Metabolomics of Small Numbers of Cells: Metabolomic Profiling of 100, 1000, and 10000 Human Breast Cancer Cells

In cellular metabolomics, it is desirable to carry out metabolomic profiling using a small number of cells in order to save time and cost. In some applications (e.g., working with circulating tumor cells in blood), only a limited number of cells are available for analysis. In this report, we describe a method based on high-performance chemical isotope labeling (CIL) nanoflow liquid chromatography mass spectrometry (nanoLC-MS) for high-coverage metabolomic analysis of small numbers of cells (i.e., ≤10000 cells). As an example, ¹²C-/¹³C-dansyl labeling of the metabolites in lysates of 100, 1000, and 10000 MCF-7 breast cancer cells was carried out using a new labeling protocol tailored to handle small amounts of metabolites. Chemical-vapor-assisted ionization in a captivespray interface was optimized for improving metabolite ionization and increasing robustness of nanoLC-MS. Compared to microflow LC-MS, the nanoflow system provided much improved metabolite detectability with a significantly reduced sample amount required for analysis. Experimental duplicate analyses of biological triplicates resulted in the detection of 1620 ± 148, 2091 ± 89 and 2402 ± 80 (<i>n</i> = 6) peak pairs or metabolites in the amine/phenol submetabolome from the ¹²C-/¹³C-dansyl labeled lysates of 100, 1000, and 10000 cells, respectively. About 63–69% of these peak pairs could be either identified using dansyl labeled standard library or mass-matched to chemical structures in human metabolome databases. We envisage the routine applications of this method for high-coverage quantitative cellular metabolomics using a starting material of 10000 cells. Even for analyzing 100 or 1000 cells, although the metabolomic coverage is reduced from the maximal coverage, this method can still detect thousands of metabolites, allowing the analysis of a large fraction of the metabolome and focused analysis of the detectable metabolites

Chemical Isotope Labeling LC-MS for High Coverage and Quantitative Profiling of the Hydroxyl Submetabolome in Metabolomics

A key step in metabolomics is to perform accurate relative quantification of the metabolomes in comparative samples with high coverage. Hydroxyl-containing metabolites are an important class of the metabolome with diverse structures and physical/chemical properties; however, many of them are difficult to detect with high sensitivity. We present a high-performance chemical isotope labeling liquid chromatography mass spectrometry (LC-MS) technique for in-depth profiling of the hydroxyl submetabolome, which involves the use of acidic liquid–liquid extraction to enrich hydroxyl metabolites into ethyl acetate from an aqueous sample. After drying and then redissolving in acetonitrile, the metabolite extract is labeled using a base-activated ¹²C- or ¹³C-dansylation reaction. A fast step-gradient LC-UV method is used to determine the total concentration of labeled metabolites. On the basis of the concentration information, a ¹²C-labeled individual sample is mixed with an equal mole amount of a ¹³C-labeled pool or control for relative metabolite quantification. The ¹²C-/¹³C-labeled mixtures are individually analyzed by LC-MS, and the resultant peak pairs of labeled metabolites in MS are measured for relative quantification and metabolite identification. A standard library of 85 hydroxyl compounds containing MS, retention time, and MS/MS information was constructed for positive metabolite identification based on matches of two or all three of these parameters with those of an unknown. Using human urine as an example, we analyzed samples of 1:1 ¹²C-/¹³C-labeled urine in triplicate with triplicate runs per sample and detected an average of 3759 ± 45 peak pairs or metabolites per run and 3538 ± 71 pairs per sample with 3093 pairs in common (<i>n</i> = 9). Out of the 3093 peak pairs, 2304 pairs (75%) could be positively or putatively identified based on metabolome database searches, including 20 pairs positively identified using the dansylated hydroxyl standards library. The majority of detected metabolites were those containing hydroxyl groups. This technique opens a new avenue for the detailed characterization of the hydroxyl submetabolome in metabolomics research

High-Performance Chemical Isotope Labeling Liquid Chromatography Mass Spectrometry for Exosome Metabolomics

Circulating exosomes in bodily fluids such as blood are being actively studied as a rich source of chemical biomarkers for cancer diagnosis and monitoring. Although nucleic acid analysis is a primary tool for the discovery of circulating biomarkers in exosomes, metabolomics holds the potential of expanding the chemical diversity of biomarkers that may be easy and rapid to detect. However, only trace amounts of exosomes can be isolated from a small volume of patient blood, and thus a very sensitive technique is required to analyze the metabolome of exosomes. In this report, we present a workflow that involves multiple cycles of ultracentrifugation for exosome isolation using a starting material of 2 mL of human serum, freeze–thaw-cycles in 50% methanol/water for exosome lysis and metabolite extraction, differential chemical isotope labeling (CIL) of metabolites for enhancing liquid chromatography (LC) separation and improving mass spectrometry (MS) detection, and nanoflow LC-MS (nLC-MS) with captivespray for analysis. As a proof-of-principle, we used dansylation labeling to analyze the amine- and phenol-submetabolomes in two sets of exosome samples isolated from the blood samples of five pancreatic cancer patients before and after chemotherapy treatment. The average number of peak pairs or metabolites detected was 1964 ± 60 per sample for a total of 2446 peak pairs (<i>n</i> = 10) in the first set and 1948 ± 117 per sample for a total of 2511 peak pairs (<i>n</i> = 10) in the second set. There were 101 and 94 metabolites positively identified in the first and second set, respectively, and 1580 and 1590 peak pairs with accurate masses matching those of metabolites in the MyCompoundID metabolome database. Analyzing the mixtures of ¹²C-labeled individual exosome samples spiked with a ¹³C-labeled pooled sample which served as an internal standard allowed relative quantification of metabolomic changes of exosomes of blood samples collected before and after treatment

Impact of Low-Intensity Pulsed Ultrasound on Transcript and Metabolite Abundance in <i>Saccharomyces cerevisiae</i>

The interactions of ultrasound with biological materials are exploited for diagnostic, interventional, and therapeutic applications in humans and can improve productivity in industrial-scale generation of organic molecules such as biofuels, vaccines, and antibodies. Accordingly, there is great interest in better understanding the biological effects of ultrasound. We studied the impact of low-intensity pulsed ultrasound (LIPUS) on RNA expression and metabolism of <i>S. cerevisiae</i>. Although the transcript expression signature of LIPUS-treated cells does not differ significantly from that of untreated cells after 5 days, metabolomic profiling by chemical-isotopic-labeling–liquid-chromatography–mass-spectrometry suggests that LIPUS has an impact on the pathways of pyrimidine, proline, alanine, aspartate, glutamate, and arginine metabolism. Therefore, LIPUS triggers metabolic effects beyond reprogramming of the core pathways of carbon metabolism. Further characterization of metabolism will likely be important for elucidation of the biological effects of LIPUS

DataSheet_2_Metabolomic and immune alterations in long COVID patients with chronic fatigue syndrome.xlsx

IntroductionA group of SARS-CoV-2 infected individuals present lingering symptoms, defined as long COVID (LC), that may last months or years post the onset of acute disease. A portion of LC patients have symptoms similar to myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS), which results in a substantial reduction in their quality of life. A better understanding of the pathophysiology of LC, in particular, ME/CFS is urgently needed.MethodsWe identified and studied metabolites and soluble biomarkers in plasma from LC individuals mainly exhibiting ME/CFS compared to age-sex-matched recovered individuals (R) without LC, acute COVID-19 patients (A), and to SARS-CoV-2 unexposed healthy individuals (HC).ResultsThrough these analyses, we identified alterations in several metabolomic pathways in LC vs other groups. Plasma metabolomics analysis showed that LC differed from the R and HC groups. Of note, the R group also exhibited a different metabolomic profile than HC. Moreover, we observed a significant elevation in the plasma pro-inflammatory biomarkers (e.g. IL-1α, IL-6, TNF-α, Flt-1, and sCD14) but the reduction in ATP in LC patients. Our results demonstrate that LC patients exhibit persistent metabolomic abnormalities 12 months after the acute COVID-19 disease. Of note, such metabolomic alterations can be observed in the R group 12 months after the acute disease. Hence, the metabolomic recovery period for infected individuals with SARS-CoV-2 might be long-lasting. In particular, we found a significant reduction in sarcosine and serine concentrations in LC patients, which was inversely correlated with depression, anxiety, and cognitive dysfunction scores.ConclusionOur study findings provide a comprehensive metabolomic knowledge base and other soluble biomarkers for a better understanding of the pathophysiology of LC and suggests sarcosine and serine supplementations might have potential therapeutic implications in LC patients. Finally, our study reveals that LC disproportionally affects females more than males, as evidenced by nearly 70% of our LC patients being female.</p

DataSheet_1_Metabolomic and immune alterations in long COVID patients with chronic fatigue syndrome.pdf

IntroductionA group of SARS-CoV-2 infected individuals present lingering symptoms, defined as long COVID (LC), that may last months or years post the onset of acute disease. A portion of LC patients have symptoms similar to myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS), which results in a substantial reduction in their quality of life. A better understanding of the pathophysiology of LC, in particular, ME/CFS is urgently needed.MethodsWe identified and studied metabolites and soluble biomarkers in plasma from LC individuals mainly exhibiting ME/CFS compared to age-sex-matched recovered individuals (R) without LC, acute COVID-19 patients (A), and to SARS-CoV-2 unexposed healthy individuals (HC).ResultsThrough these analyses, we identified alterations in several metabolomic pathways in LC vs other groups. Plasma metabolomics analysis showed that LC differed from the R and HC groups. Of note, the R group also exhibited a different metabolomic profile than HC. Moreover, we observed a significant elevation in the plasma pro-inflammatory biomarkers (e.g. IL-1α, IL-6, TNF-α, Flt-1, and sCD14) but the reduction in ATP in LC patients. Our results demonstrate that LC patients exhibit persistent metabolomic abnormalities 12 months after the acute COVID-19 disease. Of note, such metabolomic alterations can be observed in the R group 12 months after the acute disease. Hence, the metabolomic recovery period for infected individuals with SARS-CoV-2 might be long-lasting. In particular, we found a significant reduction in sarcosine and serine concentrations in LC patients, which was inversely correlated with depression, anxiety, and cognitive dysfunction scores.ConclusionOur study findings provide a comprehensive metabolomic knowledge base and other soluble biomarkers for a better understanding of the pathophysiology of LC and suggests sarcosine and serine supplementations might have potential therapeutic implications in LC patients. Finally, our study reveals that LC disproportionally affects females more than males, as evidenced by nearly 70% of our LC patients being female.</p

High-Performance Chemical Isotope Labeling Liquid Chromatography–Mass Spectrometry for Profiling the Metabolomic Reprogramming Elicited by Ammonium Limitation in Yeast

Information about how yeast metabolism is rewired in response to internal and external cues can inform the development of metabolic engineering strategies for food, fuel, and chemical production in this organism. We report a new metabolomics workflow for the characterization of such metabolic rewiring. The workflow combines efficient cell lysis without using chemicals that may interfere with downstream sample analysis and differential chemical isotope labeling liquid chromatography mass spectrometry (CIL LC–MS) for in-depth yeast metabolome profiling. Using ¹²C- and ¹³C-dansylation (Dns) labeling to analyze the amine/phenol submetabolome, we detected and quantified a total of 5719 peak pairs or metabolites. Among them, 120 metabolites were positively identified using a library of 275 Dns-metabolite standards, and 2980 metabolites were putatively identified based on accurate mass matches to metabolome databases. We also applied ¹²C- and ¹³C-dimethylaminophenacyl (DmPA) labeling to profile the carboxylic acid submetabolome and detected over 2286 peak pairs, from which 33 metabolites were positively identified using a library of 188 DmPA-metabolite standards, and 1595 metabolites were putatively identified. Using this workflow for metabolomic profiling of cells challenged by ammonium limitation revealed unexpected links between ammonium assimilation and pantothenate accumulation that might be amenable to engineering for better acetyl-CoA production in yeast. We anticipate that efforts to improve other schemes of metabolic engineering will benefit from application of this workflow to multiple cell types

DataSheet_3_Metabolomic and immune alterations in long COVID patients with chronic fatigue syndrome.pdf

IntroductionA group of SARS-CoV-2 infected individuals present lingering symptoms, defined as long COVID (LC), that may last months or years post the onset of acute disease. A portion of LC patients have symptoms similar to myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS), which results in a substantial reduction in their quality of life. A better understanding of the pathophysiology of LC, in particular, ME/CFS is urgently needed.MethodsWe identified and studied metabolites and soluble biomarkers in plasma from LC individuals mainly exhibiting ME/CFS compared to age-sex-matched recovered individuals (R) without LC, acute COVID-19 patients (A), and to SARS-CoV-2 unexposed healthy individuals (HC).ResultsThrough these analyses, we identified alterations in several metabolomic pathways in LC vs other groups. Plasma metabolomics analysis showed that LC differed from the R and HC groups. Of note, the R group also exhibited a different metabolomic profile than HC. Moreover, we observed a significant elevation in the plasma pro-inflammatory biomarkers (e.g. IL-1α, IL-6, TNF-α, Flt-1, and sCD14) but the reduction in ATP in LC patients. Our results demonstrate that LC patients exhibit persistent metabolomic abnormalities 12 months after the acute COVID-19 disease. Of note, such metabolomic alterations can be observed in the R group 12 months after the acute disease. Hence, the metabolomic recovery period for infected individuals with SARS-CoV-2 might be long-lasting. In particular, we found a significant reduction in sarcosine and serine concentrations in LC patients, which was inversely correlated with depression, anxiety, and cognitive dysfunction scores.ConclusionOur study findings provide a comprehensive metabolomic knowledge base and other soluble biomarkers for a better understanding of the pathophysiology of LC and suggests sarcosine and serine supplementations might have potential therapeutic implications in LC patients. Finally, our study reveals that LC disproportionally affects females more than males, as evidenced by nearly 70% of our LC patients being female.</p

Impact of Oxygen Concentration on Metabolic Profile of Human Placenta-Derived Mesenchymal Stem Cells As Determined by Chemical Isotope Labeling LC–MS

The placenta resides in a physiologically low oxygen microenvironment of the body. Hypoxia induces a wide range of stem cell cellular activities. Here, we report a workflow for exploring the role of physiological (hypoxic, 5% oxygen) and original cell culture (normoxic, 21% oxygen) oxygen concentrations in regulating the metabolic status of human placenta-derived mesenchymal stem cells (hPMSCs). The general biological characteristics of hPMSCs were assessed via a variety of approaches such as cell counts, flow cytometry and differentiation study. A sensitive ¹³C/¹²C-dansyl labeling liquid chromatography–mass spectrometry (LC–MS) method targeting the amine/phenol submetabolome was used for metabolic profiling of the cell and corresponding culture supernatant. Multivariate and univariate statistical analyses were used to analyze the metabolomics data. hPMSCs cultured in hypoxia display smaller size, higher proliferation, greater differentiation ability and no difference in immunophenotype. Overall, 2987 and 2860 peak pairs or metabolites were detected and quantified in hPMSCs and culture supernatant, respectively. Approximately 86.0% of cellular metabolites and 84.3% of culture supernatant peak pairs were identified using a dansyl standard library or matched to metabolite structures using accurate mass search against human metabolome libraries. The orthogonal partial least-squares discriminant analysis (OPLS-DA) showed a clear separation between the hypoxic group and the normoxic group. Ten metabolites from cells and six metabolites from culture supernatant were identified as potential biomarkers of hypoxia. This study demonstrated that chemical isotope labeling LC–MS can be used to reveal the role of oxygen in the regulation of hPMSC metabolism, whereby physiological oxygen concentrations may promote arginine and proline metabolism, pantothenate and coenzyme A (CoA) biosynthesis, and alanine, aspartate and glutamate metabolism