Literature DB >> 23389989

Quantifying spillover spreading for comparing instrument performance and aiding in multicolor panel design.

Richard Nguyen1, Stephen Perfetto, Yolanda D Mahnke, Pratip Chattopadhyay, Mario Roederer.   

Abstract

After compensation, the measurement errors arising from multiple fluorescences spilling into each detector become evident by the spreading of nominally negative distributions. Depending on the instrument configuration and performance, and reagents used, this "spillover spreading" (SS) affects sensitivity in any given parameter. The degree of SS had been predicted theoretically to increase with measurement error, i.e., by the square root of fluorescence intensity, as well as directly related to the spectral overlap matrix coefficients. We devised a metric to quantify SS between any pair of detectors. This metric is intrinsic, as it is independent of fluorescence intensity. The combination of all such values for one instrument can be represented as a spillover spreading matrix (SSM). Single-stained controls were used to determine the SSM on multiple instruments over time, and under various conditions of signal quality. SSM values reveal fluorescence spectrum interactions that can limit the sensitivity of a reagent in the presence of brightly-stained cells on a different color. The SSM was found to be highly reproducible; its non-trivial values show a CV of less than 30% across a 2-month time frame. In addition, the SSM is comparable between similarly-configured instruments; instrument-specific differences in the SSM reveal underperforming detectors. Quantifying and monitoring the SSM can be a useful tool in instrument quality control to ensure consistent sensitivity and performance. In addition, the SSM is a key element for predicting the performance of multicolor immunofluorescence panels, which will aid in the optimization and development of new panels. We propose that the SSM is a critical component of QA/QC in evaluation of flow cytometer performance. Published 2013 Wiley Periodicals, Inc.

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Year:  2013        PMID: 23389989      PMCID: PMC3678531          DOI: 10.1002/cyto.a.22251

Source DB:  PubMed          Journal:  Cytometry A        ISSN: 1552-4922            Impact factor:   4.355


  7 in total

1.  Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats.

Authors:  M Roederer
Journal:  Cytometry       Date:  2001-11-01

2.  Compensation is not dependent on signal intensity or on number of parameters.

Authors:  M Roederer
Journal:  Cytometry       Date:  2001-12-15

Review 3.  A practical approach to multicolor flow cytometry for immunophenotyping.

Authors:  N Baumgarth; M Roederer
Journal:  J Immunol Methods       Date:  2000-09-21       Impact factor: 2.303

4.  Publication of optimized multicolor immunofluorescence panels.

Authors:  Yolanda Mahnke; Pratip Chattopadhyay; Mario Roederer
Journal:  Cytometry A       Date:  2010-09       Impact factor: 4.355

5.  Quality assurance for polychromatic flow cytometry.

Authors:  Stephen P Perfetto; David Ambrozak; Richard Nguyen; Pratip Chattopadhyay; Mario Roederer
Journal:  Nat Protoc       Date:  2006       Impact factor: 13.491

Review 6.  Optimizing a multicolor immunophenotyping assay.

Authors:  Yolanda D Mahnke; Mario Roederer
Journal:  Clin Lab Med       Date:  2007-09       Impact factor: 1.935

7.  OMIP-015: human regulatory and activated T-cells without intracellular staining.

Authors:  Yolanda D Mahnke; Margaret H Beddall; Mario Roederer
Journal:  Cytometry A       Date:  2012-11-16       Impact factor: 4.355

  7 in total
  22 in total

1.  Intracellular Cytokine Detection by Flow Cytometry in Surface Marker-Defined Human Peripheral Blood Mononuclear T Cells.

Authors:  Fredine T Lauer; Jesse L Denson; Ellen Beswick; Scott W Burchiel
Journal:  Curr Protoc Toxicol       Date:  2017-08-04

2.  Toward the measurement of multiple fluorescence lifetimes in flow cytometry: maximizing multi-harmonic content from cells and microspheres.

Authors:  Patrick Jenkins; Mark A Naivar; Jessica P Houston
Journal:  J Biophotonics       Date:  2015-02-26       Impact factor: 3.207

3.  Bioconjugatable, PEGylated Hydroporphyrins for Photochemistry and Photomedicine. Narrow-Band, Red-Emitting Chlorins.

Authors:  Mengran Liu; Chih-Yuan Chen; Amit Kumar Mandal; Vanampally Chandrashaker; Rosemary B Evans-Storms; J Bruce Pitner; David F Bocian; Dewey Holten; Jonathan S Lindsey
Journal:  New J Chem       Date:  2016-07-21       Impact factor: 3.591

4.  Teaching advanced flow cytometry in Africa: 10 years of lessons learned.

Authors:  Elisa Nemes; Wendy A Burgers; Catherine Riou; Erica Andersen-Nissen; Guido Ferrari; Clive M Gray; Thomas Scriba
Journal:  Cytometry A       Date:  2016-11-04       Impact factor: 4.355

5.  Bioconjugatable, PEGylated Hydroporphyrins for Photochemistry and Photomedicine. Narrow-Band, Near-Infrared-Emitting Bacteriochlorins.

Authors:  Nuonuo Zhang; Jianbing Jiang; Mengran Liu; Masahiko Taniguchi; Amit Kumar Mandal; Rosemary B Evans-Storms; J Bruce Pitner; David F Bocian; Dewey Holten; Jonathan S Lindsey
Journal:  New J Chem       Date:  2016-07-22       Impact factor: 3.591

6.  A guide to choosing fluorescent protein combinations for flow cytometric analysis based on spectral overlap.

Authors:  Benjamin Kleeman; Andre Olsson; Tess Newkold; Matt Kofron; Monica DeLay; David Hildeman; H Leighton Grimes
Journal:  Cytometry A       Date:  2018-03-13       Impact factor: 4.355

7.  OMIP-044: 28-color immunophenotyping of the human dendritic cell compartment.

Authors:  Florian Mair; Martin Prlic
Journal:  Cytometry A       Date:  2018-01-22       Impact factor: 4.355

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

Authors:  David R Parks; Faysal El Khettabi; Eric Chase; Robert A Hoffman; Stephen P Perfetto; Josef Spidlen; James C S Wood; Wayne A Moore; Ryan R Brinkman
Journal:  Cytometry A       Date:  2017-02-03       Impact factor: 4.355

9.  Guidelines for standardizing T-cell cytometry assays to link biomarkers, mechanisms, and disease outcomes in type 1 diabetes.

Authors:  Jennie H M Yang; Kirsten A Ward-Hartstonge; Daniel J Perry; J Lori Blanchfield; Amanda L Posgai; Alice E Wiedeman; Kirsten Diggins; Adeeb Rahman; Timothy I M Tree; Todd M Brusko; Megan K Levings; Eddie A James; Sally C Kent; Cate Speake; Dirk Homann; S Alice Long
Journal:  Eur J Immunol       Date:  2022-01-28       Impact factor: 5.532

10.  High-Dimensional Data Analysis Algorithms Yield Comparable Results for Mass Cytometry and Spectral Flow Cytometry Data.

Authors:  Laura Ferrer-Font; Johannes U Mayer; Samuel Old; Ian F Hermans; Jonathan Irish; Kylie M Price
Journal:  Cytometry A       Date:  2020-04-15       Impact factor: 4.355

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