Spectral FCM technology allows the acquisition of a continuous range of wavelengths for each individual cell regardless of the fluorochrome spectrum emission ( 6– 8). Technological developments in conventional FCM instrumentation have focused on increasing the number of detectors, reaching as many as 30 in some instruments. The most widely available cytometry technology is fluorescence-based conventional FCM however, it suffers from limitations in its multiparametric capability because of fluorochrome spectral overlap ( 3– 5). FCM has undergone a technological revolution in recent years, affecting both instrumentation and reagent availability, greatly increasing the number of parameters that can be analyzed simultaneously in a single cell ( 1, 2). Instead, we propose to configure instrument acquisition to use all available fluorescence detectors and to avoid integration and compensation procedures, thereby using raw spectral or pseudospectral data for improved automated phenotypic analysis.įlow cytometry (FCM) is the most widely used single-cell analysis technique because of its ability to measure multiple parameters, which allows the phenotypic and functional study of cell populations. Our results thus challenge the paradigm of one-fluorochrome/one-parameter acquisition in FCM for unsupervised cluster-based analysis. Raw spectral data or pseudospectral data acquired with the full set of available detectors by conventional cytometry consistently outperformed datasets acquired and compensated according to FCM standards. An analysis workflow was set up and tested for the detection of changes in reference bead subsets and in a rare subpopulation of murine lymph node CD103 + dendritic cells acquired by conventional or spectral cytometry. In this study, we tested different data input modes to evaluate how cytometry acquisition configuration and data compensation procedures affect the performance of unsupervised phenotyping tools. Advances in flow cytometry (FCM) increasingly demand adoption of computational analysis tools to tackle the ever-growing data dimensionality.

Flow cytometry interpretation