High-throughput interspecies profiling of acidic plant hormones using miniaturised sample processing

doi:10.21203/rs.3.rs-1526015/v1

Download PDF

Method Article

High-throughput interspecies profiling of acidic plant hormones using miniaturised sample processing

https://doi.org/10.21203/rs.3.rs-1526015/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background: Acidic phytohormones are small molecules controlling many physiological functions in plants. A comprehensive picture of their profiles including the active forms, precursors and metabolites provides an important insight into ongoing physiological processes and is essential for many biological studies performed on plants.

Results: A high-throughput sample preparation method for liquid chromatography–tandem mass spectrometry determination of 25 acidic phytohormones classed as auxins, jasmonates, abscisates and salicylic acid was optimised. The method uses a small amount of plant tissue (less than 10 mg fresh weight) and acidic extraction in 1 mol/L formic acid in 10% aqueous methanol followed by miniaturised purification on reverse phase sorbent accommodated in pipette tips organised in a 3D printed 96-place interface, capable of processing 192 samples in one run. The method was evaluated in terms of process efficiency, recovery and matrix effects as well as establishing validation parameters such as accuracy and precision. The applicability of the method in relation to the amounts of sample collected from distantly related plant species was evaluated and the results for phytohormone profiles are discussed in the context of literature reports.

Conclusion: The method developed enables high-throughput profiling of acidic phytohormones with minute amounts of plant material, and it is suitable for large scale interspecies studies.

plant hormones

miniaturisation

in-tip microSPE

high-throughput

3D printing

liquid chromatography

mass spectrometry

evolutionarily distant plant species

The acidic phytohormones are structurally diverse signalling molecules regulating plant growth, development and defence. They comprise the phytohormonal classes of auxins (AUXs), jasmonates (JAs), abscisates (ABAs) and salicylates (SAs). AUXs are indolic compounds, precursors and metabolites of bioactive indole-3-acetic acid (IAA), which is crucial to the regulation of almost every aspect of growth and development throughout a plant life [1]. The catabolism of IAA can happen either reversibly through the formation of indole-3-acetyl-1-O-ß-D-glucose (IAA-glc), indole-3-acetyl aspartate (IAA-Asp) and indole-3-acetyl glutamate (IAA-Glu) [2] or irreversibly through the formation of 2-oxindole-3-acetic acid (oxIAA) [3] and, in turn, oxIAA can be further glycosylated to 2-oxindole-3-acetyl-1-O-ß-D-glucose (oxIAA-glc) [4–6]. The IAA conjugation and oxidation mechanisms appeared early during plant evolution and are evolutionarily conserved across the whole plant kingdom [7–10]. JAs are cyclopentenones or cyclopentanones induced by mechanical stresses or responding to wounding or necrotrophic pathogen and herbivore attacks and they contribute to the regulation of plant growth and developmental processes [11]. The pathway for jasmonate biosynthesis begins with the conversion of α-linolenic and roughanic acid to cis-(+)-12-oxo-phytodienoic acid (cis-OPDA) and dinor-oxo-phytodienoic acid (dn-OPDA) respectively. These two cyclopentenone-ring-containing molecules are subsequently subjected to reduction and β-oxidation reactions that lead to the formation of jasmonic acid (JA). JA can be conjugated to amino acids to produce the biologically active form (+)-7-iso-jasmonyl-L-isoleucine (JA-Ile) [12] or hydroxylated to inactive 12-hydroxyjasmonic acid (12-OHJA) (reviewed in [13]). While jasmonates are widely distributed in seed plants, the presence of JA and its derivatives in lower plants and algae is less understood, as the evidence regarding function of JAs in extant plant species is contradictory (reviewed in [14]). ABAs are isoprenoids derived from abscisic acid (ABA), which is responsive to abiotic stresses such as drought, salinity, heat and cold [15]. There are three different ABA hydroxylation pathways which oxidise one of the methyl groups of the ring structure (C-7’, C-8’, and C-9’), and the hydroxylation of ABA triggers further inactivation steps (reviewed in [16]). Hydroxylation at the C-8’ position is commonly thought to be the predominant ABA catabolic pathway. Consistent with this, the hydroxylated compounds phaseic acid (PA) and dihydrophaseic acid (DPA) are the most widespread and abundant ABA catabolites [16]. The 7’-hydroxyABA (7´-OHABA) form is found in a variety of plant species as a minor catabolite and 9’-hydroxyABA and its isomer neophaseic acid (neoPA) have been identified in several angiosperms [16]. SAs are phenolic compounds that have emerged as key plant defence molecules with critical roles in different aspects of plant immunity (reviewed in [17]). Despite the importance of salicylic acid (SA) in plant defence, its metabolic and signalling pathways are not fully understood. This is mainly due to the fact that these phenolic compounds have been relegated for a long time to the category of ‘secondary metabolites’ as they were considered non-essential for critical processes [18, 19].

The endogenous levels of the signalling forms of hormones and hence their activities in plants are tightly modulated by a combination of biosynthesis, metabolism, transport or release from their conjugated forms [16, 20–22]. Data on their concentrations reflect processes occurring in plants and offer an important insight into plant physiology. Depending on the plant tissue or the plant species itself, phytohormones usually occur at very low concentrations. The analytical methods employed in the determination of plant hormone levels therefore need to be very sensitive and capable of covering wide ranges of concentrations in order to track both low basal levels and levels that are elevated in response to stimuli. Moreover, the plant matrix is quite challenging from the analytical point of view. Nowadays plant hormone quantification is mostly performed by means of liquid chromatography–tandem mass spectrometry (LC-MS/MS) systems with electrospray ionisation (ESI). The current trend is to analyse large numbers of phytohormones in decreasing amounts of plant material using simple, high-throughput sample preparation techniques to provide comprehensive information about phytohormonal levels with sufficient statistical significance.

Due to the different chemical natures of targeted analytes, sample preparation from plant material prior to multi-hormone LC-MS/MS analysis is based on one of two strategies: simple, more generic, or laborious, comprising several selective steps. The simplest way is to only employ an extraction step [23–26]. This approach usually requires larger amounts of plant material, hundreds of mg fresh weight (FW) or tens of mg dry weight (DW), reaching compromise between extraction efficiency and ion suppression caused by interfering matrix present in the extracted sample. Another approach is extraction combined with derivatisation, which selectively enhances ionisation of the targeted analytes and lowers the limits of mass spectrometric (MS) detection in the presence of plant matrix. For this approach very small amounts (units of mg and lower) of plant tissue are sufficient [27]. The most common form of sample preparation is extraction followed by solid phase extraction (SPE) purification using ion exchange or reverse phase sorbents [28–30], which can be also carried out in a 96-well set up [31]. SPE purification reduces the amount of interfering matrix in the sample and enables pre-concentration of the analytes, and it usually requires tens of mg FW plant tissue.

Small-scale purification based on classical SPE takes advantage of sensitive MS/MS detection and the ability to pre-concentrate the extracted analytes, simultaneously reducing the consumption of sample, sorbents and solvents [32, 33]. Miniaturisation also facilitates automation, increases throughput in sample preparation and decreases the economic and environmental impact. The in-tip microSPE approach using small plates of Empore discs placed in ordinary pipette tips, originally developed for the purification of proteins [34], has been applied to the purification of phytohormone classes such as cytokinins [35] and AUXs [36] in units of mg FW Arabidopsis thaliana plant tissue.

The aim of this work was to develop and validate a simple, miniaturised, high-throughput method for acidic phytohormone profiling from small amounts of plant samples that would be widely applicable in large-scale interspecies studies. Here we use this approach to analyse different amounts of nine matrices from plant species ranging from algae to land plants. Determination of endogenous phytohormone profiles from evolutionarily related landmark species provides further substantial tiles to identify patterns that may have contributed to the diversification of extant plant lineages.

Lc-ms/ms Optimisation

LC-MS analysis of plant hormones predominantly employs reverse stationary phases, particularly columns with C18 chemistry [24, 25, 27–31]. To retain acidic phytohormones, taking into account the requirements for MS detection and the type of stationary phase, the mobile phases typically consist of volatile additives (formic or acetic acid) in water and acidified or pure methanol or acetonitrile. In this study, to optimise separation conditions two reverse phase C18 columns were tested: UPLC CSH C18 (2.1 x 100 mm, 1.7 µm, Waters) and Kinetex Evo C18 (2.1 x 150 mm, 2.6 µm, Phenomenex). The analysis performed on the CSH column (1.7 µm particle size) under conditions using 10 mmol/L formic acid and acetonitrile as mobile phases [29] generated backpressures close to the limits of the 1290 Infinity LC system (Agilent), and the elution of polar analytes was concentrated at the very beginning of the linear gradient. Using a Kinetex Evo column with a larger particle size (2.6 µm) lowered the system backpressure, and methanol instead of acetonitrile increased retention of the most polar compounds (e.g. oxIAA-glc), improved the overall chromatographic separation of the analytes and shortened the analytical run to 20 min (Table 1, Fig. 1).

Table 1

LC-MS/MS method parameters
Analyte	Abbreviation	Multiple reaction monitoring transitions ^a	Collision energy, eV ^a	Ionisation	Retention time, min ^b	Internal standard	Limit of detection, fmol ^c	Linear range, pmol	pK_a ^d
indole-3-acetic acid	IAA	176.1–130.1	24	[M + H]⁺	6.85 ± 0.01	[¹³C₆]-IAA	0.5	0.005–50	4.66
2-oxindole-3-acetic acid	oxIAA	192.1–146.0	12	[M + H]⁺	4.34 ± 0.01	[¹³C₆]-oxIAA	10	0.05–50	3.74
indole-3-acetyl aspartate	IAA-Asp	291.1–130.1	36	[M + H]⁺	5.54 ± 0.01	[¹³C₆]-IAA-Asp	25	0.05–50	3.74
indole-3-acetyl glutamate	IAA-Glu	305.2–130.1	24	[M + H]⁺	5.94 ± 0.01	[¹³C₆]-IAA-Glu	2.5	0.005–50	3.65
idole-3-acetyl-1-O-ß-D-glucose	IAA-glc	336.1–174.0	8	[M-H]^-	4.02 ± 0.02	[¹³C₆]-IAA-glc	10	0.05–50	5.10
2-oxindole-3-acetyl-1-O-ß-D-glucose	oxIAA-glc	352.2–190.0	8	[M-H]^-	2.66 ± 0.02	[¹³C₆]-oxIAA-glc	1	0.005–50	NP
cis-(+)-12-oxo-phytodienoic acid	cis-OPDA	293.2–275.2	12	[M + H]⁺	13.80 ± 0.01	[²H₅]-OPDA	2.5	0.005–5	4.78
(Z)-8-[3-oxo-2-(pent-2-enyl)cyclopentyl]octanoic acid	OPC-8	295.2–135.0	20	[M + H]⁺	14.36 ± 0.01	[²H₅]-OPDA	25	0.05–5	4.72
(Z)-6-[3-oxo-2-(pent-2-enyl)cyclopentyl]hexanoic acid	OPC-6	267.1–135.0	28	[M + H]⁺	13.18 ± 0.01	[²H₅]-OPDA	25	0.05–5	4.65
(Z)-4-[3-oxo-2-(pent-2-enyl)cyclopentyl]butanoic acid	OPC-4	237.2–58.8	20	[M-H]^-	11.71 ± 0.01	[²H₆]-JA	25	0.05–50	4.55
dinor-oxo-phytodienoic acid	dn-OPDA	265.2–247.1	4	[M + H]⁺	12.51 ± 0.01	[²H₂]-JA-Ile	25	0.05–5	4.60
(-)-jasmonic acid	JA	209.2–58.8	8	[M-H]^-	10.08 ± 0.01	[²H₆]-JA	0.75	0.005–50	4.71
(±)-methyl jasmonate	MeJA	225.3–151.2	12	[M + H]⁺	11.71 ± 0.01	[²H₆]-MeJA	2.5	0.005–5	NP
(±)-9,10-dihydrojasmonic acid	9,10-dhJA	211.2–58.8	16	[M-H]^-	11.09 ± 0.01	[²H₆]-JA	2.5	0.005–50	4.77
11-hydroxyjasmonic acid/ 12-hydroxyjasmonic acid	11-/12-OHJA	225.1–59.0	8	[M-H]^-	5.13 ± 0.03	[²H₆]-JA	5	0.05–50	4.46
(-)-jasmonyl-L-valine	JA-Val	310.3–151.3	16	[M + H]⁺	11.12 ± 0.01	[²H₂]-JA-Ile	1	0.005–5	4.01
(-)-jasmonyl-L-isoleucine	JA-Ile	324.3–151.2	16	[M + H]⁺	12.22 ± 0.01	[²H₂]-JA-Ile	2.5	0.005–5	4.06
(-)-jasmonyl-L-tryptophan	JA-Trp	397.3–351.3	12	[M + H]⁺	12.00 ± 0.01	[²H₂]-JA-Ile	50	0.1–5	3.39
(-)-jasmonyl-L-phenylalanine	JA-Phe	358.8–151.2	16	[M + H]⁺	12.52 ± 0.01	[²H₂]-JA-Ile	2.5	0.005–4.5	3.97
salicylic acid	SA	137.1–92.8	16	[M-H]^-	8.03 ± 0.03	[²H₄]-SA	25	0.05–50	2.79
(+)-cis,trans-abscisic acid	ABA	263.2–153.1	8	[M-H]^-	9.10 ± 0.01	[²H₆]-ABA	0.25	0.005–50	4.50
phaseic acid	PA	279.1–205.1	12	[M-H]^-	6.61 ± 0.01	[²H₃]-PA	5	0.01–50	4.30
dihydrophaseic acid	DPA	281.2–237.1	8	[M-H]^-	4.96 ± 0.02	[²H₃]-DPA	25	0.05–50	4.34
neophaseic acid	neoPA	279.1–205.1	12	[M-H]^-	8.41 ± 0.00	[²H₃]-neoPA	1	0.005–50	4.30
7'-hydroxyabscisic acid	7´-OHABA	279.1–151.1	12	[M-H]^-	7.90 ± 0.01	[²H₄]-7´-OHABA	5	0.01–50	4.37
^a Optimised using chemical standards, ^b means ± standard deviation (SD) (n = 5), ^c Limit of detection S/N > 3, expressed as amount of substance injected, ^d predicted pK_a in chemicalize.com, NP – not predicted.

Using the optimised chromatographic method was not possible to distinguish between 11-OHJA and 12-OHJA, because both compounds have the same retention times and fragmentation pattern. As in [29], the sum of unresolved 11-OHJA and 12-OHJA has been used in this study. The 11-OHJA and 12-OHJA can be separated by a gas chromatography MS method [37] or by using LC-MS with isocratic elution (Acquity UPLC BEH C18 column, 92% of 0.1% formic acid in water and 8% of 0.1% formic acid acetonitrile) [38].

High-throughput Protocol Optimisation

An in-tip microSPE stagetip assembled from SDB-XC (poly(styrenedivinylbenzene)) and C18 sorbents (Fig. 2) was used to optimise sample preparation conditions. Type and amount of the sorbent were set as previously published for AUXs [36]. Both microSPEs made up of SDB-XC/C18 and SPE Oasis® HLB (poly(divinylbenzene-co-N-vinylpyrrolidone) copolymer) sorbents provide reverse phase retention of analytes. The predicted pK_a values for acidic phytohormones range from 2.8 to 5.1 (Table 1). In order to improve their retention on reverse phase sorbents, analytes need to be in an uncharged form at a pH below their pK_a, which in this case requires acidic conditions. This corresponds to the following SPE purification methods for individual acidic phytohormone classes. For auxin analysis, plant extracts in sodium phosphate buffer (50 mmol/L, pH 7) were adjusted to pH 2.7 with hydrochloric acid before loading on an Oasis® HLB SPE column [39]. A similar approach was chosen for auxin metabolite profiling using a microSPE made from SDB-XC and C18 sorbents [36]. Purification of stress hormones (JAs, ABA, SA and IAA) on an Oasis® HLB SPE column included a preconditioning step with 0.1% formic acid in water before loading plant extracts in 10% aqueous methanol [29]. ABA and its metabolites were extracted in 10% aqueous methanol containing 1% acetic acid before being purified on an Oasis® HLB SPE column [40].

The impact of sample acidification on the retention of standards on microSPE sorbent (SDB-XC/C18) was tested. Authentic standards (1 pmol) in 10% aqueous methanol and in 1 mol/L formic acid in 10% aqueous methanol were loaded on microSPE tips and processed as described in Fig. 2. The mean purification recovery of all phytohormones was 50% for standards loaded in 10% aqueous methanol and 64% for standards in 1 mol/L formic acid in 10% aqueous methanol. Acidification was beneficial for the retention and subsequent elution of all phytohormones except for cis-OPDA and OPC-8 (Additional file 1: Fig. S1). Next, the concentration of acid in the extraction solvent and the method of acidification were investigated. Process efficiency (PE) was assessed using 2 mg FW of Arabidopsis thaliana 10-day-old seedling matrix spiked with internal standard (IS; 1 pmol of all AUXs and JAs detected in ESI+; 2 pmol for JAs detected in ESI-, ABAs and SA) extracted in 10% aqueous methanol containing different concentrations of formic acid (0.1–0.25–0.5–1 mol/L) and 10% aqueous methanol acidified to pH 2.7 (HCl) before purification. The mean PEs for extracts in 0.1–0.25–0.5–1 mol/L formic acid in 10% aqueous methanol and 10% aqueous methanol acidified to pH 2.7 (HCl) before purification were 55%, 59%, 58%, 57% and 60%, respectively. The results thus show no significant trend regardless of formic acid concentration or whether the sample was extracted in acidified solution or acidified before purification (Fig. 3).

A time-dependent increase in estimated cis-OPDA levels was previously reported in ground Arabidopsis thaliana plant material kept on ice [23]. This phenomenon was explained by possible residual activity of lipases releasing cis-OPDA from galactolipids - Arabidopsides [41]. Likewise to cis-OPDA, dn-OPDA is a constituent of the Arabidopsides [42]. When the influence of extraction approach on measured endogenous levels of phytohormones in Arabidopsis thaliana 10-day-old seedlings was studied (acidic extraction in 1 mol/L formic acid in 10% aqueous methanol and extraction in 10% aqueous methanol with pH adjusted before purification), endogenous cis-OPDA and dn-OPDA levels were found to be hugely dependent on the type of extraction (Additional file 1: Fig. S2). From these results we concluded that extraction in 1 mol/L formic acid can prevent potential enzyme activity and provide stable extraction conditions. On the other hand, the acidic conditions may decrease the stability of some analytes, especially JA or IAA conjugates with amino acids and glucose [30]. Regardless of stability, acidic conditions are crucial for the retention of the majority of analytes on reverse phase sorbent. The impact of analyte stability on precise quantification is compensated by appropriate internal standards, which are added to the plant material before extraction and get exposed to acidic conditions in the same way as the endogenous analytes.

To elute acidic plant hormones retained on the SPE reverse phase sorbent, a high percentage of organic solvent (e.g. 80% aqueous methanol) is utilised [29, 36, 39, 40]. In reverse phase chromatography of acidic phytohormones the gradients usually start at a low organic content. In order to achieve high quality of chromatographic peak shapes while injecting larger sample volumes (e.g. 10 µL), it is beneficial to include an evaporation step, which removes the excess organic solvent, and reconstitute the samples in a solvent with a low organic content similar to the composition of mobile phases at the beginning of the gradient [43]. This step also provides an opportunity to pre-concentrate the analytes. The optimum means of sample evaporation after elution (50 µL of 80% aqueous methanol) was investigated. Direct lyophilisation of 96-well plates was the most high-throughput approach available. The best recoveries were obtained using evaporation in vacuo (94%), followed by lyophilisation (93%); evaporation under a stream of nitrogen (89%) gave the lowest recovery (Additional file 1: Fig. S3). The most problematic compound was MeJA, in all probability due to its volatility [44].

Method Validation

The optimised high-throughput method was then validated. With the exception of OPC-8, the accuracies fell in the range 85–115% (Table 2.). Despite the very similar structure and physico-chemical properties of OPC-8 and cis-OPDA, which differ only in the presence of a double bound on the cyclopentane ring (cis-OPDA), the internal standard, [²H₅]-OPDA, was not able to fully compensate for losses during sample preparation, as can be seen from the different recoveries of these compounds (OPC-8 23% compared to cis-OPDA 43%) (Table 2). Precisions were lower than 15% for all analytes (Table 2).

Matrix effects (ME) express the signal suppression (ME below 100%) or enhancement (ME above 100%) caused by constituents of the sample matrix co-eluting with analytes; this occurs frequently when using MS/MS detection [45]. The ME for 2 mg FW of 10-day-old Arabidopsis thaliana seedlings ranged from 40 to 114%. The most pronounced ME was for MeJA (40%). Rather than ion suppression, the result is a consequence of the known volatility of MeJA [44] manifested during sample evaporation/lyophilisation, as the calculation of ME and also PE is related to neat standard solution, which was not subjected to sample preparation including evaporation (lyophilisation).

The recovery (RE) represents the losses of analyte in the course of sample preparation processes [45]. In our experiment the recoveries ranged from 22 to 105%.

The process efficiency (PE) reflects both the influence of sample loss during sample preparation (RE) and ME [45]. The PE values were 27–107%. The lowest RE and PE results were obtained for oxIAA-glc (22 and 28% respectively). The low stability of oxIAA-glc contributed to these results, since spiking before extraction/purification resulted in longer exposure to acidic conditions compared to spiking after extraction/purification or neat standard solution.

Table 2

Validation parameters
Analyte	Method accuracy / precision ^a,b					RE ^b,c, %	PE ^b,c,d, %	ME ^b,c, %
Spike	0.1 pmol	0.5 pmol	1.0 pmol	5.0 pmol	10.0 pmol
IAA	88.02 / 5.15	93.55 / 2.17	105.00 / 1.83	114.94 / 1.32	114.04 / 2.54	105.23 ± 6.53	61.61 ± 7.08	62.21 ± 3.14
oxIAA	94.16 / 9.37	106.24 / 1.08	113.01 / 2.06	113.97 / 2.66	113.74 / 1.21	32.11 ± 4.85	39.38 ± 9.66	98.02 ± 0.97
IAA-Asp	90.85 / 6.00	97.81 / 4.11	102.30 / 3.75	112.48 / 2.82	110.10 / 2.20	40.02 ± 2.63	46.25 ± 14.26	90.82 ± 1.34
IAA-Glu	92.73 / 0.70	109.57 / 4.04	112.09 / 1.71	113.40 / 2.40	112.48 / 2.47	66.38 ± 5.49	75.65 ± 14.20	111.86 ± 2.38
IAA-glc	NC	105.37 / 9.55	100.37 / 1.74	105.25 / 14.48	106.74 / 11.55	52.11 ± 7.83	37.71 ± 7.89	64.11 ± 4.83
oxIAA-glc	103.95 / 5.91	110.18 / 1.78	89.80 / 2.87	92.13 / 0.48	92.50 / 2.23	21.87 ± 8.24	28.25 ± 12.74	114.27 ± 3.34
cis-OPDA	94.92 / 0.87	102.94 / 1.72	114.65 / 2.57	112.29 / 2.88	113.96 / 1.73	42.71 ± 15.20	55.29 ± 15.00	102.80 ± 16.40
OPC-8	NC	NC	72.38 / 7.51	64.15 / 11.98	62.94 / 11.8	23.33 ± 1.30	-	112.27 ± 9.56
OPC-6	NC	89.02 / 7.16	86.27 / 3.67	90.84 / 6.03	87.64 / 4.25	71.02 ± 14.11	-	99.59 ± 8.32
OPC-4	111.04 / 5.83	97.33 / 2.63	93.69 / 7.36	103.65 / 2.7	100.87 / 0.58	103.03 ± 6.78	-	77.40 ± 4.28
dn-OPDA	101.73 / 7.03	105.74 / 4.96	108.58 / 3.32	103.68 / 4.26	97.80 / 4.47	86.43 ± 7.96	-	76.40 ± 0.82
JA	89.30 / 1.38	108.26 / 1.87	111.87 / 0.92	114.77 / 3.25	113.74 / 2.09	99.63 ± 2.60	70.731 ± 11.17	79.13 ± 1.21
MeJA	85.63 / 4.50	100.14 / 2.40	111.67 / 2.21	114.85 / 0.84	114.40 / 1.54	79.32 ± 5.84	40.40 ± 10.69	39.56 ± 7.87
9,10-dhJA	86.61 / 2.54	97.66 / 3.57	102.26 / 1.93	114.96 / 1.82	114.81 / 0.83	101.32 ± 2.33	-	81.84 ± 3.36
11-/12-OHJA	112.15 / 9.14	87.40 / 5.35	85.25 / 1.46	85.07 / 4.43	85.18 / 2.74	54.65 ± 7.25	-	70.76 ± 3.70
JA-Val	90.66 / 4.48	85.38 / 1.03	93.01 / 1.01	89.40 / 4.77	85.66 / 4.77	101.71 ± 5.96	-	100.09 ± 1.21
Ja-Ile	85.28 / 2.75	94.01 / 3.96	109.18 / 0.63	114.86 / 2.07	113.61 / 3.25	92.39 ± 14.39	106.74 ± 10.44	111.27 ± 1.01
JA-Trp	NC	114.94 / 6.34	103.75 / 4.66	114.06 / 3.99	112.02 / 3.34	90.55 ± 7.99	-	103.73 ± 0.90
JA-Phe	99.06 / 2.83	89.18 / 3.08	105.49 / 3.90	112.06 / 2.98	111.16 / 4.16	87.20 ± 14.06	-	106.15 ± 1.81
SA	111.82 / 5.7	85.06 / 3.55	89.71 / 2.95	110.52 / 1.24	110.79 / 1.01	86.12 ± 6.98	62.84 ± 4.10	62.73 ± 2.57
ABA	85.89 / 1.73	114.81 / 2.25	114.98 / 2.19	112.09 / 3.47	113.61 / 2.05	83.58 ± 2.41	77.69 ± 12.72	81.49 ± 1.42
PA	99.81 / 4.01	112.12 / 6.21	114.6 / 0.92	112.97 / 1.55	109.87 / 2.96	79.54 ± 1.98	62.77 ± 27.34	86.47 ± 1.19
DPA	NC	103.70 / 6.61	93.87 / 3.85	100.43 / 2.05	106.91 / 1.44	33.70 ± 5.31	42.65 ± 9.28	110.19 ± 2.37
neoPA	90.64 / 1.55	108.63 / 1.72	111.65 / 2.31	112.84 / 1.86	114.97 / 0.20	79.65 ± 3.86	74.24 ± 2.84	99.67 ± 2.05
7´-OHABA	88.33 / 5.83	105.69 / 3.28	106.62 / 3.12	114.27 / 3.72	110.59 / 3.10	83.88 ± 3.91	86.43 ± 3.27	108.82 ± 2.25
^a estimated by spiking 2 mg FW of 10-day-old Arabidopsis thaliana seedlings with authentic standards at four concentration levels, ^b values are means ± SD (n = 4), ^c estimated by spiking 2 mg FW of 10-day-old Arabidopsis thaliana seedlings with authentic or internal standards at a single concentration level, ^d assessed for internal standards labelled with stable isotopes, NC – not calculated.

Phytohormone profiles of plant species representatives

The optimised high-throughput sample preparation protocol was applied to profile phytohormones in nine matrices of different plant species across the Plantae (Fig. 4). To evaluate the influence of sample amount on the ability of the method to quantify the acidic phytohormones, different amounts of these matrices (1 – 2 – 4 – 6 – 8 mg of FW and 0.1 – 0.2 – 0.4 – 0.6 – 0.8 mg of DW, Stichococcus bacillaris) were processed. The endogenous levels and the minimal sample weight necessary for detection and quantification of acidic phytohormones are listed in Table 3. Importantly, the sample weight and the levels of phytohormones found in that sample showed a linear relationship expressed as R² ranging from 0.9820 to 0.9999 (Additional file 1: Table S1). Finally, we investigated how AUXs, JAs and ABAs contributed to the total pool in several distantly related species (Fig. 4).

TABLE 3 – in a separate file

Free IAA was quantified in all the plant species analysed (Table 3). While free IAA was the only auxin detected in the chlorophyte S. bacillaris, in the monocot T. aestivum and in the two asterids, N. tabacum and S. lycopersicum, oxIAA followed by the glucosylesters of IAA and oxIAA were the most abundant auxin forms in the moss P. patens, confirming previous findings reported for microalgae and bryophytes [46-48]. Among the seed plants, the proportion of ester conjugates in the total auxin pool was high in the conifer P. abies and in the two Brassicaceae species, A. thaliana and B. rapa. IAA-glc was the major glucosylester form in gymnosperms, while oxIAA-glc represented the majority of the total auxin pool in Brassicaceae, in accordance with published data [6, 10, 49]. The two amide conjugates of IAA, IAA-Asp and IAA-Glu, made up the majority of the total auxin pool in the woody eudicot hybrid poplar, confirming a previous finding reported in poplar where the IAA conjugate concentration was found to be higher than that of free IAA [50]. Interestingly, oxIAA was not detected in hybrid poplar leaves, while oxIAA was previously found to contribute to the asymmetric IAA distribution in bent roots of black poplar [51]. These differences in the auxin pool in poplar species might reflect the different tissues analyzed here (leaves, this study; roots, [51]) and/or species-specific metabolic pathways (hybrid poplar, this study; black poplar, [51]). Our results seem to corroborate the finding that universal elements exist among land plants that appear to form a metabolome in order to efficiently modulate auxin levels via conjugation and/or oxidation.

In jasmonate profiles, JA was detected in all the species analysed, with the exception of the moss P. patens and the chlorophyte S. bacillaris. Interestingly, the dihydro-oxylipin 9,10-dhJA and JA-Ile were the only JAs detected in the chlorophyte S. bacillaris, and to our knowledge this is the first report of a study where jasmonates were analyzed in this species. 9,10-dhJA was also found in fungi and plants, but descriptions of the presence of this dihydro-pentanone in plants are still limited. However, it has been reported that cis-OPDA was detected in the charophyte Klebsormidium flaccidum, but not in the charophyte Chara braunii (reviewed in [14]). Cis-OPDA was the only jasmonate detected in the moss P. patens and this finding is in agreement with previous studies in the two bryophytes P. patens and Marchantia polymorpha, where JA and JA-Ile were not detected [52-54], whereas these metabolites could be found in a wide range of other bryophytes [47]. Literature on the distribution of JA and its derivatives in the lower plants, bryophytes and algae, appears to be contradictory and the presence or absence of jasmonates in bryophytes and algae requires further studies for confirmation [14]. Nonetheless, currently available evidence suggests that cis-OPDA is not only the precursor of JA biosynthesis but has distinct physiological roles from those of JA-Ile, acting through a JA-independent signalling pathway, and that its biosynthesis originated in the algal lineage, before the emergence of land plants [14, 21, 55, 56]. For gymnosperms, cis-OPDA was found to be the most abundant jasmonate in the conifer P. abies, followed by JA and JA-Ile (Fig. 4) and this is in agreement with findings described in [57]. In the grass T. aestivum, cis-OPDA and, to a lesser extent, 12-OHJA, were the two most abundant jasmonates detected (Fig. 4), while JA, JA-Ile and 9,10-dhJA were found only at very low concentrations (Table 3). With respect to the dicotyledonous species, JA and JA-Ile similarly contributed to the total jasmonate pool, whereas apparent dissimilarities were found for the other jasmonates profiled (Fig. 4). For instance, dn-OPDA was detected only in Arabidopsis samples and, interestingly, it was at a similar concentration to that of cis-OPDA (Table 3). The dihydro-pentanone 9,10-dhJA was found only in the two asterids and B. rapa, not in A. thaliana or hybrid poplar (Table 3).

ABA was detected in all the species analyzed. ABA, followed by neoPA, contributed the most to the total abscisate pool in the chlorophyte S. bacillaris, confirming the presence of ABA in this species (reviewed in [58]). In the bryophytes, ABA was the only abscisate found in the moss P. patens. However, various ABA metabolites were detected in several other bryophytes, as reported in [47]. The conifer P. abies seems to prefer to catabolise ABA through the formation of neoPA, as this metabolite was found to be more abundant than DPA and 7´-OHABA. This is in contrast with findings reported in [59] where the main hydroxylated ABA catabolite was PA, followed by 9´-hydroxyABA and DPA, whereas neoPA and 7´-OHABA were below detection limits. These differences might be due to the age of the plants analyzed (2-week-old, this study; 6-week-old, [59]) and/or on the growth conditions used (vermiculite-grown seedlings, this study; hydroponic-grown seedlings, [59]). Among the angiosperms, the grass and the asterids appear to preferentially catabolise ABA through the formation of PA, while hybrid poplar catabolises ABA through DPA formation (Fig. 4). ABA was the only abscisate detected in A. thaliana, whereas DPA was found to be the most abundant ABA catabolite, followed by PA, 7´-OHABA and neoPA in B. rapa (Table 3). It should be noted that the catabolic dynamics that regulate the ABA content in plants can be subject to significant changes upon stresses that trigger the ABA response.

The acidic plant hormones are important regulators of plant processes, and comprehensive data on their profiles is often fundamental for plant biology research. A simple method for acidic plant hormone profiling comprising acidic extraction, high-throughput reverse phase in-tip microSPE sample purification using a 3D printed device and sensitive LC-MS/MS analysis was optimised and validated. The method allows simultaneous screening of 25 phytohormones and their precursors and metabolites and makes it possible to track their presence and the dynamics of their turnover in various species, requiring only units of mg of plant samples. Our protocol currently represents a fast and simple approach to phytohormone profiling that facilitates the processing of large sets of diverse plant samples.

Chemicals

Authentic and stable isotope labelled standards (Table 1) were provided from in-house standard library of JAs (cis-OPDA, OPC-8, OPC-6, OPC-4, dn-OPDA, JA, MeJA, 9,10-dhJA, 11-OHJA, 12-OHJA, JA-Val, JA-Ile, JA-Trp, JA-Phe and [²H₅]-OPDA, [²H₆]-JA, [²H₂]-(-)-JA-Ile, [²H₆]-(±)-MeJA) [29], AUXs (IAA, oxIAA, IAA-Asp, IAA-Glu, IAA-glc, oxIAA-glc and [¹³C₆]-IAA, [¹³C₆]-oxIAA, [¹³C₆]-IAA-Asp, [¹³C₆]-IAA-Glu, [¹³C₆]-IAA-glc, [¹³C₆]-oxIAA-glc) [36], ABAs (ABA, PA, DPA, neoPA, 7´-OHABA and [²H₆]-ABA, [²H₃]-PA, [²H₃]-DPA, [²H₃]-neoPA, [²H₄]-7´-OHABA) [40] and salicylates – SA (SA and [²H₄]-SA). Methanol, acetonitrile, formic acid, all LC-MS grade, acetic acid of gradient grade and hydrochloric acid were purchased from Sigma-Aldrich (St. Luis, MO, USA). Purified Milli-Q water from a Simplicity 185 System was used for preparation of aqueous solutions.

Plant Material And Growth Conditions

Arabidopsis thaliana - seedlings of ten-day-old Arabidopsis thaliana ecotype ‘Col-0’ grown in vitro in Murashige and Skoog medium Petri dishes in a growth chamber at 20°C, under long-day photoperiod (16 h light, 8 h dark) with a light intensity of 90 µmol photons m^− 2 s^− 1, were used for optimisation of the extraction and purification protocol, method validation and determination of phytohormones in a series of matrices from different specimens.

Populus tremula x alba - leaves from 90-day-old hybrid poplar seedlings grown under long-day conditions (16 h light at 22°C, 8 h dark at 18°C) as described in [60] were used in this study for phytohormone quantification.

Brassica rapa - leaves from 4-week-old Brassica rapa (ecotype ‘Pekinensis’) seedlings, cultivated in a growth chamber under long-day conditions at 21°C as described in [61], were used for phytohormone quantification.

Solanum lycopersicum - leaves from 8-week-old tomato seedlings (ecotype ‘Tornado’) cultivated in soil under environmentally controlled greenhouse conditions during spring 2020 (Olomouc, Czech Republic), were used for phytohormone quantification.

Nicotiana tabacum - leaves from 8-week-old tobacco seedlings, cultivated in a growth chamber under long-day conditions (16 h light at 20°C, 8 h dark at 18°C) as described in [40], were used for phytohormone quantification.

Triticum aestivum - leaves from 7-day-old seedlings of winter type wheat (ecotype ‘Turandot’) were used in this study for phytohormone quantification. Seedlings were obtained from dormant seeds that were soaked for 5 hours in tap water to germinate, sown into vermiculite, cultivated under long-day conditions at 21°C and irrigated every second day with 0.5 L of Hoagland’s nutrient solution [62].

Picea abies − 14-day-old spruce whole seedlings, grown in a growth chamber under long-day conditions (16 h light at 22°C, 8 h dark at 18°C) as described in [10], were used in this study for phytohormone quantification.

Physcomitrella patens − 3-week-old whole gametophores from Physcomitrella patens (Hedw.) Bruch & Schimp (ecotype ‘Gransden 2004’) wild type were used in this study for phytohormone quantification. The plants were grown axenically in 9-cm Petri dishes on Knop medium (100 mg/L Ca(NO₃)₂·4H₂O, 25 mg/L KCl, 25 mg/L KH₂PO₄, 25 mg/L MgSO₄·7H₂O and 1.25 mg/L FeSO₄·7H₂O, pH 5.8). Knop medium was supplemented with 92.05 mg/L ammonium tartrate, 0.5 mg/L nicotinic acid, 0.125 mg/L p-amino benzoic acid, 2.5 mg/L thiamine HCl, trace-element solution (0.614 mg/L H₃BO₃, 0.389 mg/L MnCl₂·4H₂O, 0.059 mg/L NiCl₂·6H₂O, 0.055 mg/L CoCl₂·6H₂O, 0.055 mg/L CuSO₄·5H₂O, 0.055 mg/L ZnSO₄·7H₂O, 0.0386 mg/L Al₂(SO₄)₃·18H₂O, 0.028 mg/L KBr, 0.028 mg/L KI, 0.028 mg/L LiCl, 0.028 mg/L SnCl₂·2H₂O) and 200 mg/L glucose. Medium was solidified with 1.5% (w/v) plant agar. Plants were cultured under standard conditions in a growth chamber at 20 ± 1°C, under a 16/8 h light/dark photoperiod with a light intensity of 90 µmol photons m^− 2 s^− 1. Plants were subcultured onto fresh medium every 3 weeks.

Stichococcus bacillaris - lyophilised green algae isolated from a pond in a region of the town Nové Hrady (Czech Republic) in summer 2013 were used in this study for phytohormone quantification.

When harvested, all plant tissues were immediately frozen in liquid nitrogen and stored at -80°C.

Sample Extraction

Plant tissue was homogenised and powdered with a mortar and pestle under liquid nitrogen and reweighed into samples of approximately 10 mg FW or 1 mg of DW (Stichococcus bacillaris). The samples were extracted in cold 1 mol/L formic acid in 10% aqueous methanol so that 1 mL of the extraction solvent contained 10 mg FW or 1 mg DW. Internal standards were added to the samples to give 5 pmols of [¹³C₆]-IAA, [¹³C₆]-oxIAA, [¹³C₆]-IAA-Asp, [¹³C₆]-IAA-Glu, [¹³C₆]-IAA-glc, [¹³C₆]-oxIAA-glc, [²H₅]-OPDA, [²H₂]-(-)-JA-Ile, [²H₆]-(±)-MeJA and 10 pmols of [²H₆]-JA, [²H₆]-ABA, [²H₃]-PA, [²H₃]-DPA, [²H₃]-neoPA, [²H₄]-7´-OHABA, [²H₄]-SA per 1 mL. To each of the samples, 4 ceria-stabilised zirconium oxide 2 mm beads (Retsch GmbH, Haan, Germany) were added, the samples were placed on an MM 400 mixer mill (Retsch GmbH, Haan, Germany) (29 Hz, 10 min, precooled holders), and centrifuged (25 200 g, 20 min, 8°C), and 100 µL (1 mg of FW, 0.1 mg DW) or 200 µL (2 mg of FW, 0.2 mg of DW) of supernatant was loaded on a stagetip (Fig. 2A). The samples were handled at 8°C in a CoolRack® in a CoolBox™ (Biocision, Larkspur, CA, USA) in the course of the sample preparation steps.

In-tip Microspe High-throughput Purification

For in-tip microSPE columns, the stagetips [34] were self-assembled using ordinary pipette tips (2–200 µL, Eppendorf) and 3 layers of SDB-XC and C18 sorbent (3M™ Empore™, St. Paul, MN, USA) similarly to the method published for auxin metabolite profiling [36] (Fig. 2B).

A 96-place tip-holder was projected in 123D Design (AutoDesk Inc., San Rafael, CA, USA) and printed by DeltiX (Trilab, Czech Republic) using polylactic acid filament. This holder was designed to be compatible with regular 96-well plates and a centrifuge rotor for 96-well plates (M-20, 75003624, Heraeus Megafuge 16K centrifuge, Thermo Fisher Scientific, Waltham, MA, USA) (Fig. 2B). The design and dimensions of the device are provided in Additional file 1: Fig. S4. Depending on number of samples, the stagetips were accommodated in 3D-printed 96-place tip-holders adaptable to process different number of large sets of samples, up to 192 samples in one run.

The purification protocol was adopted from [36] with modifications (Fig. 2A). The stagetips, located in a centrifuge, were conditioned with 50 µL of acetone (320 g, 10 min, 8°C), 50 µL of methanol (320 g, 10 min, 8°C), and 50 µL of water (627 g, 15 min, 8°C). 100 or 200 µL of sample extract (1742 g, 20 min; 8°C) was applied, washed with 50 µL of 0.1% aqueous acetic acid in water (819 g, 20 min, 8°C) and eluted with 80% aqueous methanol (1280 g, 20 min, 8°C) into a 96-well plate suitable for direct injection onto an LC-MS/MS system. Apart from the sample loads, all liquids were applied with an eight-channel pipette (Fig. 2B). Finally, the eluents in the 96-well plate were lyophilized (Labconco, Kansas city, MO, USA) and the plate stored at -20°C until required for analysis. When evaporation techniques tested TurboVap LV system (Caliper Life Sciences, Hopkinton, MA, USA) for evaporation under gentle stream of nitrogen or Acid-Resistant CentriVaps benchtop concentrator (Labconco, Kansas city, MO, USA) for evaporation in vacuo were used.

Lc-ms/ms Analysis

All analyses were carried out on an Agilent 6490 Triple Quadrupole LC/MS system coupled to a 1290 Infinity LC system (Agilent Technologies, Santa Clara, CA, USA). The data were processed in the MassHunter Quantitative software package version B.09.00 (Agilent Technologies, Santa Clara, CA, USA).

Purified and freeze-dried extracts were each dissolved in 40 µL of 20% aqueous acetonitrile and injected (10 µL) onto a Kinetex Evo C18 reverse phase column 2.1 x 150 mm, particle size 2.6 µm (Phenomenex, Torrance, CA, USA) protected by an inlet filter, using 10 mmol/L formic acid in water and methanol as mobile phases at a flow rate of 0.3 mL/min and a temperature of 60°C. Gradient elution started at 2 min, going from 20–90% of methanol at 13.5 min, continuing within 0.5 min to 100% methanol. After 1 min of 100% methanol, the methanol content was decreased to 20% over the next 0.5 min and the column was equilibrated with 20% methanol for 3.5 min before the next injection.

The MS system was operated in dynamic multiple reaction monitoring mode in ESI positive and negative ionisation mode simultaneously (Table 1). The MS settings, multiple reaction monitoring transitions, and collision energies were optimised to previous standards. The nozzle voltage was set to 0 V, the capillary voltage to 2800/3000 V positive/negative mode, and the drying gas was at 130°C with a flow rate of 14 L/min. The sheath gas was heated to 400°C and its flow rate was set to 12 L/min.

Method Validation

The method was validated in terms of accuracy and precision at five concentration levels using 2 mg FW of 10-day-old Arabidopsis thaliana seedlings spiked with authentic (0.1, 0.5, 1.0, 5.0 and 10.0 pmol) and internal standards (1 pmol for all AUXs, JAs detected in ESI+, 2 pmol for JAs, ABAs, SA detected in ESI-) (Table 1) with four replicates. The accuracy was calculated using the determined levels of the analytes (pmols) with the endogenous levels subtracted divided by the nominal level of the spike (pmol) and expressed as a percentage of the nominal level. Precision was calculated as the relative standard deviation of determined levels (%). The PE, RE and ME were assessed as in [45] using 2 mg FW of plant matrix spiked before or after extraction and the purification procedure at one concentration level (4 pmol) with four replicates. Briefly, to evaluate PE, the mean peak area of the IS spiked before sample preparation was divided by the mean peak area of the neat solution of IS without any extraction and purification, expressed as a percentage. RE was calculated as the percentage of the mean peak rate of authentic standards spiked before and after the extraction and purification process. ME was calculated by dividing the mean peak area of authentic standards spiked after sample preparation by the peak area of the neat solution with the same amount of analyte. All validation samples were processed by the optimised extraction and purification protocol (Fig. 2).

To evaluate the influence of the method of drying the samples after purification, 50 µL aliquots of solutions of all authentic standards (at 0.5 pmol) in 80% aqueous methanol were processed using evaporation under a stream of nitrogen, in vacuo drying and lyophilisation, with four replicates of each. The dried samples were redissolved in 50 µL 20% aqueous acetonitrile mimicking the conditions immediately before LC-MS/MS analysis. The recovery was calculated by dividing the peak area of a dried standard by that of the original authentic standard solution, expressed as a percentage.

Calibration And Quantification

The contents of all samples were quantified using logarithmically transformed calibration curves constructed by plotting the responses of calibration standards (analyte peak area divided by IS area multiplied by IS concentration) against their known concentrations. The calibrations spanned from six to three orders of magnitude (Table 1).

Amount Of Plant Matrix Required For Phytohormone Profiling

The weight of sample necessary for quantification of the analytes was evaluated using 1–2 – 4–6 – 8 mg FW or 0.1–0.2–0.4–0.6–0.8 mg DW of plant matrix. Either 1 or 2 mg FW (0.1 or 0.2 mg of DW) plant matrix were loaded on a stagetip, and eluates of 2 mg (0.2 mg of DW) were combined to obtain samples of 4 to 8 mg FW (0.4 to 0.8 mg DW). Each sample amount was analysed in either triplicate, or, for 1 mg FW (0.1 mg DW), in duplicate. The mean concentration of each analyte was calculated from all samples in which the analyte was quantified, e.g. an analyte quantified at 1 mg (n = 2) was also quantified at 2 mg (n = 3), 4 mg (n = 3), 6 mg (n = 3) and 8 mg (n = 3), giving a total of 14 samples (n = 14).

7´-OHABA: 7´-hydroxyabscisic acid; 9,10-dhJA: 9,10-dihydrojasmonic acid; 11-OHJA: 11-hydroxyjasmonic acid; 12-OHJA: 12-hydroxyjasmonic acid; ABA: abscisic acid; ABAs: abscisates; AUXs: auxins; cis-OPDA: cis-(+)-12-oxo-phytodienoic acid; dn-OPDA: dinor-oxo-phytodienoic acid; DPA: dihydrophaseic acid; DW: dry weight; ESI: electrospray ionisation; FW: fresh weight; IAA-Asp: indole-3-acetyl aspartate; IAA-Glu: indole-3-acetyl glutamate; IAA: indole-3-acetic acid; IAA-glc: indole-3-acetyl-1-O-ß-D-glucose; IS: internal standard; JA: jasmonic acid; JA-Ile: jasmonyl-isoleucine; JAs: jasmonates; LC: liquid chromatography; ME: matrix effects; MeJA: methyl jasmonate; MS: mass spectrometry; MS/MS: tandem mass spectrometry; neoPA: neophaseic acid; oxIAA: 2-oxindole-3-acetic acid; oxIAA-glc: 2-oxindole-3-acetyl-1-O-ß-D-glucose; OPC-4: (Z)-4-[3-oxo-2-(pent-2-enyl)cyclopentyl]butanoic acid; OPC-6: (Z)-6-[3-oxo-2-(pent-2-enyl)cyclopentyl]hexanoic acid; OPC-8: (Z)-8-[3-oxo-2-(pent-2-enyl)cyclopentyl]octanoic acid; PA: phaseic acid; PE: process efficiency; RE: recovery; SA: salicylic acid; SAs: salicylates; SD: standard deviation; SPE: solid phase extraction.

Acknowledgements

The authors thank Hana Svobodová and Miroslava Špičáková for technical support, and David Friedecký and Lukáš Kouřil for 3D printing.

Author contributions

J.Š. designed and performed the analytical experiments, wrote and organised the manuscript. F.B. wrote the biological part of the background and the results and discussion sections of the manuscript. O.N. contributed to manuscript writing. A.P. supervised development of the sample preparation protocol. V.M. and A.Ž. synthesised analytical standards. K.F., M.S. and O.N. supervised the research, discussed results and reviewed the manuscript. All authors reviewed and approved the manuscript.

Funding

This work was financially supported by Czech Science Foundation project No. 19-10464Y and by the Palacký University Olomouc Young Researcher grant no. JG_2020_002.

Availability of data and materials

The data used for this manuscript are available from the corresponding author on request.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Casanova-Saez R, Mateo-Bonmati E, Ljung K. Auxin Metabolism in Plants. Cold Spring Harb Perspect Biol. 2021;13:a039867.
Hayashi K, Arai K, Aoi Y, Tanaka Y, Hira H, Guo RP, et al. The main oxidative inactivation pathway of the plant hormone auxin. Nat Commun. 2021;12:6752.
Porco S, Pencik A, Rashed A, Voss U, Casanova-Saez R, Bishopp A, et al. Dioxygenase-encoding AtDAO1 gene controls IAA oxidation and homeostasis in Arabidopsis. Proc Natl Acad Sci. 2016;113:11016–21.
Tanaka K, Hayashi K, Natsume M, Kamiya Y, Sakakibara H, Kawaide H, et al. UGT74D1 Catalyzes the Glucosylation of 2-Oxindole-3-Acetic Acid in the Auxin Metabolic Pathway in Arabidopsis. Plant Cell Physiol. 2014;55:218–228.
Brunoni F, Collani S, Simura J, Schmid M, Bellini C, Ljung K. A bacterial assay for rapid screening of IAA catabolic enzymes. Plant Methods 2019;15.
Muller K, Dobrev PI, Pencik A, Hosek P, Vondrakova Z, Filepova R, et al. DIOXYGENASE FOR AUXIN OXIDATION 1 catalyzes the oxidation of IAA amino acid conjugates. Plant Physiol. 2021;187:103–115.
Ludwig-Muller J. Auxin conjugates: their role for plant development and in the evolution of land plants. J Exp Bot. 2011;62:1757–73.
Cooke TJ, Poli D, Sztein AE, Cohen JD. Evolutionary patterns in auxin action. Plant Mol Biol. 2002;49:319–38.
Zhang J, Peer WA. Auxin homeostasis: the DAO of catabolism. J Exp Bot. 2017;68:3145–54.
Brunoni F, Collani S, Casanova-Saez R, Simura J, Karady M, Schmid M, et al. Conifers exhibit a characteristic inactivation of auxin to maintain tissue homeostasis. New Phytol. 2020;226:1753–65.
Wasternack C, Hause B. Jasmonates: biosynthesis, perception, signal transduction and action in plant stress response, growth and development. An update to the 2007 review in Annals of Botany. Ann Bot. 2013;111:1021–58.
Fonseca S, Chini A, Hamberg M, Adie B, Porzel A, Kramell R, et al. (+)-7-iso-Jasmonoyl-L-isoleucine is the endogenous bioactive jasmonate. Nat Chem Biol. 2009;5:344–50.
Wasternack C. Jasmonates: An update on biosynthesis, signal transduction and action in plant stress response, growth and development. Ann Bot. 2007;100:681–97.
Han GZ. Evolution of jasmonate biosynthesis and signaling mechanisms. J Exp Bot. 2017;68:1323–31.
Chen K, Li GJ, Bressan RA, Song CP, Zhu JK, Zhao Y. Abscisic acid dynamics, signaling, and functions in plants. J Integr Plant Biol. 2020;62:25–54.
Nambara E, Marion-Poll A: Abscisic acid biosynthesis and catabolism. Ann Rev Plant Biol. 2005;56:165–85.
Zhang YL, Li X. Salicylic acid: biosynthesis, perception, and contributions to plant immunity. Curr Opin Plant Biol. 2019;50:29–36.
Lefevere H, Bauters L, Gheysen G. Salicylic Acid Biosynthesis in Plants. Front Plant Sci. 2020;11:338.
Dempsey MA, Vlot AC, Wildermuth MC, Klessig DF. Salicylic Acid Biosynthesis and Metabolism. The Arabidopsis Book. 2011;9:e0156.
Ljung K. Auxin metabolism and homeostasis during plant development. Development 2013;140:943–50.
Wasternack C, Strnad M. Jasmonate signaling in plant stress responses and development - active and inactive compounds. New Biotechnol. 2016;33:604–13.
Maruri-Lopez I, Yaniri Aviles-Baltazar N, Buchala A, Serrano M. Intra and Extracellular Journey of the Phytohormone Salicylic Acid. Front Plant Sci. 2019;10:423.
Trapp MA, De Souza GD, Rodrigues E, Boland W, Mithofer A. Validated method for phytohormone quantification in plants. Front Plant Sci. 2014;5:417.
Erland LAE, Shukla MR, Glover WB, Saxena PK. A simple and efficient method for analysis of plant growth regulators: a new tool in the chest to combat recalcitrance in plant tissue culture. Plant Cell Tissue Organ Cult. 2017;131:459–70.
Sheflin AM, Kirkwood JS, Wolfe LM, Jahn CE, Broeckling CD, Schachtman DP, et al. High-throughput quantitative analysis of phytohormones in sorghum leaf and root tissue by ultra-performance liquid chromatography-mass spectrometry. Anal Bioanal Chem. 2019;411:4839–48.
Yonny ME, Ballesteros-Gomez A, Adamo MLT, Torresi AR, Nazareno MA, Rubio S. Supramolecular solvent-based high-throughput sample treatment for monitoring phytohormones in plant tissues. Talanta. 2020;219:121249.
Cai WJ, Yu L, Wang W, Sun MX, Feng YQ. Simultaneous Determination of Multiclass Phytohormones in Submilligram Plant Samples by One-Pot Multifunctional Derivatization-Assisted Liquid Chromatography-Tandem Mass Spectrometry. Anal Chem. 2019;91:3492–9.
Cao ZY, Sun LH, Mou RX, Zhang LP, Lin XY, Zhu ZW, et al. Profiling of phytohormones and their major metabolites in rice using binary solid-phase extraction and liquid chromatography-triple quadrupole mass spectrometry. J Chromatogr A. 2016;1451:67–74.
Flokova K, Tarkowska D, Miersch O, Strnad M, Wasternack C, Novak O. UHPLC-MS/MS based target profiling of stress-induced phytohormones. Phytochemistry. 2014;105:147–57.
Simura J, Antoniadi I, Siroka J, Tarkowska D, Strnad M, Ljung K, et al. Plant Hormonomics: Multiple Phytohormone Profiling by Targeted Metabolomics. Plant Physiol. 2018;177:476–89.
Balcke GU, Handrick V, Bergau N, Fichtner M, Henning A, Stellmach H, et al. An UPLC-MS/MS method for highly sensitive high-throughput analysis of phytohormones in plant tissues. Plant Methods. 2012;8:47.
Miggiels P, Wouters B, van Westen GJP, Dubbelman AC, Hankemeier T. Novel technologies for metabolomics: More for less. Trends Anal Chem. 2019;120:115323.
Burato JSD, Medina DAV, de Toffoli AL, Maciel EVS, Lancas FM. Recent advances and trends in miniaturized sample preparation techniques. J Sep Sci. 2020;43:202–25.
Rappsilber J, Mann M, Ishihama Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat Protoc. 2007;2:1896–906.
Svacinova J, Novak O, Plackova L, Lenobel R, Holik J, Strnad M, et al. A new approach for cytokinin isolation from Arabidopsis tissues using miniaturized purification: pipette tip solid-phase extraction. Plant Methods. 2012;8:17.
Pencik A, Casanova-Saez R, Pilarova V, Zukauskaite A, Pinto R, Micol JL, et al. Ultra-rapid auxin metabolite profiling for high-throughput mutant screening in Arabidopsis. J Exp Bot. 2018;69:2569–79.
Miersch O, Neumerkel J, Dippe M, Stenzel I, Wasternack C. Hydroxylated jasmonates are commonly occurring metabolites of jasmonic acid and contribute to a partial switch-off in jasmonate signaling. New Phytol. 2008;177:114–27.
Glauser G, Grata E, Dubugnon L, Rudaz S, Farmer EE, Wolfender JL. Spatial and temporal dynamics of jasmonate synthesis and accumulation in Arabidopsis in response to wounding. J Biol Chem. 2008;283:16400–7.
Novak O, Henykova E, Sairanen I, Kowalczyk M, Pospisil T, Ljung K. Tissue-specific profiling of the Arabidopsis thaliana auxin metabolome. Plant J. 2012;72:523–36.
Tureckova V, Novak O, Strnad M. Profiling ABA metabolites in Nicotiana tabacum L. leaves by ultra-performance liquid chromatography-electrospray tandem mass spectrometry. Talanta. 2009;80:390–9.
Stelmach BA, Muller A, Hennig P, Gebhardt S, Schubert-Zsilavecz M, Weiler EW. A novel class of oxylipins, sn1-O-(12-oxophytodienoyl)-sn2-O-(hexadecatrienoyl)-monogalactosyl diglyceride, from Arabidopsis thaliana. J Biol Chem. 2001;276:12832–8.
Genva M, Akong FO, Andersson MX, Deleu M, Lins L, Fauconnier ML. New insights into the biosynthesis of esterified oxylipins and their involvement in plant defense and developmental mechanisms. Phytochem Rev. 2019;18:343–58.
Keunchkarian S, Reta M, Romero L, Castells C. Effect of sample solvent on the chromatographic peak shape of analytes eluted under reversed-phase liquid chromatogaphic conditions. J Chromatogr A. 2006;1119:20–8.
Jang G, Shim JS, Jung C, Song JT, Lee HY, Chung PJ, et al. Volatile methyl jasmonate is a transmissible form of jasmonate and its biosynthesis is involved in systemic jasmonate response in wounding. Plant Biotechnol Rep. 2014;8:409–19.
Matuszewski BK, Constanzer ML, Chavez-Eng CM. Strategies for the assessment of matrix effect in quantitative bioanalytical methods based on HPLC-MS/MS. Anal Chem. 2003;75:3019–30.
Stirk WA, Ordog V, Novak O, Rolcik J, Strnad M, Balint P, et al. Auxin and cytokinin relationships in 24 microalgal strains. J Phycol. 2013;49:459–67.
Zaveska Drabkova L, Dobrev PI, Motyka V. Phytohormone Profiling across the Bryophytes. Plos One 2015;10:e0125411.
Kosakivska IV, Voytenko LV, Likhnyovskiy RV, Ustinova AY. Effect of temperature on accumulation of abscisic acid and indole-3-acetic acid in Triticum aestivum L. seedlings. Genet Plant Physiol. 2014; 4(3–4):201–8.
Pavlovic I, Petrik I, Tarkowska D, Lepedus H, Vujcic Bok V, Radic Brkanac S, et al. Correlations between Phytohormones and Drought Tolerance in Selected Brassica Crops: Chinese Cabbage, White Cabbage and Kale. Int J Mol Sci. 2018;19:2866.
Junghans U, Polle A, Duchting P, Weiler E, Kuhlman B, Gruber F, et al. Adaptation to high salinity in poplar involves changes in xylem anatomy and auxin physiology. Plant Cell Environ. 2006;29:1519–31.
De Zio E, Trupiano D, Karady M, Antoniadi I, Montagnoli A, Terzaghi M, et al. Tissue-specific hormone profiles from woody poplar roots under bending stress. Physiol Plant. 2019;165:101–13.
Stumpe M, Gobel C, Faltin B, Beike AK, Hause B, Himmelsbach K, et al. The moss Physcomitrella patens contains cyclopentenones but no jasmonates: mutations in allene oxide cyclase lead to reduced fertility and altered sporophyte morphology. New Phytol. 2010;188:740–9.
De Leon IP, Schmelz EA, Gaggero C, Castro A, Alvarez A, Montesano M. Physcomitrella patens activates reinforcement of the cell wall, programmed cell death and accumulation of evolutionary conserved defence signals, such as salicylic acid and 12-oxo-phytodienoic acid, but not jasmonic acid, upon Botrytis cinerea infection. Mol Plant Pathol. 2012;13:960–74.
Yamamoto Y, Ohshika J, Takahashi T, Ishizaki K, Kohchi T, Matusuura H, et al. Functional analysis of allene oxide cyclase, MpAOC, in the liverwort Marchantia polymorpha. Phytochemistry. 2015;116:48–56.
Stintzi A, Weber H, Reymond P, Browse J, Farmer EE. Plant defense in the absence of jasmonic acid: The role of cyclopentenones. Proc Natl Acad Sci. 2001;98:12837–42.
Monte I, Kneeshaw S, Franco-Zorrilla JM, Chini A, Zamarreno AM, Garcia-Mina JM, et al. An Ancient COI1-Independent Function for Reactive Electrophilic Oxylipins in Thermotolerance. Curr Biol. 2020;30:962–71.
Alallaq S, Ranjan A, Brunoni F, Novak O, Lakehal A, Bellini C. Red Light Controls Adventitious Root Regeneration by Modulating Hormone Homeostasis in Picea abies Seedlings. Front Plant Sci. 2020;11:586140.
Hartung W. The evolution of abscisic acid (ABA) and ABA function in lower plants, fungi and lichen. Funct Plant Biol. 2010;37:806–12.
Pashkovskiy PP, Vankova R, Zlobin IE, Dobrev P, Ivanov YV, Kartashov AV, et al. Comparative analysis of abscisic acid levels and expression of abscisic acid-related genes in Scots pine and Norway spruce seedlings under water deficit. Plant Physiol Biochem. 2019;140:105–12.
Edwards KD, Takata N, Johansson M, Jurca M, Novak O, Henykova E, et al. Circadian clock components control daily growth activities by modulating cytokinin levels and cell division-associated gene expression in Populus trees. Plant Cell Environ. 2018;41:1468–82.
Pavlovic I, Pencik A, Novak O, Vujcic V, Brkanac SR, Lepedus H, et al. Short-term salt stress in Brassica rapa seedlings causes alterations in auxin metabolism. Plant Physiol. Biochem. 2018;125:74–84.
Hoagland DR, Arnon DI. The water-culture method for growing plants without soil. 2nd ed. California agricultural experiment station; 1950.

Table 3 is available in the Supplementary Files section

No competing interests reported.

Additionalfile1.pdf
Supplementary informationThe online version contains supplementary material available at: Additional file 1: Fig. S1. Purification recoveries of standards in acidified and non-acidified solution. Calculated as mean area for standard solution (1 pmol) processed by microSPE divided by the area for the corresponding amount of standard without microSPE × 100. Error bars represent ± SD, n = 3. Fig. S2. Endogenous levels of dn-OPDA and cis-OPDA found in 10-day-old Arabidopsis thaliana seedlings using different extraction conditions over time. Error bars represent ± SD, n = 4. Fig. S3. Influence of the method of drying samples after purification using evaporation under a stream of nitrogen, in vacuo drying and lyophilisation, expressed as recovery after evaporation. Calculated as mean peak area for dried and re-dissolved standard divided by area for the standard solution at the same concentration without evaporation, expressed as a percentage. Error bars represent ± SD, n = 4. Table S1. Range of sample weights (1 – 2 – 4 – 6 – 8 mg of FW or 0.1 – 0.2 – 0.4 – 0.6 – 0.8 mg of DW) in which the analyte was quantified, R² of regression of sample weight and analyte level found in sample. Fig. S4. Design and dimensions of 3D printed 96-place microSPE holder.
Table3.docx

Download PDF

Editorial decision: Major revision
16 Jul, 2022
Reviews received at journal
15 Jul, 2022
Reviewers agreed at journal
05 Jul, 2022
Reviews received at journal
17 Jun, 2022
Reviewers agreed at journal
16 May, 2022
Reviewers invited by journal
12 May, 2022
Editor assigned by journal
06 Apr, 2022
Submission checks completed at journal
06 Apr, 2022
First submitted to journal
05 Apr, 2022

You are reading this latest preprint version

High-throughput interspecies profiling of acidic plant hormones using miniaturised sample processing

Status:

Version 1

Abstract

Figures

Background

Results And Discussion

Lc-ms/ms Optimisation

High-throughput Protocol Optimisation

Method Validation

Conclusion

Methods

Chemicals

Plant Material And Growth Conditions

Sample Extraction

In-tip Microspe High-throughput Purification

Lc-ms/ms Analysis

Method Validation

Calibration And Quantification

Amount Of Plant Matrix Required For Phytohormone Profiling

Abbreviations

Declarations

References

Table

Additional Declarations

Supplementary Files

Status:

Version 1