Fungi are the subject of extensive research due to their significant impact on human life. However, the development of tools based on fluorescent proteins has been slow in these species. Trichoderma reesei, a filamentous fungus known for its high secretion capability, shows great potential for producing heterologous proteins. However, the limited range of available fluorescent proteins for T. reesei hinders progress in its biological study and industrial applications. The goal of this study is to expand the panel of fluorescent markers for T. reesei by testing six new fluorescent proteins. To enable accurate comparisons, the method previously designed for eGFP was optimized to insert coding genes as a single copy by replacing the native pTEF promoter with the Neurospora crassa equivalent. The benchmarking assay identified one yellow-fluorescent protein and two green-fluorescent proteins that outperformed eGFP for transformant screening applications and a red fluorescent protein whose screening performance is comparable to that of eGFP, which would be valuable for colabeling purposes. These results could not have been predicted from the values reported in the Fluorescent Protein Database and the relative excitation of each protein at 488 nm accounts for only part of the difference. Additionally, the data obtained on fluorescent proteins and the approach used may be beneficial for investigating other types of fungi.

The reliability of clinical flow cytometry assays hinges on the availability of robust assay controls that accurately reflect representative samples. Widely used conventional assay controls, such as cell lines, often fail to replicate the phenotypical and biophysical properties of representative primary samples resulting in gating variability and limited confidence in assay performance as certain markers may not be expressed. To address these limitations, we developed a custom synthetic multiple myeloma (MM) cell mimic using Slingshot Biosciences TruCytes® technology. Currently, the MM1.S cell line can be used as an assay control in a flow cytometry panel to evaluate key markers, B cell maturation antigen (BCMA) and G protein-coupled receptor, class C, group 5, member D (GPRC5D), on plasma cells (PCs) and B cells in bone marrow aspirate, for MM clinical trials. Although MM.1S shows strong expression of the key markers BCMA and GPRC5D, it lacks CD45 and CD19 which are needed for PC and B cell identification. We aimed to engineer a MM cell mimic to simulate the scatter properties and antigen density for our fresh MM bone marrow aspirate sample testing. Representative MM bone marrow mononuclear cells (BMMCs) were reviewed to find scatter properties and representative median fluorescence intensity (MFI) of CD45, CD19, CD38, CD138, BCMA, GPRC5D, CD56, and PD-L1 on PCs and B cells. We engineered the MM cell mimic with one population representing PCs and another population representing B cells, based on MM BMMC samples. The MM cell mimic overcame the lack of CD45 and CD19 expression and the divergent scatter and MFI profiles observed in traditional MM cell lines like MM1.S. Qualification studies demonstrated intra- and inter-assay precision (%CV ≤20%), robust inter-instrument reproducibility, post-reconstitution stability up to 7 days at 4°C, and closed-vial storage stability of up to 13 months at -20°C. In summary, these results establish the MM mimics as an improved and alternate assay control to MM1.S, facilitating longitudinal assay performance monitoring and ongoing quality assurance in clinical flow cytometry.

Accurate immunophenotypic evaluation of natural killer (NK)-cell neoplasms is challenging because NK-cells lack somatically rearranged antigen receptors, are comprised of phenotypically distinct subsets, and exhibit tissue-specific subset distribution. We developed a flow cytometry (FC) panel for NK-cell immunophenotypic analysis and clonality assessment and applied it to 120 specimens, including normal peripheral blood (PB), bone marrow (BM), lymph node (LN), and body fluids, as well as a series of NK-cell neoplasms and NK-cell clones of uncertain significance (NK-CUS). The expression ranges for NK-cell receptors (NKR) including killer immunoglobulin-like receptors (KIR, CD158) in the PB were consistent with those reported previously by other groups. The distribution of NK-cell subsets differed by the specimen type. While PB and BM NK-cells showed predominance of the CD56[dim] subset, LN and fluid samples were enriched for the CD56[bright] subset, resulting in higher CD94/NKG2A and lower KIR expression compared to PB. Epstein-Barr-virus (EBV)-positive NK/T-cell neoplasms exhibited a relatively consistent CD56[bright] CD16[-] CD94/NKG2A[+] CD57[-] KIR[-] immunophenotype that resembled normal CD56[bright] NK-cells, emphasizing the importance of immunophenotypic aberrancies and increased scatter properties rather than restricted KIR expression in diagnosing such neoplasms. NK-large granular lymphocytic leukemia (NK-LGLL) demonstrated a mature CD56[dim] phenotype with restricted KIR expression. Twelve cases fulfilled the proposed criteria for NK-CUS, comprising both putative cytomegalovirus (CMV)-related NK-cell clones and phenotypically distinct (likely) CMV-unrelated clones with more pronounced immunophenotypic abnormalities. Our findings suggest that interpretation of NK-cell clonality should take into account NK-cell subset composition and tissue-specific receptor expression. These results provide practical considerations for NK-cell immunophenotypic analysis and contribute to a better understanding of the role of FC in diagnosing such neoplasms.

When infection spreads from an initially infected cell through a cell monolayer, the resulting plaque records successive rounds of virus-cell interaction. Fluorescent reporter viruses permit this process to be followed at single-cell resolution, but whether secondary infected cells retain detectable signatures associated with their plaque of origin remains unclear. Here, we tracked enhanced green fluorescent protein expression in 154 cells from eight rhinovirus plaques and systematically compared feature-based, clustering, dimensionality-reduction, and sequence-alignment methods. Primary and secondary infected cells were distinguished mainly by the time of first detectable fluorescence; no consistent differences were detected in reporter-trajectory kinetics or amplitude. Among secondary cells, most methods detected no plaque-associated structure beyond chance. Affinity propagation detected a small plaque-label association that persisted after removal of the first detection time, although its biological interpretation was limited by session-associated intensity differences. Thus, by the secondary round, cell-to-cell heterogeneity largely exceeded any plaque-associated signature detectable under the present conditions. This work provides a framework for comparing single-cell trajectories across early rounds of plaque growth and for designing studies to resolve subtle lineage-associated effects.

Acute myeloid leukemia (AML) is characterized by high genotypic and immunophenotypic heterogeneity. We collected an extensive dataset containing 5366 flow cytometry files from 885 AML patients, stained with the EuroFlow acute leukemia orientation tube (ALOT) and AML/MDS panel, acquired in a standardized way at eight centers over a period of 10 years. Unsupervised clustering identified groups of patients based on FlowSOM-derived cell population percentages. In addition, we investigated immunophenotypic patterns in World Health Organization (WHO) patient classes and NPM1[mut] subclasses, both at the cell population and the individual marker level. Some WHO classes, for example, AML with t(8;21)(q22;q22)/RUNX1::RUNX1T1 or t(15;17)(q24;q21)/PML::RARA, showed homogeneous immunophenotypes. Characterization of maturation arrest using FlowSOM confirmed maturation arrest at early stages in distinct WHO classes. Finally, a machine learning model was trained to predict WHO genetic classes from immunophenotypic data. The model allowed accurate prediction in 77% of cases, reproducible in an independent validation cohort. In conclusion, we show that EuroFlow standardized protocols allow analysis of multi-centric data measured over an extended period of time. Computational analysis demonstrated inter- and intrapatient immunophenotypic heterogeneity and allowed prediction of genetic abnormalities.

Flow cytometers are highly sensitive scientific instruments that need to be in optimal condition to provide accurate and reliable data. To ensure consistent performance, cytometer performance is regularly assessed by measuring standardized reference particles during a quality control (QC) measurement. Cytometer manufacturers have developed dedicated modules in their operating software to monitor cytometer performance and award a pass or a fail for each QC to indicate if the instrument is performing to specifications. However, these are usually limited in their ease of use and data visualization options. For example, usually only a single parameter can be visualized and visualization over time is limited to 1 year or to the last baseline. Additionally, a shared interface to simultaneously monitor multiple cytometers from multiple manufacturers is not available. Furthermore, all instruments generate data that are not taken into account during QC, while these data can actually provide more insight in the performance of a flow cytometer and can allow early identification of problems. To facilitate more comprehensive analysis of instrument performance we have developed FlowCytoMonitoR, an interactive R-shiny program that does not require coding experience and allows users to analyze QC data in full detail for multiple instruments. The program is currently compatible with instruments from three different vendors (BD Biosciences, Beckman Coulter, and Cytek Biosciences) and allows visualization of all QC metrics. FlowCytoMonitoR has proved invaluable to our Shared Resource Laboratory (SRL) in identifying and potentially predicting problems, thereby increasing flow cytometer uptime and service quality.

Natural killer (NK) cell neoplasms span indolent to highly aggressive entities, and flow cytometric immunophenotyping is central to their detection and classification. However, distinguishing reactive from neoplastic NK cell expansions can be challenging because NK cells lack somatically recombined antigen receptors, and reactive states may produce restricted phenotypes that mimic clonality. This best-practices manuscript outlines a practical, flow cytometry-based approach to NK cell analysis that integrates (i) robust lineage definition and gating, (ii) subset-aware interpretation of CD16/CD56 patterns, (iii) inference of clonality using killer cell immunoglobulin-like receptor (KIR; CD158) repertoire restriction and other NK cell receptors. We highlight key pitfalls-particularly the limited utility of KIR-based clonality inference in CD56[bright]CD16[-] NK cells and the potential for adaptive NK cell expansions to appear KIR-restricted-and propose an operational reflex algorithm triggered by NK cell expansion and/or defined immunophenotypic abnormalities in screening tubes. Case-based examples illustrate common scenarios, including NK-large granular lymphocytic leukemia, extranodal NK/T-cell lymphoma with circulating CD56[bright]CD16[-] cells, reactive adaptive NK cell expansions, and concurrent neoplastic and reactive NK clones. This framework supports standardized, reproducible NK cell assessment and improves specificity of flow-based clonality inference in routine clinical practice.

We evaluated morphologic and immunophenotypic features of adult T-cell leukemia/lymphoma (ATLL) using morphology and flow cytometry (FCM), compared tumor cell proportions in bone marrow (BM) and peripheral blood (PB), and assessed whether these parameters could improve prognostication beyond existing indices for aggressive ATLL. We retrospectively analyzed PB and BM morphology, FCM, and clinical records of individuals newly diagnosed with ATLL at our institution from 2010 to 2024. Morphologic reevaluation was performed in 47 of 58 enrolled individuals, all with leukemic manifestations. ATLL cells showed heterogeneity. Cytoplasmic vacuoles were present in 61.7% of cases, including a previously undescribed pattern of small peripheral vacuoles (38.3%). Nuclear forms included flower-like (66.0%), cerebriform (10.6%), and small indented lymphocyte-like (36.2%) cells. Cells typically expressed CD4, CD25, CD2, and CD5. However, CD25 was negative in 26.3% of cases when assessed with a weakly conjugated fluorochrome. CD8, CD7, and CD56 were consistently absent, CD3 was variable, whereas TRBC1 showed preliminary variability in the limited subset. Both cytology (with more ATLL cells showing abnormal morphology in PB than in BM) and flow cytometry demonstrated a higher tumor burden in PB than in BM. FCM detected BM infiltration more sensitively than conventional BM smears. In this single-center derivation cohort without validation, the PB tumor load prognostic index (PBTL-PI), incorporating PB abnormal lymphocyte percentage (PB-PAL) and corrected calcium, showed excellent apparent discrimination (area under the curve [AUC] = 0.871). PBTL-PI outperformed the Japan Clinical Oncology Group prognostic index in risk stratification (log-rank p = 0.0002 vs. 0.0147) and prognostic accuracy (AUC: 0.871 vs. 0.738, DeLong's p = 0.024). However, these findings are exploratory and require independent validation. ATLL shows morphologic and immunophenotypic heterogeneity. Cytology and flow cytometry both confirmed higher tumor burden in PB (vs. BM). FCM improves the detection of BM infiltration. PBTL-PI may identify high-risk ATLL and guide risk-adapted management.

Circulating tumor plasma cells (CTCs) at diagnosis are independent prognostic markers in newly diagnosed multiple myeloma (NDMM). Flow cytometry (FC) enables sensitive and cost-effective CTC quantification, but prognostic cut-offs vary across studies. The methodological approach used for quantification may affect the determination of the CTC percentage. In this study we compare head-to-head two FC techniques [single-platform FC versus Next Generation Flow (NGF)] to measure CTC levels in NDMM patients. Methodological differences between the two techniques can be found in sample processing (NGF includes fixation and permeabilization steps that are not required for CTC quantification by single-platform FC) and analytical sensitivity, which is higher for NGF than for single-platform FC. The percentage of CTCs was simultaneously assessed with both techniques in the peripheral blood (PB) of 34 patients enrolled in the REAL MM trial (NCT03829371) at screening. Single-platform FC detected CTCs in 29/34 patients (85.3%) patients, while NGF detected CTCs in 31/34 patients (91.2%), including 2 cases that were negative by single-platform FC. The CTC percentage measured using the NGF technique (median 0.0049%, range 0%-0.377%) was significantly lower than that obtained using single-platform FC (median 0.009%, range 0%-0.59%) (p < 0.0001). Indeed, quantifying CTCs with FC after fixation and permeabilization (comparably to NGF) led to results that were similar to NGF. In conclusion, NGF demonstrated higher sensitivity than single-platform FC to detect CTCs at diagnosis in MM patients. Employing standardized techniques like NGF may avoid variability across different laboratories and centers.

Suspension flow cytometry enables high-throughput cellular profiling at the single cell level, but these data lack positional information. Conversely, tissue-based imaging cytometry techniques reveal a cell's location within the tissue architecture and can provide insight into cell biology. It would be especially valuable if data analysis tools could incorporate data from imaging and flow cytometry platforms to gain complementary strengths when quantifying features of cells and populations. We hypothesized that per-cell Marker Enrichment Modeling (MEM) might provide a way to register cells between flow and imaging cytometry analysis. Here, we developed the Velociraptor machine learning workflow for cross-platform cytometry analysis. Velociraptor begins with a graph-based implementation of MEM to calculate per-cell quantitative phenotype labels. With this information, Velociraptor can then quickly calculate similarity between each cell's phenotype and search terms describing established cell types, cells of interest, or cells observed in other samples. Velociraptor was effective in registering cells within and between cytometry platforms. Integrated identification of cell populations was tested in several challenges, including comparisons of high dimensional datasets from cancer and immunology. Tested instrument types included imaging mass cytometry (IMC), cyclic immunohistochemistry (cycIHC), suspension mass cytometry (CyTOF), and suspension spectral flow cytometry (SFC). Between IMC and CyTOF, a comparison across imaging and flow cytometry platforms that use the same mass tag probes, Velociraptor accurately identified and registered immune cell types (median F1-measure of 0.81). Between SFC and CyTOF, a comparison of two fundamentally different probe types-fluorophores and metal tags-in suspension flow cytometry, Velociraptor was even more accurate at identifying and registering cells (concordance correlation coefficient of 0.99). Velociraptor was especially useful in heterogeneous samples where individual cells diverged in phenotype from the bulk population. In IMC imaging of human breast cancer, a previously unappreciated tumor cell subset was revealed by Velociraptor, characterized as CD15[+], and validated as spatially segregated to the tumor core. In both cycIHC (8-dimensional imaging) and IMC imaging (40-dimensional imaging), Velociraptor accurately identified macrophages using a single search label as input. Notably, Velociraptor worked effectively with both extremely rare and highly abundant cell types and with cell search labels calculated from data and theoretical labels based on literature and expertise. The Velociraptor algorithm is freely available at https://github.com/cytolab.