ReNcell differentiation: Total proteomics during differentiation with small molecule perturbagens. Dataset 3 of 3 - Dataset (ID:20347)
|HMS Dataset ID:||20347|
|Dataset Title:||ReNcell differentiation: Total proteomics during differentiation with small molecule perturbagens. Dataset 3 of 3|
|Screening Lab Investigator:||Yuyu Song, Matt Berberich|
|Screening Principal Investigator:||Peter K. Sorger, Tim Mitchison|
|Assay Description:||ReNcell-VM neuroprogenitors were differentiated by growth factor (EGF and FGF) removal for 15 days in culture, giving rise to dopaminergic neurons, astrocytes, and oligodendrocytes. Drugs (Mevastatin and Kenpaullone) previously screened for their promoting effects on neural differentiation or DMSO control were added to the growth factor free maintenance media during the course of differentiation. Cells were collected at 10 time points and processed for total proteomics by liquid chromatography mass spectrometry (LC/MS) using 10-plex tandem mass tag (TMT) labelling to achieve deep quantification of proteome dynamics.|
1. Human ReNcell-VM immortalized neural progenitor cells were plated on matrigel-coated plates in ReNcell NSC Maintenance Medium supplemented with epidermal and basic fibroblast growth factors. Growth fators were withdrawn and perturbagens (Mevastatin at 2 µM final concentration (Tocris, 1526), or Kenpaullone at 2 µM (Sigma, K3888-5MG) or DMSO as a control) added at t=0. Half of the media was changed every 2 days with fresh drug added.
2. Cells were harvested at 10 time points (t = 0, 6 hr or 1, 2, 3, 4, 7, 10, 14 or 15 days). To harvest, cells were rinsed with PBS and scraped off plates in cold PBS (pH 7.4) supplemented with protease and phosphatase inhibitors before centrifugation at 300g for 3min.
3. Cell pellets were solubilized in lysis buffer (2% SDS, 150mM NaCl, 50mM Tris, pH 7.4) with protease and phosphatase inhibitors, reduced, and alkylated, followed by methanol/chloroform precipitation.
4. Precipitates were solubilized in 8M urea in 20mM EPPS, pH 8.5 and 60µg of solubilized protein from each sample was used for Lys-C and Trypsin digestion. A digest check was performed to determine the missed cleavage rate. Only samples with a missed cleavage rate <15% were processed further.
5. Equal amount of protein was taken from each sample and labelled using a TMT10plex Mass Tag Labelling Kit. The labelling efficiency and ratio checks were measured by LC-MS3 analysis of a combined 10-plex sample after mixing equal volumes (about 1 µg) from each time point. Based on the ratio check results, equal amounts of labelled peptides (> 98% labelling) from each time point were combined for further analysis.
6. TMT labelling reactions were quenched by adding hydroxylamine to a final concentration of 0.5% (v/v) and samples were combined for desalting using a SepPak tC18 Vac RC cartridge.
7. HPLC fractionation was then performed on the combined sample using an Agilent 1200 Series instrument with a flow rate of 600µl/min over a period of 75 minutes. Fractions were collected over the last 65 minutes and pooled into 24 samples, followed by sample cleanup using the Stage Tip protocol.
8. Samples were dried and resuspended in MS loading buffer (3% acetonitrile, 5% FA).
9. Resuspended samples were separated by HPLC and injected into an Orbitrap Fusion Lumos Tribrid MS using a multi-notch MS3 method. LC-MS were performed in the Orbitrap over a scan range of 400-1400m/z with dynamic exclusion. Top 10 ions with charge states from 2 to 6 were selected for MS/MS. Rapid rate scans were performed in the Ion Trap with a collision energy of 35% and a maximum injection time of 120 ms. TMT quantification was performed using SPS-MS3 in the Orbitrap with a scan range of 100-1000 m/z and an HCS collision energy of 55%.
10. Data processing: A compilation of commercially available software (Core software program) was used to convert mass spectrometric data (Thermo “.RAW” files) to mzXML format and correct monoisotopic m/z measurements and erroneous peptide charge state assignments. Assignment of MS/MS spectra was performed using the Sequest and the Human Uniprot database.
11. Data processing: A compilation of commercially available software (Core software program) was used to convert mass spectrometric data (Thermo “.RAW” files) to mzXML format and correct monoisotopic m/z measurements and erroneous peptide charge state assignments. Assignment of MS/MS spectra was performed using the Sequest and the Human Uniprot database.
12. Principal component analyses, hierarchical and K-means clustering, differential expression, and enrichment analyses were performed using customized code written in python (https://github.com/datarail/msda). The neuronal gene set library was customized and downloaded from GSEA by searching for terms across all libraries that contained “neuron” in their name. All code that takes the datasets from Synapse, computes and plots the results shown in Figures 2-5 of Song et al (2018) is available at https://github.com/sorgerlab/rencell.
Data columns and descriptions for HMS-LINCS datasets 20345-20347 are detailed in this file download: Data Columns.
|HMS Dataset Type:||Proteomics|
|Date Publicly Available:||2018-06-30|
|Most Recent Update:||2018-06-30|