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Sample GSM5510471

Query DataSets for GSM5510471

Status

Public on Nov 10, 2021

Title

follicular_granulosa_cell_layers-SWF_ChIP-seq-H3K27ac_replicated_1

Sample type

SRA

Source name

granulosa cells

Organism

Gallus gallus

Characteristics

cell type: granulosa cells
breed: Luhua hens
developmental stage: prehierarchical follicles
age: 31 weeks
follicle growth stage: SWF
chip antibodies: Anti-flag (14793, Cell Signaling Technology)

Extracted molecule

genomic DNA

Extraction protocol

Chicken ovarian GCs were washed twice in cold PBS buffer and cross-linked with 1% formaldehyde for 10 minutes at room temperature and then quenched by adding glycine (125 mmol/L final concentration). Afterwards, samples were lysed and chromatins were obtained on ice. Chromatins were sonicated to get soluble sheared chromatin (average DNA length of 200–500 bp). Then 2ul chromatin was saved at -20°C for input DNA, and 100ul chromatin was used for immunoprecipitation by Anti-flag (14793, Cell Signaling Technology). A total of 5 μg of antibody was used in the immunoprecipitation reactions at 4 °C overnight. The next day, 30 μL of protein beads were added and the samples were further incubated for 3 h. The beads were next washed once with 20 mM Tris/HCL (pH 8.1), 50 mM NaCl, 2 mM EDTA, 1% Triton X-100, 0.1% SDS; twice with 10 mM Tris/HCL (pH 8.1), 250 mM LiCl, 1 mMEDTA, 1% NP-40, 1% deoxycholic acid; and twice with TE buffer 1× (10 mM Tris-Cl at pH 7.5. 1 mM EDTA). Bound material was then eluted from the beads in 300 μL of elution buffer (100 mM NaHCO3, 1% SDS), treated first with RNase A (final concentration 8 μg/mL) during 6 h at 65°C and then with proteinase K (final concentration 345 μg/mL) overnight at 45°C.
Immunoprecipitated DNA was used to construct sequencing libraries following the protocol provided by the I NEXTFLEX® ChIP-Seq Library Prep Kit for Illumina® Sequencing (NOVA-5143, Bioo Scientific) and sequenced on a Illumina Xten with PE 150 method.
We performed H3K27ac (a canonical histone marks of enhancers) ChIP-seq for 2 replicates in each of three stages, including SWF, F1, and POF. Briefly, chicken ovarian granulosa cells were washed twice in cold PBS buffer and cross-linked with 1% formaldehyde for 10 minutes at room temperature and then quenched by addition of glycine (125 mmol/L final concentration). Afterwards, samples were lysed and chromatins were obtained on ice. Chromatins were sonicated to get soluble sheared chromatin (average DNA length of 200–500 bp). 2ul chromatin was saved at -20°C for input DNA, and 100ul chromatin was used for immunoprecipitation by Anti-flag (14793, Cell Signaling Technology).5 μg of antibody was used in the immunoprecipitation reactions at 4 °C overnight. The next day, 30 μL of protein beads was added and the samples were further incubated for 3 h. The beads were next washed once with 20 mM Tris/HCL (pH 8.1), 50 mM NaCl, 2 mM EDTA, 1% Triton X-100, 0.1% SDS; twice with 10 mM Tris/HCL (pH 8.1), 250 mM LiCl, 1 mMEDTA, 1% NP-40, 1% deoxycholic acid; and twice with TE buffer 1× (10 mM Tris-Cl at pH 7.5. 1 mM EDTA). Bound material was then eluted from the beads in 300 μL of elution buffer (100 mM NaHCO3, 1% SDS), treated first with RNase A (final concentration 8 μg/mL) during 6 h at 65°C and then with proteinase K (final concentration 345 μg/mL) overnight at 45°C. Immunoprecipitated DNA was used to construct sequencing libraries following the protocol provided by the I NEXTFLEX® ChIP-Seq Library Prep Kit for Illumina® Sequencing (NOVA-5143, Bioo Scientific) and sequenced on Illumina Xten with PE 150 method.

Library strategy

ChIP-Seq

Library source

genomic

Library selection

ChIP

Instrument model

Illumina NovaSeq 6000

Description

IP-SWF_AllEnhancers.table.txt

Data processing

TPM-submission.xlsx: bulk-RNAseq reads were aligned to the galGal6 using kallisto version 0.44.0 ,then tpm and counts matrixs were generated using R version 3.6.1.
bulk.RNAseq.all_counts.filt.0.5tpm.more.than.3num.xlsx: bulk-RNAseq reads were aligned to the galGal6 using kallisto version 0.44.0 ,then tpm and counts matrixs were generated using R version 3.6.1.
SWF.filtered_feature_bc_matrix.h5: single cell reads were aligned to the galGal6 using cellranger version 6.0.1
F1.filtered_feature_bc_matrix.h5: single cell reads were aligned to the galGal6 using cellranger version 6.0.1
POF.filtered_feature_bc_matrix.h5: single cell reads were aligned to the galGal6 using cellranger version 6.0.1
ATAC-SWF_peaks.xls: ATAC-seq reads were algned to the galGal6 using bowtie2 version 2.2.6, mitochondrial alignments and PCR duplicates were filtered using removeChrom and Picard version 1.126 and peaks were called using macs2 version 2.1.1.
ATAC-F1_peaks.xls: ATAC-seq reads were algned to the galGal6 using bowtie2 version 2.2.6, mitochondrial alignments and PCR duplicates were filtered using removeChrom and Picard version 1.126 and peaks were called using macs2 version 2.1.1.
ATAC-POF_peaks.xls: ATAC-seq reads were algned to the galGal6 using bowtie2 version 2.2.6, mitochondrial alignments and PCR duplicates were filtered using removeChrom and Picard version 1.126 and peaks were called using macs2 version 2.1.1.
IP-SWF_AllEnhancers.table.txt: chip-seq reads were aligned to the galGal6 using BWA version 0.7.15, data were filtered using samtools version 1.3.1 and super-enhancers were defined using ROSE algorithms.
IP-F1_AllEnhancers.table.txt: chip-seq reads were aligned to the galGal6 using BWA version 0.7.15, data were filtered using samtools version 1.3.1 and super-enhancers were defined using ROSE algorithms.
IP-POF_AllEnhancers.table.txt: chip-seq reads were aligned to the galGal6 using BWA version 0.7.15, data were filtered using samtools version 1.3.1 and super-enhancers were defined using ROSE algorithms.
F1.POF.silico.bulk.DEG.result.by.DEseq2.txt: single cell reads were aligned to the galGal6 using cellranger version 6.0.1, silico bulk-RNAseq counts matrixs were extracted using Python 2.7 and DEGs were generated using DEseq2 algorithms.
SWF.F1.silico.bulk.DEG.result.by.DEseq2.txt: single cell reads were aligned to the galGal6 using cellranger version 6.0.1, silico bulk-RNAseq counts matrixs were extracted using Python 2.7 and DEGs were generated using DEseq2 algorithms.
SWF.POF.silico.bulk.DEG.result.by.DEseq2.txt: single cell reads were aligned to the galGal6 using cellranger version 6.0.1, silico bulk-RNAseq counts matrixs were extracted using Python 2.7 and DEGs were generated using DEseq2 algorithms.
Genome_build: galGal6
Supplementary_files_format_and_content: bulk-RNAseq (xlsx files)
Supplementary_files_format_and_content: single cell RNAseq (matrix.h5 files)
Supplementary_files_format_and_content: ATAC-seq (peaks.xls files)
Supplementary_files_format_and_content: chip-seq (AllEnhancers.table.txt files)
Supplementary_files_format_and_content: single cell RNAseq (silico.bulk.DEG.result.by.DEseq2.txt files)

Submission date

Aug 10, 2021

Last update date

Nov 10, 2021

Contact name

hua kui

E-mail(s)

2020102003@stu.sicau.edu.cn

Organization name

Sichuan Agricultural University

Street address

No 211 Huimin Road