Cytometry Part A最新文献

Myelofibrosis is a myeloproliferative neoplasm with potential to transform to acute myeloid leukemia. This evolution is unpredictable and current assays lack the sensitivity and applicability needed to predict this transformation. While population-level data utilizing comprehensive genomic profiling can identify subgroups at higher risk of progression, they do not provide individualized information on the likelihood or timing of leukemia. Cytogenetic alterations are typically present in secondary leukemia. We aimed to determine whether these changes could be detected at an early stage. To achieve this we established and tested a single-cell imaging flow cytometric method for chromosomal aberrations using fluorescence in situ hybridization (FISH) probes to analyze circulating CD34/CD45-positive cells. Peripheral blood samples from 14 patients, collected at up to eight timepoints over a 34-month period, were analyzed for defects involving chromosomes 1, 5, and 17. Following cell immunophenotyping and FISH probe hybridization, a mean of 174,216 mononuclear cells was assessed per sample. Chromosomal abnormalities including gain(1q), del(5q), idic (5), monosomy 17, and/or del(17p) were identified in eight patients, at frequencies down to 0.2% of mononuclear cells. Serial analyses revealed emergence of new chromosomal lesions, clonal evolution, dominance, and multi-hit abnormalities. In three patients, acquired chromosome 17 abnormalities preceded progression to secondary leukemia by up to 7 months. This pilot study demonstrates that imaging flow cytometry-based FISH of circulating CD34/CD45-positive cells enables real-time, blood-based surveillance for cytogenetic evolution in myelofibrosis. The ability to dynamically track clone size and hierarchy highlights its potential as an early predictor of leukemic transformation in myelofibrosis.

We present a new end-to-end neural network approach for real-time biological cell detection and classification via label-free quantitative imaging flow cytometry based on digital holography, offering a comprehensive representation of cellular structures without the need for chemical cell staining. In contrast to previous studies, our method is the first to obtain classification and detection of cells, imaged during flow using large-magnification microscopy, in 0.44 msec, allowing real-time label-free imaging flow cytometry, with more than 10× speedup compared to YOLOv8n. The custom-made two-stage neural network consists of fixed convolution layers using image processing filters to detect a single location per object, followed by two convolutional layers that classify each detected cell. This approach enables reducing computational complexity and offers high-throughput, label-free imaging-based analysis suitable for real-time imaging flow cytometry. We validate the method on two cell datasets, T-cells at different activation stages and cancer cells of different metastatic potentials, demonstrating the method's adaptability. Our results show the ability to image, detect, and classify thousands of cells per second during flow, highlighting the potential of label-free imaging flow cytometry for real-time cell monitoring, early disease detection, and high-speed diagnostics.

Cell lineage detection refers to the inference of differentiation pathways from immature cells to distinct mature cell types. We developed TimeFlow 2, a new method for lineage inference in large flow cytometry datasets. It uses a single static snapshot of unordered cells and does not require prior knowledge of the number of pathways, cell types or temporal labels. TimeFlow 2 uses the cell orderings from TimeFlow and defines coarse cell states along pseudotime segments. By connecting these states, it constructs paths at the cell state level. To approximate the trajectory structure, it further groups the paths based on an optimal transport-based cost function. We used TimeFlow 2 on three healthy bone marrow samples and accurately assigned monocytes, neutrophils, erythrocytes and B-cells of different maturation stages to four distinct pathways. Marker dynamics across the inferred pathways showed highly correlated patterns for the corresponding lineages in all three patients. We compared the performance of TimeFlow 2 and three other established methods using standard classification and correlation metrics. TimeFlow 2 outperformed the others on flow cytometry datasets and remained competitive on the challenging mass cytometry datasets. Overall, TimeFlow 2 detects biologically informative pathways, allowing bioinformaticians to model and compare marker dynamics across cell lineages in a data-driven way. Source code in Python and tutorials are available at https://github.com/MargaritaLiarou1/TimeFlow2.

The increasing uptake of adoptive CAR-T and TCR-T cell therapies into clinical practice has intensified the need for robust and detailed immunophenotyping of ex vivo expanded T cells. We have developed and optimized an extended 36-color full-spectrum flow cytometry panel suitable for in-depth immunophenotyping of both peripheral blood and ex vivo expanded human T cells. Our panel allows for analysis of CD4⁺ and CD8⁺ memory subpopulations (Tn/Tscm, Tcm, Tem, and Temra), Treg subsets (CD45RA⁺ and CD39⁺), and helper T cell subsets (Tfh, Th1, Th2, and Th17). Intracellular staining facilitates the evaluation of functional markers granzyme B (cytotoxicity) and Ki-67 (proliferation). Our panel allows detailed investigation of T cell activation using markers carrying a range of expression kinetics. Exhaustion and senescence features, known to correlate with resistance to immunotherapies, can also be thoroughly evaluated. TCR staining with pHLA-dextramer or anti-TCR antibody is also included to allow evaluation of TCR specificity and/or transduction efficiency. A PD-1 receptor occupancy component enables quantification of T cells bound to antibodies from immune checkpoint inhibitor (ICI) therapies. Overall, we have generated a fully optimized and fit-for-purpose extended T cell profiling panel with particular relevance to the field of immunotherapies including ICIs and adoptive T cell therapies.

Inherent cross-reactivity for shared epitopes is thought to be critical for vaccines, since this is likely the only way in which a vaccine can be anticipatory, similar to immune responses induced by natural infections. However, measuring antigen-specific and cross-reactive B cells remains technically challenging due to low abundance of these cells and background binding. Here, we have assessed different antigen-labeling methods to optimize the measurement of B cell specificity, cross-reactivity, heterocliticity, and average affinity for antigen by flow cytometry. Antigens covalently labeled with NHS ester amine-reactive dyes gave reliable results compared to other antigen labeling methods: namely conjugation of His-tagged antigen on fluorescent streptavidin nanoparticles, or on soluble streptavidin tetramers, bridged via the biomolecular adapter biotin-trisNTA(Ni). Blocking with unlabeled H1 or H7 clearly outcompeted H1- and/or H7-specific cells, indicating that cross-reactive antibodies possess lower affinities compared to the mono-reactive or the rare heteroclitic antibodies. Overall, these studies suggest that evaluating the antigen specificity and breadth of candidate vaccines, including multi-season or universal vaccines, may be best accomplished using flow cytometric quantification of clonally distributed B lineage cells using antigens covalently derivatized with fluorescent dyes, which is also analogous with the standard flow cytometry approach of quantifying antigen biomarkers on cells using fluorescent monoclonal antibodies (mAbs).

Spectral flow cytometry has evolved from a contentious idea into a mainstay of high-parameter single-cell analysis, yet its vocabulary (and the statistical reasoning behind it) remains a patchwork of overlapping, sometimes contradictory terms. This position paper highlights the terminological fragmentation and translation gap, and aims to harmonize the field's lexicon while providing a cohesive information-theoretic framework for panel optimization. We expose the limits of popular ad hoc heuristics, matrix condition numbers, and pairwise cosine similarities, and promote stronger surrogates: effective rank and information efficiency, which together capture spectral independence and numerical stability, and the Cramér-Rao lower bound (CRLB) matrix, which directly predicts fluorochrome-specific spreading error. Building on this foundation, we revive the optimal-design criteria: D-optimality maximizes total information; A-optimality minimizes the average parameter variance; E-optimality constrains worst-direction inflation. By aligning precise definitions with actionable design rules, we provide a roadmap for consistent terminology, next-generation panel construction, and objective instrument benchmarking.

Flow cytometry, a complex and important methodology, is exacerbating the systemic reproducibility crisis in biological research. Our systematic review of 100 published manuscripts on PD-1 flow cytometry data found systemic deficiencies in the clarity and completeness of methodological documentation across top-tier journals. We urgently request that the International Society for Advancement of Cytometry (ISAC) and dedicated journal editors from the scientific publishing community lead a global initiative mandating a unique common standard for all submissions (MIFlowCyt). This adoption is critical for enforcing transparency, enabling rigorous peer review, and fundamentally strengthening the scientific integrity of published cytometry data.