The flow cytometry bioinformatics CRO used by 6 of the top 10 pharma

We developed an automated analysis workflow based on flowCore and flowDensity. Data was generated using DuraClone’s dry reagent. pre-formatted panel antibody cocktails technology (Beckman Coulter). The performance of automated pipelines was assessed by their ability to match on a per-event basis values obtained by an expert manual analyzer (i.e., the reference manual), currently considered the “gold standard” approach. In addition, we compared the reference manual values with those obtained by 2 additional manual analyzers who followed an identical gating strategy. Automated analyses produced results that were highly correlated with those obtained in the reference manual analysis. For example, the Spearman’s rank correlation coefficient [ρ, rs] comparing reference manual to automated analysis for the 14 Basic panel cell populations were all > 0.8. Most median F1 scores were > 0.9, with an overall F1 average of 0.93. To demonstrate robustness, we obtained an independent data set from the ONE Study, which used the same antibody panel and fluorescence intensity settings, and reanalyzed these data manually and with our automated pipelines. When all populations analyzed were combined for the two datasets, correlation values between automated and manual gating were all > 0.9, demonstrating that automated gating pipelines developed with one set of data can readily be used to accurately analyze independent data if they are collected using the same standardized methodology. To test the robustness of the analysis pipelines to alternate instrument platforms, we analyzed parallel samples acquired on a Navios (Beckman Coulter, 3 lasers) or a Fortessa X20 (BD Biosciences, 4 lasers), and obtained correlation values > 0.99. In general, we found that automated gating agreed slightly less with the reference manual than other manual gating. As in the Conrad study, lower agreement occurred especially for low-abundance, poorly defined populations (e.g., plasmablasts). Indistinct boundaries, such as between CD14+ and CD14++ (particularly in the CD16+ population), as well as between CD16– and CD16+ led to variability in manual gating (Manual 1 and Manual 2 vs. reference manual, both rs = 0.83), as well as in automated versus reference manual (rs = 0.83).

Peer-reviewed publication is availble here.

Data was generated using two staining panels that identified effector and memory or helper and regulatory T cells. A core panel of 8–10 fluorochromes was used to identify immune subsets of interest to which three fluorochromes can be added to measure 6–15 activation markers per patient per time point, depending on the desired depth of analysis. After acquisition, FCS files were opened with FlowJo software to adjust compensation and the resulting workspaces were read into R for all further data processing. While a significant focus has been rightly placed on automated gating, the adoption of automated approaches for cell population identification opens up additional avenues for improving the rigor and reproducibility of clinical trial data analysis within the analysis pipeline once data enters a computational stream. After pre-gating quality checking was competed using such approaches, gates were then set by flowDensity. Unlike typical clustering algorithms that tend to identify populations by examining all dimensions simultaneously, flowDensity is based on a sequential bivariate gating approach that generates a set of predefined cell populations using a pre-specified approach customized for each cell population of interest. The algorithm mimics manual gating steps, but chooses the best cut-off for individual markers using characteristics of their density distribution such as the slope of density, or the valley between the two peaks of the density. For activation marker gating, a gate boundary set on the fluorescence minus one control was applied to matching samples with the fluorochrome present. Gates are set independently for each data file based on these rules. Once the parameterization of the gating steps is complete, gating steps run in sequence by a single script, only requiring manual input to specify the directory where the files are located and the desired location for the outputs. The approach tends to be robust, as long as new data files are generally similar to those used to set the gate boundaries. This study then tested how the analysis pipeline with flowDensity performed compared with manual gating conducted by domain experts on data from five patients. Manual and automated analysis were performed on the same 11 populations of interest over a total of 33 time points (2–4 tests per time point), yielding 1,077 matched populations. Comparison of these populations showed a significant correlation between the manual and automated analysis of the T cell panel (see below). While the bulk of the populations showed strong agreement, manual gating of populations with few events were not matched as closely by automated analysis. The larger disagreement can be attributed primarily to the small number of events in that population (the impact of small shifts in gate placement is magnified on smaller cell populations) and secondarily to the lack of resolution between populations (i.e., smears). When controls are not used for such rare populations, there is often no objective information available to guide either automated or gating. This makes evaluation of automated analysis approaches for such cell populations especially challenging. Despite these differences, the observed trends were the same as those obtained from manual gating, which is an essential criterion for using this method in longitudinal studies.

Peer-reviewed publication is availble here.