title: GSC272_ishCLOCK_Rep2

city: CHICAGO

first: Peiwen

last: Chen

state: IL

address: 303 East Superior Street, Suite 6-512

institute: Northwestern University

description: Gene expression data from GSC272 ishCLOCK cells

hyb_protocol: Standard hyb protocol per the Sequencing & Non-coding RNA facility at MD Anderson Cancer Center. For more details, please contact Chang-Gong Liu <cgliu@mdanderson.org>

scan_protocol: Standard scan protocol per the Sequencing & Non-coding RNA facility at MD Anderson Cancer Center. For more details, please contact Chang-Gong Liu <cgliu@mdanderson.org>

data_processing: The data were analyzed with Microarray Suite version 5.0 (MAS 5.0) using Affymetrix default analysis settings and global scaling as normalization method.

source_name: Glioma stem cell

label_protocol: Standard label protocol per the Sequencing & Non-coding RNA facility at MD Anderson Cancer Center. For more details, please contact Chang-Gong Liu <cgliu@mdanderson.org>

growth_protocol: SF763 cells were cultured in NSC proliferation media (Millipore Corporation, Billerica, MA) containing 20 ng/ml EGF and 20 ng/ml bFGF.

extract_protocol: Trizol extraction of total RNA was performed according to the manufacturer's instructions.

treatment_protocol: CLOCK knockdown was performed in GSC272 cells using inducible CLOCK shRNA system.

value: Glioma stem cell

tag: cell line


title: GSC272_ishCLOCK_Rep1

status: Public on Dec 21 2019

type: RNA

country: USA

label: biotin

molecule: total RNA

organism: Homo sapiens

tag: genotype

tag: tissue


title: GSC272_ishC_Rep2

description: Gene expression data from GSC272 ishC cells


title: GSC272_ishC_Rep1


title: PAO1_WT_2 RNAseq

status: Public on Dec 20 2019

type: SRA

city: Zurich

first: Julian

last: Trouillon

address: Otto-Stern-Weg 3

department: Institute of Molecular Systems Biology

country: Switzerland

institute: ETH Zurich

description: rRNA-depleted RNA

library_source: transcriptomic

data_processing:
Read trimming using Trimmomatic
Alignment using Bowtie2
Feature count using htseq-count
Differential expression analysis using DESeq2
Genome_build: IHMA879472, PAO1F
Supplementary_files_format_and_content: excel table, DESeq2 analysis results

organism: Pseudomonas aeruginosa

source_name: LB liquid culture at OD=1

growth_protocol: LB liquid culture at OD=1

extract_protocol:
Hot Phenol RNA Extraction
RNA were treated with Dnase I, then rRNA were depleted using the Illumina RiboZero kit. Libraries were constructed using the NEBNext Ultra II Directional RNA lib prep kit from NEB.

tag: strain


title: PAO1_WT_1 RNAseq

description: rRNA-depleted RNA


title: IHMA87_derfA_2 RNAseq

description: rRNA-depleted RNA


title: PAO1_neg_1

description: adaptor-ligated sheared genomic DNA

library_source: genomic

data_processing:
Library strategy: DAP-seq
Read trimming using Trimmomatic
Alignment using Bowtie2
Peak calling using MACS2
closest features called with BEDtools
Genome_build: IHMA879472, PAO1b, PA23, CHA0, KT2440
Supplementary_files_format_and_content: excel table, MACS2 analysis results with closest features

molecule: genomic DNA

source_name: purified genomic DNA from liquid cultures

extract_protocol:
Purified proteins bound to magnetic beads were incubated with adaptor-ligated sheared genomic DNA. After washes and isolation of bead-protein-DNA complexes, the bound DNA regions were eluted by heating and then used for library preparation.
DNA libraries were prepared for sequencing using standard Illumina protocols

tag: erfa origin


title: IHMA87_WT_1 RNAseq

description: rRNA-depleted RNA


title: IHMA87_derfA_1 RNAseq

description: rRNA-depleted RNA


title: IHMA87_WT_2 RNAseq

description: rRNA-depleted RNA


title: IHMA87_WT_2 ChIPseq

description: ChIPed genomic DNA

data_processing:
Read trimming using Trimmomatic
Alignment using Bowtie2
Peak calling using MACS2
closest features called with BEDtools
Genome_build: IHMA879472, PAO1
Supplementary_files_format_and_content: excel table, MACS2 analysis results with closest features

extract_protocol:
Cells were lysed by sonication after cross-linking. Protein-DNA complexes were coIP with anti-VSVG beads and DNA was then eluted after Rnase and Proteinase treatment and further purified on silica columns.
Libraries were constructed using the NEBNext Ultra II DNA library prep kit from NEB.


title: IHMA_Erf_I_2

description:
adaptor-ligated sheared genomic DNA
I_E_I_processed.xlsx

value: P. aeruginosa


title: IHMA87_WT_1 ChIPseq

description: ChIPed genomic DNA


title: PAO1_WT_2 ChIPseq

description: ChIPed genomic DNA


title: PAO1_VSVG_1 ChIPseq

description: ChIPed genomic DNA


title: IHMA87_VSVG_1 ChIPseq

description: ChIPed genomic DNA


title: IHMA87_VSVG_2 ChIPseq

description: ChIPed genomic DNA


title: RH4 shScr 48 DNase 30x

city: Bethesda

first: Javed

last: Khan

state: MD

address: 37 Convent Dr.

department: Genetics Branch

institute: NCI, NIH

description: DNA (DNase hypersensitive)

data_processing:
ChIP enriched DNA reads were mapped to reference genome using BWA version 0.7.10
The ChIP-seq peaks were called by MACS2 version 2.1.0.  Duplicates were allowed
Enrichment of known and de novo motifs were found using HOMER version 4.8.2
Genome_build: Hg19
Supplementary_files_format_and_content: peak files are tab delimited text files (BED coordinates) for each sample.

growth_protocol: Cells were maintained in DMEM with 10% FBS and Penn/Strep at 37 °C incubation until harvesting for ChIP.

extract_protocol:
ChIP samples harvested after 1% formaldehyde fixation for 12 minutes, sonicated, processed and ChIP-enriched via standard protocols (Active Motif ChIP-IT HS kit).  ChIP enriched DNA was treated with Proteinase K at 55 ˚C for 30 minutes followed by reversal of crosslinks by heating to 80 ˚C for 2 hours, and DNA purification by column (MiniElute PCR purification kit, Qiagen).
DNA was extracted from ChIP-seq samples prepared following the Active Motif ChIP-IT High Sensitivity kit protocol.
Standard Illumina barcodes were introduced during library preparation with the Illumina TruSeq ChIP Sample Prep Kit 48 Samples - Set A Cat# IP-202-1012

tag: antibody

value: DNase

tag: enrichment target

tag: condition

value: Open Chromatin

tag: target function

value: shRNA-scramble


title: RH4 CHD4nFlag

description: DNA (ChIP-enriched)

value: anti-FLAG, Sigma Aldrich (clone M2, catF1804)

value: NuRD Complex

value: no treatment


title: RH4 shCHD4 48 DNase 30X

description: DNA (DNase hypersensitive)


title: RH4 shCHD4 Pol2

description: DNA (ChIP-enriched)

value: RNA Pol 2, Active Motif (cat61669)

value: Transcription


title: RH4 shCHD4 48h P3F

description: DNA (ChIP-enriched)

value: anti-PAX3-FOXO1 (made in house)

value: Fusion Transcription Factor


title: PAO1_WT_1 ChIPseq

description: ChIPed genomic DNA


title: RH4 shscr 48h P3F

description: DNA (ChIP-enriched)


title: RH4 P3F

description: DNA (ChIP-enriched)


title: RH4 H3K9ac

description: DNA (ChIP-enriched)

value: H3K9ac, Active Motif (cat61663 lot04415001 mAb)

value: Active Histone Mark


title: RH4 RBBP4

description: DNA (ChIP-enriched)

value: RBBP4, Epigentek (catA-2703 lotA511004 pAb)


title: RH4 Flag

description: DNA (ChIP-enriched)

value: Flag

value: IP control


title: RH4 shScr Pol2

description: DNA (ChIP-enriched)


title: GPC16_MLL1_MLL2_siRNA_3

city: London

first: David

last: Michod

address: Gower St, Bloomsbury

country: United Kingdom

institute: UCL

data_processing:
Sequencing was performed on Illumina NextSeq 500 instrument
Illumina’s bcl2fastq Conversion Software v2.19 was used to demultiplex and convert the run data to fastq files
The reads were aligned to the human genome UCSC hg38 using RNA-STAR 2.5.2b and deduplicated using Picard Tools 1.79
FeatureCounts was used to count the reads per transcript
Counts were normalized with DESeq2
Genome_build: hg38
Supplementary_files_format_and_content: normalized counts in tab-delimited text files

molecule: polyA RNA

source_name: Primary patient-derived stem cells (GPC16)

growth_protocol:
NeuroCult NS-A Basal medium supplemented with EGF, FGF, PDGF (20 ng/ml each) and NeuroCult NS-A Proliferation Supplement. The flasks were laminin coated.
The media was supplemented with penicillin (100 units/ml), streptomycin (100 μg/ml).

extract_protocol:
Total RNA was extracted from collected cells using the RNeasy Plus Mini Kit
200 ng total RNA were processed using the NEBNext RNA Ultra II kit with Poly A+ selection

treatment_protocol: Dharmacon ON-TARGETplus Control pool Non-Targeting pool was used for control, Dharmacon ON-TARGETplus SMART pool Human MLL4 (different name for MLL2) and ON-TARGETplus SMART pool Human MLL (different name for MLL1) were used for the knockdown of MLL1/MLLL2. Cells were plated in 6 well plates, and transfected on the following day using siRNA diluted in OPTI-MEM (gibco by Life Technologies) and Lipofectamine RNAiMAX (Invitrogen) according to the manufacturer’s instructions.

tag: disease state

value: Pediatric high grade glioma

tag: tumor grade

value: IV

tag: cell type

tag: treatment

value: 250 pmol MLL1/MLL2 siRNA


title: GPC16_MLL1_MLL2_siRNA_2

value: Primary patient-derived stem cells (GPC16)


title: SF188_CsiRNA_3

growth_protocol:
DMEM/F-12 supplemented with FBS(10%)
The media was supplemented with penicillin (100 units/ml), streptomycin (100 μg/ml).

value: cell line

value: 25 pmol control siRNA


title: EXP3-EBM2_longRNA

city: Houston

first: Aleksandar

last: Milosavljevic

state: Texas

address: 1 Baylor Plaza

department: Genetics and Genomics

institute: Baylor College of Medicine

data_processing:
base-calling: All libraries then had 75 bp paired end sequencing on the NextSeq500 with  default parameters using a 150 cycle high output kit. Raw Illumina reads were quality filtered as follows. First, ends of the reads were trimmed to remove N's and bases with quality less than 20. After that the quality scores of the remaining bases were sorted and the quality at the 20th percentile was computed. If the quality at the 20th percentile was less than 15, the whole read was discarded. Also, reads shorter than 40 bases after trimming were discarded. If at least one of the reads in the pair failed the quality check and had to be discarded, we discarded the mate as well.
sample quality assessment: Prior to mapping for transcript-level quantification, to assess sample integrity all fastq files from long-RNA sequencing runs were uploaded to the Genboree workbench and mapped to hg19 and all exogenous genomes using the exceRpt [v4.6.3] small RNA-seq pipeline (Kaczor-Urbanowicz et. al., 2017). Biological Replicate 2 was excluded due to low sample quality.
read quality filtering: Data quality assessment and read clipping was performed using TrimGalore [v0.4.1] with CutAdapt [v1.15] and FastQC[v0.11.6] (Martin 2011).
alignment: Paired-reads were mapped simultaneously to the GRCh37.83 cDNA and ncRNA transcriptomes (Kinsella et.al., 2011) using Kallisto[v0.44.0] (Bray et. al., 2016). Kallistos were imported to R with tximport. All reads mapping to ncRNA were excluded from the analysis, then transcript abundances were aggregated by Ensembl Gene ID. Genes with less than 10 reads across all samples were excluded from further analysis.
Differential transcript abundance was characterized by DeSeq2 with EBM as the baseline for all treatments, and samples treated as paired within replicates. Threshold values for differential expression were set at 2 and 0.05, respectively, for fold change and adjusted p-value.
Genome_build: GRCh37
Supplementary_files_format_and_content: tab delimited, transcript abundance fold change, p-value

growth_protocol: HBMVECs (500,000/well) were cultured on Matrigel™-coated (BD Matrigel™ 10mg/mL, BD Biosciences, Franklin Lakes, NJ, USA) wells in a 6-well plate in endothelial basal medium (EBM-2)

extract_protocol:
Total RNA for this study was isolated from samples using the Qiagen (Exiqon) miRCURY RNA isolation kit.
RNA-sequencing was performed using the Clontech/Takara SMARTer Stranded Total RNA-seq PICO v2 kit (Clontech/Takara 634414) for long RNA expression profiling.

value: Brain Microvascular Endothelial Cell

tag: commercial source

value: HBMVEC Cell Systems (Catalogue #ACBRI-376, Kirkland, WA, USA)


title: SF188_CsiRNA_1


title: EXP3-GBM8-EV_longRNA

treatment_protocol: EV pellet from GBM8-cell-conditioned medium


title: GPC16_MLL1_MLL2_siRNA_1


title: SF188_CsiRNA_2


title: GPC16_CsiRNA_3

value: 250 pmol control siRNA


title: GPC16_CsiRNA_2


title: SF188_MLL1_MLL2_siRNA_3

value: 25 pmol MLL1/MLL2 siRNA


title: SF188_MLL1_MLL2_siRNA_2


title: GPC16_CsiRNA_1


title: EXP1-EGM_longRNA

treatment_protocol: angiogenic growth factors; EGM-2 SingleQuot Kit Suppl. & Growth Factors (Lonza, Basel, Switzerland) supplemented with 5% EV-depleted FBS according to manufacturer protocol.


title: EXP3-EGM_longRNA


title: EXP1-EBM2_longRNA


title: SF188_MLL1_MLL2_siRNA_1


title: Human_PrefrontalCortex_NeuNnegative_ATAC_2

city: Utrecht

first: Menno

middle: P

last: Creyghton

address: Uppsalalaan 8

country: Netherlands

institute: Hubrecht Institute

data_processing:
ChIP-seq: Illumina sequencing reads were aligned using bowtie version 1.1.2 ((http://bowtie-bio.sourceforge.net) excluding reads with more than 1 mismatch (seed length of 36 base pairs (bp)) or with multiple alignment. Duplicate reads were discarded using Samtools version 1.3.1. Peak-calling was done using MACS2 version 2.1.1.20160309 (pvalue < 10-5, extsize 300, local lambda 100,000). Reads in peaks were counted using Bedtools version 2.26.
ATAC-seq: Illumina sequencing reads were trimmed to remove Nextera adapters using cutadapt (1.13). Reads were mapped using bowtie version 1.1.2 (http://bowtie-bio.sourceforge.net) excluding reads with more than 1 mismatch (seed length 36 base pairs) or with multiple aligment. Duplicate reads were discarded using Samtools verions 1.3.1. Peak-calling was done using MACS2 version 2.1.1.20160309 (pvalue < 10-5, extsize 300, local lambda 100,000). Reads in peaks were counted using Bedtools version 2.26
Genome_build: Homo sapiens: hg38, Pan troglodytes: panTro5, Macaca mulatta: rheMac8, Callithrix jacchus: calJac3
Supplementary_files_format_and_content: ChIP-seq and ATAC-seq peaks called by MACS2 are in _peaks.narrowPeak; H3K27ac and ATAC bigwig files are in .bw

source_name: Human Prefrontal Cortex

extract_protocol: NEBNext High-Fidelity PCR (NEB)

value: Human Prefrontal Cortex


title: Human_PrefrontalCortex_NeuNpositive_ATAC_2


title: Human_PrefrontalCortex_NeuNpositive_ATAC_1


title: EXP1-GBM8-EV_longRNA


title: Human_WhiteMatter_H3K27ac_3

source_name: Human White Matter

extract_protocol: TruSeq DNA sample preparation kit (Illumina)

value: Human White Matter


title: RH4 H3K9me1

description: DNA (ChIP-enriched)

value: H3K9me1, Active Motif (cat39887 lot04413002 pAb)

value: Histone Mark

value: no treatment


title: RH4 H3K9me3

description: DNA (ChIP-enriched)

value: H3K9me3, Active Motif (cat39765 lot16513004 pAb)

value: Repressed Chromatin


title: Human_WhiteMatter_H3K27ac_2


title: Human_PrefrontalCortex_NeuNnegative_ATAC_1


title: MCF7-Estrogen-ERA_1hr

city: Wuhan

first: Kaiwei

last: Liang

state: Hubei

address: # 185 Dong Hu Road

country: China

institute: Wuhan University

data_processing:
ChIP-seq was done according to a previously published protocol (Liang et al., 2015). Briefly, cells were crosslinked with 1% paraformaldehyde for 10 min and were quenched with glycine for 5 min at room temperature. Fixed chromatin was sonicated with a Covaris Focused-ultrasonicator for 6 minutes and immunoprecipitated with the indicated antibody. Libraries were prepared with the HTP Library Preparation Kit for Illumina (KAPA Biosystems) and sequenced on a NextSeq 500. ChIP-seq reads were aligned to the human genome (UCSC hg19). Alignments were processed with Bowtie version 1.1.2, allowing only uniquely mapping reads with up to two mismatches within the 50 bp read. The resulting reads were extended to 150 bases toward the interior of the sequenced fragment and normalized to total reads aligned (reads per million, rpm). Peaks were called using MACS (model based analysis of ChIP-Seq) (Zhang et al., 2008) version 1.4.2 using default parameters. Heatmaps tables were made for the indicated windows around the TSS or TES using the average coverage (r.p.m) in 25bp & 50bp bins (50bp bins for 50kb downstream of the TSS). Meta gene tables were made by approximating the coverage across all genes to the same length. All ChIP-seq heatmaps were sorted by the decreasing coverage in indicated windows by the control sample a. Average profile plots were made by averaging the coverage for all genes using NGSPLOT V2.41.
Total RNA-seq reads were trimmed from the 3′ end until the final base had a quality score > 30, using Trimmomatic version 0.33 (Bolger et al., 2014) and then aligned to the human genome (UCSC hg19, using Tophat version 2.1.0) with the following options -no-novel-juncs --read-mismatches 2 --read-edit-dist 2 --num-threads 10 --max-multihits 20 then post filtering for uniquely mapped reads using the NH flag. Protein coding genes from Ensembl version 75 that also had a refseq identifier were only considered for analysis. Raw read counts were normalized to r.p.m. per sample and then displayed in the UCSC genome browser as bigWig-formatted coverage tracks.  Reads were assigned to specific genes from Ensembl release 75 using HTseq-count. We used the R Bioconductor package DEseq2 to fit the data to a negative binomial generalized log–linear model and estimate a dispersion parameter. GO term analysis was done using Metascape
Genome_build: hg19
Supplementary_files_format_and_content: Library normalized bigWig files were provided for all samples

source_name: MCF7_10 nM estrogen

growth_protocol: MCF7 cells were cultured in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum.

extract_protocol:
Total RNA was extracted using Trizol. DNA after ChIP protocol was isolated using QIAGEN PCR purification kit.
ChIP-sequencing libraries were prepared using the KAPA HTP Library Preparation Kit complemented with NEXTflex DNA Barcodes from Bioo Scientific. 10 ng of DNA was used as starting material for input and ip samples. Libraries were amplified using 13 cycles on the thermocycler. Post amplification libraries were size selected at 250- 450bp in length using Agencourt AMPure XP beads from Beckman Coulter. Libraries were validated using the Agilent High Sensitivity DNA Kit. RNA-sequencing  libraries were prepared using the Illumina TruSeq Total RNA Preparation Kit with  oligo(dT) selection. Input RNA quality was validated using the Agilent RNA 6000 Nano Kit. 1 µg  of total RNA was used as starting material. Libraries were validated using the Agilent DNA  1000 Kit

treatment_protocol: Treatment with 20 uM OBHS or 10 nM estrogen was administrated for 1 hours in ChIP-seq experiment and 12 hours in RNA-seq

tag: tissue source

value: Mammary gland, breast

value: derived from metastatic site; pleural effusion

value: 10 nM estrogen

tag: treatment time

value: for 1 hour

tag: chip antibody

value: ERA (Estrogen Receptor α (D8H8) Rabbit mAb #8644, from CST)


title: Human_WhiteMatter_H3K27ac_1


title: MCF7-DMSO-ERA_1hr

tag: cell line

value: DMSO


title: Human_WhiteMatter_RNAseq_2

data_processing:
ChIP-seq: Illumina sequencing reads were aligned using bowtie version 1.1.2 ((http://bowtie-bio.sourceforge.net) excluding reads with more than 1 mismatch (seed length of 36 base pairs (bp)) or with multiple alignment. Duplicate reads were discarded using Samtools version 1.3.1. Peak-calling was done using MACS2 version 2.1.1.20160309 (pvalue < 10-5, extsize 300, local lambda 100,000). Reads in peaks were counted using Bedtools version 2.26.
RNA-seq:  Sequencing reads were aligned using HISAT2 version 2.0.5. Reads that mapped to multiple locations and duplicate reads were removed using Samtools version 1.3.1. Reads in genes were counted using the featureCount function of the Rsubread package in R.  The GTF file of Marmoset genes was obtained here: ftp://ftp.ensembl.org/pub/release-91/gtf/callithrix_jacchus.
Genome_build: Homo sapiens: hg38, Pan troglodytes: panTro5, Macaca mulatta: rheMac8, Callithrix jacchus: calJac3
Supplementary_files_format_and_content: ChIP-seq peaks called by MACS2 are in _peaks.narrowPeak; H3K27ac bigwig files are in .bw; RNA-seq count values are in _RNAseq.txt;

extract_protocol: TruSeq Stranded mRNA Library Preparation (Illumina)

value: White Matter


title: MCF7-OBHS-ERA_1hr

source_name: MCF7_20 uM OBHS

value: 20 uM OBHS


title: MCF7-Estrogen-Pol_II_1hr

value: PolII (Rpb1 NTD (D8L4Y) Rabbit mAb, from CST)


title: InPUT-MCF7

value: none (input)


title: Human_WhiteMatter_RNAseq_1


title: MCF7-OBHS-Pol_II_1hr


title: Lung-1

city: Boston

first: Carlos

last: Villacorta-Martin

state: MA

address: 670 Albany St

institute: Center for Regenerative Medicine at Boston University

data_processing:
The sample was processed using the standard Cellranger v.2 pipeline
Reads were mapped to GRCh38
Downstream analysis and QC done on Seurat
Differential expression using MAST models
Genome_build: GRCh38
Supplementary_files_format_and_content: the processed  files are the standard output from combining the four libraries with the cellranger aggr pipeline in HD5 format

organism: Mus musculus

source_name: Lung

growth_protocol: Cell suspensions were filtered through 30 μm FACS strainers (Miltenyi Biotech, Cat No: 130-041-407) and stained with calcein blue (1:500 dilution) directly before sorting. Cells were sorted at the Boston University Flow Cytometry Core Facility using the MoFlo high speed cell sorter and collected in HBSS+ buffer. Freshly-sorted cells were spun briefly to concentrate the cells, and then cell concentration and viability of flow-sorted samples were measured using the Countess II instrument (ThermoFisher Scientific) and ranged from 3 – 8 x 105 cells/ml with 80% viability. Cell samples were input into 10x

extract_protocol:
single cells were captured for sequencing library prepration using a 10X Chromium
We used the cellranger v2 workflow and processed according to the manufacturer’s instructions for a targeted recovery of 1000 cells (10x Genomics, USA). Resulting cDNA libraries were sequenced on an Illumina NextSeq 500 instrument with 1.95pM input and 1% PhiX control library spike-in (Illumina, USA). Single cell capture was conducted at the Boston University Single Cell Sequencing Core using a 10X Genomics Chromium System. The resulting reads were aligned and gene level unique molecular identifier (UMI) counts obtain using Cell Ranger version 2.0.1 (10x Genomics). Four libraries derived from embryonic mouse (two “lung” domain, one “thyroid” and one “forebrain” domain totaling 1316 cells and a post-normalization mean coverage of 165238 reads per cell) were combined for downstream analysis.
3' RNA-seq single cells

tag: developmental stage

value: Lung


title: Thyroid

source_name: Thyroid

tag: tissue

value: Thyroid


title: Lung-2


title: Brain

source_name: Brain

value: Brain


title: HSCs, [Ca2+]low-4

city: minato-ku

first: Tsuyoshi

last: Fukushima

state: tokyo

address: siroganedai 4-6-1

department: Cell therapy

country: Japan

institute: Institute of Medical Science

data_processing:
Each obtained read was mapped to the reference sequence “GRCm38/mm10” using CLC genomic workbench v11.0.0 (Qiagen), and expression levels were normalized and subjected to the statistical analyses based on EdgeR.
Genome_build: mm10
Supplementary_files_format_and_content: tab-delimited text files include RPKM values for each Sample

source_name: Hematopoietic stem cell

extract_protocol:
One hundred  [Ca2+]high HSCs and  [Ca2+]low HSCs were sorted into RNA lysis buffer. The first strand of cDNA was synthesized by using PrimeScript RT reagent kit (TaKaRa Bio Inc.) and not-so random primers. Following the synthesis of the first strand, the second strand was synthesized by using Klenow Fragment (3’, -5’, exo-; New England Biolabs Inc.) and complement chains of not-so random primers. Using purified double-strand cDNA, the library for RNA-Seq was prepared and amplified using Nextera XT DNA sample Prep kit (Illumina Inc.).
These prepared libraries were sequenced on Next-Seq system (Illumina Inc.), according to the manufacturer’s instruction.

tag: strain

value: bone marrow

value: Hematopoietic stem cell (HSC)

tag: cell population


title: HSCs, [Ca2+]low-5


title: HSCs, [Ca2+]low-6


title: HSCs, [Ca2+]high-5


title: HSCs, [Ca2+]high-7


title: HSCs, [Ca2+]high-6


title: HSCs, [Ca2+]low-3


title: HSCs, [Ca2+]low-2


title: HSCs, [Ca2+]low-1


title: HSCs, [Ca2+]high-3


title: HSCs, [Ca2+]high-1


title: HSCs, [Ca2+]high-2


title: MCF7-DMSO-Pol_II_1hr


title: HSCs, [Ca2+]low-7


title: 2D-Nkx2-1.positive.EPCAM.positive.rep3

description:
PolyA RNA
Sample name synonym: Gelatin_mCherry-EpCAM_4
processed data file: fpkm.matrix.ENSEMBL.xlsx

data_processing:
Fastq files were aligned to the GRCm38 reference genome with an additional mCherry reporter using STAR and default parameters.
Ensembl Mus_musculus.GRCm38.92.gtf gene annotations were used for gene count summarization, gene counts were obtained using STAR.
Raw gene counts were transformed using EdgeR "TMM" method for size-factor normalization. Expression values were exported as fragments per kilobase per million of mapped reads (FPKM).
Genome_build: GRCm38 + mCherry
Supplementary_files_format_and_content: fpkm.matrix.ENSEMBL.xlsx: Excel file includes FPKM normalized data (EdgeR with "TMM" normalization).

source_name: iPSC

growth_protocol: For Nkx2-1 induction, embryoid bodies were plated on 6-well plates coated in gelatin, or 300 μl of Matrigel (Corning 356231) at 10e6 cells/well in cSFDM with 10 ng/ml rhBMP4 (R&D 314-BP), 100 ng/ml rmWnt3a (R&D 1324-WN), and 10 μM ROCK inhibitor (Tocris Y-27632). Cells were then fed every other day with the same media minus the ROCK inhibitor. On D14, Matrigel was disrupted using 2 mg/ml dispase for 1 h, then spun down at 100×g for 1 minute to enrich for clusters of epithelial cells. These cells were then broken down into a single cell suspension using 0.25% trypsin and stained with an anti-EPCAM antibody (Biolegend, 118225, 1:500) and the live/dead stain DRAQ7 (Biolegend, 424001, 1:100). EPCAM+/Nkx2-1mCherry+ live cells were then enriched by fluorescence-activated cell sorting.

extract_protocol:
For each replicate (3 replicates per population) to be used in RNA-Seq, samples from several sorts were pooled. The mRNA was isolated using magnetic beads-based poly(A) selection, fragmented, and randomly primed for reverse transcription.
These cDNA fragments were then end-repaired, added with a single ‘A’ base, and ligated to Illumina® Paired-End sequencing adapters. The products were purified and PCR-amplified to create the final cDNA library. The libraries from individual samples were pooled in groups of four for cluster generation on the Illumina® cBot using Illumina® TruSeq® Paired-End Cluster Kit.

tag: culture media

value: 2D gelatin

tag: isolation method

value: mCherry/Nkx2-1 positive; EPCAM positive

value: Lung epithelial progenitors


title: 3D-Nkx2-1.positive.EPCAM.positive.rep1

description:
PolyA RNA
Sample name synonym: 3D_mCherry-EpCAM_2
processed data file: fpkm.matrix.ENSEMBL.xlsx

value: 3D matrigel


title: 2D-Nkx2-1.positive.EPCAM.positive.rep1

description:
PolyA RNA
Sample name synonym: Gelatin_mCherry-EpCAM_2
processed data file: fpkm.matrix.ENSEMBL.xlsx


title: 3D-Nkx2-1.positive.EPCAM.positive.rep3

description:
PolyA RNA
Sample name synonym: 3D_mCherry-EpCAM_4
processed data file: fpkm.matrix.ENSEMBL.xlsx


title: 2D-Nkx2-1.positive.EPCAM.positive.rep2

description:
PolyA RNA
Sample name synonym: Gelatin_mCherry-EpCAM_3
processed data file: fpkm.matrix.ENSEMBL.xlsx


title: 3D-Nkx2-1.negative.EPCAM.positive.rep2

description:
PolyA RNA
Sample name synonym: 3D_EpCAM_3
processed data file: fpkm.matrix.ENSEMBL.xlsx

value: mCherry/Nkx2-1 negative; EPCAM positive

value: Non-lung epithelial progenitors


title: Mouse_amygdala12

first: Meeta

last: Mistry

address: 655 Huntington Ave

institute: Harvard School of Public Health

data_processing:
Illumina Casava software used for basecalling.
Sequenced reads were assessed for low-quality sequence, then mapped against the mm10 transcriptome where reads were quantified at the transcript level using Salmon
Transcript-level abundance estimates  from Salmon were collapsed to gene-leve estimates and  converted to raw count values using the R Bioconductor package tximport
Differentially expressed genes were identified using the R Bioconductor package DESeq2
Genome_build: mm10
Supplementary_files_format_and_content: tab-delimited text file is a raw count matrix where columns correspond to samples and rows correspond to genes

source_name: amygdala

extract_protocol:
Bilateral amygdalar Vibratome 1000-sliced biopsy tissue was homogenized, bathed in RNAase-free phosphate-buffered saline, and centrifuged. The precipitate was then extracted and snap frozen in liquid nitrogen. The tissue was placed in Trizol (ThermoFisher). Ribonucleic acid was extracted using the phenol-chloroform phase separation and prepared for subsequent RNA Library preparation.
Uniquely indexed libraries were pooled in equimolar ratios and sequenced on a single Illumina NextSeq500 run with single-end 75bp reads

treatment_protocol: Amygdala were extracted following rapid decapitation followed by neurosurgery

value: amygdala

tag: Sex

value: female

tag: percent time in light

tag: percent distance in light

tag: percent open time

tag: total entry

tag: boxzone ratio (3min)

tag: boxzone ratio (5min)

tag: boxzone ratio (10min)

tag: predicted anxiety state

value: less anxious


title: 3D-Nkx2-1.negative.EPCAM.positive.rep1

description:
PolyA RNA
Sample name synonym: 3D_EpCAM_2
processed data file: fpkm.matrix.ENSEMBL.xlsx

tag: cell type


title: HSCs, [Ca2+]high-4


title: Mouse_amygdala10

value: more anxious


title: Mouse_amygdala7


title: Mouse_amygdala6


title: Mouse_amygdala8

value: anxious


title: Mouse_amygdala3


title: Mouse_amygdala5


title: Mouse_amygdala2


title: Mouse_amygdala1


title: 3D-Nkx2-1.positive.EPCAM.positive.rep2

description:
PolyA RNA
Sample name synonym: 3D_mCherry-EpCAM_3
processed data file: fpkm.matrix.ENSEMBL.xlsx


title: 3D-Nkx2-1.negative.EPCAM.positive.rep3

description:
PolyA RNA
Sample name synonym: 3D_EpCAM_4
processed data file: fpkm.matrix.ENSEMBL.xlsx


title: Mouse_amygdala9


title: Mouse_amygdala11


title: Mouse_amygdala4


title: U3112MG ESC_to_SFC

city: Uppsala

first: Lene

last: Uhrbom

address: Dag Hammarskjölds väg 20

department: Immunology, Genetics and Pathology

country: Sweden

institute: Uppsala University

description: Not established but maintained in NSC medium

hyb_protocol: GeneChip® ST Arrays (GeneChip® Human Transcriptome Array 2.0) were hybridized for 16 hours in a 45°C incubator, rotated at 60 rpm. According to the GeneChip® Expression Wash, Stain and Scan Manual (PN 702731 Rev. 3, Affymetrix Inc., Santa Clara, CA) the arrays were washed and stained using the Fluidics Station 450.

scan_protocol: The arrays were scanned using the GeneChip® Scanner 3000 7G.

data_processing: The raw data was normalized in R and the R-library Oligo using the robust multi-array average (RMA) function.

source_name: Primary GBM

label_protocol: 250 nanograms of total RNA from each sample were used to generate amplified and biotinylated sense-strand cDNA from the entire expressed genome according to the GeneChip® WT PLUS Reagent Kit User Manual (P/N 703174 Rev 1 Affymetrix Inc., Santa Clara, CA).

growth_protocol:
GC_EFBS cells were explanted and maintained in serum-containing media (MEM medium (Sigma) supplemented with 10% fetal bovine serum (FBS, Sigma), 1% L-Glutamine 200mM (Gibco) and 1% penicillin/streptomycin (Sigma)).
EFBS_to_SF cells were cultured in DMEM/F12 Glutamax (Gibco) and Neurobasal medium (Gibco) mixed 1:1 with addition of 1% B27 (Invitrogen), 0.5% N2 (Invitrogen), 1% Penicillin/Streptomycin (Sigma), 10 ng/ml each of EGF and FGF2 (Peprotech) on poly-ornithine/laminin-coated dishes for at least two passages.

extract_protocol: Total mRNA was extracted using the QIAGEN RNeasy kit (QIAGEN)

value: Glioblastoma cell culture

tag: culture conditions

value: NSC media


title: U3097MG ESC_to_SFC


title: U3040MG ESC

description: Exclusively established and maintained in serum-containing medium

value: Serum-containing media


title: U3138MG ESC_to_SFC


title: U3158MG ESC_to_SFC


title: Nkx2-1+ lung endoderm E17.5

city: Philadelphia

first: Michael

middle: Patrick

last: Morley

state: PA

address: 3400 Civic Center Blvd, Bldg 421

department: Penn Cardiovascular Institute

institute: Perelman School of Medicine at the University of Pennsylvania

data_processing:
The Cell Ranger Single Cell Software Suite was used to perform sample demultiplexing, barcode processing, and single cell 3’ gene counting (http://software.10xgenomics.com/single-cell/overview/welcome). 10bp UMI tags were extracted from Read2.
Genome_build: mm10
Supplementary_files_format_and_content: HDF5;  Cell ranger HDF5 Feature Barcode Matrix Format, this file  contains a single HDF5 group, called matrix, and metadata stored as HDF5 attributes. Within the matrix group are datasets containing the dimensions of the matrix, the matrix entries, as well as the features and cell-barcodes associated with the matrix rows and columns, respectively.

source_name: Lung endoderm

extract_protocol: Cellular suspensions were loaded on a GemCode Single Cell Instrument (10x Genomics, Pleasanton, CA) to generate single cell GEMs. Approximately ~1200 to 2800 cells were loaded per channel. Two biological replicates were generated per sample. Single cell RNA-Seq libraries were prepared using GemCode Single Cell 3’ Gel Bead and Library Kit (now sold as P/N 120230, 120231, 120232, 10x Genomics) following the protocols outlined in Zheng et. al. Sequencing libraries were loaded at 2.1pM on an Illumina Hiseq 2500  with 2 × 100 paired-end kits using the following read length: 98bp Read1, 14bp I7 Index, 8bp I5 Index and 5bp Read2.

tag: genotype

tag: age


title: Nkx2-1+ lung endoderm E15.5


title: PAO1_VSVG_2 ChIPseq

description: ChIPed genomic DNA

tag: strain


title: U3045MG ESC_to_SFC


title: U3097MG ESC


title: Tumor-bearing splenic cells from Rosa26-HGAL+Mb1-Cre, biological replicate 1

city: Salamanca

first: Isidro

last: Sánchez García

address: Campus Miguel de Unamuno

department: Centro de inv. del Cáncer (CIC).

country: Spain

institute: Inst. de Inv. Biomédica de Salamanca (IBSAL). H. U. de Salamanca. Inst. de Biología Molecular y Celular del Cáncer (IBMCC).

description: Gene expression data from Tumor-bearing splenic cells from Rosa26-HGAL+Mb1-Cre

hyb_protocol: Samples were hybridized using Affymetrix hybridization kit materials • Washing and scanning were performed using GeneChip System of Affymetrix (GeneChip Hybridization Oven 640, GeneChip Fluidics Station 450)

scan_protocol: Affymetrix Gene ChIP Scanner 3000 7G

data_processing: The RMA function provided by the package oligo from R/Bioconductor was applied to the raw expression data.

label_protocol: Samples were enzymatically fragmented and biotinylated using the WT Terminal Labeling Kit (Ambion)

growth_protocol: Animals were housed in conventional facility and they have been exposed to common pathogens.

extract_protocol: Trizol extraction of total RNA was performed according to the manufacturer's instructions.

treatment_protocol: Not applicable

tag: tissue

value: Total Spleen

tag: genotype/variation


title: Tumor-bearing splenic cells from Rosa26-HGAL+Mb1-Cre, biological replicate 2


title: Splenic cells from Control-Wild Type mouse, biological replicate 2

description: Gene expression data from Splenic cells from Control-Wild Type mouse

source_name: WT 2

value: Wild Type


title: Tumor-bearing splenic cells from Rosa26-HGAL+Aid-Cre , biological replicate 1

description: Gene expression data from Tumor-bearing splenic cells from Rosa26-HGAL+Aid-Cre


title: Tumor-bearing splenic cells from Rosa26-HGAL+Sca1-Cre, biological replicate 4

description: Gene expression data from Tumor-bearing splenic cells from Rosa26-HGAL+Sca1-Cre

source_name: L78B Tumoral_4


title: Splenic cells from Control-Wild Type mouse, biological replicate 3

source_name: WT 3


title: Pre-tumoral splenic cells from Rosa26-HGAL+Sca1-Cre, biological replicate 3

description: Gene expression data from Pre-tumoral splenic cells from Rosa26-HGAL+Sca1-Cre


title: Splenic cells from Control-Wild Type mouse, biological replicate 1

source_name: WT 1


title: Tumor-bearing splenic cells from Rosa26-HGAL+Aid-Cre , biological replicate 2


title: Tumor-bearing splenic cells from Rosa26-HGAL+Mb1-Cre, biological replicate 3


title: Tumor-bearing splenic cells from Rosa26-HGAL+Aid-Cre , biological replicate 4


title: Tumor-bearing splenic cells from Rosa26-HGAL+Sca1-Cre, biological replicate 1

source_name: L78B Tumoral_1


title: Tumor-bearing splenic cells from Rosa26-HGAL+Mb1-Cre, biological replicate 4


title: Tumor-bearing splenic cells from Rosa26-HGAL+Aid-Cre , biological replicate 3


title: Pre-tumoral splenic cells from Rosa26-HGAL+Sca1-Cre, biological replicate 2


title: Pre-tumoral splenic cells from Rosa26-HGAL+Sca1-Cre, biological replicate 1


title: Tumor-bearing splenic cells from Rosa26-HGAL+Mb1-Cre, biological replicate 5


title: Tumor-bearing splenic cells from Rosa26-HGAL+Sca1-Cre, biological replicate 3

source_name: L78B Tumoral_3


title: Nkx2-1+ lung endoderm E13.5


title: U3040MG ESC_to_SFC


title: Tumor-bearing splenic cells from Rosa26-HGAL+Sca1-Cre, biological replicate 2

source_name: L78B Tumoral_2


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_95]

city: Shanghai

first: Xiaonan

last: Wang

address: 227 Chongqing South Road

department: School of Public Health

institute: Shanghai Jiao Tong University School of Medicine

data_processing:
RNA-seq reads were aligned to mm10 using GSNAP (version 2015-09-29) with parameters (-B 5 -t 24 -n 1 -Q -N 1).
Reads in features were counted with htseq-count (HTSeq version 0.6.0) with the parameter (-s no).
Genome_build: mm10
Supplementary_files_format_and_content: HT-Seq counts / gene counts (.txt)

source_name: Bone marrow from femurs and tibias

extract_protocol:
BM hematopoietic cells was isolated from femurs and tibias by flushing and depleted of red blood cells by ACK Lysing Buffer (Thermo Fisher Scientific).
CD150+CD48-cKit+Sca1+ cells were FACS-purified.
Single-cells were sorted by FACS directly into individual wells of a 96-well plate containing lysis buffer.
Libraries were prepared using the Illumina Nextera XT DNA preparation kit.
Single-cell RNA-seq analysis was performed as described previously (Picelli et al., 2014).
Pooled libraries were run on the Illumina Hi-Seq 2500.
Samples were used to construct Nextera XT libraries (Illumina) from 125 pg of preamplified cDNA. Libraries were purified and size selected (0.5X-0.7X) using Ampure XP beads. Then, libraries were quantified using KAPA qPCR quantification kit (KAPA Biosystems).

tag: treatment


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_93]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_94]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_96]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_95]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_96]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_94]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_96]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_95]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_90]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_94]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_92]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_93]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_92]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_91]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_91]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_90]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_89]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_89]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_92]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_93]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_86]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_85]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_87]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_87]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_86]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_88]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_85]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_90]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_85]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_88]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_91]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_89]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_87]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_84]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_86]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_83]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_82]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_81]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_88]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_83]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_83]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_81]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_81]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_80]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_84]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_82]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_84]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_79]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_78]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_77]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_79]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_79]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_80]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_77]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_78]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_78]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_74]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_76]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_82]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_77]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_76]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_74]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_73]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_75]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_76]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_74]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_80]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_75]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_75]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_72]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_70]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_71]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_72]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_73]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_69]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_71]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_70]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_69]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_73]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_72]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_71]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_69]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_70]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_66]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_67]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_68]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_67]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_65]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_68]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_66]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_65]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_66]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_62]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_67]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_64]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_62]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_61]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_64]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_63]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_65]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_63]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_62]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_68]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_60]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_59]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_63]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_58]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_57]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_60]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_59]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_61]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_64]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_56]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_58]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_55]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_53]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_54]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_58]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_55]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_59]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_54]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_56]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_60]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_56]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_61]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_53]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_55]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_51]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_52]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_52]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_50]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_51]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_49]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_49]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_57]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_50]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_57]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_51]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_50]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_53]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_49]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_47]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_48]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_54]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_45]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_48]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_46]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_47]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_47]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_45]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_45]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_44]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_43]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_42]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_46]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_41]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_43]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_41]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_42]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_44]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_52]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_42]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_43]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_41]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_39]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_40]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_46]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_48]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_38]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_40]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_37]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_39]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_38]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_37]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_40]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_39]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_44]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_37]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_38]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_36]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_33]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_35]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_34]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_34]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_33]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_36]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_35]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_34]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_33]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_31]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_32]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_32]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_29]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_31]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_30]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_30]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_31]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_29]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_29]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_35]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_36]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_26]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_25]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_28]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_30]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_27]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_26]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_26]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_27]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_23]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_21]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_28]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_25]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_22]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_24]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_24]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_32]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_27]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_23]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_28]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_22]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_21]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_24]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_25]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_22]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_20]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_15]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_17]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_18]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_20]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_19]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_17]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_18]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_20]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_19]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_23]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_15]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_14]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_13]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_21]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_17]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_19]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_13]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_14]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_18]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_11]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_10]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_16]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_16]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_14]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_15]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_13]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_9]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_16]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_12]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_11]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_11]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_12]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_10]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_8]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_10]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_6]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_7]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_7]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_8]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_5]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_6]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_8]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_5]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_4]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_6]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_7]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_4]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_1]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_3]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_2]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_9]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_2]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_3]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_12]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_LPS_treat_9]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_4]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_LPS_treat_1]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_3]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_2]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_1]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_96]

value: Ctrl


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_96]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_LPS_treat_5]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_93]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_95]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_94]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_93]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_95]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_94]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_91]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_92]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_93]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_90]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_92]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_91]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_90]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_89]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_92]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_87]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_89]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_89]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_91]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_85]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_90]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_86]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_88]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_96]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_87]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_87]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_88]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_85]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_86]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_84]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_88]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_85]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_95]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_94]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_81]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_84]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_83]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_82]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_81]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_83]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_84]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_82]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_81]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_79]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_83]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_82]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_77]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_86]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_80]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_80]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_79]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_77]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_79]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_78]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_80]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_77]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_78]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_78]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_76]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_75]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_75]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_73]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_73]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_76]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_74]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_75]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_72]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_70]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_72]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_69]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_74]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_73]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_74]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_71]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_71]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_70]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_71]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_69]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_76]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_72]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_67]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_65]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_70]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_69]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_68]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_66]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_67]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_65]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_66]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_68]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_62]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_68]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_61]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_64]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_64]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_65]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_61]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_63]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_63]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_67]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_57]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_62]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_59]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_63]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_58]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_58]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_60]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_57]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_64]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_60]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_61]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_59]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_59]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_57]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_56]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_60]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_58]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_55]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_55]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_54]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_54]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_62]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_53]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_66]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_56]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_53]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_54]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_51]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_56]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_52]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_50]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_49]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_52]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_50]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_49]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_51]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_51]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_50]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_52]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_47]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_45]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_46]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_55]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_48]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_46]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_48]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_47]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_45]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_45]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_47]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_43]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_43]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_42]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_41]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_42]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_44]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_43]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_41]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_38]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_40]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_44]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_49]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_53]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_40]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_41]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_37]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_42]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_46]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_48]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_37]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_38]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_44]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_40]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_39]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_37]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_36]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_38]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_34]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_39]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_35]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_36]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_33]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_35]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_34]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_36]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_32]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_31]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_33]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_32]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_39]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_31]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_29]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_29]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_32]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_30]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_28]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_27]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_29]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_31]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_26]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_25]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_27]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_30]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_25]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_27]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_28]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_34]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_35]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_33]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_26]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_24]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_30]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_22]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_25]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_21]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_26]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_23]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_24]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_23]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_22]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_21]


title: Macaque_PrefrontalCortex_NeuNpositive_ATAC_3

organism: Macaca mulatta

source_name: Macaque Prefrontal Cortex

value: Macaque Prefrontal Cortex


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_24]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_22]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_23]


title: Macaque_PrefrontalCortex_NeuNnegative_ATAC_3


title: Macaque_WhiteMatter_H3K27ac_3

source_name: Macaque White Matter

tag: antibody

value: Macaque White Matter


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_28]


title: Macaque_WhiteMatter_H3K27ac_1


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_20]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_19]


title: Macaque_WhiteMatter_RNAseq_2

value: White Matter


title: Macaque_WhiteMatter_H3K27ac_2


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_17]


title: Macaque_WhiteMatter_RNAseq_1


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_19]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_18]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_20]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_18]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_17]


title: Macaque_PrefrontalCortex_NeuNpositive_ATAC_2


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_19]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_21]


title: HCT-116/6.5 rep3

city: Brussels

first: Cyril

last: Corbet

state: Select State/Prov.

address: Avenue Hippocrate 57

department: Institut de Recherche Expérimentale et Clinique (IREC)

country: Belgium

institute: UCLouvain

data_processing:
Illumina Casava1.8 software used for basecalling.
Low quality ends (<Q20) were trimmed with FastX 0.0.13 (HannonLab). Reads that were shorter than 35bp after trimming were removed.
Adapters were trimmed only at the end (at least 10bp overlap and 90% match) with cutadapt 1.7.1 (Martin 2011 EMBnet.journal). Reads that were shorter than 35bp after adapter trimming were removed.
Using FastX 0.0.13 and ShortRead 1.16.3, polyA-reads (more than 90% of the bases equal A), ambiguous reads (containing N), low quality reads (more than 50% of the bases < Q25) and artifact reads (all but 3 bases in the read equal one base type) were removed.
Using bowtie 2.2.4, reads that aligned to phix_illumina were removed.
High-quality reads were then imported into Tophat v2.0.13 for alignment into BAM files. As parameter options, we used: --library-type fr-firststrand  --min-intron-length 50 --max-intron-length 500000 --no-coverage-search --no-mixed --read-realign-edit-dist 3. With samtools 1.1, we removed reads from the alignment that were non-primary mappings or had a mapping quality ≤20. With samtools 1.1, we also sorted the reads from the alignment according to the chromosomes and indexed the resulting BAM files.
The number of reads in the alignments that overlap with the gene features was counted using featureCounts 1.4.6. As parameters, we took -Q 0 -s 2 -t exon -g gene_id. Some reads were not counted, since they could be attributed to more than one gene (ambiguous) or could not be attributed to any gene (no feature). 44602 genes for which all samples had less than 1 counts-per-million (absent genes) were further removed. As such we continued with 18550 genes. GC-content per sample was corrected using full quantile normalization on bins of GC-content with the EDASeq package from Bioconductor (Risso et al 2011). Sample-specific variation in library size and RNA composition was corrected using full quantile normalization with the EDASeq package from Bioconductor (Risso et al 2011). FPKM values were obtained by first dividing, for each sample, the normalized counts by the total number of counts (in millions), and then by dividing for each gene the scaled counts by the gene length (in kbp). Only the genes with a false discovery rate (FDR) < 0.01 and FPKM > 0.5 were considered for analysis. FPKM values were log2-transformed with an offset value of 1.0.
Genome_build: Preprocessed reads were aligned to the reference genome of Homo sapiens GRCh37.73
Supplementary_files_format_and_content: Excel file with 3 spreadsheets including raw counts, FPKM values as well as normalized abundance measurements (log2-transformed values with an offset value of 1.0) for each sample.

growth_protocol: Native and acidosis-adapted cancer cells were cultured in DMEM medium supplemented with heat-inactivated FBS, 10 mM glucose, 2 mM glutamine and buffered with HEPES and PIPES (25 mM of each) at pH 7.4 or 6.5, respectively (as previously described in Corbet et al. Cancer Res 2014).

extract_protocol:
Total RNA from subconfluent cells (80-90% confluence) was extracted using Maxwell® RSC SimplyRNA tissue kit (Promega). RNA quantity and quality were evaluated with a Nanodrop 1000 spectrophotometer (Thermo Fisher Scientific) and RNA 6000 nano chips on a Bioanalyzer 2100 system (Agilent), respectively. Illumina TruSeq RNA Library Prep Kit V2 was used with 2 ug of total RNA for the construction of sequencing libraries.
RNA libraries were prepared for sequencing using standard Illumina protocols.
RNA-Seq using the NextSeq 500 High Output Kit V2 (75 cycles; Illumina).

tag: cell line

tag: cancer tissue source

value: colon

tag: medium ph


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_15]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_16]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_18]


title: HCT-116/6.5 rep2


title: HCT-116/6.5 rep1


title: Macaque_PrefrontalCortex_NeuNnegative_ATAC_2


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_14]


title: HCT-116/7.4 rep3


title: FaDu/6.5 rep3

source_name: FaDu_pH 6.5

value: FaDu

value: pharynx


title: HCT-116/7.4 rep2


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_16]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_13]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_15]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_17]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_20]


title: FaDu/6.5 rep2


title: HCT-116/7.4 rep1


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_13]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_16]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_14]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_15]


title: FaDu/7.4 rep3

source_name: FaDu_pH 7.4


title: FaDu/6.5 rep1


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_14]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_13]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_12]


title: SiHa/6.5 rep3

source_name: SiHa_pH 6.5

value: SiHa

value: cervix


title: SiHa/6.5 rep2


title: FaDu/7.4 rep1


title: SiHa/6.5 rep1


title: SiHa/7.4 rep2

source_name: SiHa_pH 7.4


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_9]


title: SiHa/7.4 rep3


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_10]


title: FaDu/7.4 rep2


title: SiHa/7.4 rep1


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_10]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_12]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_12]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_9]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_11]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_11]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_10]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_9]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_11]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_7]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_8]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_5]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_6]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_8]


title: GW5T

city: New York

first: Sara

last: Parsa

state: NY

department: Cancer Biology and Genetics

institute: Memorial Sloan-Kettering Cancer Center

data_processing:
Raw output BAMs were converted back to FASTQ using PICARD Sam2Fastq.
Gene level counts were computed using htseq-count and the same gene models as used in the mapping step.
HTSeq v0.6.0 is used for feature counting.
Counts were normalized and rescaled into log2 counts using the limma R package
Genome_build: The genome used was HG19 with junctions from ENSEMBL (GRCh37.69_ENSEMBL) and a read overhang of 49.
Supplementary_files_format_and_content: txt files, normalized count tables from each experiment

source_name: lymphoma cells from transformed Follicular lymphoma-patient

extract_protocol:
total RNA extraction was performed using TRIzol (Ambion) using the manufacturer's protocol
After RiboGreen quantification and quality control by Agilent BioAnalyzer, 160ng of total RNA underwent polyA selection and TruSeq library preparation according to instructions provided by Illumina (TruSeq RNA Library Prep Kit v2, catalog # RS-122-2001), with 8 cycles of PCR.
Samples were barcoded and run on a HiSeq 2500 in Rapid mode using the TruSeq Rapid SBS Kit-HS and a HiSeq 2000 with a TruSeq SBS Kit-HS v3 (Illumina) in 50bp/50bp paired end runs.

tag: subject status/id

value: Follicular lymphoma-patient 5

tag: tumor stage

value: Transformed follicular lymphoma


title: GW6T

value: Follicular lymphoma-patient 6


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_6]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_7]


title: GW3T

value: Follicular lymphoma-patient 3


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_5]


title: GW2T

value: Follicular lymphoma-patient 2


title: GW1T

value: Follicular lymphoma-patient 1


title: GW4T

value: Follicular lymphoma-patient 4


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_6]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_7]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_8]


title: GW5F

source_name: lymphoma cells from Follicular lymphoma-patient

value: Follicular lymphoma


title: GW6F


title: GW4F


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27high_5]

value: Ctrl


title: ependymoma_1_region_E

type: genomic

city: SAN FRANCISCO

first: John

last: Liu

state: California

address: 35 Medical Center Way

institute: UCSF

hyb_protocol: Samples are then hybridized overnight to an 8-sample BeadArray, in which the WGA-DNA molecules anneal to locus-specific DNA oligomers linked to individual bead types. The oligomer probe designs follow the Infinium I and II chemistries, in which base extension follows hybridization to a locus-specific oligomer. With respect to the Infinium I probes, there are two different bead types for each locus, one with an oligomer that anneals specifically to the methylated version of the locus, while the other oligomer anneals to the unmethylated version of the locus. The probes terminate complementary to the interrogated CpG site for methylated loci, or complementary to the TpG for unmethylated alleles. A matched oligomer-template DNA molecule hybrid will allow for the incorporation of a labeled nucleotide immediately upstream (5’) to the interrogated CpG (or TpG) site. However, if the probe and template are mismatched, then primer extension will not occur. Adenine and thymine nucleotides are labeled with cy5 (red), while cytosine nucleotides are labeled with cy3 (green). No insertion of guanine nucleotides occurs in Infinium I assays. Of note, the identity of the dye is representative of the nucleotide adjacent to the CpG dinucleotide. The methylation discrimination is derived from separate measurements from the two different types of beads present for each locus. For some loci, both measurements will be cy3, and for others both will be cy5. The Infinium type II chemistry is a true two-color system. A matched oligomer-template DNA molecule hybrid will allow for the incorporation of a labeled nucleotide at the interrogated C or T of the CpG site. Adenine nucleotides labeled with cy5 (red) are incorporated across from unmethylated (TpG) sites, while guanine nucleotides labeled with cy3 (green) are incorporated across from methylated (CpG) sites.

scan_protocol: After the chemistry steps, BeadArrays are scanned and the raw signal intensities are extracted from the *.IDAT files using the ‘noob’ function in the minfi R package.

label: Cy5 and Cy3

source_name: ependymoma

label_protocol: Genomic DNA (1000 ng) is bisulfite converted using the Zymo EZ DNA methylation kit (Zymo Research, Irvine, CA) according to the manufacturer’s recommendations. The amount of bisulfite-converted DNA as well as the completeness of bisuflite conversion for each sample are assessed using a panel of MethyLight-based real-time PCR quality control assays as described previously (PMID: 18987824). Bisulfite-converted DNA is then used as a substrate for the Illumina EPIC BeadArrays, as recommended by the manufacturer and first described in Moran, 2016 (PMID: 26673039).   Specifically, each sample is whole genome amplified (WGA) and then enzymatically fragmented.

growth_protocol: Patient resection specimen

extract_protocol: DNA were isolated from tumor samples using the All-Prep Universal Kit (QIAGEN, Valencia, CA)

treatment_protocol: Patient resection specimen

tag: region

value: E


title: GW2F


title: ependymoma_1_region_D

data_processing: After the chemistry steps, BeadArrays are scanned and the raw signal intensities are extracted from the *.IDAT files using the ‘noob’ function in the minfi R package.

value: D


title: CRC ≤ 45 years. Sample 150

first: Juan Luis

last: Garcia

state: Salamanca

institute: Centro de Investigación del Cáncer IBMCC-CIC

hyb_protocol: Hybridization was carried out in a Roche NimbleGen Hybridisation Chamber (Roche NimbleGen, Inc.) for 48 h.

scan_protocol: Slides were washed and scanned using a NimbleGenMS 200 Microarray Scanner (Roche NimbleGen, Inc.).

data_processing: Array image ﬁles produced by the MS 200 Data Collection Software were imported into DEVA v1.2.1(Roche NimbleGen Inc.) for analysis. In addition,segmentation analysis was performed in all cases byusing circular binary segmentation (CBS) available in waviCGH software (http://wavi.bioinfo.cnio.es/).

source_name: Left colon

label_protocol: One microgram of genomic DNA(gDNA) from each test and reference sample(Human Genomic DNA [Male G1471/FemaleG1521] from Promega Corporation, Madison, WI) was labeled using the NimbleGen Dual-C olor DNALabeling Kit.

extract_protocol: DNA was extracted using proteinase K digestion, phenol/chloroform extraction, Phase Lock Gel Light (Eppendorf AG, Hamburg, Germany), and ethanol precipitation.

tag: Sex

value: Female

tag: molecular classification

value: MSS y CIMP-Low/0

tag: sample type

value: reference


title: CRC ≤ 45 years. Sample 155

source_name: Rectum

value: Male

value: MSS y CIMP-High

source_name: Human Genomic DNA [Male G1471/FemaleG1521] from Promega Corporation


title: CRC ≤ 45 years. Sample 156


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_4]


title: CRC ≤ 45 years. Sample 151


title: GW3F


title: ependymoma_1_region_F

value: F


title: CRC ≤ 45 years. Sample 149

value: MSI y CIMP-Low/0


title: ependymoma_1_region_B

value: B


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_1]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_3]


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27negative_2]


title: CRC ≤ 45 years. Sample 148


title: ependymoma_1_region_A

value: A


title: GW1F


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_2]


title: CRC ≤ 45 years. Sample 154


title: RNAseq_RT4_Ni_washout_Dup

city: Charlottesville

first: Zhenjia

state: VIRGINIA

address: 1300 Jefferson Park Avenue

department: Center for Public Health Genomics

institute: University of Virginia

data_processing:
ChIP-Seq reads were aligned to the human reference genome (GRCH38/hg38) using BWA. SAM files were then converted into BAM format using samtools. Bedtools was used to convert BAM files into BED format. Only 1 copy of the redundant reads that were mapped to the exact same location in the genome was retained. All non-redundant reads from H3K4me3 ChIP-Seq datasets were included for downstream analyses without peak calling.
RNA-Seq raw sequence were mapped to human genome using STAR ; multi hits removed. Gene expression levels were quantified as count, TPM and FPKM. Differential gene expression was calculated using DESeq2.
Genome_build: GRCh38/hg38
Supplementary_files_format_and_content: ChIP-seq: promoter (TSS +/-2kb) RPKM, RNA-seq: gene level count, TPM, FPKM

growth_protocol: Human lung epithelial BEAS-2B cells (ATCC, CRL-9609) were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM, Cellgro) supplemented with 1% Penicillin-Streptomycin and 10% Fetal Bovine Serum (FBS, Atlanta Biologicals) at 37°C and 5 % CO2. Human urinary bladder epithelial RT4 cells were cultured in McCoy's 5A Modified Medium (Life Technologies, Carlsbad, CA), supplemented with 10% FBS (Atlanta Biologicals) and 0.5% Penicillin‐Streptomycin at 37°C and 5% CO2. Human urinary bladder epithelial RT4 cells (a generous gift from Dr. Tang, New York University School of Medicine, NY) were cultured in McCoy's 5A Modified Medium (Life Technologies, Carlsbad, CA), supplemented with 10% FBS (Atlanta Biologicals) and 0.5% Penicillin‐Streptomycin at 37°C and 5% CO2. The cells were exposed to 100 μM NiCl2 for 6 weeks. Following exposure, the cells were washed and cultured in Ni‐free medium for 2 weeks to obtain Ni‐washed‐out cells.

extract_protocol:
Total RNA was isolated using RNeasy Kit (Qiagen 74104). ChIP was performed using samples isolated from two biological replicates. The cells were crosslinked with 1% formaldehyde for 10 minutes at 25°C and sonicated to obtain 200-500bp fragments. ChIP was performed as described earlier using ChIP grade antibodies against H3K4me3 (Millipore, 07-473).
RNA-Seq libraries were prepared using Illumina TruSeq RNA Sample Preparation Kit (RS-122-2002) according to manufacturer’s protocol. ChIP-Seq libraries were prepared using Illumina TruSeq ChIP sample preparation Kit (IP -202-1024), according to manufacturer’s protocol.

treatment_protocol: Both BEAS-2B and RT4 cells cells were exposed to  100 μM NiCl2  for 6 weeks. Following exposure, the cells were washed and cultured in Ni‐free medium for 2 weeks to obtain Ni‐washed‐out cells. For BEAS-2B cells, following 6-week exposure, a homogenous populations of Ni-exposed cells was isolated as described. Briefly, the Ni-exposed cells were then plated in 15 cm plates at the rate of 1000, 500, 100 and 50 cells per plate in Ni-free medium. Nicely separated single colonies were randomly isolated and the populations were expanded in Ni-free medium. A randomly selected homogenous population of Ni-washed-out cells (Ni-W)  in culture for >6 months post Ni exposure was used for a detailed examination.

value: Human urinary bladder


title: long_term_HSC_CD150+CD48-cKit+Sca1+ cells [SLAM_KSL_p27low_4]