Abstract 1910: flowTOTAL: A comprehensive bioinformatics workflow for flow cytometry automatic analysis

Decisions of automated systems in healthcare can have far-reaching consequences such as delayed or incorrect treatment and thus must be explainable and comprehensible for medical experts. This also applies to the field of automated Flow Cytometry (FCM) data analysis. In leukemic cancer therapy, FCM samples are obtained from the patient’s bone marrow to determine the number of remaining leukemic cells. In a manual process, called gating, medical experts draw several polygons among different cell populations on 2D plots in order to hierarchically sub-select and track down cancer cell populations in an FCM sample. Several approaches exist that aim at automating this task. However, predictions of state-of-the-art models for automatic cell-wise classification act as black-boxes and lack the explainability of human-created gating hierarchies. We propose a novel transformer-based approach that classifies cells in FCM data by mimicking the decision process of medical experts. Our network considers all events of a sample at once and predicts the corresponding polygons of the gating hierarchy, thus, producing a verifiable visualization in the same way a human operator does. The proposed model has been evaluated on three publicly available datasets for acute lymphoblastic leukemia (ALL). In experimental comparison it reaches state-of-the-art performance for automated blast cell identification while providing transparent results and explainable visualizations for human experts.KeywordsSelf-explainable deep learning modelsTransformerFlow cytometry gatingAcute lymphoblastic leukemia

Simple SummaryB-cell Acute Lymphoblastic Leukaemia is one of the most common cancers in childhood, with 20% of patients eventually relapsing. Flow cytometry is routinely used for diagnosis and follow-up, but it currently does not provide prognostic value at diagnosis. The volume and the high-dimensional character of this data makes it ideal for its exploitation by means of Artificial Intelligence methods. We collected flow cytometry data from 56 patients from two hospitals. We analysed differences in intensity of marker expression in order to predict relapse at the moment of diagnosis. We finally correlated this data with biomolecular information, constructing a classifier based on CD38 expression.Artificial intelligence methods may help in unveiling information that is hidden in high-dimensional oncological data. Flow cytometry studies of haematological malignancies provide quantitative data with the potential to be used for the construction of response biomarkers. Many computational methods from the bioinformatics toolbox can be applied to these data, but they have not been exploited in their full potential in leukaemias, specifically for the case of childhood B-cell Acute Lymphoblastic Leukaemia. In this paper, we analysed flow cytometry data that were obtained at diagnosis from 56 paediatric B-cell Acute Lymphoblastic Leukaemia patients from two local institutions. Our aim was to assess the prognostic potential of immunophenotypical marker expression intensity. We constructed classifiers that are based on the Fisher’s Ratio to quantify differences between patients with relapsing and non-relapsing disease. We also correlated this with genetic information. The main result that arises from the data was the association between subexpression of marker CD38 and the probability of relapse.

The maternal-fetal interface represents a unique immune privileged site that maintains the ability to defend against pathogens while orchestrating the necessary tissue remodeling required for placentation. The recent discovery of novel cellular families (innate lymphoid cells, tissue-resident NK cells) suggests that our understanding of the decidual immunome is incomplete. To understand this complex milieu, new technological developments allow reproductive immunologists to collect increasingly complex data at a cellular resolution. Polychromatic flow cytometry allows for greater resolution in the identification of novel cell types by surface and intracellular protein. Single-cell RNA-seq coupled with microfluidics allows for efficient cellular transcriptomics. The extreme dimensionality and size of data sets generated, however, requires the application of novel computational approaches for unbiased analysis. There are now multiple dimensionality reduction (tSNE, SPADE) and visualization tools (SPICE) that allow researchers to efficiently analyze flow cytometry data. Development of computational tools has also been extended to RNA-seq data (including scRNA-seq), which requires specific analytical tools. Here, we provide an overview and a brief primer for the reproductive immunology community on data acquisition and computational tools for the analysis of complex flow cytometry and RNA-seq data.

BCyto: A shiny app for flow cytometry data analysis

Improved Detection of Minimal Residual Disease By Flow Cytometry in AML By Combining Manual Gating and Visne Clustering

Death receptor 3 (DR3) is a tumor necrosis factor receptor superfamily member (TNFRSF25), which is minimally expressed on resting conventional T cells (though readily inducible upon cell activation), yet highly expressed on resting FoxP3+ regulatory T cells (Treg). We recently demonstrated that activation of DR3 with an agonistic antibody (4C12) leads to selective expansion and activation of Treg in healthy mice and suppression of graft-versus-host disease (GVHD) in recipient mice when donor mice are treated. However, given the long antibody half-life and concomitant safety concerns, along with the lack of a humanized agonistic antibody to DR3, both human and murine fusion proteins incorporating the natural DR3 ligand TL1A (TL1A-Ig) have been developed. Herein, we show that DR3 activation with 4C12 or with TL1A-Ig, with or without the addition of low dose IL-2 to the treatment regimen, led to a significant expansion of murine Treg in spleen, lymph nodes, and peripheral blood. Bioluminescent imaging revealed peak Treg expansion around day 7–8, with return to near baseline after 2–3 weeks. In addition to expansion, all DR3 agonist treatment regimens led to increased activation of Tregs, with significant upregulation of the activation markers ICOS, KLRG-1, PD-1, and CD103, and the proliferation marker Ki-67. The near absence of activated Treg populations in control treated spleens was also detected on tSNE analysis of flow cytometry data. Subtly different patterns of splenic Treg activation by the different DR3 agonists were noted in both tSNE analysis of flow cytometry data and RNA-sequencing analysis. However, upregulation of gene transcripts which play important roles in cell proliferation, trafficking, activation, and effector function were observed regardless of the DR3 agonist treatment regimen used. In the major MHC-mismatch model of hematopoietic cell transplantation, DR3 agonist-mediated expansion and activation of Tregs in donor mice led to a significant improvement in GVHD in recipient mice. These data provide important preclinical information regarding the outcome of DR3 activation with an agonistic antibody or natural ligand and provide insight into the therapeutic use of this approach to reduce GVHD in recipients and improve outcomes of hematopoietic cell transplantation.

A rule-based expert system is being developed to assist clinicians in the analysis of multivariate flow cytometry data for patients with leukemias or lymphomas. The cells are stained with fluorescently labeled monoclonal antibodies and the cell fluorescence is measured with a flow cytometer. Cluster analysis is used to isolate subpopulations in the data on which the clinical decisions are made. Symbolic facts for the expert system are instantiated using these numerical data and the knowledge of the clinicians and experts in flow cytometry. The first prototype used a decision tree and rigid rules. Is successfully classified only nine of eleven leukemia cases. A second prototype incorporating certainty factors into the rules is now being developed that should remove the need for a rigid decision tree. 9 refs.

Background. The sodium-dependent phosphate transporter NaPi2b is expressed in a number of tumors and serves as a target for monoclonal antibody therapy. The NaPi2b transporter has a large extracellular domain containing 4 cysteines and an MX35 epitope recognized by the corresponding monoclonal antibodies. Aim. To study the recognition of the MX35 epitope in cysteine mutants C303 and C350 of the sodium-dependent phosphate transporter NaPi2b by monoclonal antibodies in ovarian cancer cells. Material and methods. Ovarian cancer cells of the OVCAR-8 line were transfected with plasmids encoding mutant forms of the NaPi2b transporter. The recognition of these forms by monoclonal antibodies was studied by Western blot and flow cytometry. Statistical analysis of flow cytometry data was performed using Pearsons Chi-squared test with Yates correction. Results. Western blot analysis of lysates of OVCAR-8 cells expressing the wild-type NaPi2b transporter demonstrated the presence of a specific signal only in samples without the addition of dithiothreitol, while in all samples with NaPi2b cysteine mutants, even without the addition of dithiothreitol, no specific signal from monoclonal antibodies was detected. The flow cytometry results showed that proportion of OVCAR-8 cells with positive staining of the MX35 epitope with antibodies is less when expressing the NaPi2b mutants compared to the wild type. Conclusion. Cysteines C303 and C350 are involved in disulfide bonds formation, which is of key importance for the formation of the conformation of the extracellular domain of the sodium-dependent phosphate transporter NaPi2b, which ensures the MX35 epitope recognition by monoclonal antibodies.

Automated clustering workflows are increasingly used for the analysis of high parameter flow cytometry data. This trend calls for algorithms which are able to quickly process tens of millions of data points, to compare results across subjects or time points, and to provide easily actionable interpretations of the results. To this end, we created Tailor, a model-based clustering algorithm specialized for flow cytometry data. Our approach leverages a phenotype-aware binning scheme to provide a coarse model of the data, which is then refined using a multivariate Gaussian mixture model. We benchmark Tailor using a simulation study and two flow cytometry data sets, and show that the results are robust to moderate departures from normality and inter-sample variation. Moreover, Tailor provides automated, non-overlapping annotations of its clusters, which facilitates interpretation of results and downstream analysis. Tailor is released as an R package, and the source code is publicly available at www.github.com/matei-ionita/Tailor.

This unit provides several approaches to flow cytometry data analysis. Frequency determinations based on analysis of single-parameter fluorescence histograms and dual-parameter contour plots are presented. Next, steps are described for calculating values for signal-to-noise ratios when logarithmic amplification is used for data collection. This calculation can be used to compare the amounts of antigenic determinants per cell between different cells stained with the same reagent. Finally, a procedure is described for constructing a calibration curve for logarithmic amplifiers. This calibration curve is required for calculation of signal-to-noise values and can also be used to determine if the amplifiers are working properly.

Summary form only given. A description is given of the application of neural net computer technology to the analysis of flow cytometry data. Although the data used in this study are from oceanographic research, the results are general and should be directly applicable to flow cytometry data or any sort. The neural network described offers the advantages of adaptability to changing conditions and potential real-time analysis. High accuracy and processing speed, near that required for real-time classification, have been achieved in a software simulation of the neural network on a Macintosh SE personal computer. >

Background: Assessment of the likelihood of platelet activation (i.e. platelet reactivity) identifies patients at high and low risk of subsequent thrombotic events. Turbidometric platelet aggregation has been used to assess platelet function for more than 4 decades. We have developed a method to assess individual components of platelet activation with the use of flow cytometry that is performed in minimally altered whole blood. Aims: To compare assessment of platelet reactivity determined with the use of aggregometry and flow cytometry. Material and Methods: Twenty adult patients with atherosclerotic vascular disease were included in this study. Blood from each patient was used to determine turbidometric platelet aggregation and to assess platelet activation by flow cytometry. ADP was used as the agonist. Values are means ± SEM. Comparison was performed with the use of Student’s t test and correlation was assessed with the use of Pearson’s correlation analysis. Results: Both maximal aggregation and the slope of aggregation correlate with the percentage of platelets that bound fibrinogen in response to 0.2 μM ADP. The best correlation was seen between the slope of aggregation induced by 0.2 or 1 μM ADP and the percentage of platelets that bound fibrinogen in response to 0.2 μM ADP (for 0.2 μM r = 0.62, p = 0.038; for 1 μM r = 0.71, p = 0.025). Conclusion: The binding of fibrinogen to activated platelets assessed with the use of flow cytometry correlates best with the slope of turbidometric aggregation and appears to reflect the propensity of platelets to activate.

Flow cytometry is a useful and efficient method for the rapid characterization of a cell population based on the optical and fluorescence properties of individual cells. Ideally, the cell population would consist of only healthy viable cells as dead cells can confound the analysis. Thus, separating out healthy cells from dying and dead cells, and any potential debris, is an important first step in analysis of flow cytometry data. While gating of debris can be conducted using measured optical properties, identifying dead and dying cells often requires utilizing fluorescent stains (e.g. Sytox, a nucleic acid stain that stains cells with compromised cell membranes) to identify cells that should be excluded from downstream analyses. These stains prolong the experimental preparation process and use a flow cytometer’s fluorescence channels that could otherwise be used to measure additional fluorescent markers within the cells (e.g. reporter proteins). Here we outline a stain-free method for identifying viable cells for downstream processing by gating cells that are dying or dead. AutoGater is a weakly supervised deep learning model that can separate healthy populations from unhealthy and dead populations using only light-scatter channels. In addition, AutoGater harmonizes different measurements of dead cells such as Sytox and CFUs.

The scid mouse. Characterization and potential uses: Bosma, M.J., Phillips, R.A. & Schuler, W. (Current Topics in microbiology and immunology, 152.) 1 vol. (17 × 24.5 cm), 263 + xii pages. Springer-Verlag, Heidelberg, Berlin, 1989

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