Gimple et al.

SUPPLEMENTAL METHODS

METHOD DETAILS

Tumor dissociation and GSC culture

Xenografted tumors were dissociated using a papain dissociation system according to the manufacturer’s instructions. GSCs were then cultured in Neurobasal medium supplemented with 2% B27, 1% L-glutamine, 1% sodium pyruvate, 1% penicillin/streptomycin, 10 ng/ml basic fibroblast growth factor (bFGF), and 10 ng/ml epidermal growth factor (EGF) for at least 6 hours to recover expression of surface antigens. GSC phenotypes were validated by expression of stem cell markers (SOX2 and OLIG2) functional assays of self-renewal (serial neurosphere passage), and tumor propagation using in vivo limiting dilution. For differentiation experiments, glioma stem cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS; Sigma, St. Louis, MO, USA) for one week to induce differentiation.

Glioblastoma Stem Cell Model or Tissue

Patient Age (Years)

Patient Sex

Tumor Grade

GSC387

76

Female

Glioblastoma (Grade IV)

GSC3565

32

Male

Glioblastoma (Grade IV)

GSC23

63

Male

Recurrent Glioblastoma (Grade IV)

GSC1919

53

Male

Glioblastoma (Grade IV)

GSC3028

65

Female

Recurrent Glioblastoma (Grade IV)

GSC3264

65

Female

Recurrent Glioblastoma (Grade IV)

MGG8

Restricted by Institutional Requirements

Female

Glioblastoma (Grade IV)

GSC1517

54

Female

Glioblastoma (Grade IV)

GSC3136

52

Male

Glioblastoma (Grade IV)

GSC-CW738

37

Male

Recurrent Glioblastoma (Grade IV)

MGG6

Restricted by Institutional Requirements

Female

Glioblastoma (Grade IV)

Proliferation and neurosphere formation assays

Cell proliferation experiments were conducted by plating cells of interest at a density of 2,000 cells per well in a 96-well plate with 5 replicates. Alamar Blue (Thermo Fisher Scientific) was used to measure cell viability. Data is presented as mean +/- standard deviation. Neurosphere formation was measured by in vitro limiting dilution assay, as previously reported (Flavahan et al., 2013). Briefly, decreasing numbers of cells per well (50, 20, 10, and 1) were plated into 96-well plates. The presence and number of neurospheres in each well were recorded seven days after plating. Extreme limiting dilution analysis was performed using software available at http://bioinf.wehi.edu.au/software/elda, as previously described (Flavahan et al., 2013; Hu and Smyth, 2009).

Western blotting

Cells were collected and lysed in RIPA buffer (50 mM Tris-HCl, pH 7.5; 150 mM NaCl; 0.5% NP-40; 50 mM NaF with protease inhibitors) and incubated on ice for 30 minutes. Lysates were centrifuged at 4C for 10 minutes at 14,000 rpm, and supernatant was collected. The Bradford assay (Bio-Rad Laboratories) was utilized for determination of protein concentration. Equal amounts of protein samples were mixed with SDS Laemmli loading buffer, boiled for 10 minutes, and electrophoresed using NuPAGE Bis-Tris Gels, then transferred onto PVDF membranes. TBS-T supplemented with 5% non-fat dry milk was used for blocking for a period of 1 hour followed by blotting with primary antibodies at 4°C for 16 hours. Blots were washed 3 times for 5 minutes each with TBS-T and then incubated with appropriate secondary antibodies in 5% non-fat milk in TBS-T for 1 hour. For all western immunoblot experiments, blots were imaged using BioRad Image Lab software and subsequently processed using Adobe Illustrator to create the figures.

Quantitative RT-PCR

Trizol reagent (Sigma Aldrich) was used to isolate total cellular RNA from cell pellets according to the manufacturer’s instructions. The high-capacity cDNA reverse transcription Kit (ThermoFisher scientific, catalog 4368814) was used for reverse transcription into cDNA. Quantitative real-time PCR was performed using Applied Biosystems 7900HT cycler using Radiant Green Hi-ROX qPCR kit (catalog number QS2050). qPCR primers used in this study were:

Gene Name

Forward Primer

Reverse Primer

SOX2

TACAGCATGTCCTACTCGCAG

GAGGAAGAGGTAACCACAGGG

OLIG2

TGGCTTCAAGTCATCCTCGTC

ATGGCGATGTTGAGGTCGTG

ELOVL2-1

ATGTTTGGACCGCGAGATTCT

CCCAGCCATATTGAGAGCAGATA

ELOVL2-2

CTGCTCTCAATATGGCTGGGT

TCCCCTGCGCTGGTAAGAT

WSCD1

GAGGCACCTACATTGGATGCT

CGTAGACATAGGACCGCTCA

KLHDC8A

ATGGAGGTGCCTAACGTCAAG

CCGTTGTCGTCACATCCCC

FADS2

TGACCGCAAGGTTTACAACAT

AGGCATCCGTTGCATCTTCTC

GFAP

CTGCGGCTCGATCAACTCA

TCCAGCGACTCAATCTTCCTC

GAPDH

GGAGCGAGATCCCTCCAAAAT

GGCTGTTGTCATACTTCTCATGG

18S

GGCCCTGTAATTGGAATGAGTC

CCAAGATCCAACTACGAGCTT

MYC

GGCTCCTGGCAAAAGGTCA

CTGCGTAGTTGTGCTGATGT

EGFR

AGGCACGAGTAACAAGCTCAC

ATGAGGACATAACCAGCCACC

MEK (MAP2K1)

GGGCTTCTATGGTGCGTTCTA

CCCACGGGAGTTGACTAGGAT

Plasmids and lentiviral transduction

The following lentiviral clones expressing shRNAs directed against human genes were used:

TRCN Name

Name in Manuscript (Gene Name and start site of shRNA targeting)

Gene Name (location of shRNA target sequence)

TRCN0000314664

shELOVL2.308

ELOVL2 (CDS)

TRCN0000004965

shELOVL2.843

ELOVL2 (CDS)

TRCN0000314660

shELOVL2.1460

ELOVL2 (3’ UTR)

TRCN0000314663

shELOVL2.503

ELOVL2 (CDS)

TRCN0000138761

shKLHDC8A.1842

KLHDC8A (3’ UTR)

TRCN0000138219

shKLHDC8A.883

KLHDC8A (CDS)

TRCN0000137011

shWSCD1.1087

WSCD1 (CDS)

TRCN0000135544

shWSCD1.952

WSCD1 (CDS)

TRCN0000064753

shFADS2.1062

FADS2 (CDS)

TRCN0000064755

shFADS2.456

FADS2 (CDS)

TRCN0000064757

shFADS2.699

FADS2 (CDS)

TRCN0000355694

shSOX2.780

SOX2 (CDS)

TRCN0000355638

shSOX2.1038

SOX2 (CDS)

TRCN0000355637

shSOX2.1517

SOX2 (3’ UTR)

pLKO.1 Non-targeting Vector (SHC002)

shCONT

No Targets

CRISPR Analyses

The following sgRNA sequences directed against human genes were used. CHOP-CHOP was used for guide design (http://chopchop.cbu.uib.no/).

Target Name

Forward Sequence

Reverse Sequence

CRISPR Type

Non-Targeting

CACCGCTCTGCTGCGGAAGGATTCG

AAACCGAATCCTTCCGCAGCAGAGC

LentiCrisprV2

(Addgene #52961)

ELOVL2 Gene

CACCGACTTCTCTCCGCGTACATGC

AAACGCATGTACGCGGAGAGAAGTC

LentiCrisprV2

(Addgene #52961)

ELOVL2 Super-Enhancer #1

CACCGTTATCAAGTACTGACCAGAG

AAACCTCTGGTCAGTACTTGATAAC

dCas9-KRAB

(Addgene #71236)

ELOVL2 Super-Enhancer #2

CACCGGTGTCCAGCTAGACAAGAAT

AAACATTCTTGTCTAGCTGGACACC

dCas9-KRAB

(Addgene #71236)

Synthego ICE Analyses

To assess CRISPR editing efficiency, we used the Synthego ICE analysis software https://ice.synthego.com. Briefly, genomic DNA was extracted from cells treated with sgCONT or sgELOVL2, and PCR amplified using the following primers: Forward GCTCTCAATATGGCTGGGTAAC, and Reverse CAAGGACTTCCAGGATTTTCAG. Samples were PCR purified and subjected to Sanger sequencing using the primers above. Sequencing ab1 files were uploaded to the Synthego ICE website for analysis.

ChIP-qPCR Analyses

Cells (5x106) per condition were collected, and 5 mg SOX2 antibody (R & D Systems,#AF2018-SP) or goat-IgG (R & D Systems,#AB-108-C) was used for the immunoprecipitation of the DNA protein immunocomplexes. ChIP was performed using the Millipore Magna ChIP (MAGNA0017) according to the manufacturer’s protocol. The purified DNA was subjected to quantitative PCR using the following primer sets:

Target Name

Target Region (hg19)

Forward Primer

Reverse Primer

Negative Control #1

chr11:35,158,607-35,159,750

AGGGTGAGGGCTCTGAAGAT

GCCATCCCCCTATGCATTCA

Negative Control #2

chr9:105,125,389-105,126,868

CATGGAAGTACCTGGCCCAG

TGCCACATCAGGAGTGAGTG

ELOVL2 Enhancer Primer #1

chr6:11,023,240-11,023,864

GGACACCTTGGAACTGTACCA

ACCAGAGCACACACAGACAG

ELOVL2 Enhancer Primer #2

chr6:11,023,240-11,023,864

TCAGGACACCTTGGAACTGT

TGCTTCCCCTCTGGTCAGTA

Three technical replicates were performed with SOX2 ChIP-PCR data presented as fold change relative to the ChIP input.

Apoptosis assays

Apoptosis was assessed using the Dead Cell Apoptosis Kit with Annexin V Alexa Fluor™ 488 from ThermoFisher Scientific (Cat # V13241) according to the manufacturers instructions. Samples were analyzed using flow cytometry on a BD LSR Fortessa Flow Cytometer.

EGFR membrane localization by flow cytometry

Live glioma stem cells were dissociated using accutase, washed in PBS, and blocked with cell staining buffer for 5 minutes. Cells were incubated on ice for 20 minutes with PE-conjugated mouse antibody to anti-human EGFR (AY13, Biolegend, Cat # 352903). Cells were washed twice with cell staining buffer and subjected to flow cytometry on a BD LSR Fortessa Flow Cytometer.

EGFR Cloning to lentiviral vector

The EGFR coding sequence was cloned from the EGFR-WT plasmid (Addgene plasmid #11011) using the following primer sequences: Forward: GACTCAGATCTCGAGGCCACCATGCGACCCTCCGGGACGGCCGG and Reverse: AGAGTCGCGGGATCCTCATGCTCCAATAAATTCACTGCTTTGTG. The pLVX-Puro plasmid (Clontech, Catalog No. 632164) was cut using BamH1 and Xho1 and the EGFR sequence was inserted into this vector for lentiviral expression.

H3K27ac ChIP-sequencing Data Analysis

H3K27ac ChIP-sequencing data for glioblastoma samples were accessed through GSE101148 (1) and GSE72468 (2). Single-end H3K27ac and input ChIP-seq reads were trimmed using Trim Galore v0.4.3 (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) and cutadapt 1.14 (http://cutadapt.readthedocs.io/en/stable/guide.html). Reads were aligned to the hg19 human genome with BWA-MEM v0.7.17 (Heng Li arXiv preprint arXiv:1303.3997, 2013). BAM files were processed using SAMtools (Heng Li et al. Bioinformatics 2009) and PCR duplicates removed with PicardTools (http://broadinstitute.github.io/picard/). H3K27ac ChIP-sequencing data from normal brain specimens were accessed through the ENCODE and Roadmap Epigenomics projects (3) as BAM files. For GSC vs DGC comparisons, processed bigwig and H3K27ac peak files were accessed through GSE54047 (4).

H3K27ac peaks were called using MACS2 (v2.1.1) using a ChIP input file as a control with a q-value cutoff of 0.001 (5). BIGWIG track coverage files were generated from merged BAM files using the DeepTools (v2.4.1) bamCoverage command with RPKM normalization (6). Genomic coverage heatmaps were generated using the DeepTools “computeMatrix” and “plotHeatmap” functions or by viewing in the integrative genomics viewer (IGV) (7,8). Super-enhancers were called with ROSE (9) on the hg19 human genome with a stitching distance (-s) of 12,500bp and a transcription start site exclusion distance (-t) of 2,500bp. Super-enhancers were ranked by counting the H3K27ac signal in the ChIP file compared to the matched input file. Typical enhancer correlation analysis was performed using the DeepTools multiBamSummary function in “BED-file” mode over all H3K27ac peaks in glioblastoma and normal brain specimens. Super-enhancer correlation analysis and principal component analysis was performed using DiffBind (https://bioconductor.org/packages/release/bioc/html/DiffBind.html). Glioblastoma specific super-enhancers were defined as any super-enhancer occurring in a glioblastoma specimen that did not overlap with a normal brain super-enhancer. Super-enhancers were linked to the closest gene by the ROSE algorithm. Glioblastoma stem cell-specific constituent enhancers were defined as those with more than 3 gained H3K27ac enhancer peaks in glioblastoma stem cells compared with differentiated glioblastoma cells. The “GSC specificity score” was calculated by multiplying the mRNA fold change difference between glioblastoma stem cells and differentiated glioblastoma cells by the number of gained H3K27ac peaks in glioblastoma stem cells compared with differentiated glioblastoma cells.

Motifs were called from GSC-specific enhancer regions within glioblastoma-specific super-enhancers using the HOMER “findmotifsgenome.pl” script using the hg19 genome. Top scoring de novo and known motifs were presented.

Patient database bioinformatics

For survival analyses, TCGA data (10) was downloaded using the “TCGA2STAT” R package (11). The Cox Proportional Hazards model and log-rank analysis were used to assess prognostic significance of every gene in the TCGA GBM HG-U133A microarray dataset regardless of IDH mutation status. The GEPIA web-server was used to determine genes that were differentially expressed between glioblastoma specimens and normal brain specimens based on TCGA and GTEx RNA-seq data (12). For gene correlation analyses and reverse phase protein array (RPPA) analysis, data was accessed through the Gliovis web portal http://gliovis.bioinfo.cnio.es/ (13). Glioblastoma and low grade glioma tissue metabolite data was accessed from http://www.sanderlab.org/pancanmet/ (14).

Fatty acid supplementation methods

Fatty acids were conjugated with fatty acid-free BSA (Sigma) and generated a 3.18 mM stock of free fatty acid. Briefly, fatty acids were dissolved in 0.01 M NaOH by incubating at 70℃ for 20 minutes, and then complexed with 5% fatty acids-free BSA solution at a 5:1 molar ratio of fatty acid to BSA. Control BSA solution (vehicle) was prepared by adding equal amount of NaOH and BSA without fatty acids. Fatty acid-BSA conjugates were dissolved in cell culture media immediately before use.

Targeted Quantification of Total Fatty Acids in Cell Lysates Using HPLC Online Tandem Mass Spectrometry (LC/MS/MS)

1. Chemicals and solvents

Chemical standards of all the fatty acids (FA) from 10 carbons to 22 carbons including the saturated and unsaturated were purchased from Sigma-Aldrich (3050 Spruce St., St. Louis, MO 63103). The isotope labeled standards of FA were purchased from Avanti Polar Lipids, Inc. (700 Industrial Park Drive, Alabaster, Alabama 35007).

2. Sample preparation

A 200 µl cell lysate was mixed with 20 µl of mixed internal standards, 200 µl 20 mM BHT, 500 µl 2M NaOH and then hydrolyzed at 60 C for 120 min. After the hydrolysis, 600 ul 2M HCl was added to make the pH < 3. The lipids were extracted into the hexane layer using the Liquid/Liquid extraction method. The hexane layer is dried under nitrogen flow and the dried pellet was suspended with 100 µl 85% methanol. After centrifugation at 12000 rcf for 10 min, 50 µl of supernatant was transferred into a vial for FA analysis by LC/MS/MS.

3. LC/MS/MS analysis

A triple quadrupole mass spectrometer (Shimadzu LCMS-8050) was used for analysis of FA. A volume at 5 µl was injected onto a C18 column (Gemini, 3 µm, 2 x 150mm, Phenomenex) for the separation of FA species. Mobile phases were A (water containing 1.2% acetic acid) and B (methanol/acetonitrile (50/50) containing 0.1% acetic acid) and the pH was adjusted with ammonium hydroxide to 8.0 respectively. The run started with 85% mobile phase B from 0 to 2 min at the flow rate of 0.3 ml/min. Solvent B was then increased linearly to 100% B from 2 to 8 min and held at 100% B from 8 to 18 min. The column was finally re-equilibrated with 85% B for 8 min. The HPLC eluent was directly injected into the triple quadrupole mass spectrometer (Shimadzu LCMS-8050) and the FA species were ionized using electrospray ionization at negative mode. All the fatty acids were analyzed using Selected Reaction Monitoring (SRM) and the SRM transitions (m/z) were their precursor to product ions (m/z), such as 277 > 233 for FA(18:3), 279 > 261 for FA(18:2), 301 > 257 for FA(20:5), 303 > 259 for FA(20:4), 327 > 283 for FA(22:6), 329 > 285 for FA(22:5) and cis-FA(22:5), 331 > 287 for FA(22:4).

4. Data analysis

Peak areas for all the FA species and the internal standards are integrated using the software Labsolutions. Internal standard calibration curves were used for quantitation of FA species in cell lysate.

Global metabolomics method details

Metabolite extraction

The cell pellet was first mixed with1 mL ice-cold quenching solution (MeOH:CAN:H2O (2:2:1, v/v/v)) and votexed. Three freeze-thaw cycles were then performed including cycles of shock-freezing in liquid nitrogen and subsequent thawing at room temperature and sonication at 4 ºC for 15 min. The lysed cell samples were incubated in -20 ºC overnight (at least 4 h) for protein precipitation and then centrifuged at 13,000 rpm for 15 min. The supernatant was transferred to new 1.5 mL glass vials and was dried down using vacuum concentrator (Labconco, Kansas City, MO) till complete dryness. The dried samples were resuspended in 60 µL H2O/ACN (50/50, v/v) and transferred to a 1.5 mL eppendorf vial, vortexed and spin down at 13,000 rpm for 15 min. Clear supernatant was pipetted into LC insert in glass HPLC vials for LC-MS analysis.

LC-MS analysis

Complementary metabolomic profiling were carried out using a Bruker Impact II QTOF mass spectrometer (Billerica, MA, U.S.A.) coupled with an Agilent 1100 series capillary HPLC system (Palo Alto, CA, U.S.A.) in two different analytical modes to achieve a comprehensive metabolome coverage. These modes include RPLC−MS in ESI positive mode and HILIC− MS in ESI negative mode.

For RPLC−MS(+) metabolomics analysis, an Agilent ZORBAX 300SB-C18 LC column (300 Å, 5 µm, 150 × 0.5 mm) was used. Mobile phase A was 0.1% formic acid in water and mobile phase B was 0.1% FA in ACN. The LC-gradient was: t = 0.0 min, 95% A; t = 5 min, 95% A; t = 50 min, 5% A; t = 60 min; 5% A; t = 61 min, 95% A; t = 64 min, 95% A. At the end of the LC gradient, a 10-min re-equilibration time at 95% A was applied. The LC flow rate was 20 µL/min. The sample injection volume was 4 µL.

MS conditions for RP(+) analysis were set as follows: capillary voltage, 4500; nebulizer gas flow, 1.6 Bar; dry gas, 6.0 L/min at 220 ºC; funnel 1 RF 150 Vpp; funnel 2 RF, 200 Vpp; isCID energy, 0 eV; hexapole RF: 50 Vpp; Quadrupole ion energy, 4 eV; low mass 50 m/z; collision cell energy, 7.0 eV; pre pulse storage 5.0 µs; collision RF, ramp from 350 to 800 Vpp; transfer time ramp from 50 to 100 µs; detection mass range 25 to 1000 m/z; spectra collection rate 2.0 Hz.

For HILIC−MS(-) metabolomics analysis, a Phenomenex Luna NH2 LC column (100 Å, 3 µm, 150 × 1 mm) was used. Mobile phase A was 20 mM NH4AC in H2O (pH 9.7) with 5% ACN and mobile phase B was ACN with 5% H2O. The LC gradient was: t = 0.0 min, 5% A; t = 5 min, 5% A; t = 50 min, 95% A; t = 63 min, 95% A. At the end of the LC gradient, a 15-min re-equilibration time at 5% A was applied to the HILIC column. The flow rate was 50 µL/min. The sample injection volume was 4 µL.

MS conditions for HILIC(-) were set as follows: capillary voltage, 4500; nebulizer gas flow, 1.6 Bar; dry gas, 6.0 L/min at 220 ºC; funnel 1 RF 150 Vpp; funnel 2 RF, 200 Vpp; isCID energy, 0 eV; hexapole RF: 50 Vpp; Quadrupole ion energy, 4 eV; low mass 50 m/z; collision cell energy, 7.0 eV; pre pulse storage 5.0 µs; collision RF, ramp from 350 to 800 Vpp; transfer time, ramp from 50 to 100 µs; detection mass range 25 to 1000 m/z; spectra rate 2.0 Hz.

Global Lipidomics Data Analysis and Presentation

Metabolites were identified and metabolite pathway analysis was performed using the XCMS Online web portal (15). Heatmaps were generated using the R programming language

Shotgun lipidomics analysis

We used a shotgun lipidomics approach to semiquantitatively analyze lipid quantities in human patient-derived glioblastoma stem cell models as described previously (16,17). In brief, 50 μL of 100 μM internal standards were added to cell homogenates and lipids were extracted by adding by adding MeOH/CHCl3 (v/v, 2/1) in the presence of dibutylhydroxytoluene (BHT) to limit oxidation. The CHCl3 layer was collected and dried under N2 flow. The dried lipid extract was dissolved in 1 ml the MeOH/CHCl3 (v/v, 2/1) containing 5mM ammonium acetate for injection. The solution containing the lipid extract was pumped into the TripleTOF 5600 mass spectrometer (AB Sciex LLC, 500 Old Connecticut Path, Framingham, MA 01701, USA) at a flow rate of 40 μL/min for 2 minutes for each ionization mode. Lipid extracts were analyzed in both positive and negative ion modes for complete lipidome coverage using the TripleTOF 5600 System. Infusion MS/MSALL workflow experiments consisted of a TOF MS scan from m/z 200- 1200 followed by a sequential acquisition of 1001 MS/MS spectra acquired from m/z 200 to 1200.12 The total time required to obtain a comprehensive profile of the lipidome was approximately 10 minutes per sample. The data was acquired with high resolution (>30000) and high mass accuracy (~5 ppm RMS). Data processing using LipidView Software identified 150-300 lipid species, covering diverse lipids classes including major glycerophospholipids and sphingolipids. The peak intensities for each identified lipid, across all samples were normalized against an internal standard from same lipid class for quantification (16).

For downstream data analysis, fold change was calculated between shCONT and either of two shRNAs targeting ELOVL2 within each technical replicate. Metabolites showing consistent and significant trends between both independent nonoverlapping shRNAs were displayed as a bar chart.

Fluorescence Recovery after Photobleaching (FRAP) Analysis

Glioblastoma stem cell models were attached to glass-bottom plates coated with Matrigel by incubating them overnight. Cells were stained for 10 minutes with CellMask Green cell stain (ThermoFisher Scientific, Cat# C37608) according to the manufacturer’s instructions and washed two times with fresh media. Zeiss Laser Scanning Microscopy (LSM) 880 with is used to perform the imaging acquisition and FRAP. All live cell image is performed at 37°C, 5% CO2 in Dulbecco’s modified Eagle medium (FluroBriteTM DMEM, Gibco) with 10% (vol/vol) FBS and 1% (vol/vol) penicillin streptomycin and 4mM glutamine. For fluorescence recovery after photobleaching (FRAP) imaging, Image and exposure were controlled by ZEN lite software (Zeiss, Germany). Photobleaching was achieved by focusing 405nm laser to a 27μm x 27μm area on the cell for an exposure of 200ms. Wide-field fluorescent images of all the cells were acquired before and after photobleaching with 514nm laser in time series scan mode. Three pre-bleach images were taken, one bleach image, and 96 post-bleach images were taken every 200 milliseconds. Images were processed using Airyscan processing and imported into ImageJ/FIJI (18) for downstream analysis. Three regions were selected for subsequent analysis: (1) region of bleaching, (2) total cell image (3) background region. Data was uploaded and analyzed using the easyFRAP webportal (https://easyfrap.vmnet.upatras.gr/) (19).

Super-resolution stochastic optical reconstruction microscopy (STORM) Imaging

Glioblastoma stem cells were labeled with a mouse antibody to anti-human EGFR (AY13) (Biolegend, Cat # 352901). STORM imaging was performed on a Nikon Ti inverted microscope equipped with a 60X 1.49NA TIRF objective lens using a maximum 639 nm laser (300 mw, Coherent Genesis) for Alexa647 stained samples. The imaging buffer (50mM Tris pH8.0, 100mM NaCL, 5% glucose) contains 0.5 mg/ml glucose oxidase (Sigma G6125), 143 mM β-mercaptoethanol (Sigma M6250). A time series of 20000 frames per cell were recorded from a FOV of 400x400 pixels with an EMCCD (Andor iXon3) at rate of 20 Hz for the reconstruction of the super-resolution imaging. A freely available ThunderSTORM plug-in for ImageJ is used for raw image data analysis. The high resolution images are further rendered with Matlab software.

Super-resolution stochastic optical reconstruction microscopy (STORM) quantification of protein distribution

The high-resolution images show only one section of the distribution of proteins of whole cell. The objective used here is CFI Apo TIRF 60XC Oil, Nikon (NA = 1.49), refractive index n of immersion oil is 1.515, the smallest distance e that can be resolved by our EMCCD (Andor iXon3) is , lateral magnification M is 150X, laser wavelength . Then the depth of focus (DOF) of our system equals to:

Assuming that thickness of cell membrane is T (); in volume V, density of protein d(V) on the membrane and in the cell plasma are and , respectively; average blinks for each protein is K. Then total blink number N we collect is:

Figure 1. Schematic diagram of a cell

As shown in Figure 1, in the center parts of a cell:

When it comes, to the edge of a cell, where is mainly consist of cell membrane:

If , then ; otherwise, if , we have ; and with , .

In order to compare blink numbers in different parts of a cell, we choose some sections (width = ) of cells with different conditions.All the sections are chosen crossing the center of a cell. The density of blink is in direct proportion to the density of EGFR.

RNAseq Data Analysis

Trizol reagent (Sigma Aldrich) was used to isolate total cellular RNA from cell pellets according to the manufacturer’s instructions. RNA was purified and subjected to RNA-sequencing. FASTQ sequencing reads were trimmed using Trim Galore (https://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) and transcript quantification was performed using Salmon in the quasi-mapping mode (20). Salmon “quant” files were converted using Tximport (https://bioconductor.org/packages/release/bioc/html/tximport.html) and differential expression analysis was performed using DESeq2 (21). Gene set enrichment analysis was performed by selecting differentially expressed genes (FDR-corrected p-value < 0.05), generating a pre-ranked list, and inputting a pre-ranked list into the GSEA desktop application (http://software.broadinstitute.org/gsea/downloads.jsp) (22,23). Pathway enrichment bubble plots were generated using the Bader Lab Enrichment Map Application (24) and Cytoscape (http://www.cytoscape.org). Phospholipid remodeling signature scores for each sample were calculated using single sample GSEA (ssGSEA) from Gene Pattern (25).

Single Cell RNA-seq Data Analysis

We analyzed publicly available single cell RNA-seq as described previously (26-28). In brief, only single cell libraries with at least 1000 genes with greater than 1 count per million and genes with expression greater than 1 count per million in at least 2 cells per tumor or brain specimen were retained for analysis. After median centering gene counts, we visualized the relationship between cells in two dimensions through t-SNE performed using top 10 PCA components and perplexity of 30 in Matlab. We assigned each cluster to respective cell identity by marker gene expressions described in previous reports.

Synergy Calculations

In vitro drug synergy calculations were performed using the SynergyFinder R program (29). The Zero Interaction Potency (ZIP) score was calculated for synergy calculations, where a score greater than 1 indicates synergy (30).

Cancer Therapeutics Response Portal Analysis

Data from the Cancer Therapeutics Response Portal (https://portals.broadinstitute.org/ctrp.v2.1/) was accessed. Cells in any growth mode and primary site/subtype were considered for analysis.

Immunofluorescence Staining and Imaging

For immunofluorescence microscopy, GSC387 cells treated with either an empty CRIPSR-dCas9-KRAB vector or a vector targeting the ELOVL2 super-enhancer region were plated on matrigel coated coverslips. Cells were incubated in Neurobasal medium without EGF and FGF. After 24 hours, cells were fixed twice using 2% PFA (15 min each time) and processed as described previously (31). Briefly, fixed cells were washed in PBS, neutralized (10 min; 50 mM glycine in PBS), blocked in PBS with 1 mg/ml BSA (blocking buffer) for 10 min and permeabilized in blocking buffer containing 0.05% saponin. Cells were incubated with EGFR antibody (CST cat # 4267) at a 1:50 dilution in blocking buffer at 4°C overnight. Next day, the cells were washed three times with blocking buffer and incubated with donkey anti-rabbit secondary antibody (Life Technologies # A21206). Cells were washed three times in blocking buffer and coverslips were mounted using Prolong Gold Antifade (Life Technologies). Optical sections Z-stacks were imaged using 60x Magnification on Leica Confocal SPE (Sanford Consortium, UCSD facility) and processed using ImageJ software (NIH, Bethesda, MD).

References

1.Wang X, Huang Z, Wu Q, Prager BC, Mack SC, Yang K, et al. MYC-regulated Mevalonate Metabolism Maintains Brain Tumor Initiating Cells. Cancer research 2017 doi 10.1158/0008-5472.CAN-17-0114.

2.Liu F, Hon GC, Villa GR, Turner KM, Ikegami S, Yang H, et al. EGFR Mutation Promotes Glioblastoma through Epigenome and Transcription Factor Network Remodeling. Mol Cell 2015;60(2):307-18 doi 10.1016/j.molcel.2015.09.002.

3.Roadmap Epigenomics C, Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, et al. Integrative analysis of 111 reference human epigenomes. Nature 2015;518(7539):317-30 doi 10.1038/nature14248.

4.Suva ML, Rheinbay E, Gillespie SM, Patel AP, Wakimoto H, Rabkin SD, et al. Reconstructing and reprogramming the tumor-propagating potential of glioblastoma stem-like cells. Cell 2014;157(3):580-94 doi 10.1016/j.cell.2014.02.030.

5.Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol 2008;9(9):R137 doi 10.1186/gb-2008-9-9-r137.

6.Ramirez F, Ryan DP, Gruning B, Bhardwaj V, Kilpert F, Richter AS, et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res 2016;44(W1):W160-5 doi 10.1093/nar/gkw257.

7.Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative genomics viewer. Nature biotechnology 2011;29(1):24-6 doi 10.1038/nbt.1754.

8.Thorvaldsdottir H, Robinson JT, Mesirov JP. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Briefings in bioinformatics 2013;14(2):178-92 doi 10.1093/bib/bbs017.

9.Whyte WA, Orlando DA, Hnisz D, Abraham BJ, Lin CY, Kagey MH, et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 2013;153(2):307-19 doi 10.1016/j.cell.2013.03.035.

10.Brennan CW, Verhaak RG, McKenna A, Campos B, Noushmehr H, Salama SR, et al. The somatic genomic landscape of glioblastoma. Cell 2013;155(2):462-77 doi 10.1016/j.cell.2013.09.034.

11.Wan YW, Allen GI, Liu Z. TCGA2STAT: simple TCGA data access for integrated statistical analysis in R. Bioinformatics 2016;32(6):952-4 doi 10.1093/bioinformatics/btv677.

12.Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 2017;45(W1):W98-W102 doi 10.1093/nar/gkx247.

13.Bowman RL, Wang Q, Carro A, Verhaak RG, Squatrito M. GlioVis data portal for visualization and analysis of brain tumor expression datasets. Neuro Oncol 2017;19(1):139-41 doi 10.1093/neuonc/now247.

14.Reznik E, Luna A, Aksoy BA, Liu EM, La K, Ostrovnaya I, et al. A Landscape of Metabolic Variation across Tumor Types. Cell Syst 2018;6(3):301-13 e3 doi 10.1016/j.cels.2017.12.014.

15.Gowda H, Ivanisevic J, Johnson CH, Kurczy ME, Benton HP, Rinehart D, et al. Interactive XCMS Online: simplifying advanced metabolomic data processing and subsequent statistical analyses. Anal Chem 2014;86(14):6931-9 doi 10.1021/ac500734c.

16.Gromovsky AD, Schugar RC, Brown AL, Helsley RN, Burrows AC, Ferguson D, et al. Delta-5 Fatty Acid Desaturase FADS1 Impacts Metabolic Disease by Balancing Proinflammatory and Proresolving Lipid Mediators. Arterioscler Thromb Vasc Biol 2018;38(1):218-31 doi 10.1161/ATVBAHA.117.309660.

17.Simons B, Kauhanen D, Sylvanne T, Tarasov K, Duchoslav E, Ekroos K. Shotgun Lipidomics by Sequential Precursor Ion Fragmentation on a Hybrid Quadrupole Time-of-Flight Mass Spectrometer. Metabolites 2012;2(1):195-213 doi 10.3390/metabo2010195.

18.Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods 2012;9(7):676-82 doi 10.1038/nmeth.2019.

19.Koulouras G, Panagopoulos A, Rapsomaniki MA, Giakoumakis NN, Taraviras S, Lygerou Z. EasyFRAP-web: a web-based tool for the analysis of fluorescence recovery after photobleaching data. Nucleic Acids Res 2018;46(W1):W467-W72 doi 10.1093/nar/gky508.

20.Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 2017;14(4):417-9 doi 10.1038/nmeth.4197.

21.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15(12):550 doi 10.1186/s13059-014-0550-8.

22.Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005;102(43):15545-50 doi 10.1073/pnas.0506580102.

23.Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 2003;34(3):267-73 doi 10.1038/ng1180.

24.Merico D, Isserlin R, Stueker O, Emili A, Bader GD. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One 2010;5(11):e13984 doi 10.1371/journal.pone.0013984.

25.Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP. GenePattern 2.0. Nat Genet 2006;38(5):500-1 doi 10.1038/ng0506-500.

26.Darmanis S, Sloan SA, Croote D, Mignardi M, Chernikova S, Samghababi P, et al. Single-Cell RNA-Seq Analysis of Infiltrating Neoplastic Cells at the Migrating Front of Human Glioblastoma. Cell Rep 2017;21(5):1399-410 doi 10.1016/j.celrep.2017.10.030.

27.Pollen AA, Nowakowski TJ, Chen J, Retallack H, Sandoval-Espinosa C, Nicholas CR, et al. Molecular identity of human outer radial glia during cortical development. Cell 2015;163(1):55-67 doi 10.1016/j.cell.2015.09.004.

28.Tirosh I, Venteicher AS, Hebert C, Escalante LE, Patel AP, Yizhak K, et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 2016;539(7628):309-13 doi 10.1038/nature20123.

29.Ianevski A, He L, Aittokallio T, Tang J. SynergyFinder: a web application for analyzing drug combination dose-response matrix data. Bioinformatics 2017;33(15):2413-5 doi 10.1093/bioinformatics/btx162.

30.Yadav B, Wennerberg K, Aittokallio T, Tang J. Searching for Drug Synergy in Complex Dose-Response Landscapes Using an Interaction Potency Model. Comput Struct Biotechnol J 2015;13:504-13 doi 10.1016/j.csbj.2015.09.001.

31.Patwardhan A, Bardin S, Miserey-Lenkei S, Larue L, Goud B, Raposo G, et al. Routing of the RAB6 secretory pathway towards the lysosome related organelle of melanocytes. Nature communications 2017;8:15835 doi 10.1038/ncomms15835.

1