Measurable residual disease (MRD) is a key prognostic marker in B-lymphoblastic leukemia (B-ALL). Because immunophenotypic and cytogenetic features vary by genetic subtype and treatment phase, the clinical value of an integrated MRD strategy using multiparametric flow cytometry (MFC), fluorescence in situ hybridization (FISH), and immunoglobulin clonality next-generation sequencing (NGS) warrants evaluation. This retrospective single-center study included 136 patients with B-ALL and 491 bone marrow MRD assessments. MRD was evaluated by an 8-color MFC panel adapted from the modified EuroFlow protocol, targeted FISH probes with an exploratory integrated interpretation for low-level signals, and IGH/IGK NGS. Immunophenotypic marker expression and associations with genetic subtypes were analyzed in MFC MRD-positive samples. MRD levels were significantly correlated across modalities: MFC-FISH (ρ = 0.762), MFC-NGS (ρ = 0.674), and FISH-NGS (ρ = 0.341). Using NGS as the molecular comparator, MFC showed higher sensitivity than FISH (41.0% vs. 31.8%) with specificity of 100.0% vs. 96.9%. Discordant MFC/FISH cases showed subtype-associated patterns, with MFC-/FISH+ findings more frequently observed in cases with numerical chromosomal abnormalities (e.g., hyperdiploidy) and immunophenotypic features resembling hematogones. Immunophenotypic marker expression varied by subtype, with near absence of CD10 in KMT2A-rearranged B-ALL and increased CD81 expression in TCF3::PBX1 cases. MRD positivity by any method was associated with significantly worse relapse-free survival. MFC, FISH, and NGS demonstrate complementary but variable performance in MRD detection across B-ALL subtypes. Immunophenotypic and cytogenetic heterogeneity influence the sensitivity of individual modalities, supporting the use of an integrated, multimodal approach for MRD surveillance.

Routine TBNK immunophenotyping detects the major lymphocyte compartments but may underestimate the biologically relevant variation contributed by less frequent but well-defined minor lymphocyte subsets. This study evaluated whether extending routine TBNK immunophenotyping with minor lymphocyte subsets provides complementary, analytically retrievable information that improves patient-level immune stratification beyond clinical diagnosis. In this retrospective cross-sectional study, peripheral blood immunophenotypes from 159 individuals were analyzed using a standardized workflow of eight DuraClone TBNK markers with manual hierarchical selection and absolute counting on a single platform. Multivariate profiling at the patient level was performed using derived immunophenotypic variables rather than single-cell event data. Canonical and extended panels were compared using nonparametric tests, PERMANOVA, principal component analysis, UMAP, k-means clustering, inter/intra distance indices, feature importance analysis, and hierarchical heat maps. Canonical immunophenotyping showed the strongest diagnostic associations for the B cell and CD4+ T cell compartments, while the extended panel identified an additional discriminatory structure, with absolute T double negative counts as the most informative minor lymphocyte subsets. Both panels were significantly associated with diagnostic clustering, but the extended panel showed a broader distribution of variance than the diagnostic panel. Compared with the canonical panel, the extended panel increased the inter/intra distance index (0.369 vs. 0.302, p = 0.0027) and substantially repositioned individuals within the immune space, with 59.1% shifting more than 10 percentiles. The clustering agreement between models was partial (ARI = 0.28; NMI = 0.42), and heat maps revealed continuous immunophenotypic axes with an internal substructure enriched in minor lymphocyte subsets. Extended routine immunophenotyping refines the multivariate representation of peripheral immune organization without requiring additional cytometry markers. Rather than replacing standard TBNK analysis, it complements it by revealing complementary, analytically retrievable information from minor lymphocyte subsets, thereby improving patient-level stratification and facilitating a more accurate, context-specific interpretation of immune status.

Flow cytometry can establish T cell clonality by detecting a restricted expression pattern of the T cell receptor (TCR) β constant region (TRBC), expressed in association with CD3. However, T cell neoplasms frequently lose surface expression of the CD3/TCR complex, posing a challenge to demonstrating T cell lineage and clonality. To address this challenge, here we present a 12-color flow cytometry panel, called cytoTCR, to characterize cytoplasmic expression of CD3/TCR complex components. We apply cytoTCR to 38 patient specimens with immunophenotypically abnormal T cell populations, demonstrating this approach can efficiently establish T cell lineage and clonality in challenging T cell neoplasms that have lost surface CD3 expression. While we show that natural killer (NK)-lineage neoplasms can express cytoplasmic CD3 at similar levels to T cells, we show that absent expression of cytoplasmic TCR components by mature lymphocytes can help confirm NK cell lineage. We demonstrate that cytoTCR can detect cytoplasmic TRBC-restriction in challenging cases of null-phenotype anaplastic large cell lymphoma, which lack surface expression of pan-T cell antigens. In cases of T-lymphoblastic leukemia, cytoTCR shows that cytoplasmic TRBC expression matches the expected developmental stage of the leukemia. Finally, we use cytoTCR to characterize atypical cCD3[-]CD7[-] T cells in a patient with a history of T-lymphoblastic leukemia as well as recent CAR-T therapy, showing that this atypical population is polytypic and represents CAR-T product rather than residual disease. Our study presents a broadly applicable flow cytometric approach to simultaneously assess T cell lineage and clonality in suspected T lineage populations with absent surface CD3 expression.

Flow cytometry has transformed platelet biology and diagnostics through high-resolution, single-cell analysis of surface markers, activation states, and intracellular signaling. This review traces its evolution from early applications in glycoprotein profiling and immune thrombocytopenia to clinical roles in HIT, VITT, and FNAIT. Advances in spectral cytometry, phosphoflow, and fluorescent barcoding have increased analytical depth and supported development of whole blood-compatible platelet function assays. Flow cytometry now enables characterization of platelet subpopulations, assessment of complement deposition, and functional evaluation across immune and thrombotic disorders. These capabilities support applications in diagnostics, bleeding and cardiovascular risk assessment, drug-induced thrombocytopenia, and platelet product quality control. As these technologies mature, flow cytometry is expanding its role in clinical workflows and may enable more biologically informed and individualized approaches in precision hematology.

Measurable residual disease (MRD) assessment is a key determinant of risk stratification and treatment modification in B-cell acute lymphoblastic leukemia (B-ALL). Multicolour flow cytometry (MFC) is a globally adopted rapid and sensitive MRD technique. However, technical practices remain significantly variable across laboratories, compromising assay sensitivity, reproducibility, and comparability. Currently, systemic recommendations providing guidelines on the technical aspects of MFC-MRD in B-ALL are unavailable. The Cytometry Society (TCS)-India undertook a structured consensus process to standardize technical aspects of MFC-MRD testing. A two-round Delphi survey was conducted among laboratory professionals and clinicians across India to capture the prevailing practices and priorities. Consensus recommendations were prepared through survey findings and further refined during an in-person consensus congress with national and international experts from Australia, Egypt, Spain, and Russia. This document provides comprehensive recommendations across pre-analytic, analytic, and post-analytic phases of MFC-MRD including minimal antibody panel, best practices for sample collection, transport, and processing, Instrument setup, quality control, acquisition targets, gating strategies, approaches for LOD/LLOQ definition, hemodilution assessment, etc. A structured MRD report format with quality caveats is also provided. The recommendations are categorized into "Must," "Preferred," and "Acceptable" levels to suit diverse resource settings while promoting harmonization. Implementation of these standards will enhance assay sensitivity, reproducibility, and comparability, thereby strengthening MRD-guided clinical decision-making in B-ALL.

This study aimed to evaluate the feasibility of CD72 as a complementary CD19-independent B-lineage gating marker for longitudinal measurable residual disease (MRD) surveillance in relapsed/refractory B-cell acute lymphoblastic leukemia (R/R B-ALL) following CD19 CAR-T therapy. Correlation analyses were performed in 66 B-ALL samples to compare MRD detection using CD72, CD19, and cytoplasmic CD79a. CD72 expression specificity was further evaluated in 129 leukemia patients. In addition, 129 patients with R/R B-ALL treated with autologous CD19 CAR-T therapy in registered clinical trials (ChiCTR-IIh-16008711; NCT03173417) between January 2021 and December 2022 were retrospectively analyzed, with follow-up continued until January 2025. CD72 gating showed excellent concordance with both CD19- and cCD79a-based strategies for MRD assessment. CD72 expression demonstrated high specificity in B-ALL, with a positivity rate of 95.77%, compared with 29.27% in AML and 23.53% in T-ALL. All 129 heavily pretreated patients achieved MRD-negative CR at day 28 after CAR-T infusion and subsequently underwent allo-HSCT, with a median interval of 54 days (range, 40-338). A total of 16 patients experienced MRD relapse during follow-up, including four clinically confirmed CD19-negative relapses that retained CD72 expression. Patients with pre-CAR-T MRD ≤1% showed earlier B-cell recovery than those with MRD >1% (median 30 [18-45] vs. 32 [26-79] days, p = 0.028). The MRD ≤1% cohort demonstrated significantly improved 3-year overall survival compared with the MRD >1% cohort (88.1% vs. 69.2%, p = 0.014). The 3-year cumulative incidence of MRD relapse was significantly lower in the MRD ≤1% cohort than in the MRD >1% cohort (3.96% vs. 17.95%, p = 0.019), while non-relapse mortality was also numerically lower in the MRD ≤1% cohort (5.92% vs. 17.95%, p = 0.053). Multivariate analysis identified KMT2A rearrangement, IKZF1 mutation, TP53 mutation, and elevated pre-CAR-T MRD as independent predictors of inferior outcomes. CD72 represents a feasible complementary B-lineage marker for longitudinal MRD surveillance following CD19 CAR-T therapy. Retention of CD72 expression in clinically confirmed CD19-negative relapses supports its potential utility when CD19 expression is lost after targeted therapy.

Manual gating for plasma cell (PC) identification in multiparametric flow cytometry (MFC) is time-consuming and operator-dependent, especially when PCs are scarce. Artificial intelligence approaches such as unsupervised clustering (e.g., FlowSOM) map high-dimensional data that still require expert interpretation. The primary aim of this work was to develop a lightweight, transparent, spreadsheet-based algorithm for a classification model that integrates with automated clustering outputs for standardized B-cell and PC identification in research flow cytometry datasets. Bone marrow aspirates were stained with a standard BD OneFlow™ PC screening tube (CD38, CD56, β2-microglobulin, CD19, cyIgκ, cyIgλ, CD45, CD138) and acquired on a BD FACSLyric™ flow cytometer. FCS files were exported to CellEngine cytometry software and singlet nucleated events underwent FlowSOM clustering (8 clusters/sample). Cluster-level median fluorescence intensities (MFIs) were exported to an Excel "PC Trainer Classifier" that (i) normalizes markers to an in-sample B-cell anchor, (ii) computes a PCscore with CD138 as a hard gate and CD38 as a soft gate, plus secondary features (CD19↓, CD45↓, CD56↑), (iii) applies a forced core fallback (highest CD38/CD138 core score) when strict criteria yield no PCs, and (iv) derives a NEOscore (CD56↑, CD19↓, CD45↓) for neoplastic phenotype. The rule-based classifier was trained on expert assigned PC and B-cell clusters from 40 samples (30 clonal and 10 polyclonal). Validation was done on a new set of 52 samples, independent of the model. Elements of the Excel formula design and error-proofing were co-developed with ChatGPT (OpenAI); all outputs were verified by the authors. Across 52 validation cases (8 clusters/case; 416 clusters total), B-cell detection achieved: Sensitivity 0.902 (0.79-0.96), Specificity 0.984 (0.96-0.99), Precision 0.885 (0.77-0.94), Accuracy 0.973 (0.95-0.99) and F1 0.893. PC identification achieved: Sensitivity 0.651 (0.54-0.75), Specificity 0.828 (0.79-0.86), Precision 0.458 (0.37-0.55), Accuracy 0.796 (0.76-0.83) and F1 0.537. A transparent Excel-based classifier integrated with FlowSOM clustering enables highly reproducible B-cell identification and provides a structured approach to PC classification in research flow cytometry datasets. While the B-cell classifier demonstrated excellent discriminatory performance, PC identification yielded moderate sensitivity and precision, likely reflecting underlying biological and phenotypic heterogeneity. Consequently, the PC classification component is best interpreted as a triage or augmented-intelligence tool intended to support, rather than replace, expert assessment. This approach provides a structured and auditable framework that reduces operator dependency and improves inter-case harmonization. Its interpretability, low cost and portability make it particularly suited to research laboratories operating in resource-variable settings. Further optimisation and prospective validation may refine PC classification performance.

Assessment of T-cell clonality is critical for diagnosing T-cell non-Hodgkin lymphomas (T-NHL). Flow cytometric T-cell receptor (TCR)-Vβ-repertoire analysis is specific but technically complex, multi-tube, and often limited in paucicellular specimens. T-cell receptor-beta-constant-region-1 (TRBC1) has emerged as a simpler alternative, but normal distribution ranges in clinically relevant T-cell subsets remain insufficiently characterized and data on direct comparison with TCR-Vβ restriction is limited. We prospectively analyzed 70 blood samples from healthy donors and 58 samples (bone marrow, peripheral blood, fine-needle aspirates, body fluids) from T-NHL patients. Multiparameter flow cytometry (MFC) was used for diagnostic immunophenotyping in T-NHL cases and for TRBC1 assessment in T-NHL cases and 28 immunophenotypic T-cell subsets in healthy individuals. In T-NHL, clonality was determined by TRBC1 restriction in aberrant T-cell populations and, where sufficient material was available, corroborated by TCR-Vβ repertoire analysis (IOTest Beta Mark).In healthy donors, median TRBC1-positive fractions across T-cell subsets ranged from 21.39% to 78.61%, with narrow 99% confidence intervals in major CD4[+] and CD8[+] compartments. Minor subsets occasionally showed extreme values (7%-93%) but remained polyclonal by TCR-Vβ analysis, leading to thresholds of ≤6% or ≥94% TRBC1 positivity as indicative of clonality in our cohort. Among 57 evaluable T-NHL cases, all CD3-positive neoplastic populations showed monotypic TRBC1 expression, with 100% concordance with TCR-Vβ results in 18 tested cases. TRBC1 was non-informative on surface-CD3-negative T-NHL, although cytoplasmic staining was sometimes helpful. Flow cytometric TRBC1 analysis is a rapid, cost-effective, single-tube method for T-cell clonality assessment, with applicability to small T-cell subsets such as paucicellular and measurable residual disease samples.

Flow cytometry immunophenotyping is essential for diagnosing hematologic malignancies, but accurate antibody panel selection depends on clinical context that is often fragmented across the electronic health record (EHR). At our institution, clinical laboratory scientists (CLS) use a standardized decision-tree algorithm incorporating specimen characteristics, laboratory values, and prior diagnoses to select panels. This process requires manual review and synthesis of longitudinal clinical documentation and remains time-intensive. We evaluated whether ChatEHR-an institutionally developed, EHR-integrated large language model (LLM) platform-could automate clinical history extraction and support flow cytometry panel selection across 100 cases. Performance was compared with the current CLS-driven workflow using hematopathologist-reviewed panel selections as the reference standard. ChatEHR achieved comparable accuracy to CLS in extracting and categorizing prior hematologic diagnoses (78% vs. 78%). Average processing time was 20.3 s for ChatEHR versus 41 s for manual review, corresponding to an estimated annual reduction of approximately 120 staff hours. Direct panel selection accuracy was lower for ChatEHR (47% vs. 78%), primarily due to errors in deterministic protocol execution and structured data interpretation. Major error categories included incorrect decision-tree logic (39%), diagnostic conflation (22%), failure to retrieve or apply clinical history (22%), laboratory value misinterpretation (9%), and hallucination of nonexistent panels (7%). ChatEHR demonstrated strong performance in extracting and classifying clinical history but was less reliable for deterministic protocol execution and structured data interpretation. Integration of LLM-based clinical history extraction with rules-based laboratory algorithms provides a practical hybrid workflow that preserves diagnostic reliability while reducing manual chart review burden. This approach enables scalable automation while maintaining appropriate hematopathologist oversight in clinical flow cytometry workflows.

We investigated the prognostic value of aberrant lymphoblasts (ALB) detected by flow cytometry (FCM) for predicting lymphoid transformation in patients with newly diagnosed chronic myeloid leukemia (CML). Data from patients with chronic phase CML were retrospectively interrogated to analyze covariates associated with lymphoid transformation-free survival (LTFS) via Cox, Firth-penalized Cox, Inverse Probability of Treatment Weighting-weighted Cox, and Fine-Gray competing risk regression approaches. Among 1081 patients, with a median follow-up of 43 (IQR, 16-70) months, 84 (8%) transformed to lymphoid or myeloid blast phase (LBP, 3%; MBP, 5%). 5-year cumulative incidence of lymphoid transformation and probabilities of LTFS, transformation-free survival and survival were 4% (95% CI, 3%-5%), 96% (95%-97%), 91% (89%-93%), and 95% (93%-96%), respectively. In the 54 patients (5%) with detectable ALB (median 0.3%; IQR, 0.1%-1.1%) at diagnosis, 24 (44%) transformed to LBP, none transformed to MBP. 0.4% ALB was identified as the optimal cutoff for LTFS by X-tile analysis and the maximally selected rank statistics method. All patients were stratified into ALB low- (ALB = 0), intermediate- (0< ALB < 0.4%), and high-risk (ALB ≥0.4%) groups with significant differences in 5-year probabilities of lymphoid transformation (2% [95% CI, 1%-2%] vs. 18% [3%-32%] vs. 91% [70%-100%]; p< 0.001) and LTFS (98% [98%-99%] vs. 82% [69%-98%] vs. 9% [2%-49%]; p< 0.001). Multivariable analyses confirmed that ALB intermediate-/high-risk groups and higher white blood cell count at diagnosis were significantly associated with a higher likelihood of lymphoid transformation. Low-level ALB detected by FCM at diagnosis identifies patients with chronic phase CML at high risk for lymphoid transformation, especially those with ALB ≥0.4%.

Reproducibility in clinical flow cytometry is essential for diagnosis, longitudinal monitoring, and interlaboratory harmonization, particularly when threshold-based lymphocyte measurements such as absolute CD4 counts guide clinical management. Although standardization of instruments, reagents, antibody panels, and acquisition protocols has reduced variability at the level of signal generation, variability introduced by human interpretation during manual gating has not been quantitatively separated from biological and technical sources. Here, we applied a two-stage workflow audit anchored to a fixed, invariant automated analysis used as an analytical reference to quantify interpretive variability in routine clinical TBNK (T-cell, B-cell, and natural killer cell) flow cytometry and to assess its impact on CD4 decision-band classification. Standardized quality-control materials and 320 consecutive clinical samples, independently analyzed by six technologists on three harmonized cytometers, were used to define an empirical analytical performance envelope (i.e., the expected range of performance) and to evaluate departures from this envelope under routine conditions. Under quality-control conditions, automated-manual differences were stable and interpretable: percentage-based endpoints showed near-zero bias with narrow dispersion, absolute counts exhibited small, consistent offsets, and CD4 decision-band assignment was preserved. In contrast, routine clinical samples showed substantially greater dispersion that was structured primarily by operator identity rather than instrument configuration. Operator-associated disagreement exceeded instrument-associated variability, was low dimensional and reproducible, and was concentrated near established CD4 thresholds, yielding discordant but adjacent decision-band classifications. These findings quantify a previously unmeasured source of analytical variability in routine hematology testing that can affect threshold-based clinical classification despite acceptable quality-control performance.

Flow cytometry is an essential component of routine hematological lab testing. Many computational methods have been proposed for the analysis of flow cytometry data, but most have focused on supervised learning for just one or a few specific disorders. To maximize clinical utility, we develop a method that enables identification of multiple common disorders and quality indicators. Our method includes a self-supervised pretraining component as well as a new, transformer-based model architecture. The self-supervised training algorithm is based on the DINO method while the model architecture is a relatively simple transformer encoder stack that includes a class (CLS) token, similar to BERT or vision-transformer models. Using a dataset of 52,625 samples obtained during routine clinical testing at our laboratory, we show that our pretraining method develops informative tube-level representations that clearly separate important diagnostic classes. We then evaluate performance on multiple downstream tasks, including sample viability estimation and five common hematological disorders. We compare our method to self-organizing maps, convolutional neural networks, attention-based multiple-instance learning models, and two varieties of set-transformer-based models, and demonstrate that our method delivers higher classification performance than other approaches.