Implementation and Validation of an Automated Flow Cytometry Analysis Pipeline for Human Immune Profiling.

Automated reagent preparation, sample processing, and data acquisition have increased the rate at which flow cytometry data can be generated. Furthermore, advances in technology and flow cytometry instrumentation continually increase the complexity and dimensionality of this data. Together, this leads to increased pressure on manual data analysis, which has inherent limitations including subjectivity of the analyst and the length of time needed for data processing. These issues can create bottlenecks in the data processing workflow and potentially compromise data quality. To address these issues, as well as the challenges associated with manual gating in a high-volume human immune profiling laboratory, we sought to implement an automated analysis pipeline. In this report, we discuss considerations for selecting an automated analysis method, the process of implementing an automated pipeline, and detail our successful incorporation of an automated gating strategy with flowDensity into our analysis workflow. This validated pipeline augments our laboratory's ability to provide rapid high-throughput immune profiling for patients participating in cancer immunotherapy clinical trials. © International Society for Advancement of Cytometry.

flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification.

SUMMARY: flowDensity facilitates reproducible, high-throughput analysis of flow cytometry data by automating a predefined manual gating approach. The algorithm is based on a sequential bivariate gating approach that generates a set of predefined cell populations. It chooses the best cut-off for individual markers using characteristics of the density distribution. The Supplementary Material is linked to the online version of the manuscript. AVAILABILITY AND IMPLEMENTATION: R source code freely available through BioConductor (http://master.bioconductor.org/packages/devel/bioc/html/flowDensity.html.). Data available from FlowRepository.org (dataset FR-FCM-ZZBW). CONTACT: rbrinkman@bccrc.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes.

The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of computational methods for identifying cell populations in multidimensional flow cytometry data. Here we report the results of FlowCAP-IV where algorithms from seven different research groups predicted the time to progression to AIDS among a cohort of 384 HIV+ subjects, using antigen-stimulated peripheral blood mononuclear cell (PBMC) samples analyzed with a 14-color staining panel. Two approaches (FlowReMi.1 and flowDensity-flowType-RchyOptimyx) provided statistically significant predictive value in the blinded test set. Manual validation of submitted results indicated that unbiased analysis of single cell phenotypes could reveal unexpected cell types that correlated with outcomes of interest in high dimensional flow cytometry datasets.

Dynamic molecular changes during the first week of human life follow a robust developmental trajectory.

Systems biology can unravel complex biology but has not been extensively applied to human newborns, a group highly vulnerable to a wide range of diseases. We optimized methods to extract transcriptomic, proteomic, metabolomic, cytokine/chemokine, and single cell immune phenotyping data from <1 ml of blood, a volume readily obtained from newborns. Indexing to baseline and applying innovative integrative computational methods reveals dramatic changes along a remarkably stable developmental trajectory over the first week of life. This is most evident in changes of interferon and complement pathways, as well as neutrophil-associated signaling. Validated across two independent cohorts of newborns from West Africa and Australasia, a robust and common trajectory emerges, suggesting a purposeful rather than random developmental path. Systems biology and innovative data integration can provide fresh insights into the molecular ontogeny of the first week of life, a dynamic developmental phase that is key for health and disease.

A standardized immune phenotyping and automated data analysis platform for multicenter biomarker studies.

The analysis and validation of flow cytometry-based biomarkers in clinical studies are limited by the lack of standardized protocols that are reproducible across multiple centers and suitable for use with either unfractionated blood or cryopreserved PBMCs. Here we report the development of a platform that standardizes a set of flow cytometry panels across multiple centers, with high reproducibility in blood or PBMCs from either healthy subjects or patients 100 days after hematopoietic stem cell transplantation. Inter-center comparisons of replicate samples showed low variation, with interindividual variation exceeding inter-center variation for most populations (coefficients of variability <20% and interclass correlation coefficients >0.75). Exceptions included low-abundance populations defined by markers with indistinct expression boundaries (e.g., plasmablasts, monocyte subsets) or populations defined by markers sensitive to cryopreservation, such as CD62L and CD45RA. Automated gating pipelines were developed and validated on an independent data set, revealing high Spearman's correlations (rs >0.9) with manual analyses. This workflow, which includes pre-formatted antibody cocktails, standardized protocols for acquisition, and validated automated analysis pipelines, can be readily implemented in multicenter clinical trials. This approach facilitates the collection of robust immune phenotyping data and comparison of data from independent studies.

T-Cell Phenotypes Predictive of Frailty and Mortality in Elderly Nursing Home Residents.

OBJECTIVES: To determine whether immune phenotypes associated with immunosenescence are predictive of frailty and mortality within 1-year in elderly nursing home residents. DESIGN: Cross sectional study of frailty; prospective cohort study of mortality. SETTING: Thirty-two nursing homes in four Canadian cities between September 2009 and October 2011. PARTICIPANTS: Nursing home residents aged 65 and older (N = 1,072, median age 86, 72% female). MEASUREMENTS: After enrollment, peripheral blood mononuclear cells were obtained and analyzed using flow cytometry for CD4+ and CD8+ T-cell subsets (naïve, memory (central, effector, terminally differentiated, senescent), and regulatory T-cells) and cytomegalovirus (CMV)-reactive CD4+ and CD8+ T-cells. Multilevel linear regression analysis was performed to determine the relationship between immune phenotypes and frailty; frailty was measured at the time of enrollment using the Frailty Index. A Cox proportional hazards model was used to determine the relationship between immune phenotypes and time to death (within 1 year). RESULTS: Mean Frailty Index was 0.44 ± 0.13. Multilevel regression analysis showed that higher percentages of naïve CD4+ T-cells (P = .001) and effector memory CD8+ T-cells (P = .02) were associated with a lower mean Frailty Index, whereas a higher percentage of CD8+ central memory T-cells was associated with a higher mean Frailty Index score (P = .02). One hundred fifty one (14%) members of the cohort died within 1 year. Multivariable analysis showed a significant negative multiplicative interaction between age and percentage of CMV-reactive CD4+ T-cells (hazard ratio = 0.87, 95% confidence interval = 0.79-0.96). No other significant factors were identified. CONCLUSION: Immune phenotypes found to be predictive of frailty and mortality in this study can help further understanding of immunosenescence and may provide a rationale for future intervention studies designed to modulate immunity.

Standardizing Flow Cytometry Immunophenotyping Analysis from the Human ImmunoPhenotyping Consortium.

Standardization of immunophenotyping requires careful attention to reagents, sample handling, instrument setup, and data analysis, and is essential for successful cross-study and cross-center comparison of data. Experts developed five standardized, eight-color panels for identification of major immune cell subsets in peripheral blood. These were produced as pre-configured, lyophilized, reagents in 96-well plates. We present the results of a coordinated analysis of samples across nine laboratories using these panels with standardized operating procedures (SOPs). Manual gating was performed by each site and by a central site. Automated gating algorithms were developed and tested by the FlowCAP consortium. Centralized manual gating can reduce cross-center variability, and we sought to determine whether automated methods could streamline and standardize the analysis. Within-site variability was low in all experiments, but cross-site variability was lower when central analysis was performed in comparison with site-specific analysis. It was also lower for clearly defined cell subsets than those based on dim markers and for rare populations. Automated gating was able to match the performance of central manual analysis for all tested panels, exhibiting little to no bias and comparable variability. Standardized staining, data collection, and automated gating can increase power, reduce variability, and streamline analysis for immunophenotyping.