Format

Send to

Choose Destination
J Immunol. 2018 May 15;200(10):3319-3331. doi: 10.4049/jimmunol.1800446.

Flow Cytometry Data Preparation Guidelines for Improved Automated Phenotypic Analysis.

Author information

1
Unidad de Celómica, Área de Biología Celular y del Desarrollo, Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid E28029, Spain; and.
2
Unidad de Celómica, Área de Biología Celular y del Desarrollo, Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid E28029, Spain; and jmligos@cnic.es mmontoya@cnic.es.
3
Laboratorio de Inmunobiología, Área de Fisiopatología del Miocardio, Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid E28029, Spain.

Abstract

Advances in flow cytometry (FCM) increasingly demand adoption of computational analysis tools to tackle the ever-growing data dimensionality. 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. 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. 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. Our results thus challenge the paradigm of one-fluorochrome/one-parameter acquisition in FCM for unsupervised cluster-based analysis. 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.

PMID:
29735643
DOI:
10.4049/jimmunol.1800446
[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for HighWire
Loading ...
Support Center