ISAC Probe Tag Dictionary: Standardized Nomenclature for Detection and Visualization Labels Used in Cytometry and Microscopy Imaging.

Since the advent of microscopy imaging and flow cytometry, there has been an explosion in the number of probes, consisting of a component binding to an analyte and a detectable tag, to mark areas of interest in or on cells and tissue. Probe tags have been created to detect and/or visualize probes. Over time, these probe tags have increased in number. The expansion has resulted in arbitrarily created synonyms of probe tags used in publications and software. The synonyms are problematic for readability of publications, accuracy of text/data mining, and bridging data from multiple platforms, protocols, and databases for Big Data analysis. Development and implementation of a universal language for probe tags will ensure equivalent quality and level of data being reported or extracted for clinical/scientific evaluation as well as help connect data from many platforms. The International Society for Advancement of Cytometry Data Standards Task Force composed of academic scientists and industry hardware/software/reagent manufactures have developed recommendations for a standardized nomenclature for probe tags used in cytometry and microscopy imaging. These recommendations are shared in this technical note in the form of a Probe Tag Dictionary. © 2020 International Society for Advancement of Cytometry.

High-fidelity detection and sorting of nanoscale vesicles in viral disease and cancer.

Biological nanoparticles, including viruses and extracellular vesicles (EVs), are of interest to many fields of medicine as biomarkers and mediators of or treatments for disease. However, exosomes and small viruses fall below the detection limits of conventional flow cytometers due to the overlap of particle-associated scattered light signals with the detection of background instrument noise from diffusely scattered light. To identify, sort, and study distinct subsets of EVs and other nanoparticles, as individual particles, we developed nanoscale Fluorescence Analysis and Cytometric Sorting (nanoFACS) methods to maximise information and material that can be obtained with high speed, high resolution flow cytometers. This nanoFACS method requires analysis of the instrument background noise (herein defined as the "reference noise"). With these methods, we demonstrate detection of tumour cell-derived EVs with specific tumour antigens using both fluorescence and scattered light parameters. We further validated the performance of nanoFACS by sorting two distinct HIV strains to >95% purity and confirmed the viability (infectivity) and molecular specificity (specific cell tropism) of biological nanomaterials sorted with nanoFACS. This nanoFACS method provides a unique way to analyse and sort functional EV- and viral-subsets with preservation of vesicular structure, surface protein specificity and RNA cargo activity.

Methodology for evaluating and comparing flow cytometers: A multisite study of 23 instruments.

We demonstrate improved methods for making valid and accurate comparisons of fluorescence measurement capabilities among instruments tested at different sites and times. We designed a suite of measurements and automated data processing methods to obtain consistent objective results and applied them to a selection of 23 instruments at nine sites to provide a range of instruments as well as multiple instances of similar instruments. As far as we know, this study represents the most accurate methods and results so far demonstrated for this purpose. The first component of the study reporting improved methods for photoelectron scale (Spe) evaluations, which was published previously (Parks, El Khettabi, Chase, Hoffman, Perfetto, Spidlen, Wood, Moore, and Brinkman: Cytometry A 91 (2017) 232-249). Those results which were within themselves are not sufficient for instrument comparisons, so here, we use the Spe scale results for the 23 cytometers and combine them with additional information from the analysis suite to obtain the metrics actually needed for instrument evaluations and comparisons. We adopted what we call the 2+2SD limit of resolution as a maximally informative metric, for evaluating and comparing dye measurement sensitivity among different instruments and measurement channels. Our results demonstrate substantial differences among different classes of instruments in both dye response and detection sensitivity and some surprisingly large differences among similar instruments, even among instruments with nominally identical configurations. On some instruments, we detected defective measurement channels needing service. The system can be applied in shared resource laboratories and other facilities as an aspect of quality assurance, and accurate instrument comparisons can be valuable for selecting instruments for particular purposes and for making informed instrument acquisition decisions. An institutionally supported program could serve the cytometry community by facilitating access to materials, and analysis and maintaining an archive of results. © 2018 International Society for Advancement of Cytometry.

Evaluating flow cytometer performance with weighted quadratic least squares analysis of LED and multi-level bead data.

We developed a fully automated procedure for analyzing data from LED pulses and multilevel bead sets to evaluate backgrounds and photoelectron scales of cytometer fluorescence channels. The method improves on previous formulations by fitting a full quadratic model with appropriate weighting and by providing standard errors and peak residuals as well as the fitted parameters themselves. Here we describe the details of the methods and procedures involved and present a set of illustrations and test cases that demonstrate the consistency and reliability of the results. The automated analysis and fitting procedure is generally quite successful in providing good estimates of the Spe (statistical photoelectron) scales and backgrounds for all the fluorescence channels on instruments with good linearity. The precision of the results obtained from LED data is almost always better than that from multilevel bead data, but the bead procedure is easy to carry out and provides results good enough for most purposes. Including standard errors on the fitted parameters is important for understanding the uncertainty in the values of interest. The weighted residuals give information about how well the data fits the model, and particularly high residuals indicate bad data points. Known photoelectron scales and measurement channel backgrounds make it possible to estimate the precision of measurements at different signal levels and the effects of compensated spectral overlap on measurement quality. Combining this information with measurements of standard samples carrying dyes of biological interest, we can make accurate comparisons of dye sensitivity among different instruments. Our method is freely available through the R/Bioconductor package flowQB. © 2017 International Society for Advancement of Cytometry.

A Quantitative Method for Comparing the Brightness of Antibody-dye Reagents and Estimating Antibodies Bound per Cell.

We present a quantitative method for comparing the brightness of antibody-dye reagents and estimating antibodies bound per cell. The method is based on complementary binding of test and fill reagents to antibody capture microspheres. Several aliquots of antibody capture beads are stained with varying amounts of the test conjugate. The remaining binding sites on the beads are then filled with a second conjugate containing a different fluorophore. Finally, the fluorescence of the test conjugate compared to the fill conjugate is used to measure the relative brightness of the test conjugate. The fundamental assumption of the test-fill method is that if it takes X molecules of one test antibody to lower the fill signal by Y units, it will take the same X molecules of any other test antibody to give the same effect. We apply a quadratic fit to evaluate the test-fill signal relationship across different amounts of test reagent. If the fit is close to linear, we consider the test reagent to be suitable for quantitative evaluation of antibody binding. To calibrate the antibodies bound per bead, a PE conjugate with 1 PE molecule per antibody is used as a test reagent and the fluorescence scale is calibrated with Quantibrite PE beads. When the fluorescence per antibody molecule has been determined for a particular conjugate, that conjugate can be used for measurement of antibodies bound per cell. This provides comparisons of the brightness of different conjugates when conducted on an instrument whose statistical photoelectron (Spe) scales are known. © 2016 by John Wiley & Sons, Inc.

Modern flow cytometry: a practical approach.

The demonstration that CD T-cell counts can be used to monitor HIV disease progression opened the way to the first clinical application for fluorescence activated cell sorting (FACS) technology. Modern FACS methodologies such multicolor staining and sorting has opened the way to new and constructive therapeutic and clinical applications. This article outlines approaches in which current users can use to improve the quality of their FACS work without undue effort. FACS technology development and the emergence of new software support for this technology are cooperating in this effort.

A new "Logicle" display method avoids deceptive effects of logarithmic scaling for low signals and compensated data.

BACKGROUND: In immunofluorescence measurements and most other flow cytometry applications, fluorescence signals of interest can range down to essentially zero. After fluorescence compensation, some cell populations will have low means and include events with negative data values. Logarithmic presentation has been very useful in providing informative displays of wide-ranging flow cytometry data, but it fails to adequately display cell populations with low means and high variances and, in particular, offers no way to include negative data values. This has led to a great deal of difficulty in interpreting and understanding flow cytometry data, has often resulted in incorrect delineation of cell populations, and has led many people to question the correctness of compensation computations that were, in fact, correct. RESULTS: We identified a set of criteria for creating data visualization methods that accommodate the scaling difficulties presented by flow cytometry data. On the basis of these, we developed a new data visualization method that provides important advantages over linear or logarithmic scaling for display of flow cytometry data, a scaling we refer to as "Logicle" scaling. Logicle functions represent a particular generalization of the hyperbolic sine function with one more adjustable parameter than linear or logarithmic functions. Finally, we developed methods for objectively and automatically selecting an appropriate value for this parameter. CONCLUSIONS: The Logicle display method provides more complete, appropriate, and readily interpretable representations of data that includes populations with low-to-zero means, including distributions resulting from fluorescence compensation procedures, than can be produced using either logarithmic or linear displays. The method includes a specific algorithm for evaluating actual data distributions and deriving parameters of the Logicle scaling function appropriate for optimal display of that data. It is critical to note that Logicle visualization does not change the data values or the descriptive statistics computed from them.

Identification of B-cell subsets: an exposition of 11-color (Hi-D) FACS methods.

In the last few years, the effectiveness of developmental and functional studies of individual subsets of cells has increased dramatically owing to the identification of additional subset markers and the extension of fluorescence-activated cell sorter (FACS) capabilities to simultaneously measure the expression of more markers on individual cells. For example, introduction of a 6-8 multiparameter FACS instrument resulted in significant advances in understanding B-cell development. In this chapter, we describe 11-color high-dimensional (Hi-D) FACS staining and data analysis methods that provide greater clarity in identifying the B-cell subsets in bone marrow, spleen, and peritoneal cavity. Further, we show how a single Hi-D FACS antibody reagent combination is sufficient to unambiguously identify most of the currently defined B-cell developmental subsets in the bone marrow (Hardy fractions A-F) and the functional B-cell subsets (B-1a, B-1b, B-2, and marginal zone [MZ] B cells) in the periphery. Although we focus on murine B-cell subsets, the methods we discuss are relevant to FACS studies conducted with all types of cells and other FACS instruments. We introduce a new method for scaling axes for histograms or contour plots of FACS data. This method, which we refer to as Logicle visualization, is particularly useful in promoting correct interpretations of fluorescence-compensated FACS data and visual confirmation of correct compensation values. In addition, it facilitates discrimination of valid subsets. Application of Logicle visualization tools in the Hi-D FACS studies discussed here creates a strong new base for in-depth analysis of B-cell development and function.

New approaches to fluorescence compensation and visualization of FACS data.

The Fluorescence Activated Cell Sorter (FACS) is an invaluable tool for clinicians and researchers alike in phenotyping and sorting individual cells. With the advances in FACS methodology, notably intracellular staining for cytokines, transcription factors and phosphoproteins, and with increases in the number of fluorescence detection channels, researchers now have the opportunity to study individual cells in far greater detail than previously possible. In this chapter, we discuss High-Definition (Hi-D) FACS methods that can improve analysis of lymphocyte subsets in mouse and man. We focus on the reasons why fluorescence compensation, which is necessary to correct for spectral overlap between two or more fluorochromes used in the same staining combination, is best done as a computed transformation rather than using the analog circuitry available on many flow cytometers. In addition, we introduce a new data visualization method that scales the axes on histograms and two-dimensional contour (or dot) plots to enable visualization of signals from all cells, including those that have minimal fluorescence values and are not properly represented with traditional logarithmic axes. This "Logicle" visualization method, we show, provides superior representations of compensated data and makes correctly compensated data look correct. Finally, we discuss controls that facilitate recognition of boundaries between positive and negative subsets.