Panel Design and Optimization for Full Spectrum Flow Cytometry

Technological advancements in fluorescence flow cytometry and an ever-expanding understanding of the complexity of the immune system have led to the development of large flow cytometry panels, reaching up to 40 markers at the single-cell level. Full spectrum flow cytometry, which measures the full emission range of all the fluorophores present in the panel instead of only the emission peaks, is now routinely used in laboratories around the world, and the demand for this technology is rapidly increasing. With the ability to use larger and more complex staining panels, optimized protocols are vital for achieving the best panel design, panel optimization, and high-dimensional data analysis outcomes. In addition, a better understanding of how to fully characterize the autofluorescence of the sample, coupled with an intelligent panel design approach, allows improved marker resolution on highly autofluorescent tissues or cells. Here, we provide optimized step-by-step protocols for full spectrum flow cytometry, covering panel design and optimization, autofluorescence evaluation and strategy selection, and methods for performing longitudinal studies.

Monitoring Cell Proliferation by Dye Dilution: Considerations for Panel Design

High dimensional studies that include proliferation dyes face two inherent challenges in panel design. First, the more rounds of cell division to be monitored based on dye dilution, the greater the starting intensity of the labeled parent cells must be in order to distinguish highly divided daughter cells from background autofluorescence. Second, the greater their starting intensity, the more difficult it becomes to avoid spillover of proliferation dye signal into adjacent spectral channels, with resulting limitations on the use of other fluorochromes and ability to resolve dim signals of interest. In the third and fourth editions of this series, we described the similarities and differences between protein-reactive and membrane-intercalating dyes used for general cell tracking, provided detailed protocols for optimized labeling with each dye type, and summarized characteristics to be tested by the supplier and/or user when validating either dye type for use as a proliferation dye. In this fifth edition, we review: (a) Fundamental assumptions and critical controls for dye dilution proliferation assays; (b) Methods to evaluate the effect of labeling on cell growth rate and test the fidelity with which dye dilution reports cell division; and. (c) Factors that determine how many daughter generations can be accurately included in proliferation modeling. We also provide an expanded section on spectral characterization, using data collected for three protein-reactive dyes (CellTrace™ Violet, CellTrace™ CFSE, and CellTrace™ Far Red) and three membrane-intercalating dyes (PKH67, PKH26, and CellVue® Claret) on three different cytometers to illustrate typical decisions and trade-offs required during multicolor panel design. Lastly, we include methods and controls for assessing regulatory T cell potency, a functional assay that incorporates the "know your dye" and "know your cytometer" principles described herein.

Managing a multi-panel clinic with heterogeneous patients

Primary care providers (PCPs) are considered the first-line defenders in preventive care. Patients seeking service from the same PCP constitute that physician's panel, which determines the overall supply and demand of the physician. The process of allocating patients to physician panels is called panel design. This study quantifies patient overflow and builds a mathematical model to evaluate the effect of two implementable panel assignments. In specialized panel assignment, patients are assigned based on their medical needs or visit frequency. In equal panel assignment, patients are distributed uniformly to maintain a similar composition across panels. We utilize majorization theory and numerical examples to evaluate the performance of the two designs. The results show that specialized panel assignment outperforms when (1) patient demands and physician capacity are relatively balanced or (2) patients who require frequent visits incur a higher shortage penalty. In a simulation model with actual patient arrival patterns, we also illustrate the robustness of the results and demonstrate the effect of switching panel policy when the patient pool changes over time.

A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer

BACKGROUND

Cell-free DNA's (cfDNA) use as a biomarker in cancer is challenging due to genetic heterogeneity of malignancies and rarity of tumor-derived molecules. Here we describe and demonstrate a novel machine-learning guided panel design strategy for improving the detection of tumor variants in cfDNA. Using this approach, we first generated a model to classify and score candidate variants for inclusion on a prostate cancer targeted sequencing panel. We then used this panel to screen tumor variants from prostate cancer patients with localized disease in both in silico and hybrid capture settings.

METHODS

Whole Genome Sequence (WGS) data from 550 prostate tumors was analyzed to build a targeted sequencing panel of single point and small (< 200 bp) indel mutations, which was subsequently screened in silico against prostate tumor sequences from 5 patients to assess performance against commonly used alternative panel designs. The panel's ability to detect tumor-derived cfDNA variants was then assessed using prospectively collected cfDNA and tumor foci from a test set 18 prostate cancer patients with localized disease undergoing radical proctectomy.

RESULTS

The panel generated from this approach identified as top candidates mutations in known driver genes (e.g. HRAS) and prostate cancer related transcription factor binding sites (e.g. MYC, AR). It outperformed two commonly used designs in detecting somatic mutations found in the cfDNA of 5 prostate cancer patients when analyzed in an in silico setting. Additionally, hybrid capture and 2500X sequencing of cfDNA molecules using the panel resulted in detection of tumor variants in all 18 patients of a test set, where 15 of the 18 patients had detected variants found in multiple foci.

CONCLUSION

Machine learning-prioritized targeted sequencing panels may prove useful for broad and sensitive variant detection in the cfDNA of heterogeneous diseases. This strategy has implications for disease detection and monitoring when applied to the cfDNA isolated from prostate cancer patients.

Explaining attitudes toward minority groups with human values in Germany - What is the direction of causality?

This paper examines the reciprocal relations between values and attitudes toward minorities over a period of fourteen months in 2015 and 2016. A representative sample of the adult population in Germany completed four waves of a panel study in which attitudes and values were each measured two times. Reciprocal relations over time between Schwartz's (1992) higher-order value of conservation and the value of universalism as well as attitudes toward four different minorities (Muslims, refugees, foreigners, Sinti/Roma) were examined using a modified cross-lagged longitudinal design. The results showed that values and attitudes had reciprocal longitudinal effects on one another, meaning that values predicted changes in attitudes and attitudes predicted changes in values. The findings also revealed that (1) values were more stable over time than attitudes, and (2) the longitudinal effect of values on attitudes was not significantly stronger than the longitudinal effect of attitudes on values.

Phenotyping of tumor infiltrating immune cells using mass-cytometry (CyTOF)

The anti-tumor immune response plays a crucial role in cancer patient outcome as well as in response to the growing family of immunotherapeutic treatments. Improving patient prognostic and therapeutic management requires a better knowledge of the tumor microenvironment, for which a deep characterization of tumor-infiltrating immune populations is instrumental. Mass cytometry represents an excellent tool in tumor Immunology, as it allows the simultaneous analysis of >40 markers on single cells. In this chapter, we review challenging technical aspects of the mass cytometry phenotyping of tumor infiltrating immune cells, focusing on fresh human solid tumor samples. We first explain how to design mass cytometry experiments, then provide detailed protocols to isolate mononuclear immune cells from solid tissues and to stain them for an acquisition on a mass cytometer. We also discuss how to optimize the preparation of single immune cell samples, and how to ensure the reproducibility of data generated from distinct fresh human samples. Finally, we provide troubleshooting suggestions for difficult sample acquisitions on a mass cytometer.

High-Dimensional Immunophenotyping with Fluorescence-Based Cytometry: A Practical Guidebook

Recent technological advances have greatly diversified the platforms that are available for high-dimensional single-cell immunophenotyping, including mass cytometry, single-cell RNA sequencing, and fluorescent-based flow cytometry. The latter is currently the most commonly used approach, and modern instrumentation allows for the measurement of up to 30 parameters, revealing deep insights into the complexity of the immune system.Here, we provide a practical guidebook for the successful design and execution of complex fluorescence-based immunophenotyping panels. We address common misconceptions and caveats, and also discuss challenges that are associated with the quality control and analysis of these data sets.

Visualization of Mass Cytometry Signal Background to Enable Optimal Core Panel Customization and Signal Threshold Gating

Signal interference or overlap in mass cytometry is minimal compared to flow cytometry but must still be considered for optimal panel design and assay sensitivity. Here we describe a procedure for evaluating signal interference dynamics in the context of a 25-parameter core immunophenotyping panel. Specifically, a mass-minus-many (MMM) approach was used to assess background signals in "empty" or "blank" channels intended for further customization. Through this approach cell type-specific variability in signal background is revealed. Further panel customization can thus be performed with an understanding of cell type and channel-specific background levels to enable rational panel design and the objective delineation of gating thresholds during analysis.

Multiparameter Phenotyping of Human PBMCs Using Mass Cytometry

The standard for single-cell analysis of phenotype and function in recent decades has been fluorescence flow cytometry. Mass cytometry is a newer technology that uses heavy metal ions, rather than fluorochromes, as labels for probes such as antibodies. The binding of these ion-labeled probes to cells is quantitated by mass spectrometry. This greatly increases the number of phenotypic and functional markers that can be probed simultaneously. Here, we review topics that must be considered when adapting existing flow cytometry panels to mass cytometry analysis. We present a protocol and representative panels for surface phenotyping and intracellular cytokine staining (ICS) assays.