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.

Non-Parametric Comparison of Single Parameter Histograms

A number of methods have been developed to compare single parameter histograms. Some perform a channel-by-channel analysis and others give a single statistic about how the histograms may or may not differ. If they do differ, then the significance of the difference or confidence limit is usually provided. The specific location(s) for the greatest deviations may also be given. Some are more effective at resolving severely overlapping populations and others work poorly when there is any significant overlap. Each method makes certain assumptions about the data. It is important to understand the assumptions being made and to understand the limitations of each method. It is essential to know how to identify when a comparison method will work for a given set of histograms. This unit explores the different methods, and provides a guide for the reader to choose the most appropriate method(s) to use for a specific data set(s). © 2018 by John Wiley & Sons, Inc.

Establishing and maintaining system linearity

A flow cytometer system transforms a received optical signal into an electrical signal whose amplitude ideally is directly proportional to the optical signal but in practice is at least related in a linear fashion. Establishment and maintenance of system linearity are prerequisites to accurate quantitative measurements. The many potential sources of offsets and nonlinearity are found in all parts of the detection, amplification, and data acquisition systems of a flow instrument. This unit aims to help investigators evaluate performance of the hardware in flow cytometer detection systems.

Characterization of flow cytometer instrument sensitivity

Fluorescence sensitivity, measured in terms of resolution, allows the researcher to more accurately answer the question of how dim a cell can be and still be resolvable from another population. This measure focuses on the width, i.e., the standard deviation, of the population distributions and not on the location, i.e., the mean intensity, of populations on a histogram scale relative to the background. To determine the fluorescence sensitivity in terms of the resolution, both the detection efficiency, Q, and optical background, B, need to be measured. Both factors affect the ability to resolve dimly fluorescent subpopulations, and both factors are required to uniquely characterize the performance of a flow cytometer. Using Q and B, it is possible to determine the minimum fluorescence intensity that is resolvable from the background or from another population. This unit describes a practical and robust approach to measure the critical factors that determine the ability of a flow cytometer to analyze dimly fluorescent particles.