Automated reticulocyte counting: advances, standardization challenges, and clinical accessibility

Introduction

Reticulocytes are immature, anucleate red blood cell precursors released from the bone marrow into the peripheral circulation. They retain residual ribosomal RNA and mature into erythrocytes within 1–2 days. In 1992, the International Council for Standardization in Haematology (ICSH) issued guidelines specifying the microscopic assessment of reticulocytes on supravitally stained blood films as the standard method for their enumeration [1]. According to ICSH, a reticulocyte is defined as “an anucleate erythrocyte whose cytoplasm contains at least two blue-staining granules or an attached filamentous structure.” In addition, fields such as post-chemotherapy marrow regeneration and blood-doping surveillance in athletes also rely on reticulocyte counts for key information [2]. In pathological states, an elevated absolute reticulocyte count suggests increased erythropoietic activity, whereas a decreased count typically reflects bone marrow dysfunction or impaired marrow regeneration. The absolute reticulocyte count is essential for the differential diagnosis of anemia, the evaluation of marrow function, and the monitoring of therapeutic response. At present, the widely accepted adult reference interval for automated absolute reticulocyte count is 25−100 × 10⁹/L [3].

Traditional manual counting relies on supravital staining with new methylene blue and microscopic examination; it is complex and subjective, with inferior precision and reproducibility, and thus fails to meet modern clinical demands for high-throughput and accuracy [1], 4]. Since the 1980s, reticulocyte counting has gone through a crucial phase – the technological breakthrough based on flow cytometry. Tanke et al. demonstrated RNA-dependent fluorescent staining with pyronin Y and flow cytometric quantification of reticulocyte frequency and maturation distribution [5], thereby establishing the feasibility of cytometric measurement. Davis et al. introduced thiazole orange-based flow cytometry along with the Reticulocyte Maturity Index (RMI), and validated its clinical applicability [6]. Subsequently, Van Hove et al. validated a rapid thiazole-orange assay incorporating gating strategies to mitigate common interferences, facilitating routine laboratory implementation and laying the groundwork for subsequent integration into hematology analyzers [7]. With advances in flow cytometry and fluorescent dye technologies, automated reticulocyte counting has matured and been incorporated into fully automated hematology analyzers [8]; beyond improving accuracy and reproducibility, automation provides additional parameters – such as the immature reticulocyte fraction (IRF) and reticulocyte production index (RPI) – that offer a more comprehensive picture of erythropoietic function [9].

Despite major technological advances, automated reticulocyte counting still faces challenges in standardization and clinical accessibility. On the one hand, differences among analyzers in dye reagents, optical detection, and result calculation, together with the lack of a unified calibration framework, undermine the cross-platform comparability of results [10]. Relevant international hematology bodies have recognized this issue and initiated collaborations aimed at improving standardization and harmonization [11]. On the other hand, automated reticulocyte analyzers are expensive and maintenance-intensive, limiting uptake in low-income countries and primary-care settings; according to the Lancet Commission on diagnostics, approximately 47 % of the global population lacks access to basic diagnostic tests [12]. In many low-resource settings, even basic complete blood count testing is not widely available, making reticulocyte counting even harder to implement [13]. To better harness this technology for global health, innovative solutions are needed to advance both standardization and accessibility.

Physiology and pathophysiology

Reticulocytes represent the immediate post-nuclear extrusion stage of erythropoiesis [14], bridging the transition from late normoblasts to mature erythrocytes. They retain ribosomal RNA and organelle remnants that support residual hemoglobin synthesis [15]. Under homeostatic conditions, reticulocytes are released from the bone marrow into the circulation at a rate that balances the clearance of senescent red cells. Their dwell time in peripheral blood is approximately 24–48 h, during which ribosomal RNA is degraded and the cells progressively lose reticular material before maturing into erythrocytes. Erythropoietin (EPO) is the principal hormonal regulator [16]: rising EPO levels, triggered by hypoxia or anemia, expand the erythroid progenitor pool and accelerate reticulocyte release, leading to measurable increases in reticulocyte counts within 2–3 days of stimulation.

Alterations in reticulocyte production or release reflect diverse hematologic and systemic conditions. Elevated reticulocyte counts signify accelerated erythropoiesis [17], typically in response to hemolysis, acute blood loss, or recovery from marrow suppression. In these scenarios, an increased proportion of immature reticulocytes – quantified as the immature reticulocyte fraction (IRF) – is often observed, indicating “stress erythropoiesis.” Conversely, in hypoproliferative states such as aplastic anemia, myelodysplastic syndromes, or chemotherapy-induced marrow suppression, reticulocyte counts are markedly reduced. Functional iron deficiency or erythropoiesis-limiting chronic inflammation leads to blunted reticulocyte responses and a decline in reticulocyte hemoglobin content (RET-He or equivalent), serving as an early warning sign of impaired hemoglobinization [18], 19].

Historic overview

Reticulocyte measurement originally relied on manual microscopic enumeration [8]. According to the Clinical and Laboratory Standards Institute (CLSI) H44-A2 guideline [20], which was developed in cooperation with the ICSH, the recommended approach is to prepare fresh blood films, apply a supravital stain (new methylene blue), and, under oil immersion, count several thousand erythrocytes and enumerate the reticulocytes among them. Because manual counting is laborious and constrained by operator error and limited tallies, H44-A2 recommends adjusting the total number of red cells counted according to the reticulocyte proportion to control statistical error. Even when using a Miller disk, if the reticulocyte fraction is 1 %, counting only 1,100 red cells yields a statistical variation exceeding 10 %; to reduce the variation to ≤5 %, at least 4,400 red cells must be counted. Clearly, manual methods struggle to achieve such large tallies, resulting in poor reliability at low reticulocyte levels and limiting clinical utility.

In the 1980s, with advances in flow cytometry, automated reticulocyte analyzers entered clinical practice [21]. In 1998, the ICSH proposed a flow cytometry–based reference method for reticulocyte enumeration [22], recommending calibration of flow-cytometric counts to the red-cell ratio derived from manual counting. Since then, automated reticulocyte counting has been widely adopted and has gradually supplanted manual methods as the laboratory standard. Compared with traditional manual methods, automated approaches offer high throughput and high precision, enable analysis of large numbers of cells in a short time, and provide additional reticulocyte indices that facilitate a more comprehensive assessment of erythropoiesis [23]. Typical coefficients of variation (CVs) for automated counting can be <5 %, markedly outperforming the ∼20 % variability of manual methods [24]. Moreover, automated counting provides better resolution for low-reticulocyte specimens (e.g., aplastic anemia) [25], allowing earlier detection of subtle changes.

Current methodology

At present, reticulocyte-detection technologies in mainstream hematology analyzers exhibit diverse development paths worldwide, including three-dimensional fluorescence flow cytometry (3D flow cytometry), multi-angle light scatter combined with fluorescent staining, impedance methods coupled with non-fluorescent staining, and digital image cytometry. Table 1 provides a technical comparison of the latest representative fully automated hematology analyzers with reticulocyte-counting capability from major manufacturers, offering a clear side-by-side view of similarities and differences in the breadth of reticulocyte parameters and underlying detection principles across systems.

Pre-analytical factors

As metabolically active cells, reticulocytes exhibit rapid ex vivo degradation of cytoplasmic RNA. The CLSI/ICSH H44-A2 guideline recommends analyzing EDTA-anticoagulated blood within 6 h at room temperature [20]; beyond that, storage at 4 °C is advised to slow cellular deterioration. Multicenter studies further indicate that acceptable holding times for the same specimen vary markedly among analyzers [35], and pre-analytical CVs often exceed acceptable limits. For instance, a study based on the Sysmex XN platform found that [23], after 6 h of storage at room temperature, the total Retic% decreased by approximately 10–12 %, with the HFR fraction experiencing a decline of up to 18 %. Additionally, research conducted using the ADVIA 2120 and Cell-Dyn Sapphire platforms revealed that even during 72 h of storage at 4 °C [36], vibrations during transportation and variations between batches of anticoagulants could still introduce additional counting biases.

Standardization challenges

The core of quality control and standardization is the establishment of commutable reference materials and a calibration chain traceable to SI units [11]. However, available calibrators – fluorescent beads, fixed cells, or synthetic particles – differ fundamentally from clinical whole blood in light scattering, fluorescence behavior, and chemical stability, making it difficult to replicate the full workflow from staining to signal acquisition [37]. Statistical recalibration methods such as Passing–Bablok regression can improve concordance within the same sample batch, but they lack true transferability across laboratories and analytical platforms [10]. In addition, any modification of analyzer algorithms or firmware typically requires renewed regulatory submission, further delaying implementation and broad uptake of reference materials. Without large-scale commutable standards, inter-platform systematic bias is hard to eliminate, constraining cross-center comparability of reticulocyte parameters.

The fragmentation of reticulocyte counting parameters is a major obstacle to standardization. Different manufacturers employ disparate parameters to characterize reticulocytes. For example, Sysmex provides RET-He (reticulocyte hemoglobin equivalent) and Delta-He (the hemoglobin difference between reticulocytes and mature erythrocytes), Siemens reports RHCc (reticulocyte hemoglobin content), Abbott reports MCHr (mean reticulocyte hemoglobin) [32], whereas Beckman lacks corresponding reticulocyte hemoglobin parameters [24]. Although the International Council for Standardization in Haematology (ICSH) advocates the harmonized use of RET% (reticulocyte percentage), RET# (absolute reticulocyte count), and IRF (immature reticulocyte fraction) [10], multiple parallel terminologies – RETIC, %RET, LFR/MFR/HFR, RETL%/RETM%/RETH% – remain common in clinical and research settings.

Another challenge is the lack of clinical decision thresholds and reference intervals. While analytes such as hemoglobin and platelet count have widely accepted diagnostic cut-offs, clinical thresholds for RET and related parameters (e.g., IRF; Delta-He; RET-He) remain largely confined to single-center practice [38]. For example [39], a RET-He <28 pg in Sysmex systems is considered sensitive for detecting iron deficiency and dynamic changes in erythropoiesis; however, its linear correlation with metrics on other platforms (e.g., Siemens RHCc; Abbott MCHr) is suboptimal, limiting cross-platform applicability and thus generalizability in clinical use. Moreover, methodological heterogeneity and the absence of absolute reticulocyte standards mean that reference ranges still depend on the dyes/fluorochromes used; consequently, no universally accepted reference intervals have been established [20]. Most laboratories rely on manufacturer-provided or locally derived intervals; however, values can drift by more than 20 % across populations, regions, and analytical platforms, impeding multicenter studies and the implementation of guideline recommendations. These discrepancies preclude direct cross-platform data integration in clinical research, thereby affecting clinical decision-making.

A further challenge arises in the context of moderate or severe anemia. Not all automated reticulocyte systems perform result correction; when automatic correction is absent, the reticulocyte count should be adjusted for the degree of anemia so that reductions in red cell count do not spuriously inflate the reticulocyte count. The absolute reticulocyte count in peripheral blood accurately reflects erythropoietic activity. However, in moderate or severe anemia, the bone marrow prematurely releases reticulocytes into the bloodstream. These prematurely released reticulocytes are termed “shift reticulocytes”, and they circulate longer in peripheral blood than normally released reticulocytes. In this setting, laboratory professionals must report corrected counts expressed as the reticulocyte index (RI) or RPI to avoid misleading results. Their formulas are as follows [40]: (a) RI=observed reticulocyte[%] × patient’s hemoglobin or hematocrit/standard hemoglobin or hematocrit; (b) RPI= RI × (1/reticulocyte maturation time in days). In hematology and oncology, these indices retain clinical relevance: an RPI <2 indicates inadequate marrow response, whereas an RPI >3 reflects regenerative erythropoiesis, particularly useful for differentiating aplastic anemia from hemolytic anemia [41].

Quality control

As the CLSI/ICSH H44-A2 guideline–recommended reference method, the New Methylene Blue (NMB) microscopic technique is highly dependent on operating conditions, stain quality, and operator factors [20]; its CV can reach 20 %, and when the reticulocyte content is <1 %, the CV may exceed 30 % [42], 43]; this intrinsic high variability also carries over into the performance verification of automated analyzers. Accordingly, since 2010 the ICSH has actively promoted tricolor or multicolor flow cytometry as the reference procedure for reticulocytes and, in its 2014 guidance, proposed an alternative recommendation [24], 44]; the standardization roadmap is expected to yield a first-edition guideline in the coming years, aimed at improving the accuracy and comparability of reticulocyte measurements.

Automated reticulocyte enumeration is most challenged, methodologically, by foundational heterogeneity in detection principles among vendors’ analyzers [10], leading to potential inter-platform systematic bias that undermines result harmonization and guideline implementation [33]. In fluorescence assays, nucleic-acid stains bind residual RNA in reticulocytes and maturity is inferred from signal intensity; impedance approaches classify cells by size and intracellular complexity; digital imaging distinguishes reticulocytes using high-resolution imaging and computational analysis of morphology, such as membrane contours and RNA-granule patterns. Inter-platform bias in RET% can reach as high as 15 % [45] and is more pronounced in specimens with low reticulocyte counts [46], because the scarcity of reticulocytes makes the signal harder to detect reliably. In staining systems, differences in dye affinity and quantum yield also introduce substantial variability. Minor changes in dye concentration and incubation parameters can induce fluctuations in fluorescence intensity, compromising the reliability of derived indices such as RET-He and Delta-He [8], 47].

Moreover, vendor-defined fluorescence thresholds and scatter-signal parsing rules – the proverbial “black box” – further widen result dispersion: for example, Sysmex employs an LFR/MFR/HFR tripartite model [27], 28], whereas Siemens uses a RETL%, RETM%, and RETH% categorization scheme [26]. Although each aims to depict the maturation spectrum of reticulocytes [48], these thresholds are trained on platform-specific datasets and lack unified calibration, rendering parameters such as IRF non-interchangeable and precluding straightforward cross-platform comparison. Discrepancies attributable to algorithms are magnified in pathologic samples – such as hemolysis or recovery after chemotherapy [49] – and, particularly when immature cells are abundant, inter-platform divergence in very low or high fluorescence regions becomes accentuated, heightening the risk of clinical misclassification.

Reticulocyte parameters

Beyond reporting the reticulocyte absolute count, automated reticulocyte counting also acquires scatter and fluorescence distribution data, from which multiple parameters – such as the reticulocyte maturity index and the immature reticulocyte fraction – are derived [9], providing deeper insight into the dynamics of erythropoiesis (see Table 2). With growing recognition of the clinical significance of these metrics, accumulating studies advocate their integration into clinical diagnostic and treatment decisions.

Interfering factors

Endogenous sources of interference. Most automated analyzers use fluorescent dyes (e.g., thiazole orange) to detect cytoplasmic RNA; accordingly, NRBCs, large platelets, and RBC fragments containing RNA or nucleic acids are often spuriously counted as RET, leading to falsely elevated RET%. It has been reported that NRBC >10 per 100 WBC may raise RET% falsely by as much as 8.6 % and generate “pseudo-reticulocyte” readings [23]. Platelets contain small amounts of RNA and can emit signals under fluorescent staining; when systems lack platelet-exclusion markers such as anti-CD61, a mild RET overestimation may occur [30].

Exogenous sources of interference. Drugs with autofluorescence – such as hydroxychloroquine and doxorubicin – may compete for nucleic-acid dye sites, perturbing RNA fluorescence detection in flow cytometry and artifactually lowering RET-He [38]. Additionally, after IV dextran-iron infusion, plasma non-transferrin-bound iron (NTBI) rises sharply over a short interval, directly stimulating erythroid precursors to accelerate hemoglobin synthesis; newly released reticulocytes thus contain more hemoglobin, producing a transiently elevated RET-He [19]. Such interference may mask true iron deficiency and confound evaluation of therapeutic response in anemia.

Pathological factors. Parasitic infections such as malaria and Babesia generate accessory RNA/DNA signals within erythrocytes, leading to spurious elevation of reticulocyte counts, especially in fluorescence-based systems [50], 51]; similar artifacts have been documented in dogs with Babesia[52]. Hemoglobinopathies and morphological abnormalities – including thalassemia, chronic liver disease, and hereditary spherocytosis – produce abnormal scatter orfluorescence profiles that compromise accuracy of indicessuch as IRF and RET-He [23]. RBC inclusions such as Howell–Jolly and Heinz bodies mimic RNA-positive signals, causing either under- or overestimation of RET counts, with the degree of interference varying by platform [53], 54].

Critically, analyzer methodology influences susceptibility to these artifacts [55]. 3D fluorescence flow cytometry shows relative resilience but may under-detect weakly fluorescent thalassemic reticulocytes [27]. Multi-angle scatter with fluorescence improves morphologic resolution but remains affected by abnormal target cells (codocytes) [29]. Impedance-based analyzers with non-fluorescent dyes are particularly vulnerable to inclusions and abnormal morphology, often leading to false elevations [23], 53], 54]. Digital image cytometry provides visual confirmation of inclusions but its accuracy depends on slide quality and algorithm robustness [33]. Thus, interpretation of pathological interferents requires integration of clinical context with analyzer-specific technologies.

Result reporting and interpretation

Clinical accessibility

Traditionally, reticulocyte counting was regarded as a test confined to large hospital laboratories, with limited use in primary settings due to technical constraints. Advances in automated analyzers have markedly ameliorated this limitation. Modern five-part differential hematology analyzers, when equipped with a reticulocyte module, require only an additional reagent and dedicated channel to report reticulocyte results alongside the routine CBC. Consequently, in countries including China, India, Brazil, and Mexico, reticulocyte testing can shift from a specialized examination to routine practice. Driven by increased health-sector spending and medical-tourism demand [66], more secondary and county hospitals in China, India, Brazil, and Mexico have upgraded instrumentation, expanding access to anemia screening and iron-metabolism evaluation across frontline healthcare communities. Thus, the main challenge is ensuring uniform access to instrumentation, reagent supply, and technical expertise across different tiers of health-care systems.

Under resource constraints, automation lessens the complexity of sample handling and reliance on expert operators, and effectively diminishes subjective bias from manual staining and between-laboratory variability. This significantly enhances the capacity of primary-level laboratories for emergency and routine testing. In parallel, numerous non-governmental organizations and regional quality control hubs have established reticulocyte external quality assessment schemes across Latin America, sub-Saharan Africa, and Southeast Asia [67], using remote benchmarking and online training to operationalize standardized workflows and calibration in frontline labs and to strengthen practical use of advanced indices – including Ret-He and Delta-He – by local personnel. Equally important, the effective use of advanced indices such as Ret-He or Delta-He depends not only on instrument performance but also on proper training of laboratory staff and continuing education of clinicians by laboratory hematologists, so that test results can be translated into appropriate clinical decisions.

Although dissemination of technology and training proceeds apace, comprehensive uptake at the primary level is still hampered globally by acquisition costs and insufficient clinical recognition. Where funding is limited or instruments are obsolete, manual enumeration persists, and the testing-to-clinic loop for emerging reticulocyte indices is underdeveloped, so reports tend to be purely analytic and fail to guide patient management. With ongoing advances in AI-driven remote diagnostics, smartphone microfluidic assays, and low-cost, low-maintenance automation, reticulocyte counting is poised to become a standard fixture comparable to the CBC at all levels of health care worldwide, providing timely and accessible readouts of erythropoietic kinetics for conditions including anemia and hemolysis.

Future outlooks

With the release of the latest report from the International Council for Standardization in Haematology (ICSH) working group on reticulocyte parameter standardization, establishing unified calibration systems and reference intervals has become an urgent priority [10]. The ICSH working group systematically evaluated measurement biases for RET%, RET#, IRF, and related indices across commercially available automated hematology analyzers, and for the first time proposed using stable cell lines or artificial microparticles as “gold-standard” calibrators to enable inter-instrument traceability and result comparability. Looking ahead, multicenter collaborations may establish globally applicable reference intervals and clinical decision thresholds for reticulocyte parameters, enabling harmonized interpretation of emerging indices such as IRF and Ret-He and thereby markedly improving inter-regional comparability in anemia classification and treatment monitoring.

From a deeper phenotyping perspective, investigators are exploring intracellular biomarkers of reticulocytes to expand dynamic evaluation systems. Flow cytometric quantification of transferrin receptor (TfR) expression on reticulocyte membranes has been shown to reflect intracellular iron utilization and to distinguish functional fromabsolute iron deficiency [68]. More advanced approaches – cytometry by time-of-flight (CyTOF) and single-cell RNA sequencing – are being explored to delineate receptor downregulation and RNA content dynamics during reticulocyte maturation [69], with the aim of developing new metrics such as a “maturation-time index” or “RNA content index” to provide deeper, molecular-level insights into erythroid proliferative kinetics. Integration and AI-driven intelligence are set to usher reticulocyte analysis into a “one-tube, multi-index” paradigm. New-generation automated analyzers are capable of concurrent capture of digital imagery and fluorescence readouts in a single test, enabling comprehensive reports that unify RBC morphology, reticulocyte parameters, and WBC classification [70]. Within anti-doping programs, RET% together with the OFF-Score (also known as the OFF-hr score or stimulation index, =[Hb] – 60 × √RET%) is embedded in the Athlete Biological Passport to algorithmically detect illegal EPO administration and autologous transfusion practices [71], 72]. Concurrently, AI and large-scale data infrastructures are being leveraged to fuse reticulocyte indices with iron-metabolism and clinical variables via machine learning, enabling automated anemia classification and therapeutic response forecasting and providing timely, accurate decision support in frontline and remote care [73], 74]. As portable testing and digital quality-control networks mature, reticulocyte counting is poised to truly transform into a multidimensional hub for erythropoietic dynamics.

Conclusions

Following its formal definition by ICSH in 1992, reticulocyte enumeration transitioned from labor-intensive, new methylene blue microscopic methods to high-throughput, flow cytometry–based automated systems with specialized fluorochromes, markedly improving analytical precision and clinical repeatability. Although major manufacturers differ in detection principles, dye chemistries, and algorithmic parameters, they collectively provide multidimensional erythropoietic indices – such as IRF, Ret-He, and Delta-He – that greatly enrich anemia classification, marrow response evaluation, and iron-status assessment. Looking forward, there is an urgent need to build a global calibration chain based on commutable reference materials and to set unified thresholds to eliminate inter-platform systematic bias; in parallel, emerging single-cell phenotyping and RNA content indices, integrated digital imaging diagnostics (including AI-enhanced imaging flow cytometry), and machine-learning–driven report interpretation will collectively advance the shift from a single assay to an information platform, providing more precise and accessible erythropoietic insights for anemia classification, post-chemotherapy marrow monitoring, management of renal anemia, and telemedicine.