Comprehensive metabolomics as

a tool in seed quality research

Ric de Vos

Plant Research Wageningen, Bioscience

ric.devos@wur.nl

Metabolites are key to the quality of seeds and their

products

Primary metabolites: central metabolism

● “all” organisms

Secondary metabolites: mostly plant-specific

● huge diversity in nature

● key to plant survival

● propagation: pollination, fruit and seed dispersal

● defense: pathogens, pest insects, climate

● key to quality of crops and their products

● attractiveness: colour, taste, aroma

● nutritional aspects, including phytochemicals

The plant metabolome

fats phospholipids alkaloids, saponins nucleosides sugars

volatiles, carotenoids, sterols polyphenols, glucosinolates phenols, organic acids

highly apolar highly polar semi-polar

Plant metabolites: highly diverse molecules

No single extraction or detection method for all compounds

Metabolites are unequally distributed in plant/tissue

● Representative sampling is key

> 250.000 natural compounds known

Metabolite analysis approaches

Targeted / Classical analytical chemistry

● Dedicated analysis of (a set of) target compounds:

● known compounds only

● e.g. carotenoids, vitamins, sugars, ......

● quantitative by using standards

● Limitation: standards are needed

● secondary metabolites: only few standards per plant species

Untargeted / Comprehensive metabolomics

● All metabolites detected taken into account

● both known and (yet) unknown compounds

● relative levels of hundreds to thousands metabolites

● broadest view of sample composition

GCMS and LCMS based platforms

.... besides various targeted analysis methods

Scheme of comprehensive metabolomics

grinding

extract preparation

screening

LC-MS / GC-MS profiling

02-Sep-200420:44:46Asun pot tuber stage 5 A

Time5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00

%

0

100

%

0

100

M01542 1: TOF MS ES- TIC

3.50e4

14.05707.1696

2.86292.9232

4.31565.0417

12.49472.2482

32.40896.4771

31.95912.4769

29.62636.285217.08

353.0888 19.49693.3477

32.49896.4791

38.191107.5034

M01566 1: TOF MS ES+ TIC

3.50e4

32.02868.4592

1.66151.0312 14.10

355.09472.53308.0835

4.58166.0769

12.56474.2542

5.62266.1653

29.67638.3051

19.58695.3643

17.13355.1063

32.50852.4523

42.031085.2338

33.47868.5077

39.90906.611538.21

1063.5403

44.901145.0308

02-Sep-200420:44:46Asun pot tuber stage 5 A

Time5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00

%

0

100

%

0

100

M01542 1: TOF MS ES- TIC

3.50e4

14.05707.1696

2.86292.9232

4.31565.0417

12.49472.2482

32.40896.4771

31.95912.4769

29.62636.285217.08

353.0888 19.49693.3477

32.49896.4791

38.191107.5034

M01566 1: TOF MS ES+ TIC

3.50e4

32.02868.4592

1.66151.0312 14.10

355.09472.53308.0835

4.58166.0769

12.56474.2542

5.62266.1653

29.67638.3051

19.58695.3643

17.13355.1063

32.50852.4523

42.031085.2338

33.47868.5077

39.90906.611538.21

1063.5403

44.901145.0308

02-Sep-200420:44:46Asun pot tuber stage 5 A

Time5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00

%

0

100

%

0

100

M01542 1: TOF MS ES- TIC

3.50e4

14.05707.1696

2.86292.9232

4.31565.0417

12.49472.2482

32.40896.4771

31.95912.4769

29.62636.285217.08

353.0888 19.49693.3477

32.49896.4791

38.191107.5034

M01566 1: TOF MS ES+ TIC

3.50e4

32.02868.4592

1.66151.0312 14.10

355.09472.53308.0835

4.58166.0769

12.56474.2542

5.62266.1653

29.67638.3051

19.58695.3643

17.13355.1063

32.50852.4523

42.031085.2338

33.47868.5077

39.90906.611538.21

1063.5403

44.901145.0308

02-Sep-200420:44:46Asun pot tuber stage 5 A

Time5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00

%

0

100

%

0

100

M01542 1: TOF MS ES- TIC

3.50e4

14.05707.1696

2.86292.9232

4.31565.0417

12.49472.2482

32.40896.4771

31.95912.4769

29.62636.285217.08

353.0888 19.49693.3477

32.49896.4791

38.191107.5034

M01566 1: TOF MS ES+ TIC

3.50e4

32.02868.4592

1.66151.0312 14.10

355.09472.53308.0835

4.58166.0769

12.56474.2542

5.62266.1653

29.67638.3051

19.58695.3643

17.13355.1063

32.50852.4523

42.031085.2338

33.47868.5077

39.90906.611538.21

1063.5403

44.901145.0308

LC-MS / GC-MS profiles

Identification of most relevant metabolites

Data analyses

• multivariate analysis

• regression analysis

• Student T-tests

• ...........

plant material

Unbiased data preprocessing

• Peak picking and alignment

• Data filtering

• Relative abundance each

compound in all samples

De Vos et al. 2007 Nature Protocols Lommen et al. 2009 Analytical Chemistry Tikunov et al. 2012 Metabolomics MS/MS, standards, additional studies

100s – 1.000s compounds 10.000s – 1.000.000 peaks

Data visualization and statistics

PCA to visualize differences in metabolomes Heat map to group samples and metabolites

Identifying metabolite-QTLs Identifying predictors /markers of quality traits

correlation with quality traits: quality markers

● resistance, plant performance, flavour, .....

understanding the accumulation of these quality-related compounds

genetic variation, genetical metabolomics (mQTLs)

effects of environment, development, ripening,

post-harvest processing, storage, ageing, .......

perception and metabolism of plant compounds in human

generic approach: (m)any plant species and their products, including seeds ● tomato, cabbages, potato, lettuce, onion, sugar beet, pepper, rice, cacao, coffee, ........

A myriad of applications

Metabolomics in Seed Quality research

Seed development and maturation

Post-harvest treatments

● Drying, Priming, Storage / Ageing

Seed germination and seedling growth

121 a

Scan200 400 600 800 1000 1200 1400 1600 1800 2000 2200 2400 2600 2800

%

0

100

M04875 1: TOF MS ES+ BPI

3.42e414.23

355.0632

9.79355.0838

2.16381.0690

2.34325.1030

4.72166.0729

54.39555.2798

28.38499.0972

25.35509.2131

20.41369.0826

18.27339.0988

23.28365.1797

30.46517.1085

36.39371.1199

52.36331.1859

plantation in South of Minas Gerais, Brazil

20h fermentation (washing)

Coffea arabica var. Topásio

sampling embryos in liquid N2 during drying

selection of fruits

ripe fruits

sun-drying

dry-processed

40°C 60°C

wet-processed

Green coffee post-harvest processing

0,00

10,00

20,00

30,00

40,00

50,00

60,00

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250

Time (hours)

Mo

istu

re c

on

ten

t (%

)

Drying Curve of the endosperm

Natural Sun drying

Washed Sun drying

Natural 40ºC

Washed 40ºC

Natural 60ºC

Washed 60ºC

0,00

10,00

20,00

30,00

40,00

50,00

60,00

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250

Time (hours)

Mo

istu

re c

on

ten

t (%

)

Drying Curve of the endosperm

Natural Sun drying

Washed Sun drying

Natural 40ºC

Washed 40ºC

Natural 60ºC

Washed 60ºC

Processing method influences the rate of

embryo drying

60ºC

40%

30%

11%

20%

m.c. 50%

40% 30%

11% 20% Sun

40ºC

Alteration in metabolites of coffee seed embryos

I: dry-processed beans

50% 40ºC

Sun

60ºC

Alteration in metabolites of coffee seed embryos

II: wet-processed beans

0

200

400

600

800

1000

1200

50 40 30 20 11

moisture (% w.b.)

gu

an

osid

e s

ign

al

natural (dry) sun

natural (dry) 40oC

natural (dry) 60oC

washed (wet) 60oC

guanosine

adenosine

Markers for drying coffee seed at too high temperature

Heat stressed induced membrane damage in dry-

processed coffee

40 °C 60 °C

Borém et al . 2008, Biosystems Engineering 99

0.00000

0.02000

0.04000

0.06000

0.08000

0.10000

0.12000

0.14000fr

esh

W S

un 4

0W

Sun 3

0W

Sun 2

0W

Sun 1

1W

60/4

0 4

0W

60 3

0W

60 2

0W

60 1

1W

40 3

0W

40 2

0W

40 1

1N

Sun 4

0N

Sun 3

0N

Sun 2

0N

Sun 1

1N

60 4

0N

60 3

0N

60 2

0N

60 1

1N

40 4

0N

40 3

0N

40 2

0N

40 1

1

All wet processed samples All dry processed samples

Sig

nal n

um

ber

117-2

805

Markers for wet and dry processed coffee seed

clustering of metabolite patterns

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

50 40 30 20 11

moisture content (% w.b.)

su

cc

inate

(G

CM

S r

es

po

nse

) natural (dry)

washed (wet)

e.g. glucose, fructose

0

50

100

150

200

250

300

50 40 30 20 11

moisture (% w.b.)

cit

rate

(L

C-M

S s

ign

al)

natural (dry)

washed (wet)

e.g. citric acid

0

500

1000

1500

2000

50 40 30 20 11

moisture (% w.b.)

dic

aff

eo

ylq

uin

ic a

cid

sig

nal natural (dry)

washed (wet)

e.g. dicaffeoylquinic acid

0

0.5

1

1.5

2

2.5

3

3.5

4

50 40 30 20 11

moisture content (% w.b.)

GA

BA

(G

CM

S r

esp

on

se

)

natural (dry)

washed (wet)

e.g. GABA

Patterns in metabolite changes upon seed drying

Seed priming

lettuce

Metabolite patterns in lettuce seeds

primed primed wet aged (CD)

aged (CD)

root tips

cotyls

cotyledons root tips

Example: markers for primed seed

Range of LCMS markers higher in all

primed seeds as compared to

original, non-primed seeds

● in both root and cotyledons

cotyledons root tip

Expre

ssio

n V

alu

es

ARRAY

-4-3

-2-1

01

2

1 c

oty

l ori

gin

al 0

001A

1 c

oty

l ori

gin

al 0

001B

2 c

oty

l pri

med w

et

1 4

001

2 c

oty

l pri

med w

et

2 4

002

3 c

oty

l pri

med d

ry 1

4011

3 c

oty

l pri

med d

ry 2

4012

4 c

oty

l P

rim

ed

CD

1 4

003

4 c

oty

l P

rim

ed

CD

1 4

006

5 r

oot

tip o

rigin

al 0

001A

5 r

oot

tip o

rigin

al 0

001B

6 r

oot

tip p

rim

ed w

et

1 4

001

6 r

oot

tip p

rim

ed w

et

2 4

002

7 r

oot

tip p

rim

ed d

ry 1

4011

7 r

oot

tip p

rim

ed d

ry 2

4012

8 r

oot

tip P

rim

ed

CD

1 4

003

8 r

oot

tip P

rim

ed

CD

2 4

006

rootscotyledons

rel. abundance of individual markers

Seed storage = seed ageing

Ageing involves oxidative stress

● oxidative damage to DNA, proteins,

lipids, .....

Ageing will also affect seed metabolome

● Antioxidants: vitamins C and E,

glutathione (GSH)

● Lipid oxidation related-compounds

● Volatile and non-volatile

Water-soluble antioxidant glutathione

GSH

and G

SSG

levels

Years of storage

GSSG

/GSH

ratio

Lipid-soluble antioxidant vitamin E

2012

Comprehensive metabolomics as a tool for

assessing lipid peroxidation

Changes in lipid composition

● LHs: PLs, GLs, TAGs

● lipidomics (LCMS)

LPO-derived compounds

● LOOH, FaOOH, lyso-lipids

● lipidomics (LCMS)

● Volatiles (aldehydes)

● headspace trapping (GCMS)

Lipidomics: metabolomics of apolar compounds

High resolution UPLC-MS:

● Hundreds of lipid species

detectable based on their

specific accurate mass

Lipid databases available:

● lipid classes:

● TAG, PL, GL, ....

● numbers of carbons and

unsaturated bonds

Series of PCs detectable

Metabolomics to determine rice quality

Previous EU-project METAPHOR

6 European and 10 different

East-Asian countries, incl. IRRI

44 rice samples:

● Different genotypes

● Fresh and stored seed lots Cover of Metabolomics journal

Mumm et al., Sept. 2015

standaard

Time2.50 5.00 7.50 10.00 12.50 15.00 17.50 20.00 22.50 25.00 27.50 30.00 32.50 35.00 37.50 40.00 42.50 45.00

%

0

100

2.50 5.00 7.50 10.00 12.50 15.00 17.50 20.00 22.50 25.00 27.50 30.00 32.50 35.00 37.50 40.00 42.50 45.00

%

0

100

Metaphor_rice_04 Scan FI+ TIC

1.63e7

12.64

9.84

3.685.84

11.03

11.87

31.21

30.2729.64

28.6818.94

18.57

18.10

17.1613.53

25.5122.38

21.6719.45 23.61 28.26

31.7435.33

32.15

32.54

33.7337.43

Metaphor_rice_03 Scan FI+ TIC

1.63e712.64

9.84

3.68

5.845.56

8.61

11.03

11.87

25.5220.01

18.73

18.57

17.45

13.5317.02

15.73

22.38

22.14

20.7923.61

28.68

28.4537.4431.6629.16

Basmati is stored paddy before selling

freshly sun-dried

(m.c .14%)

stored 12 months

Rice volatile profiles

Series of alkanes / alkenes

RT: 0.00 - 20.00

0 2 4 6 8 10 12 14 16 18 20

Time (min)

20

40

60

80

100

Rela

tive A

bundance

20

40

60

80

100

Rela

tive A

bundance

20

40

60

80

100

Rela

tive A

bundance

20

40

60

80

100

Rela

tive A

bundance 12.54 12.61

13.15

3.80 11.94

5.4511.530.62 13.977.896.98 8.54 14.78 19.471.12

3.82

5.48

12.557.93 13.1412.028.570.66 7.30 19.451.16 15.31

12.68

13.27

12.203.845.47

0.65 11.657.95 14.158.586.66 15.05 19.411.14 3.16

3.86 5.50

7.94 12.69 13.268.59 12.07 19.411.18 7.283.45 15.17

NL: 5.00E6

Base Peak F: FTMS

+ c ESI Full ms

[95.00-1300.00] MS

f008133

NL: 5.00E6

Base Peak F: FTMS

+ c ESI Full ms

[95.00-1300.00] MS

f008118

NL: 5.00E6

Base Peak F: FTMS

+ c ESI Full ms

[95.00-1300.00] MS

f008109

NL: 5.00E6

Base Peak F: FTMS

+ c ESI Full ms

[95.00-1300.00] MS

f008112

Fresh versus stored Basmati rices

TAGs are lost during

storage of Basmati

rice

UPLC-FTMS

Super Basmati

Basmati 2000

fresh

fresh

stored

stored Basmati 2000

Super Basmati

TAGs

Also PLs are altered upon rice storage .....

RT: 0.00 - 20.00

0 2 4 6 8 10 12 14 16 18 20

Time (min)

10

20

30

40

50

60

70

80

12.54

12.61

12.66

13.15

13.27

13.31

3.80 11.94

11.90

5.45

11.53

0.6213.977.89

5.16 6.986.59 8.54 14.78 15.00 19.479.491.12 11.05 15.871.56 3.41 16.79

3.82

12.550.66

18.88

12.72

13.27

3.84

5.47

0.65

14.1513.26

NL: 5.00E6

Base Peak F: FTMS + c

ESI Full ms

[95.00-1300.00] MS

f008133

NL: 5.00E6

Base Peak F: FTMS + c

ESI Full ms

[95.00-1300.00] MS

f008118

NL: 5.00E6

Base Peak F: FTMS + c

ESI Full ms

[95.00-1300.00] MS

f008109

NL: 5.00E6

Base Peak F: FTMS + c

ESI Full ms

[95.00-1300.00] MS

f008112

RT: 6.00 - 9.26

6.0 6.5 7.0 7.5 8.0 8.5 9.0

Time (min)

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

7.0

7.5

8.0

8.5

9.0

9.5

10.0

10.5

11.0

Rela

tive A

bundance

7.897.84

6.986.598.54

6.88

6.49 8.34

7.24

6.28 7.61

8.92

7.93

8.57

7.636.886.776.426.11

8.58

6.66

7.01

7.66

7.94

7.28

NL: 5.00E6

Base Peak F: FTMS

+ c ESI Full ms

[95.00-1300.00] MS

f008133

NL: 5.00E6

Base Peak F: FTMS

+ c ESI Full ms

[95.00-1300.00] MS

f008118

NL: 5.00E6

Base Peak F: FTMS

+ c ESI Full ms

[95.00-1300.00] MS

f008109

NL: 5.00E6

Base Peak F: FTMS

+ c ESI Full ms

[95.00-1300.00] MS

f008112

PC36:2

PC36:3

TAGs

stored

fresh

…… as well as the antioxidant vitamin E.

Indicative of lipid oxidation upon seed storage:

> Basmati aroma related to, or due to, oxidative processes during storage

Metabolomics of naturally aged Brassica seed

Brassica oleracea seeds

● Stored on the bench for up to 2.5 years

● aliquots taken day 0 and every 9 months -> -20°C

Lipidomics (LCMS)

● organic solvent extracts, each sample in 3 replicates

Discovering those metabolites changing in time

● understanding seed ageing process

● metabolite markers for actual age?

The power of untargeted metabolomics

Untargeted data processing workflow:

● data matrix with relative intensities of 318

apolar metabolites in each of the 12 samples

By eye: view peaks

visible above noise

Software Metalign:

7243 real peaks present

(s/n ratio > 3)

RT: 9.71 - 10.42

9.8 9.9 10.0 10.1 10.2 10.3 10.4

Time (min)

0

20

40

60

80

100

0

20

40

60

80

100

Re

lative

Ab

un

da

nce 10.09

149.022959.97

149.0230410.33

149.0229810.24

149.02292

10.28789.59302

9.94765.68506

10.09967.79230

9.87703.59839 10.24

890.721449.76965.77557

NL:4.28E4

Base Peak F: FTMS + c ESI Full ms [95.00-1300.00] MS f008167

NL:2.51E4

Base Peak F: + c ESI Full ms [94.00-1260.00] MS f008167

Raw LCMS profiles

Unbiased peak picking software

Changes in lipidome upon Brassica storage

Several metabolites increase or decrease according to

storage time: markers for ageing process

HCA PCA

Lyso-PCs increase during Brassica seed storage

318 compounds detected by LCMS-lipidomics:

● 40 (=12.5%) are significantly different between 0 and 918 days

● Lyso-PCs are among the most upregulated compounds

● produced from oxidatively-damaged membrane lipids

Peak h

eig

ht

Metabolomics of tomato seedling growth

> Tissue is the main determinant in metabolite composition

Gomez-Roldan et al 2014, Metabolomics

Heat map of 66 annotated compounds in

tomato seedlings

Light-dependent metabolite changes

cotyledons hypocotyls

Summary

Comprehensive metabolomics: a generic, deep phenotyping tool in both fundamental and applied seed research

To understand biochemical processes

To find markers for quality / process

Seed-based food products

Rice, barley, coffee, oils, etc.

Seed physiology and technology

Development, ripening

Germination, seedling growth

Storage, priming, .....

Thanks to many others…..

►Bioscience, WUR

| Bert Schipper

| Harry Jonker

| Henriette van Eekelen

| Jeroen van Arkel

| Roland Mumm

| Yozo Okazaki

| Victoria Gomez-Roldan

| Steven Groot

| Linda Bakker

| Ron Wehrens

| Robert Hall

►Univ. of Lavras, Brazil

| Flavio Borem

► IRRI, Philippines

| Melissa Fitzgerald

| MariaFe Calingacion

►EU projects

| METAPHOR, Fuel4Me

►Netherlands Genomics Initiative

| CBSG, NMC