Summary of Study ST002353
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 | ST002353 |
| Study Title | Biomolecular condensates create phospholipid-enriched microenvironments (Part 3) |
| 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 |
| jdumes98@gmail.com | |
| Phone | 6465690174 |
| Submit Date | 2022-11-16 |
| 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: | SU002593 |
| Subject Type: | Synthetic sample |
Factors:
Subject type: Synthetic sample; Subject species: - (Factor headings shown in green)
| mb_sample_id | local_sample_id | metabolite_source | protein | RNA | fraction |
|---|---|---|---|---|---|
| SA249779 | MED1 Library 100 nM Aqueous Sample 3 | 100 nM library | MED1 | 150 nM | aqueous |
| SA249780 | MED1 Library 100 nM Aqueous Sample 2 | 100 nM library | MED1 | 150 nM | aqueous |
| SA249781 | MED1 Library 100 nM Aqueous Sample 1 | 100 nM library | MED1 | 150 nM | aqueous |
| SA249782 | MED1 Library 100 nM Condensate Sample 1 | 100 nM library | MED1 | 150 nM | condensate |
| SA249783 | MED1 Library 100 nM Condensate Sample 3 | 100 nM library | MED1 | 150 nM | condensate |
| SA249784 | MED1 Library 100 nM Condensate Sample 2 | 100 nM library | MED1 | 150 nM | condensate |
| SA249785 | MED1 Library 100 nM Input Sample 3 | 100 nM library | MED1 | 150 nM | input |
| SA249786 | MED1 Library 100 nM Input Sample 1 | 100 nM library | MED1 | 150 nM | input |
| SA249787 | MED1 Library 100 nM Input Sample 2 | 100 nM library | MED1 | 150 nM | input |
| SA249770 | MED1 Library 10 uM Aqueous Sample 3 | 10 uM library | MED1 | 150 nM | aqueous |
| SA249771 | MED1 Library 10 uM Aqueous Sample 2 | 10 uM library | MED1 | 150 nM | aqueous |
| SA249772 | MED1 Library 10 uM Aqueous Sample 1 | 10 uM library | MED1 | 150 nM | aqueous |
| SA249773 | MED1 Library 10 uM Condensate Sample 2 | 10 uM library | MED1 | 150 nM | condensate |
| SA249774 | MED1 Library 10 uM Condensate Sample 1 | 10 uM library | MED1 | 150 nM | condensate |
| SA249775 | MED1 Library 10 uM Condensate Sample 3 | 10 uM library | MED1 | 150 nM | condensate |
| SA249776 | MED1 Library 10 uM Input Sample 3 | 10 uM library | MED1 | 150 nM | input |
| SA249777 | MED1 Library 10 uM Input Sample 2 | 10 uM library | MED1 | 150 nM | input |
| SA249778 | MED1 Library 10 uM Input Sample 1 | 10 uM library | MED1 | 150 nM | input |
| SA249761 | MED1 Library 1 uM Aqueous Sample 2 | 1 uM library | MED1 | 150 nM | aqueous |
| SA249762 | MED1 Library 1 uM Aqueous Sample 3 | 1 uM library | MED1 | 150 nM | aqueous |
| SA249763 | MED1 Library 1 uM Aqueous Sample 1 | 1 uM library | MED1 | 150 nM | aqueous |
| SA249764 | MED1 Library 1 uM Condensate Sample 1 | 1 uM library | MED1 | 150 nM | condensate |
| SA249765 | MED1 Library 1 uM Condensate Sample 3 | 1 uM library | MED1 | 150 nM | condensate |
| SA249766 | MED1 Library 1 uM Condensate Sample 2 | 1 uM library | MED1 | 150 nM | condensate |
| SA249767 | MED1 Library 1 uM Input Sample 2 | 1 uM library | MED1 | 150 nM | input |
| SA249768 | MED1 Library 1 uM Input Sample 1 | 1 uM library | MED1 | 150 nM | input |
| SA249769 | MED1 Library 1 uM Input Sample 3 | 1 uM library | MED1 | 150 nM | input |
| Showing results 1 to 27 of 27 |
Collection:
| Collection ID: | CO002586 |
| Collection Summary: | Chemical library to perform condensate metabolomics with a defined set of lipids. To analyze a defined set of lipids at known concentrations, the following molecules were purchased: phosphatidylcholine (3:0/3:0)(Cayman Chemical, 32703), phosphatidylcholine (9:0/9:0)(Cayman Chemical, 10009874), phosphatidylcholine (12:0/12:0)(Echelon Biosciences, L-1112), phosphatidylcholine (16:0/16:0)(Echelon Biosciences, L-1116), phosphatidylcholine (18:0/18:0)(Echelon Biosciences, L-1118), sn-glycero-3-phosphocholine, lysophosphatidylcholine (16:0)(Echelon Biosciences, L-1516), palmitic acid (Sigma Aldrich, P5585), phosphatidylethanolamine (16:0/16:0)(Avanti Polar Lipids, 850705), phosphatidylglycerol (16:0/16:0)(Avanti Polar Lipids, 840455), phosphatidylinositol (16:0/16:0)(Echelon Biosciences, P-0016), PIP2 (16:0/16:0)(Echelon Biosciences, P-4516), phosphatidylserine (16:0/16:0)(Echelon Biosciences, L-3116). These molecules were first dissolved in an appropriate organic solvent and then either 0.33 pmoles, 3.3 pmoles, or 33 pmoles of each molecule were combined in an eppendorf tube. The organic solvents were removed using a SpeedVac Concentrator (Savant, SPD131DDA) at 25oC and the dried chemical libraries were stored at -80oC. Each tube containing a chemical library was used to perform a single condensate metabolomics experiment. These libraries were initially re-suspended in condensate buffer (50 mM NH4HCO3 pH 7.5, 50 mM NaCl, 1 mM DTT). Molecules 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 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. 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. 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: | Synthetic Mixture |
| Collection Method: | 80% methanol |
| Storage Conditions: | -80℃ |
Treatment:
| Treatment ID: | TR002605 |
| Treatment Summary: | Chemical libraries were added with individual molecules at a concentration of 100 nM, 1 uM or 10 uM to the condensate-forming low-complexity domain MED1 tagged with mCherry. Condensates were stimulated with 150 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: | SP002599 |
| Sampleprep Summary: | Metabolites were 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. 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 untargeted metabolite profiling. |
| Extract Storage: | -80℃ |
Combined analysis:
| Analysis ID | AN004099 | AN004100 |
|---|---|---|
| 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: | CH003035 |
| 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: | MS003846 |
| Analysis ID: | AN004099 |
| 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: | MS003847 |
| Analysis ID: | AN004100 |
| 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 |
