Heat-map visualization of gas chromatography-mass ...Heat-Map Visualization of Gas Chromatography-Mass Spectrometry Based Quantitative Signatures on Steroid Metabolism Ju-Yeon Moon,

Heat-Map Visualization of GasChromatography-Mass Spectrometry BasedQuantitative Signatures on Steroid Metabolism

Ju-Yeon Moon,a,b Hyun-Jin Jung,a,b Myeong Hee Moon,b

Bong Chul Chung,a and Man Ho Choiaa Life Sciences Division, Korea Institute of Science and Technology, Seoul, Koreab Department of Chemistry, Yonsei University, Seoul, Korea

Abnormalities in steroid hormones are responsible for the development and prevention ofendocrine diseases. Due to their biochemical roles in endocrine system, the quantitativeevaluation of steroid hormones is needed to elucidate altered expression of steroids. Gaschromatographic-mass spectrometric (GC-MS) profiling of 70 urinary steroids, containing 22androgens, 18 estrogens, 15 corticoids, 13 progestins, and 2 sterols, were validated and itsquantitative data were visualized using hierarchically clustered heat maps to allow “steroidsignatures”. The devised method provided a good linearity (r2 � 0.994) with the exception ofcholesterol (r2 � 0.983). Precisions (% CV) and accuracies (% bias) ranged from 0.9% to 11.2%and from 92% to 119%, respectively, for most steroids tested. To evaluate metabolic changes,this method was applied to urine samples obtained from 59 patients with benign prostatichyperplasia (BPH) versus 41 healthy male subjects. Altered concentrations of urinary steroidsfound and heat maps produced during this 70-compound study showed also differencesbetween the ratios of steroid precursors and their metabolites (representing enzyme activity).Heat maps showed that oxidoreductases clustered (5�-reductase, 3�-HSD, 3�-HSD, and17�-HSD, except for 20�-HSD). These results support that data transformation is valid, since5�-reductase is a marker of BPH and 17�-HSD is positively expressed in prostate cells.Multitargeted profiling analysis of steroids generated quantitative results that help to explaincorrelations between enzyme activities. The data transformation and visualization describedmay to be found in the integration with the mining biomarkers of hormone-dependentdiseases. (J Am Soc Mass Spectrom 2009, 20, 1626–1637) © 2009 American Society for MassSpectrometry

Mass spectrometry based metabolite profilingreveals the metabolic states of biological sys-tems and provides comprehensive insights by

allowing comparisons between many metabolites si-multaneously present in cells, tissues, or organisms [1,2]. This technique promotes the establishment of rela-tionships between phenotypes and metabolisms byproviding descriptions of the distributions of metabo-lites and their biological functions. In fact, quantitativeanalyses of sets of metabolites in biochemical pathwayshave been used for physiological monitoring, toxicolog-ical evaluations, and clinical diagnosis [3–6].Many naturally occurring steroids with similar

chemical structures could yield biological information[7]. Endogenous steroids are divided into five groups,namely, androgens, estrogens, corticoids, progestins,and sterols, which are generally synthesized from cho-lesterol in the adrenal cortex, ovaries, and testes(Scheme 1). In biosynthetic pathways of steroid hor-

mone, two major types of enzymes are involved, cyto-chrome P450 and steroid oxidoreductase. Abnormalities ofthese enzymes often lead to hormonal imbalances thathave serious consequences, and which are responsiblefor the development of hormone-dependent diseases(see Supplementary Table 1, which can be found in theelectronic version of this article). For example, concen-trations of corticoids and their metabolic ratios providediagnostic evidence of apparent mineralocorticoid ex-cesses caused by 11�-HSD deficiency [8] and congenitaladrenal hyperplasia, which are caused by deficienciesof enzymes like hydroxylase (at C-11, 17, and 21) or3�-HSD [9]. In addition, enhanced androgen activitygenerated by the conversion of testosterone to dihy-drotestosterone (DHT) by 5�-reductase was utilized toallow early therapeutic intervention in young men [10].Enzyme activity profiles can be used to describe the

functional diversities of biological systems, which aredriven by genetic diversity. Although enzyme activitieshave been monitored by following reactions betweentargeted enzymes and substrate molecules, over-estimations by radioimmunoassay (RIA) and enzymeimmunoassay (EIA), because of cross reacting antibod-

Address reprint requests to Dr. M. H. Choi, Life Sciences Division, KoreaInstitute of Science and Technology, 39-1 Hawolkok-dong, Seongbuk-ku,Seoul 136-791, Korea. E-mail: [email protected]

Published online May 5, 2009© 2009 American Society for Mass Spectrometry. Published by Elsevier Inc. Received February 7, 20091044-0305/09/$32.00 Revised April 25, 2009doi:10.1016/j.jasms.2009.04.020 Accepted April 28, 2009

ies, limit the applicability of these assays, and further-more, only single enzymes can be estimated at a time[11–14]. In contrast to conventional enzyme assays, gaschromatography-mass spectrometry (GC-MS)-basedtechniques have better quantitative reproducibility [15,16]. For these reasons, GC-MS profiling has been widelyused for steroid analysis [17–20], and offers the basis fortechniques that can be applied to large-scale clinicalstudies [21].Clinical significances are generally expressed in ta-

bles or bar graphs that show changes in analytes acrossgroups of interest. For studies involving few com-pounds, these visualizations are enough to differentiateclasses showing metabolic differences. However, quan-titative datasets of multiple compounds are much moredifficult to represent visually. Statistical clustering of-fers one such approach, and has been utilized to sup-port genomic and proteomic studies [22–24]. In a simi-lar fashion, quantitative results obtained by metaboliteprofiling can be directly compared between samplesand utilized as metabolic biomarkers. In recent, MS-based quantitative data generated by hierarchical clus-tering analysis (HCA) has been subjected to patternanalysis for metabolite profiling [2, 25]. The concept of“metabolite signature” is a result of this process, andsuch signatures are useful for measuring and visualiz-ing the relative analyte concentrations. Although many

groups have used GC-MS based steroid analysis formining biomarkers, technical improvements that moreeffectively allow the visualization of steroid metabolitesare required [26]. However, no studies have reportedMS-based steroid signatures generated by HCA to date.Here, we introduce the GC-MS quantitative profiling

of 70 urinary steroids, including 22 androgens, 18estrogens, 15 corticoids, 13 progestins, and 2 sterols,generated by HCA to evaluate metabolic changes andenzyme reactions in steroid analysis. The aim of thisstudy was to validate a GC-MS profiling method and amethod that allows the quantitative visualization ofmultiple urinary steroids. To visualize quantitative re-sults, a microarray map (a type of heat map) wasdesigned to present the urine sample results of 59patients with benign prostatic hyperplasia (BPH) and 41healthy male subjects; BPH was chosen because steroidmetabolism is known to play a role in the progress ofprostate diseases [27–29]. This study focused on illus-trating the usefulness of steroid signatures for explain-ing both the concentrations of individual steroids andthe activities of enzymes correlated global steroidogen-esis in BPH, and suggest that enzyme activity profilingmay be a useful diagnostic tool and provide a means ofidentifying mining biomarkers in hormone-dependentdiseases.

Progestins, Sterols Androgens

EstrogensCorticoids

HO

O

H

OH

HO

O

HOH

DHEA

ααααββββββββ-diol

An

5α-reductase 17β-HSD3α-HSD

3β-HSD

17β-HSD

aromatase

5αααα−−−−dione

O

O

17β-HSD

3α-HSD

O

HOH

Epi-An

3β-HSD5α-reductase

O

Epi-T

OH

17α-HSD

17αααα-OH-PregPreg

Chol

17α-hydroxylase

H

HOH

O

Etio

OH

ββββααααββββ-diol

HOH

ββββαααααααα-diolHO

H

OH

16αααα-OH-DHEA

24S-OH-CholHO

OH

HO

O

OH

HO

O

HO

O

H

OH

17αααα-OH-ProgProg

3β-HSD 3β-HSD

O

O

O

OH

O

20,22-desmolase

11-DeoxyB 11-DeoxyF

21-hydroxylase 21-hydroxylase

O

OH

O

11β-hydroxylase 11β-hydroxylase

Cortisol (F)O

OHHO OH

O

O

OHOH

O

O

O

HO OH

THF / allo-THF, allo-DHF5β- / 5α-reductase

3β-HSD

11-DehydroB

17α-hydroxylase11β-hydroxylase

O

O

HO

11ββββ-OH-Prog

HO

O

HOH

HO

O

OHOOH

O

11β-hydroxylase

11β-HSD

Cortisone (E)

O

O

OHO

O

OH

HOH

HOOH

O

O OH

HOH

OH

O

HOH

OH

THDOC

THE

5β- / 5α-reductase3β-HSD

H

HO OH

O

O

allo-DHB

20αααα-OH-progH

O

O

5αααα-DHP

HOH

O

P-oneP-diol

21β-hydroxylase

5β-reductase3β-HSD

5α-reductase

17,20-lyase

17,20-lyase

3β-HSD

A-dione

O

O

HO

OH

A-diol

17β-HSD

O

OH3β-HSD

O

OH

HDHT

HOH

OH

ααααααααββββ-diol

5ββββ−−−−dione

O

OH

5β-reductase

HO

O

HO

OH

E1 17ββββ-E2

2-OH-E1HO

O

HO

aromatase

2-OH-E2

2-MeO-E2

2-MeO-E1HO

OH

HO

HO

OH

H3CO

HO

O

H3CO

4-OH-E1

4-OH-E2

4-MeO-E2

4-MeO-E1

16αααα-OH-E1 Estriol 16-keto-E2

16-Epi-E317-Epi-E3

HO

O

OH

HO

OH

O

HO

OH

OH

HO

OH

OCH3

HO

O

OCH3

HO

OH

OH

HO

OH

OH

HO

OH

O

HO

OH

OH

17β-HSD

methyltransferase

methyltransferase methyltransferase

methyltransferase

2-hydroxylase

17β-HSDO

O

HO OH

21-DeoxyF

5α-reductase

3α-HSD 17β-HSD

17α-HSD

HO

20α-HSD 3α-HSD

11β-HSD

20α-HSD

16α-hydroxylase

4-hydroxylase 16-hydroxylase

B

T

Scheme 1. General scheme for steroid metabolism in man. See Supplementary Table 1 for the fullnames of steroid hormones.

1627J Am Soc Mass Spectrom 2009, 20, 1626–1637 QUANTITATIVE STEROID SIGNATURES BY GC-MS

Experimental

Chemicals and Materials

Reference standards of the 70 steroids examined in thisstudy (Table 1) were obtained from Sigma (St. Louis, MO,USA), Steraloids (Newport, RI, USA), or NARL (Pumble,Australia). The internal standards used were, 16,16,17-d3-testosterone and methyltestosterone for 22 androgens,2,4,16,16-d4-estradiol for 18 estrogens, 9,11,12,12-d4-cortisolfor 15 corticoids, 2,2,4,6,6,17�,21,21,21-d9-progesteroneand 2,2,4,6,6,21,21,21-d8-17�-hydroxyprogesterone for 13progestins, and 2,2,3,4,4,6-d6-cholesterol for 2 sterols werepurchased from NARL and C/D/N isotopes (Pointe-Claire, Quebec, Canada). For solid-phase extraction (SPE),Oasis HLB cartridge (3 mL, 60 mg; Waters, Milford, MA,USA) was preconditioned with 3 mL of methanol fol-lowed by 3 mL of deionized water. Sodium acetate (re-agent grade), acetic acid (glacial, 99.99�%) and L-ascorbicacid (reagent grade) were obtained from Sigma (St. Louis,MO, USA). A solution of �-glucuronidase/arylsulfatasefromHelix pomatiawas purchased fromRoche DiagnosticsGmbH (Mannheim, Germany). The trimethylsilylating(TMS) agents, N-methyl-N-trifluorotrimethylsilyl acet-amide (MSTFA), ammonium iodide (NH4I), and dithio-erythritol (DTE) were purchased from Sigma. All organicsolvents used were of analytical or HPLC grade and werepurchased from Burdick and Jackson (Muskegan, MI,USA). Deionized water was prepared using the Milli-Qpurification system (Millipore, Billerica, MA, USA).

Standard Solutions and Quality-Control Samples

Stock solutions of all reference standards were preparedat a concentration of 1000 �g/mL in methanol andworking solutions were made up with methanol atconcentrations in the range 0.1 to 10 �g/mL. L-Ascorbicacid (1 mg/mL) was added to prevent the oxidations oflabile steroid metabolites. All standard solutions werestored at �20 °C until required and all were stable for aminimum of 3 mo. The urine samples used for calibra-tion and quality-control (QC) purposes were preparedin house from steroid-free urine [30]. Steroids-free urinesamples were prepared by percolating urine samplesthrough Serdolit PAD-1 (0.1–0.2 mm analytical grade;Serva, Heidelberg, Germany). In this way, urinary ste-roids and other potential interfering compounds areretained in the cartridges and the eluates were collected.After checking for endogenous steroids with negativeresult following the solvent extraction procedure, theywere used as matrix for steroid profiling.

Subjects and Sample Collection

First-morning urine samples were collected from 59BPH patients (age: 65.3 � 8.2 y male) and 41 healthymale subjects (age: 56.7 � 7.1 y) in the Department ofUrology at Severance Hospital (Seoul, Korea). All pa-tients underwent a transrectal ultrasound-guided oc-

tant biopsy and had either a suspicious finding bydigitorectal examination or an elevated level of serumprostate-specific antigen (PSA). The age-matched nor-mal controls showed no evidence of serum PSA levelelevation or of BPH. Steroid levels in urine were nor-malized versus urinary creatinine values using Jaffemethod [31].

Sample Preparation

Quantitative metabolite profiling of urinary steroidswas based on previous reports [4, 8, 32]. Briefly, urinesamples (2 mL) spiked with 20 �L of the 7 internalstandards (d3-testosterone and d4-estradiol; 1 �g/mL,d4-cortisol and d8-17�-hydroxyprogesterone; 5 �g/mL,methyltestosterone, d9-progesterone, and d6-cholesterolto 10 �g/mL) were extracted with Oasis HLB SPEcartridges coupled to a peristaltic pump. After loadinga sample onto a cartridge, the cartridge was washedwith 2 mL water and eluted twice with 2 mL ofmethanol. Combined methanol eluates were evapo-rated under a stream of nitrogen and then 1 mL of 0.2 Macetate buffer (pH 5.2), 100 �L of aqueous 0.2% ascorbicacid, and 50 �L of �-glucuronidase/arylsulfatase werewas added. After incubation at 55 °C for 3 h, thesolution was extracted twice with 2.5 mL of ethylacetate: n-hexane (2:3, vol/vol). Combined organic sol-vents were evaporated using a N2 evaporator at 40 °Cand further dried in a vacuum desiccator over P2O5-KOH for at least 30 min. Finally, the dried residue wasderivatized with MSTFA/NH4I/DTE (40 �L; 500:4:2,vol/wt/wt) at 60 °C for 20 min, and 2 �L of theresulting mixture was subjected to GC-MS in selected-ion monitoring (SIM) mode. To maximize extractionefficiencies, two organic solvents, tetrahydrofuran(THF) and ethyl acetate, were evaluated in the pH range5 to 9. In addition, the potential uses of diethyl ether,methyl tert-butyl ether, and n-hexane were investigatedfor promoting the partition of water-miscible THF andethyl acetate.

Instrumental Conditions

GC-MS was performed with an Agilent 6890 Plus gaschromatograph interfaced with a single-quadrupoleAgilent 5975 MSD at an electron energy of 70 eV and anion source temperature of 230 °C. Each sample (2 �L)was injected in split mode (10:1) at 280 °C and separatedthrough an Ultra-1 capillary column (25 m � 0.2 mmi.d., 0.33 �m film thickness; Agilent Technologies; PaloAlto, CA, USA). The GC oven temperature was initiallyset at 215 °C, then ramped to 260 °C at 1 °C/min, andfinally increased to 320 °C at 15 °C/min and held for 1min. The carrier gas was helium at a column headpressure of 210.3 kPa (column flow: 1.0 mL/min at oventemperature of 215 °C). For quantitative analysis, char-acteristic ions of each steroid were determined as theirTMS derivatives. Peak identifications were achieved by

1628 MOON ET AL. J Am Soc Mass Spectrom 2009, 20, 1626–1637

Tab

le1.

TheGC-MSinformationforquantitativeanalysisofsteroidsstudied

Co

mp

ou

nd

s(t

rivi

aln

ame)

Ab

bre

viat

ion

Ion

sele

cted

a

Ret

enti

on

tim

e(m

in)

Co

mp

ou

nd

s(t

rivi

aln

ame)

Ab

bre

viat

ion

Ion

sele

cted

Ret

enti

on

tim

e(m

in)

An

dro

gen

sE

stro

gen

s5�

-an

dro

stan

-3�

,17�

-dio

l�

��

-dio

l25

612

.12

17�

-est

rad

iol

17�

-E2

416

18.6

05�

-an

dro

stan

-3�

,17�

-dio

l�

��

-dio

l25

612

.87

Est

ron

eE

141

419

.12

An

dro

ster

on

eA

n43

415

.27

17�

-est

rad

iol

17�

-E2

416

19.9

8E

tio

cho

lan

olo

ne

Eti

o43

415

.44

2-m

eth

oxy

-17�

-est

rad

iol-

3-m

eth

ylet

her

2-M

eO-E

2-3-

met

hyl

eth

er38

823

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5�-a

nd

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an-3

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7�-d

iol

��

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iol

241

16.0

05 �

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dro

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l25

616

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eth

oxy

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rad

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4-M

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244

623

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nd

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an-3

�,1

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iol

��

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iol

241

17.0

42-

met

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xyes

tro

ne

2-M

eO-E

144

424

.57

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idih

ydro

test

ost

ero

ne

Ep

i-D

HT

434

17.4

62-

hyd

roxy

-17�

-est

rad

iol-

3-m

eth

ylet

her

2-O

H-E

2-3-

met

hyl

eth

er44

625

.06

11-k

eto

-an

dro

ster

on

e11

-ket

o-A

n52

017

.55

11-k

eto

-eti

och

ola

no

lon

e11

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o-E

tio

520

17.6

02-

met

ho

xy-1

7�-e

stra

dio

l2-

MeO

-E2

446

25.5

3D

ehyd

roep

ian

dro

ster

on

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A43

217

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2-h

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tro

ne

2-O

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150

225

.92

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ian

dro

ster

on

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419

18.1

02-

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-est

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250

426

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dro

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418

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150

227

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ne

432

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250

428

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432

18.7

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350

429

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241

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504

29.8

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ster

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434

19.3

416

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248

730

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dro

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ne

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ion

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hyd

roxy

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on

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H-E

148

730

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tost

ero

ne

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220

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pie

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pi-

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504

31.1

611

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xyan

dro

ster

on

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n52

220

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OH

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592

37.3

011

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hyd

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cho

lan

olo

ne

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-OH

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o52

221

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E2

420

19.8

8

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roxy

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EA

16�

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EA

505

28.4

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sto

ster

on

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3-T

435

20.4

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sto

ster

on

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446

24.2

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Pro

ges

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548

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85 �

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ne

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445

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rod

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634

38.6

6P

reg

nan

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ne

447

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F63

641

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ne

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DO

C54

842

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Pre

gn

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iol

269

25.0

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cort

iso

lA

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HF

636

42.3

9P

reg

nan

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P-t

rio

l43

526

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cort

iso

l21

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634

42.5

5P

reg

nen

olo

ne

Pre

g44

527

.36

11-d

eoxy

cort

iso

l11

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544

42.7

65 �

-dih

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pro

ges

tero

ne

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HP

445

28.5

711

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B54

643

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g45

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on

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615

46.0

320

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ne

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-DH

P44

530

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ehyd

roco

rtic

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ero

ne

11-d

ehyd

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617

46.8

017

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xyp

reg

nen

olo

ne

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-OH

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g54

832

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dih

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cort

ico

ster

on

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llo-D

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636

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717

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ron

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H-P

rog

546

35.7

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llod

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rtis

ol

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F63

447

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-hyd

roxy

pro

ges

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ne

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-OH

-Pro

g53

141

.32


comparing retention times and matching the heightratios of characteristic ions (Table 1).

Method Validation

QC samples containing all 70 analytes were quantifiedusing MS peak height ratios versus the IS. Calibrationsamples were made up at 15 different concentrations,which depended on the sensitivity and reference valuesof the urinary steroids. Least-squares regression analy-sis was performed on peak height ratios at increasinganalyte levels to obtain calibration linearity. Limits ofdetection (LOD) and quantification (LOQ) were definedas the lowest concentration with a signal-to-noise (S/N)ratio � 3 for LOD and of S/N ratio � 10 for LOQ.Precisions are expressed as coefficients of variation (%CV) and accuracies as percent relative errors (% bias),and were determined using QC samples at three differ-ent concentrations (low; 2 � 50 ng/mL, medium; 20 �200 ng/mL, and high; 100 � 2000 ng/mL) dependingon the sensitivity and reference values of the urinarysteroids. To determine within-day repeatabilities, fourreplicates were analyzed, whereas day-to-day repro-ducibility was measured by running samples on 5different days.Extraction recoveries were determined using QC

samples at three concentration levels in triplicate foreach steroid by adding known amounts of mixed work-ing solutions to steroid-free urine samples. Absoluterecoveries were calculated by comparing peak heightratios of extracted samples versus those of their nonex-tracted counterparts, using the IS added just before thederivatization step.

Statistical Analysis and Steroid Signatures

Data manipulation was performed using Excel 2007spreadsheets (Microsoft Corp., Seattle, WA, USA), Sig-maplot (SYSTAT Software Inc., San Jose, CA, USA), andSIMCA software (Umetrics Inc., Umeå, Sweden). Quan-titative results are expressed as means � SD, and groupcomparisons are made using the unpaired two-tailedStudent’s t-test. Statistical significance was consideredat P 0.01.To visualize differences between the steroid signa-

tures of BPH patients and healthy subjects, a supervisedhierarchical clustering algorithm based on Pearson’scorrelation coefficient was used with a TIBCO SpotfireDecisionSite Browser (TIBCO Spotfire, Inc., Somerville,MA, USA). Concentrations of steroids and ratios ofmetabolites to precursors (an indicator of enzyme ac-tivity) in BPH patients and healthy subjects were com-pared using the Student’s t-test, and statistically sig-nificant variables were selected and processed. Forclustering analysis, all prefiltered quantitative results ofsteroids measured and their ratios were log trans-formed and normalized using z-scores by subtractingpopulation means from individual raw scores and di-viding the result by the population standard deviation.T

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Z-score transformation [z � (observed value – baselinemedian)/baseline standard variation] ensures that eachanalyte in the subject population has a median value of0 and a standard deviation of 1. In the heat mapgenerated using HCA, color coding in maps indicatingauto range by difference of concentration in each com-pound was gradated at three point; red indicates z-scores � 0, blue z-scores 0, and white z-scores 0 ofa row (subjects) across all columns (urinary steroids).

Results and Discussion

Urinary Steroid Profiling

Steroid hormones of significance under different biolog-ical conditions fall into several distinct groups, and arefrequently used to evaluate endocrine functions. Com-prehensive extraction methods for GC-MS based ste-roid analysis mainly focus on specific steroid metabo-lites which have the same biological function andchemical structures [15–17, 19]. Due to the chemical andphysical diversities within androgens, estrogens, corti-coids, progestins, and sterols, the described profilingtechnique was devised to simultaneously quantify ste-roid analytes as diverse as lipophilic cholesterol andhydrophilic corticoids in a single extract.In SPE experiment, urinary steroids are purified

using a unique copolymer sorbent, Oasis HLB, whichallows high and reproducible results to be obtained [8,30, 32, 33]. In general, SPE absorbent retains steroidsbased on hydrophobic interactions with free and con-jugated steroids. The following two extractions meth-ods were therefore investigated: (1) 2 mL of urine wasprocessed with a SPE cartridge and the sample obtainedwas hydrolyzed, and (2) urine was first hydrolyzed andthen processed in SPE. However, difficulties in loadingsamples into SPE cartridges after hydrolyzing themwith �-glucuronidase/arylsulfatases, due to an increasein the viscosity of urinary solution hydrolyzed, wereobserved. This result was not observed if �-glucuronidaseonly was present in the enzyme solution [6]. Therefore,SPE procedure was processed first in sample prepara-tion steps.Although catechol estrogens, such as 2-OH-E1, 2-OH-

E2, 4-OH-E1, 4-OH-E2, and 2-OH-E3, are degraded whenexposed to light or to a pH higher than 9.5 [34], 0.2%aqueous L-ascorbic acid was added to prevent oxidativedecomposition before enzymatic hydrolysis. After enzy-matic hydrolysis, deconjugated steroids were extractedwith an ethyl acetate: n-hexane (2:3, vol/vol) mix. Inextraction, THF and ethyl acetate were primarily investi-gated, and diethyl ether, t-butyl methyl ether, and n-hexane were added to expedite THF and ethyl acetatepartitioning. When relatively polar organic solvents, suchas diethyl ether and t-butyl methyl ether, were used,chromatographic interference was encountered. Althoughboth THF and ethyl acetate had high extraction efficien-cies, ethyl acetate was finally selected because of theobjectionable smell and high polarity of THF.

Overall extraction efficiencies were not signifi-cantly different for all steroids studied at pH valuesof 5 to 7. The recoveries of catechol estrogens (10.4%� 48.9%) were poor at pH 9 because they are oxidizedto quinones at this pH [35]. In addition, differentratios of ethyl acetate to n-hexane were examined anda 2:3 (vol/vol) mix was chosen having consideredextraction yield and chromatographic properties. Us-ing optimized conditions, all urinary steroids studiedwere extracted in good yields, except 2-OH-E3 and24S-OH-Chol, which are still detectable in urine afterextraction (Figure 1).

Gas Chromatography-Mass Spectrometry

Using the described GC-SIM/MS conditions, 70 steroidhormones were analyzed as their TMS derivatives andpeak identification was straightforward using retentiontimes, characteristic ions, chromatograms, and massspectra, the latter were compared with those of refer-ence standards. The GC oven program used resulted inwell separated, good peak shapes for most steroids withretention times from 12.12 min for ��-diol to 48.99 minfor 24S-OH-Chol. On the other hand, 11-keto-An and11-keto-Etio, and 16-keto-E2 and 16�-OH-E1 were co-eluted and estimated totally in quantification (Figure 2).However, some partial-overlapped compounds (e.g.,5�-dione and 17�-E2, 2-OH-E2-3-methylether and P-diol, 4-OH-E1 and Preg) were differentiated by SIMusing their different characteristic ions (Table 1).Most steroids were monitored using their molecular

ions as base peaks, except: ��-diol, ��-diol, ��-diol,which had an abundant [M-180; M-2OTMS]� ion at m/z256; ��-diol, ��-diol, ��-diol with an [M-195;M-2OTMS-CH3]

� ion at m/z 241; 5�-DHP, 5�-DHP,Preg with an [M-15; M-CH3]

� ion at m/z 445; Epi-P-one,P-one, and Allo-P-one with an [M-15]� at m/z 447;16-keto-E2, 16�-OH-E1 with an [M-15]� ion at m/z 487;THS with an [M-90; M-OTMS]� ion atm/z 548; THE andAllo-DHF with an [M-90]� ion at m/z 634; THF andAllo-THF with an [M-90]� ion at m/z 636; 11-deoxyFwith an [M-90]� ion at m/z 544; cortisone (E) with an[M-90-15; M-OTMS-CH3]

� ion at m/z 615; and 11-dehydroB with an [M-15]� at m/z 617. In the cases ofP-diol, P-triol, and 11�-OH-Prog, less intense fragmentsat m/z 269, 435 and 531, respectively, were chosen toimprove selectivity rather than most intense peaks (m/z117, 255, and 546); this was attributed to urinary inter-ference. Regarding the TMS ethers of P-diol and P-triol,the characteristic ions at m/z 269 and 435 were moni-tored, which are attributed to [M-195; M-2OTMS-CH3]

�

and [M-117; M-CH[OSi(CH3)3]CH3 side-chain]� ions.

The base peak of P-diol is m/z 117 and other importantfragments are m/z 269 [M-90-90-15]�, m/z 284 [M-90-90]�, m/z 347 [M-117]�, and m/z 449 [M-15]�. Amongthese fragments, m/z 269 instead of m/z 117 was chosento improve selectivity from urinary backgrounds. Alsothe P-triol has m/z 255 [M-117-90-90]�, m/z 345 [M-117-90]�, and m/z 435 [M-117]� as major fragments formed


by the loss of 117 mass units, butm/z 435 was monitoredto improve selectivity. In addition, 11�-OH-Prog elutesTHF at the similar retention time (41.30 min), but atypical loss of 90 Da (-OTMS) from the THF (m/z 636)would interfere the detection of 11�-OH-Prog in case ofusing m/z 546, the molecular ion. Therefore, m/z 531[M-90-15]�, which rarely did not exist in THF, waschosen instead of m/z 546 (see Supplementary Figure 1).

Method Validation

Method validation requires the evaluations of linearity,LOD, LOQ, precision, and accuracy using calibrationsamples prepared from in-house steroid-free urine [30].Calibration curves were generated for all analytes usingQC samples fortified with all reference standards at

different concentrations. The devised method wasfound to be linear (correlation coefficient, r2 � 0.994) forall analytes, except for cholesterol (r2 � 0.983). Mostcompounds had an LOQ in the 0.05� 2.0 ng/mL range,while those of DHEA, Epi-An, 5�-dione, ��-diol,11-dehydroB, 17�-OH-Preg, and 11�-OH-Prog rangedbetween 5.0 and 50 ng/mL (Table 2).Assay precisions and accuracies were determined by

analyzing three QC samples at different concentrationsof the individual steroids (5, 20, 100, 200, 500, and 2000ng/mL). Intra-day (n � 4) precisions (expressed as %CV) ranged from 0.9% to 11.2%, whereas accuracies(expressed as % bias) ranged from 95.8 to 119.1%, andinter-day (n � 5) precisions (% CV) and accuracies (%bias) ranged from 1.6% to 10.5% and from 91.6% to115.5%, respectively (Table 2). Especially, the accuracy

Figure 1. Extraction recoveries of 70 urinary steroids analyzed using the described method. Absoluterecoveries were calculated by comparing peak height ratios of extracted samples versus theirnon-extracted counterparts.

15.00 20.00 25.00 30.00 35.00 40.00 45.00Time (min)

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Androsterone Etiocholanolone

THF

Allo-THF

THS 17α-OH-preg

THE

Cortisone

Cortisol

P-diol

P-triol

5 -androstan-3 ,17 -diol

11-keto-androsterone11-keto-etiocholanolone

16-keto-17 -estradiol16 -hydroxyestrone

24S-hydroxycholesterol

Figure 2. A representative total ion chromatogram obtained from a healthy male subject fordetection of 70 urinary steroids.


Table 2. The validation results of overall method with intra- and inter-day assays

Compounds (trivial name)LODa

(ng/mL)LOQb

(ng/mL)Calibration

range (ng/mL)Linearity

(r2)

intra-day (n � 4) inter-day (n � 5)

CVc

(%)Accuracyc

(%)CV(%)

Accuracy(%)

Androgens��-diol 0.5 1.0 1–200 0.998 5.9 104.0 3.8 97.9��-diol 0.2 0.5 0.5–200 0.998 5.7 107.8 3.1 105.0An 0.1 0.5 0.5–3000 0.996 3.2 119.1 3.5 108.8Etio 0.1 0.5 0.5–3000 0.998 2.5 106.1 2.2 109.7��-diol 0.5 1.0 1–200 0.998 6.8 111.4 3.3 109.6��-diol 0.2 0.5 0.5–200 0.998 4.4 106.2 3.0 109.1��-diol 1.0 2.0 2–200 0.995 5.2 107.5 3.4 103.4Epi-DHT 0.1 0.2 0.2–200 0.994 8.0 106.0 3.4 108.911-keto-An & Etio 0.1 0.5 0.5–1000 0.996 3.7 108.4 3.4 112.3DHEA 5.0 50.0 50–3000 0.997 3.5 106.6 3.3 108.2Epi-An 2.0 5.0 5–1000 0.994 2.5 98.0 2.1 109.5A-diol 0.5 2.0 2–200 0.996 7.4 116.3 10.5 97.25�-dione 0.5 5.0 10–200 0.995 4.8 106.2 2.2 111.8Epi-T 0.05 0.1 0.1–200 0.997 3.8 100.7 1.6 100.8��-diol 10.0 20.0 20–500 0.998 6.9 104.0 3.0 104.0DHT 0.1 0.2 0.2–200 0.998 4.7 109.6 2.4 102.7A-dione 0.05 0.1 0.1–200 0.999 5.7 103.4 2.4 101.7T 0.05 0.1 0.1–200 0.998 4.7 106.1 1.9 102.511�-OH-An 0.2 0.5 0.5–3000 0.997 3.4 104.6 5.8 112.911�–OH-Etio 0.2 0.5 0.5–3000 0.998 2.7 101.3 4.1 113.416�-OH-DHEA 0.1 0.2 0.2–1000 0.996 4.0 104.3 1.7 105.8

Estrogens17�-E2 0.05 0.1 0.1–200 0.998 3.9 98.6 2.6 96.3E1 0.1 0.2 0.2–200 0.998 2.9 99.0 2.0 97.317�-E2 0.05 0.1 0.1–200 0.997 3.5 99.8 2.3 97.22-MeO-E2-3-methylether 0.1 0.2 0.2–200 0.998 2.9 97.3 2.1 96.44-MeO-E2 0.2 0.5 0.5–200 0.998 2.1 98.7 1.8 95.62-MeO-E1 0.05 0.1 0.1–200 0.998 1.9 99.0 1.8 95.82-OH-E2-3-methylether 0.1 0.2 0.2–200 0.998 4.2 101.3 2.4 97.62-MeO-E2 0.05 0.1 0.1–200 0.998 4.3 100.2 2.2 97.12-OH-E1 0.1 0.2 0.2–200 0.998 4.9 104.7 3.7 99.52-OH-E2 0.1 0.2 0.2–200 0.998 6.1 106.4 3.4 99.34-OH-E1 0.1 0.2 0.2–200 0.998 2.9 103.2 2.5 98.84-OH-E2 0.1 0.2 0.2–200 0.998 4.6 100.1 4.2 97.317-epi-E3 0.5 1.0 1–200 0.997 2.4 106.7 2.4 100.3E3 0.2 0.5 0.5–200 0.997 2.7 101.6 2.8 99.716-keto-E2 and 16�-OH-E1 0.1 0.2 0.2–500 0.998 2.7 98.7 8.8 92.516-epi-E3 0.5 1.0 1–200 0.997 3.5 103.2 2.3 100.52-OH-E3 0.5 1.0 1–100 0.997 5.0 107.4 5.3 101.0

CorticoidsTHS 0.1 0.2 0.2–200 0.997 0.9 99.2 3.0 98.4THDOC 0.2 0.5 0.5–200 0.996 7.1 100.4 4.1 100.0THE 1.0 2.0 2–500 0.997 7.6 151.4 9.9 151.3THF 0.2 0.5 0.5–200 0.998 6.6 106.1 4.9 104.0DHDOC 0.5 1.0 1–200 0.997 4.2 101.2 4.1 104.9Allo-THF 0.2 0.5 0.5–500 0.996 4.8 103.9 2.6 102.621-deoxyF 0.5 1.0 1–200 0.997 7.3 108.5 4.8 109.311-deoxyF 0.2 0.5 0.5–200 0.998 2.9 98.5 3.5 91.611-deoxyB 0.5 1.0 1–200 0.997 6.9 100.3 2.9 105.1Cortisone (E) 0.2 0.5 0.5–500 0.998 3.2 101.2 2.7 99.811-dehydroB 0.2 5.0 5–200 0.994 2.1 101.8 2.7 100.4Allo-DHB 0.5 1.0 1–200 0.997 6.9 113.1 5.3 108.0Allo-DHF 1.0 2.0 2–200 0.997 2.3 97.5 2.9 107.3Corticosterone (B) 0.2 0.5 0.5–200 0.997 3.3 99.0 3.3 100.3Cortisol (F) 0.1 0.2 0.2–200 0.998 3.0 100.5 3.5 98.0

Progestins5�-DHP 0.1 0.2 0.2–100 0.997 9.9 109.2 3.5 110.5Epi-P-one 1.0 2.0 2–200 0.995 4.1 106.0 3.0 105.8P-one 1.0 2.0 2–200 0.997 7.4 110.1 3.3 115.5Allo-P-one 0.2 0.5 0.5–200 0.994 2.9 109.9 2.2 103.7


of THE resulted in abnormal values, and it might beproduced by low calibration slope because of poorGC-MS properties.

Quantitative Results of Benign ProstaticHyperplasia

The validated quantitative profiling method was ap-plied to measure 70 endogenous steroids in urinesamples obtained from 59 BPH patients and 41 age-matched healthy male subjects. All quantitative results,corrected by urinary creatinine levels, were compared,and differences between patients and healthy subjectswere evaluated using the Student’s t-test. Although itwas not the purpose of this study to elucidate clinicalimplications, some features were notable, and signifi-cant differences were found between patients andhealthy subjects (see Supplementary Figure 2).In patients, urinary levels of T, Epi-T, ��-diol,

��-diol, ��-diol, 17�-E2, 11�-OH-Prog, and 16�-OH-DHEA were significantly decreased (P � 4.52 � 10–4 �2.77 � 10–13), and those of 11�-OH-Etio, ��-diol,2-OH-E3, 17�-OH-Prog, and 11-deoxyF were signifi-cantly increased (P � 4.56 � 10–7 � 9.42 � 10–18). Someother urinary steroids quantified were also significantlydifferent in patients and controls (P 0.01). Changes inthe urinary levels of ��-diol and ��-diol were inaccord with previous findings [36, 37], but other steroidchanges observed in this study have not been reportedto date. In particular, urinary 17�-E2 and 2-OH-E3showed remarkable changes with P values of 2.77 �10–13 and 4.25 � 10–9, which might be associated withthe different biochemical roles of estrogen receptor �(ER�) and � (ER�) in various types of prostate cells [28, 38,39]. The involvements of other urinary steroids needs tofuture study.

Steroid Signatures by Hierarchical ClusteringAnalysis

The present MS-based steroid profiling has become anessential clinical tool and has produced many results inBPH, but it is difficult to identify critical differencesbetween patients and controls because of the largenumber of results. Hierarchical clustering, which in-volves calculating the distance matrices of data objectsand then merging objects that are close to each other toform sub-clusters, has been utilized in biomedical stud-ies to interpret the expressions of biomolecules [2,22–25]. In the present study, after determining therelative levels of each steroid, all data were z-scoretransformed to produce a heat map using a supervisedHCA based on Pearson’s correlation coefficient. Theraw data were the relative concentrations of the 70urinary steroids, and these were filtered by statisticalsignificance (P 0.01) using the Student’s t-test. Theclustering method and similarity measure were setunweighted average and Euclidean distance, respec-tively. Urinary concentrations were represented by col-ors in the heat map, and each steroid was representedby a single row of colored boxes, whereas columnsrepresented different subjects. Heat maps were com-pared with respect to individual quantitative results,and indicated the effectiveness of steroid signatures.Figure 3 illustrates in a heat map showing altered

concentrations of urinary steroids in BPH patients andhealthy subjects. The heat map readily shows changes inthe concentrations of urinary steroids. Increased urinaryexcretion patterns of several steroids, from Epi-DHTdown to 11-deoxyF, were detected in patients, while othersteroid levels were significantly reduced. In addition,visual inspections of steroid signatures complementedquantitative statistics. Although 2-OH-E3 was present at asignificantly higher level in patients (P � 4.25 � 10–9; seeSupplementary Figure 2), these increases were only ob-

Table 2. Continued

Compounds (trivial name)LODa

(ng/mL)LOQb

(ng/mL)Calibration

range (ng/mL)Linearity

(r2)

intra-day (n � 4) inter-day (n � 5)

CVc

(%)Accuracy

c (%)CV(%)

Accuracy(%)

P-diol 0.5 1.0 1–500 0.997 6.2 105.6 3.6 107.5P-triol 0.2 0.5 0.5–500 0.999 6.0 102.8 2.3 105.5Preg 1.0 2.0 20–200 0.995 8.8 104.1 3.5 98.55�-DHP 0.2 0.5 0.5–200 0.995 7.0 99.2 3.8 104.0Prog 0.5 1.0 1–100 0.998 4.8 96.0 2.5 98.620�-DHP 0.2 0.5 0.5–100 0.997 9.1 101.7 4.9 103.817�-OH-Preg 5.0 10.0 20–500 0.998 3.6 98.7 3.0 102.917�-OH-Prog 0.2 0.5 0.5–200 0.996 3.1 95.8 2.3 96.111�-OH-Prog 2.0 5.0 5–500 0.996 11.2 103.9 7.3 94.3

SterolsChol 1.0 2.0 50–1000 0.983 7.3 113.0 7.8 115.024S-OH-Chol 0.5 2.0 2–200 0.994 6.5 96.3 4.1 100.9

aThe limit of detection was measured S/N ratio � 3.bThe limit of quantification was measured S/N ratio � 10.cPrecision and accuracy were expressed as the mean values of data obtained from three different concentrations for each analyte.


served in some patients in the heat map. The levels ofother steroids, 11�-OH-Etio and 11-deoxyF, were alsoinconsistency different in patients (Figure 3).To illustrate further the usefulness of a steroid sig-

nature, the ratio of steroid metabolite to precursor,which reflects enzyme activity (Scheme 1), was exam-ined. The cluster and heat maps of all analyzed sam-ples in this 70 compound study showed differencesbetween groups in these ratios (Figure 4). A dendro-gram of ratios revealed that the oxidoreductasesformed a cluster (5�-reductase, 3�-HSD, 3�-HSD, and17�-HSD, except for 20�-HSD; see SupplementaryTable 1 for full enzyme names), which are differenti-ated with a cytochrome P450 enzyme, 11�-hydroxy-lase. These results support the validity of the datatransformation, since 5�-reductase is a marker of

BPH [27–29], and 17�-HSD is positively expressed inprostate cells [40, 41]. Other up-regulated enzymes inthe map might be biomarkers of BPH. Furthermore,the method can be used to visualize quantitativesteroid data in a single graphic using a hierarchicallyclustered heat map. The need for this type of visual-ization arises from the complexity of large experi-mental datasets. One important advantage of using aheat map is that it can be used to visualize clinicaldata across multiple subjects for many analytes,which allows patterns to be easily identified.

Conclusions

Due to the clinical requirement to quantify trace levelsof urinary endogenous steroids, we devised an analyt-

Healthy BPH

Figure 3. Hierarchically clustered heat map of the urinary steroids levels of 59 BPH patients and 41healthy male subjects. Differences between steroid levels in controls and patients were examinedusing Student’s t-test, and only those that were significantly different at the P 0.01 level wereselected. Row represents subjects and column urinary concentration of individual steroids. Cells arecolored based on concentrations in urinary samples. Red represents highly excreted into urine andblue showed a concentration in urine.

Healthy BPH

Figure 4. A heat map for measuring enzyme activities related to steroid metabolism. The ratio ofprecursor to metabolite for each steroid was calculated, and only steroids with level differencessignificant at the P 0.01 level were selected. Rows represent subjects and column metabolic ratio.Red represents a high ratio and blue a low ratio.


ical method to measure the individual concentrations of70 urinary steroids and several enzyme activities basedon steroid signatures using a single assay, which sub-stantially reduces analytical time and sample size. Incontrast to classical biochemical approaches using RIAand EIA that mainly focus on single enzymes, singlemetabolic reactions, and kinetic properties, profilinganalysis involves the collation of quantitative results fora broad series of metabolites to gain an overall under-standing of metabolism.Steroid signatures as represented by hierarchically

clustered heat maps use color to communicate under-lying quantitative results, which are much harder toappreciate when presented numerically. This method issimple to implement and maintains the biological in-tegrity of data. Using this technique, inter-relationshipsbetween absolute concentrations and enzyme activitiesin samples from different sources can be recognized.The combined GC-MS profiling technique describedhas adequate sensitivity that could be used for highthroughput analysis, and could be adopted to enablethe quantifications of urinary steroids and indicatemetabolic alterations at biologically relevant levels.

AcknowledgmentsThe authors acknowledge support for this work by an intramuralgrant from the Korean Institute of Science and Technology (KIST),and by grants from the National R and D Program of the KoreanMinistry of Education, Science, and Technology (MEST), and theKorean Science and Engineering Foundation (KOSEF).

Appendix ASupplementary Material

Supplementary material associated with this articlemay be found in the online version at doi:10.1016/j.jasms.2009.04.020.

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