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• Depression is the most common major mental illness and emotional disorder that occurs throughout the entire lifecycle, such as, children and adolescents depression, peripartum depression, adult depression, geriatric depression, and refractory depression [1]. Recent studies have indicated that depression has been displaying younger trend with a prevalence of 14.3% in children and adolescent populations [2,3]. Unfortunately, due to the intricate clinical presentation and limited therapeutic interventions with suboptimal efficacy, the disease burden in children and adolescents has significantly escalated [2,4]. Therefore, elucidating the pathogenesis of depression is crucial for overcoming the challenges in diagnosis and treatment. Importantly, multi-faceted evidences have gradually revealed that dyshomeostasis in lipid metabolism was a key node in serial etiological hypothesis of depression including the hypothalamic-pituitary-adrenal (HPA) axis hyperfunction, neuroinflammation, mitochondrial dysfunction, and lipid peroxidation [5-9]. Additionally, several studies have demonstrated a significant correlation between the effectiveness of antidepressants and the restoration of lipid dyshomeostasis, suggesting that lipid metabolism and corresponding state changes may serve as a novel target for rapid antidepressant action [10,11]. However, as a result of that discrepancy among the intricate multiple brain regions on spatial distribution and structures of lipids, traditional 2D methods are not necessarily appropriate. Precise and comprehensive analysis of lipids from spatial level is crucial for understanding the regulated relationship between lipid variations and depression [12].

  Mass spectrometry (MS)-based omics technologies have been widely utilized to identify disease-related lipids from both qualitative and quantitative perspectives, facilitating further exploration of their metabolic roles in disease progression [12]. In addition to 2D analysis, MS imaging (MSI) enables the spatial mapping of different compounds simultaneously on tissue section with high spatial resolution [13]. Previous studies have identified specific lipids exhibiting significant alterations within a specific brain region in association with neurological disorders, as detected by MSI, such as down-regulated phosphatidylinositol (PI) in hippocampus of a mouse model for Alzheimer's disease [14] and abnormal distributions of phosphatidylcholine (PC) in the prefrontal cortex from postmortem brain of a patient with schizophrenia [15]. The investigation into impacts of lipid metabolism along with its spatial distributions across multiple brain regions for depressive disorder still remains inadequate.

  In this study, we first established a depressive-like behavior model in rats induced by multiple early life stresses (mELS), in which, we further utilized matrix-assisted laser desorption ionization (MALDI)-MSI with histomorphological analysis to ensure lipid mapping of whole brain sections on spatial level in situ.

  Construction and assessment of depressive-like behavior rat model. All animal experimental procedures were approved by the animal care and use committees at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences under protocol number SIAT-IACUC-220518-SLG-ZZJ-A2147. We successfully constructed the rat model of depressive-like behavior through a multiple mELS paradigm (Fig. 1A) [16-18]. Firstly, the dam experienced daily randomized restraint stress and bright light exposure during the final week of pregnancy (Gestational day 15 to day 21). After birth, the offspring were subjected to a daily separation from the dam lasting 6 h for 20 days. Finally, the offspring underwent post-weaning chronic unpredictable mild stress exposure for 4 weeks during the juvenile period. Afterward, mELS induced depressive-like behavior in young adult rat, namely the depressed rats aged around 60 days, were identified by using the behavioral assessment [19]. In comparison to the control group, the depressed rats exhibited the core symptoms of depression, such as, the reduced sucrose consumption rate and prolonged immobile duration in forced swimming test with decreased body weight (Figs. 1B-D). However, no significant differences both in motor function and anxiety levels were observed in time spent in center zone and total distance during the open field test between the two groups (Fig. 1E). Further, based on body weight, sucrose preference test and forced swimming test results, the rat that exhibited the most depression-like behavior (purple dot) was selected along with a control rat to investigate alterations in lipid distribution throughout the whole brain sections (Fig. 1F).

  Figure 1

  

  Figure 1.  Depressive-like behavior induced by mELS in young adult rats. (A) Flow chart of model construction for the depressive-like behavior induced by mELS. Body weight and behavioral assessment: (B) Body weight of offspring, (C) sucrose consumption rate in sucrose preference test, (D) immobile duration in forced swimming test, (E) time spent in the center zone and total distance traveled in the whole area in open field test. (F) The chosen of depressed rat that underwent MALDI-MSI. Error bars indicated standard deviation (n = 7 in control group and n = 5 in depressed group). ^*P < 0.05, ^**P < 0.01, ^***P < 0.001, ^****P < 0.0001, ns, no significance. G, gestational day; P, postnatal day; PS, prenatal stress; MS, maternal separation; CUMS, chronic unpredictable mild stress.

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  Selection and preparation of brain section for MSI analysis. For MSI scanning, eight paired representative sections from control and depressed rats were chosen according to the functional magnetic resonance atlas of rat brain [20-22], and the Rat Brain Atlas (compact sixth edition). Adjacent sections were operated the Nissl stain for histomorphological analysis and brain region confirmation (Fig. 2A). Sections 1 to 8 indicated by distance to bregma (4.68, 2.52, 1.28, 0.36, −0.6, −2.4, −3.6, and −5.04 mm, respectively) were located in the different spatial positions of the brain, including the level of frontal pole of cerebrum in sections 1 and 2, the end of the corpus callosum in section 3, the level of the anterior commissure in sections 4 and 5, the level of the internal capsule in section 6, the level of the thalamus in section 7, and the level of the midbrain in section 8. Among these eight paired brain sections, multiple brain regions reported associated with the regulation of emotions/mood disorder could be observed [23], including the medial prefrontal cortex (mPFC), insular cortex (IC), nucleus accumbens (NAc), amygdala (AMG), hippocampus (Hip), thalamus (Th) and hypothalamus. Brain regions in this study were selected between control and depressed groups without any hypothesis and supervision.

  Figure 2

  

  Figure 2.  Eight brain sections underwent MALDI-MSI scanning and analysis. (A) Location of sections based on the Rat Brain Atlas (left hemisphere of the brain section picture) and histomorphological analysis based on Nissl staining (right hemisphere of the brain section picture). Distances to bregma were marked below each section. (B) Data processing of MALDI-MSI in each brain section including the segmentation analysis, chi-square test, PLSA and analysis of lipid spatial distributions.

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  After the preparation of brain sections, the specimens were treated by vacuum drying and matrix spraying using 2,5-dihydroxybenzoic acid, and then MALDI-MSI scanning was performed for in situ analysis of lipid. MSI data were sequentially subjected to the segmentation of spatial metabolic profile and evaluation of disturbance degree at the whole brain level, as well as the spatial distribution of specific lipid in multiple brain regions (Fig. 2B).

  Segmentation of spatial metabolic profile at the whole brain level. To investigate the features of metabolic heterogeneity at molecular level between control and depressed rats, the spatial segmentation of eight brain sections sourced from MALDI-MSI data was performed using the bisecting k-Means algorithm on SCiLS Lab software (Fig. 2B). The segmentation results revealed metabolic disturbance of depressed rat compared to control rat within the whole brain, as demonstrated by the distinct clustering of brain section features into 4 clusters (Fig. 3A). Accordingly, analysis of the spatial omics data (metabolome and lipidome) from control and depressed rats revealed a clear separation in sections 1 to 7, with the exception of section 8 (Fig. 3A).

  Figure 3

  

  Figure 3.  Segmentation analysis and component analysis revealed metabolic disturbance in eight pairs of sections between control and depressed rats. (A) Segmentation analysis. Spectral information was partitioned into four clusters, visually represented by unique colors. The cluster tree was showed beneath each section. Numbers of spectra of control or depressed rat in each cluster were given beneath the color square. (B) Chi-square values of sections between control and depressed rats. (C) Percentage of differential molecules related to total features.

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  Analysis of metabolic disturbance degree at the whole brain level. We combined the Chi-square test from micro-region level and component analysis from molecular level to explore the metabolic disturbance degree at the whole brain level. Firstly, we employed the chi-square test to compare the cluster of spectra composition from micro-region level in 4 clusters for each pair of brain sections, and ranked the disparities between each pair of brain sections based on their respective Chi-square values. The results of chi-square test indicated that clusters of spectra classified by segmentation analysis showed significant differences in all eight pairs of sections between control and depressed rats (Fig. 3B, Tables S1 and S3 in Supporting information). We further ranked the degree of difference of each pair of sections as follows from highest to lowest: Section 4 (χ² = 23,714), section 3 (χ² = 19,019), section 5 (χ² = 17,741), section 7 (χ² = 15,899), section 2 (χ² = 15,714), section 6 (χ² = 12,520), section 1 (χ² = 6053), and section 8 (χ² = 1137) (Fig. 3B).

  Subsequently, probabilistic latent semantic analysis (PLSA) with a 95% confidence interval was conducted on each pair of brain sections with three principal components (Fig. 2B and Table S1). Numerous molecules with significantly different expressions were achieved to reveal the spatial metabolic heterogeneity between control and depressed rats from molecular level. Firstly, we assessed the proportion of differential molecules between each pair of sections in relation to the total number of detected molecules, to obtain a deeper understanding of the extent of variation in different brain sections from depressed rat compared to control rat. The orders of differential molecule percentages from highest to lowest were the section 2 (13.16%), section 1 (10.19%), section 7 (10%), section 5 (9.86%), section 3 (9.32%), section 8 (8.33%), section 4 (6.56%), and section 6 (4.49%) (Fig. 3C). Together, the most severe metabolic disturbance in depressed rat, in descending order of degree, were the section 7, 5, 2, 3, 4, 1, 6 and 8 in paired brain sections according to the chi-square test and component analysis.

  Spatial distributions of specific lipid in multiple brain regions. Subsequently, we investigated the dyshomeostatic lipid species in multiple brain regions in depressed rat from spatial level using PLSA and MALDI-TOF/TOF MSI-based lipid identification (Figs. S1-S3 in Supporting information). The results demonstrated the 17 lipids exhibited the distinct differences in spatial distribution patterns across one or more brain regions (Fig. 4, Tables S2 and S3 in Supporting information). Among them, it was especially noteworthy 10 of up-regulated lipids showed the great expression differences between inter-group (control and depressed rats) and intra-group (multiple brain regions) (Figs. 4A-K). More specifically, the expression level of PC (35:3) in section 5 (Fig. 4B) or PC (39:6) in section 7 (Fig. 4C) was significantly upregulated only in the preoptic area (POA) or AMG, respectively. Similar high expressions of 8 lipids were also found in multiple brain regions associated with emotion regulation (AMG, Hip, mPFC, NAc, etc.), including the sphingomyelin (SM) (d39:1) (Fig. 4D), ceramide (Cer) (d48:1) (Fig. 4E), phosphatidylglycerol (PG) (34:2) (Fig. 4F) and PC (34:3) (Fig. 4G) in section 2, phosphatidylserine (PS) (36:5) (Fig. 4H), phosphatidylethanolamine (PE) (38:2) (Fig. 4I) and PG (36:3) (Fig. 4J) in section 7, as well as PG (36:4) (Fig. 4K) in section 8. For instance, PG (34:2) expression in depressed rat was increased by approximately 1.99-fold in mPFC, 2.23-fold in IC, and 1.96-fold in ACC relative to control rat (Fig. 4F). Nevertheless, 7 of down-regulated lipids displayed the obvious differences in inter-group (control and depressed rats) rather than intra-group (multiple brain regions) (Figs. 4L-P), such as, PE (40:7p) expression in section 1 was dramatically decreased in mPFC, insular cortex (IC) and orbitofrontal cortex (OFC) from depressed rat (Fig. 4L). Our results further emphasized that multiple brain regions related to emotion regulation/mood disorder in depressed rat suffered a severe lipid dyshomeostasis from spatial perspective.

  Figure 4

  

  Figure 4.  Variations of lipids on spatial distribution patterns in control and depressed rats. (A) Schematic diagrams of brain sections and regions according to the functional magnetic resonance atlas of rat brain and the Rat Brain Atlas. (B-P) Spatial distributions of lipids. All differences were depressed versus control rat. The brain regions of interest were delineated by the black or white line in depressed or control rat, respectively. ^**** P < 0.0001. In addition, there was no expression in PC (37:3) and TG (48:2) from depressed rat. The fold changes of PC (37:3) and TG (48:2) were not available between control and depressed rats. Abbreviates of brain regions were as follows: POA, preoptic area; AMG, amygdala; mPFC, medial prefrontal cortex; IC, insular cortex; NAc, nucleus accumbens; CPu, corpus striatum; ACC, anterior cingulate cortex; OT, olfactory tubercle; Hip, hippocampus; Th, thalamus; PiC, piriform cortex; Ent, entorhinal cortex; SN, substantia nigra; OFC, orbitofrontal cortex; SC, sensory cortex; BNST, bed nucleus of the stria terminalis; LS, lateral septum.

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  In sum, our study observed lipid dyshomeostasis across multiple brain regions as well as disturbance patterns of lipid molecules at spatial scales simultaneously. It revealed that lipid dyshomeostasis happened in early adult rats of depressive-like behavior caused by multiple stress events in early life stage from a spatial level for the first time. The results further indicated that unique spatial distributions of lipid metabolism might underlie the susceptibility phenotypes for depression occurring at a younger age. Meanwhile, it further suggested these lipid dyshomeostasis might form potential spatial biomarker candidates and targets for precise interventions of depressive disorder.

  Results of previous research indicated that lipid dyshomeostasis involved in the pathogenesis of depression in different paths [24], such as, the HPA axis and neuroinflammation from chronic stress-induced depression model. Chronic stress induced HPA hyperactivity, further enhanced PC/PE conversion into the lyso-PC/lyso-PE, and then converted into diacylglycerol and promoted the triacylglycerol (TG) biosynthesis. Up-regulated Cer concentrations also contributed to the depression progression, as it changed the dopamine transporter function [25].

  Whereas, mostly results revolved around 2D-MS-based omics profiling to investigate the variation and function of lipid in depression model, such as, increased 12-lipoxygenase metabolites in the NAc derived from depressed mice [26], increased desmosterol and glycerol-1,3-diphosphate in olfactory bulb of depressed rats [27], dyshomeostasis of sphingolipid and glycerophospholipid metabolism within the Hip, prefrontal cortex, and AMG of depressed cynomolgus macaque [28]. During antidepressant treatment, dyshomeostasis of phospholipids (PC, PE, PS, SM [d18:1]) emerged in multiple brain regions of rodent model of depression, such as, Hip and prefrontal cortex [29-31]. These findings consistently suggested that lipid dyshomeostasis exhibit in depression, but lacked the spatial information of molecules in situ. For depression, MSI-based spatial distribution of lipids was in the initial stage at present. It appeared to focus only on a single brain section and single brain region, for example, purine metabolism in the striatum and energy metabolism in the mPFC were screened in depressed model [32].

  One limitation of this study was that the spatial distributions of lipids were only evaluated in animal model based on a small sampling of brain tissues. Future studies investigating the lipid variations on multiple samples are warranted, especially verifying repeated using the cross-species or -organ samples. Additionally, the molecular function of screened lipids in depression needs to be further studied by using the empirical experiments.

  Declaration of competing interest

  The authors declare no competing financial interest.

  CRediT authorship contribution statement

  Chao Zhao: Writing – review & editing, Writing – original draft, Supervision, Software, Project administration, Methodology, Investigation, Funding acquisition, Data curation. Chenyu Gao: Methodology. Zhiyi Yang: Methodology. Tianyou Cao: Methodology. Qian Luo: Supervision, Project administration, Funding acquisition. Zhijun Zhang: Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology, Investigation, Funding acquisition.

  Acknowledgments

  The work was supported by the China Science and Technology Innovation 2030-Major Project (Nos. 2022ZD0211701, 2021ZD0200700), the National Natural Science Foundation of China (Nos. 82130042, 81830040, 22176195, 82127801), Shenzhen Science and Technology Serial Funds (Nos. GJHZ20210705141400002, KCXFZ20211020164543006, JCYJ20220818101615033, ZDSYS20220606100606014, KQTD20221101093608028), the National Key R&D Program of China (No. 2022YFF0705003), Guangdong Province Zhu Jiang Talents Plan (No. 2021QN02Y028), and the Guangdong Science and Technology Department (No. 2021B1212030004).

  Supplementary materials

  Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.cclet.2025.111089.

  
  
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  Figure 1  Depressive-like behavior induced by mELS in young adult rats. (A) Flow chart of model construction for the depressive-like behavior induced by mELS. Body weight and behavioral assessment: (B) Body weight of offspring, (C) sucrose consumption rate in sucrose preference test, (D) immobile duration in forced swimming test, (E) time spent in the center zone and total distance traveled in the whole area in open field test. (F) The chosen of depressed rat that underwent MALDI-MSI. Error bars indicated standard deviation (n = 7 in control group and n = 5 in depressed group). ^*P < 0.05, ^**P < 0.01, ^***P < 0.001, ^****P < 0.0001, ns, no significance. G, gestational day; P, postnatal day; PS, prenatal stress; MS, maternal separation; CUMS, chronic unpredictable mild stress.

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  Figure 2  Eight brain sections underwent MALDI-MSI scanning and analysis. (A) Location of sections based on the Rat Brain Atlas (left hemisphere of the brain section picture) and histomorphological analysis based on Nissl staining (right hemisphere of the brain section picture). Distances to bregma were marked below each section. (B) Data processing of MALDI-MSI in each brain section including the segmentation analysis, chi-square test, PLSA and analysis of lipid spatial distributions.

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  Figure 3  Segmentation analysis and component analysis revealed metabolic disturbance in eight pairs of sections between control and depressed rats. (A) Segmentation analysis. Spectral information was partitioned into four clusters, visually represented by unique colors. The cluster tree was showed beneath each section. Numbers of spectra of control or depressed rat in each cluster were given beneath the color square. (B) Chi-square values of sections between control and depressed rats. (C) Percentage of differential molecules related to total features.

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  Figure 4  Variations of lipids on spatial distribution patterns in control and depressed rats. (A) Schematic diagrams of brain sections and regions according to the functional magnetic resonance atlas of rat brain and the Rat Brain Atlas. (B-P) Spatial distributions of lipids. All differences were depressed versus control rat. The brain regions of interest were delineated by the black or white line in depressed or control rat, respectively. ^**** P < 0.0001. In addition, there was no expression in PC (37:3) and TG (48:2) from depressed rat. The fold changes of PC (37:3) and TG (48:2) were not available between control and depressed rats. Abbreviates of brain regions were as follows: POA, preoptic area; AMG, amygdala; mPFC, medial prefrontal cortex; IC, insular cortex; NAc, nucleus accumbens; CPu, corpus striatum; ACC, anterior cingulate cortex; OT, olfactory tubercle; Hip, hippocampus; Th, thalamus; PiC, piriform cortex; Ent, entorhinal cortex; SN, substantia nigra; OFC, orbitofrontal cortex; SC, sensory cortex; BNST, bed nucleus of the stria terminalis; LS, lateral septum.

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