The field of plant hormonomics focuses on the qualitative and quantitative analysis of the hormone complement in plant samples, akin to other omics sciences. Plant hormones, alongside primary and secondary metabolites, govern vital processes throughout a plant's lifecycle. While active hormones have received significant attention, studying all related compounds provides valuable insights into internal processes. Conventional single-class plant hormone analysis employs thorough sample purification, short analysis and triple quadrupole tandem mass spectrometry. Conversely, comprehensive hormonomics analysis necessitates minimal purification, robust and efficient separation and better-performing mass spectrometry instruments. This review summarizes the current status of plant hormone analysis methods, focusing on sample preparation, advances in chromatographic separation and mass spectrometric detection, including a discussion on internal standard selection and the potential of derivatization. Moreover, current approaches for assessing the spatiotemporal distribution are evaluated. The review touches on the legitimacy of the term plant hormonomics by exploring the current status of methods and outlining possible future trends. Plant hormonomics, a term coined recently –4, sharePlant hormones are low molecular weight naturally occurring plant growth regulators. Interestingly, their production is not exclusive to plants as they are also found in microorganisms and fungi. These substances govern virtually all essential processes in a plant’s lifecycle, including germination, plant development and growth, interaction with the biotic and abiotic environment, the reproductive phase and fruit development and seed formation –18. HoweEach class of plant hormones performs a different role and induces different responses and changes in plants. Previously, these groups were categorized into two main groups, namely growth and stress hormones. However, this categorization is invalid  as stresMetabolomics utilizes different workflow designs of untargeted (semiquantitative) analyses and targeted (quantitative) analyses such as metabolic profiling, fingerprinting or footprinting . Plant hIn this review, we summarize recent methodologies employed in multiple-class plant hormone analysis , explaining their possibilities and weaknesses. By revisiting the basic workflow of these methods, we discuss and suggest changes required for hormonomics and outline possible future trends in the field of plant hormone analysis. We aim to provide a nuanced perspective on the legitimacy of the term “plant hormonomics”.The prominent role of plant hormones provides opportunities for a wide variety of uses and applications in research, agriculture and biotechnology. Identification of genes associated with biosynthesis, catabolism or perception and their modification can enable the development and improvement of crops with agronomically valuable traits  , 43. CroIn addition, plant growth regulators  are used as chemicals in industry or directly in agriculture. For example, in plant biotechnology, they have been used in plant in vitro manipulation and propagation , 59 or dUnderstanding the complex interaction of hormonal crosstalk requires extensive information about as many as possible plant hormones in a given sample. Crosstalk between hormones can take different forms, such as regulation of biosynthesis, inter-tissue transport, catabolism, signal perception and signal transduction of other hormones . InformaThe notion of broad-scale plant hormone analysis is hampered by the problematic selection of which hormone metabolites should be included in the analysis as some prior knowledge is required. Good candidates are compounds unique to the pathway of interest that play a significant role or which are a part of a regulatory rate-limiting step. CKs profiling serves as a suitable example. Methods for CKs analysis commonly include CK nucleotides (biosynthetic precursors), nucleosides (transport forms), free bases  and glucosides  from chorismic acid, conjugation of IC with glutamate (IC-9-Glu) and subsequent hydrolysis to form SA. IC is not commercially available and IC-9-Glu is unstable . TherefoNotably, evolution of the ET and JA biosynthetic and signaling pathways in the plant kingdom has led to the formation of intriguing networks. These pathways are characterized by the presence of multiple active compounds in biosynthetic pathways, e.g., 1-amino-cyclopropane-1-carboxylic acid (ACC) in the ET signaling pathway . BiosyntAlthough plant hormone analysis targets a relatively small number of analytes, hormones and their metabolites (currently hundreds), compared to metabolomics (tens of thousands) –81, manyCurrently, liquid chromatography–tandem mass spectrometry (LC–MS/MS) dominates as the method of choice for hormonal analysis , 82. TheThe problem of ME is especially pronounced in plant hormone analysis because enrichment of sample extracts without sufficient purification also leads to enrichment of the matrix components. There are two possible ways to mitigate or eliminate ME. One way is to employ intensive sample purification “” sectionThe nature of the extraction solvent and extraction conditions determines which analytes are efficiently extracted from the sample matrix. So far, no analytical method provides a universal solution and each of them introduces bias. The nine plant hormone classes form a set of compounds with widely different physicochemical properties, stability and volatility. Common practice is to use aqueous mixtures with a high content of organic solvents, e.g. methanol (MeOH), acetonitrile (ACN) and isopropanol (IPA). Typical solvents utilized for the extraction of plant hormones are listed in Table The most frequently used solvents include modified Bieleski solvent  and aqueos mixtures of MeOH or ACN (Table tert-butyl ether:methanol (MTBE:MeOH) mixture used in untargeted plant metabolomics  is preferable to linear regression without any transformation or weighting; log–log transformation improves curve fitting, increases robustness and avoids massive leveraging effects by evenly spacing calibration points  [Data analysis is an essential aspect of experiments involving instrumental analyses, capable of extracting valuable insights from the chemical information obtained. Preprocessing workflows differ for targeted and untargeted LC–MS analysis. Essentially, targeted analysis does not require extensive data preprocessing, unlike untargeted LC–MS studies. As the identities of compounds are known, the main aim is their accurate quantification. Therefore, little to no emphasis is placed on the identification of unknown compounds and data are normalized using IS “” section series) –235. Thet-test for two group comparison and ANOVA  for three or more groups are essential. When a normal distribution of data cannot be assumed, the Mann–Whitney test U test and Kruskal–Wallis are non-parametric alternatives to the t-test and ANOVA. However, normality tests  have low power when performed on a small sample size (n < 30) and might lead to erroneous assumptions [The experimental design, whether descriptive or hypothesis-driven, and research objectives should guide the selection of statistical analyses. A crucial consideration is the small sample size in plant hormone analysis, which is challenging to overcome. Limitations stem from the preparation of the plant material and the laboriousness of the analytical procedure. A large sample size is difficult to achieve when working with rare mutants, small plant organs, such as root tips, apical meristems and similar organs, or when monitoring dynamic changes with multiple time-point collections. Despite these problems, some biological questions may only require simple statistical comparison, e.g., differences in levels of relevant chemical species (hormones). For hypothesis testing, tools such as the umptions , 237. Toumptions . These lumptions . Given tMultivariate analysis plays a pivotal role when looking for insights within complex datasets. Instrumental analyses allow the collection of large amounts of data, especially untargeted analyses. This also applies to large-scale targeted analyses. Thus, multivariate analysis may provide new insights into the data obtained. In this review, only principal component analysis (PCA) and clustering analysis will be briefly mentioned. As the volume of data increases, so does the difficulty of recognizing patterns. Large datasets often include variables  that are correlated, making some information redundant. PCA is frequently used to visualize 2D (or 3D) plots of datasets with large sets of variables for visual inspection and pattern recognition . This isA new popular term has been coined—plant hormonomics. Similarly to other omics sciences, plant hormonomics aims to achieve qualitative and quantitative analysis of the hormone complement in a given sample. Hormones represent a standalone set  of low molecular weight compounds alongside primary and secondary metabolites. They possess high biological activity and govern all processes during a plant’s lifecycle, attracting the attention of many researchers. Not all related compounds exhibit high biological activity, but data on the biosynthetic precursors, transport forms, storage forms and catabolites of biologically active hormones provide additional and valuable information that can help to elucidate the internal processes.In general, plant hormone analysis shares some common ground with metabolomics. The key difference is the several orders of magnitude lower endogenous levels of plant hormones in vivo, in contrast to other metabolites. To counteract matrix effects hindering correct signal responses, samples for hormonal analysis are typically purified to remove matrix components and enriched to obtain the highest possible signal response. However, comprehensive hormonomics analysis requires little or no purification, otherwise some analytes may be removed during sample purification. Therefore, efficient and robust chromatographic separation is necessary to minimize matrix effects. Notably, current methods do not fully explore the possibilities available in the field of liquid chromatography, such as other chromatographic systems , and generally incline toward extremely short analysis of highly purified samples. Furthermore, technological improvements in HRMS have led to the development of instruments that offer equal or greater sensitivity compared to triple quadrupoles. At the same time, they offer the advantages associated with high resolution. However, the advent of these technologies has been so far reserved. Sample derivatization could help when dealing with small sample sizes or low signal responses in general. The improved physicochemical properties of derivatized analytes are often unparalleled when compared to the native state. However, despite the increased hormonome coverage, complications may arise, such as artifact formation and degradation of analytes under harsh derivatization conditions. Therefore, derivatization is mainly reserved for non-hormonomics applications.Destructive plant hormone analysis remains an indispensable tool in plant sciences. However, conventional single hormone class analysis employs a purification protocol that eliminates the majority of other matrix components, including other hormone classes. Comprehensive multiple/all-class plant hormonomics analysis cannot be achieved using a minimal purification protocol, fast chromatographic separation and triple quadrupole tandem mass spectrometry.


Biological assay and imaging techniques have made visible a great deal of the machinery of mental illness. Over fifty years of investigation of mood disorders using these technologies has identified several biological regularities in these disorders. Here we present a narrative connecting genetic, cytokine, neurotransmitter, and neural-systems-level findings in major depressive disorder (MDD). Specifically, we connect recent genome-wide findings in MDD to metabolic and immunological disturbance in this disorder and then detail links between immunological abnormalities and dopaminergic signaling within cortico-striatal circuitry. Following this, we discuss implications of reduced dopaminergic tone for cortico-striatal signal conduction in MDD. Finally, we specify some of the flaws in the current model and propose ways forward for advancing multilevel formulations of MDD most efficiently. Over the last half-century, in-vivo imaging and biological assay techniques have developed at a rapid pace. These techniques have made visible in mood disorders a broad variety of biological processes that were previously inaccessible. While a rough theoretical scaffolding has guided biological inquiry into mood disorders, most of this research has been exploratory given the complexity of the systems in play. More recently, and with many investigations of mood disorders conducted, several important regularities have emerged across biological levels including genetics, endocrine and cytokine signaling, and metabolomics, in addition to the neural-systems level as assessed with molecular and functional neuroimaging. In this review, we present the status of a developing multilevel biological perspective on major depressive disorder (MDD). We show that it is now possible to present an integrative thread from genetics to cytokines to neurotransmitters up to the neural-systems level in MDD. In presenting this formulation, we hope to show that cross-level integration in MDD is necessary in the conceptualization of this disorder.Developing a biologically coherent understanding of major depression must first take into account the marked heterogeneity of MDD, given that putative depressive subtypes have shown distinct biological profiles . SubtypiIn spite of evidence from twin-concordance studies for moderate (33–42%) heritability of MDD , early gThis most recent MDD GWAS identified a strong genetic link between MDD and metabolic dysfunction. This link is most readily seen from the results of genetic correlation analysis for estimating the degree of genetic correspondence between depression and a variety of traits. Among 31 traits found to have a significant, positive genetic correlation with MDD, nine were from the domain of metabolic disturbance .The link between the genetics of MDD and metabolic disturbance is not surprising given consistent evidence connecting MDD and metabolic syndrome, a syndrome characterized by abdominal obesity, increased fasting-glucose levels, high blood pressure, high triglyceride levels, and decreased high-density lipoprotein cholesterol . For exaIn the context of metabolic disturbance in MDD, it is important to consider a potential role for the gut microbiome in the pathophysiology of depression. Several putative links between MDD and gut-microbe composition are under consideration . One intImportantly, in the context of MDD, metabolic disturbances and inflammation are consistently linked. A large cross-sectional study examined waking cortisol response, inflammatory cytokine levels, and metabolic syndrome in melancholic MDD and atypical MDD. This investigation showed that metabolic syndrome and increased inflammation, but not heightened cortisol response, co-occur in atypical MDD whereas exaggerated waking cortisol response occurs in melancholic MDD in the absence of elevated inflammation and metabolic syndrome . This asInflammation is a protective response of the body to pathogens or injury. Researchers have noted the resemblance between depressive symptoms and the sickness-syndrome induced by inflammation, which is characterized by increased sleep, inactivity, and social withdrawal . This, iInflammation in both the peripheral and central nervous system can lead to the activation of the brain’s microglia and dendritic cells, prompting an immune response that alters neural functioning . AdminisOf many genes previously predicted to associate with MDD, one of the few found to do so is the KYNU gene  which coSeveral kynurenine pathway metabolites have neuroactive effects, some of which are neurotoxic and others which are neuroprotective or neurotrophic. 3-HK is considered neurotoxic by virtue of being a generator of free radicals, whereas QUIN is excitotoxic through agonism at N-methyl-D-aspartate (NMDA) receptors . Among tA shift from neuroprotective to neurotoxic activity in the kynurenine pathway could affect brain-structure volume in MDD. Brain areas that are reliably found to be altered in MDD, such as the hippocampus, amygdala, and striatum, are especially vulnerable to the effects of neurotoxic kynurenine pathway metabolites. Volumetric reductions in the hippocampus and amygdala, regions with a high expression of the GluN2B subunit on which QUIN acts , have beAs we detail above, inflammation impacts dopaminergic function. The first speculations about the involvement of dopaminergic abnormalities in MDD conceptualized these abnormalities in terms of their relation to cognitive and behavioral stereotypies in depression, such as repetitive, unwanted focus on negative events and aspects of the self . This la2)—most abundant in the striatum relative to other brain regions—have averaged across anatomically defined striatal regions-of-interest. Based on functional connectivity maps of the striatum, however, this region appears to be organized at a much finer spatial scale than its anatomical boundaries suggest [2 receptors, a more consistent pattern of findings emerges. Three of these studies report increased binding potential in MDD [2 receptors could hypothetically result from reduced competition of radioligand with endogenous dopamine, or increased striatal D2 receptor density, or a combination of these factors. Striatal D2 receptor imaging can detect altered competition with endogenous ligand. For example, injection of amphetamine, which increases extra-synaptic striatal-dopamine levels via effects on monoamine transporters, results in reduced striatal D2 receptor binding [2 receptors is reliably observed [2 receptor binding studies using depressed samples—with the capability of disambiguating endogenous-competition versus receptor-density accounts in MDD—are lacking, with just one pilot study reporting equivocal effects [As we have reported elsewhere , the str suggest . If we tl in MDD , 64, 65,l in MDD , with onl in MDD . It is i binding . Conversobserved . Ampheta effects .p < 10E−13) within the DRD2 gene, which codes for the D2 receptor. Interestingly, no significant gene-based hits were identified for genes involved in the synthesis, post-synaptic transport, or receptor expression for serotonin. Similarly, drug-gene interaction analyses identified several hits for dopaminergic drugs and only one for a drug with direct serotonergic properties—the non-selective serotonin reuptake inhibitor vilazodone [While any assertions about the coherence in the dopamine neuroimaging literature in MDD might be preliminary, more consistent findings have been reported from a recent meta-analysis of studies assaying CSF for the dopamine metabolite homovanillic acid (HVA). Unlike serotonin and norepinephrine metabolites in CSF that do not consistently differ in depressed relative to control samples, HVA levels are reliably reduced, with 19 of 23 studies reporting lower HVA in the CSF in depression . As CSF lazodone .The cortico-striatal-pallido-thalamic circuit  represents a macro-architectural facet of neural organization. This re-entrant circuit is a major pathway through which cortical and brain-stem functions are united. The first conceptualizations of the cortico-striatal circuit proposed that it had an aggregating function, funneling a broad set of inputs from all of cerebral cortex toward a behavioral output ultimately implemented in motor cortex . Data coMore recent work has challenged the assertion that the sub-circuits of the cortico-striatal circuit architecture are segregated. This work has been fueled by the claim that communication among the functionally specialized circuits is necessary for implementing complex, goal-directed behaviors. Consistent with this assertion, it was observed that in the striatum there are subregions of convergence between terminals from anterior cingulate, dorsolateral prefrontal, and motor regions . FurtherThe cortico-striatal circuit has a “logic-board” configuration that determines whether signal conduction through this circuit is facilitated or inhibited. See Fig. The most important development in recent years with respect to our knowledge of the cortico-striatal architecture is that its subdivisions are not just a connected set of re-entrant, go/no-go circuits, but rather are connected in an orderly way that constitutes a “motivation-to-movement interface.”  The primFirst, the ventral striatum—which is strongly connected with affective and reward-related regions such as the amygdala, hippocampus, orbitofrontal cortex, and anterior cingulate cortex—receives limited, and highly localized dopaminergic input from neurons in the ventral tegmental area extending to the substantia nigra pars compacta. Dopaminergic input from the ventral striatum back to midbrain regions, however, is much more topographically diffuse and includes parts of the midbrain that send dopaminergic projections to the central striatum, which receives glutamatergic projections from cortical regions implicated in higher cognitive functions. The central striatum, in turn, sends dopaminergic projections back to parts of the midbrain with dopaminergic connectivity to the dorsolateral aspect of the striatum. Finally, the dorsolateral striatum receives glutamatergic connections with motor regions and projects only limited dopaminergic connections to the midbrain. By virtue of this pattern of dopaminergic influence along an ascending spiral starting with the ventral striatum and ending with motor cortex, affective, cognitive, and motor processing are integrated . See Fig2 receptor is that this G-protein coupled receptor (GPCR) is relatively fast acting and inhibitory where Gαi subunits bind to and block adenylyl cyclases, preventing production of cyclic AMP [2 receptors can signal through beta-arrestin-2-dependent pathways and that this mode of action is complementary to canonical GPCR signaling, acting over the longer term to facilitate activity in D2 receptor-expressing neurons [2 receptors that recruit arrestin but lack G-protein activity restore locomotor activity in D2 receptor knockout mice when expressed virally in the ventral striatum [The interactions between the core cortico-striatal architecture and striatum-midbrain-striatum connections described here are not well characterized. There is evidence, however, that the dopaminergic projections from the substantia nigra pars compacta to the striatum reinforce cortical activation of cortico-striatal sub-circuits via contrasting actions on direct and indirect cortico-striatal pathways . These nclic AMP . Nonethe neurons . Consiststriatum .2 receptor binding potential using positron emission tomography (PET) and neural activity using resting functional magnetic resonance imaging in depressed and healthy comparison samples. Consistent with our hypotheses, we observed increased D2 receptor binding potential in depressed participants in regions of the ventral and dorsal striatum. Moreover, we saw that as binding potential increased in ventral striatum and dorsal striatum in MDD, connectivity with the respective default-mode and posterior salience network targets of these striatal regions decreased [In a meta-analysis of task-based functional neuroimaging studies of depression, we found reliably increased activation in MDD of the amygdala and ACC and reduced response of DLPFC to negative affective challenge. Moreover, we found reduced striatal response in MDD to both negative and positive affective provocation. We interpreted these findings in the context of the dopaminergically mediated, ascending spiral cortico-striatal circuit model detailed above. Specifically, we proposed that in MDD amygdalar and ACC inputs to the “bottom,” affective subdivision of the cortico-striatal spiral were exaggerated  and that signal to higher cortical regions was dampened due to reductions in striatal dopaminergic activity facilitating signal conduction up the cortico-striatal spiral . Embeddeecreased . This fiecreased  are dimiIn the continued development of cortico-striatal-thalamic models in MDD it will be important to integrate habenular function. The lateral habenula exerts an inhibitory influence on dopaminergic neurons of the ventral tegmental area and substania nigra , which aHere we present a narrative connecting abnormalities from genetics to metabolic and cytokine and neurotransmitter signaling to the neural-systems level in major depression. See Fig. The account we present suggests at least as many testable hypotheses as it addresses in the development of a multilevel account of major depression. As we state above, formulations relating immunological alterations and dopamine signaling in MDD require more and better testing. In this context, multimodal radioligand imaging for relating immunological and neurotransmitter levels—tandem translocator protein and raclopride PET imaging, for example—in a case-control design could be useful. Further, our assertions that inflammation causes changes in dopaminergic signaling in the brain are derived only from animal-model studies. To test this causal hypothesis in humans it would be feasible to conduct an experimental medicine study assessing dopaminergic neurotransmission in individuals during inflammation challenge  versus