from patients undergoing cytotoxic chemotherapy, the impedance count is sometimes more accurate. This is probably due to the erroneous staining of white cell fragments following apoptosis. A switching algorithm has been designed on the analyzers to report the most accurate platelet count, either optical or impedance. On the most recently introduced Sysmex analyzers, the XN series, the fluorescent platelet count is available within a new dedicated channel (PLT-F) exclusively for platelets using a new dye Fluorocell PLT17 (Fig. 32.8) which specifically labels intraplatelet organelles.18 During routine analysis, reflex rules can be used to allow increased counting time in

Foward scatter

Fig. 32.6 An example of a scattergram produced by the Abbott CELL-DYN 4000, showing the optical platelet count (left panel) and impedance platelet size distribution (right panel). See text for details. (Used with permission from Abbott Diagnostics.)

RBC-O

LFR

MFR

HFR

IRF

PLT Fluorescence Fig. 32.7 An example of a scattergram produced by the Sysmex XE-2100 hematology analyzer in both cartoon (left) and dot plot (right) formats. The vertical axis indicates forward scattered light or cell volume. The horizontal axis indicates fluorescence intensity. The scattergram is divided into a platelet (PLT) area, a mature red cell area (RBC-O), and the various immature reticulocyte fractions (IRF): LFR, MFR, and HFR. The immature platelet fraction (IPF) is represented as green dots on the dot plot format. (Courtesy of Sysmex Corporation.)

32

586

PART III Clinical Tests of Platelet Function

FSC

SFL Fig. 32.8 An example of a scattergram produced by the Sysmex XN hematology analyzer. The vertical axis indicates forward scattered light (FSC) or cell volume. The horizontal axis indicates fluorescence intensity (SFL). The scattergram is divided into a platelet (PLT-F) area, with the immature platelet fraction (IPF) showing the highest fluorescent platelets; a red cell area (RBC); and a white blood cell area (WBC). (Courtesy of Sysmex Corporation.)

thrombocytopenic samples (i.e., five times longer) in order to increase accuracy and precision. Furthermore, the specificity of the dye clearly distinguishes platelets and nonplatelet fragments unlike the PLT-I and PLT-O channels, subsequently reducing systematic flagging when analyzing severely thrombocytopenic and/or atypical samples.17,18

Quality Control for Automated Hematology Analyzers Modern analyzers are very precise, but care still needs to be taken to ensure that they are producing accurate platelet counts. Most modern instruments are precalibrated by the manufacturer but often perform adjustments according to the individual blood sample characteristics. Despite improved calibration, each instrument requires regular maintenance and cleaning (according to the manufacturer’s specifications) to ensure optimum performance. Each laboratory also should establish an in-house reference range for each measured cell type, including the platelet count. Quality control procedures should be performed regularly (e.g., at least daily) to check for accuracy. Each analyzer manufacturer produces quality control material that can be purchased to monitor performance of the instrument. The control can usually be used only on those manufacturers’ instruments with their specific reagents. The control consists of treated stabilized human erythrocytes in an isotonic bacteriostatic medium, with the addition of a stabilized platelet-sized component and white blood cells or fixed erythrocytes to simulate blood cells. The controls are usually available with low, normal, or high levels of white cells, red cells, and platelets. Each control has assigned values and expected ranges. Expected ranges include variation between lots and between individual instruments, and represent 95% confidence limits for well-maintained instrument systems. The United Kingdom National External Quality Assessment Service in Hematology (UK NEQAS [H]) is an external quality control service. Participating clinical laboratories are sent stabilized blood samples on a regular basis that they treat as a normal patient sample for testing. The samples are analyzed in each of the hematology laboratory’s instruments, and results are returned to NEQAS. NEQAS then provides a report

that compares the participating laboratory’s performance to that of all laboratories using the same test method or analyzer. Other countries have developed similar external quality controls. However, the lack of an internationally recognized standard for platelets necessitates the use of a consensus target value to establish limits of performance. As the many different blood cell counters available use a variety of technologies and diluents, they may respond in different ways to the stabilized blood used in the surveys. Performance is therefore individually assessed within instrument groups against the consensus target value. Recently, more regular samples with low platelet counts have been sent to laboratories by UK NEQAS (H). Twenty-nine specimen pools with platelet counts between 5 and 64  109/L have been distributed to 23 analyzer groups from five main manufacturers of automated hematology instruments. The same samples were analyzed by the ICSH/ISLH flow cytometric reference method for platelet counting at three different reference centers. The flow cytometric platelet counts were on average lower than all methods on automated analyzers. Sixty-seven percent of results returned from the analyzers overestimated the platelet count; however, performance differed between analyzer models.19 Differences may be in part due to the nature of fixed blood survey material, but the study also confirms the results found by Segal et al. in their multicenter study on the performance of different hematology analyzers using fresh blood from thrombocytopenic patients.20 These results are therefore likely to significantly influence decisions on whether prophylactic platelet transfusions are required in patients with severe thrombocytopenia with platelet counts close to transfusion thresholds.

Immunological Platelet Counting With the widespread availability of flow cytometers within hematology and research laboratories, a number of different groups began to investigate the applicability of this technology (Chapter 35) to enumerate accurately various cells within whole blood, including platelets. The principle of this methodology involves simply labeling EDTA-anticoagulated blood with a suitable antiplatelet monoclonal antibody that has been fluorescently conjugated with, for example, fluorescein isothiocyanate (FITC). Because older flow cytometers cannot measure a fixed volume of sample, counting procedures involve indirect derivation of cell number using the ratio of fluorescent platelets to either added bead preparations or the inherent number of red cells within the sample. A number of flow cytometric counting procedures were reviewed by the ICSH expert panel on cytometry.21,22 The ICSH panel identified the variables and problems associated with this methodology, which enabled the ISLH task force panel to develop, evolve, and test a new candidate reference method with a multilaboratory study.4,5 The preferred method simply derives the platelet count from the ratio of fluorescent platelets to red cells within the sample (Fig. 32.9). The main advantage of the red cell (RBC) ratio is that, providing the blood sample is well mixed and that coincident events (RBC/RBC and RBC/platelet) are eliminated by optimal dilution, the count obtained is not only accurate and precise but also independent of potential pipetting artifacts. The method is also superior to derived counts from bead ratios, because these methods are dependent on a stable bead preparation (with an accurate bead count) in combination with very accurate/precise pipetting.6 However, bead-derived platelet counts may be useful for simply counting platelets within purified preparations when the red cells have been removed. More recently with the advent of new flow cytometers that can determine cell counts and analysis volumes simultaneously during acquisition it is also possible to determine accurate and

Platelet Counting

Platelets

587

Platelets

Plt/RBC coincidence

F L 1 C D 6 1

Plt/RBC coincidence

7°

Nonplatelet events

Fig. 32.10 An example of an ImmunoPLT scattergram of light scatter (horizontal axis) versus fluorescence (vertical axis) produced by the Abbott CELL-DYN 4000. The fluorescent platelets are clearly resolved from nonplatelet events and Plt/RBC coincidence events. (Used with permission from Abbott Diagnostics.)

Debris

RBC

Fig. 32.9 Immunological platelet counting. Flow cytometry scattergram of log fluorescence (CD61-FITC, FL1, vertical axis) versus log forward scatter (horizontal axis). The fluorescent platelets are clearly resolved from noise/debris, red cells (RBCs), and platelet (Plt)/RBC coincidence events.

precise platelet counts directly just using fluorescent labeling. This methodology performs perfectly adequately providing the instrument volume determination has been calibrated correctly and can be used to measure platelet counts within any sample providing platelets can be sufficiently discriminated and instrument settings and sample handling/labeling and instrument settings are optimized for their measurement.23

Automated Immunological Counting With the more recent convergence of flow cytometry and analyzer technology, it became feasible not only to perform optical counting by light scatter and fluorescence, but also to simultaneously measure cells identified with fluorescent monoclonal antibodies. Currently, the only commercially available hematology platforms that can measure antibody-labeled platelets are the Abbott CELL-DYN and Alinity platforms. Unlike the flow cytometric method, the ImmunoPLT method is a fully automated procedure. It labels platelets within whole blood by using anti-CD61 antibodies contained within a lyophilized pellet inside special evacuated tubes (Becton Dickinson, San Jose, CA). During analysis, the analyzers simply aspirate a small quantity of blood into the antibody-containing tube and perform a standard incubation. Final counting is performed within a fixed volume and includes PLT/RBC coincidence events but is therefore not based on a cell ratio (Fig. 32.10). The method has been shown to provide an accurate platelet count, especially in thrombocytopenic samples.24 As expected, it has also been shown to agree closely with immunocounting by flow cytometry. The fully automated immunological technique has obvious advantages and will be very useful in clinical situations in which rapid, accurate platelet counts are required.

IMAGE-BASED PLATELET COUNTING Rather than fluidic technology used by automated counting instruments, the cobas M511 (Roche) locates and measures individual blood cells based on morphology. This instrument

can perform a complete blood count (including platelets), whole blood count (WBC) differential, and reticulocyte counts based on rapid multispectral image analyses applied to automatically printed and stained microslides. To achieve this, a precise volume of blood is fused over a microslide creating a monolayer. Cells are labeled with the Romanowsky staining procedure and transferred to an imaging station. Populations are imaged using a black and white charge-coupled device (CCD) under high or low magnification and characteristics are defined by measuring light absorbance using a 3–4 LED light source (3-LED light source for WBC and 4-LED for reticulocytes). Platelet and RBC counts are discriminated under low magnification. Images are generated from individual platelets and relayed onto a viewing station for validation (Fig. 32.11). A platelet distribution curve is constructed and the position of individual platelets can be located accordingly (Fig. 32.12). From this histogram, platelets can be organized into groups dependent on size for further analysis. Platelet MPV is determined by averaging the volume of approximately 700 high magnification platelet images. Individual platelet volumes are calculated from digital images by measuring light absorbance from four different wavelengths (blue, green, yellow, and red light). Absorbance is an indicator of cell height and is recorded at multiple regions depending on platelet size (Figs. 32.12 and 32.13). Once a measurement of height is taken at each point, platelet volume is defined. Image-based platelet

Fig. 32.11 Platelet histogram showing the platelet volume distribution on the cobas M511. Individual platelets can be selected in the gallery (see Fig. 32.12) and their size can be compared to the general PLT distribution as indicated by the marked location on the histogram. (Used with permission from Roche Diagnostics.)

32

588 PART III Clinical Tests of Platelet Function Fig. 32.12 Section of a platelet gallery on the cobas M511 viewing station. Individual platelets can be selected and the MPV can be shown. Full images showing the platelet with its surrounding cells can be displayed and magnified. (Used with permission from Roche Diagnostics.)

Platelet Counting

589

leukocyte and 1 μL for RBC and platelet analysis) is flushed with PBS onto a micro-fluidic biochip. Dependent on size, cell subsets are quantified using varying electrical currents. RBCs and platelets are discriminated via 303 kHz and leukocytes by 1.7 MHz. Good correlations with a commercially based automated analyzer has recently been reported for this system.29 However, when using biochip electrical impedance without hydrodynamic focusing the platelet signal can be lost if the standard deviation (STD) of the noise is 0.05 V. Despite developments in POC platelet counting, care should be taken when using the finger-prick method for blood analysis. Platelet counts have been shown to inversely correlate with successive drops of blood due to consumption during ex vivo clot formation.30

RETICULATED PLATELETS/IMMATURE PLATELET FRACTION Fig. 32.13 Height of the platelet at individual points is related to the optical absorption at each point. Measurements taken at four different wavelengths of light are combined to determine the volume of an individual platelet. (Used with permission from Roche Diagnostics.)

counting and volume measurements show good correlations with current hematology instruments.25

POC PLATELET COUNTING POC whole blood analysis can now be undertaken in as little as 5 minutes from a single finger prick of blood. Such rapid acquisition has the potential to facilitate rapid diagnosis of thrombocytopenia/thrombocytosis and abnormal platelet size within surgeries or remote locations away from pathology or platelet laboratories.26 Unlike complex laboratory instruments, portable blood counters are inexpensive, lightweight and require minimal training to use. Blood is taken directly into precalibrated cassettes,27,28 which eliminates potential hemolysis, clotting, and anticoagulant-induced differentiation often associated before processing clinical samples. HemoScreen (PixCell) is a commercially available POC instrument which combines aspects of flow cytometry with image-based analysis. This innovative method relies completely on precalibrated disposable cartridges and a microfluidic chip for blood analysis. Cartridges contain the necessary reagents to perform a whole blood count (RBC and platelets) and a 5-part differential count. Blood is collected directly into a sampler, flushed inside reagent chambers, mixed and passed into a translucent flow chamber for optical analysis. Imaging analysis uses propriety dyes to depict differences in cellular properties such as mRNA content, nuclei and cytoplasm. To control for coincident events, HemoScreen acquisition relies on viscoelastic focusing. Due to the rheological properties of the suspension media, cells migrate laterally to the flow chamber on a single plane where they are imaged. The use of imaging assists in rapid diagnosis of atypical blood samples that would otherwise be false on nonimage-based commercial instruments. Furthermore, HemoScreen platelet counts show excellent correlation with the international referencing method.28 Other POC instruments which use a microfluidic biochip and electrical impedance counting are; under development.29 Whole blood is collected into measuring tubes and using a metering method, a regulated volume of blood (10 μL for

After labeling with specific immunological markers and a fluorescent dye that binds RNA, one is able to identify young platelets with a higher RNA content through flow cytometry (Chapter 35). By analogy with the reticulocyte count, these have been called “reticulated platelets,” and it has been suggested that an increased number in the circulation is a sensitive and early indication of recovery of thrombopoiesis.31 Platelet RNA can be detected by a variety of nucleic acid staining dyes. The reticulated platelets can then be distinguished from the mature platelets that do not theoretically take up the dye. In 1990, Kienast and Schmitz pioneered flow cytometric analysis of thiazole orange positive platelets in thrombocytopenic disorders and studied a variety of clinical conditions.32 Many modifications of this method were later published (some involving the use of dual-color flow cytometry and use of a platelet-identifying antibody to discriminate platelets from noise and other cells).33–35 Although thiazole orange has been the most widely used fluorochrome, many other dyes have also been used. There is much variation in the published reference ranges for this parameter using different flow cytometric methods and even between laboratories using the same methodology.31 Despite this, a number of clinical papers on reticulated platelet analysis have been published.34,36,37 They have clearly shown that under conditions of thrombocytopenia, platelet RNA content correlates directly with megakaryocyte activity. This offers the ability to determine if thrombocytopenia is due to marrow failure or to increased peripheral destruction/ consumption. Reticulated platelets have also been reported to increase in patients after peripheral blood progenitor cell transplantation 4 days before platelet recovery. The ability to predict platelet recovery should theoretically improve decisions of whether to give prophylactic platelet transfusions. More recently, an automated method to quantitate reticulated platelets reliably, expressed as the immature platelet fraction (IPF), was developed utilizing the Sysmex XE series blood cell counters.38 The IPF is identified by flow cytometry techniques and the use of nucleic acid specific dyes, polymethine and oxazine, in the reticulocyte/optical platelet channel. The stained cells are passed through a semiconductor diode laser beam, and the resulting forward scatter light (cell volume) and fluorescence intensity (RNA content) are measured. Mature and IPFs are identified by the intensity of such parameters. Fig. 32.7 illustrates optical (fluorescence) platelet scattergrams with forward scattered light on the y-axis and fluorescence on the x-axis. Mature platelets appear as light blue dots, and the immature platelets are displayed as green dots, the latter constituting the IPF parameter. IPF data are usually expressed as a proportional value of the total optical platelet count to

32

590

PART III Clinical Tests of Platelet Function

indicate the rate of platelet production, although an absolute count can easily be obtained. On the more recent Sysmex XN series of instruments, the IPF is measured in a dedicated platelet channel (PLT-F), also using optical fluorescence, but with a new dye that theoretically stains only platelets (Fig. 32.8).17 The clinical utility of the IPF has been established in laboratory diagnosis and monitoring of thrombocytopenia due to increased peripheral platelet destruction, particularly autoimmune thrombocytopenic purpura and thrombotic thrombocytopenic purpura.31,39,40 The IPF is predicted to rise in diseases in which there is increased platelet destruction or consumption and decrease in bone marrow failure (BMF). However, in BMF the IPF has been shown to be high when the levels are theoretically low.41 Despite this limitation there is a rise in the IPF% preceding the rise in platelet counts in most chemotherapy and transplant patients. The rise in IPF% in peripheral blood stem cell-derived transplant patients occurs earlier and is more closely related to platelet recovery than for bone marrow transplant patients. In the former patients, it is suggested there is an IPF threshold to predict platelet recovery, and in the future, an established IPF-based transfusion policy is possible.42 Care must be taken when using the IPF as a predictor of platelet recovery as platelet transfusions often dilute this fraction due to an increase in circulating platelets. However, the absolute immature platelet count has proven to remain stable post transfusion, giving a more accurate representation of BM activity but not bleeding risk.43,44 Data reported by a number of different laboratories show that the measurement of the IPF is standardized, precise and shows identical normal ranges.31 However, this method is somewhat limited when analyzing severely thrombocytopenic samples.45 On the Abbott analyzers, there is also a method for measuring reticulated platelets. The stain used for reticulocytes, which are measured by fluorescence and scattered light, is used, but the scattered light trigger is lowered to include stained platelets. The raw data from the instrument are downloaded to a PC using specialized software for analysis. The normal range has been quoted as 0.49%–4.4%, which is slightly lower than the range quoted for Sysmex instruments.

CONCLUSIONS Many methods for counting platelets are now available, and the number of alternatives is no doubt due to the difficulties in counting small cells that are activated easily, aggregate, and are also difficult to resolve from extraneous matter. For the research or nonspecialized laboratory setting, the manual count still offers the least expensive and easiest methodology if there is no access to a large hematology analyzer. Alternatively, some laboratories invest in small impedance analyzers, which provide a rapid and precise way of counting platelets. The recent development of a new immunological platelet counting method allows laboratories with access to a flow cytometer to count platelets very accurately by ratioing to either red cell number (in whole blood) or to added bead preparations (in whole blood, PRP, or purified platelet preparations) or within the measured volumes. Accurate and precise platelet counts in severely thrombocytopenic patients have become more important in recent years due to increased cytotoxic treatments resulting in prolonged thrombocytopenia, and the desire to reduce the frequency and threshold of platelet transfusions (Chapter 64). With the development of new automated platelet counting methods and two-dimensional analysis using light scatter or fluorescence, many of the limitations that exist with so-called one-dimensional analyzers (e.g., impedance and single light scatter) are reduced. In two-dimensional analysis, platelets of a similar size to red cells should be included in the count, and red cell fragments, cell debris, and other

particulate matter should be excluded. Alternative platelet counting approaches using immunological markers to identify platelets unequivocally have improved the accuracy of the count still further. The flow cytometric method for counting platelets has been recommended as a potential reference method and has been the subject of review by the ICSH expert panel on cytometry.4,5 A fully automated immunological technique, as in the Abbott systems, has obvious advantages. Using the immunological platelet counting reference method, manufacturers of all hematology analyzers will now be able to calibrate the platelet count with more accuracy. External quality control programs (e.g., NEQAS and CAP) must now develop suitable stabilized and calibrated materials to assess the accuracy of counting in thrombocytopenia. These developments would lead to the reporting of reliable low platelet counts on which clinicians can base their treatment or transfusion decision making with confidence. Comparative studies with different analyzers and immunocounting will facilitate the re-evaluation of current platelet transfusion thresholds and may lead to the threshold being reduced from 10  109/L to 5  109/L, as has been proposed in the past.46–49 A large multicenter study to compare the inaccuracy of platelet counts of current analyzers in severe thrombocytopenia (compared to a reference flow cytometric method) showed that most analyzers overestimated the count, which would result in under-transfusion of platelets at any set threshold.20 This study highlights the inaccuracies of hematology analyzers in platelet counting and re-emphasizes the need for external quality control to improve analyzer calibration for samples with a low platelet count. It also suggests that the optimal thresholds for prophylactic platelet transfusions should be re-evaluated. Current methods of optical platelet counting may not be superior to impedance counts for all patient populations. In the future, high throughput image-based analysis will become increasingly popular on hematology analyzers. The ability to perform accurate counting based on single cell morphology will be useful to distinguish immunologically similar platelet subsets and potentially eliminate laborious microscopic reviews. For example, a reliable method to discriminate immature platelets from terminally differentiated platelets and subsequently measure platelet production, may be useful to distinguish the cause of thrombocytopenia and possibly to reduce the number of prophylactic platelet transfusions, providing correlation has taken place with well-characterized clinical situations. The ability to perform a full blood count from a single fingerprick of blood has potential to revolutionize rapid POC diagnosis and management of hematological disorders in remote locations and clinics. Precalibrated disposable cartridges and use of dry reagents have pioneered lightweight, easy to use systematic designs. However, the utility of these instruments away from well-controlled laboratory settings will require the introduction of suitable quality control procedures to ensure accuracy and reliability of results within these settings. REFERENCES 1. Machin SJ. Carol Briggs (1958–2015). Int J Lab Hematol 2015; 37:575. 2. Brecher G, Schneiderman M, Cronkite EP. The reproducibility and constancy of the platelet count. Am J Clin Pathol 1953;23:15–26. 3. England JM, et al. Recommended methods for the visual determination of white cell and platelet counts. WHO LAB;1998. p. 88. 4. Harrison P, et al. An interlaboratory study of a candidate reference method for platelet counting. Am J Clin Pathol 2001;115:448–59. 5. International Council for Standardization in Haematology Expert Panel on C. & International Society of Laboratory Hematology

Platelet Counting

6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19.

20. 21. 22. 23. 24. 25.

26. 27. 28.

Task Force on Platelet, C. Platelet counting by the RBC/platelet ratio method. A reference method. Am J Clin Pathol 2001; 115:460–4. Harrison P, Horton A, Grant D, Briggs C, Machin S. Immunoplatelet counting: a proposed new reference procedure. Br J Haematol 2000;108:228–35. Alugupalli KR, Michelson AD, Barnard MR, Leong JM. Serial determinations of platelet counts in mice by flow cytometry. Thromb Haemost 2001;86:668–71. Harrison P, Briggs C, Machin SJ. Platelet counting. Methods Mol Biol 2004;272:29–46. Harrison P, Goodall AH. Studies on mean platelet volume (MPV)—new editorial policy. Platelets 2016;27:605–6. Martin JF, Bath PM, Burr ML. Influence of platelet size on outcome after myocardial infarction. Lancet 1991;338:1409–11. Chu SG, et al. Mean platelet volume as a predictor of cardiovascular risk: a systematic review and meta-analysis. J Thromb Haemost 2010;8:148–56. Braekkan SK, et al. Mean platelet volume is a risk factor for venous thromboembolism: the Tromso study, Tromso, Norway. J Thromb Haemost 2010;8:157–62. Soranzo N, et al. A novel variant on chromosome 7q22.3 associated with mean platelet volume, counts, and function. Blood 2009;113:3831–7. Coulter, W.H., Vol. U.S. Patent 2656508; 1953. Kunicka JE, Fischer G, Murphy J, Zelmanovic D. Improved platelet counting using two-dimensional laser light scatter. Am J Clin Pathol 2000;114:283–9. Briggs C, Harrison P, Grant D, Staves J, Machin SJ. New quantitative parameters on a recently introduced automated blood cell counter—the XE 2100. Clin Lab Haematol 2000;22:345–50. Briggs C, Longair I, Kumar P, Singh D, Machin SJ. Performance evaluation of the Sysmex haematology XN modular system. J Clin Pathol 2012;65:1024–30. Wada A, Takagi Y, Kono M, Morikawa T. Accuracy of a new platelet count system (PLT-F) depends on the staining property of its reagents. PLoS One 2015;10:e0141311. De la Salle BJ, et al. The accuracy of platelet counting in thrombocytopenic blood samples distributed by the UK National External Quality Assessment Scheme for General Haematology. Am J Clin Pathol 2012;137:65–74. Segal HC, et al. Accuracy of platelet counting haematology analysers in severe thrombocytopenia and potential impact on platelet transfusion. Br J Haematol 2005;128:520–5. Tanaka C, Ishii T, Fujimoto K. Flow cytometric platelet enumeration utilizing monoclonal antibody CD42a. Clin Lab Haematol 1996;18:265–9. Davis B, Bigelow NC. Indirect platelet immunoplatelet counting by flow cytometry as a reference method for platelet count calibration. Lab Haematol 1999;5:15–21. Masters A, Harrison P. Platelet counting with the BD Accuri(TM) C6 flow cytometer. Platelets 2014;25:175–80. Kunz D, Kunz WS, Scott CS, Gressner AM. Automated CD61 immunoplatelet analysis of thrombocytopenic samples. Br J Haematol 2001;112:584–92. Winkelman JW, Tanasijevic MJ, Zahniser DJ. A novel automated slide-based technology for visualization, counting, and characterization of the formed elements of blood: a proof of concept study. Arch Pathol Lab Med 2017;141:1107–12. van Berkel C, et al. Integrated systems for rapid point of care (PoC) blood cell analysis. Lab Chip 2011;11:1249–55. Erhabor O, et al. Evaluation of the QBC Star centrifugal three-part differential haematology system. Br J Biomed Sci 2013;70:67–74. Ben-Yosef Y, et al. The HemoScreen, a novel haematology analyser for the point of care. J Clin Pathol 2016;69:720–5.

591

29. Hassan U, et al. A microfluidic biochip for complete blood cell counts at the point-of-care. Technol (Singap World Sci) 2015;3:201–13. 30. Bond MM, Richards-Kortum RR. Drop-to-drop variation in the cellular components of fingerprick blood: implications for point-of-care diagnostic development. Am J Clin Pathol 2015; 144:885–94. 31. Harrison P, Goodall AH. “Message in the platelet”—more than just vestigial mRNA! Platelets 2008;19:395–404. 32. Kienast J, Schmitz G. Flow cytometric analysis of thiazole orange uptake by platelets: a diagnostic aid in the evaluation of thrombocytopenic disorders. Blood 1990;75:116–21. 33. Chavda N, et al. Rapid flow cytometric quantitation of reticulated platelets in whole blood. Platelets 1996;7:189–94. 34. Richards EM, Baglin TP. Quantitation of reticulated platelets: methodology and clinical application. Br J Haematol 1995; 91:445–51. 35. Ault KA, et al. The significance of platelets with increased RNA content (reticulated platelets). A measure of the rate of thrombopoiesis. Am J Clin Pathol 1992;98:637–46. 36. Richards EM, et al. Measurement of reticulated platelets following peripheral blood progenitor cell and bone marrow transplantation: implications for marrow reconstitution and the use of thrombopoietin. Bone Marrow Transplant 1996;17:1029–33. 37. Chaoui D, et al. Reticulated platelets: a reliable measure to reduce prophylactic platelet transfusions after intensive chemotherapy. Transfusion 2005;45:766–72. 38. Briggs C, Kunka S, Hart D, Oguni S, Machin SJ. Assessment of an immature platelet fraction (IPF) in peripheral thrombocytopenia. Br J Haematol 2004;126:93–9. 39. Kickler TS, Oguni S, Borowitz MJ. A clinical evaluation of high fluorescent platelet fraction percentage in thrombocytopenia. Am J Clin Pathol 2006;125:282–7. 40. Abe Y, et al. A simple technique to determine thrombopoiesis level using immature platelet fraction (IPF). Thromb Res 2006; 118:463–9. 41. Cybulska A, Meintker L, Ringwald J, Krause SW. Measurements of immature platelets with haematology analysers are of limited value to separate immune thrombocytopenia from bone marrow failure. Br J Haematol 2017;177:612–9. 42. van der Linden N, et al. Immature platelet fraction measured on the Sysmex XN hemocytometer predicts thrombopoietic recovery after autologous stem cell transplantation. Eur J Haematol 2014; 93:150–6. 43. Bat T, Leitman SF, Calvo KR, Chauvet D, Dunbar CE. Measurement of the absolute immature platelet number reflects marrow production and is not impacted by platelet transfusion. Transfusion 2013;53:1201–4. 44. Vinholt PJ, Hvas AM, Nybo M. An overview of platelet indices and methods for evaluating platelet function in thrombocytopenic patients. Eur J Haematol 2014;92:367–76. 45. Meintker L, Fritsch JD, Ringwald J, Krause SW. Immature platelets do not reliably predict platelet recovery in patients with intensive chemotherapy or stem cell transplantation. Vox Sang 2017; 112:132–9. 46. Gmur J, Burger J, Schanz U, Fehr J, Schaffner A. Safety of stringent prophylactic platelet transfusion policy for patients with acute leukaemia. Lancet 1991;338:1223–6. 47. Murphy WG. Prophylactic platelet transfusion in acute leukaemia. Lancet 1992;339:120–1. 48. Ancliff PJ, Machin SJ. Trigger factors for prophylactic platelet transfusion. Blood Rev 1998;12:234–8. 49. Norfolk DR, et al. Consensus conference on platelet transfusion, Royal College of Physicians of Edinburgh, 27–28 November 1997. Synopsis of background papers. Br J Haematol 1998;101:609–17.

32