PMID- 26951559
OWN - NLM
STAT- MEDLINE
DCOM- 20170105
LR  - 20240325
IS  - 1879-1123 (Electronic)
IS  - 1044-0305 (Print)
IS  - 1044-0305 (Linking)
VI  - 27
IP  - 5
DP  - 2016 May
TI  - Optimizing Mass Spectrometry Analyses: A Tailored Review on the Utility of Design 
      of Experiments.
PG  - 767-85
LID - 10.1007/s13361-016-1344-x [doi]
AB  - Mass spectrometry (MS) has emerged as a tool that can analyze nearly all classes 
      of molecules, with its scope rapidly expanding in the areas of post-translational 
      modifications, MS instrumentation, and many others. Yet integration of novel 
      analyte preparatory and purification methods with existing or novel mass 
      spectrometers can introduce new challenges for MS sensitivity. The mechanisms 
      that govern detection by MS are particularly complex and interdependent, 
      including ionization efficiency, ion suppression, and transmission. Performance 
      of both off-line and MS methods can be optimized separately or, when appropriate, 
      simultaneously through statistical designs, broadly referred to as "design of 
      experiments" (DOE). The following review provides a tutorial-like guide into the 
      selection of DOE for MS experiments, the practices for modeling and optimization 
      of response variables, and the available software tools that support DOE 
      implementation in any laboratory. This review comes 3 years after the latest DOE 
      review (Hibbert DB, 2012), which provided a comprehensive overview on the types 
      of designs available and their statistical construction. Since that time, new 
      classes of DOE, such as the definitive screening design, have emerged and new 
      calls have been made for mass spectrometrists to adopt the practice. Rather than 
      exhaustively cover all possible designs, we have highlighted the three most 
      practical DOE classes available to mass spectrometrists. This review further 
      differentiates itself by providing expert recommendations for experimental setup 
      and defining DOE entirely in the context of three case-studies that highlight the 
      utility of different designs to achieve different goals. A step-by-step tutorial 
      is also provided.
FAU - Hecht, Elizabeth S
AU  - Hecht ES
AD  - W. M. Keck FTMS Laboratory for Human Health Research, Department of Chemistry, 
      North Carolina State University, Raleigh, NC, 27695, USA.
FAU - Oberg, Ann L
AU  - Oberg AL
AD  - Division of Biomedical Statistics and Informatics, Department of Health Sciences 
      Research, Mayo Clinic, Rochester, MN, 55905, USA.
FAU - Muddiman, David C
AU  - Muddiman DC
AD  - W. M. Keck FTMS Laboratory for Human Health Research, Department of Chemistry, 
      North Carolina State University, Raleigh, NC, 27695, USA. dcmuddim@ncsu.edu.
LA  - eng
GR  - R01 GM112662/GM/NIGMS NIH HHS/United States
GR  - T32 GM008776/GM/NIGMS NIH HHS/United States
GR  - R01 GM112662 02/GM/NIGMS NIH HHS/United States
GR  - T32GM008776/GM/NIGMS NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Review
DEP - 20160307
PL  - United States
TA  - J Am Soc Mass Spectrom
JT  - Journal of the American Society for Mass Spectrometry
JID - 9010412
SB  - IM
MH  - *Mass Spectrometry
MH  - Models, Statistical
MH  - Research Design
PMC - PMC4841694
MID - NIHMS766756
OTO - NOTNLM
OT  - Design of experiments
OT  - Mass spectrometry
OT  - Modeling
OT  - Optimization
OT  - Tutorial
EDAT- 2016/03/10 06:00
MHDA- 2017/01/06 06:00
PMCR- 2017/05/01
CRDT- 2016/03/09 06:00
PHST- 2015/11/02 00:00 [received]
PHST- 2016/01/16 00:00 [accepted]
PHST- 2016/01/14 00:00 [revised]
PHST- 2016/03/09 06:00 [entrez]
PHST- 2016/03/10 06:00 [pubmed]
PHST- 2017/01/06 06:00 [medline]
PHST- 2017/05/01 00:00 [pmc-release]
AID - 10.1007/s13361-016-1344-x [pii]
AID - 10.1007/s13361-016-1344-x [doi]
PST - ppublish
SO  - J Am Soc Mass Spectrom. 2016 May;27(5):767-85. doi: 10.1007/s13361-016-1344-x. 
      Epub 2016 Mar 7.