Interpretation Of Flow Cytometry Data Research Articles

Deep Learning in Hematology: From Molecules to Patients.

Deep learning (DL), a subfield of machine learning, has made remarkable strides across various aspects of medicine. This review examines DL's applications in hematology, spanning from molecular insights to patient care. The review begins by providing a straightforward introduction to the basics of DL tailored for those without prior knowledge, touching on essential concepts, principal architectures, and prevalent training methods. It then discusses the applications of DL in hematology, concentrating on elucidating the models' architecture, their applications, performance metrics, and inherent limitations. For example, at the molecular level, DL has improved the analysis of multi-omics data and protein structure prediction. For cells and tissues, DL enables the automation of cytomorphology analysis, interpretation of flow cytometry data, and diagnosis from whole slide images. At the patient level, DL's utility extends to analyzing curated clinical data, electronic health records, and clinical notes through large language models. While DL has shown promising results in various hematology applications, challenges remain in model generalizability and explainability. Moreover, the integration of novel DL architectures into hematology has been relatively slow in comparison to that in other medical fields.

Microbial water quality investigation through flow cytometry fingerprinting: from source to tap

Abstract Ensuring the quality of treated drinking water is crucial for preventing potential health impacts, regulatory fines, and reputation damage. Traditional culture-based microbiological methods often fail to capture the heterogeneity of the bacterial communities in drinking water. This study employed daily interstage monitoring and flow cytometry (FCM) analysis over a period of one year to investigate the dynamics of water treatment processes and service reservoirs. The objective of this study was to test the utility of FCM fingerprints for aiding in microbial event detection. We found that the chlorine concentration contact time was pivotal for microbial log reduction across the treatment works. FCM fingerprints exhibited significant deviations during operational events, such as process interruptions, but did not correlate with the presence of bacterial indicator organisms in the distributed and tap water. Furthermore, the diversity of bacterial fingerprints, quantified by the Bray–Curtis dissimilarity index, served as an indicator for identifying potentially poor microbial water quality. In chlorinated waters with low cell counts, the background signal shows potential as a metric to differentiate between different water sources, thereby offering the possibility to characterize breakthrough events in these circumstances that challenge most other microbial analytical methods. Interestingly, groundwater from simpler treatment works showed a higher occurrence of bacterial indicators, whereas surface water works had a lower incidence. These findings underpin the importance of appropriate disinfection even for “low-risk” source waters and the added value that the statistical interpretation of FCM data can offer objective decision making.

Diagnosis of chronic B-cell lymphoproliferative disease in peripheral blood=how machine learning may help to the interpretation of flow cytometry data.

Flow cytometry (FCM) has become a method of choice for immunologic characterization of chronic lymphoproliferative disease (CLPD). To reduce the potential subjectivities of FCM data interpretation, we developed a machine learning random forest algorithm (RF) allowing unsupervised analysis. This assay relies on 16 parameters obtained from our FCM screening panel, routinely used in the exploration of peripheral blood (PB) samples (mean fluorescence intensity values (MFI) of CD19, CD45, CD5, CD20, CD200, CD23, HLA-DR, CD10 in CD19-gated B cells, ratio of kappa/Lambda, and different ratios of MFI B-cells/T-cells [CD20, CD200, CD23]). The RF algorithm was trained and validated on a large cohort of more than 300 annotated different CLPD cases (chronic B-cell leukemia, mantle cell lymphoma, marginal zone lymphoma, follicular lymphoma, splenic red pulp lymphoma, hairy cell leukemia) and non-tumoral selected from PB samples. The RF algorithm was able to differentiate tumoral from non-tumoral B-cells in all cases and to propose a correct CLPD classification in more than 90% of cases. In conclusion the RF algorithm could be proposed as an interesting help to FCM data interpretation allowing a first B-cells CLPD diagnostic hypothesis and/or to guide the management of complementary analysis (additional immunologic markers and genetic).

A Method for the Interpretation of Flow Cytometry Data Using Genetic Algorithms

Background: Flow cytometry analysis is the method of choice for the differential diagnosis of hematologic disorders. It is typically performed by a trained hematopathologist through visual examination of bidimensional plots, making the analysis time-consuming and sometimes too subjective. Here, a pilot study applying genetic algorithms to flow cytometry data from normal and acute myeloid leukemia subjects is described. Subjects and Methods: Initially, Flow Cytometry Standard files from 316 normal and 43 acute myeloid leukemia subjects were transformed into multidimensional FITS image metafiles. Training was performed through introduction of FITS metafiles from 4 normal and 4 acute myeloid leukemia in the artificial intelligence system. Results: Two mathematical algorithms termed 018330 and 025886 were generated. When tested against a cohort of 312 normal and 39 acute myeloid leukemia subjects, both algorithms combined showed high discriminatory power with a receiver operating characteristic (ROC) curve of 0.912. Conclusions: The present results suggest that machine learning systems hold a great promise in the interpretation of hematological flow cytometry data.

BayesFlow: latent modeling of flow cytometry cell populations.

BackgroundFlow cytometry is a widespread single-cell measurement technology with a multitude of clinical and research applications. Interpretation of flow cytometry data is hard; the instrumentation is delicate and can not render absolute measurements, hence samples can only be interpreted in relation to each other while at the same time comparisons are confounded by inter-sample variation. Despite this, most automated flow cytometry data analysis methods either treat samples individually or ignore the variation by for example pooling the data. A key requirement for models that include multiple samples is the ability to visualize and assess inferred variation, since what could be technical variation in one setting would be different phenotypes in another.ResultsWe introduce BayesFlow, a pipeline for latent modeling of flow cytometry cell populations built upon a Bayesian hierarchical model. The model systematizes variation in location as well as shape. Expert knowledge can be incorporated through informative priors and the results can be supervised through compact and comprehensive visualizations.BayesFlow is applied to two synthetic and two real flow cytometry data sets. For the first real data set, taken from the FlowCAP I challenge, BayesFlow does not only give a gating which would place it among the top performers in FlowCAP I for this dataset, it also gives a more consistent treatment of different samples than either manual gating or other automated gating methods. The second real data set contains replicated flow cytometry measurements of samples from healthy individuals. BayesFlow gives here cell populations with clear expression patterns and small technical intra-donor variation as compared to biological inter-donor variation.ConclusionsModeling latent relations between samples through BayesFlow enables a systematic analysis of inter-sample variation. As opposed to other joint gating methods, effort is put at ensuring that the obtained partition of the data corresponds to actual cell populations, and the result is therefore directly biologically interpretable. BayesFlow is freely available at GitHub.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0862-z) contains supplementary material, which is available to authorized users.

Towards automation of flow cytometric analysis for quality-assured follow-up assessment to guide curative therapy for acute lymphoblastic leukaemia in children

Minimal residual disease (MRD) is of high prognostic value in risk stratification in childhood acute lymphoblastic leukaemia. Flow cytometry (FCM) was shown to yield reliable results in MRD measurement. However, the interpretation of FCM data relies largely on operator skills and experience. While sample preparation, antibody panels, staining procedures and flow cytometric acquisition can be standardized, easily controlled and be made available worldwide, the availability of experienced operators represents the current bottleneck to a growing number of laboratories to the benefit of an increasing number of patients with leukaemia. Currently, international paediatric studies—throughout Europe, South America, to Australia—aim at stratifying the treatment according to the FCM-MRD methodology. The measurements are still operator-dependent leading to substantial costs regarding training and quality control. This article introduces a new European Union-funded project (AutoFLOW) aiming at the standardization and automation of FCM-MRD analysis by machine-learning technology.

A novel statistical analysis and interpretation of flow cytometry data

A recently developed class of models incorporating the cyton model of population generation structure into a conservation-based model of intracellular label dynamics is reviewed. Statistical aspects of the data collection process are quantified and incorporated into a parameter estimation scheme. This scheme is then applied to experimental data for PHA-stimulated CD4+T and CD8+T cells collected from two healthy donors. This novel mathematical and statistical framework is shown to form the basis for accurate, meaningful analysis of cellular behaviour for a population of cells labelled with the dye carboxyfluorescein succinimidyl ester and stimulated to divide.

Update for the logicle data scale including operational code implementations

Since its introduction in the early 2000s, the logicle data scale (1,2) has been widely adopted by the cytometry community. The characteristics of logicle displays and their use to facilitate interpretation of flow cytometry data have been discussed in several papers (3,4). FlowJo software (Tree Star, Ashland, OR) offers the option of setting logicle transformation as the default for some data types. Diva software (BD Biosciences, San Jose, CA) offers logicle displays with scaling customized to the distribution of the data in each plot (called “Biexponential” but using the logicle constraints on the biexponential and logicle methods for selecting display parameters). In our experience with data from many different biological applications and a variety of instruments, logicle transformation provides good representation of all cytometry data for which direct linear presentation is not the most appropriate choice and has shown no tendency to generate artifactual features in displays. On this basis, we now recommend logicle as the appropriate default method for nonlinear transformation of cytometric data. In support of this recommendation, this communication provides (1) minor updates to the logicle specification, (2) a rigorously defined parameterization that should clarify questions that have come from people who were implementing logicle transformations in software, (3) technical details that were not considered suitable for the original publication but have become important for the standardization and consistent application of the method, (4) reference implementation code for both high-precision and routine calculations, and (5) a more detailed mathematical exposition on Biexponential Functions (Supporting Information). We expect that the formulas presented here will be the normative definition for the logicle transformation included in the ISAC Standards Committee’s recommendation in Gating-ML (Analytical Cytometry Standard (ACS)—Gating-ML Component, http://flowcyt.sourceforge.net/gating/). Implementations of the logicle scale in the Java, C++, and C programming languages are provided in the Supporting Information and are released under the Revised Berkeley Software Distribution open source license for free use in cytometry software. A module for integrating the logicle scale into the R statistical programming environment is available in the Bio-conductor repository (http://www.bioconductor.org/). Several algorithms and computational methods used in this work were adopted or modified from Ref. 5 (NR3, www.cambridge.org/numericalrecipes and www.nr.com).

State of the Art Symposium: Flow cytometry in the diagnosis of lymphoproliferative disorders by fine-needle aspiration

Immunophenotyping is an essential component of the diagnosis of lymphoma using fine-needle aspiration (FNA) and flow cytometry is a powerful technique that is well suited to FNA material. The current article addresses basic principles of flow cytometry data collection and interpretation, presents a differential diagnosis of common lymphomas based on immunophenotype, and discusses potential pitfalls in the interpretation of flow cytometry data.

Computer-assisted interpretation of flow cytometry data in hematology.

A computer program has been developed for computer-assisted diagnosis (including subclassification) of flow cytometry data of acute leukaemias and non-Hodgkin lymphomas by means of artificial intelligence. The knowledge base for the system has been formulated as semantic networks that describe physiological hematopoiesis as well as the pathological situation (e.g., aberrant antigen expression) of hematological disorders. The semantic networks reflect the hierarchy of cells and their occurrence in diseases, the normal and pathological antigen expression patterns of cells, cell maturation, and the frequency of cell populations in normal blood and bone marrow. Using these semantic networks, the diagnosis algorithm compares the characteristic antigen expression pattern of a disease with the actual findings in the blood or bone marrow sample. The algorithm can separate mixed populations by taking double staining findings into account. Finally, a diagnosis text is generated that describes all identified cell populations and the resulting diagnosis. The validation of the program showed a correct diagnosis (disease group and subclassification) in 97% of the cases (n = 633) with slight differences between the disease groups (e.g., B-NHL: 99%, B-cell ALL: 84%).

A knowledge-based system for the interpretation of flow cytometry data in leukemias and lymphomas.

Flow cytometry immunophenotyping and analysis of DNA ploidy and proliferative activity have become extremely helpful techniques for diagnosing and subclassifying hematopoietic cell populations in a modern, full-service hematopathology laboratory. The number of physicians with special training in the interpretation of these studies is limited. A knowledge-based computer system has been designed to aid in the interpretation of immunophenotyping and DNA flow cytometry results in hematopoietic disease. The system, known as "Professor Fidelio," is a heuristic classification system that reasons on the basis of defined diagnostic patterns. In this study, Fidelio was tested as a stand-alone system on 366 specimens from two large tertiary medical centers. Fidelio's interpretation was considered to be appropriate in all cases. In 300 of 366 (82%) specimens, the system's interpretation agreed with the diagnosis of record. Many of the disagreements could be traced to errors in the recording of the original diagnosis and minor differences in diagnostic criteria between Fidelio's knowledge base and the criteria in use at the medical centers. When used in a stand-alone mode, Fidelio's interpretation was less specific than the diagnosis of record in certain lymphoproliferative disorders that require morphologic information for subclassification. Professor Fidelio is one module in a workstation for the diagnostic hematology laboratory. This workstation is designed for interpretive reporting, education, and database functions for clinical research. Clinical and morphologic information are shared between Fidelio and the other modules for peripheral blood analysis, bone marrow morphology, and lymph node interpretation by means of a relational database. The system will be useful in hospitals that lack individuals specially trained in flow cytometry.(ABSTRACT TRUNCATED AT 250 WORDS)