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Phytochemistry

Characterization of complex photosynthetic pigment profiles in European deciduous tree leaves by sequential extraction and reversed-phase high performance liquid

chromatography.

--Manuscript Draft--

Manuscript Number: PHYTOCHEM-D-22-00163

Article Type: Full Length Article

Section/Category: Ecological Biochemistry and Chemistry (Full Length Article)

Keywords: Plant pigment composition; Chlorophyll derivatives; Carotenoid derivatives;

Deciduous tree leaves; Sequential extraction; HPLC-DAD

Abstract: Leaf pigments, including chlorophylls and carotenoids, are important biochemical indicators of plant photosynthesis and photoprotection.

In this study, we developed, optimized, and validated a sequential extraction and liquid chromatography-diode array detection method allowing for the simultaneous

quantification of the main photosynthetic pigments, including chlorophyll a, chlorophyll b, β- carotene, and the characterization of plant pigment derivatives. Chromatographic separation was accomplished with the newest generation of core-shell columns revealing numerous pigment derivatives. The sequential extraction allowed for a better recovery of the main pigments (+25 % chlorophyll a, +30 % chlorophyll b, +42 %, β- carotene), and the characterization of ca. 5.3 times more pigment derivatives (i.e., up to 60 chlorophyll and carotenoids derivatives) than with a single step extraction. A broad working range of concentrations (300 - 2000 ng/mL) was achieved for most pigments and their derivatives and the limit of detection was generally as low as few nanograms per millilitre. The method also showed adequate trueness (RSD<1%) and intermediate precision (RSD<5%).

The method was developed and validated with willow and spinach leaves and their extracts and The method was successfully performed on leaf pigment extracts of four other European deciduous tree species. Within a case study using Fagus sylvatica L.

leaves, pigment derivatives revealed a high within-individual tree variability throughout the growing season that could not be detected using the main photosynthetic pigments alone, eventually showing that the method allowed for the monitoring of pigment dynamics at unprecedented detail.

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Highlight 1: Sequential extraction reveals a wide range of photosynthetic pigments.

Highlight 2: The HPLC method resolves chlorophyll a, b, and chlorophyll derivatives.

Highlight 3: The HPLC method resolves α-, β- carotene, and carotenoids derivatives.

Highlight 4: Leaf pigment derivatives provide new insights into intraspecific variation.

Highlights

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10 20 30 40 50 10 15 20 25 30

chl a

chl b

Absorbance (-)

a chls & xan chls car

chls car & xan unknowns

b

Absorbance (-)

Retention time (min) c

Retention time (min) d

Carotenoids and derivatives

Chlorophylls and derivatives Graphical Abstract

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Characterization of complex photosynthetic pigment profiles in

1

European deciduous tree leaves by sequential extraction and

2

reversed-phase high performance liquid chromatography.

3

Fanny Petibon^a, Guido L.B. Wiesenberg^a 4

a. Department of Geography, University of Zurich, Winterthurerstrasse 190, CH-8057 Zürich, 5

Switzerland  6

[email protected] 7

[email protected] 8

Corresponding author: Fanny Petibon ([email protected]) 9

Department of Geography, University of Zurich, Winterthurerstrasse 190, CH-8057 Zurich, 10

Switzerland. Tel.: +41 446 355166.

11

E-mail address: [email protected] 12

Manuscript File

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Highlights

13

Highlight 1: Sequential extraction reveals a wide range of photosynthetic pigments.

14

Highlight 2: The HPLC method resolves chlorophyll a, b, and chlorophyll derivatives.

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Highlight 3: The HPLC method resolves α-, β- carotene, and carotenoids derivatives.

16

Highlight 4: Leaf pigment derivatives provide new insights into intraspecific variation.

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Abstract

18

Leaf pigments, including chlorophylls and carotenoids, are important biochemical indicators of plant 19

photosynthesis and photoprotection.

20

In this study, we developed, optimized, and validated a sequential extraction and liquid 21

chromatography-diode array detection method allowing for the simultaneous quantification of the 22

main photosynthetic pigments, including chlorophyll a, chlorophyll b, β- carotene, and the 23

characterization of plant pigment derivatives. Chromatographic separation was accomplished with 24

the newest generation of core-shell columns revealing numerous pigment derivatives. The 25

sequential extraction allowed for a better recovery of the main pigments (+25 % chlorophyll a, +30 % 26

chlorophyll b, +42 %, β- carotene), and the characterization of ca. 5.3 times more pigment 27

derivatives (i.e., up to 60 chlorophyll and carotenoids derivatives) than with a single step extraction.

28

A broad working range of concentrations (300 - 2000 ng/mL) was achieved for most pigments and 29

their derivatives and the limit of detection was generally as low as few nanograms per millilitre. The 30

method also showed adequate trueness (RSD<1%) and intermediate precision (RSD<5%).

31

The method was developed and validated with willow and spinach leaves and their extracts and The 32

method was successfully performed on leaf pigment extracts of four other European deciduous tree 33

species. Within a case study using Fagus sylvatica L. leaves, pigment derivatives revealed a high 34

within-individual tree variability throughout the growing season that could not be detected using the 35

main photosynthetic pigments alone, eventually showing that the method allowed for the 36

monitoring of pigment dynamics at unprecedented detail.

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Keywords

38

Plant pigment composition 39

Chlorophyll derivatives 40

Carotenoid derivatives 41

Deciduous tree leaves 42

Sequential extraction 43

Untargeted chromatography-optical detection 44

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1. Introduction

45

Chlorophylls and carotenoids are the most widespread and abundant plant leaf pigment families, 46

and fulfil vital physiological functions, including photosynthesis, and photoprotection. Changes in 47

chlorophyll and carotenoid composition have been observed within tree leaves across a light canopy 48

gradient [1], and season [2,3] as well as in response to a variety of biotic [4] and abiotic stress [5], 49

including heatwave and drought [6,7], UV-B [8–10], elevated CO2 and O3 [11–13], revealing the 50

performance and acclimation capacity of the plant photosynthetic apparatus.

51

These pigment responses to environmental impacts are typically investigated by measuring the 52

optical properties of leaf chlorophylls and carotenoids that are related to their chemical structure.

53

Chlorophylls (chls) are photosynthetic tetrapyroles, whose absorption spectra reflect the presence 54

or absence of a central metal and the nature of substituents [14]. Chlorophyll a (chl a) which is the 55

most abundant form of chlorophyll in terrestrial plants presents two absorption maxima at 430 nm 56

and 664 nm in acetone-water (90:10, v/v) [15]. Chlorophyll b (chl b), which differs from chl a by one 57

aldehydic group substituent, presents two absorption maxima at 460 nm and 647 nm in acetone- 58

water (90:10, v/v) (Appendix A.1a-b) [15]. All other chlorophylls that have similar absorption 59

maxima and can be clearly separated by chromatographic methods are hereafter referred to as 60

chlorophyll derivatives (chl dev). Carotenoids, or tetraterpernoids, are categorized into two 61

compound classes: carotenes, and xanthophylls. Carotenes are tetraterpenes counting two primary 62

isomers, α-carotene (α-car), and β-carotene (β-car) (Appendix A.1c-d). Xanthophylls are oxygenated 63

tetraterpene derivatives (Appendix A.1e-h). Up to seven hundred carotenoids have been found to 64

naturally occur in the terrestrial plant kingdom[16]. Among them, five xanthophylls are found in all 65

green plants with few exceptions: lutein (lut), neoxanthin (neo), violaxanthin (vio), antheraxanthin 66

(ant), and zeaxanthin (zea). lut and neo act as anion scavenger, while vio, ant, and zea form the 67

xanthophyll cycle (VAZ). Nonubiquitous carotenoids are hereafter referred to as carotenoid 68

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derivatives (car dev). All carotenoids are characterized by absorption maxima ranging from 400 to 69

550nm [15].

70

Leaf chlorophylls and carotenoids have been characterized through a variety of methods taking 71

advantage of their optical properties. This includes in-situ optical measurements of leaves using a 72

plant probe coupled with a field spectroradiometer [17], light absorption by bulk pigment extracts 73

using a spectrophotometer [15], and light absorption of individual pigments separated with a liquid 74

chromatography coupled with an optical detector. Approaches using liquid chromatography were 75

first developed early 1980s [18]. Development of C18 and C30 chromatographic columns have enabled 76

the identification of an increasing number of carotenoids [19] as well as the separation of 77

structurally similar carotenoid isomers (e.g., lut and zea [20], α-car and β-car [21], astaxanthin and 78

phytoene isomers [22]). Simultaneous characterization of chlorophylls and carotenoids were 79

successively applied to higher plant tissues [23]. Most of those methods however target a selected 80

number of the most abundant pigments (i.e., chl a, chl b, α-car, β-car, lut, neo, VAZ). Esteban and al.

81

[5] showed in a meta-analysis that chl a+b concentrations are constrained between 1.2 - 15.6 82

µmol.g^-1d.w. (in ca. 800 green plant species) and that pigment ratios (e.g., chl a:chl b, β-car:chls, 83

neo:chls) show little to no responsiveness to varying environmental conditions. Insight into the 84

functionality of the photosynthetic apparatus is thus limited by the concentration range and 85

responsiveness of targeted chlorophylls and carotenoids. A handful of studies tended to adopt a 86

metabolomic profiling approach to elucidate carotenoid profiles [24–27]. Among them, Wei et al.

87

[27] suggested that accounting for the entire pigment profile rather than the most abundant 88

pigments could better reflect physiological changes in seafood and provide a useful framework for 89

exploring mechanisms underlying pigment composition. However, to our knowledge no such 90

method is available for forest tree species.

91

A metabolomic profiling approach requires that the pigment extract is representative of the original 92

leaf pigment composition, and thus as less denaturised as possible. Yet, techniques that allow for 93

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high purity by eliminating the lipid matrix also tend to eliminate chlorophylls and favour conjugated 94

carotenoid hydrolysis [28]. Also, simple extraction of fresh leaf discs either with methanol [29,30], 95

acetone [31–33] or a mixture of those with water [34] have so far been preferred in ecophysiological 96

studies to avoid alteration of the pigment composition (Appendix. A.2). However, the solubility and 97

thus the extractability of polar and apolar pigments may differ depending on the polarity of the 98

extraction solvents resulting in an incomplete and selective pigment extraction. In addition, the 99

absence of pre-treatments may allow for the extraction of the extractable fraction but not bound 100

pigments. While heat treatments are to be excluded due to the pigment denaturation sensitivity to 101

ambient and hot temperatures, low temperature pre-treatments (e.g., freeze-drying) offer 102

promising alternatives to enhance pigment extractability.

103

To date, there is thus a lack of methods investigating complex leaf pigment profiles and ensuring a 104

minimum degradation of the pigments at each step of the preparation and characterization 105

procedure. Therefore, in this study, we develop a method including the sample preparation and 106

extraction and the chromatographic characterisation of the pigment extract. We show the benefits 107

of the optimized and validated method to a standardized procedure commonly reported in the 108

literature. We ask whether low temperature pre-treatment and a sequential extraction with a 109

solvent polarity gradient allows for the recovery of a broader and more representative range of plant 110

leaf pigments than a single step extraction. We further hypothesize that the metabolic profiling 111

approach accounting for low abundance pigments provides relevant information on plant physiology 112

that complement what we traditionally learn from the dynamics of the most abundant pigments.

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2. Material and methods

114

2.1. Materials

115

2.1.1. Standards and reagents 116

Chlorophyll a (≤95 %), chlorophyll b (≤95%), β-carotene (≤95%), lutein (≤95%) were purchased from 117

Sigma Aldrich (Steinheim, Germany). Standards were prepared in different solvents according to 118

their solubility and stability and later stored in amber vials in a deep freezer at -80°C.

119

Extraction solvents, acetone (≤99.9%, Carl Roth, Karlsruhe, Germany), isopropanol (≤99.9%, Sigma- 120

Aldrich, Steinheim, Germany), and n-hexane (≤95%, Carl Roth, Karlsruhe, Germany), were GC 121

Ultragrade. Eluents, ethyl acetate (≤99.8%, Merck, Darmstadt, Germany), and methanol (99.9%, Carl 122

Roth, Karlsruhe, Germany), were Hypergrade LC-MS. Water was deionised to >18.2 M Ω/cm 123

resistance, using a Milli-Q® Advantage A10 water purification system (Merck, Darmstadt, Germany).

124

Acetic acid (≤60%, Carl Roth, Karlsruhe, Germany) and formic acid (85%, Sigma-Aldrich, Steinheim, 125

Germany) were used as buffers. High performance liquid chromatography (HPLC) eluents were 126

filtered on glass microfiber filters (Whatman® GF6, Sigma Aldrich, Steinheim, Germany) before use.

127

2.1.2. Leaf samples 128

Frozen spinach leaves were bought in the local supermarket.

129

Leaf samples of willow (Salix alba L.) were collected along two pre-alpine rivers (46°45’ N 7°7’ W;

130

46◦44’ N, 7°18’ W) in the region of Fribourg in Switzerland, in 2016 [35]. The spinach and willow 131

samples were used for the development and validation of the sample preparation, extraction and 132

liquid chromatography method.

133

Leaf samples of beech (Fagus sylvatica L.), maple (Acer pseudoplatanus L.), lime (Tilia cordata Mill.), 134

and hornbeam (Carpinus betulus L.) were collected on mature trees located on the campus of the 135

University of Zurich (47°40’ N, 8°55’ E) at four different times during the growing season 2018. Three 136

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The purity of standards and reagents is wrong!

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leaf replicates were collected at different sampling heights (3, 6, 12 m) and under various light 137

exposures (sun vs. shade). Immediately after sampling, branches were stored in black plastic bags 138

and stored in polystyrene containers filled with dry ice and transported to a deep freezer at -80°C 139

within an hour. These samples were used to demonstrate the applicability of the method on a wide 140

range of samples and the potential of the method to assess intraspecific variation.

141

2.2. Methods

142

2.2.1. Sample preparation 143

Within 3 hours after the sampling, fresh leaves were individually weighed on an analytical scale 144

(0.01mg, Satorius Cubis®, Germany) to determine the moist weight. Three leaves of the same knot 145

or close from each other were stored at -80°C and analysed together, except for the leaves of Acer 146

pseudoplatanus L. that were analysed individually. Two replicates per branch were prepared. Latest 147

one week after sampling, each replicate was freeze-dried overnight (Freeze-dryer Alpha 2-4 LD plus, 148

Christ, Germany). Dry weight of the individual leaves was measured on an analytical scale before the 149

leaves were manually ground in liquid nitrogen with a mortar and pestle.

150

2.2.2. Leaf extraction 151

Pigments were extracted following a sequential extraction procedure using acetone:water (90:10, 152

v/v), pure acetone, and isopropanol:n-hexane (50:50, v/v). Solvents were kept in the freezer at - 153

20°C. Only small volumes of solvent were taken out of the fridge and solvent bottles immediately 154

returned after use. This ensured that pigment extraction was performed below 0°C to prevent 155

pigment degradation. All steps were performed under subdued light. Fraction 1 was obtained by 156

adding 1 mL of acetone:water directly in the vial containing 20 mg of leaf powder. The solution was 157

agitated using a vortex stirrer and left for 1 min, before being filtered on a 1 μm glass-fibre filter 158

(Macherey-Nagel, Germany) and transferred to a 10 mL amber vial kept cooled in ice. This step was 159

repeated with 1 mL of acetone:water two additional times. The same procedure was repeated with 160

pure acetone and isopropanol:n-hexane (50:50, v/v) and the extract was combined in the same vial.

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The eluate was concentrated under vacuum using a concentrator plus (Vaudaux-Eppendorf, 162

Switzerland), before being transferred to an amber 2 mL vial and redissolved in 250 μL acetone 163

before chromatographic analysis.

164

2.2.3. Chromatographic separation 165

Chromatographic separation of pigments was carried out with an Agilent 1290 Infinity UHPLC system 166

(Santa Clara, U.S.A) comprised of a binary pump (Agilent G4220A), an autosampler (Agilent G4226A) 167

with thermostat set to 4°C (Agilent G1330B), a column oven, and a diode array detector (DAD, 168

Agilent VL+ G1315C). The column oven was equipped with one Agilent Poroshell 120 SB-C18 in series 169

(150mm) with a particle size of 2.7µm and maintained at 10°C to prevent pigment degradation 170

during the chromatographic separation. 15μL of standards and samples were injected at a flow rate 171

of 0.5mL/min, which was kept constant during the analyses. Mobile phase A consisted of purified 172

water buffered with formic acid (98.5:1.5, v/v) to a pH value of 2.5. Mobile phase B consisted of 173

methanol:ethyl acetate (68:32, v/v).

174

Mobile phase composition, pH and gradient elution were optimized with a full factorial design of 175

experiment assessing the pH of eluent A, the composition of eluent B, the initial composition of the 176

mobile phase and the solvent gradient (Appendix B.4). The optimized chromatographic separation 177

had a total run time of 54min. The initial gradient elution yielded 80% eluent B (v/v). After 5 178

minutes, the proportion of eluent B in the mobile phase linearly increased to 100% with a ramp of 179

5%/min. The mobile phase composition was kept constant for 20 min. Re-equilibration time was 15 180

min between individual measurements.

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2..2.4 Compound identification and quantification 182

Compound detection was ensured by a DAD detector at 450 nm for carotenoids and 665 nm for 183

chlorophylls with a split width of 8 nm. The peak integration was performed with the Agilent 184

OpenLAB CDS ChemStation software. Peak identification of main chlorophylls and carotenoids (chl a, 185

chl b, β-car and lut) was based on the retention time and absorption spectra of the analytical 186

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standards. Other compound peaks were categorized as chlorophyll and carotenoid derivatives based 187

on a decision tree and the absorption spectra (Appendix B.1). In case of mixed spectra due to co- 188

elution or a low signal-to-noise ratio that did not allow for the attribution of the absorption 189

spectrum to either chlorophyll or carotenoid derivatives, the compound peaks were labelled as 190

unknown derivatives.

191

Quantification was based on compound peak areas. Calibration curves from two analytical standards 192

of chl a and β-car were used to quantify all compounds categorized as chlorophylls and carotenoids 193

(Appendix B.2). The concentrations ranged from 0.3 to 200 μg/mL. The pigment concentration was 194

normalized per dry leaf weight (mg/g). The peak areas of all unknown derivatives were summed and 195

considered as one contribution.

196

2.2.5. Statistical evaluation 197

The statistical evaluation was performed with R studio software [36], with the packages FrF2, 198

DoE.base, DoE.wrapper for the design of experiment and tidyverse, dplyr, ggplot2 for other analyses.

199

2.2.6. Method validation 200

To validate the sample preparation procedure, we verified that freeze-drying and manual milling did 201

not alter the pigment composition and recovery. We first compared the pigment composition 202

obtained from frozen willow and spinach leaf extracts and freeze-dried extracts of the same leaf 203

samples. We then compared the effect of automatic milling, manual milling with liquid nitrogen to 204

no milling on pigment composition and recovery. The same ground leaf sample was used to test all 205

preparation procedures. Three analytical replicates were prepared. We applied a one-way ANOVA 206

test on the total pigment concentration and on chl a, chl b and car concentrations to assess the 207

recovery and on the number of detected peaks and chl a:chl b and chl a:chl dev ratios to validate the 208

composition.

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The extraction procedure was validated on a selection of five samples of willow and spinach leaves.

210

The extracts obtained after each solvent were collected in separate vials, separately analysed, and 211

compared to the corresponding extract of the control procedure. In the control procedure, the same 212

samples were extracted using a unique solvent mixture of acetone: water (90:10, v/v) for all three 213

fractions. We looked at the absence/presence of compound peaks and applied a t-test to compare 214

the overall and individual fraction yields. Repeatability was tested on five analytical replicates.

215

The validation of the chromatographic separation was performed according to the 216

recommendations of Raposo and Ibelli-Bianco [37] and Stöckl et al [38]. We tested five performance 217

parameters listed in Table 1 [39,40]. The selectivity was determined for the main pigments chl a, chl 218

b, and car by calculated the selectivity factor between the main pigments and the neighbouring 219

peaks. The application range was defined based on the calibration curve including six concentrations 220

measured in two replicates over two days (1r x 2d) and validated with an ANOVA lack-of-fit test [38].

221

A calibration curve was established for each individual analytical standard of chl a, chl b, and β-car, 222

dissolved in pure acetone (Appendix B.2). The accuracy was defined as the total error including the 223

precision (random error) and the trueness (systematic error). The intra-day and inter-day precision 224

were assessed by measuring a spinach and beech extract twice a day during five consecutive days.

225

The trueness was measured at the concentrations of chl a standard for three replicates with an 226

acceptance criterion of < 5%. The limit of detection (LOD) and limit of quantification (LOQ) were set 227

at a signal-to-noise ratio of 3 and 6, respectively [38,40].

228

Table 1: Tested performance parameters of the chromatographic separation.

229

Performance parameters

Protocol (replicates x days)

Acceptance criteria

Samples Compounds Results

Selectivity

Full factorial DoE

Selectivity factor >1.3

Spinach

Beech chl a, chl b, β-car

chl a: 1.4 chl b: 1.5 β-car: 1.2

Application range 6

concentrat ions 1r x 2d

Anova lack-

of-fit test Standards chl a, β-car

P<0.05 (Appendi x B.2) 1

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Intra- and inter-

precision 3r x 5d ≤ 10% Spinach

Beech

chl a, chl b, chl dev, β-car , car dev

≤7.8%

Trueness 2r x 5d ≤ 5% Standards chl a ≤ 1%

LOD none

Signal-to- noise ratio of 3

Blank chl dev, car dev

LOQ none

Signal-to- noise ratio of 6

Standards

Spinach chl dev, car dev

3. Results & Discussion

230

3.1. Method development & validation

231

3.1.1. Sample preparation 232

We tested the influence of the preparation steps, i.e., freeze-drying and grinding, on the extraction 233

yield and the relative pigment composition (Fig. 1a).

234

Freeze-drying was carried out to remove the presence of moisture in leaf samples that could 235

promote pigment denaturation and degradation during storage [41,42]. Freeze-drying tended to 236

increase the extraction yield, though not significantly (ANOVA, F1,4=4.53, p = 0.167). The sublimation 237

of frozen water crystals under vacuum contribute to break cell walls and membranes, facilitating 238

solvent accessibility and the release of pigments from the protein matrix [41]. In this sense, higher 239

concentrations of chl a and chl b in freeze-dried extracts than in fresh frozen extracts of mint leaves 240

were found [43]. However, the opposite was also observed in extracts of algae [44]. Also, our result 241

confirmed that the effect of freeze-drying on the extraction yield is species-specific and depends on 242

the leaf structure and composition. Among drying procedures, freeze-drying is known to best 243

preserve pigments [41]. This was verified for our leaf samples, whose pigment composition was 244

statistically identical among fresh frozen and freeze-dried leaf extracts (ANOVA, F1,4=2, p>0.1) (Fig.

245

1b). Nonetheless, freeze-drying increases sample porosity and thus higher surfaces are potentially 246

exposed to oxygen oxidation and moisture reabsorption than in fresh frozen leaves [45]. Besides, 247

pigments released from the protein matrix may be more sensitive to heat, photochemical, and 248

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enzymatic degradation [41,46]. Also, to prevent any pigment degradation, freeze-dried leaf samples 249

were stored in sealed black bags at –80°C and later handled under subdued light and below 0°C.

250

Freeze-dried leaf samples were ground to further break the cell walls and membranes, facilitate 251

solvent accessibility and eventually improve the pigment extraction yield in comparison to the one 252

obtained from commonly non-ground fresh leaf discs [1,7,47]. The use of a mill ball or a mortar and 253

pestle to grind the leaf samples led to similar extraction yield and relative pigment composition 254

(ANOVA, F1,4 =3, p>0.1) (Fig. 1c). Automatic milling using, e.g., ball mills, is widely used to produce 255

homogeneous leaf sample powder. However, the repeated friction of the sample with the ball of a 256

horizontal mill may heat the sample and alter its composition, if the milling device cannot be cooled 257

or the duration of the milling process is too long [48]. In this study, the settings of the ball mill were 258

chosen to prevent degradation due to over-heating as evidenced by the pigment composition that is 259

comparable to the one obtained after manual grinding with liquid nitrogen (Fig. 1c). However, more 260

material was lost on the wall of the beaker of the ball mill than within the mortar. As in addition the 261

cleaning of the beaker of the ball mill (necessary to avoid contamination) was more time consuming 262

than the one of the mortar and pestle, manual grinding was preferred. The use of a mortar and 263

pestle with liquid nitrogen resulted in a finer and more homogenous powder than without liquid 264

nitrogen or with a ball mill, as also shown by Gomes et al. (see [49]). This eventually improved the 265

extraction of chl dev (ANOVA, F1,4 =4.8, p = 0.093) and decreased the relative contribution of chl a 266

(ANOVA, F1,4=13.9, p = 0.022) (Fig. 1d). As manual grinding was performed under subdued light with 267

liquid nitrogen, i.e., at below 0°C, we are confident that the greater proportion of chl dev 268

corresponds to the better extraction of low abundant pigment compounds using liquid nitrogen and 269

not denaturised pigments resulting from potential preparation artefacts [46].

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271

Fig. 1 Sample preparation procedure and its impact on the chlorophyll composition. (a) Selected 272

preparation steps (light grey boxes) and the relative composition of chlorophylls including chl a (dark 273

grey), chl b (grey), and chl dev (light grey) associated with (a) freeze drying, (b) grinding, (c) with or 274

without liquid nitrogen. Error bars correspond to the standard deviation (n=3).

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3.1.2. Extraction 277

On average, the sequential extraction with a solvent polarity gradient allowed for the extraction of 278

three times more compounds (Fig. 2a-d) than using acetone: water (90:10, v/v) as unique solvent 279

(Fig. 2a-b). Up to 61 chromatographically resolved pigment compounds, including 22 chlorophylls, 25 280

carotenoids and 14 unknown compounds resulted from the sequential extraction of beech leaves 281

(Appendix C.1). Apart from 10 compounds found only in fraction 1 (acetone: water), all observed 282

compounds were present in fraction 2 (pure acetone). The last extraction step (isopropanol: n- 283

hexane) improved on average by 16 ± 6% (SD) the extraction yield of compounds present in all 284

fractions (n= 25 compounds) and by 29 ± 7% the extraction of apolar compounds found in fraction 2 285

and 3 only (n=6 compounds). The extraction yield achieved by a sequential extraction with the 286

solvent polarity gradient was on average 40% greater than with a repeated extraction using acetone:

287

water (90:10 v/v) as unique solvent. Individual fractions respectively contribute 33 % (Fraction 1), 58 288

% (Fraction 2), and 9% (Fraction 3) to the extraction yield of the sequential extraction with the 289

chosen solvent polarity gradient. Fractions 2 and 3 contribute to 25, 30 and 42 % of the extraction 290

yield of chl a, chl b, and β-car, respectively.

291

Aqueous and pure acetone have been equally used and preferred amongst other solvents such as 292

ethanol, dimethylformamide and dimethyl sulfoxide for the extraction of photosynthetic pigments of 293

leaf samples (Appendix A.2). Our results confirmed that aqueous and pure acetone allow for the 294

extraction of the main pigments (chl a, chl b, and car) enabling a fast screening of the pigment 295

profile. However, aqueous acetone and pure acetone did not perform equally at extracting pigment 296

derivatives. Pure acetone allowed for the extraction of most of the pigment derivatives.

297

Nevertheless, highly polar and apolar pigment compounds were not present in the acetone fraction.

298

Highly polar chlorophylls and carotenoids were best extracted with acetone: water (90:10, v/v), 299

despite the fact that lyophilized leaf samples were expected to have a better affinity with acetone 300

than aqueous acetone mixture [15]. Besides, the solubility of carotenoids in pure acetone decreases 301

with decreasing compound polarity, as well as the ability of acetone to break carotenoid-protein 302

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complexes [28]. As highlighted by Simonoska et al. [28], the ability of the solvent to break down 303

pigment-protein complexes matters more for the extraction efficiency than for the solubility of the 304

compounds in the solvent mixture, especially if the concentration of pigments in the leaf sample is 305

low. Extended extraction times were commonly described in literature (up to 48 hours) to 306

compensate the weak extraction power of acetone but only the most abundant pigments were 307

targeted [34,46]. Besides, pigment recovery and composition was shown to differ among extraction 308

solvents, with an extraction carried for 12 hours at 4°C, the enzyme chlorophyllase was shown to 309

catalyse the degradation of chls into chlorophyllides in aqueous acetone but not in pure acetone 310

[46]. Those observations validated our first hypothesis that a single extraction with (aqueous or 311

pure) acetone led to a biased representation of the leaf pigment profile due to the polarity of the 312

extraction solvent and its interaction with the leaf constituents that potentially lead to extraction 313

artefacts. Frequently used methods might thus not be able to reliably characterize the majority of 314

the pigment profile. The presented method relied on sequential extraction with a solvent polarity 315

gradient. By gradually decreasing the polarity of the solvent, we succeeded in extracting apolar 316

pigment derivatives and significantly increased the extraction yield of all compounds. Isopropanol: n- 317

hexane showed a greater ability to extract compounds embedded in pigment-protein complexes 318

than acetone, which enabled to extract a wide range of pigments in a short time period. The short 319

extraction time and temperatures below 0°C help in addition to prevent pigment denaturation 320

resulting into a more genuine leaf pigment profile.

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322

Fig. 2 Relative abundance of chlorophylls (chl a, chl b, chl dev) and carotenoids (β-car, car 323

dev, xan dev) in each fraction of the sequential extraction (a), and corresponding chromatogram at 324

665nm (b, fraction 1 (extracted with acetone: water); c, fraction 2 (extracted with pure acetone); d, 325

fraction 3 (extracted with isopropanol: n-hexane)). The sum of the three fractions equals 100%.

326

18 20 22

Fraction 1 Fraction 2 Fraction 3 0

10 20 30 40 50 60

Relative abudance (%)

Sequential extraction b-car car dev chl a chl b chl dev a.

Absorbance (-)

b. Fraction 1

Absorbance (-)

Time d. Fraction 3

Absorbance (-)

c. Fraction 2

Time

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3.1.3. Chromatography 327

Chromatographic separation of the main pigments chl a, chl b, and car, and related pigment 328

derivatives that are partially present in low abundance was achieved within 54 min (Fig. 3). Pigment 329

derivatives represented on average 95 % of the detected peaks but less than 34 % of the integrated 330

peak area counts. Up to 68 chromatographically resolved pigments, including 28 chlorophylls, 28 331

carotenoids and 12 unknown pigments resulted from the sequential extraction of spinach leaves 332

(Spinacia oleracae) (Fig. 3). We obtained similar chromatograms for deciduous tree species, 333

including beech (Fagus sylvatica L.), lime (Tilia cordata Mill.) and maple (Acer pseudoplatanus L.) 334

(Appendix C.2). In beech leaves, we characterized up to 61 pigments, including 22 chlorophylls, 25 335

carotenoids, and 14 unknown pigments (Appendix C.1). The pigment derivatives categorized as 336

unknown were either below the limit of identification or co-eluted with other compounds.

337

Nevertheless, the latter were few and a large number of photosynthetic pigments were successfully 338

categorized based on their absorption spectra (Appendix B.1). The number of categorized 339

compounds exceeded what was previously achieved in a similar time run for spinach (up to 26 340

pigments, [18,50–52]) or beech (chl a, chl b, and up to 11 car [23,53]). Low abundance pigment 341

derivatives were mostly neglected in previous methods. Highly concentrated extracts and the use of 342

a chromatographic column with a poroshell structure, which improved peak shape compared to 343

column packed with porous particles, contributed to increase the number of detected pigments.

344

The influence of four parameters, i.e., the pH of the eluent A, the polarity of the eluent B, the initial 345

eluent ratio, and the eluent gradient, on the chromatographic separation was assessed by carrying 346

out a full factorial experimental design (Appendix B.4). pH mostly affected the chromatographic 347

separation (<3, F1,11=5.185, p < 0.05). Acidic pH increased the number of detected peaks by reducing 348

co-elution and improving peak separation. However, chls degrade into pheophytins and car 349

isomerize more easily at acidic pH than at neutral and basic pH [54,55]. Nevertheless, pigment 350

denaturation is expected to happen slower at low temperatures [56]. To validate the optimum pH, 351

an analytical standard of chl a was run at different pH values; a pH of 3 allowed for the best 352

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selectivity while limiting pigment denaturation. A pH comprised between 3 and 4 was also 353

considered as optimal to characterize chl dev as chlorophyllides and pheophytins. The solvent 354

gradient (F1,11=3.385, p=0.1) and to a lesser extent the composition of eluent B and its proportion in 355

the initial mobile phase (F1,11=2.032, p=0.182) also affected the chromatographic separation 356

(Appendix B.4). As most compounds appeared during the isocratic phase (100% of eluent B). The run 357

time was thus optimized by decreasing the polarity of the eluent B and increasing the proportion of 358

eluent B in the initial mobile phase.

359

Method validation was performed by evaluating the selectivity, application range, limits of detection 360

and quantification (LODs and LOQs), precision (inter- day and intra-day precision) and trueness for 361

the main pigments and pigment derivatives (Table 1). The main pigments (chl a, chl b, and car) as 362

well as some pigment derivatives presented a selectivity >1.3. Co-elution of some pigment 363

derivatives was still observed. However, it was considered sufficient for sample screening, as the low 364

abundance of co-eluted pigment derivatives made the identification challenging for individual 365

pigment derivatives that were eventually investigated as one group. The application range was 366

validated between 0.3 -2 µg/mL with an increasing residual error at low concentrations (Appendix 367

B.2). The intra- and inter-day precision and the trueness were respectively below the targeted 368

threshold. Also, with a large number of characterized pigments and acceptable precision for 369

ecophysiological studies, the developed method offers the opportunity to investigate the dynamics 370

of the main photosynthetic pigments and their derivatives.

371 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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372

Fig. 3 Chromatograms of a spinach sample at 450 nm (a, b) and at 665 nm (c, d). Individual compounds are listed in Appendix C.1.

373

10 20 30 40 50 10 15 20 25 30

chl a

chl b

Absorbance (-)

a chls & xan chls car

chls car & xan unknowns

b

Absorbance (-)

Retention time (min) c

Retention time (min) d

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3.2. Application

374

To demonstrate the applicability of the proposed analytical method, we characterized the pigment 375

profile of leaves originating from one individual beech tree. As it is well known that the leaf pigment 376

composition changes during the growing season [1,3], we tested and validated our method on leaves 377

sampled in the early, middle, and late growing season. We compared the pigment concentration and 378

composition obtained after a single step and sequential extraction using always the optimized 379

chromatographic procedure. The concentration of total chlorophyll (chl tot) was respectively 380

comprised between 0.3 and 2.6 µg chl a eq. g^-1 d.w., and 1.8 and 45.2 µg chl a eq./g^-1 d.w. over the 381

course of the growing season. The sequential extraction increased on average by 10 times the 382

recovery in chl tot and by up to 40 times the recovery in chl dev compared to a single step extraction 383

(Appendix C.3). The range of concentrations of chl tot obtained with a single step extraction is 384

comparable to the concentrations in beech leaves reported in the literature using similar extraction 385

methods (Appendix A.2) [1,3,12,57] and fall into the expected range of concentrations (1.2 - 15.6 386

µmol.g^-1d.w.) defined by Esteban et al. [5] in a meta-analysis including 809 plant species. The range 387

of concentrations obtained with a sequential extraction, however, exceeded this expected range of 388

concentrations but remained close to the concentration range (11.1 - 44 µmol.g^-1d.w.) suggested by 389

Antal et al. [58]. The range of concentration consequently depends on the preparation and 390

extraction methods. The greater recovery of pigments and greater contribution of pigment 391

derivatives after a sequential extraction than a single extraction resulted in a wider range of 392

concentrations that helped to better capture seasonal dynamics and variation within the tree crown.

393

Variation of chl a and chl b within the crown was observed between sunlit and shade leaves [12] or 394

along a vertical canopy gradient [1,59] which brought new insight about the photosynthetic activity 395

of a tree. Similarly, chl a and chl b are used as proxy to investigate seasonal dynamics and leaf 396

senescence [3,23]. However, Esteban et al. [5] showed that chl a:chl b tended to vary over a 397

restricted range and showed little responsiveness to environmental stress such as drought and 398

ozone exposure. Here, we showed that chl dev concentration varied across the season and within 399

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the tree crown offering an additional layer of understanding of the pigment dynamics. chl dev:chl tot 400

was significantly greater and chl a+b:chl tot lower (ANOVA, F1,16= 8.34, p<0.001) after a sequential 401

extraction than a single step extraction (Fig. 4). Irrespective of the extraction method, chl dev:chl tot 402

was highest in the early growing season (single step: 0.2 ± 0.1 , sequential: 0.7 ± 0.1) and tended to 403

be at its lowest in the middle of the growing season (single step: 0.09 ±0.01 , sequential: 0.3 ± 0.1) . 404

The opposite was observed for chl a. Independently of the extraction method, chl b:chl tot remained 405

constant over the entire growing season (single step: 0.13 ± 0.01 , sequential: 0.090 ± 0.002, F1,16= 406

2.90, p = 0.108). The standard deviation indicates the variability in pigment composition within an 407

individual tree among different sampling spots. The pigment profile obtained with a sequential 408

extraction tended to capture the most variability. This variability is mostly driven by chl a:chl tot 409

(cv=30%) and chl dev:chl tot (cv= 28%) and to a lesser extent by chl b:chl tot (8%). The variability in 410

chl a:chl totwithin an individual tree is maximum in the early season and minimal in the mid-season 411

and reversely for chl dev:chl tot. There was no variability observed of chl b:chl tot within an 412

individual tree was across the season. Pigment derivatives are precursors and degradation products 413

of the major pigments and thus can inform on the renewal of chl a and chl b, especially when chl 414

a:chl b ratio tend to be conserved in the mid-growing season [2,8,60]. Change in colour was shown 415

to not be a good indicator of chlorophyll degradation in leaves [57], potentially due to the 416

contribution of chl dev to the optical signal. Monitoring and characterizing chl dev offers the 417

opportunity to further explore seasonal dynamics and variation within the tree crown, in relation 418

with photosynthesis activity and microclimate.

419 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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420

Fig. 4 Relative composition of chlorophylls (chl a, chl b, chl dev) of beech leaves in early (May to 421

June), mid- (July to August), and late (September to November) growing season 2018 obtained after 422

a single extraction and a sequential extraction. Error bars correspond to standard deviation (n=4).

423 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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4. Conclusions

424

In this study, we described a new method for the simultaneous characterization of leaf chlorophylls, 425

carotenoids, and their derivatives. The sequential extraction allowed for the recovery of the main 426

pigments and pigment derivatives over a broader range of polarity in comparison with a single 427

extraction commonly used in the literature. The chromatographic separation was validated for 428

leaves of common European deciduous tree species with a good precision and application range for 429

ecophysiological applications. We showed on a case study that accounting for pigment derivatives 430

improved the characterization of intraspecific variation within an individual tree over one growing 431

season. We eventually used a standard HPLC-DAD system with the aim to provide a method 432

accessible to a wide range of users and suitable for diverse applications.

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Author contributions

434

CRediT taxonomy roles are listed with authors in alphabetical order. Conceptualization &

435

Methodology: F.P., G.B.L.W.; Data curation; F.P.; Investigation: F.P.; Formal analysis: F.P.;

436

Visualization: F.P.; Writing - original draft: F.P.; Writing - review & editing: F.P., G.B.L.W.; Funding 437

acquisition & Resource: G.B.L.W.; Project administration & Supervision: G.B.L.W.

438

Declaration of interests

439

The authors declare that they have no known competing financial interests or personal relationships 440

that could have appeared to influence the work reported in this paper.

441

Funding sources

442

This study was supported by the University of Zurich including the Swiss National Science Foundation 443

project 157778 who funded the HPLC system and the University Research Priority Program on Global 444

Change and Biodiversity.

445

Acknowledgments

446

The authors would like to thank Grünstadt Zürich who allowed us to collect samples and M.D. Hilf 447

who assisted with technical support and instrument maintenance. We are grateful to numerous 448

members of the working groups Soil Science and Biogeochemistry, Geochronology and Remote 449

Sensing Laboratories who helped during sampling campaigns.

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