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.

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 (Regierungspr&#228;sidium 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
<ol>
	<li>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.</li>
	<li>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 &#176;C.</li>
	<li>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. 
	&#8203;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.</li>
</ol>
2. Isolation of B and T cells from the spleen
<ol>
	<li>Pre-cool 90 mL of phosphate-buffered saline (PBS) per mouse in a fridge or on ice to 4 &#176;C. Pre-cool a centrifuge to 4 &#176;C.</li>
	<li>Isolation of spleens 
	NOTE: It is most critical to work fast, on ice, and with cold (4 &#176;C) buffers during cell isolation and the following staining steps. Otherwise, the metabolic profile of cells might change considerably.
	<ol>
		<li>Prepare 1.5 mL of PBS in a 2 mL reaction tube on ice.</li>
		<li>Euthanize a C57BL6/J mouse according to the guidelines and methods approved by local authorities.</li>
		<li>Sterilize fur with 70% ethanol.</li>
		<li>Open the abdomen with scissors and remove the spleen using fine forceps without rupturing the organ. Immediately place the spleen in cold PBS (4 &#176;C).</li>
	</ol>
	</li>
	<li>Generation of a single-cell suspension
	<ol>
		<li>Place a 70 &#181;m 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 &#176;C)</li>
		<li>Centrifuge at 300 x g for 5 min. Remove the supernatant.</li>
		<li>Resuspend the pellet in 1 mL of ACK lysis buffer and incubate for 2 min at room temperature (RT; 20 &#176;C).</li>
		<li>Add 50 mL of cold PBS (4 &#176;C). Centrifuge at 300 x g for 5 min and discard the supernatant.</li>
	</ol>
	</li>
	<li>FACS staining
	<ol>
		<li>Prepare the antibody cocktail in 500 &#181;L of cold PBS (4 &#176;C) per mouse: CD3-PE (1:200), B220-A647 (1:200).</li>
		<li>Resuspend the cell pellet in the antibody cocktail. Transfer to a 5 mL FACS tube. Incubate for 30 min at 4 &#176;C in the dark.</li>
		<li>Add 3 mL of cold PBS (4 &#176;C). Centrifuge at 300 x g for 5 min and discard the supernatant.</li>
		<li>Resuspend the cell pellet in 1 mL of cold FACS resuspension buffer (4 &#176;C). Filter the cells through a filter-cap FACS tube. Cells are ready for flow cytometry-based sorting.</li>
	</ol>
	</li>
</ol>
3. Isolation of HSCs and MPPs from bone marrow
<ol>
	<li>Dissect and isolate the bone marrow. 
	NOTE: It is most critical to work fast and keep isolated bones and the cells cold (4 &#176;C) during the whole procedure to allow accurate results. Furthermore, to minimize background signals, work and use a clean space and laboratory equipment.
	<ol>
		<li>Euthanize mice according to guidelines and methods approved by local authorities.</li>
		<li>Spray 70% Ethanol on the lower part of the mice.</li>
		<li>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.</li>
		<li>Rip or cut the hind limbs off, and make sure the legs contain the tibia, femur, and hip joint.</li>
		<li>Take the scissors and cut them close alongside the spine until completely removed.</li>
		<li>Distribute the legs and spines into 6-well-plates containing cold PBS (4 &#176;C) and keep the plates on ice.</li>
		<li>Clean the femur, tibia, hip joint, and vertebrae of soft tissue using a scalpel and scissors.</li>
		<li>Crush the cleaned bones with a mortar and pestle in 5 mL of cold PBS (4 &#176;C).</li>
		<li>Filter the cell suspension through a 40 &#181;m cell strainer into a 50 mL tube.</li>
		<li>Repeat steps 3.1.8-3.1.9&#160;until the bones appear completely white.</li>
	</ol>
	</li>
	<li>Lysis of erythrocytes:
	<ol>
		<li>Centrifuge the harvested cell suspension at 400 x g for 5 min at 4 &#176;C and discard the supernatant.</li>
		<li>Resuspend the cell pellet in 1 mL of cold ACK buffer (4 &#176;C). Incubate for 5 minutes on ice.</li>
		<li>Add cold PBS (4 &#176;C) to stop the reaction (optional).</li>
		<li>Centrifuge at 400 x g for 5 min at 4 &#176;C and discard the supernatant.</li>
	</ol>
	</li>
	<li>Lineage depletion: 
	NOTE: To enrich for lineage negative (Lin-) cells, a negative selection kit for CD4 cells was used.
	<ol>
		<li>Prepare the magnetic beads:
		<ol>
			<li>Add 500 &#181;L of beads into 2 mL microcentrifuge tubes. Place the tubes on a magnet and discard the supernatant.</li>
			<li>Wash the beads with 1 mL of cold PBS (4 &#176;C) and discard the supernatant.</li>
			<li>Add 500 &#181;L of cold PBS (4 &#176;C) and keep the beads cold (4 &#176;C).</li>
		</ol>
		</li>
		<li>Incubate the cells with 500 &#181;L of lineage cocktail (50 &#181;L of antibody provided by the kit + 450 &#181;L of PBS) for 25 min (shaking, 4 &#176;C) in the dark.</li>
		<li>Wash the cells with cold PBS (4 &#176;C).</li>
		<li>Centrifuge the tube at 400 x g for 5 min at 4 &#176;C and discard the supernatant.</li>
		<li>Resuspend the cells in 1000 &#181;L of cold PBS (4 &#176;C) and transfer the cell suspension to the bead solution.</li>
		<li>Incubate for 15 min on a rotating wheel at 4 &#176;C.</li>
		<li>Place the tubes on a magnet and wait until the solution clears. Transfer the supernatant to a fresh FACS tube.</li>
		<li>Wash the beads with 500 &#181;L of PBS.</li>
		<li>Place the tubes on a magnet and wait until the solution clears.</li>
		<li>Transfer the supernatant to the fresh FACS tube.</li>
		<li>Centrifuge at 400 x g for 5 min at 4 &#176;C and discard the supernatant.</li>
	</ol>
	</li>
	<li>Staining for FACS
	<ol>
		<li>Prepare 300 &#181;L of antibody mix per mouse in cold PBS (4 &#176;C).</li>
		<li>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)</li>
		<li>Incubate the cells for 40 min in the dark at 4 &#176;C.</li>
		<li>Wash with 1 mL of cold PBS (4 &#176;C). Centrifuge at 400 x g for 5 min at 4 &#176;C and discard the supernatant.</li>
		<li>Resuspend the cell pellet in 1 mL of FACS resuspension buffer and filter the cells through a filter-cap FACS tube.</li>
	</ol>
	</li>
</ol>
4. FACS Sorting
<ol>
	<li>Set up the cell sorter.
	<ol>
		<li>Insert 70 &#181;m nozzle tip. Activate 405 nm, 488 nm, 561 nm, and 633 nm lasers.</li>
		<li>Set sort mode to 4 Way Purity: yield mask 0, purity mask 32, phase mask 0</li>
		<li>Set the drop frequency to 90,400 Hz, and adjust the amplitude and drop delay according to the manufacturer&#39;s instructions. Set plate voltage to 5000 V.</li>
		<li>Define sorting gates according to the manufacturer&#39;s instructions.
		<ol>
			<li>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.</li>
			<li>For B cells, select the AF647-positive, PE-negative population.</li>
			<li>For T cells, select the PE-positive, AF647-negative population.</li>
			<li>For HSCs, select the PE-Cy7-low, PE-positive, APC-Cy7-positive, BV605-positive, BV421-negative population.</li>
			<li>For MPPs, select the PE-Cy7-low, PE-positive, APC-Cy7-positive, BV421-positive, BV605-negative, population.</li>
			<li>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.</li>
		</ol>
		</li>
		<li>Adjust the flow rate to obtain less than 15,000 events/s.</li>
	</ol>
	</li>
	<li>Prepare collection plate
	<ol>
		<li>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.</li>
		<li>Prepare extraction buffer: Add 10 &#181;L of 13C yeast extract stock solution to 10 mL of acetonitrile and mix.</li>
		<li>Prepare collection plate: Add 25 &#181;L 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).</li>
	</ol>
	</li>
	<li>Perform the cell sort.
	<ol>
		<li>Open the collection wells as needed to collect sorted cells and close them as soon as possible to minimize evaporation.</li>
	</ol>
	</li>
	<li>If samples cannot be measured immediately, store them in sealed containers at -80 &#176;C for several days. After thawing, sonicate the samples for 5 min to ensure complete resuspension of metabolites.</li>
</ol>
5. LC-QQQ-MS measurement
<ol>
	<li>Prepare LC Solvents
	<ol>
		<li>Prepare medronic acid stock solution (100 mM): Dissolve 17.6 mg of medronic acid in 1 mL of ultrapure H2O.</li>
		<li>Prepare Buffer A: Dissolve 1.92 g of ammonium carbonate in 1 L of ultrapure H2O, and add 50 &#181;L of medronic acid stock solution.</li>
		<li>Prepare Buffer B: Mix 50 mL of buffer with 450 mL of acetonitrile.</li>
		<li>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 &#176;C for several months; other buffers can be kept at RT for 2 days.</li>
	</ol>
	</li>
	<li>Program LC-QQQ-MS method
	<ol>
		<li>Optimize compound-specific MS settings (e.g., collision energy) according to the manufacturer&#39;s instructions. For Agilent 6495-series instruments, use the settings in Supplementary Table 1.</li>
		<li>Optimize source-specific MS settings according to the manufacturer&#39;s instructions. For instruments with JetStream heated ESI source, use the following parameters: gas temp: 240 &#176;C, gas flow: 15 L/min, nebulizer: 50 psi, sheath gas temp: 400 &#176;C, sheath gas flow: 11 L/min, capillary voltage: 2000 V, nozzle voltage: 300 V.</li>
		<li>Optimize settings of ion transfer optics according to the manufacturer&#39;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.</li>
		<li>Program the LC gradient: 0 min, 95% B, 150 &#181;L/min; 18 min, 55% B, 150 &#181;L/min; 19 min, 30% B, 150 &#181;L/min; 21.5 min, 30% B, 150 &#181;L/min; 22 min, 95% B, 150 &#181;L/min; 22.7 min, 95% B, 200 &#181;L/min, 23.5 min, 95% B, 400 &#181;L/min; stop time 28 min. Column temperature: 35 &#176;C.</li>
	</ol>
	</li>
	<li>Set up LC-MS instrument.
	<ol>
		<li>Connect the chromatographic column in the temperature-controlled column compartment.</li>
		<li>Connect the buffers and purge all lines.</li>
		<li>Run at least 4 blank samples or pool samples to equilibrate the column.</li>
	</ol>
	</li>
	<li>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.
	<ol>
		<li>Confirm that the initial backpressure is &#60;150 bar and the maximum backpressure is &#60;400 bar.</li>
		<li>Confirm that the retention times are in a &#177;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.</li>
		<li>Ensure the valley between leucine and isoleucine in the +132-&#62;86 transition is &#60; 20 % of the peak height.</li>
		<li>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&#160;min.</li>
	</ol>
	</li>
	<li>Set up worklist
	<ol>
		<li>Start with a blank sample to minimize carry-over from the LC test mix.</li>
		<li>Run the samples in randomized order.</li>
		<li>End with a blank sample to clean the column for future experiments.</li>
	</ol>
	</li>
</ol>
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.
<ol>
	<li>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.</li>
	<li>Start R.</li>
	<li>Install automRm21 according to the package documentation (only required once before first use).</li>
	<li>Start automRm GUI in R: automRm::automrm_gui()</li>
	<li>Process the batch of .d files in the tab Process &#62; Process Batch.
	<ol>
		<li>Select .xlsx file holding metabolite information. This file must match the LC-QQQ-MS method.</li>
		<li>Select .RData file holding peak picking ML model.</li>
		<li>Select .RData file holding the peak reporting ML model.</li>
		<li>Select the project folder containing .mzML files.</li>
		<li>Start data pre-processing by clicking Process Batch.</li>
	</ol>
	</li>
	<li>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.</li>
	<li>Open the peakinfo.xlsx from the project folder and check the data.
	<ol>
		<li>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.</li>
		<li>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.</li>
		<li>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.</li>
	</ol>
	</li>
</ol>

Low-Input Metabolic Studies in Mice Cell Types

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M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Interferences+and+contaminants+encountered+in+modern+mass+spectrometry.">Interferences and contaminants encountered in modern mass spectrometry.</a> Analytica Chimica Acta. 627 (1), 71-81 (2008).</li><li>Kuonen, F., Touvrey, C., Laurent, J., Ruegg, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fc+block+treatment,+dead+cells+exclusion,+and+cell+aggregates+discrimination+concur+to+prevent+phenotypical+artifacts+in+the+analysis+of+subpopulations+of+tumor-infiltrating+CD11b++myelomonocytic+cells.">Fc block treatment, dead cells exclusion, and cell aggregates discrimination concur to prevent phenotypical artifacts in the analysis of subpopulations of tumor-infiltrating CD11b+ myelomonocytic cells.</a> Cytometry. Part A The Journal of the International Society for Analytical Cytology. 77 (11), 1082-1090 (2010).</li><li>Basu, S., Campbell, H. M., Dittel, B. N., Ray, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Purification+of+specific+cell+population+by+fluorescence+activated+cell+sorting+(FACS).">Purification of specific cell population by fluorescence activated cell sorting (FACS).</a> Journal of Visualized Experiments. 41, 1546 (2010).</li><li>Broadhurst, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Guidelines+and+considerations+for+the+use+of+system+suitability+and+quality+control+samples+in+mass+spectrometry+assays+applied+in+untargeted+clinical+metabolomic+studies.">Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies.</a> Metabolomics. 14 (6), 72 (2018).</li></ol>

The authors declare no conflicts of interest.

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 &#176;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 &#176;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.

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 &#181;L 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&#39;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 &#34;3R&#34; (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.

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

targeted metabolomics on rare primary cells

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.

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.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65690/65690fig01.jpg" /> 
Figure 1: Overview of key steps of the presented protocol. This figure has been reprinted with permission from Schoenberger et al.12. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65690/65690fig01large.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/65690/65690fig02.jpg" /> 
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. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65690/65690fig02large.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/65690/65690fig03.jpg" /> 
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. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65690/65690fig03large.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/65690/65690fig04.jpg" /> 
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). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65690/65690fig04large.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/65690/65690fig05.jpg" /> 
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. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65690/65690fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/65690/65690fig06.jpg" /> 
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. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65690/65690fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
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). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65690/Supplementary Table 1.xlsx" target="_blank">Please click here to download this File.</a>

Watch this Scientific Journal Video about Advancing Metabolomics Analysis of Rare Hematopoietic Stem Cells at JoVE.com

Author Spotlight: Advancing Metabolomics Analysis of Rare Hematopoietic Stem Cells 

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.

Targeted Metabolomics on Rare Primary Cells

Cellular function critically depends on metabolism, and the function of the underlying metabolic networks can be studied by measuring small molecule ...

targeted-metabolomics-on-rare-primary-cells

Nanyang Technological University

Research

JoVE Journal

Immunology and Infection

1.8K Views.  Max Planck Institute of Immunobiology and Epigenetics. Het belangrijkste doel van mijn laboratorium is om een gezonde hematopoëtische stamcel te behouden die ouder wordt om hematologische maligniteiten te voorkomen, en dat doen we door voedingsinterventies uit te voeren. Dus in het bijzonder zijn we geïnteresseerd om te begrijpen hoe metabolieten uit de voeding stamvorming reguleren en ook intracellulaire metabolieten hoe ze het lot van stamcellen reguleren. Hematopoëtische stamcellen zijn een zeer zeldzame populatie, wat betekent dat we altij...

Video: Auteur in de schijnwerpers: Voortschrijdende metabolomics-analyse van zeldzame hematopoëtische stamcellen 

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry (UPLC-HRMS)

Here we present a workflow to analyze the metabolic profiles for biological samples of interest including; cells, serum, or tissue. The sample is first separated into polar and non-polar fractions by a liquid-liquid phase extraction, and partially purified to facilitate downstream analysis. Both aqueous (polar metabolites) and organic (non-polar metabolites) phases of the initial extraction are processed to survey a broad range of metabolites. Metabolites are separated by different liquid chromatography methods based upon their partition properties. In this method, we present microflow ultra-performance (UP)LC methods, but the protocol is scalable to higher flows and lower pressures. Introduction into the mass spectrometer can be through either general or compound optimized source conditions. Detection of a broad range of ions is carried out in full scan mode in both positive and negative mode over a broad m/z range using high resolution on a recently calibrated instrument. Label-free differential analysis is carried out on bioinformatics platforms. Applications of this approach include metabolic pathway screening, biomarker discovery, and drug development.

Untargeted Metabolomics from Biological Sources Using Ultraperformance Liquid Chromatography-High Resolution Mass Spectrometry UPLC-HRMS

Untargeted metabolomics provides a hypothesis generating snapshot of a metabolic profile. This protocol will demonstrate the extraction and analysis of metabolites from cells, serum, or tissue. A range of metabolites are surveyed using liquid-liquid phase extraction, microflow ultraperformance liquid chromatography/high-resolution mass spectrometry (UPLC-HRMS) coupled to differential analysis software.

Here we present a workflow to analyze the metabolic profiles for biological samples of interest including; cells, serum, or tissue. The sample is first ...

Chemistry

A Method for Measuring Metabolism in Sorted Subpopulations of Complex Cell Communities Using Stable Isotope Tracing

Mammalian cell types exhibit specialized metabolism, and there is ample evidence that various co-existing cell types engage in metabolic cooperation. Moreover, even cultures of a single cell type may contain cells in distinct metabolic states, such as resting or cycling cells. Methods for measuring metabolic activities of such subpopulations are valuable tools for understanding cellular metabolism. Complex cell populations are most commonly separated using a cell sorter, and subpopulations isolated by this method can be analyzed by metabolomics methods. However, a problem with this approach is that the cell sorting procedure subjects cells to stresses that may distort their metabolism.
To overcome these issues, we reasoned that the mass isotopomer distributions (MIDs) of metabolites from cells cultured with stable isotope-labeled nutrients are likely to be more stable than absolute metabolite concentrations, because MIDs are formed over longer time scales and should be less affected by short-term exposure to cell sorting conditions. Here, we describe a method based on this principle, combining cell sorting with liquid chromatography-high resolution mass spectrometry (LC-HRMS). The procedure involves analyzing three types of samples: (1) metabolite extracts obtained directly from the complex population; (2) extracts of "mock sorted" cells passed through the cell sorter instrument without gating any specific population; and (3) extracts of the actual sorted populations. The mock sorted cells are compared against direct extraction to verify that MIDs are indeed not altered by the cell sorting procedure itself, prior to analyzing the actual sorted populations. We show example results from HeLa cells sorted according to cell cycle phase, revealing changes in nucleotide metabolism.

This article describes a method for studying cellular metabolism in complex communities of multiple cell types, using a combination of stable isotope tracing, cell sorting to isolate specific cell types, and mass spectrometry.

Mammalian cell types exhibit specialized metabolism, and there is ample evidence that various co-existing cell types engage in metabolic cooperation. ...

Biochemistry

Mass Spectrometry and Luminogenic-based Approaches to Characterize Phase I Metabolic Competency of In Vitro Cell Cultures

Xenobiotic metabolizing enzymes play a key function in the biotransformation of medicines and toxicants by adding functional groups that increase solubility and facilitate excretion. On some occasions those structural modifications lead to the formation of new toxic products. In order to reduce animal testing, chemical risk can be assessed using metabolically competent cells. The expression of metabolic enzymes, however, is not stable over time in many in vitro primary culture systems and is often partial or absent in cell lines. Therefore, the study of medicines, additives, and environmental pollutants metabolism in vitro should ideally be conducted in cell systems where metabolic activity has been characterized. We explain here an approach to measure the activity of a class of metabolic enzymes (Human Phase I) in 2D cell lines and primary 3D cultures using chemical probes and their metabolic products quantifiable by UPLC mass spectrometry and luminometry. The method can be implemented to test the metabolic activity in cell lines and primary cells derived from a variety of tissues.

Mass Spectrometry and Luminogenic-based Approaches to Characterize Phase I Metabolic Competency of In Vitro Cell Cultures

Metabolic competency of in vitro systems is a key requirement for the biotransformation and disposition of drugs and toxicants. In this protocol, we describe the application of reference metabolic probes to assess phase I metabolism in cell cultures.

Xenobiotic metabolizing enzymes play a key function in the biotransformation of medicines and toxicants by adding functional groups that increase ...

Target Cell Pre-enrichment and Whole Genome Amplification for Single Cell Downstream Characterization

Rare target cells can be isolated from a high background of non-target cells using antibodies specific for surface proteins of target cells. A recently developed method uses a medical wire functionalized with anti-epithelial cell adhesion molecule (EpCAM) antibodies for in vivo isolation of circulating tumor cells (CTCs)1. A patient-matched cohort in non-metastatic prostate cancer showed that the in vivo isolation technique resulted in a higher percentage of patients positive for CTCs as well as higher CTC counts as compared to the current gold standard in CTC enumeration. As cells cannot be recovered from current medical devices, a new functionalized wire (referred to as Device) was manufactured allowing capture and subsequent detachment of cells by enzymatic treatment. Cells are allowed to attach to the Device, visualized on a microscope and detached using enzymatic treatment. Recovered cells are cytocentrifuged onto membrane-coated slides and harvested individually by means of laser microdissection or micromanipulation. Single-cell samples are then subjected to single-cell whole genome amplification allowing multiple downstream analysis including screening and target-specific approaches. The procedure of isolation and recovery yields high quality DNA from single cells and does not impair subsequent whole genome amplification (WGA). A single cell&#39;s amplified DNA can be forwarded to screening and/or targeted analysis such as array comparative genome hybridization (array-CGH) or sequencing. The device allows ex vivo isolation from artificial rare cell samples (i.e. 500 target cells spiked into 5 mL of peripheral blood). Whereas detachment rates of cells are acceptable (50 - 90%), the recovery rate of detached cells onto slides spans a wide range dependent on the cell line used (&#60;10 - &#62;50%) and needs some further attention. This device is not cleared for the use in patients.

This protocol is to recover and prepare rare target cells from a mixture with non-target background cells for molecular genetic characterization at the single-cell level. DNA quality is equal to non-treated single cells and allows for single-cell application (both screening based and targeted analysis).

Rare target cells can be isolated from a high background of non-target cells using antibodies specific for surface proteins of target cells. A recently ...

Cancer Research

Assessment of the Metabolic Profile of Primary Leukemia Cells

The metabolic requirement of cancer cells can negatively influence survival and treatment efficacy. Nowadays, pharmaceutical targeting of metabolic pathways is tested in many types of tumors. Thus, characterization of cancer cell metabolic setup is inevitable in order to target the correct pathway to improve the overall outcome of patients. Unfortunately, in a majority of cancers, the malignant cells are quite difficult to obtain in higher numbers and the tissue biopsy is required. Leukemia is an exception, where a sufficient number of leukemic cells can be isolated from the bone marrow. Here, we provide a detailed protocol for the isolation of leukemic cells from leukemia patients bone marrow and subsequent analysis of their metabolic state using extracellular flux analyzer. Leukemic cells are isolated by the density gradient, which does not affect their viability. The next cultivation step helps them to regenerate, thus the metabolic state measured is the state of cells in optimal conditions. This protocol allows achieving consistent, well-standardized results, which could be used for the personalized therapy.

Here we present a protocol for the isolation of leukemic cells from leukemia patients bone marrow and analysis of their metabolic state. Assessment of the metabolic profile of primary leukemia cells could help to better characterize the demand of primary cells and could lead up to more personalized medicine.

The metabolic requirement of cancer cells can negatively influence survival and treatment efficacy. Nowadays, pharmaceutical targeting of metabolic ...

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 ...

An Integrated Raman Spectroscopy and Mass Spectrometry Platform to Study Single-Cell Drug Uptake, Metabolism, and Effects

Cells are known to be inherently heterogeneous in their responses to drugs. Therefore, it is essential that single-cell heterogeneity is accounted for in drug discovery studies. This can be achieved by accurately measuring the plethora of cellular interactions between a cell and drug at the single-cell level (i.e., drug uptake, metabolism, and effect). This paper describes a single-cell Raman spectroscopy and mass spectrometry (MS) platform to monitor metabolic changes of cells in response to drugs. Using this platform, metabolic changes in response to the drug can be measured by Raman spectroscopy, while the drug and its metabolite can be quantified using mass spectrometry in the same cell. The results suggest that it is possible to access information about drug uptake, metabolism, and response at a single-cell level.

This protocol presents an integrated Raman spectroscopy-mass spectrometry (MS) platform that is capable of achieving single-cell resolution. Raman spectroscopy can be used to study cellular response to drugs, while MS can be used for targeted and quantitative analysis of drug uptake and metabolism.

Cells are known to be inherently heterogeneous in their responses to drugs. Therefore, it is essential that single-cell heterogeneity is accounted for in ...

Using Human Induced Pluripotent Stem Cell-derived Hepatocyte-like Cells for Drug Discovery

The ability to differentiate human induced pluripotent stem cells (iPSCs) into hepatocyte-like cells (HLCs) provides new opportunities to study inborn errors in hepatic metabolism. However, to provide a platform that supports the identification of small molecules that can potentially be used to treat liver disease, the procedure requires a culture format that is compatible with screening thousands of compounds. Here, we describe a protocol using completely defined culture conditions, which allow the reproducible differentiation of human iPSCs to hepatocyte-like cells in 96-well tissue culture plates. We also provide an example of using the platform to screen compounds for their ability to lower Apolipoprotein B (APOB) produced from iPSC-derived hepatocytes generated from a familial hypercholesterolemia patient. The availability of a platform that is compatible with drug discovery should allow researchers to identify novel therapeutics for diseases that affect the liver.

The protocol presented here describes a platform for identifying small molecules for the treatment of liver disease. A step-by-step description is presented detailing how to differentiate iPSCs into cells with hepatocyte characteristics in 96-well plates, and to use the cells to screen for small molecules with potential therapeutic activity.

The ability to differentiate human induced pluripotent stem cells (iPSCs) into hepatocyte-like cells (HLCs) provides new opportunities to study inborn ...

Developmental Biology

Carrier-assisted One-pot Sample Preparation for Targeted Proteomics Analysis of Small Numbers of Human Cells

Protein analysis of small numbers of human cells is primarily achieved by targeted proteomics with antibody-based immunoassays, which have inherent limitations (e.g., low multiplex and unavailability of antibodies for new proteins). Mass spectrometry (MS)-based targeted proteomics has emerged as an alternative because it is antibody-free, high multiplex, and has high specificity and quantitation accuracy. Recent advances in MS instrumentation make MS-based targeted proteomics possible for multiplexed quantification of highly abundant proteins in single cells. However, there is a technical challenge for effective processing of single cells with minimal sample loss for MS analysis. To address this issue, we have recently developed a convenient protein carrier-assisted one-pot sample preparation coupled with liquid chromatography (LC) - selected reaction monitoring (SRM) termed cLC-SRM for targeted proteomics analysis of small numbers of human cells. This method capitalizes on using the combined excessive exogenous protein as a carrier and low-volume one-pot processing to greatly reduce surface adsorption losses as well as high-specificity LC-SRM to effectively address the increased dynamic concentration range due to the addition of exogeneous carrier protein. Its utility has been demonstrated by accurate quantification of most moderately abundant proteins in small numbers of cells (e.g., 10-100 cells) and highly abundant proteins in single cells. The easy-to-implement features and no need for specific devices make this method readily accessible to most proteomics laboratories. Herein we have provided a detailed protocol for cLC-SRM analysis of small numbers of human cells including cell sorting, cell lysis and digestion, LC-SRM analysis, and data analysis. Further improvements in detection sensitivity and sample throughput are needed towards targeted single-cell proteomics analysis. We anticipate that cLC-SRM will be broadly applied to biomedical research and systems biology with the potential of facilitating precision medicine.

A protein carrier-assisted one-pot sample preparation coupled with liquid chromatography (LC) - selected reaction monitoring (SRM) termed cLC-SRM is a convenient method for multiplexed targeted proteomics analysis of small numbers of cells, including single cells. It capitalizes on using excessive exogenous protein as a carrier and high-specificity LC-SRM for targeted quantification.

Protein analysis of small numbers of human cells is primarily achieved by targeted proteomics with antibody-based immunoassays, which have inherent ...

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting factors, such as labor intensive sample preparations, low detection limits, slow scan speeds, intensive method optimization for each metabolite, and the inability to measure both positively and negatively charged ions in single experiments. Therefore, a novel metabolomics protocol could advance metabolomics studies. Amide-based hydrophilic chromatography enables polar metabolite analysis without any chemical derivatization. High resolution MS using the Q-Exactive (QE-MS) has improved ion optics, increased scan speeds (256 msec at resolution 70,000), and has the capability of carrying out positive/negative switching. Using a cold methanol extraction strategy, and coupling an amide column with QE-MS enables robust detection of 168 targeted polar metabolites and thousands of additional features simultaneously.&#160; Data processing is carried out with commercially available software in a highly efficient way, and unknown features extracted from the mass spectra can be queried in databases.

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. Utilizing normal-phased liquid chromatography coupled to high-resolution mass spectrometry with polarity switching and a rapid duty cycle, we describe a protocol to analyze the polar metabolic composition of biological material with high sensitivity, accuracy, and resolution.

Metabolite profiling has been a valuable asset in the study of metabolism in health and disease. However, current platforms have different limiting ...

Targeted Metabolomics on Rare Primary Cells

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