Summary of Study ST002349

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001509. The data can be accessed directly via it's Project DOI: 10.21228/M8N71K This work is supported by NIH grant, U2C- DK119886.

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Study IDST002349
Study TitleBiomolecular condensates create phospholipid-enriched microenvironments
Study TypeMetabolomes of in vitro synthesized condensates
Study SummaryProteins and RNA are able to phase separate from the aqueous cellular environment to form sub-cellular compartments called condensates. This process results in a protein-RNA mixture that is chemically distinct from the surrounding aqueous phase. Here we use mass spectrometry to characterize the metabolomes of condensates. To test this, we prepared mixtures of phase-separated proteins and cellular metabolites and identified metabolites enriched in the condensate phase. These proteins included SARS-CoV-2 nucleocapsid, as well as low complexity domains of MED1 and HNRNPA1.
Institute
Cornell University
DepartmentDepartment of Pharmacology
LaboratoryDr. Samie Jaffrey
Last NameDumelie
First NameJason
Address1300 York Ave, LC-524, New York City, NY
Emailjdumes98@gmail.com
Phone6465690174
Submit Date2022-11-04
Raw Data AvailableYes
Raw Data File Type(s)mzdata.xml
Analysis Type DetailOther
Release Date2022-11-28
Release Version1
Jason Dumelie Jason Dumelie
https://dx.doi.org/10.21228/M8N71K
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Project:

Project ID:PR001509
Project DOI:doi: 10.21228/M8N71K
Project Title:Biomolecular condensates create phospholipid-enriched microenvironments
Project Type:Metabolomics of in vitro condensates
Project Summary:Proteins and RNA are able to phase separate from the aqueous cellular environment to form sub-cellular compartments called condensates. This process results in a protein-RNA mixture that is chemically distinct from the surrounding aqueous phase. Here we use mass spectrometry to characterize the metabolomes of condensates. To test this, we prepared mixtures of phase-separated proteins and cellular metabolites and identified metabolites enriched in the condensate phase. These proteins included SARS-CoV-2 nucleocapsid, as well as low complexity domains of MED1 and HNRNPA1.
Institute:Cornell University
Department:Department of Pharmacology
Laboratory:Dr. Samie Jaffrey
Last Name:Dumelie
First Name:Jason
Address:1300 York Ave, LC-524, New York City, NY
Email:jdumes98@gmail.com
Phone:6465690174
Funding Source:This work was supported by the National Institutes of Health grants R35NS111631 and R01CA186702 (S.R.J.); R01AR076029, R21ES032347 and R21NS118633 (Q.C.); and NIH P01 HD067244 and support from the Starr Cancer Consortium I13-0037 (S.S.G.).
Publications:Under revision
Contributors:Jason G. Dumelie, Qiuying Chen, Dawson Miller, Nabeel Attarwala, Steven S. Gross and Samie R. Jaffrey1

Subject:

Subject ID:SU002438
Subject Type:Mammal
Subject Species:Mus musculus
Taxonomy ID:10090

Factors:

Subject type: Mammal; Subject species: Mus musculus (Factor headings shown in green)

mb_sample_id local_sample_id Protein RNA Fraction
SA235674HNRNPA1 Aqueous Sample 2HNRNPA1 150 nM aqueous
SA235675HNRNPA1 Aqueous Sample 3HNRNPA1 150 nM aqueous
SA235676HNRNPA1 Aqueous Sample 1HNRNPA1 150 nM aqueous
SA235677HNRNPA1 Condensate Sample 1HNRNPA1 150 nM condensate
SA235678HNRNPA1 Condensate Sample 3HNRNPA1 150 nM condensate
SA235679HNRNPA1 Condensate Sample 2HNRNPA1 150 nM condensate
SA235680HNRNPA1 Input Sample 2HNRNPA1 150 nM input
SA235681HNRNPA1 Input Sample 1HNRNPA1 150 nM input
SA235682HNRNPA1 Input Sample 3HNRNPA1 150 nM input
SA235683MED1 Aqueous Sample 1MED1 150 nM aqueous
SA235684MED1 Aqueous Sample 3MED1 150 nM aqueous
SA235685MED1 Aqueous Sample 2MED1 150 nM aqueous
SA235686MED1 Condensate Sample 1MED1 150 nM condensate
SA235687MED1 Condensate Sample 3MED1 150 nM condensate
SA235688MED1 Condensate Sample 2MED1 150 nM condensate
SA235689MED1 Input Sample 3MED1 150 nM input
SA235690MED1 Input Sample 2MED1 150 nM input
SA235691MED1 Input Sample 1MED1 150 nM input
SA235692Nucleocapsid No RNA Aqueous Sample 4SARA CoV-2 nucleocapsid 0 nM aqueous
SA235693Nucleocapsid No RNA Aqueous Sample 5SARA CoV-2 nucleocapsid 0 nM aqueous
SA235694Nucleocapsid No RNA Condensate Sample 5SARA CoV-2 nucleocapsid 0 nM condensate
SA235695Nucleocapsid No RNA Condensate Sample 4SARA CoV-2 nucleocapsid 0 nM condensate
SA235696Nucleocapsid No RNA Input Sample 4SARA CoV-2 nucleocapsid 0 nM input
SA235697Nucleocapsid No RNA Input Sample 5SARA CoV-2 nucleocapsid 0 nM input
SA235698Nucleocapsid Aqueous Sample 1SARA CoV-2 nucleocapsid 150 nM aqueous
SA235699Nucleocapsid Aqueous Sample 2SARA CoV-2 nucleocapsid 150 nM aqueous
SA235700Nucleocapsid Aqueous Sample 3SARA CoV-2 nucleocapsid 150 nM aqueous
SA235701Nucleocapsid Condensate Sample 1SARA CoV-2 nucleocapsid 150 nM condensate
SA235702Nucleocapsid Condensate Sample 3SARA CoV-2 nucleocapsid 150 nM condensate
SA235703Nucleocapsid Condensate Sample 2SARA CoV-2 nucleocapsid 150 nM condensate
SA235704Nucleocapsid Input Sample 2SARA CoV-2 nucleocapsid 150 nM input
SA235705Nucleocapsid Input Sample 1SARA CoV-2 nucleocapsid 150 nM input
SA235706Nucleocapsid Input Sample 3SARA CoV-2 nucleocapsid 150 nM input
SA235707Nucleocapsid 600 nM RNA Aqueous Sample 7SARA CoV-2 nucleocapsid 600 nM aqueous
SA235708Nucleocapsid 600 nM RNA Aqueous Sample 6SARA CoV-2 nucleocapsid 600 nM aqueous
SA235709Nucleocapsid 600 nM RNA Condensate Sample 7SARA CoV-2 nucleocapsid 600 nM condensate
SA235710Nucleocapsid 600 nM RNA Condensate Sample 6SARA CoV-2 nucleocapsid 600 nM condensate
SA235711Nucleocapsid 600 nM RNA Input Sample 6SARA CoV-2 nucleocapsid 600 nM input
SA235712Nucleocapsid 600 nM RNA Input Sample 7SARA CoV-2 nucleocapsid 600 nM input
Showing results 1 to 39 of 39

Collection:

Collection ID:CO002431
Collection Summary:Condensate metabolomics. Mouse metabolites were collected from the liver of female mice using methanol extraction. After euthanizing a mouse, the liver was immediately frozen in liquid nitrogen. We then used cold 80% methanol to extract metabolites. This method effectively quenches metabolic activity and is well-established for extracting a broad range of metabolites, including polar metabolites77,78. First, 1 ml of 80% methanol was added to the liver and incubated for 10 min at -20oC. Glass beads were added to the liver and then the liver was lysed by bead-beating for 45 s using a Tissuelyser cell disrupter (Qiagen). The lysate was incubated for 10 min at -20oC and centrifuged (13200 rpm, 5 min) to separate metabolites from macromolecules. The supernatant was collected and 200 µl of 80% methanol was added to the pellet. The incubation, shaking and centrifugation steps were repeated twice to extract more metabolites from the pellet. The three supernatants were combined and centrifuged (14000 rpm, 10 min) to separate any remaining macromolecules from the metabolites. The combined supernatants were dried using a SpeedVac Concentrator (Savant, SPD131DDA) at 25oC and the dried metabolite samples were stored at -80oC. The amount of protein in the pellet was measured using the Quick Start Bradford assay to calculate the metabolites’ protein equivalent mass. Mouse metabolites were initially re-suspended in condensate buffer (50 mM NH4HCO3 pH 7.5, 50 mM NaCl, 1 mM DTT) to a protein equivalent concentration of 938 g/l. The chosen final concentration of metabolites is slightly lower than the 200-300 g/l protein concentration observed in cells79. Metabolites that were not fully soluble in condensate buffer were removed by centrifugation (2x5 min, 16,000 g each), in which only the supernatant was retained. Due to the lack of crowding agents, phase separation required greater concentrations of protein and RNA than typically employed for nucleocapsid and MED1 condensate formation17,32. Purified protein (37.5 μM) was briefly sonicated (10 s) and centrifuged (1 min, 1,000 g) to disrupt any existing condensates and to remove any precipitated proteins. Purified protein (final concentration, 30 μM) was combined with metabolites (final concentration, 150 g/l protein equivalent) and then phage lambda RNA (final concentration, 0.15 μM) in a total volume of 300 µl. An input sample (10 µl) was saved and then the sample was allowed to incubate for 10 min at 25oC. Condensates were then separated from the aqueous environment by centrifugation (10 min, 12,500 g, 25oC). The aqueous phase was removed from the condensate phase and then equal volumes (usually ~ 2 µl) of the aqueous fraction, condensate fraction and input sample were processed for metabolomics using identical approaches as described below. Where shown, representative images of the phases were taken on an iPhone 11. Protein levels in each fraction were evaluated using gels as described above. Where indicated, RNA was added instead to the nucleocapsid at a concentration of either 0 μM or 0.6 μM. In these experiments, all other conditions, including buffer concentrations, were identical to other condensate metabolomics experiments. Notably, in a different subset of experiments, metabolites were added to MED1 condensates after the 10 min incubation rather than prior to the incubation. Metabolite enrichment in these condensates was highly correlated to the other MED1 condensates (r = 0.9, Pearson’s correlation), suggesting that the timing of metabolite addition may not be important. Metabolites were then extracted from each fraction and the input for LC-MS as follows. First the samples were diluted in ammonium bicarbonate buffer (50 mM NH4HCO3 pH 7.5) and briefly heated (2 min, 65oC) to disrupt condensates before being added immediately to 4x volume of ice-cold 100% methanol to precipitate protein and RNA. This heating step does not appear to be necessary for extracting these metabolites and can be excluded (Extended Data Fig. 2e,g). Protein and RNA were separated from metabolites by vortexing the samples (2 min), followed by incubation at -25oC (10 min) and then centrifugation (5 min, 13,000 rpm). The supernatant was saved and the process was repeated on the pellet two more times after adding 200 µl of 80% methanol each time to the pellet. The three supernatants were combined and centrifuged (10 min, 14000 rpm) to remove any additional macromolecules. The final supernatant was collected and dried using a SpeedVac Concentrator run at 25oC.
Sample Type:Liver
Collection Method:80% methanol
Storage Conditions:-80℃

Treatment:

Treatment ID:TR002450
Treatment Summary:Mouse liver metabolites were combined with either the condensate-forming low-complexity domains of HNRNPA1, MED1 or full-length SARS-CoV-2 nucleocapsid. Condensates were stimulated with either 0 nM, 150 nM or 600 nM RNA. Condensates were centrifuged to the bottom of a 600 ul tube. Equal fractions from the input sample, aqueous phase and condensate phases were collected separately. Metabolites were extracted from each fraction.

Sample Preparation:

Sampleprep ID:SP002444
Sampleprep Summary:Metabolites were then extracted from each fraction and the input for LC-MS as follows. First the samples were diluted in ammonium bicarbonate buffer (50 mM NH4HCO3 pH 7.5) and briefly heated (2 min, 65oC) to disrupt condensates before being added immediately to 4x volume of ice-cold 100% methanol to precipitate protein and RNA. This heating step does not appear to be necessary for extracting these metabolites and can be excluded (Extended Data Fig. 2e,g). Protein and RNA were separated from metabolites by vortexing the samples (2 min), followed by incubation at -25oC (10 min) and then centrifugation (5 min, 13,000 rpm). The supernatant was saved and the process was repeated on the pellet two more times after adding 200 µl of 80% methanol each time to the pellet. The three supernatants were combined and centrifuged (10 min, 14000 rpm) to remove any additional macromolecules. The final supernatant was collected and dried using a SpeedVac Concentrator run at 25oC. On the day of metabolite analysis, dried-down extracts were reconstituted in 150 µl 70% acetonitrile, at a relative protein concentration of ~ 2 µg/µl, and 4 µl of this reconstituted extract was injected for LC/MS-based targeted and untargeted metabolite profiling.
Extract Storage:-80℃

Combined analysis:

Analysis ID AN003834 AN003835
Analysis type MS MS
Chromatography type Normal phase Normal phase
Chromatography system Agilent Model 1290 Infinity II liquid chromatography system Agilent Model 1290 Infinity II liquid chromatography system
Column Cogent Diamond Hydride (150 × 2.1mm,4um) Cogent Diamond Hydride (150 × 2.1mm,4um)
MS Type Other Other
MS instrument type QTOF QTOF
MS instrument name Agilent 6550 QTOF Agilent 6550 QTOF
Ion Mode POSITIVE NEGATIVE
Units Ion counts Ion counts

Chromatography:

Chromatography ID:CH002839
Chromatography Summary:Tissue extracts were analyzed by LC/MS as described previously, using a platform comprised of an Agilent Model 1290 Infinity II liquid chromatography system coupled to an Agilent 6550 iFunnel time-of-flight MS analyzer. Chromatography of metabolites utilized aqueous normal phase (ANP) chromatography on a Diamond Hydride column (Microsolv). Mobile phases consisted of: (A) 50% isopropanol, containing 0.025% acetic acid, and (B) 90% acetonitrile containing 5 mM ammonium acetate. To eliminate the interference of metal ions on chromatographic peak integrity and electrospray ionization, EDTA was added to the mobile phase at a final concentration of 5 µM. The following gradient was applied: 0-1.0 min, 99% B; 1.0-15.0 min, to 20% B; 15.0 to 29.0, 0% B; 29.1 to 37min, 99% B.
Instrument Name:Agilent Model 1290 Infinity II liquid chromatography system
Column Name:Cogent Diamond Hydride (150 × 2.1mm,4um)
Flow Gradient:0-1.0 min, 99% B; 1.0-15.0 min, to 20% B; 15.0 to 29.0, 0% B; 29.1 to 37min, 99% B.
Solvent A:50% isopropanol/50% water; 0.025% acetic acid
Solvent B:90% acetonitrile/10% water; 5 mM ammonium acetate
Chromatography Type:Normal phase

MS:

MS ID:MS003576
Analysis ID:AN003834
Instrument Name:Agilent 6550 QTOF
Instrument Type:QTOF
MS Type:Other
MS Comments:LC/MS-based targeted and untargeted metabolite profiling. For targeted analysis, raw LC/MS data was extracted by MassProfinder 8.0 (Agilent Technologies) using an in-house annotated personal metabolite database that contains 863 metabolites (Agilent Technologies). Additionally, molecular feature extraction (MFE) was performed for untargeted metabolite profiling using MassProfinder 8.0 (Agilent Technologies). The untargeted molecular features were imported into MassProfiler Professional 15.1 (MPP, Agilent Technologies) and searched against Metlin personal metabolite database (PCDL database 8.0), Human Metabolome Database (HMDB) and an in-house phospholipid database for tentative metabolite ID assignments, based on monoisotopic neutral mass (< 5 ppm mass accuracy) matches. Furthermore, a molecular formula generator (MFG) algorithm in MPP was used to generate and score empirical molecular formulae, based on a weighted consideration of monoisotopic mass accuracy, isotope abundance ratios, and spacing between isotope peaks. A tentative compound ID was assigned when PCDL database and MFG scores concurred for a given candidate molecule. Tentatively assigned molecules were reextracted using Profinder 8.0 for confirmation of untargeted results. For phospholipids, assignment of IDs was based on the defined pattern of neutral loss and head group fragment ions. Metabolites from targeted and untargeted extraction were combined for further statistical analysis among groups of input, aqueous and condensate fractions. Metabolites were removed from our analysis if they had a low ion count or high variation in input samples. Measurements of metabolite ion counts in input samples should be replicates across experiments. As such, differences in metabolite ion counts reflect experimental variability. To determine appropriate cut-offs, we examined the relationship between metabolite ion counts and their variation across input sample technical replicates. Metabolites with a median of < 1000 ion counts/sample tended to have high variation across samples. As a result, these metabolites were removed. Metabolites were also removed with > 2.5 standard deviation in log2(ion counts) since the input measurements for these metabolites were particularly unreliable relative to what was observed for other metabolites.
Ion Mode:POSITIVE
  
MS ID:MS003577
Analysis ID:AN003835
Instrument Name:Agilent 6550 QTOF
Instrument Type:QTOF
MS Type:Other
MS Comments:LC/MS-based targeted and untargeted metabolite profiling. For targeted analysis, raw LC/MS data was extracted by MassProfinder 8.0 (Agilent Technologies) using an in-house annotated personal metabolite database that contains 863 metabolites (Agilent Technologies). Additionally, molecular feature extraction (MFE) was performed for untargeted metabolite profiling using MassProfinder 8.0 (Agilent Technologies). The untargeted molecular features were imported into MassProfiler Professional 15.1 (MPP, Agilent Technologies) and searched against Metlin personal metabolite database (PCDL database 8.0), Human Metabolome Database (HMDB) and an in-house phospholipid database for tentative metabolite ID assignments, based on monoisotopic neutral mass (< 5 ppm mass accuracy) matches. Furthermore, a molecular formula generator (MFG) algorithm in MPP was used to generate and score empirical molecular formulae, based on a weighted consideration of monoisotopic mass accuracy, isotope abundance ratios, and spacing between isotope peaks. A tentative compound ID was assigned when PCDL database and MFG scores concurred for a given candidate molecule. Tentatively assigned molecules were reextracted using Profinder 8.0 for confirmation of untargeted results. For phospholipids, assignment of IDs was based on the defined pattern of neutral loss and head group fragment ions. Metabolites from targeted and untargeted extraction were combined for further statistical analysis among groups of input, aqueous and condensate fractions. Metabolites were removed from our analysis if they had a low ion count or high variation in input samples. Measurements of metabolite ion counts in input samples should be replicates across experiments. As such, differences in metabolite ion counts reflect experimental variability. To determine appropriate cut-offs, we examined the relationship between metabolite ion counts and their variation across input sample technical replicates. Metabolites with a median of < 1000 ion counts/sample tended to have high variation across samples. As a result, these metabolites were removed. Metabolites were also removed with > 2.5 standard deviation in log2(ion counts) since the input measurements for these metabolites were particularly unreliable relative to what was observed for other metabolites.
Ion Mode:NEGATIVE
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