FlowKit: A Python Toolkit for Integrated Manual and Automated Cytometry Analysis Workflows.

An important challenge for primary or secondary analysis of cytometry data is how to facilitate productive collaboration between domain and quantitative experts. Domain experts in cytometry laboratories and core facilities increasingly recognize the need for automated workflows in the face of increasing data complexity, but by and large, still conduct all analysis using traditional applications, predominantly FlowJo. To a large extent, this cuts domain experts off from the rapidly growing library of Single Cell Data Science algorithms available, curtailing the potential contributions of these experts to the validation and interpretation of results. To address this challenge, we developed FlowKit, a Gating-ML 2.0-compliant Python package that can read and write FCS files and FlowJo workspaces. We present examples of the use of FlowKit for constructing reporting and analysis workflows, including round-tripping results to and from FlowJo for joint analysis by both domain and quantitative experts.

Physiological Fitness and the Pathophysiology of Chronic Lymphocytic Leukemia (CLL).

Chronic lymphocytic leukemia (CLL) is associated with physical dysfunction and low overall fitness that predicts poor survival following the commencement of treatment. However, it remains unknown whether higher fitness provides antioncogenic effects. We identified ten fit (CLL-FIT) and ten less fit (CLL-UNFIT) treatment-naïve CLL patients from 144 patients who completed a set of physical fitness and performance tests. Patient plasma was used to determine its effects on an in vitro 5-day growth/viability of three B-cell cell lines (OSU-CLL, Daudi, and Farage). Plasma exosomal miRNA profiles, circulating lipids, lipoproteins, inflammation levels, and immune cell phenotypes were also assessed. CLL-FIT was associated with fewer viable OSU-CLL cells at Day 1 (p = 0.003), Day 4 (p = 0.001), and Day 5 (p = 0.009). No differences between the groups were observed for Daudi and Farage cells. Of 455 distinct exosomal miRNAs identified, 32 miRNAs were significantly different between the groups. Of these, 14 miRNAs had ≤-1 or ≥1 log2 fold differences. CLL-FIT patients had five exosomal miRNAs with lower expression and nine miRNAs with higher expression. CLL-FIT patients had higher HDL cholesterol, lower inflammation, and lower levels of triglyceride components (all p < 0.05). CLL-FIT patients had lower frequencies of low-differentiated NKG2+/CD158a/bneg (p = 0.015 and p = 0.014) and higher frequencies of NKG2Aneg/CD158b+ mature NK cells (p = 0.047). The absolute number of lymphocytes, including CD19+/CD5+ CLL-cells, was similar between the groups (p = 0.359). Higher physical fitness in CLL patients is associated with altered CLL-like cell line growth in vitro and with altered circulating and cellular factors indicative of better immune functions and tumor control.

Toward development of a comprehensive external quality assurance program for polyfunctional intracellular cytokine staining assays.

The External Quality Assurance Program Oversight Laboratory (EQAPOL) Flow Cytometry Program assesses the proficiency of NIH/NIAID/DAIDS-supported and potentially other interested research laboratories in performing Intracellular Cytokine Staining (ICS) assays. The goal of the EQAPOL Flow Cytometry External Quality Assurance Program (EQAP) is to provide proficiency testing and remediation for participating sites. The program is not punitive; rather, EQAPOL aims to help sites identify areas for improvement. EQAPOL utilizes a highly standardized ICS assay to minimize variability and readily identify those sites experiencing technical difficulties with their assays. Here, we report the results of External Proficiency 3 (EP3) where participating sites performed a 7-color ICS assay. On average, sites perform well in the Flow Cytometry EQAP (median score is "Good"). The most common technical issues identified by the program involve protocol adherence and data analysis; these areas have been the focus of site remediation. The EQAPOL Flow Cytometry team is now in the process of expanding the program to 8-color ICS assays. Evaluating polyfunctional ICS responses would align the program with assays currently being performed in support of HIV immune monitoring assays.

Setting objective thresholds for rare event detection in flow cytometry.

The accurate identification of rare antigen-specific cytokine positive cells from peripheral blood mononuclear cells (PBMC) after antigenic stimulation in an intracellular staining (ICS) flow cytometry assay is challenging, as cytokine positive events may be fairly diffusely distributed and lack an obvious separation from the negative population. Traditionally, the approach by flow operators has been to manually set a positivity threshold to partition events into cytokine-positive and cytokine-negative. This approach suffers from subjectivity and inconsistency across different flow operators. The use of statistical clustering methods does not remove the need to find an objective threshold between between positive and negative events since consistent identification of rare event subsets is highly challenging for automated algorithms, especially when there is distributional overlap between the positive and negative events ("smear"). We present a new approach, based on the Fß measure, that is similar to manual thresholding in providing a hard cutoff, but has the advantage of being determined objectively. The performance of this algorithm is compared with results obtained by expert visual gating. Several ICS data sets from the External Quality Assurance Program Oversight Laboratory (EQAPOL) proficiency program were used to make the comparisons. We first show that visually determined thresholds are difficult to reproduce and pose a problem when comparing results across operators or laboratories, as well as problems that occur with the use of commonly employed clustering algorithms. In contrast, a single parameterization for the Fß method performs consistently across different centers, samples, and instruments because it optimizes the precision/recall tradeoff by using both negative and positive controls.

Managing Multi-center Flow Cytometry Data for Immune Monitoring.

With the recent results of promising cancer vaccines and immunotherapy1-5, immune monitoring has become increasingly relevant for measuring treatment-induced effects on T cells, and an essential tool for shedding light on the mechanisms responsible for a successful treatment. Flow cytometry is the canonical multi-parameter assay for the fine characterization of single cells in solution, and is ubiquitously used in pre-clinical tumor immunology and in cancer immunotherapy trials. Current state-of-the-art polychromatic flow cytometry involves multi-step, multi-reagent assays followed by sample acquisition on sophisticated instruments capable of capturing up to 20 parameters per cell at a rate of tens of thousands of cells per second. Given the complexity of flow cytometry assays, reproducibility is a major concern, especially for multi-center studies. A promising approach for improving reproducibility is the use of automated analysis borrowing from statistics, machine learning and information visualization21-23, as these methods directly address the subjectivity, operator-dependence, labor-intensive and low fidelity of manual analysis. However, it is quite time-consuming to investigate and test new automated analysis techniques on large data sets without some centralized information management system. For large-scale automated analysis to be practical, the presence of consistent and high-quality data linked to the raw FCS files is indispensable. In particular, the use of machine-readable standard vocabularies to characterize channel metadata is essential when constructing analytic pipelines to avoid errors in processing, analysis and interpretation of results. For automation, this high-quality metadata needs to be programmatically accessible, implying the need for a consistent Application Programming Interface (API). In this manuscript, we propose that upfront time spent normalizing flow cytometry data to conform to carefully designed data models enables automated analysis, potentially saving time in the long run. The ReFlow informatics framework was developed to address these data management challenges.

Optimization of a highly standardized carboxyfluorescein succinimidyl ester flow cytometry panel and gating strategy design using discriminative information measure evaluation.

The design of a panel to identify target cell subsets in flow cytometry can be difficult when specific markers unique to each cell subset do not exist, and a combination of parameters must be used to identify target cells of interest and exclude irrelevant events. Thus, the ability to objectively measure the contribution of a parameter or group of parameters toward target cell identification independent of any gating strategy could be very helpful for both panel design and gating strategy design. In this article, we propose a discriminative information measure evaluation (DIME) based on statistical mixture modeling; DIME is a numerical measure of the contribution of different parameters towards discriminating a target cell subset from all the others derived from the fitted posterior distribution of a Gaussian mixture model. Informally, DIME measures the "usefulness" of each parameter for identifying a target cell subset. We show how DIME provides an objective basis for inclusion or exclusion of specific parameters in a panel, and how ranked sets of such parameters can be used to optimize gating strategies. An illustrative example of the application of DIME to streamline the gating strategy for a highly standardized carboxyfluorescein succinimidyl ester (CFSE) assay is described.