title: Human hepatocellular carcinoma cell: MHCC97H_sh-HNRNPM rep_2

city: shanghai

first: Gui-Qi

last: zhu

address: shanghai

institute: zhongshan hospital

data_processing:
Raw sequencing data was first filtered by Trimmomatic (version 0.36), low-quality reads were discarded and the reads contaminated with adaptor sequences were trimmed. Clean Reads were further treated with in-house scripts to eliminate duplication bias introduced in library preparation and sequencing.
Deduplicated Reads were mapped to the reference genome of homo sapiens from UCSC genome (http://genome.ucsc.edu/) using STRA software (version 2.5.3a) with default parameters. Reads mapped to the exon regions of each gene were counted by featureCounts (Subread-1.5.1; Bioconductor) and then RPKM was calculated. Genes differentially expressed between groups were identified using the edgeR package (version 3.12.1). p-value cutoff of 0.05 and Fold-change cutoff of 2 were used to judge the statistical significance of gene expression differences.
For lncRNA analysis, new lncRNAs were predicted. briefly, The mapped reads were spliced to obtain new transcripts by Stringtie(version 1.3.2). Then，new transcripts were fileted by four principle:1) Filter out the transcripts less than 200bp in length; 2) Filter out the transcripts containing less than 2 exons; 3) Include the transcripts containing at least 3 reads; 4) Remove known transcripts from the genome annotation file. Then, CPC2(version beta)、CPAT(version 1.2.4)、CNCI(version 2) and Pfam(version 27.0)were used to predict the coding potential of the selected transcripts. The transcripts predicted to be non-coding in all four softwares or database were referred to them as novel lncRNA.
The expression of novel lncRNA and genome annotated known lncRNA were counted by featureCounts (Subread-1.5.1; Bioconductor) and then RPKM was calculated. LncRNA differentially expressed between groups were identified using the edgeR package (version 3.12.1). P-value cutoff of 0.05 and fold-change cutoff of 2 were used to judge the statistical significance of lncRNA expression differences.
The target genes of differentially expressed lncRNA were predicted. 1) anti-sense targets were predicted by RIsearch(version 2.0), This software could calculate the minimum binding free energy, and the candidate target genes were those with the minimum binding free energy less than -50. 2) At the same time, Cis targets were predicted that genes within 100kb upstream and downstream of the lncRNA locus were selected as candidate target genes. 3)The co-expression of mRNA and lncRNA was analyzed by WGCNA(version 1.51) with both weight value and  the signed R2 scale free topology model fit greater than 0.8. Finally, the target genes of lncRNA are the union of anti-sense and Cis, and then the intersection with co-expression.
Genome_build: mm9
Supplementary_files_format_and_content: tab-delimited text files include FPKM values for each Sample ...
Supplementary_files_format_and_content: Matrix table with raw gene counts for every gene and every sample

source_name: Human hepatocellular carcinoma cell

extract_protocol:
Total RNAs were extracted from MHCC97H wild-type and sh-HNRNPM MHCC97H cell using TRIzol reagent (Invitrogen, cat. NO 15596026) following the methods by Chomczynski et al (DOI:10.1006/abio.1987.9999). DNA digestion was carried out after RNA extraction by DNaseI. RNA quality was determined by examining A260/A280 with NanodropTM OneCspectrophotometer (Thermo Fisher Scientific Inc). RNA Integrity was confirmed by 1.5% agarose gel electrophoresis. Qualified RNAs were finally quantified by Qubit3.0 with QubitTM RNA Broad Range Assay kit (Life Technologies，Q10210).
2 μg total RNAs were used for stranded RNA sequencing library preparation using Ribo-off rRNA Depletion Kit (Human/Mouse/Rat) (Catalog NO. MRZG12324, Illumina) and KC-DigitalTM Stranded mRNA Library Prep Kit for Illumina® (Catalog NO. DR08502, Wuhan Seqhealth Co., Ltd. China) following the manufacturer’s instruction. The kit eliminates duplication bias in PCR and sequencing steps, by using unique molecular identifier (UMI) of 8 random bases to label the pre-amplified cDNA molecules. The library products corresponding to 200-500 bps were enriched, quantified and finally sequenced on NovaSeq 6000 sequencer (Illumina) with PE150 model.

treatment_protocol: Two replicates MHCC97H cells knockdown HNRNPM expression

value: MHCC97H cells

tag: genotype

value: HNRNPM-knockdown


title: HOVON95: Newly diagnosed MM_17

status: Public on Jun 01 2022

type: RNA

city: Rotterdam

first: Mark

last: van Duin

address: Wytemaweg 80

country: Netherlands

institute: Erasmus MC

hyb_protocol: 13 ug of fragmented and biotin labeled cRNA hybridized to the microarray in an Affymetrix GeneChip® Hybridization Oven 645 and washed / stained in Affymetrix GeneChip Fluidics Station FS450DX2v2

scan_protocol: Affymetrix GeneChip® System 3000Dx v.2

data_processing: MAS5.0 (Bioconductor affy package v1.36.2) default values with a trimmed mean target intensity value set to 1500. Values below 30 were truncated.

label: biotin

molecule: total RNA

organism: Homo sapiens

source_name: bone marrow aspirate

label_protocol: Affymetrix Gene Profiling MDx IVD Expression Reagent Kit in accordance with Affymetrix protocols.

extract_protocol: RNeasy

treatment_protocol: Patients were treatment naive

tag: gender

value: Female

tag: age

tag: tissue

value: bone marrow aspirate

tag: disease state

value: multiple myeloma


title: 13_1_S9_R1_001

type: SRA

city: Amsterdam

first: Leandro

last: Moreno

address: Meibergdreef 9

institute: AmsterdamUMC

description: RNA-seq from peritoneal metastatic disease

library_source: transcriptomic

data_processing:
RNA - seq transcript quantification was performed by mapping the high quality reads to the GRCh38 human transcriptome by using the software RSEM 50 and the STAR aligner
Genome_build: GRCh38
Supplementary_files_format_and_content: Raw counts table

extract_protocol:
Frozen samples were immersed in RLT buffer (AllPrep DNA/RNA/miRNA Univers al Kit, Qiagen) and disrupted and homogenized using TissueLyser LT (Qiagen). Total RNA and DNA were isolated simultaneously from tissue lysates using the AllPrep DNA/RNA/miRNA Universal Kit (Qiagen), following the manufacturer’s instructions. The RNA and D NA concentration was measured using NanoDrop 2000 (Thermo Scientific) and Qubit fluorometer (Thermo Fisher Scientific
Libraries were prepared using Kapa mRNA HyperPrep, sequencing was performed using Illumina HiSeq (Single Read, 50 bp)

value: peritoneal metastatic disease


title: Control 117

first: Anton

middle: W.

last: Langerak

address: Doctor Molewaterplein 40

description: IGH gene repertoire sequencing from pre-diagnostic CLL patients and controls

library_source: genomic

data_processing:
Library strategy: B-cell receptor repertoire
Raw FASTQ files were uploaded to the ARResT/Interrogate immunoprofiler for annotation and initial exploratory analysis. The full productive IGH repertoire for each sample was downloaded in .csv format and processed in R (R Core Team, 2020) during further analysis.
Genome_build: GRCh38
Supplementary_files_format_and_content: Tab delimited tekst file containing: sample,QC.status,scenario,rearrangements,raw.count,w.junction.count,w.junction.percent,artefacts.count,artefacts.percent,usable.count,usable.percent,w.most.pop.cln.s..count,w.most.pop.cln.s..percent,uniq.usable.seq.count,clonotypes.count,diversity..norm.ed..count,effective.species.count,R1.Q.mean.stdev.count,R2.Q.mean.stdev.count,joined.normally.count,joined.normally.percent,joined.loosely.count,joined.loosely.percent,total.joined.count,total.joined.percent,saved.from.unjoined.count,saved.from.unjoined.percent,low.quality..ee.nt...0.01..count,low.quality..ee.nt...0.01..percent,short...50nt..count,short...50nt..percent,annotated.count,annotated.percent,w.TR.receptor.count,w.TR.receptor.percent,w.B.chain.count,w.B.chain.percent,w.VJ.Vb..Db..Jb.count,w.VJ.Vb..Db..Jb.percent,w.VJ.Vb..Db..Jb.cln

molecule: genomic DNA

source_name: Buffy coat

growth_protocol: Blood  samples were collected at from healthy volunteers, buffycoats and serum were stored at -80 °C until further analysis

extract_protocol:
Total  gDNA was isolated from the buffycoat DNA and 250-500 ng was amplified through multiplex PCR of IGHV-IGHD-IGHJ gene rearrangements following the BIOMED-2 protocol. After amplification, PCR products were purified on Agencourt AMPURE beads (Beckman Coulter, Brea, CA, USA) and eluted using H2O. The purified product was checked for primer dimers using the Agilent Tapestation (Agilent,Santa Clara, CA, USA).
The intitial multiplex PCR already attached the indexes to the PCR products, so library construction was simply pooling the purified PCR product in equilmolar fashion.

tag: cell type

value: leukocytes

tag: diagnosis

value: healthy

tag: sample type

value: Control


title: HOVON95: Newly diagnosed MM_18

value: Male


title: 11_2_S4_R1_001

source_name: RNA-seq from peritoneal metastatic disease


title: Human hepatocellular carcinoma cell: MHCC97H_sh-HNRNPM rep_1

country: China

tag: cell line


title: SZ16_GEX

city: Pittsburgh

first: Patrizia

last: Fuschiotti

state: PA

address: 200 Lothrop St

country: USA

institute: University of Pittsburgh

data_processing:
Chromium scRNA-seq data produced by the 10X Chromium Platform were processed to generate sample-specific fastq files.
Processed reads were then examined by quality metrics, mapped to a reference human genome using RNA-seq aligner STAR and assigned to individual cells of origin according to the cell specific barcodes, using the Cell Ranger pipeline (10X Genomics).
To ensure that PCR amplified transcripts were counted only once, only single UMIs were counted for gene expression level 10. In this way, cell x gene UMI counting matrices were generated for downstream analyses.
Seurat, an R package developed for single-cell analysis, was used to identify distinct cell populations and visualize cell clusters in graphs. Specifically, the UMI matrix was filtered such that only cells expressing at least 200 genes were utilized in downstream analysis.
Unwanted sources of variation were regressed out of the data by constructing linear models to predict gene expression based on the number of UMIs per cell as well as the percentage of mitochondrial gene content.
Hashtags were demultiplexed based on the Seurat package, https://satijalab.org/seurat/articles/hashing_vignette.html
Genome_build: GrCh38A genome
Supplementary_files_format_and_content: H5 files for gene expression, CSV files for TCR outputs

source_name: CTCL PBMCs

extract_protocol:
Skin biopsies were minced and digested enzymatically (Whole Dissociation Skin Kit, Miltenyi Biotec) for 2 hours at 37°C and further dispersed using the Miltenyi gentleMACS Octo Dissociator. Normal skin biopsies were hashed together 2 at a time per protocol: https://www.biolegend.com/en-us/protocols/totalseq-b-or-c-with-10x-feature-barcoding-technology Normal Sample 1 and 2 are labeled with HTO1 and HTO2 respectively. Normal sample 3 and 4 are labeled with HTO9 and HTO10 respectively. PBMCs were acquired from whole blood samples using Ficoll. Experimental procedures followed established techniques using the Chromium Single Cell 5’ Library V1 kit (10x Genomics).
Experimental procedures followed established techniques using the Chromium Single Cell 5’ Library V1 kit +V(D)J Enrichment Kit, Human T Cell (10x Genomics).
Single Cell 10x Genomics 5' Library V1 Chemistry +V(D)J Enrichment Kit, Human T Cell

tag: health status

value: Sezary Syndrome


title: HOVON95: Newly diagnosed MM_20


title: Control 115


title: Control 114


title: HOVON95: Newly diagnosed MM_19


title: MF19_GEX

source_name: Skin Tumor

value: Mycosis fungoides


title: SZ22_GEX


title: HOVON95: Newly diagnosed MM_16


title: Human hepatocellular carcinoma cell: MHCC97H_wild type rep_2

value: HNRNPM-wild type


title: ALDH1B1 KO clone expressing ALDH1B1 replicate 3

city: Stanford

first: James

middle: K

last: Chen

state: CA

address: 269 Campus Dr., CCSR 3150

department: Chemical and Systems Biology

institute: Stanford University

data_processing:
Base calling was performed with a NovaSeq 6000 system and RTA3 software
Raw reads were processed through fastp, and adapter-containing, poly-N-containing, and low-quality reads were removed to obtain "clean" reads.
Clean reads were mapped to the reference genome using the STAR software and Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced (FPKM) values were calculated to estimate gene expression levels.
DESeq2 was used to determined differentially expressed genes.
Genome_build: hg38

source_name: SW480 cells

growth_protocol: SW480 cells were grown in RPMI medium with 10% FBS and 10 U/mL penicillin-streptomycin in the 6-well plate format.

extract_protocol:
Total RNA was extracted with Qiagen RNeasy Mini kit.
An NEBNext® Ultra™ II RNA Library Prep Kit for Illumina® (NEB, USA) was used for library construction according to the manufacture's protocol (https://www.neb.com/protocols/2017/03/04/protocol-for-use-with-nebnext-poly-a-mrna-magnetic-isolation-module-e7490-and-nebnext-ultra-ii-rn)
The samples were enriched for mRNA using oligo(dT) beads.

treatment_protocol: For the inhibitor treatment group, the parental SW480 line was treated with 2 µM ALDH1B1-selective inhibitor for 24 hours.


title: Human hepatocellular carcinoma cell: MHCC97H_wild type rep_1


title: HOVON95: Newly diagnosed MM_15


title: HC07MTG

city: New Haven

first: Le

last: Zhang

state: CT

address: 300 George Street

department: Neurology

institute: Yale University

description: single nucleus RNA sequencing

data_processing:
A custom pre-mRNA human genome reference was generated with GRCh38 that included pre-mRNA sequences by 10x Cell Ranger.
snRNA-seq data were aligned to this GRCh38–pre-mRNA reference to map both unspliced pre-mRNA and mature mRNA using 10x CellRanger version 3.0.2.
Genome_build: GRCh38, GRCh38-premRNA
Supplementary_files_format_and_content: Filtered gene expression matrix

source_name: brain

extract_protocol:
Nuclei were isolated from post-mortem frozen or flash-frozen human brain tissue. Frozen tissue (50 to 100 mg) was homogenized in 15 ml of ice-cold nuclei homogenization buffer [2 M sucrose, 10 mM Hepes (pH 7.9), 25 mM KCl, 1 mM EDTA (pH 8.0), 10% glycerol, and ribonuclease (RNase) inhibitors freshly added (80 U/ml)] with Dounce tissue grinder (10 strokes with loose pestle and 10 strokes with tight pestle). The homogenate was transferred into an ultracentrifuge tube on top of 10 ml of fresh nuclei homogenization buffer and centrifuged at 24,000 rpm for 60 min at 4°C on high-speed centrifuge. The supernatant was removed, and the pellet was resuspended in 1 ml of nuclei resuspension buffer [15 mM Hepes (pH 7.4), 15 mM NaCl, 60 mM KCl, 2 mM MgCl2, 3 mM CaCl2, and RNase inhibitors freshly added (80 U/ml)] and counted on a hemocytometer.
The snRNA-seq libraries were prepared by the 10x Genomics Chromium Single Cell 3′ Reagent Kit v3 chemistry according to the manufacturer’s instructions and sequenced using Illumina NovaSeq6000 S4.
Single nucleus RNA sequencing, 10x Genomics Chromium Single Cell 3′ Reagent Kit v3

value: total brain nuclei

value: middle temporal gyrus

tag: state

value: postmortem


title: Control 111


title: Control 113


title: HOVON95: Newly diagnosed MM_22


title: HC37MTG

description: single nucleus RNA sequencing


title: Control 112


title: ALDH1B1 KO clone expressing EGFP replicate 3


title: ALDH1B1 KO clone expressing ALDH1B1 replicate 2


title: ALDH1B1 KO clone expressing ALDH1B1 replicate 1


title: Control 124


title: HOVON95: Newly diagnosed MM_14


title: Control 109


title: ALDH1B1 KO clone expressing EGFP replicate 2


title: HC35MTG

description: single nucleus RNA sequencing


title: HC19MTG

description: single nucleus RNA sequencing


title: SZ16_TCR


title: SZ29_MF25_TCR

source_name: CTCL PBMCs, Skin Tumor

value: Sezary Syndrome, Mycosis fungoides


title: Control 108


title: ALDH1B1 KO clone expressing EGFP replicate 1


title: Control 107


title: HC14MTG

description: single nucleus RNA sequencing


title: Control 110


title: AD17MTG

description: single nucleus RNA sequencing


title: HC03MTG

description: single nucleus RNA sequencing


title: HOVON95: Newly diagnosed MM_13


title: Control 106


title: HOVON95: Newly diagnosed MM_25


title: HOVON95: Newly diagnosed MM_12


title: AD16MTG

description: single nucleus RNA sequencing


title: Parental line treated with ALDH1B1 inhibitor replicate 3


title: HOVON95: Newly diagnosed MM_10


title: Parental line treated with ALDH1B1 inhibitor replicate 2


title: HOVON95: Newly diagnosed MM_9


title: HOVON95: Newly diagnosed MM_11


title: HOVON95: Newly diagnosed MM_21


title: HOVON95: Newly diagnosed MM_6


title: Control 105


title: AD04MTG

description: single nucleus RNA sequencing


title: Control 104


title: Parental line replicate 3


title: AD30MTG

description: single nucleus RNA sequencing


title: HOVON95: Newly diagnosed MM_8


title: HOVON95: Newly diagnosed MM_7


title: Control 103


title: Parental line treated with ALDH1B1 inhibitor replicate 1


title: Parental line replicate 2


title: HOVON95: Newly diagnosed MM_5


title: Control 102


title: AD02MTG

description: single nucleus RNA sequencing


title: Parental line replicate 1


title: Control 100


title: HOVON95: Newly diagnosed MM_4


title: Control 99


title: 7_N

city: Beijing

first: Xuezhu

last: WANG

address: No.1 Shuaifuyuan Wangfujing Dongcheng District

institute: Peking Union Medical College Hospital

data_processing:
CASAVA base recognition
fastp for quality control and trimming of the seqeunce reads
STAR to align the sequence reads to hg38
HTSeq-count to quantify the mapped reads
Integrative circRNA prediction pipeline: circRNA_finder, find_circ, Circexplorer, and CIRI2
Genome_build: hg38
Supplementary_files_format_and_content: text files include raw count of gene expression or circRNA expression in each sample

source_name: Adjacent normal tissues of patient with intrahepatic cholangiocarcinoma

extract_protocol:
Exosomes were extracted from the plasma samples using exoRNeasy Maxi Kit (QIAGEN, Hilden, Germany), following the manufacturer’s protocol. Total RNA from the tissue and plasma exosome samples was extracted using the TRIzol reagent (Invitrogen, Carlsbad, CA, USA), and those with a RIN value >7 was used for library preparation.
lncRNA-seq library preparation protocol

tag: disease

value: intrahepatic cholangiocarcinoma

value: Adjacent normal tissues

tag: Sex

value: male


title: HOVON95: Newly diagnosed MM_3

value: bone marrow aspirate


title: 7_exosome

source_name: Plasma exosomes of patient with intrahepatic cholangiocarcinoma

value: Plasma exosomes


title: siRUNX EtOH rep3

city: Houston

first: Chin-Yo

last: Lin

state: TX

address: 3517 Cullen Blvd, Rm 3018

department: Biology and Biochemistry

institute: University of Houston

description: processed data file: EtOH_siRUNX1_responsive_genes.xlsx

data_processing:
Raw sequence data were aligned to the human reference genome hg19 using the TopHat software.
BAM-files were sorted and indexed using the Samtools software package.
FPKM (fragments per kilobase of exon per million fragments mapped) values were calculated using the Cufflinks software.
Go analysis was performed by use of GOseq
Genome_build: hg19
Supplementary_files_format_and_content: xlsx files with fpkm values and log2 fold change with corresponding p and q values

source_name: Mammary Gland

extract_protocol:
The isolation of total RNA was performed using the RNeasy Plus Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocol including DNaseI digestion.
RNA libraries were prepared for sequencing using standard Illumina protocols

value: adenocarcinoma

value: Mammary Gland

value: female


title: HOVON95: Newly diagnosed MM_2

value: multiple myeloma


title: siRUNX EtOH rep2


title: siRUNX EtOH rep1


title: Control 98

value: leukocytes

value: healthy

value: Control


title: HOVON95: Newly diagnosed MM_1

value: Female


title: AD12MTG

description: single nucleus RNA sequencing

value: total brain nuclei


title: siRUNX E2 rep3

description: processed data file: E2_siRUNX1_responsive_genes.xlsx


title: 6_Ca

source_name: Cancer tissues of patient with intrahepatic cholangiocarcinoma

value: Cancer tissues


title: Newly diagnosed PCL_56

source_name: peripheral blood

label_protocol: Affymetrix GeneChip® 3' IVT PLUS Reagent Kit or Gene Profiling MDx IVD Expression Reagent Kit in accordance with Affymetrix protocols.

value: peripheral blood

value: plasma cell leukemia


title: Newly diagnosed PCL_57

value: Male


title: 6_N

value: intrahepatic cholangiocarcinoma

value: Adjacent normal tissues


title: siRUNX E2 rep1


title: Control 96


title: Newly diagnosed PCL_55


title: Control 94


title: 5_N


title: 7_Ca

value: male


title: Newly diagnosed PCL_54


title: NT EtOH rep3


title: Control 97


title: Control 93


title: Newly diagnosed PCL_52


title: Control 95


title: Newly diagnosed PCL_53


title: Control 92


title: 5_exosome_1

value: Plasma exosomes


title: NT EtOH rep2

value: adenocarcinoma


title: NT EtOH rep1

value: Mammary Gland


title: Control 90


title: 5_Ca

value: female


title: siRUNX E2 rep2


title: 4_N


title: NT E2 rep3


title: 5_exosome_2


title: NT E2 rep2


title: Control 89


title: Newly diagnosed PCL_51


title: Newly diagnosed PCL_50


title: 4_Ca


title: Newly diagnosed PCL_49


title: Control 88


title: NT E2 rep1


title: Control 86


title: 3_N


title: 3_exosome


title: Control 87


title: Newly diagnosed PCL_48


title: Newly diagnosed PCL_47


title: 3_Ca


title: 2537821_Ca


title: 2537821_N


title: Control 85


title: Control 84


title: 2534465_N


title: Newly diagnosed PCL_46


title: Newly diagnosed PCL_45


title: Control 82


title: 2534465_exosome


title: Control 81


title: 2534465_Ca


title: 2523247_N


title: Control 83


title: 2523247_Ca


title: Control 78


title: bovine CL_MIX 11

city: Lexington

first: Phillip

middle: J

last: Bridges

state: KY

address: 206 WP Garrigus Building

department: Animal and Food Sciences

institute: University of Kentucky

description: Gene expression data from early luteal phase CL

hyb_protocol: The biotinylated cRNAs were further fragmented and used as probes to hybridize the gene chips in the GeneChip Hybridization Oven 640 (Affymetrix).After hybridization, the chips were washed and stained on a GeneChip Fluidics Station 450 (Affymetrix)

scan_protocol: The reaction image and signals were read with a GeneChip Scanner (GCS 3000, 7G; Affymetrix)

data_processing: Data were collected using the GeneChip Operating Software (GCOS, version 1.2; Affymetrix). The raw expression intensity values from the GCOS were imported into Partek Genomics Suite software (PGS version 6.6; Partek Inc.).

organism: Bos taurus

source_name: bovine corpus luteum

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

growth_protocol: Corpora lutea collected on day 7 of the estrous cycle

extract_protocol: Corpus luteum was divided into eight pieces and placed in a foil pack, flash-frozen in liquid nitrogen, and stored at -80°C. Total RNA was extracted from the frozen luteal tissue using TRIzol Reagent following the manufacturer's instructions.

treatment_protocol: not applicable

tag: agent

value: MIX

value: corpus luteum


title: Control 79

value: leukocytes

value: healthy


title: bovine CL_ISe 7

value: ISe


title: bovine CL_ISe 6


title: 2520263_N


title: Newly diagnosed PCL_40

value: Female


title: bovine CL_ISe 5


title: Newly diagnosed PCL_43


title: bovine CL_MIX 8


title: bovine CL_ISe 2


title: bovine CL_MIX 10


title: bovine CL_MIX 9


title: Newly diagnosed PCL_41


title: Newly diagnosed PCL_38


title: Control 80

value: Control


title: Newly diagnosed PCL_39

value: peripheral blood


title: Control 77


title: Newly diagnosed PCL_42

value: plasma cell leukemia


title: bovine CL_ISe 3


title: 2520263_Ca

value: Cancer tissues


title: bovine CL_MIX 1


title: Control 76


title: Newly diagnosed PCL_36


title: Newly diagnosed PCL_44


title: Newly diagnosed PCL_37

value: Male


title: 2518780_N

value: intrahepatic cholangiocarcinoma

value: Adjacent normal tissues


title: Control 75


title: Control 73


title: Scrape-3

city: Baltimore

first: Marc

last: Halushka

state: Maryland

address: 720 Ruthand ave Ross 632b

department: Pathology

institute: Johns Hopkins

description:
Control
SAMPLE 6

data_processing:
For normalization, the average of three stable and ubiquitous house keeping miRNAs were used, miR-20, miR-21, and miR-103
Target miRNA signals normalized to average of housekeeping miRNAs by 2^-deltadeltaCt, where deltadeltaCt = (Ct_Target_bulk - Ct_housekeepingmiRNA_bulk)- (Ct_Target_xMD − Ct_housekeepingmiRNAs_xMD)]
All data processing was preformed in Excel.
normalized data reports normalized signal (against housekeeping miRNAs).
Fold change data shows the fold change between bulk and microdissected tissue.

label: SYBR green

source_name: Small intestine

label_protocol: Labeling was done using standard IHC

extract_protocol: Extraction performed based on xMD protocols using phenol chloroform, which includes Dnase I.

treatment_protocol: xMD used for small intestine AE1/AE3 postive cells. With matched 4um sections of bulk tissue.

value: Small intestine


title: Scrape-2

description:
Control
SAMPLE 5


title: Scrape-1

description:
Control
SAMPLE 4


title: Newly diagnosed PCL_34


title: xMD-3

description:
Test
SAMPLE 3

source_name: Small intestine, AE1/AE3 positive cells

value: Epithelial cells


title: 2393116_Ca


title: Control 71


title: Newly diagnosed PCL_35


title: 2287602_Ca


title: 2518780_Ca

value: male


title: xMD-2

description:
Test
SAMPLE 2


title: 2287602_N

value: female


title: xMD-1

description:
Test
SAMPLE 1


title: Control 69


title: Newly diagnosed PCL_33


title: Newly diagnosed PCL_32


title: Newly diagnosed PCL_31


title: 2_N


title: 2393116_N


title: Control 72


title: Control 68


title: Control 70


title: 2_Ca


title: Brain_PBS_sample_3

city: Solna

first: Manuel

last: Zeitelhofer

address: Biomedicum

department: MBB

country: Sweden

institute: Karolinska Institute

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

scan_protocol: Scanning was done using the GeneChip® Scanner 3000 7G

data_processing: The raw data was normalized in Expression Console, provided by Affymetrix (http://www.affymetrix.com), using the robust multi-array average (RMA) method. Subsequent analysis of the gene expression data was carried out in the statistical computing language R (http://www.r-project.org) using packages available from the Bioconductor project (www.bioconductor.org). In order to search for the differentially expressed genes between the X and Y groups, an empirical Bayes moderated t-test was applied using the ‘limma’ package. To address the problem with multiple testing, the p-values were adjusted using the method of Benjamini and Hochberg.

label: Biotin

organism: Mus musculus

source_name: vascular fragments of brain, 4h post PBS ICV

label_protocol: 10 nanograms of total RNA from each sample were used to generate amplified and biotinylated sense transcript cDNA from the entire expressed genome according to the Nugen Technologies, Inc. protocols Ovation® Pico WTA System V2 (M01224v2) and Encore Biotine Module (M01111v5).

growth_protocol: Mice were supplied with a standard mouse diet and were housed in 12h light/dark cycle with free access to water and food.

extract_protocol: Total RNA extracted using Qiagen kit following manufacturer's instructions. RNA concentration was measured with ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE) and RNA quality was evaluated using the Agilent 2100 Bioanalyzer system (Agilent Technologies Inc, Palo Alto, CA).

treatment_protocol: Mice were either ICV injected with PDGF-CC or PBS 4 hours before sacrifice.

tag: strain

value: adult

tag: treatment/time point

value: 4h post PBS ICV

value: brain


title: Newly diagnosed PCL_29

source_name: bone marrow

value: bone marrow


title: Newly diagnosed PCL_27


title: Brain_PBS_sample_2


title: Newly diagnosed PCL_28


title: Newly diagnosed PCL_30


title: 1_N


title: Control 67

source_name: Buffy coat

value: leukocytes

value: healthy


title: Newly diagnosed PCL_24


title: 1_exosome

value: Plasma exosomes


title: Control 65

value: Control


title: 1_Ca


title: Control 62


title: MGUSPC2

data_processing:
CASAVA base recognition
fastp for quality control and trimming of the seqeunce reads
CellRanger to align the sequence reads to hg38 and then demultiplex the cells
Genome_build: hg38
Supplementary_files_format_and_content: gunzip file of Cell Ranger-generated one mtx file and two tsv files for gene ID and cell barcode ID

source_name: bone marrow cells of patient with monoclonal gammopathy of undetermined significance

extract_protocol:
We diluted 5 mL bone marrow aspiration with 5 mL RPMI 1640 culture medium and then separated the lymphocytes with the lymphocyte separation medium. We used magnetic beads conjugated to anti-CD138 antibodies (Miltenyi Biotech, 130-051-301) to enrich the bone marrow plasma cells (BMPCs), and then resuspend the BMPCs. The quality control standards: cell viability>85%, cell concentration 700-1200 cells/μL, cell diameter 5-30μm.
10X Genomics 3' Chromium v2 protocol

value: bone marrow cells


title: Brain_PBS_sample_1


title: Newly diagnosed PCL_26

value: Female

value: plasma cell leukemia


title: Control 64


title: Brain_PDGFCC_sample_4

source_name: vascular fragments of brain, 4h post PDGF-CC ICV

value: 4h post PDGF-CC ICV


title: Newly diagnosed PCL_23


title: normalPC1

source_name: bone marrow cells of healthy control

value: healthy control


title: Newly diagnosed PCL_25

value: Male


title: Control 61


title: Control 66


title: Newly diagnosed PCL_22


title: Control 60


title: Brain_PDGFCC_sample_3


title: pALPC3

source_name: bone marrow cells of patient with light chain amyloidosis

value: light chain amyloidosis


title: 313_322_5N_LN

city: Omaha

first: Patrick

last: Swanson

state: NE

address: 2500 California Plaza

department: Medical Microbiology and Immunology

institute: Creighton University School of Medicine

description:
seq_logo_37_dnRAG1_VH12_5N_LN_2_bm_spl.txt
seq_logo_491_dnRAG1_VH12_5N_LN_2_bm_spl.txt
seq_logo_486_dnRAG1_VH12_5N_LN_2_bm_spl.txt
seq_logo_14126_dnRAG1_VH12_5N_LN_2_bm_spl.txt
full_graph_2_gene_j_bm_spl.txt
full_graph_2_gene_v_bm_spl.txt
full_graph_2_vk37_bm_spl.txt
full_graph_2_vk491_bm_spl.txt
full_graph_2_vk486_bm_spl.txt
full_graph_2_vk1110_bm_spl.txt
full_graph_2_vk14126_bm_spl.txt

data_processing:
Library strategy: Rep-seq
Working in a linux terminal: Demultiplexing and adapter trimming: The sample sheet for the run was edited to include the correct indices and sample names for accurate demultiplexing, along with specifying the Read 1 and Read 2 adapters for adapter trimming. The bcl2fastq program (v2.20, Illumina) was used with parameters "--use-bases-mask Y*,I6N2,N2I6,Y* --barcode-mismatch 0", to convert the raw read data into FASTQ files, demultiplex samples based on the indices provided in the sample sheet, and remove the Illumina adapters used to bind the sequence amplicons to the flow cell.
Filtering: Each set of paired end reads were quality filtered using FilterSeq.py from pRESTO (v0.5.13, Immcantation) as follows: "FilterSeq.py quality -s SampleN_SN_L001_R1_001.fastq -q 20 --outname SampleN_R1 --log FS1.log // FilterSeq.py quality -s SampleN_SN_L001_R2_001.fastq -q 20 --outname SampleN_R2 --log FS2.log", where N is the sample number.
Primer masking: For each sample, the V region primers were masked on read 1 and the C region primers (primer sequences can be found in the supplementary files of the manuscript) were cut on read 2 as follows: "MaskPrimers.py score -s SampleN_R1_quality-pass.fastq -p ../V_primers.fasta --start 0 --mode mask --outname SampleN_R1 --log MP1.log // MaskPrimers.py score -s SampleN_R2_quality-pass.fastq -p ../C_primers.fasta --start 15 --mode cut --barcode --outname SampleN_R2 --log MP2.log."
Pairing of paired end reads: Reads are then subjected to the first round of sequencing pairing which sorts and matches sequence records with matching coordinates across files as follows: "PairSeq.py -1 SampleN_R1_primers-pass.fastq -2 SampleN_R2_primers-pass.fastq --2f BARCODE --coord illumina."
Build a consensus sequence: After pairing, a consensus sequence is determined for each set of reads from the sample: "BuildConsensus.py -s SampleN_R1_primers-pass_pair-pass.fastq --bf BARCODE --pf PRIMER --prcons 0.6 --maxerror 0.1 --maxgap 0.5 --outname SampleN_R1 --log BC1.log // BuildConsensus.py -s SampleN_R2_primers-pass_pair-pass.fastq --bf BARCODE --pf PRIMER --maxerror 0.1 --maxgap 0.5 --outname SampleN_R2 --log BC2.log."
Pairing of paired end reads: Reads are then subjected to a second round of sequencing pairing which Sorts and matches sequence records with matching coordinates across files as follows: "PairSeq.py -1 SampleN_R1_consensus-pass.fastq -2 SampleN_R2_consensus-pass.fastq --coord presto --1f CONSCOUNT --2f CONSCOUNT PRCONS."
Assemble reads into a single sequence: Reads are aligned and condensed into a single sequence. This requires that read 2 be transformed into the reverse complement during the process and is performed as follows: "AssemblePairs.py align -1 SampleN_R1_consensus-pass_pair-pass.fastq -2 SampleN_R2_consensus-pass_pair-pass.fastq --coord presto --rc tail --1f CONSCOUNT --2f CONSCOUNT PRCONS --outname SampleN --log AP.log."
Parse headers: Parse the pRESTO annotations into FASTA/FASTQ sequence headers as follows: "ParseHeaders.py collapse -s SampleN_assemble-pass.fastq -f CONSCOUNT --act min."
Collapse sequences: Remove duplicate sequences from FASTA/FASTQ files as follows: "CollapseSeq.py -s SampleN*reheader.fastq -n 20 --inner --uf PRCONS --cf CONSCOUNT --act sum --outname SampleN."
Parse headers: Parse the pRESTO annotations into FASTA/FASTQ sequence headers and parses records in the console log of pRESTO modules as follows: "ParseHeaders.py table -s SampleN_collapse-unique.fastq -f ID PRCONS CONSCOUNT DUPCOUNT // ParseLog.py -l FS1.log FS2.log -f ID QUALITY // ParseLog.py -l MP1.log MP2.log -f ID PRIMER BARCODE ERROR // ParseLog.py -l BC1.log BC2.log -f BARCODE SEQCOUNT CONSCOUNT PRIMER PRCONS PRCOUNT PRFREQ ERROR // ParseLog.py -l AP.log -f ID LENGTH OVERLAP ERROR PVALUE FIELDS1 FIELDS2."
Split sequences: Sorts, samples, and splits FASTQ sequence files into groups with 2+ or one UMI as follows: "SplitSeq.py group -s SampleN_collapse-unique.fastq -f CONSCOUNT --num 2 --outname SampleN --fasta."
Gene annotation using IMGT/HighV-Quest: The FASTA file for sequences with 2 or more copies mapping back to a single UMI is uploaded to IMGT/HighV-Quest using the following parameters: Analysis title - "SampleN 2", Species - Mus musculus (house mouse), Receptor type or locus - IG, Upload sequences in FASTA format - "SampleN_atleast-2.fasta".
Download updated germlines from IMGT for reference during processing: "fetch_imgtdb.sh -o ~share/germlines/imgt."
Make a databse: Creates a data table from the IMGT file and orinigal FASTA file using the Change-O package (v0.4.6, Immcantation) as follows: "MakeDb.py imgt -i SampleN_2.txz -s SampleN_atleast-2.fasta --regions --scores."
Parse the database: Select for functional sequences only: "ParseDb.py select -d SampleN_2_db-pass.tab -f FUNCTIONAL -u T."
Create germlines: Add the full germline sequences to the data table for comparison and reference: "CreateGermlines.py -d SampleN_2_db-pass_parse-select.tab -g full -r ~/share/germlines/imgt/mouse/vdj/*.fasta." Make sure the path to the reference genome matches the output location from fetch_imgtdb.sh.
Moving to RStudioServer running R (v 3.6.0):  Create an R dataframe from the ChangeO files using the Alakazam package (0.3.0, Immcantation) as follows: "loc_SampleN_2 <- readChangeoDb("SampleN_2_db-pass_parse-select_germ-pass.tab")"
Count genes: Tally the usage frequencies of the V segment genes as follows: "loc_SampleN_2_gene_v <- countGenes(loc_SampleN_2, gene = "V_CALL", mode = "gene")"
Add Sample annotation: Add the type of sample to the data frame for identification of genotype and phenotype using dplyr (0.8.3) as follows: "loc_SampleN_2_gene_v<-mutate(loc_SampleN_2_gene_v, Sample = "WT CD5- PtC-")"
Order and factor genes: Reorder the list of genes based on their location within the locus according to IMGT using gtools (3.8.1), and reassign factors and levels (base R) to maintain the new order as follows (the script for gsubGene2NumV etc. can be found in the supplementary files of the manuscript): "loc_SampleN_2_gene_v$GENE <- gsubGene2NumV(loc_SampleN_2_gene_v$GENE) // loc_SampleN_2_gene_v <- loc_SampleN_2_gene_v[mixedorder(loc_SampleN_2_gene_v$GENE, decreasing=TRUE),] // loc_SampleN_2_gene_v$GENE <- gsubNum2GeneV(loc_SampleN_2_gene_v$GENE) // loc_SampleN_2_gene_v$GENE <- factor(loc_SampleN_2_gene_v$GENE, levels = loc_SampleN_2_gene_v$GENE)"
Include all genes: Ensure that all genes on the light chain locus are represented for each sample using stringr (1.4.0) and tibble (2.1.3)  to create uniform graphs as follows: "full_loc_SampleN_2_gene_v <- loc_SampleN_2_gene_v // a <- length(full_loc_SampleN_2_gene_v$GENE) // if(str_detect(full_loc_SampleN_2_gene_v$GENE[[a]], V_GENES[[167]]) == FALSE) { // full_loc_SampleN_2_gene_v <- add_row(full_loc_SampleN_2_gene_v, GENE=V_GENES[[167]], SEQ_COUNT=0, SEQ_FREQ=0, Sample=full_loc_SampleN_2_gene_v$Sample[[1]], .after=a) // } // b <- length(V_GENES) // x <- c(1:b) // for (val in x) { // if(str_detect(full_loc_SampleN_2_gene_v$GENE[[val]], V_GENES[[val]]) == FALSE) { // full_loc_SampleN_2_gene_v <- add_row(full_loc_SampleN_2_gene_v, GENE=V_GENES[[val]], SEQ_COUNT=0, SEQ_FREQ=0, Sample=full_loc_SampleN_2_gene_v$Sample[[val]], .before=val) // } // }"
Calculate mean and SEM: Calculate the mean representation frequency and standard error of the mean of each gene in the repertoire across the biological replicates for each sample as follows: "WT_5N_LN_2_gene_v_stat <- data.frame( SEQ_FREQ_1 = full_loc_Sample1_2_gene_v$SEQ_FREQ, SEQ_FREQ_2 = full_loc_Sample2_2_gene_v$SEQ_FREQ, SEQ_FREQ_3 = full_loc_Sample3_2_gene_v$SEQ_FREQ) // WT_5N_LN_2_gene_v <- mutate(WT_5N_LN_2_gene_v_stat, MEAN_SEQ_FREQ = apply(WT_5N_LN_2_gene_v_stat, 1, mean)) // WT_5N_LN_2_gene_v <- mutate(WT_5N_LN_2_gene_v, SEM = apply(WT_5N_LN_2_gene_v, 1, sem)) // WT_5N_LN_2_gene_v <- mutate(WT_5N_LN_2_gene_v, V_GENES = V_GENES) // WT_5N_LN_2_gene_v <- mutate(WT_5N_LN_2_gene_v, SAMPLE = full_loc_WT_5N_LN1_2_gene_v$Sample)." Repeat for the remainder of the genotypes and phenotypes.
Compile all samples: Create one dataframe with the mean frequency, SEM, gene names, and sample names by combining individual dataframes as follows: "full_graph_2_gene_v <- rbind(WT_5N_LN_2_gene_v, RAG_5N_LN_2_gene_v) // full_graph_2_gene_v <- rbind(full_graph_2_gene_v, RAG_5P_LN_2_gene_v) // full_graph_2_gene_v <- rbind(full_graph_2_gene_v, RAG_5P_LP_2_gene_v) // full_graph_2_gene_v <- rbind(full_graph_2_gene_v, VH12_5N_LN_2_gene_v) // full_graph_2_gene_v <- rbind(full_graph_2_gene_v, VH12_5P_LP_2_gene_v) // full_graph_2_gene_v <- rbind(full_graph_2_gene_v, VH12_RAG_5N_LN_2_gene_v) // full_graph_2_gene_v <- rbind(full_graph_2_gene_v, VH12_RAG_5P_LP_2_gene_v)."
Order and factor genes: Reorder the list of genes based on their location within the locus according to IMGT and reassign factors and levels to maintain the correct order within the graph as follows: "full_graph_2_gene_v$V_GENES <- gsubGene2NumV(full_graph_2_gene_v$V_GENES) // full_graph_2_gene_v <- full_graph_2_gene_v[mixedorder(full_graph_2_gene_v$V_GENES, decreasing=TRUE),] // full_graph_2_gene_v$V_GENES <- gsubNum2GeneV(full_graph_2_gene_v$V_GENES) // full_graph_2_gene_v$V_GENES <- psuedogenes(full_graph_2_gene_v$V_GENES) // full_graph_2_gene_v$V_GENES <- factor(full_graph_2_gene_v$V_GENES, levels=unique(full_graph_2_gene_v$V_GENES)) // full_graph_2_gene_v$SAMPLE <- factor(full_graph_2_gene_v$SAMPLE, levels=c("WT CD5- PtC-", "dnRAG1 CD5- PtC-", "dnRAG1 CD5+ PtC-", "dnRAG1 CD5+ PtC+", "VH12 CD5- PtC-", "VH12 CD5+ PtC+", "dnRAG1 VH12 CD5- PtC-", "dnRAG1 VH12 CD5+ PtC+"))"
Graph total gene usage: Using ggplot2 (3.2.1), create a graph of total gene usage as follows: "dodge<-position_dodge(width=0.9) // full_graph_2_gene_v_plot <- ggplot(data=full_graph_2_gene_v, mapping=aes(x=V_GENES, y=MEAN_SEQ_FREQ), limitsize=FALSE)+geom_col(aes(fill=SAMPLE), position=dodge)+coord_cartesian(ylim =c(0, 1), expand = FALSE)+geom_errorbar(aes(ymin = MEAN_SEQ_FREQ-SEM, ymax = MEAN_SEQ_FREQ+SEM, group=SAMPLE,  linetype="blank"), position=dodge, width = 0.75, show.legend=FALSE )+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=90, face="bold", size=32, hjust=0, vjust=0.5, color = "black"))+theme(axis.text.y=element_text(size=32, face="bold", hjust=0.5, vjust=0.5, color ="black"))+theme(panel.background=element_rect(fill="white"))+ggtitle("Light Chain Repertoire V Gene Usage")+theme(plot.title = element_text(h=0.5, face="bold", size=48))+labs(x="\nGene Segment", y="Frequency in Repertoire\n")+theme(axis.title = element_text(face="bold", size=40))+theme(legend.text = element_text(size=40, face="bold"))+theme(legend.key.size = unit(0.75, "in"))+theme(legend.position = c(.9,.85))+theme(legend.title=element_blank())+scale_fill_manual(values = cbf_sw) // ggsave("full_graph_2_gene_v_plot.png", width=60, height=20, limitsize=FALSE)"
Repeat lines 77 through 84 for J segment usage (full scripts for this process can be found in the supplementary files of the manuscript).
Translate the CDR3: Add the amino acid sequence for the CDR3 region as follows: "loc_SampleN_2 <- mutate(loc_SampleN_2, CDR3_AA = translateDNA(loc_SampleN_2$CDR3_IMGT))"
Pull sequences using IGKV4-91: Pull all sequences from a sample that have IGKV4-91 as a V gene call to analyze J gene usage and CDR3 sequence: "z <- str_detect(loc_SampleN_2$V_CALL, "4-91") // pull_491_seq_SampleN_2 <- loc_SampleN_2 // pull_491_seq_SampleN_2 <- mutate(pull_491_seq_SampleN_2, Vk4_91 = z) // pull_491_seq_SampleN_2 <- filter(pull_491_seq_SampleN_2, Vk4_91==TRUE) // pull_491_seq_SampleN_2 <- select(pull_491_seq_SampleN_2, SEQUENCE_INPUT, FUNCTIONAL, V_CALL, J_CALL, SEQUENCE_VDJ, V_SEQ_START, V_SEQ_LENGTH, NP1_LENGTH, NP2_LENGTH, J_SEQ_START, J_SEQ_LENGTH, JUNCTION, JUNCTION_LENGTH, V_IDENTITY, J_IDENTITY, CDR3_IMGT, CDR3_AA)"
Count J gene use: Tally the usage frequencies of J gene segments among sequencies using IGKV4-91 as follows: "linear_SampleN_2_vk491 <- countGenes(pull_491_seq_SampleN_2, gene = "J_CALL", mode = "gene") // linear_SampleN_2_vk491$GENE <- gsubGene2NumJ(linear_SampleN_2_vk491$GENE) // linear_SampleN_2_vk491 <- linear_SampleN_2_vk491[mixedorder(linear_SampleN_2_vk491$GENE, decreasing=TRUE),] // linear_SampleN_2_vk491$GENE <- gsubNum2GeneJ(linear_SampleN_2_vk491$GENE) // linear_SampleN_2_vk491$GENE <- factor(linear_SampleN_2_vk491$GENE, levels = linear_SampleN_2_vk491$GENE)"
Include all J kappa genes: Ensure that all J segment genes on the kappa locus are represented for each sample to create uniform graphs as follows: "if(exists("linear_SampleN_2_vk491")==FALSE){ // linear_SampleN_2_vk491 <- data.frame(GENE=J_GENES[[7]], SEQ_COUNT=0, SEQ_FREQ=0) // } // full_linear_SampleN_2_vk491 <- linear_SampleN_2_vk491 // a <- length(full_linear_SampleN_2_vk491$GENE) // if(str_detect(full_linear_SampleN_2_vk491$GENE[[a]], J_GENES[[7]]) == FALSE) { // full_linear_SampleN_2_vk491 <- add_row(full_linear_SampleN_2_vk491, GENE=J_GENES[[7]], SEQ_COUNT=0, SEQ_FREQ=0, .after=a) // } // b <- length(J_GENES) // x <- c(1:b) // for (val in x) { // if(str_detect(full_linear_SampleN_2_vk491$GENE[[val]], J_GENES[[val]]) == FALSE) { // full_linear_SampleN_2_vk491 <- add_row(full_linear_SampleN_2_vk491, GENE=J_GENES[[val]], SEQ_COUNT=0, SEQ_FREQ=0, .before=val) // } // } // full_linear_SampleN_2_vk491 <- mutate(full_linear_SampleN_2_vk491, SAMPLE = "WT CD5- PtC-")"
Calculate mean and SEM of J gene frequencies : Calculate the mean representation frequency and standard error of the mean of each gene in the J kappa locus across the biological replicates for the sequences using IGKV4-91 from each sample as follows: "WT_5N_LN_2_vk491_stat <- data.frame( SEQ_FREQ_1 = full_linear_Sample1_2_vk491$SEQ_FREQ[1:4], SEQ_FREQ_2 = full_linear_Sample2_2_vk491$SEQ_FREQ[1:4], SEQ_FREQ_3 = full_linear_Sample3_2_vk491$SEQ_FREQ[1:4]) // WT_5N_LN_2_vk491 <- mutate(WT_5N_LN_2_vk491_stat, MEAN_SEQ_FREQ = apply(WT_5N_LN_2_vk491_stat, 1, mean)) // WT_5N_LN_2_vk491 <- mutate(WT_5N_LN_2_vk491, SEM = apply(WT_5N_LN_2_vk491, 1, sem)) // WT_5N_LN_2_vk491 <- mutate(WT_5N_LN_2_vk491, J_GENES = J_GENES) // WT_5N_LN_2_vk491 <- mutate(WT_5N_LN_2_vk491, SAMPLE = full_linear_Sample1_2_vk491$SAMPLE[1:4])" Repeat for the other genotypes and phenotypes.
Compile all samples: Create one data frame with the mean frequency, SEM, gene names, and sample names by combining individual data frames as follows: "full_graph_2_vk491 <- rbind(WT_5N_LN_2_vk491, RAG_5N_LN_2_vk491) // full_graph_2_vk491 <- rbind(full_graph_2_vk491, RAG_5P_LN_2_vk491) // full_graph_2_vk491 <- rbind(full_graph_2_vk491, RAG_5P_LP_2_vk491) // full_graph_2_vk491 <- rbind(full_graph_2_vk491, VH12_5N_LN_2_vk491) // full_graph_2_vk491 <- rbind(full_graph_2_vk491, VH12_5P_LP_2_vk491) // full_graph_2_vk491 <- rbind(full_graph_2_vk491, VH12_RAG_5N_LN_2_vk491) // full_graph_2_vk491 <- rbind(full_graph_2_vk491, VH12_RAG_5P_LP_2_vk491)"
Order and factor genes: Reorder the list of genes based on their location within the locus according to IMGT and reassign factors and levels to maintain the correct order within the graph as follows: "full_graph_2_vk491$J_GENES <- gsubGene2NumJ(full_graph_2_vk491$J_GENES) // full_graph_2_vk491 <- full_graph_2_vk491[mixedorder(full_graph_2_vk491$J_GENES, decreasing=TRUE),] // full_graph_2_vk491$J_GENES <- gsubNum2GeneJ(full_graph_2_vk491$J_GENES) // full_graph_2_vk491$J_GENES <- factor(full_graph_2_vk491$J_GENES, levels=unique(full_graph_2_vk491$J_GENES)) // full_graph_2_vk491$SAMPLE <- factor(full_graph_2_vk491$SAMPLE, levels=c("WT CD5- PtC-", "dnRAG1 CD5- PtC-", "dnRAG1 CD5+ PtC-", "dnRAG1 CD5+ PtC+", "VH12 CD5- PtC-", "VH12 CD5+ PtC+", "dnRAG1 VH12 CD5- PtC-", "dnRAG1 VH12 CD5+ PtC+"))"
Graph total J gene usage of sequences using IGKV4-91: Using ggplot2 (3.2.1), create a graph of total J gene usage of sequences using IGKV4-91 as follows: "dodge<-position_dodge(width=0.9) // full_graph_2_vk491_plot <- ggplot(data=full_graph_2_vk491, mapping=aes(x=J_GENES, y=MEAN_SEQ_FREQ))+geom_col(aes(fill=SAMPLE), position=dodge)+geom_errorbar(aes(ymin = MEAN_SEQ_FREQ-SEM, ymax = MEAN_SEQ_FREQ+SEM, group=SAMPLE,  linetype="blank"), position=dodge, width = 0.75, size = 4, show.legend=FALSE )+coord_cartesian(ylim =c(0, 1), expand = FALSE)+theme(axis.line=element_line(size=2))+theme(axis.text.x=element_text(angle=0, face="bold", size=60, hjust=0, vjust=0.5, color = "black"))+theme(axis.text.y=element_text(size=60, face="bold", hjust=0.5, vjust=0.5, color ="black"))+theme(panel.background=element_rect(fill="white"))+ggtitle("Light Chain Repertoire J Gene Pairing with Vk491")+theme(plot.title = element_text(h=0.5, face="bold", size=48))+labs(x="\nGene Segment", y="Frequency in Repertoire\n")+theme(axis.title = element_text(face="bold", size=60))+theme(legend.key.size = unit(0.75, "in"))+theme(legend.text = element_text(size=48, face="bold"))+theme(legend.title=element_blank())+scale_fill_manual(values = cbf_sw) // ggsave("full_graph_2_vk491_plot.png", width=48, height=20)."
Mode CDR3 length: Find the mode CDR3 length for sequences using IGKV4-91 and select sequences with that CDR3 length as follows: "pull_491_seq_SampleN_2 <- mutate(pull_491_seq_SampleN_2, CDR3_AA_LENGTH = str_length(pull_491_seq_SampleN_2$CDR3_AA)) // M <- getmode(pull_491_seq_SampleN_2$CDR3_AA_LENGTH) // logo_491_seq_SampleN_2 <- filter(pull_491_seq_SampleN_2, CDR3_AA_LENGTH == M)"
Compile all samples: Create one data frame with all the CDR3 sequences of the mode length from sequences using IGKV4-91 across the biological replicates for the sample as follows: "seq_logo_491_WT_5N_LN_2 <- rbind(logo_491_seq_Sample1_2, logo_491_seq_Sample2_2) // seq_logo_491_WT_5N_LN_2 <- rbind(seq_logo_491_WT_5N_LN_2, logo_491_seq_Sample3_2) // N <- length(seq_logo_491_WT_5N_LN_2$CDR3_AA)." Repeat for other genotypes and phenotypes.
Graph the CDR3 sequence logo: Using ggseqlogo (0.1), create a sequence logo for the mode CDR3 length from sequences using IGKV4-91 for each sample as follows: "graph_seq_logo_491_WT_5N_LN_2 <- ggseqlogo(seq_logo_491_WT_5N_LN_2$CDR3_AA, method = 'prob')+theme(legend.position = 'none', axis.text.x = element_blank(), axis.text.y = element_text(size=32, face="bold"), axis.title = element_text(size=40, face="bold"))+annotate('text', x=5, y=-0.1, size = 20, fontface="bold", label = paste("n =",N)) // ggsave("graph_seq_logo_491_WT_5N_LN_2.jpeg", width=14, height=10)."
Repeat lines 87 through 96 for sequences using IGKV3-7, IGKV4-58, IGKV5-43, IGKV4-86, IGKV1-110, IGKV1-135, and IGKV14-126.
Compile annotated gene calls: Combine gene usage information from all samples into one table, with the genotype, phenotype, and biological replicate included as annotations as follows (showing WT only as the example): "loc_SampleN_2 <- mutate(loc_SampleN_2, GENOTYPE = "WT") // loc_SampleN_2 <- mutate(loc_SampleN_2, PHENOTYPE = "CD5- PtC-") // loc_SampleN_2 <- mutate(loc_SampleN_2, SAMPLE = paste(GENOTYPE, PHENOTYPE)) // loc_SampleN_2 <- mutate(loc_SampleN_2, EXP = "1") // Sample1 <- select(loc_Sample1_2, V_CALL, J_CALL, GENOTYPE, PHENOTYPE, SAMPLE, EXP) // loc_Sample2_2 <- mutate(loc_Sample2_2, GENOTYPE = "WT") // loc_Sample2_2 <- mutate(loc_Sample2_2, PHENOTYPE = "CD5- PtC-" // loc_Sample2_2 <- mutate(loc_Sample2_2, SAMPLE = paste(GENOTYPE, PHENOTYPE)) // loc_Sample2_2 <- mutate(loc_Sample2_2, EXP = "2") // Sample2 <- select(loc_Sample2_2, V_CALL, J_CALL, GENOTYPE, PHENOTYPE, SAMPLE, EXP) // loc_Sample3_2 <- mutate(loc_Sample3_2, GENOTYPE = "WT") // loc_Sample3_2 <- mutate(loc_Sample3_2, PHENOTYPE = "CD5- PtC-") // loc_Sample3_2 <- mutate(loc_Sample3_2, SAMPLE = paste(GENOTYPE, PHENOTYPE)) // loc_Sample3_2 <- mutate(loc_Sample3_2, EXP = "3") // Sample3 <- select(loc_Sample3_2, V_CALL, J_CALL, GENOTYPE, PHENOTYPE, SAMPLE, EXP) // WT_5N_LN <- bind_rows(Sample1, Sample2, Sample3) // WT <- WT_5N_LN // WT <- mutate(WT, V_CALL = getGene(WT$V_CALL)) // WT <- mutate(WT, J_CALL = getGene(WT$J_CALL)) // WT <- mutate(WT, GENE = paste(V_CALL, ",", J_CALL)) "
Repeat line 98 for other genotypes and combine into one table: "all_reps <- bind_rows(WT, dnRAG1, VH12, dnRAG1_VH12) "
Calculate the RDI in log fold change: Calculate the repertoire dissimilarity index (RDI) using the RDI package (1.0.0) from the Immcantation portal as follows: "genes <- select(all_reps, V_CALL, J_CALL) // seqAnnot <- select(all_reps, SAMPLE, EXP) // vdjMan <- calcVDJcounts(genes, seqAnnot) // vdjMan <- vdjMan[, c("WT CD5- PtC- 1", "WT CD5- PtC- 2", "WT CD5- PtC- 3", "dnRAG1 CD5- PtC- 1", "dnRAG1 CD5- PtC- 2", "dnRAG1 CD5- PtC- 3", "dnRAG1 CD5+ PtC- 1", "dnRAG1 CD5+ PtC- 2", "dnRAG1 CD5+ PtC- 3", "dnRAG1 CD5+ PtC+ 1", "dnRAG1 CD5+ PtC+ 2", "dnRAG1 CD5+ PtC+ 3", "VH12 CD5- PtC- 1", "VH12 CD5- PtC- 2", "VH12 CD5- PtC- 3", "VH12 CD5+ PtC+ 1", "VH12 CD5+ PtC+ 2", "VH12 CD5+ PtC+ 3", "dnRAG1 VH12 CD5- PtC- 1", "dnRAG1 VH12 CD5- PtC- 2", "dnRAG1 VH12 CD5- PtC- 3", "dnRAG1 VH12 CD5+ PtC+ 1", "dnRAG1 VH12 CD5+ PtC+ 2", "dnRAG1 VH12 CD5+ PtC+ 3")] // rdiMan <- calcRDI(vdjMan, distMethod = "euclidean")"
Plot dendrogram: Create a dendrogram showing how much dissimilarity there is among the various repertoires as follows: "plot(hclust(rdiMan), main="Heirarchical Clustering by Repertoire Dissimilarity Index", sub= " ", xlab="Genotype, Phenotype, and Replicate", ylab="\nLog Fold Change", cex=2, cex.axis=1.5, cex.lab=2, cex.main=3, lwd=3)"
Detect and analyze IGKV chimeric sequences: Using Microsoft Excel to view tab delimited .txt files comprised of the pulled sequences from line 86, individual sequences with NP insertions > 5 nucleotides were selected and individually submitted to IMGT/V-QUEST to identify KV genes with sequence identity to the 3’ end of the hybrid KV genes. The gene call was based on the sequence with the fewest base-pair mismatches at the 3’ end of the gene. For this purpose, it was found that removing the first 60 nucleotides of the sequence improved identification.  Multiple sequence alignments were performed using Clustal Omega.
All steps were repeated for the second round of analysis using only VH12 and dnRAG1 VH12 sample from the spleen and bone marrow of animals.
Genome_build:  IMGT/V-QUEST reference directory release: 201927-2
Supplementary_files_format_and_content: Tab delimited .txt files containing annotated sequences and gene calls.
*.tab - annotated functional sequences
full_graph*.txt - J gene or V gene segment abundance measurements
seq_logo*.txt - Sequence inputs for sequence logos
V-QUEST*.pdf - Sequences with identified KV replacement

source_name: Sorted Splenocytes

extract_protocol:
Bone marrow cell suspensions were made by flushing two femurs and tibias with RF10-M (RPMI 1640 [Invitrogen] containing 2 mM l- glutamine, 10% fetal calf serum [Invitrogen], 1000 U-μg/mL peni- cillin–streptomycin [Fisher Scientific, Waltham, MA], 14mM 2-mer- captoethanol, 20 mM HEPES [GIBCO/Invitrogen, Carlsbad, CA]) using a 25-gauge needle. Single-cell suspensions from spleen were made by mincing the tissue with two needles in RF10-M and passing the material through a needleless syringe. Red blood cells were lysed in TBAC buffer (140 mM ammonium chloride, 17 mM Tris-base, pH 7.2) on ice for five minutes, and quenched with an equal volume of RF10-M. Cells were centrifuged and resuspended in 1mL of RF10-M and counted on a Model Z1 particle counter (Beckman Coulter, Fullerton, CA). For analysis for cell surface antigens, samples containing 1 × 106 cells were treated with Fc-block reagent (anti-CD16/CD32, clone 93; eBioscience RRID: AB_467132), and then incubated with antibody cocktail (BD Biosciences (San Jose, CA): anti-CD19-APC-Cy7 (1D3), eBioscience (San Diego, CA): anti-CD5-PE (53-7.3)) for thirty minutes on ice in the dark. PtC binding was detected by including 200 nm Fluorescein-DHPE/DOPC/CHOL liposomes (1:400 dilution of 0.59mg/mL stock; FormuMax Scientific) in the antibody cocktail. Cells were then washed with PBS4 (Dulbecco’s phosphate buffered saline, 4% heat inactivated FBS), incubated with the desired streptavidin conjugate, washed again with PBS4, and finally suspended in PBS4 for bulk sorting. One to three splenic CD19+CD5-PtC-, CD19+CD5+PtC-, and/or CD19+CD5+PtC+ B cell populations were sorted from WT, dnRAG1, VH12, and dnRAG1-VH12 mice using a BD FACSAria flow cytometer (BD Biosciences). Samples were kept at 4 °C and immediately pelleted by centrifugation at 300 xg for 5 minutes. Supernatants were discarded and the pellets were resuspended in 500ul of cold Trizol (Ambion, Life Technologies) and RNA isolation was performed according to the Immunological Genome Project’s (Immgen.org) protocol for “RNA extraction with Trizol”which is based on the maufacturer's instructions for Trizol extractions.
The isolated RNA was subjected to first-strand cDNA synthesis using SuperScript III Reverse Transcriptase according to manufacturer’s instructions with specific murine Ck and Cl primers (see Table). Second-strand cDNA synthesis was performed using Phusion HF Polymerase according to manufacturer’s instructions with degenerate Vk and Vl primers to amplify specific Vk and Vl gene families2. The Ck and Cl primers contained a 15-mer unique molecular identifier (UMI) sequence to differentiate unique cDNAs, and a partial Illumina adaptor sequence for amplification and sequencing steps. The Vk and Vl primers also contained a partial adaptor sequence, but no UMI. The double-stranded cDNA (ds-cDNA) was purified twice using an equal volume of Ampure XP beads (Beckman Coulter) according to manufacturer instructions, and then subjected to 30 rounds of amplification using Illumina adaptor primers (both of which contained an Illumina-validated barcode to identify the genotype and sorted cell population for each sample using a two-dimensional array) and Platinum HF Taq (initial denaturation 95 °C 2 min; 30 cycles of amplification (95 °C 30s; 65 °C 2min; 68 °C 2min); and final extension (68 °C 7 min)). The prepared libraries were purified with 0.7x volume of Ampure XP beads, which was selected to remove smaller (<250bp) nucleic acid products from the amplified samples. Library concentration was measured on a Qubit fluorimeter using the Qubit dsDNA HS Assay kit (ThermoFisher Scientific). Average product size and purity was calculated and assessed with the Bioanalyzer High Sensitivity DNA Analysis kit for the 2100 Bioanalyzer Instrument (Agilent). Samples were pooled to create a 40nM library. The concentration of the pooled library was again determined using a Qubit fluorimeter and entered into the above equation with the average of all product lengths as the product size to determine concentration of the pooled library in nM. The library was then diluted to 4nM, denatured for 5 minutes with an equal volume of 0.2N NaOH [Sodium Hydroxide 1N Concentrate (Fisher Chemical) diluted in DEPC treated water (ThermoFisher Scientific)], then diluted to 20pM in thawed HT1 Buffer from the MiSeq Reagent Nano Kit v2 (500-cycles) (Illumina). Simultaneously, PhiX DNA (PhiX Control v3, Illumina) was diluted to 4nM with 200mM TrisCl (200mM Tris hydrochloride in nuclease free water, adjusted to pH 7), then denatured for 5 minutes in 0.2N NaOH and diluted to 20pM in HT1 buffer (included with the MiSeq cartridge). 240l of the 20pM library was combined with 24l of 20pM PhiX DNA (to compensate for potentially low sequence diversity due to the small number of targets) and 336L HT1 Buffer for a total loading volume of 600L at a final loading concentration of 8pM for the library with 0.8pM PhiX DNA (to make a 10% PhiX spike in), then added to the sample well on the MiSeq cartridge. Instrument instructions were followed for loading the flow cell, reagents, and cartridge onto the MiSeq for the paired-end 2 x 250bp sequencing run. The sample sheet (example included in supplement) was set up to Generate FASTQ only, using Nextera XT as the assay type to allow for Amplicon chemistry to be specified.

tag: replicate

tag: round

tag: number of animals per sample

value: 89 days

value: Spleen

tag: animal genotype

tag: cell phenotype

value: CD19+ CD5- PtC-


title: pALPC2

tag: tissue

value: male

tag: age


title: Brain_PDGFCC_sample_2

value: adult

value: female

value: brain


title: 313_322_5P_LP

description:
seq_logo_37_dnRAG1_VH12_5P_LP_2_bm_spl.txt
seq_logo_491_dnRAG1_VH12_5P_LP_2_bm_spl.txt
seq_logo_486_dnRAG1_VH12_5P_LP_2_bm_spl.txt
seq_logo_14126_dnRAG1_VH12_5P_LP_2_bm_spl.txt
full_graph_2_gene_j_bm_spl.txt
full_graph_2_gene_v_bm_spl.txt
full_graph_2_vk37_bm_spl.txt
full_graph_2_vk491_bm_spl.txt
full_graph_2_vk486_bm_spl.txt
full_graph_2_vk1110_bm_spl.txt
full_graph_2_vk14126_bm_spl.txt

value: CD19+ CD5+ PtC+


title: Control 59

tag: cell type

value: leukocytes

tag: diagnosis

value: healthy


title: MGUSPC1

value: monoclonal gammopathy of undetermined significance

value: bone marrow cells


title: 323_324_5N_LN

description:
seq_logo_37_VH12_5N_LN_2_bm_spl.txt
seq_logo_491_VH12_5N_LN_2_bm_spl.txt
seq_logo_486_VH12_5N_LN_2_bm_spl.txt
seq_logo_14126_VH12_5N_LN_2_bm_spl.txt
full_graph_2_gene_j_bm_spl.txt
full_graph_2_gene_v_bm_spl.txt
full_graph_2_vk37_bm_spl.txt
full_graph_2_vk491_bm_spl.txt
full_graph_2_vk486_bm_spl.txt
full_graph_2_vk1110_bm_spl.txt
full_graph_2_vk14126_bm_spl.txt

value: 99 days


title: 323_324_5P_LP

description:
seq_logo_37_VH12_5P_LP_2_bm_spl.txt
seq_logo_491_VH12_5P_LP_2_bm_spl.txt
seq_logo_486_VH12_5P_LP_2_bm_spl.txt
seq_logo_14126_VH12_5P_LP_2_bm_spl.txt
full_graph_2_gene_j_bm_spl.txt
full_graph_2_gene_v_bm_spl.txt
full_graph_2_vk37_bm_spl.txt
full_graph_2_vk491_bm_spl.txt
full_graph_2_vk486_bm_spl.txt
full_graph_2_vk1110_bm_spl.txt
full_graph_2_vk14126_bm_spl.txt


title: Control 58

tag: sample type

value: Control


title: Control 63


title: pALPC1


title: Control 57


title: 313_322_AN_5P_LP

description:
seq_logo_37_dnRAG1_VH12_AN_5P_LP_2_bm_spl.txt
seq_logo_491_dnRAG1_VH12_AN_5P_LP_2_bm_spl.txt
seq_logo_486_dnRAG1_VH12_AN_5P_LP_2_bm_spl.txt
seq_logo_14126_dnRAG1_VH12_AN_5P_LP_2_bm_spl.txt
full_graph_2_gene_j_bm_spl.txt
full_graph_2_gene_v_bm_spl.txt
full_graph_2_vk37_bm_spl.txt
full_graph_2_vk491_bm_spl.txt
full_graph_2_vk486_bm_spl.txt
full_graph_2_vk1110_bm_spl.txt
full_graph_2_vk14126_bm_spl.txt

source_name: Sorted Bone Marrow Cells

value: Bone Marrow


title: Control 55


title: 313_322_AN_5N_LN

description:
seq_logo_37_dnRAG1_VH12_AN_5N_LN_2_bm_spl.txt
seq_logo_491_dnRAG1_VH12_AN_5N_LN_2_bm_spl.txt
seq_logo_486_dnRAG1_VH12_AN_5N_LN_2_bm_spl.txt
seq_logo_14126_dnRAG1_VH12_AN_5N_LN_2_bm_spl.txt
full_graph_2_gene_j_bm_spl.txt
full_graph_2_gene_v_bm_spl.txt
full_graph_2_vk37_bm_spl.txt
full_graph_2_vk491_bm_spl.txt
full_graph_2_vk486_bm_spl.txt
full_graph_2_vk1110_bm_spl.txt
full_graph_2_vk14126_bm_spl.txt


title: Control 56


title: Newly diagnosed PCL_20

tag: gender

value: Male

value: bone marrow

tag: disease state

value: plasma cell leukemia


title: Control 54


title: SK-MEL-28 Etoposide 2

city: Changsha

first: MEI

last: LUO

state: Hunan

address: Kaifu District

department: Dermatology

institute: Xiangya Hospital

data_processing:
Hisat2 for mapping
StringTie for annotation
DESeq2 for differentital expression
Genome_build: hg38
Supplementary_files_format_and_content: Matrix table with raw gene counts for every gene and every sample

growth_protocol: Human malignant melanoma cell lines, A375 (Cat#CRL-1619) and SK-Mel-28 (Cat#HTB-72) cell lines were obtained from American Type Culture Collection (ATCC) and were cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 100U/ml penicillin and streptomycin at 37℃ in 5% CO2 (v/v).

extract_protocol:
The mRNA polyA-based enrichment method was performed to establish the RNA library.
RNA sequencing was performed by Illumina NovaSeq 6000 at Novogene-Tianjin, China (Table S5)

treatment_protocol: etoposide + rapamycin vs etoposide

tag: treatment

value: Etoposide

tag: cell line


title: SK-MEL-28 Etoposide 3


title: SK-MEL-28 Etoposide + Rapamycin 2

value: Etoposide + Rapamycin


title: SK-MEL-28 Etoposide + Rapamycin 3


title: SK-MEL-28 Etoposide 1


title: 313_322_AP_5P_LP

description:
seq_logo_37_dnRAG1_VH12_AP_5P_LP_2_bm_spl.txt
seq_logo_491_dnRAG1_VH12_AP_5P_LP_2_bm_spl.txt
seq_logo_486_dnRAG1_VH12_AP_5P_LP_2_bm_spl.txt
seq_logo_14126_dnRAG1_VH12_AP_5P_LP_2_bm_spl.txt
full_graph_2_gene_j_bm_spl.txt
full_graph_2_gene_v_bm_spl.txt
full_graph_2_vk37_bm_spl.txt
full_graph_2_vk491_bm_spl.txt
full_graph_2_vk486_bm_spl.txt
full_graph_2_vk1110_bm_spl.txt
full_graph_2_vk14126_bm_spl.txt

value: 89 days


title: 323_324_AN_5P_LP

description:
seq_logo_37_VH12_AN_5P_LP_2_bm_spl.txt
seq_logo_491_VH12_AN_5P_LP_2_bm_spl.txt
seq_logo_486_VH12_AN_5P_LP_2_bm_spl.txt
seq_logo_14126_VH12_AN_5P_LP_2_bm_spl.txt
full_graph_2_gene_j_bm_spl.txt
full_graph_2_gene_v_bm_spl.txt
full_graph_2_vk37_bm_spl.txt
full_graph_2_vk491_bm_spl.txt
full_graph_2_vk486_bm_spl.txt
full_graph_2_vk1110_bm_spl.txt
full_graph_2_vk14126_bm_spl.txt


title: Control 53


title: 313_322_AP_5N_LN

description:
seq_logo_37_dnRAG1_VH12_AP_5N_LN_2_bm_spl.txt
seq_logo_491_dnRAG1_VH12_AP_5N_LN_2_bm_spl.txt
seq_logo_486_dnRAG1_VH12_AP_5N_LN_2_bm_spl.txt
seq_logo_14126_dnRAG1_VH12_AP_5N_LN_2_bm_spl.txt
full_graph_2_gene_j_bm_spl.txt
full_graph_2_gene_v_bm_spl.txt
full_graph_2_vk37_bm_spl.txt
full_graph_2_vk491_bm_spl.txt
full_graph_2_vk486_bm_spl.txt
full_graph_2_vk1110_bm_spl.txt
full_graph_2_vk14126_bm_spl.txt


title: 323_324_AN_5N_LN

description:
seq_logo_37_VH12_AN_5N_LN_2_bm_spl.txt
seq_logo_491_VH12_AN_5N_LN_2_bm_spl.txt
seq_logo_486_VH12_AN_5N_LN_2_bm_spl.txt
seq_logo_14126_VH12_AN_5N_LN_2_bm_spl.txt
full_graph_2_gene_j_bm_spl.txt
full_graph_2_gene_v_bm_spl.txt
full_graph_2_vk37_bm_spl.txt
full_graph_2_vk491_bm_spl.txt
full_graph_2_vk486_bm_spl.txt
full_graph_2_vk1110_bm_spl.txt
full_graph_2_vk14126_bm_spl.txt


title: SK-MEL-28 Etoposide + Rapamycin 1


title: A375 Etoposide 3


title: Control 51


title: Control 50


title: A375 Etoposide 1


title: Control 49


title: Newly diagnosed PCL_18

value: Female


title: Control 52


title: Newly diagnosed PCL_21


title: Newly diagnosed PCL_16


title: 323_324_AP_5N_LN

description:
seq_logo_37_VH12_AP_5N_LN_2_bm_spl.txt
seq_logo_491_VH12_AP_5N_LN_2_bm_spl.txt
seq_logo_486_VH12_AP_5N_LN_2_bm_spl.txt
seq_logo_14126_VH12_AP_5N_LN_2_bm_spl.txt
full_graph_2_gene_j_bm_spl.txt
full_graph_2_gene_v_bm_spl.txt
full_graph_2_vk37_bm_spl.txt
full_graph_2_vk491_bm_spl.txt
full_graph_2_vk486_bm_spl.txt
full_graph_2_vk1110_bm_spl.txt
full_graph_2_vk14126_bm_spl.txt


title: 323_324_AP_5P_LP

description:
seq_logo_37_VH12_AP_5P_LP_2_bm_spl.txt
seq_logo_491_VH12_AP_5P_LP_2_bm_spl.txt
seq_logo_486_VH12_AP_5P_LP_2_bm_spl.txt
seq_logo_14126_VH12_AP_5P_LP_2_bm_spl.txt
full_graph_2_gene_j_bm_spl.txt
full_graph_2_gene_v_bm_spl.txt
full_graph_2_vk37_bm_spl.txt
full_graph_2_vk491_bm_spl.txt
full_graph_2_vk486_bm_spl.txt
full_graph_2_vk1110_bm_spl.txt
full_graph_2_vk14126_bm_spl.txt


title: 329_330_5P_LP

value: 98 days

value: Spleen


title: A375 Etoposide + Rapamycin 3


title: Newly diagnosed PCL_14


title: Brain_PDGFCC_sample_1

value: adult

tag: Sex

value: female

value: 4h post PDGF-CC ICV

value: brain


title: Newly diagnosed PCL_19


title: Newly diagnosed PCL_13


title: 329_330_5N_LN

tag: strain

value: CD19+ CD5- PtC-


title: Newly diagnosed PCL_10


title: Newly diagnosed PCL_17


title: Newly diagnosed PCL_15


title: Newly diagnosed PCL_12


title: Newly diagnosed PCL_11


title: Control 48

value: leukocytes

value: healthy


title: A375 Etoposide + Rapamycin 2


title: 325_326_5P_LP

value: CD19+ CD5+ PtC+


title: Newly diagnosed PCL_9


title: A375 Etoposide 2


title: Control 47

value: Control


title: A375 Etoposide + Rapamycin 1


title: 325_326_5N_LN


title: 329_330_AN_5P_LP

value: Bone Marrow


title: Control 46


title: trZMYND11K36_S10

city: Quebec City

first: Samer

last: Hussein

state: Quebec

address: 9 rue Mcmahon

department: Departement of medecine

country: Canada

institute: CHU de Québec - Université Laval

data_processing:
Library strategy: CUT&RUN-seq
For ChIP-seq and CUT&RUN-seq, the single-end reads were mapped on the human genome build hg19 using bowtie version 2.1.6 (Langmead et al., 2009), using the pre-set sensitive parameter.
MACS version 2.0.10 (model-based analysis of ChIPseq) peak finding algorithm was used to identify regions of ChIP-seq or CUT&RUN-seq enrichment over the background (Feng et al., 2012), using broad peaks parameters.
Unique mapped read values were normalized to library size. Peaks were annotated as per human genome EnsDb.Hspaiens.v75 - hg19
Genome_build: hg19
Supplementary_files_format_and_content: MBTD1H4K8_S6.IgG.broad_peaks.broadPeak is a bed file containing significant MACS-called broad peaks for the MBTD1H4K8_S6 CUT&RUN sample using the IgG control sample as input sample.
Supplementary_files_format_and_content: MBTD1K36_S9.IgG.broad_peaks.broadPeak is a bed file containing significant MACS-called broad peaks for the MBTD1K36_S9 CUT&RUN sample using the IgG control sample as input sample.
Supplementary_files_format_and_content: trZMYND11H4K8_S7.IgG.broad_peaks.broadPeak is a bed file containing significant MACS-called broad peaks for the trZMYND11H4K8_S7 CUT&RUN sample using the IgG control sample as input sample.
Supplementary_files_format_and_content: trZMYND11K36_S10.IgG.broad_peaks.broadPeak is a bed file containing significant MACS-called broad peaks for the trZMYND11K36_S10 CUT&RUN sample using the IgG control sample as input sample.
Supplementary_files_format_and_content: ZMH4K8_S8.IgG.broad_peaks.broadPeak is a bed file containing significant MACS-called broad peaks for the ZMH4K8_S8 CUT&RUN sample using the IgG control sample as input sample.
Supplementary_files_format_and_content: ZMK36_S11.IgG.broad_peaks.broadPeak is a bed file containing significant MACS-called broad peaks for the ZMK36_S11 CUT&RUN sample using the IgG control sample as input sample.

source_name: K562 from ATCC

growth_protocol: RPMI, 10% NBCS, 1ug/ml Puromycin

extract_protocol:
For CUT&RUN, cells were bound to activated ConA magnetic beads, permeabilized and incubated with antibody, overnight at 4C. After washing, cells were incubated with pAG Mnase. DNA extract was purified using the NEB monarch DNA cleanup kit.
Finally, 5–10 ng of purified CUT&RUN-enriched DNA was used to prepare Illumina library using the NEB Ultra II DNA Library Prep Kit per manufacturer’s instructions.

treatment_protocol: No Treatment

value: lymphoblast from chronic myelogenous leukemia (CML)

tag: growth conditions

value: normal growth conditions

tag: chip antibody

value: H3K36me3, rabbit polyclonal, Abcam, ab9050, RRID:AB_306966

value: Insertion of Tag only at the AAVS1 locus by Zinc finger Nuclease, puromycin selection

tag: expressing


title: Control 44


title: ZMH4K8_S8

value: H4K8ac, rabbit monoclonal, Abcam, ab45166, RRID:AB_732937

value: ZMYND11-MBTD1 fusion-3xFlag-2xstrep


title: Control 45


title: Control 43


title: Newly diagnosed PCL_8

value: Male

value: bone marrow

value: plasma cell leukemia


title: Newly diagnosed PCL_6

value: Female


title: 329_330_AP_5P_LP

tag: replicate

tag: round

tag: number of animals per sample

value: 98 days


title: Control 41


title: Newly diagnosed PCL_7

tag: tissue


title: Newly diagnosed PCL_5

tag: age


title: Control 42

tag: cell type


title: 329_330_AP_5N_LN

tag: animal genotype

tag: cell phenotype


title: MBTD1K36_S9


title: trZMYND11H4K8_S7


title: 325_326_AN_5P_LP


title: 325_326_AN_5N_LN


title: MBTD1H4K8_S6


title: Control 40

tag: diagnosis

tag: sample type


title: ZMK36_S11

tag: treatment


title: 325_326_AP_5P_LP


title: 325_326_AP_5N_LN


title: 329_330_AN_5N_LN


title: S2-2

city: Toronto

first: Fan

last: Xia

state: ON

address: 144 College Street, Room 970

institute: University of Toronto

data_processing:
Illumina Casava1.7 software used for basecalling.
Alignment of Trimmed Reads using STAR with parameters --sjdbOverhang 100 --runThreadN 8
Reads Per Kilobase of exon per Megabase of library size (RPKM) were calculated using HTSeq 0.13.15 and DESeq2 1.32.0
Genome_build: GRCh38
Supplementary_files_format_and_content: tab-delimited text files include FPKM values for each Sample ...

source_name: prostate cancer

growth_protocol: RPMI-1640 (Wisent) supplemented with 10% fetal bovine serum (Wisent) and 1% penicillin/streptomycin (Wisent), at 37 oC, 5% CO2.

extract_protocol:
RNeasy Plus Kit (Qiagen) following manufacturer’s instructions
RNA libraries were prepared for sequencing using standard Illumina protocols

treatment_protocol: CRISPR gene KO

tag: cell line

value: SLIT2 KO


title: Control 38

value: leukocytes


title: Control 39

value: healthy


title: Control 37


title: IgG_S12

value: IgG, Rabbit polyclonal, Epicypher, 13-0042


title: S1-2


title: Newly diagnosed PCL_3

tag: gender

tag: disease state


title: Newly diagnosed PCL_4


title: Newly diagnosed PCL_1


title: Newly diagnosed PCL_2


title: S2-1


title: Newly diagnosed MM_109

source_name: bone marrow aspirate

tag: age category

value: bone marrow aspirate

value: multiple myeloma


title: Newly diagnosed MM_108


title: 296_297_5P_LP

tag: strain

value: 85 days

value: Spleen

value: CD19+ CD5+ PtC+


title: Control 35

value: Control


title: 294_304_5N_LN

value: 85 days and 82 days

value: CD19+ CD5- PtC-


title: Ctrl2-2

value: Non-targeted control


title: S1-1


title: 296_297_5N_LN


title: 294_304_AN_5N_LN

value: Bone Marrow


title: Ctrl2-1


title: Control 36


title: 294_304_AN_5P_LP


title: Newly diagnosed MM_107

value: Male


title: 294_304_5P_LP


title: 294_304_AP_5P_LP


title: Ctrl1-2


title: Control 32


title: Control 34


title: Control 31


title: Ctrl1-1


title: Control 30


title: 294_304_AP_5N_LN


title: HC1and2_GEX

molecule: polyA RNA

source_name: Skin tumor

extract_protocol:
Skin biopsies were minced and digested enzymatically (Whole Dissociation Skin Kit, Miltenyi Biotec) for 2 hours at 37°C and further dispersed using the Miltenyi gentleMACS Octo Dissociator. Normal skin biopsies were hashed together 2 at a time per protocol: https://www.biolegend.com/en-us/protocols/totalseq-b-or-c-with-10x-feature-barcoding-technology Normal Sample 1 and 2 are labeled with HTO1 and HTO2 respectively. Normal sample 3 and 4 are labeled with HTO9 and HTO10 respectively. Experimental procedures followed established techniques using the Chromium Single Cell 5’ Library V1 kit (10x Genomics).
Experimental procedures followed established techniques using the Chromium Single Cell 5’ Library V1 kit +V(D)J Enrichment Kit, Human T Cell (10x Genomics).

value: Healthy control

value: Skin tumor


title: Control 33


title: 296_297_AN_5P_LP


title: Control 29


title: MF24_VDJ

data_processing:
Library strategy: VDJ-seq
Chromium scRNA-seq data produced by the 10X Chromium Platform were processed to generate sample-specific fastq files.
Processed reads were then examined by quality metrics, mapped to a reference human genome using RNA-seq aligner STAR and assigned to individual cells of origin according to the cell specific barcodes, using the Cell Ranger pipeline (10X Genomics).
To ensure that PCR amplified transcripts were counted only once, only single UMIs were counted for gene expression level 10. In this way, cell x gene UMI counting matrices were generated for downstream analyses.
Seurat, an R package developed for single-cell analysis, was used to identify distinct cell populations and visualize cell clusters in graphs. Specifically, the UMI matrix was filtered such that only cells expressing at least 200 genes were utilized in downstream analysis.
Unwanted sources of variation were regressed out of the data by constructing linear models to predict gene expression based on the number of UMIs per cell as well as the percentage of mitochondrial gene content.
Hashtags were demultiplexed based on the Seurat package, https://satijalab.org/seurat/articles/hashing_vignette.html
Genome_build: GrCh38A genome
Supplementary_files_format_and_content: H5 files for gene expression, CSV files for TCR outputs

value: Mycosis fungoides


title: 296_297_AN_5N_LN


title: Control 28

value: leukocytes


title: Newly diagnosed MM_106

data_processing: MAS5.0 (Bioconductor affy package v1.36.2) default values with a trimmed mean target intensity value set to 1500. Values below 30 were truncated.

extract_protocol: RNeasy

value: Female


title: MF21_VDJ


title: Newly diagnosed MM_103


title: Newly diagnosed MM_105


title: MF18_VDJ


title: 296_297_AP_5N_LN


title: 296_297_AP_5P_LP


title: HC3and4_GEX

source_name: Skin tissue

value: Skin tissue


title: Newly diagnosed MM_104

value: bone marrow aspirate


title: Control 27

value: healthy


title: Newly diagnosed MM_100

value: multiple myeloma


title: 242_248_5P_LP

description:
seq_logo_37_dnRAG1_VH12_5P_LP_2.txt
seq_logo_491_dnRAG1_VH12_5P_LP_2.txt
seq_logo_486_dnRAG1_VH12_5P_LP_2.txt
seq_logo_14126_dnRAG1_VH12_5P_LP_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt
V-QUEST 491 KV replacement ID EXP 2 -  first line removed.pdf
V-QUEST 486 KV replacement ID EXP 2 - first line removed.pdf

value: 70 days

value: Spleen


title: Newly diagnosed MM_101


title: Control 26

description: IGH gene repertoire sequencing from pre-diagnostic CLL patients and controls

value: Control


title: Newly diagnosed MM_99


title: Newly diagnosed MM_97


title: Newly diagnosed MM_98


title: MF17_VDJ


title: 242_248_5N_LN

description:
seq_logo_37_dnRAG1_VH12_5N_LN_2.txt
seq_logo_491_dnRAG1_VH12_5N_LN_2.txt
seq_logo_486_dnRAG1_VH12_5N_LN_2.txt
seq_logo_14126_dnRAG1_VH12_5N_LN_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt
V-QUEST 491 KV replacement ID EXP 2 -  first line removed.pdf
V-QUEST 486 KV replacement ID EXP 2 - first line removed.pdf


title: Newly diagnosed MM_95


title: Newly diagnosed MM_102


title: MF24_GEX


title: Control 24


title: Newly diagnosed MM_96


title: MF21_GEX


title: Control 25


title: Newly diagnosed MM_94


title: Newly diagnosed MM_93


title: Control 22


title: Control 23


title: MF17_GEX


title: 241_252_5N_LN

description:
seq_logo_37_VH12_5N_LN_2.txt
seq_logo_491_VH12_5N_LN_2.txt
seq_logo_486_VH12_5N_LN_2.txt
seq_logo_14126_VH12_5N_LN_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt
V-QUEST 491 KV replacement ID EXP 2 -  first line removed.pdf
V-QUEST 486 KV replacement ID EXP 2 - first line removed.pdf

value: 70 days and 68 days


title: MF18_GEX


title: 241_252_5P_LP

description:
seq_logo_37_VH12_5P_LP_2.txt
seq_logo_491_VH12_5P_LP_2.txt
seq_logo_486_VH12_5P_LP_2.txt
seq_logo_14126_VH12_5P_LP_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt
V-QUEST 491 KV replacement ID EXP 2 -  first line removed.pdf
V-QUEST 486 KV replacement ID EXP 2 - first line removed.pdf

value: CD19+ CD5+ PtC+


title: Control 21


title: 239_5P_LN

description:
seq_logo_37_dnRAG1_5P_LN_2.txt
seq_logo_491_dnRAG1_5P_LN_2.txt
seq_logo_486_dnRAG1_5P_LN_2.txt
seq_logo_14126_dnRAG1_5P_LN_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt
V-QUEST 491 KV replacement ID EXP 2 -  first line removed.pdf
V-QUEST 486 KV replacement ID EXP 2 - first line removed.pdf

value: RAG

value: CD19+ CD5+ PtC-


title: 239_5N_LN

description:
seq_logo_37_dnRAG1_5N_LN_2.txt
seq_logo_491_dnRAG1_5N_LN_2.txt
seq_logo_486_dnRAG1_5N_LN_2.txt
seq_logo_14126_dnRAG1_5N_LN_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt
V-QUEST 491 KV replacement ID EXP 2 -  first line removed.pdf
V-QUEST 486 KV replacement ID EXP 2 - first line removed.pdf

value: CD19+ CD5- PtC-


title: hCjSCs, 3D pterygium model rep2

city: La Jolla

first: ZHENG

last: ZHONG

state: California

address: 9500 Gilman Dr

department: Nanoengineering

institute: University of California, San Diego

data_processing:
All the sequenced raw reads were filtered and trimmed with Trim Galore (version 0.4.1)
the clean reads were mapped to the human genome (grc38) using Hisat2
the number of reads uniquely mapped to each gene was determined using FeatureCount
Genome_build: grc38
Supplementary_files_format_and_content: reads count per gene

source_name: human conjunctival stem cells

growth_protocol: A customized DLP-based 3D bioprinting system was built with projection optics suppled with a 365 nm light source (Hamamatsu), a motion controller (Newport) and a digital micromirror device (DMD, Texas Instruments). The digital patterns were generated with MATLAB and inputted to the DMD chip through a custom-built coordination software. The thickness of the printed structures was controlled by the motion controller covered with polydimethylsiloxane (PDMS).  The bioprinted hydrogel structures were printed on methacrylated coverslips upon light exposure, then rinsed with warm DPBS before subjected to culture in 5% CO2 at 37℃.

extract_protocol:
Total RNA was purified using RNeasy per manufacturer’s instructions (Qiagen)
PolyA-tailed RNA was purified from total RNA and libraries were constructed using the NEBNext® Ultra™ II RNA Library Prep Kit (NEB) following manufacturer’s recommendations.

treatment_protocol: The pre-polymer solution for the printing was made by dissolving GelMA, HAGM and LAP with DPBS (ThermoFisher Scientific) and filtered with 0.22 μm syringe filter (Millipore Sigma). 5% or 8% (w/v) GelMA with 0.25% (w/v) LAP, and 2.5% (w/v) GelMA with 1% (w/v) HAGM and 0.25% (w/v) LAP were made accordingly. The 5% (w/v) GelMA was used as the soft condition for hCjSCs bioprinting while the 8% (w/v) GelMA was used as the stiff condition. 2.5% (w/v) GelMA with 1% (w/v) HAGM was used for the bioprinting of HUVECs and 10T1/2s. Before the printing, the cells were digested, filtered with 70 μm cell strainers (Corning), quantified for the cell concentration, and pelleted with desired quantity. For the hCjSCs bioprinting, the bioink contained 2×10^7 cells/mL of hCjSCs. For the multilayered printing of 3D pterygium model, the stem cell layer contained 2×10^7 cells/mL of hCjSCs plus 1×10^7 cells/mL of macrophages while the vascular layer contained 2×10^7 cells/mL of HUVECs and 4×10^5 cells/mL of 10T1/2s (50:1).

value: human conjunctival stem cells

tag: growth protocol

value: 3D pterygium model


title: Control 20

data_processing:
Library strategy: B-cell receptor repertoire
Raw FASTQ files were uploaded to the ARResT/Interrogate immunoprofiler for annotation and initial exploratory analysis. The full productive IGH repertoire for each sample was downloaded in .csv format and processed in R (R Core Team, 2020) during further analysis.
Genome_build: GRCh38
Supplementary_files_format_and_content: Tab delimited tekst file containing: sample,QC.status,scenario,rearrangements,raw.count,w.junction.count,w.junction.percent,artefacts.count,artefacts.percent,usable.count,usable.percent,w.most.pop.cln.s..count,w.most.pop.cln.s..percent,uniq.usable.seq.count,clonotypes.count,diversity..norm.ed..count,effective.species.count,R1.Q.mean.stdev.count,R2.Q.mean.stdev.count,joined.normally.count,joined.normally.percent,joined.loosely.count,joined.loosely.percent,total.joined.count,total.joined.percent,saved.from.unjoined.count,saved.from.unjoined.percent,low.quality..ee.nt...0.01..count,low.quality..ee.nt...0.01..percent,short...50nt..count,short...50nt..percent,annotated.count,annotated.percent,w.TR.receptor.count,w.TR.receptor.percent,w.B.chain.count,w.B.chain.percent,w.VJ.Vb..Db..Jb.count,w.VJ.Vb..Db..Jb.percent,w.VJ.Vb..Db..Jb.cln

extract_protocol:
Total  gDNA was isolated from the buffycoat DNA and 250-500 ng was amplified through multiplex PCR of IGHV-IGHD-IGHJ gene rearrangements following the BIOMED-2 protocol. After amplification, PCR products were purified on Agencourt AMPURE beads (Beckman Coulter, Brea, CA, USA) and eluted using H2O. The purified product was checked for primer dimers using the Agilent Tapestation (Agilent,Santa Clara, CA, USA).
The intitial multiplex PCR already attached the indexes to the PCR products, so library construction was simply pooling the purified PCR product in equilmolar fashion.


title: 253_259_5P_LP

description:
seq_logo_37_dnRAG1_VH12_5P_LP_2.txt
seq_logo_491_dnRAG1_VH12_5P_LP_2.txt
seq_logo_486_dnRAG1_VH12_5P_LP_2.txt
seq_logo_14126_dnRAG1_VH12_5P_LP_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt
V-QUEST 491 KV replacement ID EXP 1 -  first line removed.pdf
V-QUEST 486 KV replacement ID EXP 1 -  first line removed.pdf

tag: replicate


title: hCjSCs, 3D pterygium model rep3


title: 239_5P_LP

description:
seq_logo_37_dnRAG1_5P_LP_2.txt
seq_logo_491_dnRAG1_5P_LP_2.txt
seq_logo_486_dnRAG1_5P_LP_2.txt
seq_logo_14126_dnRAG1_5P_LP_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt
V-QUEST 491 KV replacement ID EXP 2 -  first line removed.pdf
V-QUEST 486 KV replacement ID EXP 2 - first line removed.pdf

tag: round


title: Control 17


title: Control 19


title: 255_257_5P_LP

description:
seq_logo_37_VH12_5P_LP_2.txt
seq_logo_491_VH12_5P_LP_2.txt
seq_logo_486_VH12_5P_LP_2.txt
seq_logo_14126_VH12_5P_LP_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt
V-QUEST 491 KV replacement ID EXP 1 -  first line removed.pdf
V-QUEST 486 KV replacement ID EXP 1 -  first line removed.pdf

tag: number of animals per sample


title: hCjSCs, 3D pterygium model rep1


title: 253_259_5N_LN

description:
seq_logo_37_dnRAG1_VH12_5N_LN_2.txt
seq_logo_491_dnRAG1_VH12_5N_LN_2.txt
seq_logo_486_dnRAG1_VH12_5N_LN_2.txt
seq_logo_14126_dnRAG1_VH12_5N_LN_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt
V-QUEST 491 KV replacement ID EXP 1 -  first line removed.pdf
V-QUEST 486 KV replacement ID EXP 1 -  first line removed.pdf

tag: tissue


title: 240_5N_LN

description:
seq_logo_37_WT_5N_LN_2.txt
seq_logo_491_WT_5N_LN_2.txt
seq_logo_486_WT_5N_LN_2.txt
seq_logo_14126_WT_5N_LN_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt
V-QUEST 491 KV replacement ID EXP 2 -  first line removed.pdf
V-QUEST 486 KV replacement ID EXP 2 - first line removed.pdf

tag: age

value: WT


title: hCjSCs, 2D Culture ctrl rep2

tag: cell type

value: 2D Culture


title: hCjSCs, 2D Culture ctrl rep1


title: Newly diagnosed MM_92

value: Female


title: Newly diagnosed MM_91

value: Male


title: Control 18

value: leukocytes


title: hCjSCs, 2D Culture ctrl rep3


title: Newly diagnosed MM_89

value: bone marrow aspirate


title: Newly diagnosed MM_90

value: multiple myeloma


title: Newly diagnosed MM_87


title: Newly diagnosed MM_85


title: Newly diagnosed MM_86


title: Newly diagnosed MM_88


title: 255_257_5N_LN

description:
seq_logo_37_VH12_5N_LN_2.txt
seq_logo_491_VH12_5N_LN_2.txt
seq_logo_486_VH12_5N_LN_2.txt
seq_logo_14126_VH12_5N_LN_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt
V-QUEST 491 KV replacement ID EXP 1 -  first line removed.pdf
V-QUEST 486 KV replacement ID EXP 1 -  first line removed.pdf

value: 70 days

tag: animal genotype


title: Control 16

value: healthy


title: Control 12

value: Control


title: Control 13


title: Healthy adult 14_PB_bL

city: New York City

first: Ramin

last: Herati

state: NY

address: 429 E. 29th Street, room 308

institute: NYU School of Medicine

description:
FINAL_master_list_of_gene_counts_MIN.Fastqs.txt
FS1819-999_PB_bL_S6

data_processing:
FASTQ files were trimmed using Trimmomatic 0.32.
Bcl2fastq2 v2.20 was used to convert bcl files to fastq format.
Alignment done using STAR 2.5.2a against the hg38 reference genome.
The aligned files were processed using PORT 0.8.5 gene-based normalization (https://github.com/itmat/Normalization).
Genome_build: HG38
Supplementary_files_format_and_content: Matrix table with raw gene counts for every gene and every sample

source_name: Peripheral blood mononuclear cells

extract_protocol:
Peripheral blood mononuclear cells were sorted by FACS and checked for purity. Then, RNA was isolated using Qiagen RNeasy Micro Plus kit (Cat#74034) according to manufacturer's instructions.
Libraries were constructed using the TakaraBio SMART-Seq® HT Kit (Cat#634437) and indexing adapters added using Illumina Nextera XT (Cat#FC-131-1024) as per manufacturer's instructions.

tag: individual

value: Healthy adult 14

tag: therapy

value: Healthy control

tag: time point

value: Baseline

value: PBMC

tag: cell subset

value: Antibody-secreting B cells


title: Control 11

tag: diagnosis

tag: sample type


title: Healthy adult 14_HiHi_bL

description:
FINAL_master_list_of_gene_counts_MIN.Fastqs.txt
FS1819-999_HiHi_bL_S7

value: ICOS+CD38+ cTfh


title: 254_5P_LN

description:
seq_logo_37_dnRAG1_5P_LN_2.txt
seq_logo_491_dnRAG1_5P_LN_2.txt
seq_logo_486_dnRAG1_5P_LN_2.txt
seq_logo_14126_dnRAG1_5P_LN_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt
V-QUEST 491 KV replacement ID EXP 1 -  first line removed.pdf
V-QUEST 486 KV replacement ID EXP 1 -  first line removed.pdf

value: Spleen

value: RAG

tag: cell phenotype

value: CD19+ CD5+ PtC-


title: Healthy adult 14_ABC_bL

description:
FINAL_master_list_of_gene_counts_MIN.Fastqs.txt
FS1819-999_ABC_bL_S5

value: Activated B cells


title: 254_5N_LN

description:
seq_logo_37_dnRAG1_5N_LN_2.txt
seq_logo_491_dnRAG1_5N_LN_2.txt
seq_logo_486_dnRAG1_5N_LN_2.txt
seq_logo_14126_dnRAG1_5N_LN_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt
V-QUEST 491 KV replacement ID EXP 1 -  first line removed.pdf
V-QUEST 486 KV replacement ID EXP 1 -  first line removed.pdf

value: CD19+ CD5- PtC-


title: Control 10

description: IGH gene repertoire sequencing from pre-diagnostic CLL patients and controls

value: leukocytes


title: Control 9


title: Healthy adult 13_PB_oW

description:
FINAL_master_list_of_gene_counts_MIN.Fastqs.txt
FS1819-131_PB_oW_S9

value: Healthy adult 13

value: One week after flu vaccine


title: 254_5P_LP

description:
seq_logo_37_dnRAG1_5P_LP_2.txt
seq_logo_491_dnRAG1_5P_LP_2.txt
seq_logo_486_dnRAG1_5P_LP_2.txt
seq_logo_14126_dnRAG1_5P_LP_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt
V-QUEST 491 KV replacement ID EXP 1 -  first line removed.pdf
V-QUEST 486 KV replacement ID EXP 1 -  first line removed.pdf

value: 70 days

value: CD19+ CD5+ PtC+


title: Healthy adult 14_naiveB_bL

description:
FINAL_master_list_of_gene_counts_MIN.Fastqs.txt
FS1819-999_naiveB_bL_S4

value: Naïve B cells


title: 249_5N_LN

description:
seq_logo_37_WT_5N_LN_2.txt
seq_logo_491_WT_5N_LN_2.txt
seq_logo_486_WT_5N_LN_2.txt
seq_logo_14126_WT_5N_LN_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt
V-QUEST 491 KV replacement ID EXP 1 -  first line removed.pdf
V-QUEST 486 KV replacement ID EXP 1 -  first line removed.pdf

value: 72 days

value: WT


title: 207_208_229_5P_LP

description:
seq_logo_37_dnRAG1_VH12_5P_LP_2.txt
seq_logo_491_dnRAG1_VH12_5P_LP_2.txt
seq_logo_486_dnRAG1_VH12_5P_LP_2.txt
seq_logo_14126_dnRAG1_VH12_5P_LP_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt

value: 91 days, 91 days, and 77 days


title: Newly diagnosed MM_84

first: Mark

last: van Duin

address: Wytemaweg 80

tag: gender

value: Female

tag: age category

value: bone marrow aspirate

tag: disease state

value: multiple myeloma


title: Control 7

value: healthy

value: Control


title: Control 8


title: Healthy adult 13_naiveB_oW

description:
FINAL_master_list_of_gene_counts_MIN.Fastqs.txt
FS1819-131_naiveB_oW_S8

value: Healthy control

value: PBMC


title: Newly diagnosed MM_83

value: Male


title: Control 6

value: leukocytes


title: 207_208_229_5N_LN

description:
seq_logo_37_dnRAG1_VH12_5N_LN_2.txt
seq_logo_491_dnRAG1_VH12_5N_LN_2.txt
seq_logo_486_dnRAG1_VH12_5N_LN_2.txt
seq_logo_14126_dnRAG1_VH12_5N_LN_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt

tag: replicate

tag: strain

value: Spleen

value: CD19+ CD5- PtC-


title: Newly diagnosed MM_81


title: Newly diagnosed MM_82


title: Newly diagnosed MM_79


title: Newly diagnosed MM_80


title: Newly diagnosed MM_78


title: Control 4


title: Control 5


title: Healthy adult 13_HiHi_bL

description:
FINAL_master_list_of_gene_counts_MIN.Fastqs.txt
FS1819-131_HiHi_bL_S3

value: Healthy adult 13

value: Baseline

value: ICOS+CD38+ cTfh


title: Healthy adult 13_HiHi_oW

description:
FINAL_master_list_of_gene_counts_MIN.Fastqs.txt
FS1819-131_HiHi_oW_S10

value: One week after flu vaccine


title: 215_216_5P_LP

description:
seq_logo_37_VH12_5P_LP_2.txt
seq_logo_491_VH12_5P_LP_2.txt
seq_logo_486_VH12_5P_LP_2.txt
seq_logo_14126_VH12_5P_LP_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt

tag: round

tag: number of animals per sample

value: 82 days

value: CD19+ CD5+ PtC+


title: Healthy adult 13_ABC_bL

description:
FINAL_master_list_of_gene_counts_MIN.Fastqs.txt
FS1819-131_ABC_bL_S2

value: Activated B cells


title: 237_5P_LP

description:
seq_logo_37_dnRAG1_5P_LP_2.txt
seq_logo_491_dnRAG1_5P_LP_2.txt
seq_logo_486_dnRAG1_5P_LP_2.txt
seq_logo_14126_dnRAG1_5P_LP_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt

tag: age

value: 75 days

tag: tissue

value: RAG


title: Healthy adult 13_naiveB_bL

description:
FINAL_master_list_of_gene_counts_MIN.Fastqs.txt
FS1819-131_naiveB_bL_S1

tag: cell type

value: Naïve B cells


title: Healthy adult 12_naiveB_oW

description:
FINAL_master_list_of_gene_counts_MIN.Fastqs.txt
FS1819-130_naiveB_oW_S84

tag: individual

value: Healthy adult 12


title: Healthy adult 12_ABC_oW

description:
FINAL_master_list_of_gene_counts_MIN.Fastqs.txt
FS1819-130_ABC_oW_S85

tag: therapy

value: Healthy control


title: 237_5P_LN

description:
seq_logo_37_dnRAG1_5P_LN_2.txt
seq_logo_491_dnRAG1_5P_LN_2.txt
seq_logo_486_dnRAG1_5P_LN_2.txt
seq_logo_14126_dnRAG1_5P_LN_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt

tag: animal genotype

value: CD19+ CD5+ PtC-


title: Control 3

description: IGH gene repertoire sequencing from pre-diagnostic CLL patients and controls

value: healthy

value: Control


title: Healthy adult 11_PB_oW

description:
FINAL_master_list_of_gene_counts_MIN.Fastqs.txt
FS1819-129_PB_oW_S82

value: Healthy adult 11

tag: time point

value: PBMC

tag: cell subset

value: Antibody-secreting B cells


title: Newly diagnosed MM_76

value: Female

value: bone marrow aspirate

value: multiple myeloma


title: Newly diagnosed MM_75


title: Newly diagnosed MM_77


title: Control 2

value: leukocytes

tag: diagnosis

tag: sample type


title: 215_216_5N_LN

description:
seq_logo_37_VH12_5N_LN_2.txt
seq_logo_491_VH12_5N_LN_2.txt
seq_logo_486_VH12_5N_LN_2.txt
seq_logo_14126_VH12_5N_LN_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt

value: 82 days

value: Spleen

tag: cell phenotype

value: CD19+ CD5- PtC-


title: CSp5C2

city: Zhengzhou

first: Qian

last: Bai

address: JING BA ROAD

institute: The second affiliated hospital of Zhengzhou University

data_processing:
Poly-N, adapter and low-quality were removed by in-house perl scripts ，Quality control analysis was performed by ng_qc.
Clean reads were aligned to the mus musculus ensembl 92 using HISAT2(Langmead,  B.et al) v2.0.4. HISAT2 was run with ‘--rna-strandness RF’, other parameters were set  as default.
The mapped reads of each sample were assembled by StringTie (v1.3.1) (Mihaela  Pertea.et al. 2016) in a reference-based approach. StringTie uses a novel network flow  algorithm as well as an optional de novo assembly step to assemble and quantitate fulllength transcripts representing multiple splice variants for each gene locus.
Cuffdiff (v2.1.1) was used to calculate FPKMs of both lncRNAs and coding genes in  each sample (Trapnell, C. et al. 2010). Gene FPKMs were computed by summing the  FPKMs of transcripts in each gene group. FPKM means fragments per kilo-base of  exon per million fragments mapped, calculated based on the length of the fragments  and reads count mapped to this fragment.
Genome_build: mus_musculus_Ensembl_92
Supplementary_files_format_and_content: raw_FPKM

source_name: spinal trigeminal nucleus caudalis

extract_protocol:
RNA was harvested using Trizol reagent.
A total amount of 3 μg RNA per sample was used as input material for the RNA sample  preparations. Firstly, ribosomal RNA was removed by Epicentre Ribo-zero™ rRNA  Removal Kit (Epicentre, USA), and rRNA free residue was cleaned up by ethanol  precipitation. Subsequently, sequencing libraries were generated using the rRNAdepleted RNA by NEBNext® Ultra™ Directional RNA Library Prep Kit for Illumina®  (NEB, USA) following manufacturer’s recommendations.

value: spinal trigeminal nucleus caudalis

value: 8 weeks


title: CSp5C3


title: Healthy adult 11_HiHi_oW

description:
FINAL_master_list_of_gene_counts_MIN.Fastqs.txt
FS1819-129_HiHi_oW_S83

value: One week after flu vaccine

value: ICOS+CD38+ cTfh


title: 236_5N_LN

description:
seq_logo_37_WT_5N_LN_2.txt
seq_logo_491_WT_5N_LN_2.txt
seq_logo_486_WT_5N_LN_2.txt
seq_logo_14126_WT_5N_LN_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt

value: 75 days

value: WT


title: 237_5N_LN

description:
seq_logo_37_dnRAG1_5N_LN_2.txt
seq_logo_491_dnRAG1_5N_LN_2.txt
seq_logo_486_dnRAG1_5N_LN_2.txt
seq_logo_14126_dnRAG1_5N_LN_2.txt
full_graph_2_gene_j.txt
full_graph_2_gene_v.txt
full_graph_2_vk37.txt
full_graph_2_vk491.txt
full_graph_2_vk486.txt
full_graph_2_vk1110.txt
full_graph_2_vk14126.txt

value: RAG


title: Newly diagnosed MM_73

city: Rotterdam

institute: Erasmus MC

tag: gender

value: Male

tag: age category


title: Control 1

first: Anton

last: Langerak

address: Doctor Molewaterplein 40

value: healthy

value: Control


title: Newly diagnosed MM_71

value: Female

tag: disease state


title: Newly diagnosed MM_74

value: bone marrow aspirate


title: Healthy adult 11_naiveB_oW

description:
FINAL_master_list_of_gene_counts_MIN.Fastqs.txt
FS1819-129_naiveB_oW_S80

value: Healthy adult 11

value: Healthy control

value: PBMC

value: Naïve B cells


title: SSp5C3

tag: strain