Multi-site reproducibility of a human immunophenotyping assay in whole blood and peripheral blood mononuclear cells preparations using CyTOF technology coupled with Maxpar Pathsetter, an automated data analysis system

High-dimensional mass cytometry data potentially enable a comprehensive characterization of immune cells. In order to positively affect clinical trials and translational clinical research, this advanced technology needs to demonstrate a high reproducibility of results across multiple sites for both peripheral blood mononuclear cells (PBMC) and whole blood preparations. A dry 30-marker broad immunophenotyping panel and customized automated analysis software were recently engineered and are commercially available as the Fluidigm® Maxpar® Direct™ Immune Profiling Assay™. In this study, seven sites received whole blood and six sites received PBMC samples from single donors over a 2-week interval. Each site labeled replicate samples and acquired data on Helios™ instruments using an assay-specific acquisition template. All acquired sample files were then automatically analyzed by Maxpar Pathsetter™ software. A cleanup step eliminated debris, dead cells, aggregates, and normalization beads. The second step automatically enumerated 37 immune cell populations and performed label intensity assessments on all 30 markers. The inter-site reproducibility of the 37 quantified cell populations had consistent population frequencies, with an average %CV of 14.4% for whole blood and 17.7% for PBMC. The dry reagent coupled with automated data analysis is not only convenient but also provides a high degree of reproducibility within and among multiple test sites resulting in a comprehensive yet practical solution for deep immune phenotyping.

High-Dimensional Modeling for Cytometry: Building Rock Solid Models Using GemStone™ and Verity Cen-se'™ High-Definition t-SNE Mapping

This chapter outlines how to approach the complex tasks associated with designing models for high-dimensional cytometry data. Unlike gating approaches, modeling lends itself to automation and accounts for measurement overlap among cellular populations. Designing these models is now easier because of a new technique called high-definition t-SNE mapping. Nontrivial examples are provided that serve as a guide to create models that are consistent with data.

Human B-cell and progenitor stages as determined by probability state modeling of multidimensional cytometry data

BACKGROUND

Human progenitor and B-cell development is a highly regulated process characterized by the ordered differential expression of numerous cell-surface and intracytoplasmic antigens. This study investigates the underlying coordination of these modulations by examining a series of normal bone marrow samples with the method of probability state modeling or PSM.

RESULTS

The study is divided into two sections. The first section examines B-cell stages subsequent to CD19 up-regulation. The second section assesses an earlier differentiation stage before and including CD19 up-regulation. POST-CD19 ANTIGENIC UP-REGULATION: Statistical analyses of cytometry data derived from sixteen normal bone marrow specimens revealed that B cells have at least three distinct coordinated changes, forming four stages labeled as B1, B2, B3, and B4. At the end of B1; CD34 antigen expression down-regulates with TdT while CD45, CD81, and CD20 slightly up-regulate. At the end of B2, CD45 and CD20 up-regulate. At the end of B3 and beginning of B4; CD10, CD38, and CD81 down-regulate while CD22 and CD44 up-regulate. PRE-CD19 ANTIGENIC UP-REGULATION: Statistical analysis of ten normal bone marrows revealed that there are at least two measurable coordinated changes with progenitors, forming three stages labeled as P1, P2, and P3. At the end of P1, CD38 up-regulates. At the end of P2; CD19, CD10, CD81, CD22, and CD9 up-regulate while CD44 down-regulates slightly.

CONCLUSIONS

These objective results yield a clearer immunophenotypic picture of the underlying cellular mechanisms that are operating in these important developmental processes. Also, unambiguously determined stages define what is meant by "normal" B-cell development and may serve as a preliminary step for the development of highly sensitive minimum residual disease detection systems. A companion article is simultaneously being published in Cytometry Part A that will explain in further detail the theory behind PSM. Three short relevant videos are available in the online supporting information for both of these papers.

Automated analysis of flow cytometric data for measuring neutrophil CD64 expression using a multi-instrument compatible probability state model

BACKGROUND

Leuko64™ (Trillium Diagnostics) is a flow cytometric assay that measures neutrophil CD64 expression and serves as an in vitro indicator of infection/sepsis or the presence of a systemic acute inflammatory response. Leuko64 assay currently utilizes QuantiCALC, a semiautomated software that employs cluster algorithms to define cell populations. The software reduces subjective gating decisions, resulting in interanalyst variability of <5%. We evaluated a completely automated approach to measuring neutrophil CD64 expression using GemStone™ (Verity Software House) and probability state modeling (PSM).

METHODS

Four hundred and fifty-seven human blood samples were processed using the Leuko64 assay. Samples were analyzed on four different flow cytometer models: BD FACSCanto II, BD FACScan, BC Gallios/Navios, and BC FC500. A probability state model was designed to identify calibration beads and three leukocyte subpopulations based on differences in intensity levels of several parameters. PSM automatically calculates CD64 index values for each cell population using equations programmed into the model. GemStone software uses PSM that requires no operator intervention, thus totally automating data analysis and internal quality control flagging. Expert analysis with the predicate method (QuantiCALC) was performed. Interanalyst precision was evaluated for both methods of data analysis.

RESULTS

PSM with GemStone correlates well with the expert manual analysis, r(2) = 0.99675 for the neutrophil CD64 index values with no intermethod bias detected. The average interanalyst imprecision for the QuantiCALC method was 1.06% (range 0.00-7.94%), which was reduced to 0.00% with the GemStone PSM. The operator-to-operator agreement in GemStone was a perfect correlation, r(2) = 1.000.

CONCLUSION

Automated quantification of CD64 index values produced results that strongly correlate with expert analysis using a standard gate-based data analysis method. PSM successfully evaluated flow cytometric data generated by multiple instruments across multiple lots of the Leuko64 kit in all 457 cases. The probability-based method provides greater objectivity, higher data analysis speed, and allows for greater precision for in vitro diagnostic flow cytometric assays.

Validation of cell-based fluorescence assays: practice guidelines from the ICSH and ICCS - part III - analytical issues

Clinical diagnostic assays, may be classified as quantitative, quasi-quantitative or qualitative. The assay's description should state what the assay needs to accomplish (intended use or purpose) and what it is not intended to achieve. The type(s) of samples (whole blood, peripheral blood mononuclear cells (PBMC), bone marrow, bone marrow mononuclear cells (BMMC), tissue, fine needle aspirate, fluid, etc.), instrument platform for use and anticoagulant restrictions should be fully validated for stability requirements and specified. When applicable, assay sensitivity and specificity should be fully validated and reported; these performance criteria will dictate the number and complexity of specimen samples required for validation. Assay processing and staining conditions (lyse/wash/fix/perm, stain pre or post, time and temperature, sample stability, etc.) should be described in detail and fully validated.

Flow cytometry for hematopoietic cells

Within the last 25 years, flow cytometry and fluorescence-activated cell sorting have emerged as both routine diagnostic tools in clinical medicine and as advanced analytic tools critical in performing scientific research. This chapter aims at summarizing the use of flow cytometry in benign and malignant hematology and the monitoring of inherited and acquired immunodeficiency states. Numerous figures are provided from our laboratories at Massachusetts General Hospital that illustrate examples of these conditions. The chapter also describes novel flow cytometry-based imaging techniques, the combination of flow cytometry and mass spectrography, new software tools, and some future directions and applications of advanced instrumentation for flow cytometry.

Cell sorting in cancer research--diminishing degree of cell heterogeneity

Increasing evidence of intratumor heterogeneity and its augmentation due to selective pressure of microenvironment and recent achievements in cancer therapeutics lead to the need to investigate and track the tumor subclonal structure. Cell sorting of heterogeneous subpopulations of tumor and tumor-associated cells has been a long established strategy in cancer research. Advancement in lasers, computer technology and optics has led to a new generation of flow cytometers and cell sorters capable of high-speed processing of single cell suspensions. Over the last several years cell sorting was used in combination with molecular biological methods, imaging and proteomics to characterize primary and metastatic cancer cell populations, minimal residual disease and single tumor cells. It was the principal method for identification and characterization of cancer stem cells. Analysis of single cancer cells may improve early detection of tumors, monitoring of circulating tumor cells, evaluation of intratumor heterogeneity and chemotherapeutic treatments. The aim of this review is to provide an overview of major cell sorting applications and approaches with new prospective developments such as microfluidics and microchip technologies.