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.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

Study ID	ST002349
Study Title	Biomolecular condensates create phospholipid-enriched microenvironments (Part 1)
Study Type	Metabolomes of in vitro synthesized condensates
Study 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
Submit Date	2022-11-04
Raw Data Available	Yes
Raw Data File Type(s)	mzdata.xml
Analysis Type Detail	LC-MS
Release Date	2023-03-01
Release Version	2

Select appropriate tab below to view additional metadata details:

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:	SU002591
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
SA249704	HNRNPA1 Aqueous Sample 2	HNRNPA1	150 nM	aqueous
SA249705	HNRNPA1 Aqueous Sample 3	HNRNPA1	150 nM	aqueous
SA249706	HNRNPA1 Aqueous Sample 1	HNRNPA1	150 nM	aqueous
SA249707	HNRNPA1 Condensate Sample 1	HNRNPA1	150 nM	condensate
SA249708	HNRNPA1 Condensate Sample 3	HNRNPA1	150 nM	condensate
SA249709	HNRNPA1 Condensate Sample 2	HNRNPA1	150 nM	condensate
SA249710	HNRNPA1 Input Sample 2	HNRNPA1	150 nM	input
SA249711	HNRNPA1 Input Sample 1	HNRNPA1	150 nM	input
SA249712	HNRNPA1 Input Sample 3	HNRNPA1	150 nM	input
SA249713	MED1 Aqueous Sample 1	MED1	150 nM	aqueous
SA249714	MED1 Aqueous Sample 3	MED1	150 nM	aqueous
SA249715	MED1 Aqueous Sample 2	MED1	150 nM	aqueous
SA249716	MED1 Condensate Sample 1	MED1	150 nM	condensate
SA249717	MED1 Condensate Sample 3	MED1	150 nM	condensate
SA249718	MED1 Condensate Sample 2	MED1	150 nM	condensate
SA249719	MED1 Input Sample 3	MED1	150 nM	input
SA249720	MED1 Input Sample 2	MED1	150 nM	input
SA249721	MED1 Input Sample 1	MED1	150 nM	input
SA249722	Nucleocapsid No RNA Aqueous Sample 4	SARA CoV-2 nucleocapsid	0 nM	aqueous
SA249723	Nucleocapsid No RNA Aqueous Sample 5	SARA CoV-2 nucleocapsid	0 nM	aqueous
SA249724	Nucleocapsid No RNA Condensate Sample 5	SARA CoV-2 nucleocapsid	0 nM	condensate
SA249725	Nucleocapsid No RNA Condensate Sample 4	SARA CoV-2 nucleocapsid	0 nM	condensate
SA249726	Nucleocapsid No RNA Input Sample 4	SARA CoV-2 nucleocapsid	0 nM	input
SA249727	Nucleocapsid No RNA Input Sample 5	SARA CoV-2 nucleocapsid	0 nM	input
SA249728	Nucleocapsid Aqueous Sample 1	SARA CoV-2 nucleocapsid	150 nM	aqueous
SA249729	Nucleocapsid Aqueous Sample 2	SARA CoV-2 nucleocapsid	150 nM	aqueous
SA249730	Nucleocapsid Aqueous Sample 3	SARA CoV-2 nucleocapsid	150 nM	aqueous
SA249731	Nucleocapsid Condensate Sample 1	SARA CoV-2 nucleocapsid	150 nM	condensate
SA249732	Nucleocapsid Condensate Sample 3	SARA CoV-2 nucleocapsid	150 nM	condensate
SA249733	Nucleocapsid Condensate Sample 2	SARA CoV-2 nucleocapsid	150 nM	condensate
SA249734	Nucleocapsid Input Sample 2	SARA CoV-2 nucleocapsid	150 nM	input
SA249735	Nucleocapsid Input Sample 1	SARA CoV-2 nucleocapsid	150 nM	input
SA249736	Nucleocapsid Input Sample 3	SARA CoV-2 nucleocapsid	150 nM	input
SA249737	Nucleocapsid 600 nM RNA Aqueous Sample 7	SARA CoV-2 nucleocapsid	600 nM	aqueous
SA249738	Nucleocapsid 600 nM RNA Aqueous Sample 6	SARA CoV-2 nucleocapsid	600 nM	aqueous
SA249739	Nucleocapsid 600 nM RNA Condensate Sample 7	SARA CoV-2 nucleocapsid	600 nM	condensate
SA249740	Nucleocapsid 600 nM RNA Condensate Sample 6	SARA CoV-2 nucleocapsid	600 nM	condensate
SA249741	Nucleocapsid 600 nM RNA Input Sample 6	SARA CoV-2 nucleocapsid	600 nM	input
SA249742	Nucleocapsid 600 nM RNA Input Sample 7	SARA CoV-2 nucleocapsid	600 nM	input

Showing results 1 to 39 of 39

Showing results 1 to 39 of 39

Collection:

Collection ID:	CO002584
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:	TR002603
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.

Treatment ID:

TR002603

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:	SP002597
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	AN004095	AN004096
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 × 2.1 mm, 4um)	Cogent Diamond Hydride (150 × 2.1 mm, 4um)
MS Type	ESI	ESI
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:	CH003033
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 × 2.1 mm, 4um)
Solvent A:	50% isopropanol, containing 0.025% acetic acid
Solvent B:	90% acetonitrile containing 5 mM ammonium acetate
Chromatography Type:	Normal phase

MS:

MS ID:	MS003842
Analysis ID:	AN004095
Instrument Name:	Agilent 6550 QTOF
Instrument Type:	QTOF
MS Type:	ESI
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:	MS003843
Analysis ID:	AN004096
Instrument Name:	Agilent 6550 QTOF
Instrument Type:	QTOF
MS Type:	ESI
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