Targeted Metabolomics on Rare Primary Cells

Katharina M. Glaser; Mirijam Egg; Sebastian Hobitz; Michael Mitterer; Dominik Schain-Zota; Katharina Sch&#246;nberger; Konrad Schuldes; Nina Cabezas-Wallscheid; Tim L&#228;mmermann; Angelika Rambold; Joerg M. Buescher

doi:10.3791/65690

JoVE Journal > Immunology and Infection

免疫学と感染

Targeted Metabolomics on Rare Primary Cells

Published: February 23, 2024

doi:

10.3791/65690

Katharina M. Glaser*¹, Mirijam Egg*¹, Sebastian Hobitz¹, Michael Mitterer¹, Dominik Schain-Zota¹, Katharina Schönberger¹, Konrad Schuldes¹, Nina Cabezas-Wallscheid¹, Tim Lämmermann¹, Angelika Rambold¹, Joerg M. Buescher¹

¹Max Planck Institute of Immunobiology and Epigenetics

概要

Here, we present a protocol to accurately and reliably measure metabolites in rare cell types. Technical improvements, including a modified sheath fluid for cell sorting and the generation of relevant blank samples, enable a comprehensive quantification of metabolites with an input of only 5000 cells per sample.

Abstract

Cellular function critically depends on metabolism, and the function of the underlying metabolic networks can be studied by measuring small molecule intermediates. However, obtaining accurate and reliable measurements of cellular metabolism, particularly in rare cell types like hematopoietic stem cells, has traditionally required pooling cells from multiple animals. A protocol now enables researchers to measure metabolites in rare cell types using only one mouse per sample while generating multiple replicates for more abundant cell types. This reduces the number of animals that are required for a given project. The protocol presented here involves several key differences over traditional metabolomics protocols, such as using 5 g/L NaCl as a sheath fluid, sorting directly into acetonitrile, and utilizing targeted quantification with rigorous use of internal standards, allowing for more accurate and comprehensive measurements of cellular metabolism. Despite the time required for the isolation of single cells, fluorescent staining, and sorting, the protocol can preserve differences among cell types and drug treatments to a large extent.

Introduction

Metabolism is an essential biological process that occurs in all living cells. Metabolic processes involve a vast network of biochemical reactions that are tightly regulated and interconnected, allowing cells to produce energy and synthesize essential biomolecules¹. To understand the function of metabolic networks, researchers measure the levels of small molecule intermediates within cells. These intermediates serve as important indicators of metabolic activity and can reveal critical insights into cellular function.

Mass spectrometry (MS) is the most popular choice for the specific detection of metabolites in complex samples¹^,². Nuclear magnetic resonance (NMR) has advantages in the absolute quantification of compounds and structure elucidation, but MS can often resolve more components in complex mixtures such as biofluids or cell extracts. More often than not, MS is combined with prior separation of the compound by capillary electrophoresis (CE), gas chromatography (GC), or liquid chromatography (LC)³. The choice of separation platform is mostly driven by the range of target metabolites and the type of sample and, in a real-world setting, by the availability of machines and expertise. All three separation platforms have a broad and overlapping range of suitable metabolites but different limitations. Briefly, CE can only separate charged molecules and requires a lot of expertise to implement robust analysis of a large number of samples⁴. GC is limited to molecules that are small and apolar enough to evaporate before decomposing³. Considering all commercially available LC columns, any two metabolites can be separated by this technology⁵. However, many LC methods exhibit less resolving power than CE or GC methods of similar length.

The typical amount of starting material for metabolomics measurements is usually in the range of 5 x 10⁵ to 5 x 10⁷ cells per sample, 5-50 mg of wet tissue, or 5-50 µL of body fluid⁶. However, it can be challenging to obtain such amounts of starting material when working with primary cells of rare cell types, such as for example hematopoietic stem cells (HSCs) or circulating tumor cells. These cells are often present in very low numbers and cannot be cultivated without compromising critical cellular features.

HSCs and multipotent progenitor cells (MPPs) are the least differentiated cells of the hematopoietic system and continuously produce new blood cells throughout an organism's life. The regulation of hematopoiesis is of clinical relevance in conditions such as leukemia and anemia. Despite their importance, HSCs and MPPs are among the rarest cells within the hematopoietic system. From a single mouse, typically, about 5000 HSCs can be isolated⁷^,⁸^,⁹. As traditional metabolomics methods require more input material, pooling cells from multiple mice was often necessary to analyze rare cell types¹⁰^,¹¹.

Here, we aimed to develop a protocol that enables the measurement of metabolites in as little as 5000 cells per sample to enable the generation of metabolomics data from the HSCs of a single mouse¹². At the same time, this method allows to generate multiple replicates from a single mouse for more abundant cell types like lymphocytes. This approach reduces the number of animals required for a given project, thus contributing to the "3R" (reduction, replacement, refinement) of animal experiments.

Metabolites in cells can have very high turnover rates, often in the order of seconds¹³. However, preparing samples for fluorescence-activated cell sorting (FACS) can take hours, and FACS sorting itself can take minutes to hours, leading to potential alterations in the metabolome due to non-physiological conditions. Some of the reagents used in this protocol (such as ammonium-chloride-potassium [ACK] lysis buffer) can have similar effects. These conditions can cause cellular stress and impact the levels and ratios of metabolites within cells, leading to inaccurate or biased measurements of cellular metabolism¹⁴^,¹⁵^,¹⁶. The metabolic changes due to sample preparation are sometimes referred to as sorting artifacts. Long digestion protocols and harsh reagents that might be required to produce single-cell suspensions from hard or tough tissues can aggravate this issue. What changes can occur likely depends on the cell type and the processing condition. The precise nature of the changes remains unknown, as the metabolic state of the undisturbed cells in the living tissue cannot be measured.

The protocol presented here involves several key differences compared to traditional methods, namely the use of 5 g/L NaCl as a sheath fluid, sorting directly into extraction buffer, injecting large sample volumes on hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS), and utilizing targeted quantification, rigorous use of internal standards and background controls (Figure 1). This protocol has the potential to preserve differences among cell types and between drug treatment and vehicle control to a large extent¹². Even for cultured cells, it compares favorably to alternative approaches, such as the more established centrifugation and manual removal of supernatant. However, as sorting artifacts may still occur, data must be interpreted with caution. Despite this limitation, the protocol represents a significant improvement in the field of metabolic profiling, allowing for more accurate and comprehensive measurements of cellular metabolism in rare primary cells¹².

The ability to robustly measure broad metabolic profiles in rare primary cells opens the door to new experiments in biomedical research involving these cells. For example, metabolically mediated regulation in HSCs has been shown to impact dormancy self-renewal capacity, with implications for anemia and leukemia¹¹^,¹⁷. In patient-derived circulating tumor cells, differences in the expression of metabolic genes between tumor and adjacent cells have been shown¹⁸^,¹⁹. This protocol now allows researchers to study these differences systematically on a metabolic level, which is generally regarded as closer to the cellular phenotype than gene expression.

Protocol

Breeding and husbandry of all mice used for this protocol were conducted in a conventional animal facility at the Max Planck Institute for Immunobiology and Epigenetics (MPI-IE) according to the regulations of the local authorities (Regierungspräsidium Freiburg). Mice were euthanized with CO₂ and cervical dislocation by FELASA B-trained personnel following guidelines and regulations approved by the animal welfare committee of the MPI-IE and the local authorities. No animal experimentation was performed, and mice were without health burdens.

NOTE: Highly sensitive analytical methods such as the LC-MS method used in this protocol have the potential to detect even very minor contaminations in the sample. Therefore, it is of utmost importance to minimize such contaminations. The single most important measure is to wear clean gloves whenever touching anything that comes in contact with the samples. This includes for example, preparation of buffers and sheath fluid, labeling of sample tubes, and cleaning laboratory equipment. Moreover, single-use labware should be used whenever possible. Glassware should be rinsed with LC-MS grade solvents prior to use. A clean working environment further helps reduce contaminations.

1. General preparations

Prepare internal standard stock solution: Prepare 6 mg/mL chloro-phenylalanine (CLF) and 6 mg/mL aminoterephtalic acid (ATA) in 30% v/v 2-propanol in ultrapure water.
Prepare FACS resuspension buffer: Add 0.9 g of NaCl, 99.5 mL of ultrapure water, and 0.5 mL of internal standard stock solution. Pre-cool to 4 °C.
Prepare sheath fluid: In a sheath fluid tank, dissolve 30 g of NaCl in 6 L of deionized water and connect to a cell sorter.
NOTE: The concentration of NaCl in the sheath fluid has been optimized to allow deflection of droplets in the sorter that is as robust as when using PBS as sheath fluid and simultaneously to minimize negative effects on metabolite detection by LC-MS¹². Lower concentrations of NaCl are advantageous for metabolite detection but impair the robust sorting of cells.

2. Isolation of B and T cells from the spleen

Pre-cool 90 mL of phosphate-buffered saline (PBS) per mouse in a fridge or on ice to 4 °C. Pre-cool a centrifuge to 4 °C.
Isolation of spleens
NOTE: It is most critical to work fast, on ice, and with cold (4 °C) buffers during cell isolation and the following staining steps. Otherwise, the metabolic profile of cells might change considerably.
1. Prepare 1.5 mL of PBS in a 2 mL reaction tube on ice.
2. Euthanize a C57BL6/J mouse according to the guidelines and methods approved by local authorities.
3. Sterilize fur with 70% ethanol.
4. Open the abdomen with scissors and remove the spleen using fine forceps without rupturing the organ. Immediately place the spleen in cold PBS (4 °C).
Generation of a single-cell suspension
1. Place a 70 µm cell strainer on a 50 mL centrifuge tube and wet it with PBS. Pass the spleen through the mesh using the thumb rest of a syringe plunger and 30 mL of cold PBS (4 °C)
2. Centrifuge at 300 x g for 5 min. Remove the supernatant.
3. Resuspend the pellet in 1 mL of ACK lysis buffer and incubate for 2 min at room temperature (RT; 20 °C).
4. Add 50 mL of cold PBS (4 °C). Centrifuge at 300 x g for 5 min and discard the supernatant.
FACS staining
1. Prepare the antibody cocktail in 500 µL of cold PBS (4 °C) per mouse: CD3-PE (1:200), B220-A647 (1:200).
2. Resuspend the cell pellet in the antibody cocktail. Transfer to a 5 mL FACS tube. Incubate for 30 min at 4 °C in the dark.
3. Add 3 mL of cold PBS (4 °C). Centrifuge at 300 x g for 5 min and discard the supernatant.
4. Resuspend the cell pellet in 1 mL of cold FACS resuspension buffer (4 °C). Filter the cells through a filter-cap FACS tube. Cells are ready for flow cytometry-based sorting.

3. Isolation of HSCs and MPPs from bone marrow

Dissect and isolate the bone marrow.
NOTE: It is most critical to work fast and keep isolated bones and the cells cold (4 °C) during the whole procedure to allow accurate results. Furthermore, to minimize background signals, work and use a clean space and laboratory equipment.
1. Euthanize mice according to guidelines and methods approved by local authorities.
2. Spray 70% Ethanol on the lower part of the mice.
3. Dissect the legs and the spines using scissors. Make an incision on the back and extend the cut around to the abdomen. Peel the skin from the hind limbs.
4. Rip or cut the hind limbs off, and make sure the legs contain the tibia, femur, and hip joint.
5. Take the scissors and cut them close alongside the spine until completely removed.
6. Distribute the legs and spines into 6-well-plates containing cold PBS (4 °C) and keep the plates on ice.
7. Clean the femur, tibia, hip joint, and vertebrae of soft tissue using a scalpel and scissors.
8. Crush the cleaned bones with a mortar and pestle in 5 mL of cold PBS (4 °C).
9. Filter the cell suspension through a 40 µm cell strainer into a 50 mL tube.
10. Repeat steps 3.1.8-3.1.9 until the bones appear completely white.
Lysis of erythrocytes:
1. Centrifuge the harvested cell suspension at 400 x g for 5 min at 4 °C and discard the supernatant.
2. Resuspend the cell pellet in 1 mL of cold ACK buffer (4 °C). Incubate for 5 minutes on ice.
3. Add cold PBS (4 °C) to stop the reaction (optional).
4. Centrifuge at 400 x g for 5 min at 4 °C and discard the supernatant.
Lineage depletion:
NOTE: To enrich for lineage negative (Lin-) cells, a negative selection kit for CD4 cells was used.
1. Prepare the magnetic beads:
  1. Add 500 µL of beads into 2 mL microcentrifuge tubes. Place the tubes on a magnet and discard the supernatant.
  2. Wash the beads with 1 mL of cold PBS (4 °C) and discard the supernatant.
  3. Add 500 µL of cold PBS (4 °C) and keep the beads cold (4 °C).
2. Incubate the cells with 500 µL of lineage cocktail (50 µL of antibody provided by the kit + 450 µL of PBS) for 25 min (shaking, 4 °C) in the dark.
3. Wash the cells with cold PBS (4 °C).
4. Centrifuge the tube at 400 x g for 5 min at 4 °C and discard the supernatant.
5. Resuspend the cells in 1000 µL of cold PBS (4 °C) and transfer the cell suspension to the bead solution.
6. Incubate for 15 min on a rotating wheel at 4 °C.
7. Place the tubes on a magnet and wait until the solution clears. Transfer the supernatant to a fresh FACS tube.
8. Wash the beads with 500 µL of PBS.
9. Place the tubes on a magnet and wait until the solution clears.
10. Transfer the supernatant to the fresh FACS tube.
11. Centrifuge at 400 x g for 5 min at 4 °C and discard the supernatant.
Staining for FACS
1. Prepare 300 µL of antibody mix per mouse in cold PBS (4 °C).
2. Antibody mix for stem/progenitor cells: HSCs: CD4(1:1000)/ CD8a(1:1000)/ B220(1:1000)/ Ter119(1:500)/ Gr-1(1:1000)/ CD11b – PeCy7(1:1000), c-Kit – PE(1:1000), Sca1 – APC-Cy7(1:500), CD48 – BV421(1:1000), CD150 – BV605(1:300)
3. Incubate the cells for 40 min in the dark at 4 °C.
4. Wash with 1 mL of cold PBS (4 °C). Centrifuge at 400 x g for 5 min at 4 °C and discard the supernatant.
5. Resuspend the cell pellet in 1 mL of FACS resuspension buffer and filter the cells through a filter-cap FACS tube.

4. FACS Sorting

Set up the cell sorter.
1. Insert 70 µm nozzle tip. Activate 405 nm, 488 nm, 561 nm, and 633 nm lasers.
2. Set sort mode to 4 Way Purity: yield mask 0, purity mask 32, phase mask 0
3. Set the drop frequency to 90,400 Hz, and adjust the amplitude and drop delay according to the manufacturer's instructions. Set plate voltage to 5000 V.
4. Define sorting gates according to the manufacturer's instructions.
  1. For all cell types, first, select single cells based on forward scatter and sideward scatter signal, followed by selection based on cell type-specific staining.
  2. For B cells, select the AF647-positive, PE-negative population.
  3. For T cells, select the PE-positive, AF647-negative population.
  4. For HSCs, select the PE-Cy7-low, PE-positive, APC-Cy7-positive, BV605-positive, BV421-negative population.
  5. For MPPs, select the PE-Cy7-low, PE-positive, APC-Cy7-positive, BV421-positive, BV605-negative, population.
  6. For debris samples, select events that are too small to represent intact cells based on forward scatter and sideward scatter signal. Typical FACS plots showing these gating strategies are shown in Figure 2 and Figure 3.
5. Adjust the flow rate to obtain less than 15,000 events/s.
Prepare collection plate
1. Prepare yeast extract stock solution: Resuspend 1 aliquot of ¹³C yeast extract in 7.5 mL of ultrapure H₂O and 2.5 mL of methanol.
2. Prepare extraction buffer: Add 10 µL of ¹³C yeast extract stock solution to 10 mL of acetonitrile and mix.
3. Prepare collection plate: Add 25 µL of the extraction buffer to each well of a 96 well PCR plate that will be used to collect the sample. To minimize evaporation, cover wells with PCR lids (stripes cut into singlets).
Perform the cell sort.
1. Open the collection wells as needed to collect sorted cells and close them as soon as possible to minimize evaporation.
If samples cannot be measured immediately, store them in sealed containers at -80 °C for several days. After thawing, sonicate the samples for 5 min to ensure complete resuspension of metabolites.

5. LC-QQQ-MS measurement

Prepare LC Solvents
1. Prepare medronic acid stock solution (100 mM): Dissolve 17.6 mg of medronic acid in 1 mL of ultrapure H₂O.
2. Prepare Buffer A: Dissolve 1.92 g of ammonium carbonate in 1 L of ultrapure H₂O, and add 50 µL of medronic acid stock solution.
3. Prepare Buffer B: Mix 50 mL of buffer with 450 mL of acetonitrile.
4. Prepare LC test mix: Mix 1 ppm each of leucine, isoleucine, AMP, ADP, and ATP in 80:20 acetonitrile: ultrapure H₂O.
  NOTE: Medronic acid stock can be stored at -20 °C for several months; other buffers can be kept at RT for 2 days.
Program LC-QQQ-MS method
1. Optimize compound-specific MS settings (e.g., collision energy) according to the manufacturer's instructions. For Agilent 6495-series instruments, use the settings in Supplementary Table 1.
2. Optimize source-specific MS settings according to the manufacturer's instructions. For instruments with JetStream heated ESI source, use the following parameters: gas temp: 240 °C, gas flow: 15 L/min, nebulizer: 50 psi, sheath gas temp: 400 °C, sheath gas flow: 11 L/min, capillary voltage: 2000 V, nozzle voltage: 300 V.
3. Optimize settings of ion transfer optics according to the manufacturer's instructions. For Agilent instruments with iFunnel ion optics, use the following parameters: high-pressure RF positive: 110 V, high-pressure RF negative: 90 V, low-pressure RF positive: 80 V, low-pressure RF negative: 60 V.
4. Program the LC gradient: 0 min, 95% B, 150 µL/min; 18 min, 55% B, 150 µL/min; 19 min, 30% B, 150 µL/min; 21.5 min, 30% B, 150 µL/min; 22 min, 95% B, 150 µL/min; 22.7 min, 95% B, 200 µL/min, 23.5 min, 95% B, 400 µL/min; stop time 28 min. Column temperature: 35 °C.
Set up LC-MS instrument.
1. Connect the chromatographic column in the temperature-controlled column compartment.
2. Connect the buffers and purge all lines.
3. Run at least 4 blank samples or pool samples to equilibrate the column.
Run LC test mix to check chromatographic performance. Ensure the following criteria are met. If not, clean or troubleshoot the instrument before running the samples.
1. Confirm that the initial backpressure is <150 bar and the maximum backpressure is <400 bar.
2. Confirm that the retention times are in a ±1 min window as follows: isoleucine 6.0 min, leucine 6.4 min, AMP 9.5 min, ADP 11.2 min, and ATP 12.7 min.
3. Ensure the valley between leucine and isoleucine in the +132->86 transition is < 20 % of the peak height.
4. Ensure the difference in retention time AMP to ADP is 1.5 to 1.9 min, and the difference in retention time ADP to ATP is 1.1 to 1.9 min.
Set up worklist
1. Start with a blank sample to minimize carry-over from the LC test mix.
2. Run the samples in randomized order.
3. End with a blank sample to clean the column for future experiments.

6. Data pre-processing in automRm

NOTE: The following steps discuss conversion from .d to .mzML in msconvert, preparation of metabdb, loading of data and models and metabdb, and general strategies for manual peak review.

Convert raw data from .d format to .mzML format using ProteoWizard²⁰ with the following settings: binary encoding precision: 32-bit, MS Levels 1-2, Write Index selected, Use zlib compression selected.
Start R.
Install automRm²¹ according to the package documentation (only required once before first use).
Start automRm GUI in R: automRm::automrm_gui()
Process the batch of .d files in the tab Process > Process Batch.
1. Select .xlsx file holding metabolite information. This file must match the LC-QQQ-MS method.
2. Select .RData file holding peak picking ML model.
3. Select .RData file holding the peak reporting ML model.
4. Select the project folder containing .mzML files.
5. Start data pre-processing by clicking Process Batch.
After the pre-processing is finished, open peakoverview.pdf to check the correct detection of chromatographic peaks. Optionally, load the automRmdata_Review.RData from the Review tab of the automRm GUI to manually modify peak filtering and peak integration.
Open the peakinfo.xlsx from the project folder and check the data.
1. Check ¹³C internal standard signal in the tab 13CArea: Ensure that there are no systematic differences in the intensity values among experimental groups and between cell samples and debris samples. If such differences exist, only use data from the tabs NormArea or NormHeight for subsequent analysis; otherwise, use data from the tabs RawArea or RawHeight.
2. Check the signal intensity of internal standards ATA and CLF in tab RawArea. Ensure that there are no systematic differences in the intensity of either compound among experimental groups.
3. For each metabolite, compare the signal intensity of samples containing cells to the debris samples from the same cell suspension. Use an appropriate statistical test to identify metabolites in which samples containing cells have higher intensities than debris samples. Only include metabolites that pass this test in subsequent data analysis.

Representative Results

FACS sorting enables the isolation of clean populations of different cell types from the same cell suspension (Figure 2 and Figure 3). The specificity of this method relies on the staining of the different cell types with specific surface markers (for example, B cells and T cells from the spleen) or specific combinations of surface markers (for example HSCs and MPPs). Staining of intracellular markers typically requires permeabilization of the cell membrane. This can cause leakage of metabolites, and therefore this type of staining is not suitable to be used in this protocol.

LC provides the separation of metabolites prior to detection by MS (Figure 4). Metabolites elute from the HILIC column approximately sorted by polarity, with less polar compounds eluting early and more polar metabolites eluting later. The signal intensity is determined by the amount of a target metabolite in the sample and a metabolite-specific response factor. Consequently, signal intensities cannot be meaningfully compared across metabolites but only for one metabolite across samples. The 3 peaks that are highlighted in Figure 4 represent 1: niacinamide is detected in both debris and cell extract, albeit at different levels, 2: acetyl-carnitine that is almost exclusively detected in cell extracts, and 3: the internal standard aminoterephatalic acid that is detected at the same level in debris samples and cell extracts.

Metabolic differences among cell types from the same tissue are preserved using the combined FACS-LC-MS protocol (Figure 5). For HSCs and MPPs, cells from 6 mice were required to generate 6 samples, leading to a larger variability within each group compared to B cells and T cells, which were sorted from the same murine spleen. Debris samples representing process blank control samples are clearly separated from samples containing cell extracts. Within the debris samples, a clear separation of the two experiments is visible, highlighting the difference in metabolic environment that the cells experience prior to the sort. When represented in a clustered heatmap (Figure 6), additional information becomes visible, such as the relative levels of metabolites across samples.

Figure 1: Overview of key steps of the presented protocol. This figure has been reprinted with permission from Schoenberger et al.¹². Please click here to view a larger version of this figure.

Figure 2: Isolation of pure populations of B cells and T cells from the spleen. Cells and debris events were selected based on forward scatter (FSC) and sideward scatter (SSC) signals. B cells and T cells were selected based on cell type-specific staining signals. This figure has been adapted with permission from Schoenberger et al.¹². Please click here to view a larger version of this figure.

Figure 3: Isolation of HSC and MPP from bone marrow. Cells and debris events were selected based on forward scatter (FSC) and sideward scatter (SSC) signals. HSC and MPP populations were selected based on multiple stages of cell type-specific staining signals. This figure has been adapted with permission from Schoenberger et al.¹². Please click here to view a larger version of this figure.

Figure 4: Example chromatograms. Samples with (A) 5000 HSCs and (B) 5000 debris events from the same sort. Note that axis share the same scale in both panels. Highlighted metabolites are 1: nicotinamide, 2: acetyl-carnitine, 3: aminoterephtalic acid (ATA). Please click here to view a larger version of this figure.

Figure 5: Principal component analysis (PCA) of metabolic profiles of different cell types and debris background samples from the same experiments. Major separation is observed between the different organs and debris background samples from any organ. In addition, cell types are clearly separated within the cells of each organ. Please click here to view a larger version of this figure.

Figure 6: Heatmap representing metabolites measured across different cell types and matching background control samples (debris). Missing values were imputed with the half-minimum value of the same metabolite across all samples. For plotting, data was normalized to the maximum separately in every row. Please click here to view a larger version of this figure.

Supplementary Table 1: The table lists the compound-specific settings for the selected metabolites, including retention time (RT), polarity (Pol), Precursor m/z (Q1), Fragment m/z (Q3), and collision energy (CE). Please click here to download this File.

Discussion

The most critical steps for successful implementation of targeted metabolomics using this protocol are 1) a robust staining and gating strategy that will yield clean cell populations 2) precise handling of liquid volumes, 3) reproducible timing of all experimental steps, in particular all steps prior to metabolite extraction. Ideally, all samples belonging to one experiment should be processed and measured in one batch to minimize batch effects²². For larger experiments, we suggest collecting cell extracts at -80 °C and then measuring all extracts in one batch.

When working with low-input samples, clean handling of all materials that are used in the course of the experiments must be emphasized²³. Common contaminations are residual buffer, detergents, and skin-care products. The most important measure to minimize contamination is the use of suitable gloves throughout the experiment.

To minimize potential alterations in metabolite composition due to exposure of cells to unphysiological conditions, it is crucial to prepare the samples on ice and with cold reagents (4 °C) is crucial. Also, being as fast as possible before the FACS step will increase the quality of measurement². The indicated times for antibody staining are as short as reasonable for good staining efficiency. An additional step to block the unspecific binding of antibodies to Fc receptors using Fc-block antibodies could be added before the FACS staining procedure²⁴^,²⁵. This prevents contamination of the target cell populations and is especially advisable when working with cell suspensions that include many Fc-receptor high cell types (like monocytes or macrophages) or cells that were cultured without serum. However, it will increase the overall handling time by 10-15 min and therefore may add additional non-physiological variability to the results.

With the use of dynamic MRM methods on modern mass spectrometers, all metabolites listed in compound-specific settings can be recorded with a single injection. Limiting the number of target metabolites allows for the implementation of the described protocol on older mass spectrometers and accelerates data pre-processing and data analysis.

Some level of background signal in the LC-MS data is unavoidable. Therefore, great care must be taken to identify those metabolites that can be detected above the background. Sorting events that are too small to represent intact cells (debris) generates relevant process blank samples that represent matrix-dependent background signal intensities. In addition, internal standards allow the identification of problematic samples that should be excluded from further data analysis²⁶. Both quality assurance measures work best if a sufficiently large number of replicates is available. We recommend using at least six replicates per group.

開示

The authors have nothing to disclose.

Acknowledgements

The authors would like to thank the animal facility of the Max Planck Institute of Immunobiology and Epigenetics for providing the animals used in this study.

Materials

¹³C yeast extract	Isotopic Solutions	ISO-1
40 µm cell strainer	Corning	352340
Acetonitrile, LC-MS grade	VWR	83640.32
ACK lysis buffer	Gibco	104921	Alternatively: Lonza, Cat# BP10-548E
Adenosine diphosphate (ADP)	Sigma Aldrich	A2754
Adenosine monophosphate (AMP)	Sigma Aldrich	A1752
Adenosine triphosphate (ATP)	Sigma Aldrich	A2383
Ammonium Carbonate, HPLC grade	Fisher Scientific	A/3686/50
Atlantis Premier BEH Z-HILIC column (100 x 2.1 mm, 1.7 µm)	Waters	186009982
B220-A647	Invitrogen	103226
B220-PE/Cy7	BioLegend	103222	RRID:AB_313005
CD11b-PE/Cy7	BioLegend	101216	RRID:AB_312799
CD150-BV605	BioLegend	115927	RRID:AB_11204248
CD3-PE	Invitrogen	12-0031-83
CD48-BV421	BioLegend	103428	RRID:AB_2650894
CD4-PE/Cy7	BioLegend	100422	RRID:AB_2660860
CD8a-PE/Cy7	BioLegend	100722	RRID:AB_312761
cKit-PE	BioLegend	105808	RRID:AB_313217
Dynabeads Untouched Mouse CD4 Cells Kit	Invitrogen	11415D
FACSAria III	BD
Gr1-PE/Cy7	BioLegend	108416	RRID:AB_313381
Heat sealing foil	Neolab	Jul-18
Isoleucine	Sigma Aldrich	58880
JetStream ESI Source	Agilent	G1958B
Leucine	Sigma Aldrich	L8000
Medronic acid	Sigma Aldrich	M9508-1G
Methanol, LC-MS grade	Carl Roth	HN41.2
NaCl	Fluka	31434-1KG
PBS	Sigma Aldrich	D8537
Sca1-APC/Cy7	BioLegend	108126	RRID:AB_10645327
TER119-PE/Cy7	BioLegend	116221	RRID:AB_2137789
Triple Quadrupole Mass Spectrometer	Agilent	6495B
Twin.tec PCR plate 96 well LoBind skirted	Eppendorf	30129512
UHPLC Autosampler	Agilent	G7157B
UHPLC Column Thermostat	Agilent	G7116B
UHPLC Pump	Agilent	G7120A
UHPLC Sample Thermostat	Agilent	G4761A

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Glaser, K. M., Egg, M., Hobitz, S., Mitterer, M., Schain-Zota, D., Schönberger, K., Schuldes, K., Cabezas-Wallscheid, N., Lämmermann, T., Rambold, A., Buescher, J. M. Targeted Metabolomics on Rare Primary Cells. J. Vis. Exp. (204), e65690, doi:10.3791/65690 (2024).

Targeted Metabolomics on Rare Primary Cells