title: DKK and Dual Node multiseqlane1

status: Public on Dec 09 2024

city: Los Angeles

first: Dario

last: Boffelli

address: 700 N San Vicente Blvd

department: Pediatrics

institute: Cedars Sinai

description: 10X Genomics

data_processing:
Multiseq read alignment with barcode sequences was done with MULTI-Seq10X (https://github.com/Mignot-Lab/Multiseq10x)
Demultiplexing was done with deMULTIplex2 using default paramaters (https://github.com/Gartner-Lab/deMULTIplex2)
cellranger mkfastq V7.0.0 was used to generate the fastq files
cellranger count V7.0.0 was used to produce the filtered feature, barcode and mtx files for cDNA
Assembly: GRCm38
Supplementary files format and content: Tab-separated values and matrix files

source_name: mESC

growth_protocol: On day 0, Kdr-GFP mESC and L929 synthetic organizer cells were seeded separately in N2B27 media: 300 cellsin 40 µL/wellfor mESC,50 cellsin 40 µL/wellfor L929synthetic organizer cells, in an ultra-low attachment round bottom 96 well plate for mESC, or an ultra-low attachment round bottom 384 well plate for L929 synthetic organizer cells. On day 1, uninduced L929 nodes were transferred to the wells containing mESC. For dual node samples, one uninduced Wnt3a node and one uninduced Dkk1 node were transferred to the same well. The medium volume per well was up to 200 uL, with a daily change of 50 uL.

extract_protocol:
On Day 8, 10-20 embryoids in each condition were transferred to a 96-well U-bottom plate and were digested using TrypLE. The cells were then washed with BGJb medium and labeled with 10 uL of 2.5 uM anchor LMO-barcode for 5 min on ice. Then, 10 uL of 2.5 uM co-anchor LMO were added to each cell pool, and samples were labeled for another 5 min on ice. Following LMO labeling, cells were washed with 1% BSA in PBS to quench LMOs. After washing, the cells were resuspended in FACS buffer with DAPI and live embryoid cells were sorted using BD FACS Aria. L929 node cells were sorted separately and mixed with embryoid cells at a 2% ratio.
10x Genomics Single Cell 5’ v1 chemistry was used to generate Gel Bead-In Emulsions (GEM), and perform post GEM-RT cleanup, cDNA amplification, as well as library construction.

treatment_protocol: To induce Wnt3a expression by the L929 organizer cells, 40 µL of N2B27 medium containing doxycycline (D3447, Sigma) at final concentration of 200 ng/mL was added to wells on day 1. To induce Dkk1 expression by the L929 cells, 40 µL of N2B27 medium with grazoprevir (MK-5172, MedChemExpress) at final concentration of 0.8 uM was added to wells on day 2.

value: mESC

value: Kdr-EGFP mESC


title: DKK and Dual Node multiseqlane2

type: SRA

state: CA

country: USA

description: 10X Genomics

library_source: transcriptomic single cell

molecule: total RNA

organism: Mus musculus

tag: tissue

tag: cell line

tag: cell type


title: Wnt_cDNA_Sample2

description: 10X Genomics


title: DKK and Dual Node cDNA lane1

description: 10X Genomics


title: Wnt_Multseq_Sample2

description: 10X Genomics


title: DKK and Dual Node cDNA lane2

description: 10X Genomics


title: Wnt_cDNA_Sample1

description: 10X Genomics


title: RNA_S3848Nr25

status: Public on Nov 20 2024

city: Krakow

first: Tomasz

last: Szmatoła

state: Małopolskie

address: Mickiewicza 21

department: OMEI

country: Poland

institute: University of Agriculture in Krakow

library_source: transcriptomic

data_processing:
The bioinformatics analysis starts with demultiplexing of the sequencing by the Illumina bcl2fasq (2.20). Adapter trimming and quality filtering of paired-end reads were performed with fastp (v0.23.2) [21]. The trimming procedure included automatic detection of adapter sequences, removal of low-quality bases from both ends of reads, sliding window filtering with a mean quality threshold of 20, and exclusion of reads shorter than 36 bp or containing more than 10 ambiguous nucleotides. Additionally, polyG and polyX tails were trimmed to minimize sequencing artefacts. The quality of trimmed reads was evaluated using FastQC (v0.11.5), and overall sequencing performance across samples was summarized with MultiQC (v1.14). High-quality reads were then aligned to the Columba livia reference genome (Cliv_3.0, NCBI assembly GCF_001887795.1) using STAR (v2.7.10a) [22] The aligner was run in two-pass mode, allowing detection and incorporation of novel splice junctions to improve mapping accuracy. Alignment parameters included a maximum of 20 allowed multiple mappings per read and a mismatch tolerance of ≤4% of the read length. Gene-level quantification was subsequently verified using HTSeq-count (v0.13.5), applied to name-sorted BAM files generated from STAR alignments
Assembly: Cliv_3.0, NCBI assembly GCF_001887795.1
Supplementary files format and content: txt

organism: Columba livia

growth_protocol: All birds were raised at the same loft and fed by the schedule for competition. The training of pigeons was performed according to the widowhood method.

extract_protocol:
The performing group upon their return and the control group were sedated and deceased using the cervical dislocation method. The cerebellum was dissected from the brain, secured in RNA later solution and stored at – 20 °t. Tissue was homogenized with the use of zirconium oxide beads (diameter 0.5 mm) in BulletBlender homogenizer (Next Advance Inc., USA). The total RNA was isolated using adjusted method with the use of TriReagent (Ambion, Life Technologies) according to the method described by Chomczyński and PureLink RNA Mini Kit (Invitrogen). The concentration and quality of RNA were determined on TapeStation 2200 using RNA Screen Tape (Agilent Technologies).
The samples with RIN (RNA integrity number) ranging from 9.6 – 10.0 (Schroeder et al. 2006), were sent to Library preparation and sequencing by a commercial company (CeGat gGmbH) on NovaSeq 6000 (Ilumina), and finally 100 bp (±50 bp) paired-end reads were generated.

treatment_protocol: 5 birds (racing homers) competed in a 300 km flight and 5 birds of the same age form the same loft rested in the loft on the day of the competition.

value: cerebellum

tag: treatment

value: race


title: RNA_S3848Nr26


title: RNA_S3848Nr24


title: RNA_S3848Nr23


title: Wnt_Multseq_Sample1

description: 10X Genomics


title: RNA_S3848Nr21


title: 4_P-20_TIL (scTCR-seq)

status: Public on Dec 16 2024

city: Baltimore

first: Elana

middle: Judith

last: Fertig

state: MD

address: 670 W. Baltimore Street, 3rd floor

department: Institute for Genome Sciences

institute: University of Maryland, Baltimore

description: barcode number 4

data_processing:
Read alignment and counting was conducted using 10x Genomics CellRanger (v6.1.1).
Assembly: hg38
Supplementary files format and content: 10x genomics CellRanger aggregated expression matrix output (barcodes.tsv.gz, features.tsv.gz, matrix.mtx.gz)
Supplementary files format and content: 10x genomics CellRanger VDJ contigs

molecule: polyA RNA

organism: Homo sapiens

source_name: tumor

extract_protocol:
Fresh tumors were mechanically disruped, resuspended in RPMI media, strained by 70 um strainer, and percoll seperated to enrich for leukocytes. Single cell suspensions were cryopreserved in liquid nitrogen and thawed at 37C for sequencing.
Chromium single cell controller generated droplet emulsion of single-cells and barcoded gel beads. RNA seq libraries were generated using the single cell 5’ DGE Library and Gel Bead Kit v3.1 (10x Genomics). Single cell TCR seq libraries were generated with the Single Cell V(D)J Enrichment Kit ( Human T Cell) and Chromium Next GEM Chip G Single Cell Kit (10x Genomics). The libraries for digital gene expression were sequenced using NovaSeq 6000 system (Illumina).

treatment_protocol: Patients received priming doses of neoadjuvant cancer immunotherapy prior to surgical resection of their primary tumor. Treatments consisted of cyclophosphamide (Cy)/GVAX vaccine (Arm A), Cy/GVAX and anti-PD1 (nivolumab, Arm B), or Cy/GVAX, anti-PD1, and CD137 agonist (urelumab, a human IgG4 agonistic monoclonal antibody, Arm C).

value: tumor


title: 14_P-45_TIL (scTCR-seq)

description: barcode number 14


city: Würzburg

first: Julian

last: Konig

address: Am Hubland

country: Germany

institute: Würzburg University

library_source: other

data_processing:
*library strategy:  miCLIP2
Quality control: FastQC (v0.11.8)
Barcode and adapter trimming based on sample barcode at positions 6 to 11 of R1: Flexbar (v3.4.0) with non-default parameters: -r SAMPLE.R1.fastq.gz -p SAMPLE.R2.fastq.gz --zip-output GZ --threads 4 --stdout-log --adapter-seq AGATCGGAAGAGCGGTTCAGC --adapter-min-overlap 1 --min-read-length 15 --length-dist --umi-tags --barcode-unassigned samplebarcodes.fasta (using sample barcodes: In vitro miCLIP2, unmethylated, rep 1: NNNNNCGTGATNNNN, In vitro miCLIP2, unmethylated, rep 2: NNNNNACATCGNNNN, In vitro miCLIP2, methylated with Mettl3/Mettl14 complex, rep 1: NNNNNGCCTAANNNN, In vitro miCLIP2, methylated with Mettl3/Mettl14 complex, rep 2: NNNNNTGGTCANNNN)
Setting up list of oligonucleotide IDs and UMIs "OLIGO_UMIs_RC.txt" with entries structured as follows: XXXXX_NNNNNNNNNNNNNNN, where XXXXX is the five-digit oligonucleotide ID and NNNNNNNNNNNNNNN is the reverse complement of the 15 nt oligonucleotide UMI (unique molecular identifier).
De-multiplexing reads of individual oligonucleotides: basic Bash and seqtk (v1.3): R1 was de-multiplexed based on first 15 bp of R2: zcat SAMPLE.R2.fastq.gz | awk '{if (FNR%4==1 || FNR%4==3) printf "%s\t",$1; if (FNR%4==2) printf "%s\t",substr($1,1,15); if (FNR%4==0) printf "%s\n",substr($1,1,15);}' > SAMPLE.R2.15bp.tsv; for OLIGO in `cat OLIGO_UMIs_RC.txt`; do BC=$(echo $a | awk '{split($1,v,"_"); print v[2]}'); cat SAMPLE.R2.15bp.tsv | awk -v bc=$BC '{if($2==bc) {gsub("#2","#1",$1); gsub("@","",$1); print $1}}' > READ_IDLIST; seqtk subseq SAMPLE.R1.trimmed.fastq.gz READ_IDLIST | gzip > SAMPLE_${OLIGO}.R1.fastq.gz; done
Creation of oligonucleotide-specific mapping index: STAR (v2.7.3a) with non-default parameters: --runMode genomeGenerate --genomeDir OLIGO_STARINDEX --genomeFastaFiles OLIGO_SEQUENCE.fa --runThreadN 4 --genomeSAindexNbases 2
Mapping: STAR (v2.7.3a) with non-default parameters: --runMode alignReads --genomeDir OLIGO_STARINDEX --runThreadN 4 --alignEndsType Extend5pOfRead1 --outFilterMismatchNoverReadLmax 0.04 --outFilterMismatchNmax 999 --outFilterMultimapNmax 1 --outSAMattributes All --outReadsUnmapped Fastx --outSJfilterReads Unique --readFilesCommand zcat --outSAMtype BAM SortedByCoordinate --limitBAMsortRAM 1001076135
BAM to BED shifting one base upstream: bedtools (v2.27.1): bedtools bamtobed -i SAMPLE_XXXXX_NNNNNNNNNNNNNNN.bam | bedtools shift -m 1 -p -1 -i stdin -g GENOMESIZEINFO | sort -k1,1 -k2,2n > SAMPLE_XXXXX_NNNNNNNNNNNNNNN.shifted1bp.bed
BED to bedGraph considering only 5' end positions: bedtools (v2.27.1): bedtools genomecov -bg -strand + -5 -i SAMPLE_XXXXX_NNNNNNNNNNNNNNN.shifted1bp.bed -g GENOMESIZEINFO > SAMPLE_XXXXX_NNNNNNNNNNNNNNN.plus.bedgraph
bedGraph to bigWig using bedGraphToBigWig of the UCSC tool suite (v385)
The 47,996 bigwig files with the miCLIP2 crosslink signal were imported with rtracklayer (v1.52.0). For each oligonucleotide, the miCLIP2 signal at the positions from 50 nt upstream to 90 nt downstream of the designed DRACN sites were extracted for generating the counting table. This means the designed sites will always be at the position 51 for all oligos in the counting table.
Assembly: oligonucleotide sequences

molecule: other

source_name: in vitro transcripts

extract_protocol:
Each in vitro transcribed RNA in the library includes a transcript region of interest (146 nt), a unique molecular identifier (UMI, 15 nt) and the L3 linker sequence (21 nt). Transcript regions were selected based on miCLIP2 data from HEK293T cells to contain miCLIP2-detected m6A sites or unmodified adenosines in a DRACN context between positions 62–72 from the oligonucleotide start. In total, we selected 1,988 m6A sites in a DRACN motif ("m6A"), 1,500 unmodified adenosines in a DRACH motif ("Unmodified DRACH") and 400 permutated sequences ("Permutation"), where weak and strong DRACH motifs were exchanged within their original context. Each transcript sequence was represented by three different oligonucleotides with distinct UMIs (technical replicates). Differential methylation of the in vitro transcribed RNA libraries upon incubation with the recombinant METTL3-METTL14 writer complex or control was estimated using the published miCLIP2 protocol (PMID: 34157120), adjusted to the use of the in vitro transcribed RNA. Briefly, 500 ng purified RNA were used as input. Since fragment size was <200 nt and the L3 linker sequence was already included in the designed oligonucleotides, the initial RNA fragmentation and L3 linker ligation steps of the protocol were omitted. RNA crosslinking and immunoprecipitation with m6A-specific antibody, protein-RNA complex purification, proteinase K digestion, RNA isolation and reverse transcription were performed according to the miCLIP2 protocol. Truncations at the m6A-specific antibody crosslink are used to map the m6A position in the transcript.
Library generation was carried out according to the published miCLIP2 protocol (PMID: 34157120). Equimolarly pooled in vitro methylation miCLIP2 libraries were sequenced on an Illumina NextSeq 500 sequencing machine as 141 nt and 25 nt paired-end reads including a 15 nt sample barcode.

treatment_protocol: Purified in vitro transcribed RNAs from a designed oligonucleotide library (n=11999) were incubated with or without recombinant METTL3–METTL14 writer complexes as described in Meiser et al., 2020 (PMID: 33544495).

value: in vitro transcripts

tag: chip antibody

value: m6a-specific SySy antibody (#202003)

value: in vitro methylation


title: In vitro miCLIP2, methylated with Mettl3/Mettl14 complex, rep 1


title: In vitro miCLIP2, unmethylated, rep 2

value: untreated


title: RNA_S3848Nr20

value: control


title: RNA_S3848Nr17


title: RNA_S3848Nr19


title: HEK293T (Ribo-Seq), STM2457, 6h, rep2

description: Translation_efficiency_in_WT_and_STM2457_treated_samples.txt

data_processing:
*library strategy: Ribo-seq
Quality control: FastQC (v0.11.9)
Ribo-Seq
Barcode and adapter trimming: Flexbar (v3.5.0) with non-default parameters: --zip-output GZ --threads 4 --stdout-log --adapter-seq AGATCGGAAGAGCGGTTCAG --adapter-min-overlap 1 --min-read-length 15 --length-dist --umi-tags --barcode-unassigned samplebarcodes.fasta (using sample barcodes: HEK293T (Ribo-Seq), DMSO, 6h, rep1: NNNNNGCCTAANNNN, HEK293T (Ribo-Seq), DMSO, 6h, rep2: NNNNNCACTGTNNNN, HEK293T (Ribo-Seq), STM2457, 6h, rep1: NNNNNATTGGCNNNN, HEK293T (Ribo-Seq), STM2457, 6h, rep2: NNNNNGATCTGNNNN)
Mapping: STAR (v2.7.3a) with non-default parameters: --genomeDir Gencode_release31_GRCh38.p12 --sjdbGTFfile gencode.v31.primary_assembly.annotation.gtf --sjdbOverhang 76 --runThreadN 4 --alignEndsType Extend5pOfRead1 --outFilterMismatchNoverReadLmax 0.04 --outFilterMismatchNmax 999 --outFilterMultimapNmax 1 --outSAMattributes All --outReadsUnmapped Fastx --outSJfilterReads Unique --readFilesCommand zcat --outSAMtype BAM SortedByCoordinate
Duplicate removal: UMI-tools (v1.1.2) with non-default parameter: umi_tools dedup --method unique
BAM to BED: bedtools (v2.29.2): bedtools bamtobed -i SAMPLE.duprm.bam | sort -k1,1 -k2,2n > SAMPLE.duprm.bed
BED to bedGraph considering only 5' end positions: bedtools (v2.29.2): bedtools genomecov -bg -strand + -5 -i SAMPLE.duprm.bed -g CHROMOLENGTHTABLE > SAMPLE.duprm.plus.bedgraph; bedtools genomecov -bg -strand - -5 -i SAMPLE.duprm.bed -g CHROMOLENGTHTABLE > SAMPLE.duprm.minus.bedgraph
bedGraph to bigWig using bedGraphToBigWig of the UCSC tool suite (v385)
Counts per gene: Subread (v2.0.3) with non-default parameters: featureCounts -t CDS -g gene_id -s 1 (defaults used for -t exon -g gene_id)
RNA-Seq
Mapping: STAR (v2.7.3a) with non-default parameters: --outFilterMismatchNoverReadLmax 0.04 --outFilterMismatchNmax 999 --outSAMattributes NH HI AS nM MD --outSJfilterReads Unique --outSAMunmapped Within --outStd SAM --readFilesCommand zcat --sjdbOverhang 78 --genomeDir Gencode_release-31_GRCh38.p12_ERCC.starindex --sjdbGTFfile gencode.v31.primary_assembly.annotation_ERCC.gtf --outMultimapperOrder Random
Filter/remove secondary hits: Samtools (v1.10) with parameters: samtools view -F 256
Counts per gene: Subread (v2.0.3) with non-default parameters: featureCounts -t CDS -g gene_id -s 2 (defaults used for -t exon -g gene_id)
The count table of reads from both Ribo-seq and RNA-seq data are imported in R and merged into one, and the DESeq2 are used to normalize the read counts for each sample. Subsequently,the mean value of normalized counts for each condition are used for the calculation of translation efficiency.
Assembly: GRCh38.p12
Supplementary files format and content: Ribo-Seq: bigWig (read start positions per nucleotide)
Supplementary files format and content: RNA-Seq: tsv count tables (read count per gene)

growth_protocol: HEK293T cells were grown and cultured in DMEM supplemented with 10% fetal bovine serum, 1% L-glutamine, and 1% penicillin/streptomycin and were incubated at 37 °C with 5% CO2.

extract_protocol:
RNA for preparation of RNA-seq library was isolated from crude cell lysate using Direct-zol RNA Miniprep kit. Possible residual DNA contamination was removed by TURBO DNase digestion. RNA was further purified using RNA Clean and Concentrator kit. Ribosome profiling was performed in HEK293T cells following a modified protocol based on McGlincy and Ingolia (PMID: 22836135) and Buchbender et al. (PMID: 31610236 ). Briefly, the lysate was treated with RNase I and nuclease digestion was stopped by adding SUPERase In RNase inhibitor. Ribosomes were isolated by sucrose cushion and centrifugation with a TLA100.3 rotor at 100.000 rpm, 4°C for 1 h. RNA was purified from the resuspended ribosomal pellet using the Direct-zol kit according to the manufacturer’s instructions for purifying total RNA including small RNAs. The eluted RNA was precipitated with ethanol. For the purification of ribosome-protected fragments, RNA samples were run on a 15% polyacrylamide TBE-urea gel for 65 min at 200 V next to synthesized marker oligonucleotides of defined length (26 nt and 34 nt). The gel regions between 26–34 nt were excised for each sample and RNA was eluted in the extraction followed by phenol-chloroform extraction and RNA precipitation with isopropanol. T4 polynucleotide kinase was used to remove 2'-3' cyclic phosphate from ribosomal footprints. After 3’-linker ligation, unligated 3'-linker was removed from the reaction by the addition of 0.5 µl yeast 5'-deadenylase and 0.5 µl RecJ exonuclease. The RNA was purified with Oligo Clean and Concentrator kit according to the manufacturer’s instructions.
RNA libraries for RNA-seq were prepared using TruSeq Strand-Specific mRNA Library Prep with PolyA-Selection. For Ribo-seq, reverse transcription was performed at the following conditions: 25°C for 5 min, 55°C for 60 min, 80°C for 5 min, and held at 4°C. To remove RNA, alkaline hydrolysis was performed. The second adapter ligation to the 3' end of the cDNA was performed as described in Buchbender et al. (PMID: 31610236 ). Briefly, after reverse transcription, cDNA clean-up was performed using MyONE Silane beads. Next, a second linker ligation was performed using different L##clip2.0 oligoes for each sample. 5'-linker ligated cDNA was cleaned up using MyONE Silane beads. Different from the published protocol (ref), we omitted PCR preamplification, followed by the first ProNex size selection before preparative PCR amplification, and instead performed just one PCR amplification step with an optimized number of cycles. After PCR, different samples were multiplexed in equal molarities. The library was then purified, and size-selected on ProNex magnetic beads. The library was sequenced on an Illumina NextSeq 500 with more than 40 million reads

treatment_protocol:
m6A depletion was performed by adding medium supplemented with 20 µM STM2457 in DMSO 0.2% (v/v) or with DMSO 0.2% (v/v) alone as control for 6 h.
At 1 h prior cell lysis, 10% extra FBS was added to the cell culture medium to boost translation. Cells were treated with cycloheximide (CHX) at 100 µg/ml for 3 min at 37 °C directly before lysis.

value: Human embryonic kidney 293T cell line

tag: genotype

value: WT


title: RNA_S3848Nr15


title: HEK293T (RNA-Seq), STM2457, 6h, rep4


title: HEK293T (Ribo-Seq), STM2457, 6h, rep1


title: HEK293T (Ribo-Seq), DMSO, 6h, rep2

value: DMSO 6h


title: In vitro miCLIP2, unmethylated, rep 1


title: HEK293T (RNA-Seq), STM2457, 6h, rep3


title: HEK293T (Ribo-Seq), DMSO, 6h, rep1


title: HEK293T (RNA-Seq), STM2457, 6h, rep1


title: HEK293T (RNA-Seq), DMSO, 6h, rep4


title: HEK293T (RNA-Seq), DMSO, 6h, rep1


title: HEK293T (RNA-Seq), DMSO, 6h, rep2


title: HEK293T (RNA-Seq), DMSO, 6h, rep3


title: HEK293T (RNA-Seq), STM2457, 6h, rep2


title: RNA_S3848Nr18


title: IL13+ OM treated EPC2-hTERT ALI

status: Public on Sep 13 2024

city: Philadelphia

first: Megha

last: Lal

state: PA

address: 3615 Civic Center Blvd

institute: Children's Hospital of Philadelphia

description: 1x100bp configuration

data_processing:
Quality assessment of sequencing reads was performed using FastQC 0.12.0, and contaminant ribosomal RNA was filtered out using FastQ Screen 0.14.1. The subsequent read alignment against GRCh38.p13 reference genome using STAR aligner 2.7.1a. The alignment files were then converted to BAM format, sorted, and indexed using samtools 1.12. Post-alignment quality assessment was executed using Qualimap v2.2.1. Reads were then counted for exonic regions based on gene IDs utilizing the gencode.v29.annotation.gtf annotation file and featureCounts in subread-2.0.2. Summary plots for the output files from the intermediate analysis were generated using MultiQC 1.10. Subsequent downstream RNA-seq analysis was performed using R version 4.3.2. Genes with counts less than 10 in at least three samples were removed to ensure robustness in the analysis. The gene expression data was normalized for sequencing depth and library composition using variance stabilizing transformation followed by differential gene expression analysis using DESeq2 1.44.0.
Assembly: hg38 (GRCh38.p13)
Supplementary files format and content: tab-delimited text file that includes raw counts for each sample
Supplementary files format and content: tab-delimited text file that includes log2 normalized for each sample

growth_protocol: The immortalized human esophageal epithelial cell line EPC2-hTERT (EPC2) was cultured in keratinocyte serum-free media (KSFM, Thermo Fisher Scientific, USA) containing 0.09 mM Ca2+ supplemented with 1ng/ml recombinant human epidermal growth factor (EGF), 50 µg/ml bovine pituitary extract (BPE), 100 U/ml penicillin and 100 µg/ml streptomycin at 37 °C in a humidified 5% CO2 incubator. Upon confluency (~80%), calcium concentration was increased to 1.8 mM and cultured for 48 h to promote maturation and differentiation.  EPC2 cells were cultured in KSFM media using 0.4 µm trans wells from Corning Life Science as air-liquid interface (ALI) culture. On day 3 of the culture, the cells were switched to high-calcium (1.8 mM) containing KSFM for five days to initiate differentiation. On day 7, the culture media from the upper chamber was removed to encourage terminal differentiation and stratification.

extract_protocol:
On day 14, the upper chamber was lysed using Tri Reagent (Sigma-Aldrich), and the cell extract was stored at -80°C until further use. RNA was extracted for traditional RNA sequencing using RNeasy Mini Kit (Qiagen) according to the manufacturer’s protocol.
RNA sequencing libraries of ALI were prepared using the Illumina TruSeq RiboZero kit, followed by single-end sequencing on the Illumina NovaSeq 6000 platform.

treatment_protocol: From days 10 to 14, the basolateral chamber media was supplemented with 100 ng/ml recombinant human IL-13 (SRP3274, Sigma–Aldrich), 50 µM acid-activated omeprazole (O104, Sigma–Aldrich), or a combination of both.

value: Esophageal keratinocyte cell line

value: IL13 - 100ng/ml and Omeprazole (acid activated) - 50uM


title: OM treated EPC2-hTERT ALI

description: 1x100bp configuration

value: Omeprazole (acid activated) - 50uM


title: Mammary Glands Cells, 3M, Rep1

status: Public on Sep 29 2024

city: Farmington

first: Olga

last: Anczukow

state: CT

address: 10 Discovery Drive

institute: The Jackson Laboratory for Genomic Medicine

description: FFPE Spatial Transcriptomic Sequencing

data_processing:
For ST, FASTQ files were generated using the 10X Genomics Space Ranger. FASTQ files, the image registration JSON file, and associated OMETIFF corresponding to high resolution bright field image were used for further processing, including alignment to the GRChm38 mm10-specific filtered probe set (10x Genomics Mouse Probeset v1.0.0) using the version 2.1.0 Space Ranger count pipeline (10x Genomics).
Assembly: mm10
Supplementary files format and content: barcodes.tsv.gz: list of spatial barcodes
Supplementary files format and content: features.tsv.gz: list of gene IDs
Supplementary files format and content: matrix.mtx.gz: gene expression count data in Matrix Market Exchange Format
Supplementary files format and content: aligned_fiducials.jpg: aligned fiducials of the tissue image
Supplementary files format and content: detected_tissue_image.jpg: image of tissue and spots
Supplementary files format and content: scalefactors_json.json: scalefactors in json format
Supplementary files format and content: tissue_hires_image.png: hi-res image of tissue
Supplementary files format and content: tissue_lowres_image.png: low-res image of tissue
Supplementary files format and content: tissue_positions_list.csv: list of spatial barcodes and the coordinates specifying spots
Supplementary files format and content: aligned_tissue_image.jpg
Supplementary files format and content: cytassist_image.tiff
Supplementary files format and content: spatial_enrichment.csv
Supplementary files format and content: tissue_positions.csv
Supplementary files format and content: original_image.tiff
Library strategy: Spatial Transcriptomics

source_name: Mammary Gland

extract_protocol:
For ST, fresh mammary tissue was NBF-fixed and paraffin-embedded, and 15 um sections were cut with cryostat.
Spatial transcriptome sequencing libraries were prepared with 10X Genomics Visium CytAssist Slides and according to manufacturer's protocol construction.

value: Mammary Gland

tag: age

value: 3 months

tag: gender

value: Female

tag: strain

tag: virginity status

value: Virgin

tag: replicate


title: Mammary Glands Cells, 18M, ST, Rep1

description: FFPE Spatial Transcriptomic Sequencing

value: Mammary Glands

value: Mammary Cells (Epithelial, Stromal and Immune)

value: 18 months

tag: Sex


title: Control EPC2-hTERT ALI

description: 1x100bp configuration

value: Control


title: Sample1, Parous, scRNAseq

status: Public on Sep 12 2024

city: Santa Cruz

first: Paloma

last: Medina

address: 1156 High St.

institute: University of California Santa Cruz

description: 10x Genomics

data_processing:
Reads were mapped to the mouse genome (mm10). Additionally, we integrated an 18m NP data set from Tabula Muris Senis. For quality control, we used Scanpy’s ‘scanpy.pp.calculate_qc_metrics’68, ‘scanpy.pp.filter_genes’, and scanpy.pp.filter_cells.  We applied filters to eliminate (1) genes that are detected in less than 3 cells, (2) cells that have less than 200 genes, (3) cells with gene counts < 600 or > 8,000, (4) cells with total counts of UMIs per cell < 2,000 or > 12,000, and (5) cells with mitochondrial gene ratio > 1.5%. The mitochondrial gene ratio was defined as the percentage of UMIs mapped to mitochondrial genes compared to non-mitochondrial genes within each cell; cells with a high ratio are indicative of non-viable or apoptotic cells. Doublets were identified using Scrublet and these cells were then filtered out. After filtering, the datasets were concatenated, the UMI counts for each cell were then normalized using a target sum of 1e4, and then log transformed using ‘scanpy.pp.log1p’, which computes X=log(X+1), where log denotes the natural logarithm.
Assembly: mm10
Supplementary files format and content: tab-separated values files and matrix files

extract_protocol:
L2-5 and R2-5 mammary glands were harvested, minced, and chemically digested overnight in Advanced DMEM F/12 with 1% PSA, gentle collagenase/hyaluronidase, and DNAse I at 37C, 5% CO2, and added humidity. Partially digested glands were then mechanically digested by pipetting with a serological pipette until no tissue pieces were visible. Digested glands were washed with staining buffer (Hank’s Balanced Salt Solution, 2% Bovine Calf Serum, 1% PSA) and centrifuged (1500RPM) at 4C for 5 minutes. Red blood cells were lysed with 5mL of ACK Lysis buffer for 5 minutes, and cells were washed with 15mL of staining buffer. Cells were treated with 0.25% Trypsin with EDTA and gently pipetted continuously for 2-3 minutes to digest the basement membrane. Cells were then treated with DNAse I and Dispase and pipetted continuously for 2-3 minutes to prevent clumping. The single-cell suspension was then filtered through a 40um mesh strainer and pelleted via centrifugation (1500RPM, 4C, 5 minutes).
We performed scRNA-seq from mammary glands of 18m NP and 18m P mice using 10x chromium scRNA-seq (n=3 mice/group) as per manufacture’s protocol using the 5’ Gene expression workflow. Libraries were sequenced on the Illumina NovoSeq 6000 Platform by Medgenome, Inc.

value: C57Bl/6 WT


title: WT_MEF_Sense,rep1

status: Public on Aug 23 2024

city: Boston

first: Shengze

last: Yao

state: MA

address: 185 Cambridge St

department: Molbio

institute: Massachusetts General Hospital

library_source: genomic

data_processing:
The raw data underwent adaptor removal using trim_galore/cutadapt (versions 0.4.3/1.7.1). Subsequently, the trimmed reads were aligned to the genomes of Mus musculus (mus) and Mus castaneus (cas) using NovoAlign (version 4.03). These aligned reads were then mapped back to the reference mm10 genome using SNPs, resulting in the generation of a BAM file. To maintain uniform library sizes across the samples, random sampling with Samtools (version 1.11) was implemented. CHART coverage profiles were generated using DeepTools (version 3.1.2), resulting in the creation of bigwig files (rpkm-normalized).
To identify Xist CHART peaks on autosomes, reads from chromosome X (chrX) were initially excluded from the BAM file. Peak calling was performed using MACS2 (version 2.1) with default parameters. The resultant peak files from two replicates were merged using bedtools (version 2.30) intersect.
Assembly: mm10
Supplementary files format and content: bigwig
Supplementary files format and content: narrowPeak
Library strategy: CHART-SEQ

molecule: genomic DNA

growth_protocol: Mouse embryonic stem cells (mESCs) were initially cultured on γ-irradiated MEF feeders using DMEM medium supplemented with high-glucose, 15% FBS, and LIF. To induce differentiation, ES cell colonies were dissociated with trypsin and transferred to a LIF-devoid medium, where they were maintained in suspension for 4 days to generate embryoid bodies (EBs). On the fourth day, the EBs were plated onto gelatin-coated dishes and allowed to further differentiate until harvested at specified time points. MEF cells were cultured separately in DMEM medium supplemented with high-glucose and 10% FBS.

extract_protocol:
For CHART-seq, 25 million cells underwent crosslinking with 1% formaldehyde, followed by additional crosslinking of nuclei with 3% formaldehyde. Chromatin was then sheared via sonication to produce fragments ranging from 2 to 4kb. Pre-clearing of the chromatin was performed using MyOne Streptavidin C1 beads. Subsequently, the pre-cleared chromatin was mixed with 36 pmol of either antisense (targeting Xist) or sense (used as a control) biotin-labeled probes, and captured using MyOne Streptavidin C1 beads. After washing the beads, DNA was eluted using RNase H. Eluted DNA was extracted via phenol-chloroform and further sheared to achieve fragments below 500 bp.
The library was generated using the NEBNext® Ultra™ II DNA Library Prep Kit.

value: CAST/EiJ x 129S1

value: MEF

value: wild-type

tag: pull down_oligo

value: Control sense oligos

value: female

tag: time


title: WT_MEF_Input,rep2


title: WT_MEF_Xist,rep1

value: Antisense oligos to Xist


title: Sample2, Nulliparous, scRNAseq

description: 10x Genomics


title: WT_MEF_Xist,rep2


title: WT_MEF_Input,rep1


title: K562-wt SF3B1 iCLIP replicate 3

status: Public on Aug 19 2024

data_processing:
Quality control: FastQC (v0.11.8)
Barcode and adapter trimming: Flexbar (v3.4.0) with non-default parameters: --zip-output GZ --threads 4 --stdout-log --adapter-seq AGATCGGAAGAGCGGTTCAG --adapter-min-overlap 1 --min-read-length 15 --length-dist --umi-tags --barcode-unassigned samplebarcodes.fasta (using sample barcodes: K562-wt U2AF2 iCLIP replicate 1: NNNTATTNN, K562-wt U2AF2 iCLIP replicate 2: NNNGCGTNN, K562-wt U2AF2 iCLIP replicate 3: NNNATCGNN, K562-wt U2AF2 iCLIP replicate 4: NNNTACGNN, K562-wt SF3B1 iCLIP replicate 2: NNNGCGTNN, K562-wt SF3B1 iCLIP replicate 3: NNNATCGNN, K562-wt SF3B1 iCLIP replicate 4: NNNTACGNN, K562-K700E/wt SF3B1 iCLIP replicate 1: NNNCGAGNN, K562-K700E/wt SF3B1 iCLIP replicate 3: NNNATACNN)
U2AF2 Mapping: STAR (v2.7.3a) with non-default parameters: --genomeDir Gencode_release26_GRCh38.p10 --sjdbGTFfile gencode.v26.primary_assembly.annotation.gtf --sjdbOverhang 50 --runThreadN 4 --alignEndsType Extend5pOfRead1 --outFilterMismatchNoverReadLmax 0.04 --outFilterMismatchNmax 999 --outFilterMultimapNmax 1 --outSAMattributes All --outReadsUnmapped Fastx --outSJfilterReads Unique --readFilesCommand zcat --outSAMtype BAM SortedByCoordinate
SF3B1 Mapping: STAR (v2.6.1b) with non-default parameters: --genomeDir Gencode_release31_GRCh38.p12 --sjdbGTFfile gencode.v31.primary_assembly.annotation.gtf --sjdbOverhang 91 --runThreadN 4 --alignEndsType Extend5pOfRead1 --outFilterMismatchNoverReadLmax 0.04 --outFilterMismatchNmax 999 --outFilterMultimapNmax 1 --outSAMattributes All --outReadsUnmapped Fastx --outSJfilterReads Unique --readFilesCommand zcat --outSAMtype BAM SortedByCoordinate
Duplicate removal: UMI-tools (v1.0.0) with non-default parameter: umi_tools dedup --method unique
BAM to BED shifting one base upstream: bedtools (v2.27.1): bedtools bamtobed -i SAMPLE.duprm.bam | bedtools shift -m 1 -p -1 -i stdin -g CHROMOLENGTHTABLE | sort -k1,1 -k2,2n > SAMPLE.shifted1bp.bed
BED to bedGraph considering only 5' end positions: bedtools (v2.27.1): bedtools genomecov -bg -strand + -5 -i SAMPLE.shifted1bp.bed -g CHROMOLENGTHTABLE > SAMPLE.duprm.plus.bedgraph; bedtools genomecov -bg -strand - -5 -i SAMPLE.shifted1bp.bed -g CHROMOLENGTHTABLE > SAMPLE.duprm.minus.bedgraph
bedGraph to bigWig using bedGraphToBigWig of the UCSC tool suite (v365)
For quantitative comparisons of K562-SF3B1wt/wt and K562-SF3B1K700E/wt iCLIP, all replicates were randomly subsampled to match the crosslink events of the smallest sample before binding site definition.
Binding sites were identified from the merged crosslink events of SF3B1wt/wt and SF3B1wt/K700E as described in Busch et al., Methods, 2014, PMID:24184352. For this, the crosslink events of all five replicates were combined and subjected to peak calling with PureCLIP (version 1.3.1) with default parameters. The PureCLIP-called sites (Psites) were filtered by first removing 5% of the Psites with the lowest score associated and then keeping only the top 20% of Psites within each gene annotated (GENCODE release 36, GRCh38; only annotations with a gene support level of 1 or 2 and transcript support level from 1 to 3). The Psites were then merged into binding sites using the R/Bioconductor package BindingSiteFinder (version 1.0.3), using the following options: width of 5 nt (bsSize =5); ≥ 2 Psites (minWidth = 2, minClSites = 1) and ≥ 1 crosslink positions within each binding site (minCrosslinks = 1). In brief, Psites closer than 5 nt were merged into regions, and isolated Psites were discarded. Within each region, binding site centers were iteratively placed at the position with most crosslink events and extended by 2 nt on both sides. Binding site centers were required to harbor the maximum crosslink signal within the binding site. The optimal binding site width of 5 nt was determined by an evaluation of the ratio of crosslink events within binding sites of increasing width over the mean background signal in flanking windows of the same size. Next, binding sites that were not supported by all replicates in at least one condition (SF3B1wt/wt or SF3B1wt/K700E) were filtered out. The threshold for sufficient coverage in a replicate was determined using the 5th percentile and a lower boundary of two crosslink events as described in Busch et al., Methods, 2014, PMID:24184352. Finally, binding sites were assigned to target genes using GENCODE annotation (release 36, GRCh38; filtered as above) as described in Busch et al.
Assembly: GRCh38.p12 (SF3B1), GRCh38.p10 (U2AF2)
Supplementary files format and content: bigWig (crosslink events per nucleotide)
Supplementary files format and content: bed (coordinates of binding sites)

source_name: chronic lymphoid leukemia derived

growth_protocol: For iCLIP experiments, K562 cells were grown in Iscove Modified Dulbecco Media (IMDM) supplemented with 2 mM L-glutamine (Sigma-Aldrich) and 10% fetal bovine serum (SIGMA-Aldrich) at 37°C with 5% CO2 and 95% humidity.

extract_protocol:
iCLIP was performed as described in Huppertz et al, Methods, 2014, PMID:24184352. Exponentially growing K562 cells were pelleted, washed once with PBS and 10x10^6 were subjected to UV-cross linking at 400 mJ/cm2 at 254 nm in 6 ml PBS in a 10 cm petri dish on ice. Crosslinked cells were scraped from the dish, collected by centrifugation for 2 min at 500 xg at 4 °C, snap frozen in liquid nitrogen and stored at -80 °C. About 3x10^6 cells were immunoprecipitated using antibody. For iCLIP of K562-SF3B1wt/wt and K562-SF3B1K700E/wt, 10 µg of a monoclonal SF3B1 antibody (clone 16, MBL D221-3) was used. For U2AF2 iCLIP, 10 µg of a monoclonal U2AF2 antibody (Sigma, U4759) was used.
iCLIP library preparation was performed as described in Huppertz et al, Methods, 2014, PMID:24184352.

treatment_protocol: Exponentially growing K562 cells were pelleted, washed once with PBS and 10x10^6 were subjected to UV crosslinking at 400 mJ/cm2 at 254 nm in 6 ml PBS in a 10 cm petri dish on ice.

value: chronic lymphoid leukemia derived

tag: rrid

value: SF3B1 wt/wt

tag: provider

value: Horizon Discovery, HD PAR-131

value: UV crosslinking

tag: iclip antibody


title: K562-wt SF3B1 iCLIP replicate 2

tag: tissue

value: monoclonal SF3B1 antibody (clone 16, MBL D221-3)


title: K562-K700E/wt SF3B1 iCLIP replicate 1

tag: cell line

value: SF3B1 K700E/wt

value: Horizon Discovery, HD 181-012


title: K562-K700E/wt SF3B1 iCLIP replicate 3


title: genomic DNA from liver tissue [R8_9]

status: Public on Jul 30 2024

type: genomic

city: Rochester

first: Keith

middle: Darrell

last: Robertson

state: MN

address: 200 First Street SW, Stabile 12-70

department: Pharmacology

institute: Mayo Clinic

hyb_protocol: Bisulfite converted DNA was amplified, fragmented and hybridised to Illumina Infinium Human MethylationEPIC Beadchip using the standard Illumina protocol

scan_protocol: Standard Illumina Protocol

data_processing:
minfi
matrix processed value definition: preprocessSWAN
matrix signal intensities value definition: Unmethylated and methylated signal intensities

label: Cy5 and Cy3

source_name: Disease liver tissue

label_protocol: Standard Illumina Protocol

extract_protocol: Qiagen DNeasy

tag: tissue type

value: Disease

value: liver


title: genomic DNA from liver tissue [R8_8]


title: K562-wt U2AF2 iCLIP replicate 4

value: ATCC

tag: treatment

value: monoclonal U2AF2 antibody (Sigma, U4759)


title: K562-wt U2AF2 iCLIP replicate 3


title: K562-wt U2AF2 iCLIP replicate 1


title: K562-wt U2AF2 iCLIP replicate 2


title: genomic DNA from liver tissue [R8_7]


title: genomic DNA from liver tissue [R8_6]


title: Mammary Glands Cells, 3M, Rep2

description: FFPE Spatial Transcriptomic Sequencing

value: Mammary Gland

value: 3 months

value: Female

value: Virgin


title: genomic DNA from liver tissue [R8_5]


title: genomic DNA from liver tissue [R8_4]


title: IL13 treated EPC2-hTERT ALI

description: 1x100bp configuration

tag: cell type

value: Esophageal keratinocyte cell line

value: WT


title: genomic DNA from liver tissue [R8_3]


title: Mammary Glands Cells, 18M, ST, Rep2

description: FFPE Spatial Transcriptomic Sequencing

value: Mammary Glands

value: Mammary Cells (Epithelial, Stromal and Immune)

value: 18 months


title: genomic DNA from liver tissue [R8_11]


title: genomic DNA from liver tissue [R8_10]


title: genomic DNA from liver tissue [R8_2]


title: WT_MEF_Sense,rep2

value: CAST/EiJ x 129S1

value: MEF

tag: genotype

value: wild-type

value: Control sense oligos

value: female


title: genomic DNA from liver tissue [R7_9]

value: Disease

value: liver


title: genomic DNA from liver tissue [R8_1]


title: genomic DNA from liver tissue [R7_5]


title: genomic DNA from liver tissue [R7_8]


title: genomic DNA from liver tissue [R7_7]


title: K562-wt SF3B1 iCLIP replicate 4

value: chronic lymphoid leukemia derived

value: SF3B1 wt/wt

value: Horizon Discovery, HD PAR-131

value: UV crosslinking

value: monoclonal SF3B1 antibody (clone 16, MBL D221-3)


title: genomic DNA from liver tissue [R7_6]


title: genomic DNA from liver tissue [R7_2]


title: genomic DNA from liver tissue [R7_10]


title: genomic DNA from liver tissue [R7_4]


title: genomic DNA from liver tissue [R7_1]


title: genomic DNA from liver tissue [R7_3]


title: genomic DNA from liver tissue [R6_9]


title: genomic DNA from liver tissue [R6_5]


title: genomic DNA from liver tissue [R6_6]


title: genomic DNA from liver tissue [R6_8]


title: genomic DNA from liver tissue [R6_4]


title: genomic DNA from liver tissue [R6_3]


title: genomic DNA from liver tissue [R6_2]


title: genomic DNA from liver tissue [R6_7]


title: genomic DNA from liver tissue [R6_10]


title: genomic DNA from liver tissue [R6_1]


title: genomic DNA from liver tissue [R56_7]


title: genomic DNA from liver tissue [R56_8]


title: genomic DNA from liver tissue [R56_5]


title: genomic DNA from liver tissue [R56_1]


title: genomic DNA from liver tissue [R56_3]


title: genomic DNA from liver tissue [R55_9]


title: genomic DNA from liver tissue [R56_4]


title: genomic DNA from liver tissue [R55_6]


title: genomic DNA from liver tissue [R55_8]


title: genomic DNA from liver tissue [R55_7]


title: genomic DNA from liver tissue [R55_5]


title: genomic DNA from liver tissue [R55_3]


title: genomic DNA from liver tissue [R55_16]


title: genomic DNA from liver tissue [R55_14]


title: genomic DNA from liver tissue [R55_2]


title: genomic DNA from liver tissue [R55_17]


title: genomic DNA from liver tissue [R55_11]


title: genomic DNA from liver tissue [R55_12]


title: genomic DNA from liver tissue [R55_1]


title: genomic DNA from liver tissue [R48_8]


title: genomic DNA from liver tissue [R48_9]


title: genomic DNA from liver tissue [R48_7]


title: genomic DNA from liver tissue [R48_6]


title: genomic DNA from liver tissue [R56_6]


title: genomic DNA from liver tissue [R48_5]


title: genomic DNA from liver tissue [R48_4]


title: genomic DNA from liver tissue [R48_1]


title: genomic DNA from liver tissue [R48_2]


title: genomic DNA from liver tissue [R48_3]


title: genomic DNA from liver tissue [R47_8]


title: genomic DNA from liver tissue [R48_10]


title: genomic DNA from liver tissue [R47_9]


title: genomic DNA from liver tissue [R55_4]


title: genomic DNA from liver tissue [R47_7]


title: genomic DNA from liver tissue [R47_10]


title: genomic DNA from liver tissue [R47_5]


title: genomic DNA from liver tissue [R47_6]


title: genomic DNA from liver tissue [R55_15]


title: genomic DNA from liver tissue [R47_1]


title: genomic DNA from liver tissue [R55_13]


title: genomic DNA from liver tissue [R43_9]


title: genomic DNA from liver tissue [R55_10]


title: genomic DNA from liver tissue [R43_7]


title: genomic DNA from liver tissue [R43_6]


title: genomic DNA from liver tissue [R43_8]


title: genomic DNA from liver tissue [R43_5]


title: genomic DNA from liver tissue [R43_4]


title: genomic DNA from liver tissue [R43_3]


title: genomic DNA from liver tissue [R43_10]


title: genomic DNA from liver tissue [R4_9]


title: genomic DNA from liver tissue [R43_2]


title: genomic DNA from liver tissue [R43_1]


title: genomic DNA from liver tissue [R4_8]


title: genomic DNA from liver tissue [R4_7]


title: genomic DNA from liver tissue [R47_4]


title: genomic DNA from liver tissue [R4_5]


title: genomic DNA from liver tissue [R4_4]


title: genomic DNA from liver tissue [R4_3]


title: genomic DNA from liver tissue [R4_1]


title: genomic DNA from liver tissue [R4_6]


title: genomic DNA from liver tissue [R47_3]


title: genomic DNA from liver tissue [R4_2]


title: genomic DNA from liver tissue [R37_3]


title: genomic DNA from liver tissue [R4_10]


title: genomic DNA from liver tissue [R37_2]


title: genomic DNA from liver tissue [R37_1]


title: genomic DNA from liver tissue [R28_7]


title: genomic DNA from liver tissue [R28_8]


title: genomic DNA from liver tissue [R28_6]


title: genomic DNA from liver tissue [R28_9]


title: genomic DNA from liver tissue [R28_4]


title: genomic DNA from liver tissue [R28_3]


title: genomic DNA from liver tissue [R28_2]


title: genomic DNA from liver tissue [R28_10]


title: genomic DNA from liver tissue [R28_5]


title: genomic DNA from liver tissue [R21_8]


title: genomic DNA from liver tissue [R21_6]


title: genomic DNA from liver tissue [R21_5]


title: genomic DNA from liver tissue [R21_4]


title: genomic DNA from liver tissue [R21_9]


title: genomic DNA from liver tissue [R37_4]


title: genomic DNA from liver tissue [R21_3]


title: genomic DNA from liver tissue [R21_2]


title: genomic DNA from liver tissue [R21_10]


title: genomic DNA from liver tissue [R20_6]


title: genomic DNA from liver tissue [R20_8]


title: genomic DNA from liver tissue [R20_7]


title: genomic DNA from liver tissue [R20_9]


title: genomic DNA from liver tissue [R20_5]


title: genomic DNA from liver tissue [R20_26]


title: genomic DNA from liver tissue [R20_4]


title: genomic DNA from liver tissue [R20_3]


title: genomic DNA from liver tissue [R20_21]


title: genomic DNA from liver tissue [R20_22]


title: genomic DNA from liver tissue [R20_25]


title: genomic DNA from liver tissue [R20_24]


title: genomic DNA from liver tissue [R28_1]


title: genomic DNA from liver tissue [R20_20]


title: genomic DNA from liver tissue [R21_7]


title: genomic DNA from liver tissue [R20_19]


title: genomic DNA from liver tissue [R20_18]


title: genomic DNA from liver tissue [R20_15]


title: genomic DNA from liver tissue [R20_2]


title: genomic DNA from liver tissue [R20_16]


title: genomic DNA from liver tissue [R20_14]


title: genomic DNA from liver tissue [R20_13]


title: genomic DNA from liver tissue [R20_12]


title: genomic DNA from liver tissue [R20_11]


title: genomic DNA from liver tissue [R20_10]


title: genomic DNA from liver tissue [R2_9]


title: genomic DNA from liver tissue [R2_8]


title: genomic DNA from liver tissue [R20_1]


title: genomic DNA from liver tissue [R2_6]


title: genomic DNA from liver tissue [R20_23]


title: genomic DNA from liver tissue [R2_7]


title: genomic DNA from liver tissue [R2_4]


title: genomic DNA from liver tissue [R2_5]


title: genomic DNA from liver tissue [R2_2]


title: genomic DNA from liver tissue [R2_19]


title: genomic DNA from liver tissue [R2_3]


title: genomic DNA from liver tissue [R2_20]


title: genomic DNA from liver tissue [R2_18]


title: genomic DNA from liver tissue [R2_17]


title: genomic DNA from liver tissue [R2_16]


title: genomic DNA from liver tissue [R2_15]


title: genomic DNA from liver tissue [R2_14]


title: genomic DNA from liver tissue [R2_13]


title: genomic DNA from liver tissue [R2_12]


title: genomic DNA from liver tissue [R2_11]


title: genomic DNA from liver tissue [R2_10]


title: genomic DNA from liver tissue [R19_9]


title: genomic DNA from liver tissue [R19_8]


title: genomic DNA from liver tissue [R19_6]


title: genomic DNA from liver tissue [R19_7]


title: genomic DNA from liver tissue [R19_5]


title: genomic DNA from liver tissue [R19_4]


title: genomic DNA from liver tissue [R19_3]


title: genomic DNA from liver tissue [R19_2]


title: genomic DNA from liver tissue [R19_11]


title: genomic DNA from liver tissue [R19_10]


title: genomic DNA from liver tissue [R19_1]


title: genomic DNA from liver tissue [R18_9]


title: genomic DNA from liver tissue [R18_8]


title: genomic DNA from liver tissue [R18_7]


title: genomic DNA from liver tissue [R18_5]


title: genomic DNA from liver tissue [R18_4]


title: genomic DNA from liver tissue [R18_2]


title: genomic DNA from liver tissue [R18_1]


title: genomic DNA from liver tissue [R17_7]


title: genomic DNA from liver tissue [R17_6]


title: genomic DNA from liver tissue [R17_4]


title: genomic DNA from liver tissue [R17_10]


title: genomic DNA from liver tissue [R17_1]


title: genomic DNA from liver tissue [R16_9]


title: genomic DNA from liver tissue [R16_7]


title: genomic DNA from liver tissue [R16_8]


title: genomic DNA from liver tissue [R16_5]


title: genomic DNA from liver tissue [R16_2]


title: genomic DNA from liver tissue [R18_6]


title: genomic DNA from liver tissue [R16_12]


title: genomic DNA from liver tissue [R16_10]


title: genomic DNA from liver tissue [R16_1]


title: genomic DNA from liver tissue [R16_4]


title: genomic DNA from liver tissue [R17_9]


title: genomic DNA from liver tissue [R17_8]


title: genomic DNA from liver tissue [R15_5]


title: genomic DNA from liver tissue [R15_7]


title: genomic DNA from liver tissue [R15_9]


title: genomic DNA from liver tissue [R17_3]


title: genomic DNA from liver tissue [R17_5]


title: genomic DNA from liver tissue [R15_8]


title: genomic DNA from liver tissue [R18_3]


title: genomic DNA from liver tissue [R15_4]


title: genomic DNA from liver tissue [R17_2]


title: genomic DNA from liver tissue [R15_3]


title: genomic DNA from liver tissue [R15_2]


title: genomic DNA from liver tissue [R2_1]


title: genomic DNA from liver tissue [R15_1]


title: genomic DNA from liver tissue [R16_6]


title: genomic DNA from liver tissue [R14_9]


title: genomic DNA from liver tissue [R14_8]


title: genomic DNA from liver tissue [R16_3]


title: genomic DNA from liver tissue [R14_6]


title: genomic DNA from liver tissue [R14_2]


title: genomic DNA from liver tissue [R14_5]


title: genomic DNA from liver tissue [R14_3]


title: genomic DNA from liver tissue [R15_6]


title: genomic DNA from liver tissue [R14_4]


title: genomic DNA from liver tissue [R14_19]


title: genomic DNA from liver tissue [R14_16]


title: genomic DNA from liver tissue [R14_17]


title: genomic DNA from liver tissue [R14_18]


title: genomic DNA from liver tissue [R14_10]


title: genomic DNA from liver tissue [R14_15]


title: genomic DNA from liver tissue [R14_11]


title: genomic DNA from liver tissue [R14_13]


title: genomic DNA from liver tissue [R13_9]


title: genomic DNA from liver tissue [R14_14]


title: genomic DNA from liver tissue [R14_12]


title: genomic DNA from liver tissue [R14_1]


title: genomic DNA from liver tissue [R13_8]


title: genomic DNA from liver tissue [R13_5]


title: genomic DNA from liver tissue [R13_7]


title: genomic DNA from liver tissue [R13_6]


title: genomic DNA from liver tissue [R15_10]


title: genomic DNA from liver tissue [R13_4]


title: genomic DNA from liver tissue [R14_7]


title: genomic DNA from liver tissue [R13_3]


title: genomic DNA from liver tissue [R13_10]


title: genomic DNA from liver tissue [R13_1]


title: genomic DNA from liver tissue [R11_9]


title: genomic DNA from liver tissue [R11_8]


title: genomic DNA from liver tissue [R11_7]


title: genomic DNA from liver tissue [R11_6]


title: genomic DNA from liver tissue [R11_4]


title: genomic DNA from liver tissue [R11_5]


title: genomic DNA from liver tissue [R11_3]


title: genomic DNA from liver tissue [R11_2]


title: genomic DNA from liver tissue [R11_10]


title: genomic DNA from liver tissue [R11_1]


title: genomic DNA from liver tissue [R10_9]


title: genomic DNA from liver tissue [R10_8]


title: genomic DNA from liver tissue [R10_6]


title: genomic DNA from liver tissue [R13_2]


title: genomic DNA from liver tissue [R10_7]


title: genomic DNA from liver tissue [R10_5]


title: genomic DNA from liver tissue [ND9_0]

source_name: Normal liver tissue

value: Normal


title: genomic DNA from liver tissue [ND7_0]


title: genomic DNA from liver tissue [R10_2]


title: genomic DNA from liver tissue [ND6_0]


title: genomic DNA from liver tissue [R10_1]


title: genomic DNA from liver tissue [R10_3]


title: genomic DNA from liver tissue [ND8_0]


title: genomic DNA from liver tissue [R10_10]


title: genomic DNA from liver tissue [ND5_9]


title: genomic DNA from liver tissue [ND5_7]


title: genomic DNA from liver tissue [ND5_8]


title: genomic DNA from liver tissue [ND5_6]


title: genomic DNA from liver tissue [ND5_5]


title: genomic DNA from liver tissue [ND5_4]


title: genomic DNA from liver tissue [ND5_1]


title: genomic DNA from liver tissue [ND5_0]


title: genomic DNA from liver tissue [ND4_0]


title: genomic DNA from liver tissue [ND5_2]


title: genomic DNA from liver tissue [ND3_9]


title: genomic DNA from liver tissue [ND3_8]


title: genomic DNA from liver tissue [ND3_7]


title: genomic DNA from liver tissue [R10_4]


title: genomic DNA from liver tissue [ND3_6]


title: genomic DNA from liver tissue [ND3_1]


title: genomic DNA from liver tissue [ND3_5]


title: genomic DNA from liver tissue [ND3_4]


title: genomic DNA from liver tissue [ND3_2]


title: genomic DNA from liver tissue [ND3_3]


title: genomic DNA from liver tissue [ND3_0]


title: genomic DNA from liver tissue [ND10_7]


title: genomic DNA from liver tissue [ND10_9]


title: genomic DNA from liver tissue [ND10_3]


title: genomic DNA from liver tissue [ND10_8]


title: genomic DNA from liver tissue [ND10_0]


title: genomic DNA from liver tissue [ND10_6]


title: genomic DNA from liver tissue [ND10_4]


title: Disease Liver R8-9

data_processing:
STAR
HTseq
gencode_v22
Assembly: hg38
Supplementary files format and content: HTseq raw counts

source_name: liver

extract_protocol:
Qiagen Rneasy
Illumina TruSeq Stranded mRNA

tag: disease state


title: genomic DNA from liver tissue [ND5_3]


title: Disease Liver R8-7


title: Disease Liver R8-8


title: Disease Liver R8-5


title: Disease Liver R8-4


title: genomic DNA from liver tissue [ND10_2]


title: Disease Liver R8-3


title: Disease Liver R8-6


title: Disease Liver R8-2


title: Disease Liver R8-1


title: Disease Liver R8-11


title: Disease Liver R8-10


title: genomic DNA from liver tissue [ND10_5]


title: Disease Liver R6-2


title: Disease Liver R6-4


title: Disease Liver R6-5


title: genomic DNA from liver tissue [ND10_1]


title: Disease Liver R6-1


title: Disease Liver R56-8


title: Disease Liver R56-7


title: Disease Liver R56-3


title: Disease Liver R56-6


title: Disease Liver R56-5


title: Disease Liver R56-1


title: Disease Liver R55-9


title: Disease Liver R56-4


title: Disease Liver R55-7


title: Disease Liver R55-6


title: Disease Liver R55-8


title: Disease Liver R55-3


title: Disease Liver R55-5


title: Disease Liver R55-2


title: genomic DNA from liver tissue [R21_1]


title: Disease Liver R55-16


title: Disease Liver R55-4


title: Disease Liver R55-1


title: Disease Liver R6-6


title: Disease Liver R55-17


title: Disease Liver R6-3


title: Disease Liver R55-14


title: Disease Liver R55-15


title: Disease Liver R55-13


title: Disease Liver R55-12


title: Disease Liver R47-9


title: Disease Liver R55-10


title: Disease Liver R47-7


title: Disease Liver R47-8


title: Disease Liver R55-11


title: Disease Liver R47-6


title: Disease Liver R47-5


title: Disease Liver R47-4


title: Disease Liver R47-3


title: Disease Liver R43-9


title: Disease Liver R47-1


title: Disease Liver R43-7


title: Disease Liver R43-6


title: Disease Liver R47-10


title: Disease Liver R43-5


title: Disease Liver R43-4


title: Disease Liver R43-3


title: Disease Liver R43-2


title: Disease Liver R43-1


title: Disease Liver R37-4


title: Disease Liver R37-2


title: Disease Liver R43-10


title: Disease Liver R37-1


title: Disease Liver R33-6


title: Disease Liver R33-3


title: Disease Liver R33-5


title: Disease Liver R33-4


title: Disease Liver R33-1


title: Disease Liver R32-5


title: Disease Liver R32-7


title: Disease Liver R32-6


title: Disease Liver R32-4


title: Disease Liver R43-8


title: Disease Liver R32-12


title: Disease Liver R28-9


title: Disease Liver R32-11


title: Disease Liver R28-7


title: Disease Liver R28-6


title: Disease Liver R28-8


title: Disease Liver R37-3


title: Disease Liver R28-5


title: Disease Liver R23-8


title: Disease Liver R23-7


title: Disease Liver R23-9


title: Disease Liver R23-6


title: Disease Liver R28-1


title: Disease Liver R28-3


title: Disease Liver R28-10


title: Disease Liver R28-2


title: Disease Liver R32-9


title: Disease Liver R23-5


title: Disease Liver R32-8


title: Disease Liver R32-1


title: Disease Liver R23-2


title: Disease Liver R23-3


title: Disease Liver R22-7


title: Disease Liver R23-10


title: Disease Liver R22-6


title: Disease Liver R32-10


title: Disease Liver R22-5


title: Disease Liver R22-4


title: Disease Liver R28-4


title: Disease Liver R22-1


title: Disease Liver R23-1


title: Disease Liver R20-8


title: Disease Liver R22-3


title: Disease Liver R20-7


title: Disease Liver R20-9


title: Disease Liver R20-6


title: Disease Liver R22-2


title: Disease Liver R23-4


title: Disease Liver R20-4


title: Disease Liver R20-26


title: Disease Liver R20-5


title: Disease Liver R20-24


title: Disease Liver R20-23


title: Disease Liver R20-2


title: Disease Liver R20-20


title: Disease Liver R20-3


title: Disease Liver R20-22


title: Disease Liver R20-21


title: Disease Liver R20-18


title: Disease Liver R20-1


title: Disease Liver R20-13


title: Disease Liver R20-19


title: Disease Liver R20-12


title: Disease Liver R20-16


title: Disease Liver R20-15


title: Disease Liver R20-10


title: Disease Liver R20-11


title: Disease Liver R2-6


title: Disease Liver R2-4


title: Disease Liver R2-5


title: Disease Liver R2-7


title: Disease Liver R2-8


title: Disease Liver R2-2


title: Disease Liver R20-25


title: Disease Liver R2-3


title: Disease Liver R2-11


title: Disease Liver R2-1


title: Disease Liver R2-10


title: Disease Liver R19-7


title: Disease Liver R19-6


title: Disease Liver R19-8


title: Disease Liver R2-15


title: Disease Liver R19-1


title: Disease Liver R19-3


title: Disease Liver R19-10


title: Disease Liver R19-4


title: Disease Liver R20-14


title: Disease Liver R19-11


title: Disease Liver R18-6


title: Disease Liver R18-7


title: Disease Liver R18-9


title: Disease Liver R19-9


title: Disease Liver R2-9


title: Disease Liver R19-5


title: Disease Liver R18-8


title: Disease Liver R18-5


title: Disease Liver R18-3


title: Disease Liver R18-2


title: Disease Liver R18-1


title: Disease Liver R17-9


title: Disease Liver R17-8


title: Disease Liver R17-7


title: Disease Liver R17-6


title: Disease Liver R17-4


title: Disease Liver R17-5


title: Disease Liver R17-10


title: Disease Liver R16-8


title: Disease Liver R16-9


title: Disease Liver R16-7


title: Disease Liver R16-6


title: Disease Liver R16-3


title: Disease Liver R16-2


title: Disease Liver R16-5


title: Disease Liver R15-8


title: Disease Liver R16-4


title: Disease Liver R15-6


title: Disease Liver R16-1


title: Disease Liver R15-3


title: Disease Liver R16-12


title: Disease Liver R15-2


title: Disease Liver R15-7


title: Disease Liver R14-9


title: Disease Liver R16-10


title: Disease Liver R14-6


title: Disease Liver R14-8


title: Disease Liver R14-3


title: Disease Liver R15-5


title: Disease Liver R14-7


title: Disease Liver R14-5


title: Disease Liver R14-4


title: Disease Liver R14-2


title: Disease Liver R15-1


title: Disease Liver R17-3


title: Disease Liver R14-19


title: Disease Liver R14-1


title: Disease Liver R14-17


title: Disease Liver R14-18


title: Disease Liver R14-15


title: Disease Liver R14-14


title: Disease Liver R14-13


title: Disease Liver R14-12


title: Disease Liver R14-10


title: Disease Liver R11-7


title: Disease Liver R11-8


title: Disease Liver R11-6


title: Disease Liver R11-4


title: Disease Liver R11-5


title: Disease Liver R11-3


title: Disease Liver R11-2


title: Disease Liver R11-1


title: Normal Liver ND5-7


title: Normal Liver ND5-5-


title: Normal Liver ND5-4


title: Normal Liver ND5-3


title: Normal Liver ND5-2


title: Normal Liver ND5-1


title: Normal Liver ND3-9


title: Disease Liver R14-16


title: Normal Liver ND3-7


title: Normal Liver ND3-5


title: Normal Liver ND3-6


title: Normal Liver ND3-4


title: Disease Liver R14-11


title: Normal Liver ND3-2


title: Normal Liver ND3-3-


title: Disease Liver R11-9


title: Normal Liver ND3-1


title: Normal Liver ND10-9


title: Normal Liver ND10-8


title: Normal Liver ND10-7


title: Normal Liver ND5-9


title: Normal Liver ND10-6


title: Normal Liver ND5-6


title: Normal Liver ND10-5


title: Normal Liver ND5-8


title: Normal Liver ND10-3


title: Normal Liver ND10-4


title: Normal Liver ND10-2


title: Disease Liver R11-10


title: Normal Liver ND3-8


title: NPC_RNAseq, rep3

status: Public on Jul 10 2024

city: St. Louis

first: Jennifer

middle: E.

last: Phillips-Cremins

state: MO

address: 4515 McKinley Ave

department: Bioengineering

institute: Washington University in St. Louis

description:
GSE220103_DESeq2_normalized_counts.txt
lNPC_rep3

data_processing:
RNA-seq paired-end reads were aligned using kallisto (v0.46.1) quant with 100 bootstraps of transcript quantification (Bray et al., 2016). Following pseudoalignment by kallisto, quantifications of estimated counts were converted into DESeq2 format in R using the library ("tximportData") according to DESeq2 documentation recommendations (Love et al., 2014). Normalized counts were computed for each replicate and each cellular stage using DESeq2 (v1.22.1).
The processed data files are abundance tsv files directly from the software Kallisto and a normalized counts txt file derived from the output of the software DESeq2.
Assembly: hg38
Supplementary files format and content:DESeq2_normalized_counts.txt, abundance.tsv

growth_protocol: Neural progenitors and cortical neurons were differentiated from iPSCs using a previously described protocol (Paquet et al 2016), with some modifications. One day (24 hrs) prior to starting differentiation, StemFlex media was replaced to Essential 6 (E6) media (Thermo Fisher Scientific, A1516401) on iPSCs growing on 10 cm cell culture dishes. Induction of neural precursor cell differentiation was initiated by detaching iPSCs from the plates by addition of 4 ml Accutase (Thermo Fisher Scientific, #A1110501). Accutase was inactivated and single cell suspension was achieved by triturating cells with 16 ml of E6 media. Cells were counted and centrifuged for 4 minutes at 200 x g and then resuspended in E6 media supplemented with ROCK inhibitor (Stem Cell Technologies, #72304) at a 1 million cell/ml final concentration. 1 mL of cell suspension was seeded onto each well of a flat bottom 12-well tissue culture plate (Falcon, #353043), pre-coated with 0.75 ml Geltrex hESC-Qualified Reduced Growth Factor Basement Membrane Matrix (Thermo Fisher Scientific, A1569601). Cells were fully confluent next day. Medium was replaced to 2 mL neural induction (NI) medium (235 ml DMEM/F12 GlutaMax (Thermo Fisher Scientific, #10565-018), 235 ml Neurobasal media (Thermo Fisher Scientific, #21203-049), 5ml B27 supplement (Thermo Fisher Scientific, #17504-044), 5 ml GlutaMax supplement (Thermo Fisher Scientific, #35050) 5 ml Non-Essential Amino Acid (Thermo Fisher Scientific, #11140-050), 2.5 ml N-2 supplement (Thermo Fisher Scientific, #17502048), 2.5 ml of 500µg/ml Insulin (Sigma, #I1882), 2-mercapto-ethanol (Thermo Fisher Scientific, #21985-023) and 10 ml Penicillin-Streptomycin (Thermo Fisher Scientific, #15140-122)) supplemented with LDN-193189 (Selleckchem, #S2618, 1:10000) and SB431542 (Selleckchem, #S1067, 1:1000). The day of change to NI media was counted as day in vitro 0 (DIV0). Cells were maintained in NI media for 8 days (DIV8), changing media every day. To differentiate neural precursor cells (NPCs), DIV8 cells were enzymatically dissociated using Accutase (Thermo Fisher Scientific, #A1110501), inactivated by fresh media, and centrifuged at 150 x g. Pelleted cells were resuspended at 30 million cells per ml in NI medium, supplemented with ROCK inhibitor (Stem Cell Technologies, #72304). Cells were plated as 250-μl spots onto dry 6-well plates, pre-coated with poly-L-ornithine (Sigma-Aldrich, #P4957) for at least 8 hours and laminin (Thermo Fisher Scientific, #23017015, 20x dilution) overnight and then dried completely. Cells were left to adhere within the spots to the dry coated plate surface for ~45 minutes. After cells were attached, 2 ml NI medium supplemented with ROCK inhibitor were slowly added to avoid detachment of the cells. Media was daily changed until DIV10. On DIV10 NI media was replaced with neural maintenance (NM) media (235 ml DMEM/F12 GlutaMax (Thermo Fisher Scientific, #10565-018), 235 ml Neurobasal media (Thermo Fisher Scientific, #21203-049), 5 ml B27 supplement (Thermo Fisher Scientific, #17504-044), 5 ml GlutaMax supplement (Thermo Fisher Scientific, #35050) 5 ml Non-Essential Amino Acid (Thermo Fisher Scientific, #11140-050), 2.5 ml N-2 supplement (Thermo Fisher Scientific, #17502048), 2.5 ml of 500µg/ml Insulin (Sigma, #I1882), 450µl 2-mercapto-ethanol (Thermo Fisher Scientific, #21985-023), and 10 ml Penicillin-Streptomycin (Thermo Fisher Scientific, #15140-122)). 20 ng ml FGF-2 (Stem Cell Technologies Inc., #78003) were added for 2 days as soon as neural rosettes were apparent in the cultures. When cells with neuronal morphology started to emerge from rosettes, rosettes were manually isolated under the microscope under sterile conditions. As a first step of rosette isolation, the culture was treated with STEMdiff Neural Rosette Selection Reagent (Stem Cell Technologies Inc., #5832) for 1 hr. Manually isolated rosettes were washed with fresh media and re-plated on poly-L-ornithine/laminin-coated 6-well plates at a very high density. Cells were cultivated in NM media until DIV35, when NPC samples were harvested.   To further differentiate cells into terminally differentiated, post-mitotic neurons, NPC rosettes were triturated into single cells using Accutase, followed by inactivation with fresh media (Neurobasal medium supplemented with B-27 serum-free supplement (Thermo Fisher Scientific, #17504044), 2 mM GlutaMax (Thermo Fisher Scientific, #35050061), 1% (v/v) Penicillin-Streptomycin (Thermo Fisher Scientific, #15140-122)). Cells were seeded at a ~200,000–500,000 NPCs per well density onto a 24-well poly-L-ornithine/laminin-coated plate. In the first 7 days after plating, 10 μM DAPT (Sigma-Aldrich, #D5942) and 5-FU to promote neuronal maturation was added to the media to eliminate proliferating cells and enhancing terminal differentiation of the culture. Post-mitotic cortical neurons were harvested at DIV65.

extract_protocol:
Total RNA, including non-coding and coding RNA, was extracted from 3 biological replicates of iPSCs, NPCs and neurons using the Direct-zol total RNA isolation kit (Zymo, #R2061) according to the manufacturer’s protocol. Integrity of the RNA samples (RIN) were assessed using Agilent RNA 6000 Pico reagent kit on the Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA).
500 ng of isolated total RNA with a RIN value above 8 was used for ribosomal RNA depleted strand-specific RNA library preparation, using the TruSeq Stranded Total RNA LT sample preparation kit with Ribo-Zero Gold (Illumina, #RS-122-2301). TruSeq RNA Single Indexes Set A and B were used and ligated onto cDNA (Illumina, #20020492) to enable multiplex sequencing. Library clean-up and size selection of ~300 bp fragments was performed by Agencourt AMPure XP beads (Beckman Coulter, #A63881) prior to PCR amplification and index incorporation for 15 cycles. Library quality and quantities were assessed using the Qubit high sensitivity DNA assay kit (Thermo Fisher Scientific, Q32851) and the Agilent DNA 1000 reagent kit (Agilent Technologies, 5067–1504) on the Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA). Sequencing of multiplexed, pooled samples were performed on Illumina NextSeq500 using a 150-cycle v2.5 High Output kit (Illumina, Cat# 20024907), generating 2 x 75bp, paired-end reads.

value: iPSC derived late stage NPC

value: WT


title: Neuron_RNAseq, rep2

description:
GSE220103_DESeq2_normalized_counts.txt
neu_rep2

value: iPSC derived cortical neuron


title: NPC_RNAseq, rep2

description:
GSE220103_DESeq2_normalized_counts.txt
lNPC_rep2


title: Neuron_RNAseq, rep3

description:
GSE220103_DESeq2_normalized_counts.txt
neu_rep3


title: NPC_RNAseq, rep1

description:
GSE220103_DESeq2_normalized_counts.txt
lNPC_rep1


title: iPSC_RNAseq, rep3

description:
GSE220103_DESeq2_normalized_counts.txt
iPS_rep3

value: iPSC


title: iPSC_RNAseq, rep2

description:
GSE220103_DESeq2_normalized_counts.txt
iPS_rep2


title: iPSC_RNAseq, rep1

description:
GSE220103_DESeq2_normalized_counts.txt
iPS_rep1


title: Neuron_CTCF_H3K27ac_Input

data_processing:
We aligned RNA Polymerase II (RNAPII), CTCF, and H3K27ac ChIP-seq reads to the hg38 genome using Bowtie v0.12.7 and removed reads with more than two possible alignments. To account for sequencing depth differences, we downsampled all immunoprecipitation libraries to 24 million reads. We also downsampled input libraries to 24 million reads. We used MACS2 version 2.1.1.20160309 to call peaks with a p-value cutoff of 1 × 10−4 and a broad peak cutoff of 1 × 10−4 for RNAPII and H3K37ac. We also generated bedgraph pileups using the -B and --SPMR flags. In our analysis, we noticed that the RNAPII, CTCF, and H3K27ac ChIP-seq signals were not proportional across libraries. To address this, we divided each pileup interval by scalar size factors. We determined each size factor by finding the maximum global signal for each chromosome, finding the mean of those maximums, and then dividing that mean by 14.106 for the RNAPII libraries, 11.339 for the CTCF libraries, and 9.965 for the H3K27ac libraries.
Assembly: hg38
Supplementary files format and content: For HiC: The processed data files are the standard _allValidPairs file format from the output of the software HiC-Pro. The data includes all paired reads that are valid.
Supplementary files format and content: For ChIP-seq: The ChIP-seq processed data files are in the ENCODE narrowPeak, broadPeak, or bigwig format

growth_protocol: We differentiated human iPSC-derived cortical neurons as previously described (Paquet et al., 2016). 24 hours prior to beginning the differentiation, we replaced StemFlex media consisting of stable bFGF to Essential 6 (E6) media (Thermo Fisher Scientific, A1516401) on the undifferentiated iPS cells growing on 10 cm tissue culture dish. We detached iPSCs from the plates by adding 4 ml Accutase (Thermo Fisher Scientific, A1110501) and gently resuspended to generate single cells. We added 16 ml of E6 media to inactivate Accutase, and counted cells. We spin the cells for 4 minutes at 200g and resuspended the cells at a 1 million cell/ml cell concentration in E6 media supplemented with ROCK inhibitor (Stem Cell Technologies, #72304). We seeded iPSCs on flat bottom 12-well tissue culture plates (Falcon, #353043) coated with 0.75 ml Geltrex hESC-Qualified Reduced Growth Factor Basement Membrane Matrix (Thermo Fisher Scientific, A1569601). Next day, or when cells were 100% confluent, we replaced medium with neural induction (NI) medium  (235 ml Neurobasal media (Thermo Fisher Scientific, #21203-049), 235 ml DMEM/F12 GlutaMax (Thermo Fisher Scientific, #10565-018), 10 ml Penicillin-Streptomycin (Thermo Fisher Scientific, #15140-122), 5ml B27 supplement (Thermo Fisher Scientific, #17504-044), 5 ml GlutaMax supplement (Thermo Fisher Scientific, #35050) 5 ml Non-Essential Amino Acid (Thermo Fisher Scientific, #11140-050), 2.5 ml N-2 supplement (Thermo Fisher Scientific, #17502048), 2.5 ml of 500µg/ml Insulin (Sigma, I1882), 2-mercapto-ethanol (Thermo Fisher Scientific, #21985-023)) supplemented with SB431542 (Selleckchem, S1067, 1:1000) and LDN-193189 (Selleckchem, S2618, 1:10000). We counted the day of neural induction (NI) medium change as day in vitro 0 (DIV0). We maintained cells in NI media for 8 days (DIV8), changing media daily. On DIV8, we dissociated early neural progenitor cells using Accutase (Thermo Fisher Scientific, A1110501) and resuspended at 30 million cells per ml in NI medium supplemented with ROCK inhibitor. We seeded cells onto dried poly-L-ornithine (Sigma-Aldrich, P4957) and laminin-coated (Thermo Fisher Scientific, 23017015) 6-well plates in 250-μl spots. We left cells to adhere for ~45 minutes and then we added 2 ml NI medium supplemented with ROCK inhibitor. On DIV10 we replaced NI media with neural maintenance (NM) medium. Upon the appearance of neural rosettes, we added 20 ng ml FGF-2 (Stem Cell Technologies Inc., 78003) for 2 days. When neurons started to emerge from rosettes, we isolated those manually after treatment with STEMdiff Neural Rosette Selection Reagent (Stem Cell Technologies Inc., 5832) for 1 hour. We washed rosettes and plated on poly-L-ornithine/laminin-coated 6-well plates. Between DIV30 and DIV36 we either collected NPCs for downstream genomic analysis and immunostainings, or further cultivated for terminal differentiation to cortical neurons.

extract_protocol:
We fixed the cells for all downstream ChIP-seq assays as previously described (Beagan et al., 2017; Beagan et al., 2016; Beagan et al., 2020; Phillips-Cremins et al., 2013). Briefly, we fixed 8-10 million cells by adding 1 % (v/v) formaldehyde in DMEM/F-12 (Thermo Fisher, 11320033) for 10 min at room temperature. The complete fixation media was 50 mM HEPES-KOH (pH 7.5), 100 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 11 % formaldehyde. We quenched the fixation process by adding 125 mM glycine for 5 min at room temperature, following by 15 min at 4 °C. We washed the cross-linked cells in pre-chilled PBS before flash freezing and stored the pellets at −80 °C. We then lysed in pre-chilled cell lysis buffer (10 mM Tris pH 8.0, 10 mM NaCl, 0.2 % (v/v) NP-40, 1 mM PMSF, 0.2 % (v/v) Protease inhibitor cocktail (Sigma, P8340)) and incubated on ice for 10 min. We homogenized cells using dounce homogenizer with pestle A (30 times). We centrifuged lysates at 2,500 g for 5 min at 4 °C to pellet nuclei. We solubilized nuclei in 500 μl nuclear lysis buffer (50 mM Tris pH 8.0, 10 mM EDTA, 1 % (w/v) SDS, 1 mM PMSF, 0.2 % (v/v) Protease inhibitor cocktail) on ice for 20 min before mixing with 300 μl IP dilution buffer (20 mM Tris pH 8.0, 2 mM EDTA, 150 mM NaCl, 1 % (v/v) Triton X-100, 0.01 % (w/v) SDS, 1 mM PMSF, 0.2 % Protease inhibitor cocktail). Next, we sheared chromatins to 200-600 bp DNA fragment size using Qsonica Q800R3 (Qsonica Sonicators, CT) with parameters of 100 % amplitude and 30 sec on / 30 sec off pulses. After sonication we centrifuged lysates at 16,000 g and added pre-clearing solution (50 μg of IgG (Sigma, I8140), 175 μl of Protein A Agarose (Thermo Fisher, 15918014) and 175 μl of Protein G Agarose (Thermo Fisher, 15920010) in 3.7 ml of pre-chilled IP dilution buffer and 0.5 ml of nuclear lysis buffer) to each supernatant. We incubated the samples by rotating at 10 rpm for 2 hrs at 4 °C. Subsequently, we performed target protein-associated chromatin immunoprecipitation from supernatant using antibody-bound beads by rotating the samples at 10 rpm overnight at 4 °C. Next day, we washed pull down beads once in IP wash buffer 1 (20 mM Tris pH 8.0, 2 mM EDTA, 50 mM NaCl, 1 % (v/v) TritonX-100, 0.1 % (w/v) SDS), twice in high salt buffer (20 mM Tris pH 8.0, 2 mM EDTA, 500 mM NaCl, 1 % (v/v) TritonX-100, 0.01 % (w/v) SDS), once in IP wash buffer 2 (10 mM Tris pH 8.0, 1 mM EDTA, 250 mM lithium chloride, 1 % (v/v) NP-40, 1 % (w/v/) sodium deoxycholate), twice in 1 x TE at 4 °C. We eluted chromatins from beads in elution buffer (1 % (w/v) SDS, 100 mM sodium bicarbonate), and degraded RNA by adding RNaseA (Roche, 10109169001) at 65 °C for 1 hr. To remove residual proteins and reverse crosslink DNA, we added 2.4 U of Proteinase K (NEB, P8107S) to eluent and incubated at 65°C overnight. We purified ChIP DNA using phenol chloroform extraction and ethanol precipitation methods. We stored ChIP DNA at -20 °C until we performed library preparation. We used 5 ng of purified ChIP-seq DNA for downstream library preparation.
We prepared all sequencing libraries using the NEBNext Ultra II Library Prep Kit (NEB, E7645S) following the manufacturer's protocol. We first end repaired, and dA-tailed DNA from ChIP experiments and ligated with Illumina adaptors using NEBNext Ultra II DNA Library Prep Kit for Illumina according to the manufacturer’s protocol with minor modifications. DNA fragments <1 kb size were size-selected and amplified in subsequent steps using 7-8 PCR cycles. After additional purification using AgenCourt Ampure XP beads (Beckman Coulter, A63881) according to the manufacturer’s instruction, we assessed quality of the individual libraries using Agilent Bioanalyzer High Sensitivity DNA Analysis Kits (Agilent, 5067-4626) and quantified using Kapa Library Quantification Kit (KAPA biosysytem, KK4835) prior to library pooling sequencing on Illumina NextSeq 500. We sequenced ChIP-seq libraries with reading length of 75 bp single-ended.


title: Neuron_RNAPII_Input


title: Neuron_RNAPII_IP

tag: antibody

value: RNAPII, Active Motif, 39097


title: Neuron_H3K27ac_IP

value: H3K27ac, Abcam, ab4729


title: Neuron_HiC

data_processing:
We aligned human 37bp paired-end reads independently to hg38 genome using bowtie2 (version 2.2.5) (Langmead et al., 2012 ) (global parameters:–very-sensitive –L 30 –score-min L,-0.6,-0.2 –end-to-end–reorder; local parameters:–very-sensitive –L 20 –scoremin L,-0.6,-0.2 –end-to-end–reorder) through the HiC-Pro software (version 2.7.7) (Servant et al., 2015 ). We filtered unmapped reads, non-uniquely mapped reads, and PCR duplicates. We assembled cis-contact matrices by binning paired reads into uniform 10 kb bins using custom scripts and then merged the matrices across replicates. Then, we balanced matrices using the Knight-Ruiz matrix balancing algorithm.
Assembly: hg38
Supplementary files format and content: For HiC: The processed data files are the standard _allValidPairs file format from the output of the software HiC-Pro. The data includes all paired reads that are valid.
Supplementary files format and content: For ChIP-seq: The ChIP-seq processed data files are in the ENCODE narrowPeak, broadPeak, or bigwig format

extract_protocol:
We fixed the cells for all downstream Hi-C assays as previously described (Beagan et al., 2017; Beagan et al., 2016; Beagan et al., 2020; Phillips-Cremins et al., 2013). Briefly, we fixed cells by adding 1 % (v/v) formaldehyde in DMEM/F-12 (Thermo Fisher, 11320033) for 10 min at room temperature. The complete fixation media was 50 mM HEPES-KOH (pH 7.5), 100 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 11 % formaldehyde. We quenched the fixation process by adding 125 mM glycine for 5 min at room temperature, following by 15 min at 4 °C. We washed the cross-linked cells in pre-chilled PBS before flash freezing and stored the pellets at −80 °C. We used ~2 million cross-linked cells per replicate per condition and processed two biological replicates per condition. We used Arima-HiC kit (Arima Genomics, Inc.; Cat # A510008) for Hi-C sample preparation according to the manufacturer's recommended protocol. Arima-HiC protocol utilizes multiple restriction enzymes to digest chromatin. Following restriction of the chromatin, we filled in the 5’-overhangs to label the digested ends with a biotinylated nucleotide.  Next, we ligated spatially proximal digested ends of DNA and purified the proximally-ligated DNA. We prepared samples for library preparation by shearing purified proximally-ligated DNA. We sheared to an average size of ~400 bp using a Covaris S220 sonicator at 140 W peak incident power, 10% duty factor, and 200 cycles per burst for 55 seconds. We performed size-selection to obtain 200-600 bp DNA fragments using DNA Purification Beads (AMPure XP Beads, Beckman Coulter, A63881). We enriched biotin-tagged ligation junctions after size-selection using Enrichment Beads (provided in Arima-HiC kit). We stored streptavidin beads containing enriched DNA fragments up to 3 days before proceeding with the library preparation step.
We prepared all sequencing libraries using the NEBNext Ultra II Library Prep Kit (NEB, E7645S) following the manufacturer's protocol. We first end repaired, and dA-tailed DNA from HiC experiments and ligated with Illumina adaptors using NEBNext Ultra II DNA Library Prep Kit for Illumina according to the manufacturer’s protocol with minor modifications. We washed adaptor-ligated Hi-C libraries on streptavidin beads twice in 150 μl of wash buffer at 55 °C and once in 100 μl of elution buffer (Arima Genomics, A510008), and then eluted ligation products from streptavidin beads by boiling at 98 °C for 10 min in 15 μl elution buffer. For ChIP-seq, we size-selected adaptor-ligated libraries using AgenCourt Ampure XP beads (Beckman Coulter, A63881) and then amplified using NEBNext Ultra II DNA Library Prep Kit for Illumina (NEB, E7645S) according to the manufacturer’s protocol. We sequenced Hi-C libraries with reading length 37 bp pair-ended.


title: NPC_CTCF_H3K27ac_Input

data_processing:
We aligned RNA Polymerase II (RNAPII), CTCF, YY1, and H3K27ac ChIP-seq reads to the hg38 genome using Bowtie v0.12.7 and removed reads with more than two possible alignments. To account for sequencing depth differences, we downsampled all immunoprecipitation libraries to 24 million reads. We also downsampled input libraries to 24 million reads. We used MACS2 version 2.1.1.20160309 to call peaks with a p-value cutoff of 1 × 10−4 and a broad peak cutoff of 1 × 10−4 for RNAPII and H3K37ac. We also generated bedgraph pileups using the -B and --SPMR flags. In our analysis, we noticed that the RNAPII, CTCF, YY1, and H3K27ac ChIP-seq signals were not proportional across libraries. To address this, we divided each pileup interval by scalar size factors. We determined each size factor by finding the maximum global signal for each chromosome, finding the mean of those maximums, and then dividing that mean by 14.106 for the RNAPII libraries, 11.339 for the CTCF libraries, 13.448 for the YY1 libraries, and 9.965 for the H3K27ac libraries.
See above, unique to each sample
Assembly: hg38
Supplementary files format and content: For HiC: The processed data files are the standard _allValidPairs file format from the output of the software HiC-Pro. The data includes all paired reads that are valid.
Supplementary files format and content: For ChIP-seq: The ChIP-seq processed data files are in the ENCODE narrowPeak, broadPeak, or bigwig format


title: iPS_CTCF_IP

description: Millipore, 07-729

growth_protocol: We prepared iPSC culture plates by coating 10 cm tissue culture plates (Corning, #430167) with 6 mL Geltrex hESC-Qualified Reduced Growth Factor Basement Membrane Matrix (Thermo Fisher Scientific, #A1569601) for at least 1 hour at 37 °C. We  cultured all iPSC lines in StemFlex culture media (Thermo Fisher Scientific, #A3349401) supplemented with 1 % (v/v) penicillin-streptomycin (Thermo Fisher Scientific, #15140122) at 37 °C and 5 % CO2. To passage, we detached cells from plates using 4 ml of Versene Solution (Thermo Fisher Scientific, 15040066) at 37 °C for 3 minutes before inactivation by adding 10 ml of culture media and seeded cells onto freshly prepared Geltrex-coated plates. We split cells every 2-4 days in a 1:3 to 1:8 dilution and passaged cells only up to 3 times prior to experiments.


title: iPSC_H3K27ac_IP

description: Abcam, ab4729


title: Normal Liver ND10-1


title: NPC_RNAPII_Input


title: NPC_CTCF_IP

description: Millipore, 07-729


title: iPS_RNAPII_IP

description: Active Motif, 39097


title: iPSC_HiC

data_processing:
We aligned human 37bp paired-end reads independently to hg38 genome using bowtie2 (version 2.2.5) (Langmead et al., 2012 ) (global parameters:–very-sensitive –L 30 –score-min L,-0.6,-0.2 –end-to-end–reorder; local parameters:–very-sensitive –L 20 –scoremin L,-0.6,-0.2 –end-to-end–reorder) through the HiC-Pro software (version 2.7.7) (Servant et al., 2015 ). We filtered unmapped reads, non-uniquely mapped reads, and PCR duplicates. We assembled cis-contact matrices by binning paired reads into uniform 10 kb bins using custom scripts and then merged the matrices across replicates. Then, we balanced matrices using the Knight-Ruiz matrix balancing algorithm.
See above, unique to each sample
Assembly: hg38
Supplementary files format and content: For HiC: The processed data files are the standard _allValidPairs file format from the output of the software HiC-Pro. The data includes all paired reads that are valid.
Supplementary files format and content: For ChIP-seq: The ChIP-seq processed data files are in the ENCODE narrowPeak, broadPeak, or bigwig format


title: NPC_H3K27ac_IP

description: Abcam, ab4729


title: NPC_HiC


title: iPS_RNAPII_Input


title: ATAC-seq of mus musculus:MLN iILC2_1

status: Public on Jun 27 2024

city: New York

first: Yuefeng

last: Huang

state: NY

address: 701W 168th Street

department: Microbiology & Immunology

institute: Columbia University

data_processing:
Bowtie2
Assembly: mm10
Supplementary files format and content: bigWig file

source_name: Mesenteric LN

extract_protocol:
Tagment DNA Buffer #15027866, Tagment DNA Enzyme #15027865 from Illumina Digitonin #G9441 from Promega
New England Biolabs #M0541

value: Mesenteric LN


title: ATAC-seq of mus musculus:MLN iILC2_2


title: ATAC-seq of mus musculus:gut ILC2_2

source_name: Gut

value: Gut

value: Untreated


title: NPC_RNAPII_IP

description: Active Motif, 39097


title: Neuron_RNAseq, rep1

description:
GSE220103_DESeq2_normalized_counts.txt
neu_rep1


title: ATAC-seq of mus musculus:gut ILC2_1


title: kidney, HFD, RNA 1275

status: Public on Jun 12 2024

city: Sant Joan Despí

first: Maruja

last: Navarro-Díaz

state: Barcelona

address: Oriol Martorell, 12

department: Nephrology

country: Spain

institute: Hospital Sant Joan Despí Moisès Broggi

data_processing:
Quality assessment of RNA using TapeStation 2200 and Bioanalyzer
Removal of genomic DNA using DNase I.
Trimming of adapter sequences from reads using Cutadapt.
Mapping and quantification of transcripts using Salmon and Subread/Rsubread.
Summarization and quantification of small RNA transcripts using annotations from miRBase v22 and Ensembl release 102.
Quality control checks including multi-dimensional scaling plot analysis and Euclidean distances between samples.
Differential expression testing using DESeq2 v1.24.0 Bioconductor’s package.
Cataloging miRNA target sites using multiMiR Bioconductor’s package.
Upregulation/downregulation classification of miRNAs based on adjusted p-value and Log2 fold change.
Assembly: Rnor6.0 genome
Supplementary files format and content: Text files includes raw counts for each sample

organism: Rattus norvegicus

source_name: kidney

extract_protocol:
RNA was obtained from frozen tissue blocks using Trizol Reagent (Invitrogen, Thermo Fisher). Genomic DNA was removed by incubation with DNase I for 10 minutes (Invitrogen) and QC was assessed in a TapeStation 2200 (Agilent, Santa Clara, USA). Half of RNA were used for RNA-Seq experiments and the rest were used for qPCR purposes. SmallRNAs from tissue were purified using 1mg of total isolated RNA following an enrichment protocol with SPRIselect beads (Bee Na Lee, Beckman Coulter, California, USA). qPCR validations were done . The urine samples were collected and 500ml (total cell-free and exosomal RNA) used for smallRNA obtention following the instructions of the ZR Urine RNA Isolation Kit (Zymo Research Corp, CA, USA). In both cases, assessment of QC was done in a Bioanalyzer using a High Sensitivity chip (Agilent).
RNA-seq Libraries were prepared from 1mg of total isolated RNA with the NEBNext® Ultra™ II Directional RNA Library Prep Kit for Illumina® (New England Biolabs, NEB, Massachusetts, USA), run in a HighSeq 2500 System (Illumina, CA, USA) and 40-50M of reads per sample were obtained. The quality of reads was assessed using the FastQC tool and a trimming step was applied to remove the adapters using the Cutadapt tool. Salmon tool was used to map and quantify the aligned transcripts to the Rnor6.0 genome. DESeq2 v1.24.0 Bioconductor’s package has been used for differential expression testing and the analysis of biological significance has been based on Gene Set Enrichment Analysis (GSEA) performed over two annotation databases: Gene Ontology (GO) and the Reactome Pathway Knowledge base. smallRNA libraries were prepared following the instructions of the NebNext Small RNA Library Prep Set for Illumina (NEB). Size selection of fragments was done in a 6% Polyacrylamide Gel (Invitrogen) and QC of libraries in a TapeStation 2200 using a HighSens D1000 screentape (Agilent). Libraries were sequenced in a HighSeq 2500 System (Illumina) and 12-15M reads per sample were obtained and analyzed. The quality of reads was assessed using the FastQC tool and a trimming step was applied to remove the adapters using the Cutadapt tool. The Subread/Rsubread package was used to map and quantify the aligned transcripts to the Rnor6.0 genome. For summarization/quantification step, annotations were obtained from different databases depending on the small RNA class: miRNA precursor and mature annotation was derived from miRBase v22 (http://www.mirbase.org/ftp.shtml), whereas annotations for other non-coding smallRNAs described in rat were derived from Ensembl release 102 (https: //www.ensembl.org/info/data/ftp/index.html). smallRNA libraries were prepared following the instructions of the NebNext Small RNA Library Prep Set for Illumina (NEB). Size selection of fragments was done in a 6% Polyacrylamide Gel (Invitrogen) and QC of libraries in a TapeStation 2200 using a HighSens D1000 screentape (Agilent). Libraries were sequenced in a HighSeq 2500 System (Illumina) and 12-15M reads per sample were obtained and analyzed. The quality of reads was assessed using the FastQC tool and a trimming step was applied to remove the adapters using the Cutadapt tool. The Subread/Rsubread package was used to map and quantify the aligned transcripts to the Rnor6.0 genome. For summarization/quantification step, annotations were obtained from different databases depending on the small RNA class: miRNA precursor and mature annotation was derived from miRBase v22 (http://www.mirbase.org/ftp.shtml), whereas annotations for other non-coding smallRNAs described in rat were derived from Ensembl release 102 (https: //www.ensembl.org/info/data/ftp/index.html). Different types of quality controls were also conducted (multi-dimensional scaling plot analysis, Euclidean distances between samples) to ensure the normalized data are appropriate for the differential expression analysis; as a result, no outliers or batch effects were detected. DESeq2 v1.24.0, a Bioconductor package, was utilized for the differential expression testing. miRNA target sites were cataloged in databases based on experimental validation and computational prediction using the multiMiR Bioconductor package (http://multimir.ucdenver.edu). DESeq2 v1.24.0 Bioconductor’s package was used for differential expression testing. miRNA target sites have been catalogued in databases based on experimental validation and computational prediction using the multiMiR Bioconductor’s package (http://multimir.ucdenver.edu).

value: kidney

value: Wistar rats

value: High Fat Diet


title: kidney, HFD, RNA 1271


title: kidney, HFD, RNA 1267


title: kidney, HFD, RNA 1274


title: kideny, HFD, RNA 1255


title: kidney, HFD, RNA 1259


title: Neuron_CTCF_IP

value: CTCF, Millipore, 07-729


title: iPSC_CTCF_H3K27ac_Input


title: kidney, HFD, RNA 1256


title: kidney, HFD, RNA 1252


title: Kidney cortex, WT22

status: Public on Jun 07 2024

city: Iowa city

first: Wenjie

last: Yu

state: IA

address: 200 Hawkins Dr, 6330 PBDB

institute: The University of Iowa

data_processing:
Kallisto based pseudoalignment and quantification
Reads for different transcripts of the same gene were aggregated
Assembly: Sscrofa11.1 from RefSeq database
Supplementary files format and content: tsv format files for individual samples containing counts (est_counts) and tpms for each transcript

organism: Sus scrofa

source_name: Kidney cortex

extract_protocol:
Kidney cortex tissues from similar position of newborn pigs were collected, total RNA of the tissues was extracted using a RNeasy Lipid Tissue Kit (Qiagen) according to the manufacturer’s instructions.
Illumina TruSeq Stranded Total RNA with RiboZero

value: Kidney cortex

tag: age

value: newborn

value: Wild-type


title: Kidney cortex, WT18


title: Kidney cortex, WT17


title: Kidney cortex, WT16


title: Kidney cortex, WT12


title: Kidney cortex, HET19

value: CFTR+/-


title: Kidney cortex, HET21


title: Kidney cortex, HET15


title: Kidney cortex, HET20


title: Kidney cortex, CF9

value: CFTR-/-


title: Kidney cortex, HET14


title: Kidney cortex, HET13


title: Kidney cortex, CF8


title: Kidney cortex, CF7


title: Kidney cortex, CF4


title: Kidney cortex, CF3


title: Kidney cortex, CF6


title: Kidney cortex, CF10


title: Kidney cortex, CF5


title: Kidney cortex, CF11


title: Kidney cortex, CF1


title: RNA-seq of mus musculus:Lung iILC2_2

status: Public on Jun 05 2024

data_processing:
Genomon RNA v2.6.2
Assembly: mm10
Supplementary files format and content: a csv file containing FPKM values

source_name: Lung

extract_protocol:
RNA was harvested using miRNeasy Micro Kit (Qiagen)
RNA libraries for RNA-seq were prepared using Single Cell/Low Input RNA Library Prep Kit for Illumina (New England BioLabs)

value: Lung

value: WT


title: Kidney cortex, WT23


title: RNA-seq of mus musculus:Lung iILC2_1


title: RNA-seq of mus musculus:MLN iILC2_2


title: RNA-seq of mus musculus:MLN iILC2_1


title: RNA-seq of mus musculus:gut iILC2_2


title: RNA-seq of mus musculus:gut iILC2_1


title: RNA-seq of mus musculus:gut ILC2_2


title: RNA-seq of mus musculus:gut ILC2_1