Sometimes simpler is better: VLog, a general but easy-to-implement log-like transform for cytometry.

The fundamental purpose of log and log-like transforms for cytometry is to make measured population variabilities as uniform as possible. The long-standing success of the log transform was its ability to stabilize linearly increasing gain-dependent uncertainties and the success of the log-like transforms is that they extend this notion to include zero and negative measurement values. This study derives and examines a transform called VLog that stabilizes the three general sources of variability: (1) gain-dependent variability, (2) photo-electron counting error, and (3) signal-independent sources of error. Somewhat surprisingly, this transform has a closed-form solution and therefore is relatively simple to implement. By including some quantitation elements in its formulation, the shape-dependent arguments, α and ß, usually do not require optimization for different datasets. The simplicity and generality of the transform may make it a useful tool for cytometry and possibly other technologies. © 2016 International Society for Advancement of Cytometry.

Probability state modeling theory.

As the technology of cytometry matures, there is mounting pressure to address two major issues with data analyses. The first issue is to develop new analysis methods for high-dimensional data that can directly reveal and quantify important characteristics associated with complex cellular biology. The other issue is to replace subjective and inaccurate gating with automated methods that objectively define subpopulations and account for population overlap due to measurement uncertainty. Probability state modeling (PSM) is a technique that addresses both of these issues. The theory and important algorithms associated with PSM are presented along with simple examples and general strategies for autonomous analyses. PSM is leveraged to better understand B-cell ontogeny in bone marrow in a companion Cytometry Part B manuscript. Three short relevant videos are available in the online supporting information for both of these papers. PSM avoids the dimensionality barrier normally associated with high-dimensionality modeling by using broadened quantile functions instead of frequency functions to represent the modulation of cellular epitopes as cells differentiate. Since modeling programs ultimately minimize or maximize one or more objective functions, they are particularly amenable to automation and, therefore, represent a viable alternative to subjective and inaccurate gating approaches.

Automated analysis of flow cytometric data for CD34+ stem cell enumeration using a probability state model.

BACKGROUND: Flow Cytometry is widely used for enumeration of hematopoietic stem cell (SC) levels in bone marrow, cord blood, peripheral blood, and apheresis products. The ISHAGE single-platform gating method is considered by many to be the standard for CD34+ SC enumeration. However, attempts at uniform application of this ISHAGE method have met with only partial success. We propose an automated, multivariate classification approach for SC analysis based on Probability State Modeling™ (PSM). In this study, we compare the results from automated PSM analysis with manual ISHAGE gating analysis as performed by a trained analyst. METHODS: A total of 258 samples were assayed on BD FACSCanto II flow cytometers using a stain-lyse-no-wash technique. Populations were defined using CD34, CD45, 7-AAD, and light scatter. BD TruCount™ bead tubes were used for absolute SC concentrations. A PSM was designed to classify events into beads, debris, intact-dead cells, and intact-live SC; run unattended and record results. RESULTS: The ISHAGE and PSM methods show excellent agreement in estimating the concentration of #SC/µL: slope = 1.009, r² = 0.999. Bland-Altman Analysis for the SC concentration has an average difference (bias) of 2.018 SC/µL. The 95% confidence interval is from -59.350 to 63.380 SC/µL. The operator-to-operator agreement using PSM is perfect: r² = 1.000. CONCLUSIONS: Automated PSM analysis of SC listmode data produces results that agree strongly with ISHAGE gate-based results. The PSM approach provides higher reproducibility, objectivity, and speed with accuracy at least equivalent to the ISHAGE method.

Multiparameter flow cytometry of fluorescent protein reporters.

Reporters based on the green fluorescent protein (GFP) from the jellyfish Aequorea victoria and GFP-like proteins from other marine organisms provide valuable tools to monitor gene transfer and expression noninvasively in living cells. Stable cell lines were generated from the Sp2/0-Ag14 hybridoma that express up to three spectral enhanced versions of GFP, the enhanced cyan fluorescent protein (ECFP), the enhanced green fluorescent protein (EGFP), and the enhanced yellow fluorescent protein (EYFP), and/or a variant of the Discosoma coral red fluorescent protein (DsRed). The panel of lines was used to demonstrate a flow cytometric procedure for simultaneous analysis of all four fluorescent proteins that utilizes dual-laser excitation at 488 nm and 407 nm. Additional schemes for simultaneous detection of two, three or four of these fluorescent proteins are also presented.