The study was supported by grant Harmonia 2015/18/M/ST4/00059 from the National Science Centre. The authors would like to acknowledge MilliporeSigma, a business of Merck KGaA, Darmstadt, Germany, for providing the SPME devices used in this work. The life science business of Merck operates as MilliporeSigma in the U.S. and Canada. Also, the authors would like to thank Thermo Fisher Scientific for access to the Q-Exactive Focus orbitrap mass spectrometer.
Authors&#8217; contributions: JB: optimization of sample preparation and LC-MS parameters, performance of SPME-LC-MS experiments, data analysis, statistical analysis and data interpretation and manuscript preparation related to lipidomics part; PZG: coordination and performance of majority of samplings in hospital, optimization of sampling and sample preparation parameters, performance of SPME-LC-MS experiments, data analysis, statistical analysis and data interpretation, manuscript preparation related to metabolomics part; MG &#8211; assistance in optimization of sample preparation, LC-MS method and data analysis related to lipidomics part; KG: co-performance of SPME samplings and optimization of sampling and sample preparation, SPME-LC-MS analysis related to metabolomics part; KC: performance of several SPME samplings in hospital, assistance in optimization of sampling, sample preparation and data analysis related to metabolomics part; KJ: performance of several SPME samplings in hospital, assistance in lipidomics analysis; DP: performance of surgical procedures, recruitment of the patients; JF: performance of surgical procedures, recruitment of the patients; MH: performance of surgical procedures, coordination of clinical part of the research; BB: concept, coordination supervision of the project and manuscript preparation, performance of several samplings

The study presented herein was approved by Bioethics Committee of Collegium Medicum in Bydgoszcz at Nicolaus Copernicus University in Toru&#324; (KB 628/2015). Remember to always wear a lab coat and any other required personal safety equipment, such as (but not limited to) safety gloves and glasses. Do not touch the extraction phase of the solid phase microextraction (SPME) probes.
1. Preparation of the SPME devices
<ol>
	<li>Use probes (fibers) with mixed-mode and C18 coatings for metabolomics and lipidomics, respectively. Collect two sets of samples, one for metabolomics and one for lipidomics.</li>
	<li>Adjust the coating of the probe to an optimum length by trimming the SPME probe. In the current study, the selected coating length was 7 mm. Select the length of the fiber according to the size of the tumor under study, ensuring that the entire sorbent can be immersed in the tumor (Figure 1).</li>
	<li>Condition the coatings of the probes by soaking them in methanol: water 50:50 v/v mixture for a minimum period of 1 h before the extraction procedure. Transport fibers to the site of sampling (e.g., hospital) in a vial containing the conditioning solution.</li>
</ol>
2. Sample collection procedure
<ol>
	<li>Do not wash or pretreat the tumor in any way prior to SPME extraction.</li>
	<li>Start the sampling as soon as possible after the tumor removal (2 min in the presented study). 
	NOTE: Adjust the time depending on the on-site set-up for a given facility (distance of researcher&#8217;s working site from the operating table) and keep it constant for the entire study. Minimizing the elapsed time between tumor removal and start of the extraction is crucial for the capture of unstable metabolites that degrade after blood circulation is cut off from the studied tissue.</li>
	<li>Perform sampling at room temperature. Alternatively, place the sample on ice when the extraction is carried out. In either case, maintain the same conditions for the whole set of samples.</li>
	<li>Take the probe out from the vial.</li>
	<li>Wash fibers with liquid chromatography-mass spectrometry (LC/MS) grade water for 5 s by immersing them in LC/MS grade water. Do not let the sorbent dry prior to the fiber insertion to ensure good reproducibility of data.</li>
	<li>Insert the fibers into the brain tumor tissue as far apart as possible, ensuring that the entire extraction phase is located inside the tumor. 
	NOTE: It is recommended that extractions be carried out in replicate in order to determine the heterogeneous nature of the tumor (Figure 2). Three replicates per sample are recommended.</li>
	<li>Leave the probe for 30 min in the tissue (measured with a timer).</li>
	<li>Use blank controls to eliminate sources of error related to the presence of artefacts stemming from sources other than the sampled tumor. To obtain blank controls, subject fibers to the same analytical workflow, as described above, but without the sampling step (insertion in the tissue or any other sample/matrix). In the data processing step, compile the analytes extracted from these fibers into an &#8220;exclusion list&#8221; to exclude signals derived from contaminants stemming from solvents or fiber manufacturing. It is recommended that at least 3 replicates of blank are used. 
	NOTE: To check for the risk of contamination, it is necessary to perform sampling from gloves, tables, apparatus or any other surfaces that may pose a contamination risk. In such cases, fiber preparation, time of extraction, and desorption protocols are the same as that for the samples.</li>
	<li>While extraction is being carried out, label the vials to be used for storage of the probes after extraction.</li>
	<li>After 30 min, remove fiber(s) from the brain tumor.</li>
	<li>Wash fibers with water for 3 s by immersing them in LC/MS grade water to remove residues of blood or cell debris from the probe so the final extract contains only small molecules. A longer washing step is not recommended as it may lead to the loss of polar compounds.</li>
	<li>Immobilize the fibers in the pre-slit septa of the high-performance liquid chromatography (HPLC) vial cap by piercing the septa from the bottom with the non-coated end of the fiber.</li>
	<li>Put the fibers immobilized in the cap in separate HPLC vials and place them in the selected transportation container.</li>
	<li>Perform steps 2.11-2.13 for fibers dedicated to blank controls.</li>
</ol>
3. Transport and storage
NOTE: Several options are available for transporting samples to the laboratory. It is recommended that a liquid nitrogen Dewar or polystyrene box filled with dry ice be used for transportation; alternatively, ice packs can be used for immediate and quick transportation.
<ol>
	<li>Place the vials with fibers in the transportation container.</li>
	<li>Upon laboratory arrival, immediately place vials with SPME fibers in a -80 &#176;C or -30 &#176;C freezer. Do not store fibers longer than 3 years at -30 &#176;C or 5 years at -80 &#176;C.</li>
</ol>
4. Sample preparation for metabolomics analysis
NOTE: This step should only be performed once all samples for an experiment have been collected.
<ol>
	<li>Before instrumental analysis, prepare desorption solvent mixture: acetonitrile:water 80:20 v/v.</li>
	<li>Take out the vials containing the mixed-mode fibers from the freezer. Use these for metabolomics analysis.</li>
	<li>Label vials to be used for desorption.</li>
	<li>Pipette 300 &#956;L of the desorption solution (prepared in step 4.1) into glass inserts placed in 2 mL vials.</li>
	<li>Perform desorption from each fiber placed in a separate insert by fully immersing the coating in the desorption solvent, then agitating it for 120 min at 1,200 rpm using vortex.</li>
	<li>After 120 min (once desorption is completed) remove caps with probes.</li>
	<li>Prepare QC sample by mixing 10 &#956;L aliquots of each sample from the sample set. The sample set size depends on the experimental design. It is important to analyze all samples as one batch.</li>
	<li>Close the vials with new caps.</li>
	<li>Place the vials in the autosampler (4 &#176;C) of the liquid chromatography high resolution mass spectrometer (LC-HRMS) and move to step 5. 
	NOTE: Randomize injections order of the samples including control blanks. Inject QC sample after every 8-10 samples to monitor the stability of the instrument.</li>
</ol>
5. Metabolomics analysis using reversed phase liquid chromatography and high-resolution mass spectrometer (RPLC-HRMS analysis)
<ol>
	<li>Set up the parameters of the LC-HRMS analysis and positive ionization mode. 
	NOTE: The parameters used in the current study in positive mode were as follows: scan range: m/z 80-1000; resolution 70 000; acquisition performed using AGC (1,000,000 ions); inject time to C-trap: auto; spray voltage: 1.5 kV; S-lens RF level: 55%; S-lens voltage: 25 V; skimmer voltage: 15 V; capillary temperature: 300 &#176;C; sheath gas: 40 a.u.; aux gas: 15 a.u.; aux gas heater temperature: 300 &#176;C. This chromatographic method was adapted from Vuckovic et al.7. Injection volume: 10 &#956;L.</li>
	<li>Set up the parameters of the LC-HRMS analysis and negative ionization mode. 
	NOTE: The parameters used in the current study in negative mode: scan range: m/z 80-1000; resolution 70 000; acquisition performed using AGC (1,000,000 ions); inject time to C-trap: auto; spray voltage: 2.5 kV; S-lens RF level: 55%; S-lens voltage: -25 V; skimmer voltage: -15 V; capillary temperature: 256 &#176;C sheath gas: 48 a.u; aux gas: 11 a.u.; aux gas heater temperature: 413 &#176;C. This chromatographic method was adopted from Vuckovic et al.7. Injection volume: 10 &#956;L.</li>
	<li>Calibrate the instrument as recommended by the manufacturer. 
	NOTE: In the current study, the instrument was calibrated using external calibration every 48 h, resulting in a mass accuracy &#60;2 ppm.</li>
	<li>Start the analysis by clicking the Start button in the software operating the instrument.</li>
	<li>When the analysis is complete, replace the RPLC column with the HILIC column, change the mobile phases and go to step 6.</li>
</ol>
6. Metabolomics analysis using hydrophilic interaction liquid chromatography and high-resolution mass spectrometer (HILIC-HRMS analysis)
<ol>
	<li>Set up the parameters of the LC-HRMS analysis and positive ionization mode. 
	NOTE: The parameters used in the current study in positive mode were as follows: scan range: m/z 80-1000; resolution 70 000; acquisition performed using AGC (1,000,000 ions); inject time to C-trap: auto; spray voltage: 1.5 kV; S-lens RF level: 55%; S-lens voltage: 25 V; skimmer voltage: 15 V; sheath gas: 60 a.u.; aux gas: 40 a.u.; aux gas heater temperature: 425 &#730;C; capillary temperature: 325 &#730;C. Chromatographic method was adapted from Vuckovic et al.7. Injection volume: 10 &#956;L.</li>
	<li>Set up the parameters of the LC-HRMS analysis and negative ionization mode. 
	NOTE: The parameters used in the current study in negative mode were as follows: scan range: m/z 80-1000; resolution 70 000; acquisition performed using AGC (1,000,000 ions); inject time to C-trap: auto; spray voltage: 1.3 kV; S-lens RF level: 55%.; S-lens voltage: -25 V; skimmer voltage: -15 V; capillary temperature: 263 &#730;C; sheath gas: 60 a.u.; aux gas: 30 a.u.; aux gas heater temperature: 425 &#176;C. Chromatographic method was adapted from Vuckovic et al.7. Injection volume: 10 &#956;L.</li>
	<li>Calibrate the instrument as recommended by the manufacturer. 
	NOTE: In the current study, the instrument was calibrated using external calibration every 48 h, resulting in a mass accuracy &#60;2 ppm.</li>
	<li>Start the analysis by clicking the Start button in the software operating the instrument.</li>
</ol>
7. Sample preparation for lipidomics analysis
NOTE: This step should only be performed once all samples for the experiment have been collected.
<ol>
	<li>Before starting the analysis, prepare desorption solvent mixture: isopropanol:methanol 50:50 v/v.</li>
	<li>Take out the vials containing the C18 fibers dedicated for lipidomics analysis from the freezer.</li>
	<li>Label vials to be used for desorption.</li>
	<li>Pipette 200 &#956;L of the desorption solution (prepared in step 7.1) to silanized glass inserts placed in 2 mL vials. 
	NOTE: Non-silanized inserts can be also used, but their use may result in poor reproducibility of compounds with high logP, as such compounds can non-specifically attach to glass walls.</li>
	<li>Perform desorption from each fiber in a separate insert by fully immersing the coating in the desorption solvent, then agitating it for 60 min at 1,200 rpm using vortex.</li>
	<li>After 60 min when desorption is completed remove caps with probes.</li>
	<li>Prepare QC sample by mixing 10 &#956;L aliquots of each sample from the sample set. The sample set size depends on the experimental design. It is important to analyze all samples as one batch.</li>
	<li>Close the vials with new caps.</li>
	<li>Place the vials in the autosampler (4 &#176;C) of the liquid chromatography high resolution mass spectrometer (LC-HRMS) and move to step 8.</li>
</ol>
8. Lipidomics analysis using reversed phase liquid chromatography and high-resolution mass spectrometry (RPLC-HRMS analysis)
<ol>
	<li>Set up the parameters of the LC-HRMS analysis and positive ionization mode. 
	NOTE: The parameters used in the current study in positive ion mode were as follows: scan range: m/z 100-1000; acquisition performed using AGC (1,000,000 ions); inject time to C-trap: auto; spray voltage: 3.5 kV, S-lens RF level: 55%; S-lens voltage: 25 V; skimmer voltage: 15 V; capillary temperature 275 &#730;C; sheath gas: 30 a.u.; aux gas: 10 a.u.; spare gas: 2 a.u.; probe heater temperature 300 &#730;C. LC parameters used were: phase A: methanol:water, 40:60 with 10 mM ammonium acetate and 1 mM acetic acid ; phase B: isopropanol:methanol, 90:10 with 10mM ammonium acetate and 1 mM acetic acid.; the gradient: 0 min &#8211; 20% B; 1.0 min &#8211; 20% B; 1.5 min &#8211; 50% B; 7.5 min &#8211; 70% B; 13.0 min &#8211; 95% B; 17.0 min &#8211; 95% B; 17.1 min &#8211; 95.5 % B; 23.0 min &#8211; STOP; C18 Column, 3.5 &#956;m, 2.1 mm x 75 mm; flow: 0.2 mL/min; oven temperature: 55 &#176;C; injection volume: 10 &#956;L.</li>
	<li>Set up the parameters of the LC-HRMS analysis and negative ionization mode. 
	NOTE: The parameters used in the current study were as follows: HRMS parameters for negative ion mode: scan range: m/z 100-1000; acquisition performed using AGC (1,000,000 ions); inject time to C-trap: auto; spray voltage: 3.5 kV, S-lens RF level: 55%; S-lens voltage: -25 V; skimmer voltage: -15 V; capillary temperature 275 &#730;C; sheath gas: 30 a.u; aux gas: 10 a.u.; spare gas: 2 a.u; probe heater temperature 300 &#176;C. Chromatographic method: same as in 8.1.</li>
	<li>Calibrate the instrument as recommended by the manufacturer. 
	NOTE: In the current study, the instrument was calibrated using external calibration every 48 h, resulting in a mass accuracy &#60;2 ppm.</li>
	<li>Start the analysis by clicking the Start button in the software operating your instrument.</li>
	<li>When the analysis is completed, replace the RPLC column with the HILIC column, change the mobile phases and go to step 9.</li>
</ol>
9. Lipidomics analysis using hydrophilic interaction liquid chromatography and high-resolution mass spectrometer (HILIC-HRMS analysis)
<ol>
	<li>Set up the parameters of the LC-HRMS analysis and positive ionization mode. 
	NOTE: The parameters used in the current study in positive ion mode were as follows: scan range: m/z 100-1000; acquisition performed using AGC (1,000,000 ions); spray voltage: 1.5 kV; S-lens RF level: 55%; S-lens voltage: 25 V; skimmer voltage: 15 V; capillary temperature 325 &#176;C; sheath gas: 60 a.u.; aux gas: 30 a.u.; spare gas: 2 a.u.; probe heater temperature 320 &#176;C. LC parameters used were: phase A: 5 mM ammonium acetate in water; phase B: acetonitrile; the gradient: 0 &#8211; 2min &#8211; 4% B; 15.0 &#8211; 20% B; 15.1 &#8211; 4% B, 21.0 min &#8211; STOP; 3 &#181;m 100 mm x 2.1 mm column; flow: 0.4 mL/min; oven temperature: 40 &#176;C; injection volume: 10 &#956;L.</li>
	<li>Set up the parameters of the LC-HRMS analysis and negative ionization mode. 
	NOTE: The parameters used in the current study in negative ion mode were as follows: scan range: m/z 80-1000; acquisition performed using AGC (1,000,000 ions); spray voltage: 1.5 kV, S-lens RF level: 55%; S-lens voltage: -25 V; skimmer voltage: -15 V; capillary temperature 320 &#176;C; sheath gas: 50 a.u.; aux gas: 21 a.u.; spare gas: 3 a.u.; probe heater temperature 320 &#176;C. The chromatographic method was the same as described in 9.1.</li>
	<li>Calibrate the instrument as recommended by the manufacturer. 
	NOTE: In the current study, the instrument was calibrated using external calibration every 48 h, resulting in a mass accuracy &#60;2 ppm.</li>
	<li>Start the analysis by clicking the Start button in the software operating the instrument.</li>
</ol>
10. Data processing and statistical analysis
<ol>
	<li>Process data using software compatible with the format of the raw data files.</li>
	<li>Perform statistical analysis using the processed data. 
	NOTE: The type of test depends on the scientific hypothesis and design of the study. In the current study, Principal Component Analysis, Partial-Least Square-Discriminant Analysis, and one-way ANOVA were used.</li>
</ol>

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The authors have nothing to disclose.

Untargeted metabolomics and lipidomics are commonly used in studies focused on identifying tumor biomarkers. However, in most cases, researchers look for compounds that can be used for screening of the disease. Consequently, the preferred biological samples are blood or urine due to their relatively easy access. Analysis of tumor tissue is mainly performed to understand the mechanisms behind the disease, characterize different tumor types, etc. On-site analysis of tumor biomarkers is rarely performed, as such applications require extensive sample preparation. Alternatively, strategies based on real-time analysis of tissue profiles without pre-selection of specific biomarkers are earning the attention of the medical community3,4. The solution presented herein provides another perspective on tissue processing on-site by unveiling the type of information that can be obtained via such methods.
The combination of sampling, sample preparation, and extraction renders SPME as a very useful tool for on-site analysis. Moreover, lack of tissue consumption during sampling enables further use of the same samples for biomarker analysis and routine tests (genotyping, histological analysis), therefore, adding new information to the results of standard testing. The sampling device has a very simple design, its operation is very easy, and no special training is required to perform the extraction itself. However, achieving reliable results requires much more than just proper handling of devices. To properly perform the experiment, one needs to understand the extraction process, the nature of the sample, and be aware of potential mistakes that can influence the data.
It is important to consider the heterogeneity of cancerous tissue10; sampled tumors may contain parts undergoing necrosis, calcification, and hypoxia, and each of these processes will be reflected in the metabolome and lipidome attained, thus influencing results. Therefore, it is recommended that spatial resolution sampling be carried out by insertion of several fibers in different parts of the cancerous tissue, or alternatively, that a longer coating be used to penetrate the whole of the tumor so as to obtain averaged information on the tumor. If the spatial resolution sampling method is carried out, fibers can be all desorbed into one desorption solvent; this would not only allow for the attainment of overall information on the tumor, but also increase the sensitivity of the analysis. Alternatively, desorption of individual fibers into separate vials would enable investigations to figure out the internal diversity of the brain tumor, which consists of the core built of cancerous cells, and the outer zone, which is the border of healthy tissue. Deeper parts of the tumor are usually more damaged by the processes related to cancer11. However, investigators must keep in mind that this option compromises method sensitivity and the overall number of detectable compounds. In the current work, a 7 mm coating was used; this length was considered optimum for various sizes of tumors included in the study. The coatings penetrated the tumors, and thus provided non-special resolution, but averaged data across the sample. Regardless of the protocol selected, it is important that the same protocol be followed during the entire study, including the number of fibers used for individual sampling, the length of the coating, extraction time, and all other factors delineated in this work.
It is important to control the quality of the analysis. The pooled QC (see steps 4.7 and 7.7 in the protocol) should be prepared and used for monitoring instrument stability during the run of the entire sample batch. The blank controls (see step 2.8) can be later used to prepare an &#8220;exclusion list&#8221; to eliminate signals of contaminants originating from solvents or fiber manufacturing. On special occasions, such as a risk of contamination, it is necessary to perform sampling from gloves, tables, apparatus or any other surfaces that may pose a contamination risk. In such cases, fiber preparation, time of extraction and desorption protocols are the same as for the samples.
Metabolomic and lipidomic analyses are focused entirely on small molecules (less than 1,500 Da) appearing in an organism or specific components of the organism, such as specific organs, tissue, fluids, cells, etc. Metabolomic and lipidomics offer a snapshot of biochemical changes occurring in the body, and in the case of cancer, they integrate information related to the genome, histology, and malignancy of the tumor. These omics sciences create a connection between physiology and phenotype as metabolites are higher up in the biochemical ladder than proteins or genes12. By understanding the metabolome and lipidome of cancerous tumors, we come closer to discovering the phenotype among all -omics sciences as these branches of study offer more in-depth knowledge of dynamic changes of molecules as a response of living organisms to various stimuli. As presented in this work, the data obtained in one sampling corresponds to the histology of the cancer, its degree of malignancy, and it reflects changes occurring at the genome level. In gliomas, as the type of cancer of interest in this study, the information hidden in the genome is particularly important, as a personalized treatment is developed based on the results of genetic tests. Particular mutations are prognostic markers of the outcomes of the chemo- or radiotherapy. As demonstrated here, the selection of biomarkers reflecting a given mutation is possible with the proposed strategy. Mutation markers, as well as additional descriptors types such as those that indicate the degree of malignancy of the tumor can also be used to support routine diagnostic methods.
Ex vivo chemical biopsy with the use of solid phase microextraction fibers is the first step in the application of the method to intraoperative diagnostics. The method can be easily adopted for in vivo sampling pending permission from appropriate Ethical Boards. In such cases, sterilization of SPME devices must be performed according to the accepted sterilization procedures of the hospital where sampling is to be carried, i.e., autoclaving or ethylene oxide sterilization. The pre-conditioned and sterilized fibers must be kept in sealed packages labeled with a sterilization expiry date. It is important to note that fibers should not be cleaned with the use of surfactants. Such a procedure can cause unspecific changes in sorbent composition, thus impacting the extraction of analytes. In the studies described herein, a 30 min extraction period was used, but other reports validate that shorter times can yield satisfactory results in in vivo studies13. Huq et al. showed that analyte equilibrium time is achieved faster in tissue, as a complex matrix, than in simple matrices14. However, the reproducibility of the obtained results can be compromised under shorter extraction periods as more analytes will be extracted under pre-equilibrium conditions; therefore, precise time control must be implemented.
Both omics sciences exploited as part of this work have excellent potential as biomarker discovery tools. Once biomarkers are selected or a chemometric model is established, medical diagnostics based on the determination of target metabolites via methods applicable for on-site investigations, such as the SPME approach described in this work, can be developed and implemented as part of routine diagnostics.
The protocol proposed in the present manuscript describes how to perform untargeted metabolomic and lipidomic analyses of cancer tissue using solid phase microextraction for screening of potential biomarkers. It is designed to enable extraction of representative compounds, differentiation of tumors, and identification of discriminatory compounds characterizing a given cancer i.e., potential biomarkers. The untargeted analyses with SPME described in this article represent a starting point in the development of quick intraoperative diagnostics, where a selected panel of compounds can be determined without the necessity for screening of all compounds present in the sample. In the interest of fast diagnostic results, SPME probes used for on-site extraction could be directly coupled to analytical instrumentation located in the hospital facility. Simple extractions performed with minimum sample preparation followed by chromatography-free analysis would significantly shorten overall time from hours to a few minutes, as already described for drug monitoring15.

Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the main methods used for the real time analysis of brain lesions. Brain tumor differentiation is generally based on histopathology with additional staining and advanced immunohistochemical techniques. According to the updated guidance on central nervous brain tumors issued by the World Health Organization (WHO) in 2016, genetic tests are crucial for the differentiation and classification of these tumors1. Differentiation and classification of tumors allow physicians to choose the most effective treatment for a given type of tumor thereby expanding the life expectancy of the patient. Unfortunately, despite the availability of such advanced methods to assist physicians in selecting an optimum therapy for their patients, the life expectancy of patients diagnosed with glioblastoma (IV grade glioma) is only about 15-16 months2. Even with the sophistication and increased accuracy of the said imaging and histological methods as diagnostic tools, there is still a great need for new techniques capable of offering complementary information to aid physicians in decisions regarding the course of treatment. Over the past years, several new approaches based on mass spectrometry have been proposed for intraoperative analysis of cancer3,4. The potential of solid phase microextraction (SPME), the method presented herein, as a rapid on-site analysis tool, has already been demonstrated in a variety of studies5. The current manuscript shows one of the clinical applications of the method, untargeted metabolomics and lipidomics of human brain tumors. Untargeted investigations present an important starting point in the discovery of potential biomarkers. Once established, such biomarkers can then be used as diagnostic references to differentiate among tumors using the same technology coupled to on-site instrumentation.
SPME is an equilibrium-based sample preparation technique that extracts small molecules from sample matrices with the use of small amounts of extraction phase. In SPME&#8217;s most traditional configuration of the device (probe), a fiber is coated with an appropriate extraction phase and immobilized on a solid support i.e., a metal wire5,6. Biocompatible coatings and devices (probes) enable extraction directly from complex biological matrices without sample pretreatment e.g., homogenization and filtration. Through the extraction process, analytes are partitioned between the extraction phase and sample matrix in proportion to their initial concentrations. If extraction is carried out long enough, then equilibrium is achieved. While extraction at equilibrium provides the highest possible sensitivity and reproducibility, pre-equilibrium extraction is also possible and even preferable in some cases i.e., in vivo sampling, where time-restrictions associated with the on-site sampling (e.g., operating or emergency rooms) necessitate fast extractions. The extraction time profile of a given analyte is generally influenced by the physicochemical properties of the analyte, the matrix being sampled, the type of sorbent used, and several other extraction conditions. The plethora of factors governing their extraction kinetics makes it practically impossible to ensure equilibrium extraction of all compounds when untargeted analyses such as metabolomics or lipidomics are performed. For the above-mentioned reasons, the extraction time of the current protocol was set arbitrarily to ensure satisfactory sensitivity and coverage of metabolites on one hand, and practicality for on-site use on the other.
It should be emphasized that the very small size of probes used for the extraction of sample from tissues only causes minimum tissue damage while the sampling procedure itself does not consume any tissue but very small amounts of small molecules from the sampled area; therefore, the same sample can be further used for routine tests i.e., histological or genetic, enabling the attainment of essential and complementary information from the same sample. Such complementary, comprehensive data would enable a better understanding of tumor biology, hopefully facilitating the discovery of new treatment targets. Exploiting this method further increases the possibility of on-site intraoperative diagnostics when determining target biomarkers.
Below we present protocols for sampling of brain tumors on-site for metabolomics and lipidomics analyses and data processing.

<table><tbody><tr><td>acetic acid</td><td>Honeywell</td><td>49199</td><td>Mobile phase additive, LCMS grade</td></tr><tr><td>acetonitrile</td><td>Honeywell</td><td>34967</td><td>HPLC solvent, LCMS grade</td></tr><tr><td>ammonium acetate</td><td>Honeywell</td><td>14267</td><td>Mobile phase additive, LCMS grade</td></tr><tr><td>BenchMixer MultiTube Vortexer</td><td>Benchmark Scientific</td><td>BV1010*</td><td>Vortex mixer</td></tr><tr><td>caps</td><td>Agilent</td><td>5183-2076</td><td>Blue scrw tp,pre-slit PTFE/Si septa</td></tr><tr><td>Compound Discoverer 2.1</td><td>Thermo Scientific</td><td></td><td>software for data processing</td></tr><tr><td>Discovery HS F5 Supelguar Cartridge 2 cm &amp;times; 2.1 mm, 3 &amp;mu;m</td><td>Supelco</td><td>567571-U</td><td>Guard Column</td></tr><tr><td>Discovery HS F5, 2.1 mm x 100 mm, 3 &amp;mu;m</td><td>Merck</td><td>567502-U</td><td>HPLC Column</td></tr><tr><td>formic acid</td><td>Honeywell</td><td>56302</td><td>Mobile phase additive, LCMS grade</td></tr><tr><td>glass vial inserts 250ul , deactivated</td><td>Agilent</td><td>5181-8872</td><td></td></tr><tr><td>glass vial inserts 350ul</td><td>Agilent</td><td>5188-5321</td><td></td></tr><tr><td>glass vials 1.5ml</td><td>Agilent</td><td>5183-2030</td><td></td></tr><tr><td>glass vials, 2 mL (amber, deactivated)</td><td>Agilent</td><td>5183-2072</td><td></td></tr><tr><td>glass vials, 2mL</td><td>Agilent</td><td>5182-0715</td><td></td></tr><tr><td>HILIC Luna 3 &amp;mu;m, 200A, 100 x 2.0 mm</td><td>Shim-Pol</td><td>PHX-00D-4449-B0</td><td>HPLC Column</td></tr><tr><td>isopropanol</td><td>Honeywell</td><td>34965</td><td>HPLC solvent, LCMS grade</td></tr><tr><td>LipidSearch 4.1</td><td>Thermo Scientific</td><td></td><td>software for data processing</td></tr><tr><td>Metaboanalyst</td><td>Xia Lab @ McGill</td><td></td><td>online software for statistical analysis (Chong, J., Wishart, D.S. and Xia, J. (2019) Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis. Current Protocols in Bioinformatics 68, e86 )</td></tr><tr><td>methanol</td><td>Honeywell</td><td>34966</td><td>HPLC solvent, LCMS grade</td></tr><tr><td>Pierce LTQ Velos ESI Positive Ion Calibration Solution</td><td>Thermo Scientific</td><td>88323</td><td></td></tr><tr><td>Pierce Negative Ion Calibration Solution</td><td>Thermo Scientific</td><td>88324</td><td></td></tr><tr><td>Q Exactive Focus hybrid quadrupole-Orbitrap MS</td><td>Thermo Scientific</td><td>726049</td><td>Mass Spectrometer</td></tr><tr><td>SecurityGuard cartridge for HILIC, 4 mm &amp;times; 2.0 mm</td><td>Phenomenex</td><td>KJ0-4282</td><td>Guard Column</td></tr><tr><td>SeQuant ZIC-cHILIC 3&amp;micro;m,100&amp;Aring; 100 x 2.1 mm</td><td>Merck</td><td>1506570001</td><td>HPLC Column</td></tr><tr><td>SeQuant ZIC-HILIC Guard Kit 20 x 2.1 mm</td><td>Supelco</td><td>1504360001</td><td>Guard column</td></tr><tr><td>SPME LC fiber probes, C18</td><td>Supelco</td><td>custom order</td><td>comercial probes: 57281-U; probes used in the experiment were not needle assembled and were precut to the length described in the protocol</td></tr><tr><td>SPME LC fiber probes, mixed Mode</td><td>Supelco</td><td>custom order</td><td></td></tr><tr><td>UltiMate 3000 HPLC systems</td><td>Thermo Scientific</td><td>5200.0345</td><td>HPLC system</td></tr><tr><td>water</td><td>Merck</td><td>1153331000</td><td>HPLC solvent, LCMS grade</td></tr><tr><td>XSelect CSH C18 3.5&amp;mu;m 2.1x75mm</td><td>Waters</td><td>186005644</td><td>HPLC Column</td></tr><tr><td>XSelect CSH C18 VanGuard Cartridge, 3.5 &amp;micro;m, 3.9x5 mm</td><td>Waters</td><td>186007813</td><td>Column cartidge</td></tr></tbody></table>

on-site sampling and extraction of brain tumors for metabolomics and lipidomics analysis

Despite the variety of tools available for cancer diagnosis and classification, methods that enable fast and simple characterization of tumors are still in need. In recent years, mass spectrometry has become a method of choice for untargeted profiling of discriminatory compound as potential biomarkers of a disease. Biofluids are generally considered as preferable matrices given their accessibility and easier sample processing while direct tissue profiling provides more selective information about a given cancer. Preparation of tissues for the analysis via traditional methods is much more complex and time-consuming, and, therefore, not suitable for fast on-site analysis. The current work presents a protocol combining sample preparation and extraction of small molecules on-site, immediately after tumor resection. The sampling device, which is of the size of an acupuncture needle, can be inserted directly into the tissue and then transported to the nearby laboratory for instrumental analysis. The results of metabolomics and lipidomics analyses demonstrate the capability of the approach for the establishment of phenotypes of tumors related to the histological origin of the tumor, malignancy, and genetic mutations, as well as for the selection of discriminating compounds or potential biomarkers. The non-destructive nature of the technique permits subsequent performance of routinely used tests e.g., histological tests, on the same samples used for SPME analysis, thus enabling attainment of more comprehensive information to support personalized diagnostics.

Exploiting solid phase microextraction as a sample preparation method in combination with liquid chromatography coupled to high-resolution mass spectrometry and an advanced data processing software allowed us to successfully characterize the metabolome and lipidome of human brain tumors. The probe, the size of which was equivalent to an acupuncture needle, caused minimum damage to the studied tissue and no tissue consumption, therefore, enabling the further use of samples for histological or genetic studies. Satisfactory separation of the selected groups was obtained for both reversed phase and HILIC columns, and for both ionization modes in metabolomics and lipidomics analyses. The use of both separation methods not only in metabolomics, but also in lipidomics analysis provided valuable complementary data. The reversed phase column separate lipids with respect to their carbon chains length and the presence of unsaturated bonds, whereas the HILIC column is useful for profiling lipid groups, especially phospholipids8.
The reproducibility of the instrumental analysis was found to be very good based on the tight clustering of QC samples on the principal component analysis plot (Figure 3A, the three QC samples injected every eight patients&#8217; samples along the sequence overlap). Moreover, extraction blanks used for negative control analysis were found to separate well from real samples. The wide range of analytes extracted by the probes facilitated the discovery of representative species, thus successfully allowing for differentiation between human brain tumors based on their histological origin, malignancy, and other factors (e.g., genetic). Figure 3B shows lipidomics data for samples collected from patients with gliomas and meningiomas. Enabling differentiation between these tumors, which were characterized by different histological origin and malignancy, was an important goal of the study, as meningiomas are generally considered as benign tumors, while gliomas are one of the most malignant. Additionally, in Figure 4 presenting metabolomics data, gliomas were divided based on their degree of malignancy into high and low. These sub-groups were compared with the molecular phenotype of meningiomas. In both cases, prominent separation of clusters was observed. Nowadays, diagnostics of glioma primarily relies on determining specific genetic mutations in tumor samples. Therefore, results obtained were compared to genotyping data. Figure 5A presents separation of the samples with detected co-deletion 1p19q and samples where the mutation was not observed.
Statistical analysis also permits selection of compounds in the studied groups. These compounds might be considered as potential biomarkers in cases where an appropriately large cohort is sampled. However, more in-depth analysis, including conclusive confirmation of detected compounds by fragmentation and comparison of detected species with analytical standards, is required to draw definitive conclusions of a biological nature. Examples of such discriminant metabolites are presented in Figure 3C and Figure 5B. The identities of these compounds, i.e., sphingomielyn: SM d36:1 and proline were confirmed by comparing fragmentation patterns of the metabolites from the sample and authentic standards.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61260/61260fig01.jpg" /> 
Figure 1: SPME fibers prepared for the extraction process. <a href="https://www.jove.com/files/ftp_upload/61260/61260fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61260/61260fig02.jpg" /> 
Figure 2: Extraction of meningioma using SPME probes. <a href="https://www.jove.com/files/ftp_upload/61260/61260fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61260/61260fig03.jpg" /> 
Figure 3: PCA and box plots for glioma and meningioma.&#160;Principal component analysis plot containing (A) all analyzed samples including blanks, extraction QC, blanks, meningiomas, gliomas; (B) containing only studied groups (after exclusion of blanks and QCs); (C) box whisker plot for sphingomielyn: SM d36:1 differentiating patient with glioma and meningioma. Lipidomics data. <a href="https://www.jove.com/files/ftp_upload/61260/61260fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61260/61260fig04.jpg" /> 
Figure 4: PCA for HGG, LGG and meningioma.&#160;Principal component analysis plot showing differentiation between (A) high grade gliomas (HGG) and meningiomas (MEN)9 and (B) low grade gliomas (LGG) and meningiomas. Metabolomics data reprinted from Via Medica ref9 with permission. <a href="https://www.jove.com/files/ftp_upload/61260/61260fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/61260/61260fig05.jpg" /> 
Figure 5: PCA and box plots for glioma with and without deletion.&#160;(A) Principal component analysis plot showed differences in patients with and without co-deletion 1p19q; (B) box whisker plots for proline differentiated patients with and without co-deletion 1p19q; n-without deletion, y-with deletion. Metabolomics data <a href="https://www.jove.com/files/ftp_upload/61260/61260fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis at JoVE.com

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

The manuscript presents on-site sampling of human brain tumors with solid phase microextraction followed by their biochemical profiling towards the discovery of biomarkers.

Despite the variety of tools available for cancer diagnosis and classification, methods that enable fast and simple characterization of tumors are still ...

on-site-sampling-extraction-brain-tumors-for-metabolomics-lipidomics

Research

JoVE Journal

Medicine

5.8K Views.  Nicolaus Copernicus University in Torun. Dit protocol kan worden gebruikt om een extractie uit hersentumoren in of naast een server uit te voeren op het gebruik van vaste fase microextractievezels. Deze techniek maakt de extractie van kleine moleculen rechtstreeks uit gereseceerde tumoren mogelijk en biedt de mogelijkheid voor snelle intraoperatieve diagnostiek door extractie-apparaten rechtstreeks te koppelen aan analytische instrumentatie. Om het vaste fase microextractie-apparaat voor te bereiden, trimt u de codering van elke sol...

Video: On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

Longitudinal Intravital Imaging of Brain Tumor Cell Behavior in Response to an Invasive Surgical Biopsy

Biopsies are standard of care for cancer treatment and are clinically beneficial as they allow solid tumor diagnosis, prognosis, and personalized treatment determination. However, perturbation of the tumor architecture by biopsy and other invasive procedures has been associated with undesired effects on tumor progression, which need to be studied in depth to further improve the clinical benefit of these procedures. Conventional static approaches, which only provide a snapshot of the tumor, are limited in their ability to reveal the impact of biopsy on tumor cell behavior such as migration, a process closely related to tumor malignancy. In particular, tumor cell migration is the key in highly aggressive brain tumors, where local tumor dissemination makes total tumor resection virtually impossible. The development of multiphoton imaging and chronic imaging windows allows scientists to study this dynamic process in living animals over time. Here, we describe a method for the high-resolution longitudinal imaging of brain tumor cells before and after a biopsy in the same living animal. This approach makes it possible to study the impact of this procedure on tumor cell behavior (migration, invasion, and proliferation). Furthermore, we discuss the advantages and limitations of this technique, as well as the ability of this methodology to study changes in the cancer cell behavior for other surgical interventions, including tumor resection or the implantation of chemotherapy wafers.

Here we describe a method for high-resolution time-lapse multiphoton imaging of brain tumor cells before and after invasive surgical intervention (e.g., biopsy) within the same living animal. This method allows studying the impact of these invasive surgical procedures on tumor cells&#39; migratory, invasive, and proliferative behavior at a single cell level.

Biopsies are standard of care for cancer treatment and are clinically beneficial as they allow solid tumor diagnosis, prognosis, and personalized ...

Cancer Research

Extraction of Aqueous Metabolites from Cultured Adherent Cells for Metabolomic Analysis by Capillary Electrophoresis-Mass Spectrometry

Metabolomic analysis is a promising omics approach to not only understand the specific metabolic regulation in cancer cells compared to normal cells but also to identify biomarkers for early-stage cancer detection and prediction of chemotherapy response in cancer patients. Preparation of uniform samples for metabolomic analysis is a critical issue that remains to be addressed. Here, we present an easy and reliable protocol for extracting aqueous metabolites from cultured adherent cells for metabolomic analysis using capillary electrophoresis-mass spectrometry (CE-MS). Aqueous metabolites from cultured cells are analyzed by culturing and washing cells, treating cells with methanol, extracting metabolites, and removing proteins and macromolecules with spin columns for CE-MS analysis. Representative results using lung cancer cell lines treated with diamide, an oxidative reagent, illustrate the clearly observable metabolic shift of cells under oxidative stress. This article would be especially valuable to students and investigators involved in metabolomics research, who are new to harvesting metabolites from cell lines for analysis by CE-MS.

The purpose of this article is to describe a protocol for extraction of aqueous metabolites from cultured adherent cells for metabolomic analysis, particularly, capillary electrophoresis-mass spectrometry.

Metabolomic analysis is a promising omics approach to not only understand the specific metabolic regulation in cancer cells compared to normal cells but ...

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Metastatic spread of cancer is an unfortunate consequence of disease progression, aggressive cancer subtypes, and/or late diagnosis. Brain metastases are particularly devastating, difficult to treat, and confer a poor prognosis. While the precise incidence of brain metastases in the United States remains hard to estimate, it is likely to increase as extracranial therapies continue to become more efficacious in treating cancer. Thus, it is necessary to identify and develop novel therapeutic approaches to treat metastasis at this site. To this end, intracranial injection of cancer cells has become a well-established method in which to model brain metastasis. Previously, the inability to directly measure tumor growth has been a technical hindrance to this model; however, increasing availability and quality of small animal imaging modalities, such as magnetic resonance imaging (MRI), are vastly improving the ability to monitor tumor growth over time and infer changes within the brain during the experimental period. Herein, intracranial injection of murine mammary tumor cells into immunocompetent mice followed by MRI is demonstrated. The presented injection approach utilizes isoflurane anesthesia and a stereotactic setup with a digitally controlled, automated drill and needle injection to enhance precision, and reduce technical error. MRI is measured over time using a 9.4 Tesla instrument in The Ohio State University James Comprehensive Cancer Center Small Animal Imaging Shared Resource. Tumor volume measurements are demonstrated at each time point through use of ImageJ. Overall, this intracranial injection approach allows for precise injection, day-to-day monitoring, and accurate tumor volume measurements, which combined greatly enhance the utility of this model system to test novel hypotheses on the drivers of brain metastases.

Intracranial brain metastasis modeling is complicated by an inability to monitor tumor size and response to treatment with precise and timely methods. The presented methodology couples intracranial tumor injection with magnetic resonance imaging analysis, which when combined, cultivates precise and consistent injections, enhanced animal monitoring, and accurate tumor volume measurements.

Metastatic spread of cancer is an unfortunate consequence of disease progression, aggressive cancer subtypes, and/or late diagnosis. Brain metastases are ...

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Brain metastases are the most lethal cancer lesions; 10-30% of all cancers metastasize to the brain, with a median survival of only ~5-20 months, depending on the cancer type. To reduce the brain metastatic tumor burden, gaps in basic and translational knowledge need to be addressed. Major challenges include a paucity of reproducible preclinical models and associated tools. Three-dimensional models of brain metastasis can yield the relevant molecular and phenotypic data used to address these needs when combined with dedicated analysis tools. Moreover, compared to murine models, organ-on-a-chip models of patient tumor cells traversing the blood brain barrier into the brain microenvironment generate results rapidly and are more interpretable with quantitative methods, thus amenable to high throughput testing. Here we describe and demonstrate the use of a novel 3D microfluidic blood brain niche (&#181;mBBN) platform where multiple elements of the niche can be cultured for an extended period (several days), fluorescently imaged by confocal microscopy, and the images reconstructed using an innovative confocal tomography technique; all aimed to understand the development of micro-metastasis and changes to the tumor micro-environment (TME) in a repeatable and quantitative manner. We demonstrate how to fabricate, seed, image, and analyze the cancer cells and TME cellular and humoral components, using this platform. Moreover, we show how artificial intelligence (AI) is used to identify the intrinsic phenotypic differences of cancer cells that are capable of transit through a model &#181;mBBN and to assign them an objective index of brain metastatic potential. The data sets generated by this method can be used to answer basic and translational questions about metastasis, the efficacy of therapeutic strategies, and the role of the TME in both.

Here, we present a protocol for preparing and culturing a blood brain barrier metastatic tumor micro-environment and then quantifying its state using confocal imaging and artificial intelligence (machine learning).

Brain metastases are the most lethal cancer lesions; 10-30% of all cancers metastasize to the brain, with a median survival of only ~5-20 months, ...

A Robust Discovery Platform for the Identification of Novel Mediators of Melanoma Metastasis

Metastasis is a complex process, requiring cells to overcome barriers that are only incompletely modeled by in vitro assays. A systematic workflow was established using robust, reproducible in vivo models and standardized methods to identify novel players in melanoma metastasis. This approach allows for data inference at specific experimental stages to precisely characterize a gene's role in metastasis. Models are established by introducing genetically modified melanoma cells via intracardiac, intradermal, or subcutaneous injections into mice, followed by monitoring with serial in vivo imaging. Once preestablished endpoints are reached, primary tumors and/or metastases-bearing organs are harvested and processed for various analyses. Tumor cells can be sorted and subjected to any of several 'omics' platforms, including single-cell RNA sequencing. Organs undergo imaging and immunohistopathological analyses to quantify the overall burden of metastases and map their specific anatomic location. This optimized pipeline, including standardized protocols for engraftment, monitoring, tissue harvesting, processing, and analysis, can be adopted for patient-derived, short-term cultures and established human and murine cell lines of various solid cancer types.

This article describes a workflow of techniques employed for testing novel candidate mediators of melanoma metastasis and their mechanism(s) of action.

Metastasis is a complex process, requiring cells to overcome barriers that are only incompletely modeled by in vitro assays. A systematic workflow was ...

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

The tumor microenvironment (TME) represents a complex ecosystem comprised of dozens of distinct cell types, including tumor, stroma, and immune cell populations. To characterize proteome-level variation and tumor heterogeneity at scale, high-throughput methods are needed to selectively isolate discrete cellular populations in solid tumor malignancies. This protocol describes a high-throughput workflow, enabled by artificial intelligence (AI), that segments images of hematoxylin and eosin (H&#38;E)-stained, thin tissue sections into pathology-confirmed regions of interest for selective harvest of histology-resolved cell populations using laser microdissection (LMD). This strategy includes a novel algorithm enabling the transfer of regions denoting cell populations of interest, annotated using digital image software, directly to laser microscopes, thus enabling more facile collections. Successful implementation of this workflow was performed, demonstrating the utility of this harmonized method to selectively harvest tumor cell populations from the TME for quantitative, multiplexed proteomic analysis by high-resolution mass spectrometry. This strategy fully integrates with routine histopathology review, leveraging digital image analysis to support enrichment of cellular populations of interest and is fully generalizable, enabling harmonized harvests of cell populations from the TME for multiomic analyses.

This protocol describes a high-throughput workflow for artificial intelligence-driven segmentation of pathology-confirmed regions of interest from stained, thin tissue section images for enrichment of histology-resolved cell populations using laser microdissection. This strategy includes a novel algorithm enabling the transfer of demarcations denoting cell populations of interest directly to laser microscopes.

The tumor microenvironment (TME) represents a complex ecosystem comprised of dozens of distinct cell types, including tumor, stroma, and immune cell ...

Metabolomic Analysis of Rat Brain by High Resolution Nuclear Magnetic Resonance Spectroscopy of Tissue Extracts

Studies of gene expression on the RNA and protein levels have long been used to explore biological processes underlying disease. More recently, genomics and proteomics have been complemented by comprehensive quantitative analysis of the metabolite pool present in biological systems. This strategy, termed metabolomics, strives to provide a global characterization of the small-molecule complement involved in metabolism. While the genome and the proteome define the tasks cells can perform, the metabolome is part of the actual phenotype. Among the methods currently used in metabolomics, spectroscopic techniques are of special interest because they allow one to simultaneously analyze a large number of metabolites without prior selection for specific biochemical pathways, thus enabling a broad unbiased approach. Here, an optimized experimental protocol for metabolomic analysis by high-resolution NMR spectroscopy is presented, which is the method of choice for efficient quantification of tissue metabolites. Important strengths of this method are (i) the use of crude extracts, without the need to purify the sample and/or separate metabolites; (ii) the intrinsically quantitative nature of NMR, permitting quantitation of all metabolites represented by an NMR spectrum with one reference compound only; and (iii) the nondestructive nature of NMR enabling repeated use of the same sample for multiple measurements. The dynamic range of metabolite concentrations that can be covered is considerable due to the linear response of NMR signals, although metabolites occurring at extremely low concentrations may be difficult to detect. For the least abundant compounds, the highly sensitive mass spectrometry method may be advantageous although this technique requires more intricate sample preparation and quantification procedures than NMR spectroscopy. We present here an NMR protocol adjusted to rat brain analysis; however, the same protocol can be applied to other tissues with minor modifications.

The neurochemistry of mammalian brain is changed in many neurological and systemic diseases. Characteristic profiles of cerebral metabolites can be efficiently obtained based on crude extracts of brain tissue. To this end, high-resolution NMR spectroscopy is employed, enabling detailed quantitative analysis of metabolite concentrations (metabolomics).

Studies of gene expression on the RNA and protein levels have long been used to explore biological processes underlying disease. More recently, genomics ...

Neuroscience

Lipidomics and Transcriptomics in Neurological Diseases

Lipids serve as the primary interface to brain insults or stimuli conducive to neurological diseases and are a reservoir for the synthesis of lipids with various signaling or ligand function that can underscore the onset and progression of diseases. Often changing at the presymptomatic level, lipids are an emerging source of drug targets and biomarkers. Many neurological diseases exhibit neuroinflammation, neurodegeneration, and neuronal excitability as common hallmarks, partly modulated by specific lipid signaling systems. The interdependence and interrelation of synthesis of various lipids prompts a multilipid, multienzyme, and multireceptor analysis in order to derive the commonalities and specificities of neurological contexts and to expedite the unravelling of mechanistic aspects of disease onset and progression. Ascribing lipid roles to distinct brain regions advances the determination of lipid molecular phenotype and morphology associated with a neurological disease.
Presented here is a modular protocol suitable for the analysis of membrane lipids and downstream lipid signals along with mRNA of enzymes and mediators underlying their functionality, extracted from discrete brain regions that are relevant for a particular neurological disease and/or condition. To ensure accurate comparative lipidomic profiling, the workflows and operating criteria were optimized and standardized for: i) brain sampling and dissection of regions of interest, ii) co-extraction of multiple lipid signals and membrane lipids, iii) dual lipid/mRNA extraction, iv) quantification by liquid chromatography multiple reaction monitoring (LC/MRM), and v) standard mRNA profiling. This workflow is amenable for the low tissue amounts obtained by sampling of the functionally discrete brain subregions (i.e. by brain punching), thus preventing bias in multimolecular analysis due to tissue heterogeneity and/or animal variability. To reveal peripheral consequences of neurological diseases and establish translational molecular readouts of neurological disease states, peripheral organ sampling, processing, and their subsequent lipidomic analysis, as well as plasma lipidomics, are also pursued and described. The protocol is demonstrated on an acute epilepsy mouse model.

This article presents a modular protocol for tissue lipidomics and transcriptomics, and plasma lipidomics in neurological disease mouse models targeting lipids underlying inflammation and neuronal activity, membrane lipids, downstream messengers, and mRNA-encoding enzymes/receptors underlying lipid function. Sampling, sample processing, extraction, and quantification procedures are outlined.

Lipids serve as the primary interface to brain insults or stimuli conducive to neurological diseases and are a reservoir for the synthesis of lipids with ...

Laser Capture Microdissection of Glioma Subregions for Spatial and Molecular Characterization of Intratumoral Heterogeneity, Oncostreams, and Invasion

Gliomas are primary brain tumors characterized by their invasiveness and heterogeneity. Specific histological patterns such as pseudopalisades, microvascular proliferation, mesenchymal transformation and necrosis characterize the histological heterogeneity of high-grade gliomas. Our laboratory has demonstrated that the presence of high densities of mesenchymal cells, named oncostreams, correlate with tumor malignancy. We have developed a unique approach to understand the mechanisms that underlie glioma&#39;s growth and invasion. Here, we describe a comprehensive protocol that utilizes laser capture microdissection (LMD) and RNA sequencing to analyze differential mRNA expression of intra-tumoral heterogeneous multicellular structures (i.e., mesenchymal areas or areas of tumor invasion). This method maintains good tissue histology and RNA integrity. Perfusion, freezing, embedding, sectioning, and staining were optimized to preserve morphology and obtain high-quality laser microdissection samples. The results indicate that perfusion of glioma bearing mice using 30% sucrose provides good morphology and RNA quality. In addition, staining tumor sections with 4% Cresyl violet and 0.5% eosin results in good nuclear and cellular staining, while preserving RNA integrity. The method described is sensitive and highly reproducible and it can be utilized to study tumor morphology in various tumor models. In summary, we describe a complete method to perform LMD that preserves morphology and RNA quality for sequencing to study the molecular features of heterogeneous multicellular structures within solid tumors.

Laser microdissection (LMD) is a sensitive and highly reproducible technique that can be used to uncover pathways that mediate glioma heterogeneity and invasion. Here, we describe an optimized protocol to isolate discrete areas from glioma tissue using laser LMD followed by transcriptomic analysis.

Gliomas are primary brain tumors characterized by their invasiveness and heterogeneity. Specific histological patterns such as pseudopalisades, ...

 Advanced MALDI-MSI Mapping of Metabolites in Brain Organoids

Molecular Imaging of Human Brain Organoids Using Mass Spectrometry 

Brain organoid models serve as a powerful tool for studying human brain development and function. Mass spectrometry imaging (MSI), a cutting-edge technology, allows us to map the spatial distribution of diverse molecules such as lipids, sugars, amino acids, drugs, and their metabolites within these organoids, all without the need for specific molecular probes. High-quality MSI data hinge on meticulous sample preparation. Fixatives play a pivotal role, but conventional options such as glutaraldehyde, paraformaldehyde, and cryopreserving such as sucrose may inadvertently impact tissue metabolites. Optimal fixation entails flash freezing in liquid nitrogen. However, for small organoids, a more suitable approach involves transitioning the organoids directly from the incubator into a warmed embedding solution, followed by freezing in dry ice-cooled ethanol. Another critical step is the embedding prior to cryosectioning, which also requires materials compatible with MSI, as traditional options can interfere with matrix deposition and ionization. Here, an optimized protocol for high resolution-MALDI-MSI of human brain organoids is presented, encompassing sample preparation, sectioning, and imaging using mass spectrometry. This method showcases the molecular distribution of small metabolites, such as amino acids, with high mass accuracy and sensitivity. As such, coupled with complementary studies of brain organoids, it can assist in illuminating complex processes governing early brain development, metabolic cell fate trajectories, and distinctive metabolite signatures. Furthermore, it provides insights into the precise locations of molecules within the organoid, enriching our understanding of the spatial organization of 3D brain organoid models. As the field continues to advance, a growing number of studies leveraging MSI to delve into brain organoids and complex biological systems is anticipated, thereby deepening the understanding of the metabolic aspects of human brain function and development.

Molecular Imaging of Human Brain Organoids Using Mass Spectrometry

An advanced method was developed for mass spectrometry imaging (MSI) of brain organoids that allows mapping metabolite distributions within these models. This technology offers insights into brain metabolic pathways and metabolite signatures during early development and in disease, promising a deeper understanding of the human brain function.

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

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