Flow chart of the rostro-caudal gradient study.

<p>In the rostro-caudal gradient (RCG) study, we examined the seven following points of the RCG from a PSP patient: 1-2^nd, 10-11^th, 16-17^th, 24-25^th, 31-32^nd, 38-39^th and 44-45^th mL CSF, referred to as RCG point 1-7, respectively. Twelve samples were digested and iTRAQ labeled (114-117). A reference, (labeled with iTRAQ reagent 114) containing the same amount of each RCG point, was included in each experiment. The RCG points 1 and 7 were included twice. After digestion and iTRAQ-labeling, samples were combined as follows: Exp. 1 (reference, 44-45^th mL, 24-25^th mL and 1-2^nd mL), Exp. 2 (reference, 1-2^nd mL, 38-39^th mL and 16-17^th mL), and Exp. 3 (reference, 10-11^th mL, 44-45^th mL and the 31-32^nd mL). The three experiments were fractionated by mixed mode reversed phase-anion chromatography (MM (RP-AX)) and analyzed on an Orbitrap Velos Pro. The protein abundances were averaged for each protein in the duplicate samples.</p

Categorization of proteins based on fold change, R-squared values and major expected contributing source of origin.

<p>In order to explore how different proteins were affected by the rostro-caudal gradient, we used the fold change and R-squared values of the proteins quantified with SID-MRM and iTRAQ to categorize the proteins into the three categories: affected by the RCG, unaffected by the RCG and uncertain. The fold change was calculated between the 1-2^nd and 44-45^th mL of CSF (referred to as RCG point 1 and 7), and the classification was based on the criteria from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090429#pone-0090429-t001" target="_blank">Table 1</a>.The proteins are also categorized into groups based on the major expected contributing source of origin, with Uniprot as the primary reference unless otherwise stated. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0090429#pone.0090429.s006" target="_blank">Table S5</a> for details. The asterisk (*) marks conflicting results: affected + unaffected  =  uncertain; affected + uncertain  =  affected; unaffected + uncertain  =  unaffected. Proteins only quantified in the SID-MRM study is marked with ^a.</p

Overview of the conducted studies.

<p>Blood contamination: in experiment A protein depleted CSF was separated by SDS-PAGE, in experiment B crude CSF was in-solution digested. Progenesis LC-MS was used for data analysis. In the rostro-caudal gradient study, we used iTRAQ-labeling with mixed mode reversed phase-anion chromatography (MM (RP-AX)) fractionation. The Spectrum Mill software was used for data analysis. For verification we used stable isotope dilution (SID) multiple reaction monitoring (MRM) to monitor 70 peptides. MM (RP-AX) chromatography was used for fractionation and the MultiQuant software was used for data analysis. In the plasma/CSF ratio study equal amount of corresponding CSF and plasma from five patients were compared using dimethyl labeling. Samples were fractionated using strong cation exchange (SCX) chromatography. Proteome discoverer was used for data analysis. For all discovery experiments the samples were analyzed on an Orbitrap Velos Pro and for SID-MRM the samples were analyzed on a Q-trap 5500. SIS = Stable Isotope Standards</p

Flow chart of the plasma/CSF study.

<p>We compared the cerebrospinal fluid (CSF) and plasma protein ratio of five patients (P1-P5) using dimethyl labeling. The reference sample was a mix of equal total amount of CSF and plasma, and was labeled by light reagents. The five CSF samples were labeled by intermediate (IM) reagents, and the plasma samples were labeled by the heavy reagents. The light, IM and heavy labeled samples were combined and fractionated into eight fractions by strong cation exchange chromatography and analyzed on an Orbitrap Velos Pro. The average (and standard deviation) protein concentration of CSF and plasma, age at sampling and ratio male/female of the five patients are presented in the figure.</p

Protein concentration measurement of along the rostro-caudal gradient.

<p>Protein concentration of the cerebrospinal fluid (CSF)-derived proteins from the seven points along the rostro-caudal gradient (RCG). The CSF was measured in triplicates with Qubit, and error bars of the standard deviation are included. R squared value 0.8931.</p

In-Depth Cerebrospinal Fluid Quantitative Proteome and Deglycoproteome Analysis: Presenting a Comprehensive Picture of Pathways and Processes Affected by Multiple Sclerosis

In the current study, we conducted a quantitative in-depth proteome and deglycoproteome analysis of cerebrospinal fluid (CSF) from relapsing-remitting multiple sclerosis (RRMS) and neurological controls using mass spectrometry and pathway analysis. More than 2000 proteins and 1700 deglycopeptides were quantified, with 484 proteins and 180 deglycopeptides significantly changed between pools of RRMS and pools of controls. Approximately 300 of the significantly changed proteins were assigned to various biological processes including inflammation, extracellular matrix organization, cell adhesion, immune response, and neuron development. Ninety-six significantly changed deglycopeptides mapped to proteins that were not found changed in the global protein study. In addition, four mapped to the proteins oligo-myelin glycoprotein and noelin, which were found oppositely changed in the global study. Both are ligands to the nogo receptor, and the glycosylation of these proteins appears to be affected by RRMS. Our study gives the most extensive overview of the RRMS affected processes observed from the CSF proteome to date, and the list of differential proteins will have great value for selection of biomarker candidates for further verification

A model of parental investment in children's human capital: SKOPE Research Paper No. 15, Spring 2001

Parents' decision to invest in children's human capital is motivated by returns to education and future transfers, which are both affected by perceived gender earnings differentials. To the extent that human capital is accumulated during a time in which the decision lies largely with parents, this model may contribute to understanding differential expenditure on the education of sons and daughters that cause human capital differences prior to entering the labour market