Nanosecond Photochemical Reaction (nsPCR) for Enhanced Identification, Quantification and Visualization of Primary Amine-containing Metabolites by MALDI-Mass Spectrometry

Abstract

Many metabolites, including amino acids, neurotransmitters and pharmaceuticals contain primary amine functional groups. The analysis of these molecules by mass spectrometry (MS) plays an important role in the study of cancers and psychogenic diseases. However, the MS-based detection and visualization of these bioactive metabolites directly from real biological systems still suffers from challenges such as low ionization efficiency and/or matrix interference effects. Here, we introduce a simple and efficient strategy, the nanosecond photochemical reaction (nsPCR)-enabled fast chemical derivatization, enabling direct MS analysis of primary amine-containing metabolites, with enhanced detection sensitivity for numerous metabolites from cell culture medium and rat brain sections. Furthermore, this nsPCR-based chemical derivatization strategy was demonstrated to be a useful visualizing tool that could provide improved spatial information for these metabolites, potentially offering alternative tools for gaining novel insights in metabolic events.

Graphical Abstract

INTRODUCTION

As metabolites are the downstream product of a biosystem, metabolomics study is focused on the global analysis of metabolites in a biological specimen and is considered to contain most comprehensive and closely linked biomarker information that reflects the biological alterations.¹ Amongst the diverse metabolites, amino acids (and the analogues), neurotransmitters, and lipids are the top representative classes that attract the attention of the biological community. Reprogrammed metabolism is a hallmark of cancer cells.^{2, 3} Amino acids such as glycine and alanine have been shown to be strongly correlated to cancer proliferation and tumorigenesis for some cancers.^{3, 4} Dysregulation of neurotransmitters is considered a hallmark of various neurological and psychiatric diseases.⁵ Therefore, quantitative studies of small-molecule metabolites and visualization of spatial distributions of metabolites in samples are critical for understanding pathological pathways, improving diagnosis and treatment of diseases.

Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are two most widely used analytical tools in metabolite studies.⁶ As a non-invasive technique, NMR spectroscopy enables valuable samples kept intact and to be used in following experiments. Meanwhile, NMR can quantify analytes accurately and reproducibly.⁷ However, NMR suffers from poor sensitivity and ubiquitous signal overlap of mixtures of analytes from complex samples such as cells and tissues, limiting its power to identify metabolites. MS-based techniques offer distinct advantages in speed, specificity, sensitivity and wide detection range compared with other techniques, which enable exciting discoveries in medical research. Mass spectrometry imaging (MSI), with high molecular specificity, sensitivity, and throughput, has provided new dimensionality in the visualization of the metabolite distributions in tissues or cells. A variety of sampling/ionization methods have been employed for MSI, such as secondary ion mass spectrometry (SIMS), and laser ablation electrospray ionization (LAESI), direct liquid extraction and ionization (DLEI) and matrix-assisted laser desorption/ionization (MALDI).^{6, 11}

MALDI is a commercially available and commonly used soft ionization method. Unlike LC-MS-based analysis, which requires time-consuming and labor-intensive purification process, MALDI-MS-based analysis requires relatively simple sample preparation procedures due to its tolerance to relatively high level of salt and other contaminants. Also, commercial MALDI-MS instruments enable rapid analysis and can achieve high spatial resolution for MSI (5–10 μm or even lower).¹⁶ However, the versatility of MALDI-MS for metabolite studies is still impaired by inherently low ionization efficiency and concomitant ion suppression, especially for small molecules with lower mass overlapping with MALDI matrix peaks.¹⁷

To overcome above-mentioned challenges faced by MALDI-MS-based techniques, many chemical derivatization methods have been developed to facilitate small molecule metabolite analysis.^{18, 19} Considering the importance and widespread distribution of amine-containing metabolites, including amino acids and neurotransmitters, some chemical derivatization methods focus on improving the detection and visualization of these metabolites specifically.^{18, 20} A few amine derivatization strategies were described in a review paper.²¹ However, most of these strategies require incubation and/or heating to achieve decent derivatization efficiency, whereas can increase analyte dislocation or degradation, compromising their accuracy in identification, quantification and visualization of metabolites.

Developing fast and effective derivatization strategies for enhancing metabolite identification, quantification, and visualization is still key to advance low molecular weight molecule (LMWM) analysis with MALDI-MS. In our previous work, we developed a nanosecond PhotoChemical Reaction (nsPCR)-based MS strategy for enhancing neuropeptide visualization in brain tissues and protein identification.²² In this work, we demonstrate the capability of nsPCR-enhanced MALDI-MS analysis of LMWMs with the simple optimization of nsPCR reagents. The derivatization reaction is activated by laser without incubation or heating and can be completed at nanosecond time scale with high spatial resolution on tissue samples. With similar working principles and derivatization protocols as recently proposed, we herein validated the successful application of the nsPCR-based MS strategy in enhancing identification and quantification of a key class of LMWMs, primary amine-containing metabolites (scheme shown in Figure 1). Facilitated by the nsPCR strategy, the limit of detection (LOD) for some metabolites is improved by 3~5 fold. Notably, some small metabolites (e.g. glycine and L-alanine) have been successfully identified with nsPCR at a concentration level not detectable without derivatization. Furthermore, the nsPCR strategy also exhibits potentials in enhancing the identification and visualization of low-abundance small molecules as demonstrated by nsPCR-assisted MALDI imaging experiments.²³

Figure 1.

The design and working principle of the nsPCR strategy for on-demand matrix removal and chemical derivatization, facilitating the molecular identification and MS visualization of a wide range of biomolecules directly from tissues and other complex biological matrices.

EXPERIMENTAL DETAILS

Chemicals and Materials.

Methanol (MeOH), acetonitrile (ACN), and trifluoroacetic acid (TFA) were purchased from were purchased from Fisher Scientific (Pittsburgh, PA, USA). 2-nitrobenzaldehyde (NBA) was purchased from Chem-impex int’l Inc (Wood Dale, IL, USA). 4-bromo-2-nitrobenzaldehyde (4-Br-2-NBA) was purchased from Tokyo Chemical Industry Co; LTD (Tokyo, Japan). 2-hydroxy-5-nitrobenzaldehyde (2-NBA), 4-dimethylamino-2-nitrobenzaldehyde (4-N-2-NBA) were purchased from Sigma-Aldrich (St. Louis, MO, USA). 5-hydroxy-2-nitrobenzaldehyde (5-NBA) was purchased from Acros organics (New Jersey, USA). Dulbecco’s Modified Eagle Medium (DMEM, high glucose, GlutaMAX^™, pyruvate) was purchased from Gibco (Grand Island, NY, USA). Glycine, L-alanine, L-valine, DL-leucine, L-glutamic acid, dopamine, L-histidine, L-phenylalanine, L-arginine, L-tryptophan were purchased from Sigma-Aldrich (St. Louis, MO, USA). The matrices of α-cyano-4- hydroxycinnamic acid (CHCA) were purchased from Sigma-Aldrich (St. Louis, MO, USA). All solvents used in this study were of HPLC grade.

Sample preparation for MALDI spots.

Stock solution of glycine, lysine, L-alanine, L-valine, DL-leucine, L-glutamic acid, dopamine, L-histidine, L-phenylalanine, L-arginine, L-tryptophan were prepared as 25 mM or 50 mM in HPLC water respectively. Then the stock solution of the metabolites was mixed to get a metabolite mixture with equimolar concentrations (1 mM) for each standard. CHCA was prepared as 10 mg/mL (ACN:H₂O:TFA (v/v/v) 49.95:49.95:0.1). Each nsPCR reagent (nsPCR reagents evaluated in study shown in Figure 2a.) was first dissolved in the mixed solvent of ACN/EtOH/H₂O/TFA (84/13/2.9/0.1, v/v/v/v). Firstly, 2-NBA and its analogues were prepared at a concentration of 0 mM, 100 mM and 200 mM. Metabolite mixture and metabolite standard were prepared in serial dilutions (0 μM, 5 μM, 10 μM, 20 μM, 40 μM, 50 μM, 100 μM, 200 μM, 1000 μM) using 0.1% TFA. For each concentration of nsPCR reagents, it was tested by several concentrations of metabolite mixture. The optimal concentration of nsPCR was further used to measure the LOD of valine and leucine, the concentrations of the metabolites were optimized based on the results. Glycine in DMEM was quantified by adding serial dilutions of glycine standard. The standard glycine (0 μM, 5 μM, 10 μM, 20 μM, 40 μM and 100 μM) as calibration standard was added to 10-fold diluted DMEM media.

Figure 2.

Workflow for nsPCR-enhanced MALDI-MS analysis. (a) Five nsPCR reagents (NBAs) employed in this study. Theoretical accurate mass shifts for NBA, 5-NBA, 2-NBA, 4-N-2-NBA and 4-Br-2-NBA are 133.0164 Da, 149.0113 Da, 149.0113 Da, 175.0586 Da and 210.9269 Da per modification, respectively. (b) Generally applicable nsPCR-enhanced MALDI-MS spot analysis workflow. (c) Generally applicable nsPCR-enhanced MALDI-MS imaging workflow. This workflow involves five-step procedure, step 1: tissue sectioning and chemical preparation; step 2: matrix and nsPCR application to tissue; step 3: MALDI-MS operation and data acquisition; step 4: database searching and statistical analysis; and step 5: ion map creation and visualization.

A 1:1:1 (v/v/v) mixture of sample, matrix and nsPCR reagent was deposited onto a stainless steel MALDI plate for all experiments (workflow shown in Figure 2b). A total volume of 2 μL for each spot was deposited on the MALDI plate. At least 3 replicates for each sample. The samples were dried down for 20 minutes in fume hood in dark before MALDI-Orbitrap data acquisition.

Sample preparation for MALDI imaging.

nsPCR reagents were firstly dissolved in the mixed solvent of ACN/EtOH/FA/H₂O (84/13/0.3/2.7, v/v/v/v). Unless stated otherwise, 5 mg/mL of nsPCR reagent was used for imaging analysis. We complied with all relevant ethical regulations for animal testing and research. Animal experiments were conducted following institutional guidelines approved by University of Wisconsin-Madison IACUC. The procedure for preparing mouse tissues was described in our previous study.²² Briefly, female C57BL/6J mice were anesthetized, perfused with chilled phosphate buffered saline, decapitated, and removed brains. The brains were cut along midline and 100 mg mL⁻¹ gelatin in water was used to embed each hemisphere, which was then snap frozen in dry ice. Tissue samples were stored at −80 °C. An Olympus SZX16 stereo microscope (Olympus, Center Valley, PA) was used to take the optical images of tissue sections with bright field illumination. Tissue slides were dried for 30 minutes in a desiccator at room temperature right before matrix and nsPCR reagents application. Thereafter, 5 mg/mL CHCA matrix were deposited via the TM sprayer system using the following conditions: nozzle temperature of 80 °C, gas pressure of 10 psi, eight passes, moving velocity of 1100 mm/min, drying time of 30 seconds and flow rate of 0.2 mL/min. Then nsPCR reagent was sequentially deposited with the gentle condition of 30 °C and gas pressure of 10 psi. The sprayed slides were stored in a desiccator after drying at room temperature for 30 minutes. At this point, the tissue slides were ready to be examined with MALDI-MSI experiments (workflow shown in Figure 2c).

Data Acquisition and Analysis.

Here, we used MALDI-LTQ-Orbitrap XL mass spectrometer for spot analysis. This vacuum MALDI source was featured with 60 Hz 337 nm N2 laser. For MALDI setting, plate motion is set as survey CPS, ASF is set as off, and scan rate is set as 2 microscans/step. For laser setting, laser energy is set as 12 μJ, AGC is set as off, and number of laser shot is set as 2. The MS was operated under positive polarity mode, with mass range of m/z 70–400 and mass resolution of 60,000. The number of experiments to acquire was set as 50 for each sample spot. Xcalibur (Thermo Scientific, Bremen, Germany) was used to process mass spectra data.

The MALDI imaging experiments were performed using a Thermo QE-HF mass spectrometer (Thermo Fisher Scientific, Bremen, Germany). A SubAP-MALDI source was coupled with the QE-HF orbitrap mass spectrometer for data acquisition. The SubAP-MALDI (ng) UHR (MassTech, Columbia, MD) was used as a laser ion source and similar laser module can be found in our previous study.²² Notably, this SubAP-MALDI source was featured with a 355 nm high resolution Nd:YAG laser module along with an integrated ion funnel for improved ion collection and transmission. The typical operation source pressure was 3~5 Torr that made the source SubAP-MALDI. The starting laser parameters (unless otherwise specified) were laser energy of 18.9% and repetition rate of 1000Hz. The MS was operated under positive polarity mode, with mass range of m/z 133–2000 and mass resolution of 60,000 unless otherwise specified. To ensure consistent scan time at each pixel, the maximum injection time was set to 300 ms and automatic gain control target was tuned to 3e6. Xcalibur (Thermo Scientific, Bremen, Germany) was used to process QE-HF mass spectra; ImageQuest (Thermo Scientific, Bremen, Germany) were used for image processing, including image normalization and ion image production. Peaks of protonated ion [M+H]⁺, corresponding sodiated ion [M+Na]⁺, and potassium adduct [M+K]⁺ would be accounted for the same compound. All ions were identified through accurate mass matching from the Human Metabolome Database (HMDB) with less than 10 ppm mass error tolerance.

RESULTS AND DISCUSSION

Design and Workflow for nsPCR-based Chemical Derivatization of Metabolites.

Upon laser irradiation, the nsPCR reagents, as shown in Figure 2a, can selectively react with primary amine groups of metabolites, just like the case of proteins and peptides.^{22, 23} Similarly, we found that the nsPCR can also improve the MALDI-MS detection of metabolites, especially for those with low molecular weights overlapping with MALDI matrix peaks. On one hand, the fast and online labeling shifts small metabolites effectively in molecular weights, which allow these metabolites to be separated from overlapping and interfering matrix peaks. On the other hand, the observed apparent ionization efficiency has been greatly enhanced for most of these small metabolites. We thus speculate that, the nsPCR for enhanced metabolite analysis by MALDI-MS benefits from mass shift-associated alleviation of ion suppression effects by MALDI matrix as well as the pH jump induced by nsPCR that basically enriches the available protons during analyte ionization and desorption.²²

For nsPCR-enhanced MALDI spot analysis, the sample preparation procedure is straightforward and convenient (Figure 2b).²³ Unlike most previously reported methods for amine derivatization, we only need to mix metabolites and MALDI matrix with nsPCR reagent before data acquisition, without incubation or heating for the reaction. Therefore, this strategy enables rapid analysis of metabolites. When nsPCR strategy is used to enhance MALDI-MS imaging analysis, we only need to apply nsPCR reagent after and/or during matrix application, followed by conventional data acquisition workflow (Figure 2c). Since the derivatization reaction takes place via laser irradiation, samples can be analyzed right after the application of nsPCR reagent, leaving the localization of metabolites largely undisturbed.

Analytical Performance for nsPCR-based Chemical Derivatization of Metabolites.

Here, we evaluated five NBA-derived nsPCR reagents (structure shown in Figure 2a), NBA, 2-NBA, 5-NBA, 4-Br-2-NBA, 4-N-2-NBA, with different functional groups that might affect the reactivity of amine coupling. We chose NBA because it was previously reported for enhancing visualization of peptides through derivatizing the primary amine group of peptides.²² Also, we speculated that hydroxy group in 2-NBA and 5-NBA may affect the reaction efficiency between the derivatization reagents and primary amine group. We selected 4-Br-2-NBA because of its relatively high molecular weight (230 Da), which may help avoid matrix interference in the low molecular weight range. As for 4-N-2-NBA, we speculated that tertiary amine group could facilitate ionization, improving signal intensities of derivatized molecules. Among the five derivatization reagents, 4-Br-2-NBA and 4-N-2-NBA showed poor performance for detection of all tested primary amine-containing molecules. No observable peaks corresponding to derivatized metabolites were shown when 4-N-2-NBA or 4-Br-2-NBA were applied (Figure S1).

Instead, NBA, 2-NBA and 5-NBA can efficiently derivatize some molecules tested in the metabolite standard mixture. Glycine and L-alanine without derivatization were undetectable in amino acids standard mixture by LTQ MALDI-Orbitrap when the concentration was under 200 μM. Facilitated by derivatization via nsPCR reagents, glycine (Figure S2) and alanine (Figure S3) were detected with significantly improved signal/noise ratios of up to 68. Also, enhancement of detection for valine (Figure S4) and leucine (Figure 3) were observed via the nsPCR strategy. In Figure 3, we found when NBA or 5-NBA was applied, the S/N for derivatized leucine could achieve 414 or more, double of that without nsPCR reagents at the specific concentration. For NBA, the signal intensity of derivatized leucine was 10-fold of underivatized leucine, showing excellent derivatization efficiency (Figure 3b). Also, we found that the signal intensity for derivatized leucine was 116-fold of underivatized leucine when 5-NBA was applied, indicating almost complete derivatization (Figure 3d). In short, in the case of leucine, the optimal nsPCR reagent was determined to be 5-NBA considering overall labeling efficiency as well as absolute intensity, while we recommend further optimization using similar workflows when it is applied to other metabolite systems.

Figure 3.

Mass spectra obtained from leucine standard using LTQ MALDI Orbitrap MS platform: (a) leucine only, concentration of leucine used here was 50 μM, (b) leucine with NBA, leucine used here was 50 μM and NBA used here was 200 mM, (c) leucine with 2-NBA, leucine used here was 50 μM and 2-NBA used here was 100 mM, (d) leucine with 5-NBA, leucine used here was 50 μM and 5-NBA used here was 100 mM.

As such, we extended the metabolite system by using these nsPCR reagents and the derivatization performance evaluation results are shown in Table S1. Among these three derivatization reagents, 5-NBA shows the best properties in enhancing the detection of amino acids including glycine, alanine, valine, and leucine (Table S1). Moreover, we found that there was a tendency that molecules with lower molecular weight could benefit more from the nsPCR strategy than those with higher molecule weight. The concentration of 5-NBA was then optimized, and we found that 50 mM and 100 mM work well for most tested molecules but the optimal concentration of derivatization reagent for different primary amine-containing molecules can be different (representative data for glycine shown in Figure 4a). Here, the slope of the calibration curve represented the sensitivity for detecting glycine at the evaluated concentration range. In other words, at the specific concentration range, a higher value of slope indicated a higher sensitivity. The optimal concentration of nsPCR reagents can be used for further experiments. To test the applicability of this derivatization strategy in more complex biological samples, we used 100 mM 5-NBA for calibration curve plotting of glycine in DMEM, and we found good linearity for the quantitation though the slope of the trendline was significantly smaller than that in glycine standard (Figure 4b).

Figure 4.

Calibration curve for quantitation of glycine via 5-NBA. a) Calibration curve for quantitation of glycine in metabolite standards mixture via 5-NBA with different concentrations. b) Calibration curve for quantitation of glycine in DMEM via 5-NBA. The medium was 10-fold diluted and then serial dilutions of glycine standard were added into the medium for quantitation. Concentration of 5-NBA used here was 100 mM.

To determine the improvement of detection of L-valine and DL-leucine, we measured the probability of detection for these two metabolites (Figure 5). We found that, after derivatization with 5-NBA, the sensitivity of detecting L-valine standard was improved by 2.8 fold. For DL-leucine standard, the sensitivity was improved by 4.5 fold with derivatization.

Figure 5.

Probability of detection for valine and leucine. Probability of detection for valine a) without derivatization, b) with 5-NBA derivatization. Probability of detection for leucine c) without derivatization, d) with 5-NBA derivatization. For each concentration of valine or leucine, there were 8–10 replicates. The concentration of 5-NBA used here was 100 mM.

The probability of detection (POD) measures the probability to detect a compound at a specific concentration, so that LOD can be calculated based on the fitting curve.²⁴ Therefore, we prepared a serial dilution of valine and leucine for detection with/without nsPCR reagent. We measured the probability of detection for these two metabolites based on 8–10 replicates for each concentration (Figure 5). Here, the POD50 value represents a specific concentration, where 50% probability of detection can have a S/N equal or greater than 3. Therefore, POD50 was used here to quantitatively assess the limit of detection.

The POD50 for valine was calculated to be 9.4 μM without nsPCR reagents, while by adding 5-NBA, the POD50 for derivatized valine decreased to be 3.4 μM (Figure 5a–b). For leucine, the POD50 was 3.0 μM without nsPCR reagents, and a decreased POD50 of 0.67 μM was observed by the application of 5-NBA (Figure 5c–d). Thus, after the application of 5-NBA, the sensitivity of nsPCR-enhanced MALDI-MS was improved by 2.8 fold for valine and by 4.5 fold for leucine.

nsPCR-enhanced Molecular Visualization of Metabolite Spatial Distributions from Mouse Tissue Sections.

We also tested the performance of this derivatization strategy for MS imaging in real biological samples using mouse brain tissue section slides (Figure 6). Mass spectra obtained from the same region of 3 consecutive tissue sections showed that a high abundance of metabolites (including lipids) was observed from the 5-NBA group, indicating effectiveness of 5-NBA in enhancing the detection of metabolites in tissue specimens. A total of 174 metabolites were detected, including 120 metabolites (69% of the total number) detected in the control group, 118 metabolites (68% of the total number) detected in the 2-NBA group, 145 metabolites (83% of the total number) detected in the 5-NBA group (Figure 6a). Among these metabolites, some showed obvious enhanced signal intensity after nsPCR application (representative ion images shown in Figure 6c–h). We also observed that signal intensity for some metabolites/lipids decreased after the application of nsPCR reagents. We speculated that the application of nsPCR reagents altered the ionization of the metabolites, positively or negatively. Overall, we obtained 25 more metabolite IDs in the 5-NBA group than that of the control group, including 6 primary amine-containing metabolites (cysteamine, 5-aminopentanoic acid, aminoadipic acid, aminocaproic acid, argininic acid, norepinephrine). The application of nsPCR not only helped profile metabolites undetectable without derivatization (Figure 6c–e), but also improved the visualization of metabolite with low signals (Figure 6f–h). Also, we found that the imaging contrast of MALDI-MSI ion maps is still maintained and comparable to that of control group after the application of nsPCR reagents (Figure 6c–h), suggesting that the use of nsPCR reagents has minimal impacts on both the spatial resolution and dislocation of analytes. Notably, all the putative metabolite IDs were obtained through database searching based on accurate mass matching (< 10 ppm). Therefore, metabolite standards or MS2 level data would be beneficial for further validating these metabolite IDs. Taken together, notable enhancement of detection sensitivity for metabolites and lipids were achieved via 5-NBA derivatization, encouraging its broad application to on-tissue metabolite profiling via MALDI-MSI.

Figure 6.

MALDI-MSI ion maps of representative metabolites/lipids in mouse brain tissue: (a) number of metabolites/lipids identified in mouse brain tissue, (b) optical images of the mouse brain tissue, (c–h) (images in the control group were shown in the left, images in the 2-NBA group were shown in the middle, images in the 5-NBA group were shown in the right) MALDI–MSI maps of the tissue section shown in (c) putative ID: allantoin, [M + H]⁺, m/z 159.0518 (left), [M + 2-NBA + NH4]⁺, m/z 325.0923 (middle), [M + 5-NBA + NH4]⁺, m/z 325.0923 (right), (d) putative ID: argininic acid, [M+H]⁺, m/z 176.1035 (left), [M + 2-NBA + H]⁺, m/z 325.1149 (middle), [M + 5-NBA + H]⁺, m/z 325.1149 (right), (e) putative ID: norepinephrine, [M + H]⁺, m/z 170.0817 (left), [M + 2-NBA + Na]⁺, m/z 341.0778 (middle), [M + 5-NBA + Na]⁺, m/z 341.0778 (right), (f) putative ID: [DAG+C4H7ON+H]+, m/z 704.5804, (g) putative ID: [FAHFA+AMPP]⁺, m/z 729.5927, (h) putative ID: [PC + H]⁺, D16:1–16:0/D14:1–18:0, m/z 732.5563, (i) putative ID: [PC + H]⁺, D16:0–18:2, m/z 758.5713, (j) putative ID: [PC + Na]⁺, D16:0–18:2, m/z 780.5558, (k) putative ID: [PC + H]⁺, D18:1–22:6/D18:2–22:5, m/z 832.5869.

To compare the performance of the nsPCR-enabled MALDI MSI with previous studies for LMWM analysis, Table 1 lists some representative examples for laser-based, mostly MALDI-MSI analysis of LMWMs with various regimes employing different instrument platforms and matrices. For example, previous studies using 9-AA, or 1,5 DAN as matrix with negative ion mode enabled direct MSI of some small molecules such as Glu, Gln, Asp and AMP etc. as these molecules are easily ionized under negative ion mode (Table 1).^{25–27, 30} Meanwhile, other materials like graphene oxide, ZnO nanoparticles (NP) etc. were investigated to achieve good coverage for small molecule detection.^{29, 31, 34} It was reported that by washing away ionization suppression lipids using organic solvent, signal intensities of small metabolites could also be improved.²⁸ In a recent study, Chen et al. reported the ion images of glycine and alanine in mouse brain acquired with ZnO NP-assisted LDIMSI.³¹ Studies showed that LAESI worked well for MSI of small metabolites including glycine and alanine.^{32, 33} These prior studies suggest that each technique has varied but unique preferences in visualization of LMWMs. Compared with previous MALDI-MSI protocols applicable to LMWM visualization in mouse/rat brain tissues, nsPCR-equipped MALDI-MSI not only preserves most LMWM information and provides some complementary identifications including many lipid species (Table 1), but also offers an opportunity to quantify and visualize some classes of LMWMs with enhanced sensitivity as demonstrated by identification numbers (Table 1) and overall coverage comparing to prior methods without nsPCR.

Table 1.

Some examples for MALDI-MSI study of low molecular weight molecules comparable to this study.

Tissue type	MS platform	Matrix/NP	Acquisition mode	Metabolites detected	References
Mouse brain	MALDI-Orbitrap MS	CHCA	Positive ion mode	23 LMWM (Arg, AMP, aminoadipic acid, cysteamine, allantoin, etc.) and 122 lipids	This study
Rat brain	MALDI-TOF/TOF MS	9-AA	Negative ion mode	13 primary metabolites (AMP, ADP, ATP, etc.)	Benabdellah, et al., 200925
Mouse brain	MALDI-TOF/TOF-MS	9-AA	Negative-ion mode	More than 30 endogenous metabolites including, Glu, Asp, AMP, ADP, ATP, UMP, GST, etc.	Miura, et al., 201026
Rat brain	MALDI Orbitrap MS	1,5-DAN	Negative ion mode	Asp, Gln, Glu, creatine, adenosine, AMP, GMP, ADP, ATP, etc.	Liu, et al., 201427
Rat brain	MALDI FTICR MS	TiO2	Positive ion mode	34 LMWM (Glu, Gln, GABA, GSH, adenosine, AMP, creatine, etc.)	Yang et al., 201828
Mouse brain	MALDI hybrid Qh-FTICR MS	Graphene oxide	Negative-ion mode	22 of LMWM (inosine, IMP, AMP, GSH etc.) and 190 of lipids	Zhou, et al., 201729
Mouse brain	MALDI FTICR MS	9-AA	Negative ion mode	Asp, Glu, Gln, Tau, GSH, cAMP, AMP, IMP, GMP, ADP, GDP, CMP etc.	Rzagalinski, et al., 201930
Mouse/rat brain	MALDI TOF/TOF MS	ZnO NP	Positive ion mode	Gly, Ala, Gln, Glu, Arg, etc.	Chen, et al., 202131

Low molecular weight molecules, LMWM; glycine, Gly; alanine, Ala; arginine, Arg; glutamic acid, Glu; glutamine, Gln; aspartic acid, Asp; glutathione, GSH; adenosine monophosphate, AMP; cyclic adenosine monophosphate, cAMP; inosine monophosphate, IMP; guanosine monophosphate, GMP; adenosine 5′-diphosphate, ADP; adenosine 5′-triphosphate, ATP; guanosine 5′--diphosphate, GDP; cytidine monophosphate, CMP; uridine monophosphate, UMP; uridine diphosphate, UDP; γ-aminobutyric acid, GABA; ethanolamine, ETA; taurine, Tau; time of flight, TOF; laser ablation electrospray ionization, LAESI; Fourier-transform ion cyclotron resonance, FTICR; 9-aminoacridine, 9-AA; desorption electrospray ionization, DESI; nanoparticle, NP; zinc dioxide, ZnO; not applicable, NA.

CONCLUSIONS

In summary, improved detection of metabolites from metabolite standard and cell culture medium and more sensitive spatial characterization of metabolites from mouse brain tissue samples were achieved by nsPCR derivatization coupled with MALDI-MS. The ionization efficiencies of glycine, alanine, valine and leucine were notably improved after nsPCR derivatization, especially for glycine and L-alanine, which were not detected without nsPCR derivatization in the MALDI LTQ Orbitrap. Improved sensitivity and metabolite coverage were demonstrated by spatial mapping of metabolite distribution in mouse brain tissue. Furthermore, the nsPCR strategy enabled on-demand derivatization and detection of metabolites, simplifying the derivatization procedure potentially enabling the analysis of time-sensitive samples since there was no incubation time. This study presents a versatile strategy for profiling primary amine-containing metabolites derived from biological samples (e.g., cell culture medium, cell lysates), as well as in situ mapping the spatial distribution of metabolites directly on tissue sections with high speed and high sensitivity. In this study, all the nsPCR reagents were commercially available and inexpensive. These advancements will facilitate investigating metabolic changes related to various biological processes and to discover potential metabolite biomarkers in disease and drug development. It is possible that other analogues of the nsPCR reagents can potentially improve the sensitivity and coverage for metabolite detection. More in-depth study focusing on the optimization of the structures of nsPCR reagents for improved performance is underway in our laboratory.

Supplementary Material

supporting information

Table S1. Derivatization efficiency of metabolites with different nsPCR reagents.

Figures S1–S4. Representative mass spectra obtained from metabolite standards.