Name: Author Spotlight: Advancing Metabolomics Analysis of Rare Hematopoietic Stem Cells
Uploaded: 2024-02-23T14:30:00
Description: Watch this Scientific Journal Video about Advancing Metabolomics Analysis of Rare Hematopoietic Stem Cells at JoVE.com

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, a...

Low-Input Metabolic Studies in Mice Cell Types

<ol>
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Metabolon Inc Available from: <a target="_blank" href="https://www.metabolon.com/qp-content/uploads/2020/06/Standard-Sample-Preparation-and-Shipping-Procedure-SSGv2.0.pdf">https://www.metabolon.com/qp-content/uploads/2020/06/Standard-Sample-Preparation-and-Shipping-Procedure-SSGv2.0.pdf</a> (2023)</li><li>Rossi, L., Challen, G. A., Sirin, O., Lin, K. K. -. Y., Goodell, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hematopoietic+stem+cell+characterization+and+isolation.">Hematopoietic stem cell characterization and isolation.</a> Methods in Molecular Biology. 750, 47-59 (2011).</li><li>Cabezas-Wallscheid, N., 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=Identification+of+regulatory+networks+in+HSCs+and+their+immediate+progeny+via+integrated+proteome,+transcriptome,+and+DNA+methylome+analysis.">Identification of regulatory networks in HSCs and their immediate progeny via integrated proteome, transcriptome, and DNA methylome analysis.</a> Cell Stem Cell. 15 (4), 507-522 (2014).</li><li>Belmonte, M., Kent, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protocol+to+maintain+single+functional+mouse+hematopoietic+stem+cells+in+vitro+without+cell+division.">Protocol to maintain single functional mouse hematopoietic stem cells in vitro without cell division.</a> STAR Protocols. 2 (4), 100927 (2021).</li><li>Devilbiss, A. W., 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=Metabolomic+profiling+of+rare+cell+populations+isolated+by+flow+cytometry+from+tissues.">Metabolomic profiling of rare cell populations isolated by flow cytometry from tissues.</a> eLife. 10, 61980 (2021).</li><li>Sch&#246;nberger, K., 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=Multilayer+omics+analysis+reveals+a+non-classical+retinoic+acid+signaling+axis+that+regulates+hematopoietic+stem+cell+identity.">Multilayer omics analysis reveals a non-classical retinoic acid signaling axis that regulates hematopoietic stem cell identity.</a> Cell Stem Cell. 29 (1), 131-148 (2022).</li><li>Sch&#246;nberger, K., 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=LC-MS-based+targeted+metabolomics+for+FACS-purified+rare+cells.">LC-MS-based targeted metabolomics for FACS-purified rare cells.</a> Analytical Chemistry. 95 (9), 4325-4334 (2023).</li><li>Link, H., Kochanowski, K., Sauer, U. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+identification+of+allosteric+protein-metabolite+interactions+that+control+enzyme+activity+in+vivo.">Systematic identification of allosteric protein-metabolite interactions that control enzyme activity in vivo.</a> Nature Biotechnology. 31 (4), 357-361 (2013).</li><li>Binek, A., 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=Flow+cytometry+has+a+significant+impact+on+the+cellular+metabolome.">Flow cytometry has a significant impact on the cellular metabolome.</a> Journal of Proteome Research. 18 (1), 169-181 (2018).</li><li>Ryan, K., Rose, R. E., Jones, D. R., Lopez, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sheath+fluid+impacts+the+depletion+of+cellular+metabolites+in+cells+afflicted+by+sorting+induced+cellular+stress+(SICS).">Sheath fluid impacts the depletion of cellular metabolites in cells afflicted by sorting induced cellular stress (SICS).</a> Cytometry. Part A The Journal of the International Society for Analytical Cytology. 99 (9), 921-929 (2021).</li><li>Llufrio, E. M., Wang, L., Naser, F. J., Patti, G. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sorting+cells+alters+their+redox+state+and+cellular+metabolome.">Sorting cells alters their redox state and cellular metabolome.</a> Redox biology. 16, 381-387 (2018).</li><li>Zhang, Y. W., 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=Hyaluronic+acid-GPRC5C+signalling+promotes+dormancy+in+haematopoietic+stem+cells.">Hyaluronic acid-GPRC5C signalling promotes dormancy in haematopoietic stem cells.</a> Nature Cell Biology. 24 (7), 1038-1048 (2022).</li><li>Zafeiriadou, A., 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=Metabolism-related+gene+expression+in+circulating+tumor+cells+from+patients+with+early+stage+non-small+cell+lung+cancer.">Metabolism-related gene expression in circulating tumor cells from patients with early stage non-small cell lung cancer.</a> Cancers. 14 (13), 3237 (2022).</li><li>Chen, J., 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=Metabolic+classification+of+circulating+tumor+cells+as+a+biomarker+for+metastasis+and+prognosis+in+breast+cancer.">Metabolic classification of circulating tumor cells as a biomarker for metastasis and prognosis in breast cancer.</a> Journal of Translational Medicine. 18 (1), 59 (2020).</li><li>Chambers, M. C., 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=A+cross-platform+toolkit+for+mass+spectrometry+and+proteomics.">A cross-platform toolkit for mass spectrometry and proteomics.</a> Nature Biotechnology. 30 (10), 918-920 (2012).</li><li>Eilertz, D., Mitterer, M., Buescher, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=automRm:+An+R+package+for+fully+automatic+LC-QQQ-MS+data+preprocessing+powered+by+machine+learning.">automRm: An R package for fully automatic LC-QQQ-MS data preprocessing powered by machine learning.</a> Analytical Chemistry. 94 (16), 6163-6171 (2022).</li><li>Han, W., Li, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluating+and+minimizing+batch+effects+in+metabolomics.">Evaluating and minimizing batch effects in metabolomics.</a> Mass Spectrometry Reviews. 41 (3), 421-442 (2022).</li><li>Keller, B. O., Sui, J., Young, A. B., Whittal, R. 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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. 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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...

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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>13C yeast extract</td>
			<td>Isotopic Solutions</td>
			<td>ISO-1</td>
			<td></td>
		</tr>
		<tr>
			<td>40 &#181;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 &#181;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&#160;</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.

Author Spotlight: Advancing Metabolomics Analysis of Rare Hematopoietic Stem Cells 

Targeted Metabolomics on Rare Primary Cells

The main goal of my lab is to maintain a healthy hematopoietic stem cell pull up in aging to avoid hematological malignancies, and we do so by performing dietary interventions. So in particular, we're interested to understand how dietary derived metabolites regulate stemness and also intracellular metabolites how they regulate the stem cell fate. Hematopoietic stem cells is a very rare population, meaning that we always face the challenge that we have not enough material to perform our experiments.But now with the establishment of single cell methods, and also with low input methods, we have achieved a new knowledge on how these stem cells are regulated. Hematopoietic stem cells is a very rare population and on top, they're very tiny. Now, with the methods that we have developed such, for example, low input metabolomics based on mass spec, we now can detect with very low number of cells, hundreds of metabolites, this now allowed us to understand what is the role of certain metabolites for stem cell regulation.In the past, metabolomics analysis of rare primary cells required many mice and days of work and sample preparation. With our new protocol, we need fewer mice and less time. Additionally, we have reduced the number of manual steps, so the whole protocol becomes more robust.For the future, we have planned to adapt our low input protocol to enable additional analysis and rare primary cells. For example, non-targeted metabolomics and lipidomics.

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. Thi...

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

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.

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

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