Literature DB >> 17531779

High-content flow cytometry and temporal data analysis for defining a cellular signature of graft-versus-host disease.

Ryan Remy Brinkman1, Maura Gasparetto, Shang-Jung Jessica Lee, Albert J Ribickas, Janelle Perkins, William Janssen, Renee Smiley, Clay Smith.   

Abstract

Acute graft-versus-host disease (GVHD) is diagnosed by clinical and histologic criteria that are often nonspecific and typically apparent only after the disease is well established. Because GvHD is mediated by donor T cells and other immune effector cells, we sought to determine whether changes within a wide array of peripheral blood lymphocyte populations could predict the development of GvHD. Peripheral blood samples from 31 patients undergoing allogeneic blood and marrow transplant were analyzed for the proportion of 121 different subpopulations defined by 4-color combinations of lymphocyte phenotypic and activation markers at progressive time points posttransplant. Samples were processed using a newly developed high content flow cytometry technique and subjected to a spline- and functional linear discriminant analysis (FLDA)-based temporal analysis technique. This strategy identified a consistent posttransplant increase in the proportion and extent of fluctuation of CD3+CD4+CD8beta+ cells in patients who developed GVHD compared to those that did not. Although larger prospective clinical studies will be necessary to validate these results, this study demonstrates that high-content flow cytometry coupled with temporal analysis is a powerful approach for developing new diagnostic tools, and may be useful for developing a sensitive and specific predictive test for GVHD.

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Year:  2007        PMID: 17531779      PMCID: PMC2000975          DOI: 10.1016/j.bbmt.2007.02.002

Source DB:  PubMed          Journal:  Biol Blood Marrow Transplant        ISSN: 1083-8791            Impact factor:   5.742


  40 in total

1.  Nonparametric mixed effects models for unequally sampled noisy curves.

Authors:  J A Rice; C O Wu
Journal:  Biometrics       Date:  2001-03       Impact factor: 2.571

2.  Assessing homeostasis through circadian patterns.

Authors:  R A Irizarry; C Tankersley; R Frank; S Flanders
Journal:  Biometrics       Date:  2001-12       Impact factor: 2.571

3.  Clustering of time-course gene expression data using a mixed-effects model with B-splines.

Authors:  Yihui Luan; Hongzhe Li
Journal:  Bioinformatics       Date:  2003-03-01       Impact factor: 6.937

4.  Statistical analysis of a small set of time-ordered gene expression data using linear splines.

Authors:  M J L De Hoon; S Imoto; S Miyano
Journal:  Bioinformatics       Date:  2002-11       Impact factor: 6.937

5.  Customized molecular phenotyping by quantitative gene expression and pattern recognition analysis.

Authors:  Shreeram Akilesh; Daniel J Shaffer; Derry Roopenian
Journal:  Genome Res       Date:  2003-07       Impact factor: 9.043

6.  Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes.

Authors:  Ziv Bar-Joseph; Georg Gerber; Itamar Simon; David K Gifford; Tommi S Jaakkola
Journal:  Proc Natl Acad Sci U S A       Date:  2003-08-21       Impact factor: 11.205

7.  Rat peripheral CD4+CD8+ T lymphocytes are partially immunocompetent thymus-derived cells that undergo post-thymic maturation to become functionally mature CD4+ T lymphocytes.

Authors:  Eva Jiménez; Rosa Sacedón; Angeles Vicente; Carmen Hernández-López; Agustín G Zapata; Alberto Varas
Journal:  J Immunol       Date:  2002-05-15       Impact factor: 5.422

8.  Early changes in gene expression profiles of hepatic GVHD uncovered by oligonucleotide microarrays.

Authors:  Tamotsu Ichiba; Takanori Teshima; Rork Kuick; David E Misek; Chen Liu; Yuichiro Takada; Yoshinobu Maeda; Pavan Reddy; Debra L Williams; Samir M Hanash; James L M Ferrara
Journal:  Blood       Date:  2003-03-27       Impact factor: 22.113

9.  Macrophage migratory inhibitory factor (MIF) expression in acute graft-versus-host disease (GVHD) in allogeneic hemopoietic stem cell transplant recipients.

Authors:  J W S Lo; A Y H Leung; X R Huang; A K W Lie; C Metz; R Bucala; R Liang; H Y Lan
Journal:  Bone Marrow Transplant       Date:  2002-09       Impact factor: 5.483

10.  Persistence of host dendritic cells after transplantation is associated with graft-versus-host disease.

Authors:  Geoffrey W Chan; Gullu Gorgun; Kenneth B Miller; Francine M Foss
Journal:  Biol Blood Marrow Transplant       Date:  2003-03       Impact factor: 5.742

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  21 in total

1.  Rapid cell population identification in flow cytometry data.

Authors:  Nima Aghaeepour; Radina Nikolic; Holger H Hoos; Ryan R Brinkman
Journal:  Cytometry A       Date:  2011-01       Impact factor: 4.355

2.  Using flowViz to visualize flow cytometry data.

Authors:  D Sarkar; N Le Meur; R Gentleman
Journal:  Bioinformatics       Date:  2008-02-01       Impact factor: 6.937

Review 3.  A chromatic explosion: the development and future of multiparameter flow cytometry.

Authors:  Pratip K Chattopadhyay; Carl-Magnus Hogerkorp; Mario Roederer
Journal:  Immunology       Date:  2008-12       Impact factor: 7.397

4.  Data augmentation for models based on rejection sampling.

Authors:  Vinayak Rao; Lizhen Lin; David B Dunson
Journal:  Biometrika       Date:  2016-05-06       Impact factor: 2.445

5.  Data reduction for spectral clustering to analyze high throughput flow cytometry data.

Authors:  Habil Zare; Parisa Shooshtari; Arvind Gupta; Ryan R Brinkman
Journal:  BMC Bioinformatics       Date:  2010-07-28       Impact factor: 3.169

6.  Transcriptome analysis of GVHD reveals aurora kinase A as a targetable pathway for disease prevention.

Authors:  Scott N Furlan; Benjamin Watkins; Victor Tkachev; Ryan Flynn; Sarah Cooley; Swetha Ramakrishnan; Karnail Singh; Cindy Giver; Kelly Hamby; Linda Stempora; Aneesah Garrett; Jingyang Chen; Kayla M Betz; Carly G K Ziegler; Gregory K Tharp; Steven E Bosinger; Daniel E L Promislow; Jeffrey S Miller; Edmund K Waller; Bruce R Blazar; Leslie S Kean
Journal:  Sci Transl Med       Date:  2015-11-25       Impact factor: 17.956

7.  Analysis of High-Throughput Flow Cytometry Data Using plateCore.

Authors:  Errol Strain; Florian Hahne; Ryan R Brinkman; Perry Haaland
Journal:  Adv Bioinformatics       Date:  2009-10-11

8.  A survey of flow cytometry data analysis methods.

Authors:  Ali Bashashati; Ryan R Brinkman
Journal:  Adv Bioinformatics       Date:  2009-12-06

9.  flowClust: a Bioconductor package for automated gating of flow cytometry data.

Authors:  Kenneth Lo; Florian Hahne; Ryan R Brinkman; Raphael Gottardo
Journal:  BMC Bioinformatics       Date:  2009-05-14       Impact factor: 3.169

10.  The curvHDR method for gating flow cytometry samples.

Authors:  Ulrike Naumann; George Luta; Matthew P Wand
Journal:  BMC Bioinformatics       Date:  2010-01-22       Impact factor: 3.169

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