maldi-tof fingerprinting of seminal plasma lipids in the study of human male infertility

ORIGINAL ARTICLE

MALDI-TOF Fingerprinting of Seminal Plasma Lipidsin the Study of Human Male Infertility

Mariana Camargo • Paula Intasqui • Camila Bruna de Lima • Daniela Antunes Montani •

Marcılio Nichi • Eduardo Jorge Pilau • Fabio Cesar Gozzo • Edson Guimaraes Lo Turco •

Ricardo Pimenta Bertolla

Received: 19 March 2014 / Accepted: 31 May 2014

� AOCS 2014

Abstract This study proposed lipid fingerprinting of

human seminal plasma by mass spectrometry as an ana-

lytical method to differentiate biological conditions. For

this purpose, we chose infertile men as a model to study

specific conditions, namely: high and low seminal plasma

lipid peroxidation levels (sub-study 1.1), high and low

sperm nuclear DNA fragmentation (sub-study 1.2), and

intervention status: before and after subinguinal micro-

surgical varicocelectomy (study 2). Study 1 included 133

patients, of which 113 were utilized for sub-study 1.1 and

89 for sub-study 1.2. Study 2 included 17 adult men

submitted to subinguinal varicocelectomy, before and

90 days after varicocelectomy. Lipids were extracted from

seminal plasma and submitted to Matrix-Assisted Laser

Desorption Ionization Quadrupole-Time-of-Flight Mass

Spectrometry in the positive ionization mode. Spectra

were processed using Waters� MassLynx, and Metabo-

Analyst online software was used for statistical analyses.

For sub-studies 1.1 and 1.2, and study 2, univariate ana-

lysis revealed 8, 87 and 34 significant ions, respectively.

Multivariate analysis was performed through PCA and

PLS-DA. PCA generated 56, 32 and 34 components

respectively for each study and these were submitted to

logistic regression. A ROC curve was plotted and the area

under the curve was equal to 97.4, 92.5 and 96.5 %. PLS-

DA generated a list of 19, 24 and 23 VIP ions for sub-

studies 1.1 and 1.2, and study 2, respectively. Therefore,

this study established the lipid profile and comparison of

patterns altered in response to specific biological

conditions.

Keywords DNA fragmentation � Lipid fingerprinting �Lipid peroxidation � Mass spectrometry � Semen �Spermatozoa � Varicocelectomy

Abbreviations

CNPq National Council of Technological and

Scientific Development

DNA Deoxyribonucleic acid

DTT Dithiothreitol

EDTA Ethylenediamine tetraacetic acid

FAPESP Sao Paulo Research Foundation

LPO Lipid peroxidation

m/z Mass-to-charge ratio

MALDI Matrix-assisted laser desorption

ionization

Electronic supplementary material The online version of thisarticle (doi:10.1007/s11745-014-3922-7) contains supplementarymaterial, which is available to authorized users.

M. Camargo � P. Intasqui � C. Bruna de Lima �D. A. Montani � E. G. Lo Turco � R. P. Bertolla (&)

Department of Surgery, Division of Urology, Human

Reproduction Section, Sao Paulo Federal University, R Embau,

231, Sao Paulo 04039-060, SP, Brazil

e-mail: [email protected]

M. Nichi

Department of Animal Reproduction, School of Veterinary

Medicine, University of Sao Paulo, Sao Paulo, Brazil

E. J. Pilau � F. C. Gozzo

Institute of Chemistry, University of Campinas and National

Institute of Science and Technology Bioanalytical, Sao Paulo,

Brazil

E. J. Pilau

Department of Chemistry, Center for Exact Sciences, Maringa

State University, Maringa, PR, Brazil

E. G. Lo Turco � R. P. Bertolla

Hospital Sao Paulo, Sao Paulo, Brazil

123

Lipids

DOI 10.1007/s11745-014-3922-7

http://dx.doi.org/10.1007/s11745-014-3922-7

MALDI-TOF

MS

Matrix-assisted laser desorption

ionization time-of-flight mass

spectrometry

MDA Malondialdehyde

mRNA Messenger ribonucleic acid

MS Mass spectrometry

OS Oxidative stress

PCA Principal component analysis

PLS-DA Partial least square-discriminant analysis

ROC Receiver operating characteristics

SD Standard deviation

SPSS Statistical package of the social sciences

TBA Thiobarbituric acid

TBARS Thiobarbituric acid reactive substances

TBE Tris, boric acid and EDTA

TCA Trichloroacetic acid

UNIFESP Sao Paulo Federal University

VIP Variable importance in projection

Introduction

Medicine has been facing changes in its approach, moving

from reactive, passive and isolated treatments to a more

proactive, preventive and individualized medicine [1]. This

has been stimulated by studies focused on the post-

genomics pathways, which comprise new approaches based

in the ‘‘Omics’’ cascade: transcription of a gene (genome) to

mRNA (transcriptome) and its translation into proteins

(proteome). The molecular pathways performed by prote-

ome include metabolic studies (metabolome) [2], such as

studies of sugars (glycome) and lipids (lipidome).

Many ‘‘Omics’’ approaches are based on the application

of mass spectrometry (MS) and would not have been

possible without the significant progress achieved in this

field during the last decades [3]. Among the most widely

used MS techniques, matrix-assisted laser desorption ion-

ization time-of-flight mass spectrometry (MALDI-TOF

MS) has been performed successfully to estimate the rel-

ative lipid composition of an unknown sample in an easy

and rapid manner [4–7].

MALDI-TOF MS lipid analysis is sensitive, relatively

unaffected by impurities, and allows for convenient sample

preparation, making it an excellent analytical approach for

rapid screening of lipid components in biological matrices

[8]. MALDI-TOF MS appears to be useful for screening

and quantitative determination of lipid classes from lipo-

proteins, liposomes, and lipid extracts of cells and tissues

[9]. Moreover, very small amounts of sample are sufficient

for lipid analysis. Thus, MALDI-TOF MS fulfills many

important criteria, such as high mass accuracy (resolution

over 4,000, errors under 50 ppm) and reproducibility, fast

and simple performance, high sample throughput, and

considerable sensitivity, qualifying this method as a potent

tool for biological sample analysis [10].

Some current limitations of lipidomics studies are: (a) it

is difficult to comprehensively identify the diversity of

lipids present in a biological sample in a single MS run,

and (b) lipid structure is not always easily determined

during tandem MS experiments, impairing lipid identifi-

cation, and (c) although it is possible to determine the

presence of double bonds in tandem MS experiments, they

are not easily located [11]. On the other hand, current lipid

databases allow determination of lipid classes (and sub-

classes) based on exact masses, when using high resolution

equipment, and a threshold of 50 ppm allows determina-

tion of most lipids present in a sample [6, 7, 12]. Fur-

thermore, it has become an important area of research,

particularly in the biomarker discovery field [13], high-

lighting potential biomarkers for diseases, when compared

to healthy controls [14–18]. The biggest challenge

regarding lipid fingerprinting studies is to process and to

analyze these large datasets in order to obtain useful bio-

logical information [19]. Currently, the most successful

approaches are those that incorporate existing biological

knowledge into statistical analyses [20].

Abnormal lipid metabolism was already observed in

numerous human lipid-related diseases, such as obesity,

diabetes, atherosclerosis, preeclampsia, and Alzheimer’s

disease [21–24]. As a method for separation of biological

conditions in reproductive biology, lipid fingerprinting has

been used successfully to demonstrate a lipid profile dif-

ference between spermatozoa of Ruminantia and Feloidae,

and, thus, differentiate these by MALDI-TOF MS analysis

[25]. Moreover, Ferreira et al. used a lipid fingerprinting

approach to differentiate bovine embryos subjected to

different metabolic states [6].

Seminal plasma lipids act as energy substrates for

sperm, and they seem to work on the modulation of semen

anti or pro-oxidative potential [25]. Moreover, seminal

plasma is essential for the many steps that occur after

ejaculation up to fertilization, and alterations to its com-

position may affect male fertility potential [26]. Therefore,

studying the seminal plasma lipid fingerprints could be of

medical relevance regarding infertility. We hypothesized

that seminal plasma lipid fingerprinting can be used as a

method to differentiate biological conditions related to

male infertility. In order to test our hypothesis, we chose

three main male fertility alterations as disease models:

(a) men with low and high semen oxidative stress status

(b) men with low and high sperm DNA fragmentation, and

(c) men before and after surgical correction of varicocele

(varicocelectomy). We expect our results to demonstrate

differential lipid fingerprints as capable of detecting these

Lipids

123

diverse biological conditions using standard univariate

statistical analysis and multivariate analysis.

Methods

Study Design

Institutional Review Board approval was obtained from the

Sao Paulo Federal University (UNIFESP) Research Ethics

Committee, and a signed written informed consent was

obtained from each individual enrolled in the study. Two

separate studies were performed to analyze the lipid fin-

gerprints of seminal plasma: (a) according to lipid perox-

idation (LPO) levels and sperm function by means of

sperm DNA integrity (study 1) and (b) in adult men before

and after varicocelectomy (study 2). Semen samples were

collected by masturbation after 2–5 days of ejaculatory

abstinence and analyzed according to the World Health

Organization criteria [27]. All reagents used in this study

were purchased from Sigma (Sigma-Aldrich, St. Louis,

Missouri, USA), unless otherwise described.

For study 1, a cross-sectional study was employed

including 133 consecutive patients (over 6 months) under

conjugal infertility evaluation. This study included only

patients whose semen presented sperm progressive motility

[32 %, sperm concentration C15 9 106/mL and normal

sperm forms[4 % [27]. After liquefaction, a semen aliquot

was used for semen analysis. Another semen aliquot was used

for evaluation of sperm DNA fragmentation using an alkaline

comet assay and the remaining semen volume was centri-

fuged at 8009g for 30 min to separate the seminal plasma

supernatant and the cell fraction. The seminal plasma

obtained was aliquoted and stored at -20 �C until the eval-

uation of oxidative stress (OS) status and the lipid analysis.

For study 2, a prospective study was carried out

including 17 adult men with varicocele. All patients

included in this study were submitted to bilateral varicocele

repair using subinguinal microsurgical varicocelectomy

according to Marmar et al. [28]. Inclusion criteria were

adult men between 20 and 40 years of age, with surgical

indication, referred to the Division of Urology of UNI-

FESP. Patients presenting fever in the 90 day period prior

to semen analysis, with evidence of urogenital infection,

and patients with a history of cancer or endocrinopathies

(and their treatments) were excluded from the study.

Study 1: Lipid Fingerprinting of Seminal Plasma

According to Lipid Peroxidation Levels and Sperm

Function by Means of Sperm DNA Integrity

Of the 133 selected patients, OS status of seminal plasma

could be evaluated in 113 patients, and sperm DNA

fragmentation was successfully analyzed in 89 patients.

Study 1 was thus subdivided into two sub-studies:

(a) according to LPO levels (low or high, sub-study 1.1),

and (b) according to sperm DNA fragmentation rate (low

or high, sub-study 1.2). The experiments were performed

as described below.

Sub-study 1.1: Lipid Fingerprinting of Seminal Plasma

According to Lipid Peroxidation Levels, Assessed

by the TBARS Method

Sperm peroxidation levels were assessed as previously

described by Ohkawa et al. [29]. The technique is based on

the reaction of two molecules of thiobarbituric acid (TBA)

with malondialdehyde (MDA), a by-product of LPO under

specific conditions, such as low pH and high temperatures.

This reaction results in a red complex (TBARS), which

absorbs light at 532 nm and may be quantified using

spectrophotometry.

After sample thawing, 500 lL of seminal plasma were

diluted in 1 mL of a 10 % (v/v) solution of trichloroacetic

acid (TCA 10 %) and centrifuged at 16,1009g, at 15 �C

for 15 min in order to avoid non-specific reactions to

proteins. Following centrifugation, 500 lL of the super-

natant were added to 500 lL of a 1 % (v/v) solution of

TBA in 0.05 N sodium hydroxide, and incubated for

10 min at 100 �C in a water bath. Samples were quickly

placed in ice after incubation in order to quench the reac-

tion. TBARS levels were measured in a spectrophotometer

at a 532 nm wavelength. A standard curve was previously

prepared using a standard solution of MDA (Acros

Organics, NJ, USA), prepared from Malonaldehyde bis

(diethyl acetate) hydrolyzed by H2SO4, at different con-

centrations for further comparison of results, which are

expressed in ng TBARS/mL. The concentration of TBARS

is determined using the values divided by 1.56 9 105 M-1

cm-1 [30]. A descriptive statistical analysis of the results

obtained was performed to divide the patients into quartiles

and, therefore, form the experimental groups: low (bottom

25 % of subjects) and high (top 25 % of subjects) LPO

levels. A Student’s t test for unpaired samples was applied

to assess whether the groups were indeed statistically

constituted (p \ 0.05). The seminal plasma samples from

these patients were then used for lipid analysis, as descri-

bed below.

Sub-study 1.2: Lipid Fingerprinting of Seminal Plasma

According to the Sperm Nuclear DNA Fragmentation Rate,

Assessed by Comet Assay

Sperm nuclear DNA integrity was evaluated by an alkaline

Comet assay, as previously established by our group [31,

32]. Slides (Precision Glass Line, China) were precoated

Lipids

123

with 1 mL 1 % normal-melting-point agarose in TBE

(0.089 M Tris, 0.089 M borate and 0.002 M EDTA)

overnight. To each slide, 100 lL of an aliquot of fresh

semen diluted in 0.75 % low-melting-point agarose in TBE

to a final concentration of 1 9 106/mL were added. This

was covered with a coverslip (Precision Glass Line, China)

and kept for 10 min at 4 �C to solidify. Coverslips were

then removed and 300 lL of 0.75 % low-melting-point

agarose in TBE were added. The slides were covered once

again with a coverslip and kept at 4 �C. After 10 min, the

coverslips were removed and the slides were immersed in

cold lysis solution (100 mM Na2-EDTA, 10 mM Tris,

2.5 M NaCl, pH 11.0, 4 mM DTT, 2 % Triton X-100) for

2 h (the solution was reapplied after 1 h). The slides were

washed with Milli-Q water (2 9 5 min) and immersed

immediately afterwards in alkaline electrophoresis solution

(300 mM NaOH, 1 mM Na2-EDTA, pH[13.0, T = 4 �C)

for 20 min. Electrophoresis was then performed in the

same solution for 20 min at 1.5 V/cm, \270 mA. The

slides were washed with TBE (2 9 5 min) and fixed in

ethanol (3 9 5 min). For Comet visualization, the slides

were stained with ethidium bromide (20 lg/mL) for

15 min and washed with TBE (3 9 15 min) to remove

background staining, and were observed under epifluores-

cent illumination at 4009 magnification (Olympus BX-51,

Olympus, Japan). Damaged DNA migrates during elec-

trophoresis from the nucleus towards the anode, forming

the shape of a comet with a head (cell nucleus with intact

DNA) and a tail (fragmented DNA).

A total of 60 sperm were analyzed by the Comet assay

described above and the results were subsequently pro-

cessed using the Komet 6.0.1 software (Andor Technology,

Ulster, UK). The software’s manufacturer recommends the

analysis of 50 cells for quantification of DNA damage [33].

The Olive Tail Moment variable, calculated by the soft-

ware for each sample, was used for descriptive statistical

analysis to form the experimental groups: low (bottom

25 % of subjects) and high (top 25 % of subjects) sperm

DNA fragmentation. This variable is defined as the product

of total DNA percentage in comet tail and the distance

between the centers of mass in comet head and tail and is

considered one of the main parameters to evaluate DNA

migration [33]. A Student’s t test for unpaired samples was

applied to assess whether the groups were indeed statisti-

cally constituted (p \ 0.05). The seminal plasma samples

selected to compose the groups were then used for lipid

analysis, as described below.

Study 2: Lipid Fingerprinting of Seminal Plasma

of Men Before and After Varicocelectomy

For study 2, initially, varicocele was diagnosed by clinical

analysis through scrotal palpation in a temperature-

controlled room ([23 �C) with adequate illumination, and

varicocele was graded according to Dubin and Amelar

[34]. The criteria used were:

• Varicocele grade I: dilatation of spermatic cord palpa-

ble only with a Valsalva maneuver

• Varicocele grade II: dilatation of spermatic cord easily

palpable, with the patient standing, demonstrating

marked venous dilatation during a Valsalva maneuver

• Varicocele grade III: massive dilatation of spermatic

cord easily visualized with patient standing and inten-

sified ectasia during a Valsalva maneuver

Following varicocele diagnosis, patients provided one

semen sample as described in study 1, and were submitted

to varicocelectomy using a subinguinal microsurgical

approach [28]. After 90 days of recovery (one full sper-

matogenic cycle), patients provided another semen sample,

as previously described.

Lipid Analysis of Studies 1 and 2

Lipid Extraction

Initially, lipids were extracted using the protocol proposed

by Bligh and Dyer [35] to separate water-soluble constit-

uents (proteins, sugars, and salts) from the organic phase.

Briefly, 50 lL of seminal plasma was placed in a micro-

centrifuge tube and 50 lL of water, 125 lL of chloroform

and 250 lL of methanol were added. The homogenous

mixture was submitted to manual agitation. Then, polar and

apolar phases were separated by addition of 100 lL of

water and 125 lL of chloroform. This mixture was sub-

mitted to centrifugation at 5009g for 5 min. The lower

phase containing the lipids was transferred to another tube,

which was maintained open at room temperature until

evaporation of the solvent was complete.

Mass Spectrometry (MALDI-TOF MS)

The air-dried organic phase of the samples was diluted in

10 lL of chloroform and 1 lL of this dilution was spotted

onto a stainless steel MALDI target, dried through evapo-

ration, and overlaid with 1 lL of a 0.15 M 2,5-dihy-

droxybenzoic acid (2,5-dihydroxybenzoic acid; Sigma,

USA) solution in methanol as an organic matrix. Samples

were run in triplicate.

MS spectra were obtained using a Synapt HDMS

(Waters, Manchester, UK) MALDI-TOF mass spectrome-

ter equipped with a 200 Hz solid state laser at 355 nm. An

accelerating voltage of 20 kV (sample plate) was used, and

the operating condition used was 250 a.u. (laser energy).

The spectra were recorded in reflectron mode within a mass

range of m/z 400–1,000 in the positive ionization mode,

Lipids

123

where the sample was ionized by diverse shots from the

MALDI laser at random spots for 45 s. Calibration was

performed using PEG 600/1000/2000 sodium adducts

between m/z 300 and 2,000.

Spectra Analysis

For processing and analysis of the raw mass spectra data, the

software MassLynx 4.1 was used. Each composed chro-

matogram (produced from the total ion current for the

MALDI analysis) was summed to produce their mass spec-

tra, and the triplicates were analyzed and processed for

background subtraction, smoothing, and peak centroiding,

considering a resolution of 4,000 and a lock mass correction

using a ‘‘constitutive’’ peak (present in all samples at a high

amount) of [M ? H]? 725.6789 (±0.300). We chose to use

m/z 725.6789 because it is the same peak, present at a high

constant amount in all samples. Because we did not want to

spike in a lipid standard in order to avoid further suppression,

we chose this as a cross-sample normalization method. The

±0.3 was chosen only as a wide enough mass region in order

to avoid mass shifts (in cross-sample standardization) to lead

to detection of the same peaks as different peaks. Deisot-

oping was then achieved considering the whole m/z range

(400–1,000) and a maximum charge state of ?1 (Supple-

mentary Fig. 1). To avoid background noise, we used a TOF

Mass Measure function in the MassLynx software that sub-

tracted background at 40 % below a linear curve.

Each peak list was copied and pasted into a spreadsheet

software (Microsoft Excel 2007, Microsoft Corporation,

WA, USA), with a total of 600 data points for each list. All

data were then normalized to the constitutive peak and the

average value of the triplicate was generated.

Statistical Analyses

Statistical analyses of clinical data were performed separately

for study 1 and 2 using SPSS (PASW) 18.0 software. For all

analyses, an a of 5 % was adopted. For semen analysis of

study 1, initially, distribution normality was assessed using a

Kolmogorov–Smirnov Test. Normally distributed variables

were compared using a Student’s t test for unpaired samples

and non-normally distributed variables were compared using

a Mann–Whitney test. For study 2, a Kolmogorov–Smirnov

test was performed to test the data for normality of distribu-

tion. Normally distributed variables were then compared

using a Student’s t test for paired samples, and non-normally

distributed variables were compared using the non-parametric

Wilcoxon test for paired samples.

For the analysis of spectral data generated by MS

methods, the online software MetaboAnalyst 2.0 [36, 37]

was utilized. The software focuses on exploratory statisti-

cal analysis and functional interpretation for quantitative

metabolomics studies. Furthermore, it allows for different

analyses for different data processing such as univariate

and multivariate analysis, among others [36, 37].

Initially a peak intensity table was constructed with sam-

ples in rows and features in columns. Each initial data matrix

used for our studies contains 600 ions (m/z) and all values are

non-negative numbers. For statistical homogenization, values

of ‘‘0’’ (zero) were replaced by a value of half the minimum

value found within the data. Data were normalized by log

transformation to achieve Gaussian distribution.

For univariate spectral data analysis, Student’s t test for

unpaired and paired samples were used respectively for

studies 1 and 2. For unpaired Student’s t test, fold changes are

reported as results, in which values over 1 represent hyper-

representation in the study group (high semen oxidative

stress or high sperm DNA fragmentation), and values under

one represent hypo-representation in the study group, when

compared to controls (low semen oxidative stress or low

sperm DNA fragmentation). For paired analysis, results are

presented as increasing or decreasing in post-varicocelec-

tomy when compared with pre-varicocelectomy samples.

For multivariate spectral data analysis, a principal

component analysis (PCA) was utilized as an unsupervised

method and a partial least squares-discriminant analysis

(PLS-DA) as a supervised method. PCA summarized data

by generating components, and scores meaning the

weighted average of original variables. Then, considering

these scores, we utilized SPSS 18.0 to perform a PCA

regression using the ‘‘Forward Stepwise’’ method, which

includes significant scores on a stepwise manner until no

significant alteration to the model is observed, maximizing

positive and negative predictive values. A receiver oper-

ating characteristic (ROC) curve was then plotted with the

generated predicted values. For PLS-DA, we chose to

report the Variable Importance in Projection (VIP) scores.

Statistically important ions were submitted to database

identification using the software SimLipid 3.0, allowing for

H?, Na? or K? adducts, and searching the Lipid Maps

database (www.lipidmaps.org). Only lipids from the glyc-

erophospholipids, sphingolipids, fatty acyls, glycerolipids,

prenol lipids and sterol lipids categories were searched.

The mass error was calculated for all identified ions, with a

maximum threshold of 50 parts per million (ppm).

Results

Clinical Data of Experimental Groups

Study 1

Clinical information regarding the 133 patients included in

study 1 and each group analyzed are presented in Table 1.

Lipids

123

http://www.lipidmaps.org

For sub-study 1.1, 113 patients were included and, of these,

27 patients were selected to compose the low and 29 to

compose the high lipid peroxidation levels groups. Only

Thiobarbituric acid-reactive substances (TBARS) levels

were statistically different between the groups. For sub-

study 1.2, 89 patients were included, being 17 selected to

compose the low and 15 to compose the high sperm DNA

fragmentation groups. Only the Olive Tail Moment vari-

able was statistically different between the groups

(Table 1).

Study 2

Clinical data regarding semen analysis in all the patients

are presented in Table 1. Only motility ‘‘a’’ was higher in

the post-varicocelectomy samples (Table 1). Mean; stan-

dard deviation age was 30.6; 4.7 years. Of the 17 men

included in this study, 9 presented bilateral varicocele

grade II left, grade I right, 2 presented bilateral varicocele

grade II left, grade II right, 2 presented bilateral varico-

cele grade II on both sides, 1 presented bilateral varico-

cele grade III on both sides, 1 presented varicocele grade

III left, grade I right, 1 presented unilateral varicocele

grade III left and 1 presented unilateral varicocele grade

II left.

Univariate Analysis

Univariate analysis revealed 8, 87 and 10 significant ions

respectively for sub-studies 1.1 and 1.2, and study 2

(Supplementary Table 1). For study 1.1, 8 ions were hypo-

represented in the study group, of which 7 ions were

identified, and 1 was not. For study 1.2 of 87 ions, 29 were

hypo-represented in the study group (high sperm DNA

fragmentation), of which 24 were identified and 5 were not,

and 58 hyper-represented in study group, of which 43 were

identified and 15 were not. In study 2, from 10 ions hyper-

represented, 3 were not identified in Lipid Maps database,

all of them from post group. There were 4 ions identified in

pre group and 3 ions identified in Post group.

Multivariate Analysis

Data were submitted to PCA including all the variables.

PCA generated 56, 32, and 34 components for sub-studies

1.1 and 1.2 and for study 2, respectively. For sub-study 1.1

the following principal components were used to compose

the mathematical model: PC4, PC7, PC14, PC22, PC23,

PC34, PC47, PC53 and PC56; for sub-study 1.2 the com-

ponents were PC2, PC6 and PC22; and finally for study 2,

we utilized PC4, PC6 PC9 and PC19.

Table 1 Clinical data regarding the studied population in study 1 (groups for sub-study 1.1 [low 9 high lipid peroxidation levels] and sub-study

1.2 [low 9 high sperm DNA fragmentation]) and study 2

Study 1 Study 2

Studied

population

(n = 133)

Sub study 1.1 oxidative stress

subset sample (n = 113)

Sub study 1.2 DNA

fragmentation subset

sample (n = 89)

Varicocelectomy subset

Low

(n = 27)

High

(n = 29)

P (t test) Low

(n = 17)

High

(n = 15)

P (t test) Pre

(n = 17)

Post

(n = 17)

P (t test)

Age (years) 34.5; 7.14 36.3; 6.71 34.7; 6.89 0.392 33.5; 4.93 32.5; 9.09 0.679 30.6; 4.7 – –

Volume (mL) 3.5; 1.38 4.1; 1.64 3.5; 1.17 0.119 3.6; 1.51 4.1; 1.33 0.299 3.3; 1.31 3.8; 1.79 0.051

Motility

(a ? b ? c, %)

57.4; 9.58 58.7; 9.22 58.0; 9.98 0.775 57.9; 9.28 61.2; 7.31 0.283 44.3; 24.0 52.7; 17.7 0.06

Concentration

(9106/mL)

72.5; 52.49 68.6; 41.91 93.0; 71.99 0.130 80.9; 82.8 67.1; 44.4 0.568 5.1; 30.9 13.9; 24.2 0.68

Morphology

(% normal)

7.7; 3.18 8.4; 2.99 7.3; 3.06 0.211 8.5; 4.42 7.8; 3.43 0.638 4.2; 2.99 4.4; 2.20 0.76

Round Cells

(9106/mL)

1.9; 2.07 1.8; 2.26 1.7; 1.12 0.810 1.8; 2.34 1.8; 2.03 0.918 2.2; 2.50 2.0; 2.34 0.81

Neutrophils

(9106/mL)

0.4; 1.33 0.6; 1.93 0.4; 0.86 0.690 0.1; 0.20 0.4; 0.77 0.639 0.2; 1.1 0.1; 0.5 0.46

Olive tail moment

(a.u.)

– – – – 0.2; 0.05 1.1; 0.18 \0.001* – – –

TBARS (ng/mL) – 166.2; 35.5 356.6; 43.08 \0.0001* – – – – – –

Values are means; SD

Lipids

123

f xð Þ ¼ 1

1þ e�z

After logistic regression, a ROC curve was plotted and

the area under the curve was 97.4 % in study 1.1, 92.5 % in

study 1.2 and 96.5 % in study 2 (Fig. 1).

PLS-DA demonstrated the variations explained by each

model and the complete separation between the groups of

each study using VIP ions the most important ions

responsible for the variance among the groups. Only VIP

ions with values greater than 2 were considered and pre-

sented (Fig. 2; Table 2).

Concerning the ions in Table 3, in study 1.1, from 19

VIP ions, 13 ions were identified and, 6 were not. In group

1.2 from 24 VIP ions, 20 ions were identified and 4 were

not. In group 2, from 23 VIP ions, there were 22 ions

identified and only one was not.

Discussion

Lipid research and profiling is extremely challenging due

to many reasons: (a) lipid metabolism is highly complex

and plays roles at different levels in the organism, fre-

quently influenced by external factors (b) many lipid

metabolites are structurally complex and have not yet been

well studied or classified (c) analytical outcomes are highly

Fig. 1 Receiver operating characteristics (ROC) curves obtained

from principal component analysis––logistic regression models of:

a sub-study 1 (low 9 high lipid peroxidation levels); b sub-study 2

(low 9 high sperm DNA fragmentation); c study 2 (before and

90 days after varicocelectomy). The areas under the curve were

97.4 % in sub-study 1.1, 92.5 % in sub-study 1.2 and 96.5 % in study

2 (p \ 0.001)

Lipids

123

dependent on the lipid extraction protocols, solvents uti-

lized and laboratory techniques [38].

With that in mind, our approach proposed a lipidomic

screening strategy, which provides us a complete data set

from all spectra generated from extracted lipids. More

specifically, in the male infertility context, our goal was

to verify if our analytical method could be used to dif-

ferentiate (a) biological conditions, namely: high and

low LPO levels and high and low sperm nuclear DNA

fragmentation, and (b) intervention status: before and

after subinguinal microsurgical varicocelectomy. It is

important to note that our objective was to verify the

lipid profile of seminal plasma, and establish the com-

parison of patterns that change in response to a specific

biological condition.

Although a variety of analytical MS approaches could

have been used in this study, we chose to utilize the

MALDI-TOF MS, because this technique offers important

advantages: (a) minimal sample preparation (b) analysis

speed and simplicity (c) direct solubility of matrices and

lipid samples in organic solvents and (d) high sensitivity,

resolution and reproducibility of measurements regarding a

potential clinical application of lipid fingerprinting, the

analysis speed is essential and must be considered [39].

Through direct analysis of lipid extracts by high reso-

lution MS scans, we were able to demonstrate that our

method is capable of reliably distinguishing biological

groups. Furthermore, lipid quantification based on relativ-

ized peaks intensities is not only feasible, but presents itself

as a rapid and comprehensive lipidomic method that can

Fig. 2 Partial least square-discriminant analysis plotting the individ-

uals using the first three generated components in: a sub-study 1 (low

[0] 9 high [1] lipid peroxidation levels); b sub-study 2 (low [0] 9

high [1] sperm DNA fragmentation); c study 2 (before [0] and

90 days after [1] varicocelectomy). C1 Component 1, C2 component

2 and C3 component

Lipids

123

Table 2 The most representative ions in PLS-DA regarding sub-studies 1.1 and 1.2 and study 2. Only ions with variable importance in

projection (VIP) scores greater than 2, indicating likely importance to the biological condition, were included

Peaks (m/z) Lipid category Class Mass error (ppm)

Sub-study 1.1

405.1870 Glycerophospholipids Glycerophosphates 36.54

453.2279 – – –

496.3396 Glycerophospholipids Glycerophosphoethanolamines 0.33

502.4583 – – –

528.2503 – – –

529.2312 Sterol lipids Steroid conjugates 19.18

532.2819 Glycerophospholipids Glycerophosphoserines 31.49

556.2771 Glycerophospholipids Glycerophosphocholines 6.20


598.4899 – – –


654.5536 – – –

693.3141 Sterol lipids Sterols 48.99



823.5867 Glycerophospholipids Glycerophosphoinositols 20.89


924.4887 – – –


Sub-study 1.2

428.2010 – – –

431.3040 Fatty acyls Fatty acids and conjugates 26.44

451.2369 Fatty acyls Fatty acids and conjugates 40.20


587.4228 Prenol lipids Isoprenoids 39.27

598.5033 – – –

604.4821 Sphingolipids Ceramides 41.36

614.4748 – – –



685.5219 Sphingolipids Phosphosphingolipids 6.04




720.4268 – – –

753.5012 Glycerophospholipids Glycerophosphoglycerols 4.56







853.6360 Glycerolipids Triradylglycerols 4.44

890.6161 Sphingolipids Acidic Glycosphingolipids 25.22

Study 2

403.2331 Fatty acyls Eicosanoids 1.13


Lipids

123

lead us to find molecules and identify them as potential

biomarkers for medical condition, ultimately aiding in

diagnostics [39].

All the analytical processes followed a workflow that

can be summarized in sample fractioning, MS detection,

and production of lipid data with relative concentrations.

Peak lists were acquired from all analyzed samples,

including technical and biological replicates. These lists

were aligned by their m/z, deisotoped and submitted to

rigorous statistical data analysis [38]. Such MS-based lipid

fingerprinting technique provides large amounts of data,

which makes bioinformatics an indispensable tool [3]. In

this context, both univariate and multivariate statistical

analysis were utilized to reduce data complexity [11].

Univariate analysis is responsible for providing a pre-

liminary overview about important characteristics that can

potentially be discriminant under the conditions of the

studies. Thus, we produced Volcano plots, which combine

fold-change and t test information. Our results revealed 8,

87 and 10 significant ions (both using statistical analysis

and Fold-change thresholds), respectively for sub-studies

1.1 and 1.2 and study 2. Because quantitation of ions using

MALDI as an ionization source may be subject to criticism

due to (a) ionization efficiency strongly depends on the

MALDI matrix used in the experiments [40] and (b) diffi-

culties in the analysis of some lipids species due to ion

suppression by other ones [41], we used a 45-s ionization

time and technical triplicate runs for each biological rep-

licate. This allowed us to propose these ions as putative

markers of biological condition.

It is interesting to note that 1 ion was hyper-represented

in samples with high sperm DNA fragmentation and in

samples before varicocelectomy (Table 3), a lipid belong-

ing to the Sphingoid bases (SP) class. This indicates it

could be a biomarker of sperm homeostasis, as the presence

of varicocele (biological effect) demonstrates a same

marker as of sperm DNA fragmentation (cellular effect).

Multivariate analyses, such as PCA and PLS-DA,

evaluate the effect of all variables on the studied response,

with less regard to their quantitative value [42]. Such

analyses are essential for reduction of data dimensionality,

clustering tendency and multicollinearity, and for detection

of relevant differences between two biological conditions

within all the variables observed [8, 19]. Therefore, mul-

tivariate analysis is almost always used to assist the iden-

tification of novel molecular species as potential

biomarkers, which can reflect an established condition, but

it can also be used as a reference to target discoveries. It is

Table 2 continued

Peaks (m/z) Lipid category Class Mass error (ppm)



435.2062 Sterol lipids Steroids 26.52

436.2077 Sphingolipids Sphingoid bases 35.15

447.3256 Sterol lipids Secosteroids 2.90







561.2456 Fatty acyls Fatty acyls glycosides 1.76

627.3848 – – –



753.5981 Glycerolipids Triradylglycerols 3.73





975.4928 Glycerophospholipids Glycerophosphoinositol monophosphates 9.90

Lipids

123

important to note that the discovery of potential biomarkers

is only the first step on the slow process of validation for

clinical or diagnostic purposes [38].

In our study, PCA was used to provide a simplified

representation of the obtained data, and the rotated PCA

components were used to achieve the best data separation

[8]. In order to better explain the separation between the

groups of each study, a ROC curve was generated with the

significant components of PCA, which showed an area

under the curve of 97.4 % in sub-study 1.1, 92.5 % in sub-

study 1.2 and 96.5 % in study 2, suggesting that this test

have a high specificity and sensibility to truly separate the

groups. Additionally, we undertook PLS-DA, as it is a

supervised method that uses multivariate regression tech-

niques to extract variables and, through a linear combina-

tion, classify these variables to build the best model. In our

study, PLS-DA showed 40.6, 44.5 and 50.6 % of separa-

tion of the groups considering the 3 first components for

sub-studies 1.1 and 1.2 and study 2 respectively (Fig. 2).

Another important measure of PLS-DA is the VIP, which is

a weighted sum of squares of the PLS loadings. Only VIP

values greater than two were considered and we found 19,

Table 3 Common ions

revealed both by univariate and

multivariate analysis,

considered as possible

biomarkers for the respective

sub-studies

Peaks

(m/z)

Lipid category Class Error mass

(ppm)

Fold-change

Study 1.1: Possible markers of lipid peroxidation High/low

532.2819 Glycerophospholipids Glycerophosphoserines 31.49 0.34

556.2771 Glycerophospholipids Glycerophosphocholines 6.20 0.38

823.5867 Glycerophospholipids Glycerophosphoinositols 20.89 0.43

862.5090 Glycerophospholipids Glycerophosphoethanolamines 31.62 0.40

Study 1.2: Possible markers of DNA fragmentation High/low

428.2010 – – – 11.11

431.3040 Fatty acyls Fatty acids and conjugates 26.44 7.14

451.2369 Fatty acyls Fatty acids and conjugates 40.20 0.18

509.2649 Glycerophospholipids Glycerophosphates 0.66 2.44

587.4228 Prenol lipids Isoprenoids 39.27 0.33

598.5033 – – – 6.25

604.4821 Sphingolipids Ceramides 41.36 2.38

614.4748 – – – 6.25

627.3742 Sterol lipids Sterols 0.50 0.48




699.4283 Prenol lipids Isoprenoids 6.62 2.70

720.4268 – – – 3.22

753.5012 Glycerophospholipids Glycerophosphoglycerols 4.56 0.22






853.6360 Glycerolipids Triradylglycerols 4.44 4.76

890.6161 Sphingolipids Acidic glycosphingolipids 25.21 8.33

Study 2: Possible markers for biological condition (before and after varicocelectomy) Counts up-

down

403.2331 Fatty acyls Eicosanoids 1.13 04–09

424.2350 Glycerophospholipids Glycerophosphoethanolamines 25.61 09–01

433.2484 Fatty acyls Eicosanoids 18.94 02–10

436.2077 Sphingolipids Sphingoid bases 35.15 11–01

469.2604 Glycerophospholipids Glycerophosphoglycerols 9.17 09–00

760.4914 Glycerophospholipids Glycerophosphoethanolamines 0.29 10–01

Lipids

123

24 and 23 representative ions in sub-studies 1.1 and 1.2 and

study 2, respectively (Table 2).

Moreover, 2 ions were VIP in both studies 1.1 and 1.2,

belonging to the glycerophosphates and glycerophos-

phoserines classes. While in univariate analysis no differ-

ence was observed in study 1.1, these ions were hyper-

represented in the high DNA fragmentation semen samples.

Glycerophosphoserines (GPS) are located normally to

the inner side of the membrane bilayer. Apoptosis causes

membrane asymmetry and translocation of GPS onto the

outer side. For this reason, the detection of GPS exposure

has been well established as an early apoptotic marker [43,

44]. At present, little is known about the circumstances

responsible for this fact in spermatozoa, but some authors

suggest that PS translocation might be a consequence that

occurs in the sperm membrane due to capacitation and

acrosomal reaction processes, resulting from cellular age-

ing or still due to a pathological process of spermatozoa

elimination [45]. Besides, mitochondrial exposure to ROS

results in the release of apoptosis inducing factor (AIF),

which directly interacts with the DNA and leads to DNA

fragmentation [46, 47]. Although we were not able to find

any report in literature, we suppose that PS could be

released in seminal plasma and could be a possible marker

for the consequences of OS in spermatozoa.

The glycerophosphates class (GP), has been found to be

released after the activation of phospholipase D during

inflammatory conditions, known as a pro oxidative state

[48]. The consequence of GP release is the production of

superoxide anion by neutrophils, confirming such an

involvement with OS [49].

Some study limitations should be highlighted. MALDI-

TOF MS analysis of samples without previous chromato-

graphic separation can promote the major detection of

some lipid classes, such as glycerophosphocholines, and

the suppression of other classes. This is important since

glycerophosphocholines are a very abundant class of lipids.

It also complicates the analysis of other lipid classes, such

as free fatty acids and cholesterol [3, 19]. Moreover, the

Bligh and Dyer method [35] is useful for glycerophos-

pholipids extraction, but more polar compounds are

incompletely extracted. Additionally, some lipids precipi-

tate with the proteins and are not analyzed within the

organic phase [3]. Another limitation is related to moni-

toring the oxidation of lipids. Although the TBARS method

has been used extensively for this purpose due to the low

cost and convenience, it offers a limited sensitivity because

it only provides an indirect measure of lipid peroxidation

[50]. Observation of oxide, peroxide, and hydroxyl radicals

in lipids could constitute a possible advantage of using a

mass-spectrometry-based approach in future studies.

In summary, our statistical analyses demonstrated that

the MS-based lipid fingerprinting method proposed in this

study is capable of characterizing and separating different

male fertility conditions. Some ions presented differences

in both univariate and multivariate analyses, and we con-

sider that this indicates a better possible candidate bio-

marker. We suggest lipids 509.2649, 629.4153, 687.4100,

697.4607, 755.5575, 825.5352 and 829.5751 (glycero-

phosphates), 532.2819, 756.533 and 782.4875 (glycero-

phosphoserines), 431.304 and 451.2369 (fatty acids),

587.4228 and 699.4283 (isoprenoids), 556.2771 (glycer-

ophosphocholines), 823.5867 (glycerophosphoinositols),

424.2350, 760.4914 and 862.509 (glyceropho-

sphoethanolamines), 604.4821 (ceramides), 469.2604 and

753.5012 (glycerophosphoglycerols), 853.636 (triradyl-

glycerol), 890.6161 (acid glycosphingolipids), 627.3742

(sterols), 436.2077 (sphingoid bases) and, 403.2331 and

433.2484 (eicosanoids). The lipids 428.201, 598.5033,

614.4748, 720.4268 could not be identified on the available

platforms (Table 3).

In our study, lipid fingerprinting was useful to separate

(a) biological condition, which may assist in the diagnosis

of male infertility, and (b) intervention status, which may

assist in the follow-up of patients undergoing intervention

for treatment of male infertility.

Acknowledgments The authors wish to acknowledge National

Council of Technological and Scientific Development (CNPq) for

providing funding for the research (Process 472941/2012-7) and a

scholarship to Ms. Camargo, and Ms. Bruna de Lima. We also thank

the Sao Paulo Research Foundation (FAPESP) for providing a

scholarship to Ms. Intasqui. The fund providers had no role in the

study design, data collection and analysis, decision to publish, or

preparation of the manuscript.

Conflict of interest The authors have no conflict of interest to

disclose.

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maldi-tof fingerprinting of seminal plasma lipids in the study of human male infertility

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