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Text-fig. 2. Light micrographs of Pinus spp. cuticles prepared with the modified, gentle bleaching procedure. a: Cuticle 1, Pinus sp. 1. Nearly the entire width of the leaf has been preserved. Five parallel rows of stomata are visible. b: Cuticle 1, close-up of (a). Two guard cells are visible around each stoma. c: Cuticle 1, close-up of eight stomata. Two guard cells and eight subsidiary cells are visible around each stoma. d: Cuticle 2, Pinus sp. 2. Some folding of the cuticle occurred during preparation, but many parallel rows of stomata on both sides of a thin, central midvein are evident. e: Cuticle 2, close-up of (d). Pairs of guard cells surround each stoma. f: Cuticle 2, close-up of (e). Subsidiary and epithelial cells can be observed around the stomata. in A Modified, Step-By-Step Procedure For The Gentle Bleaching Of Delicate Fossil Leaf Cuticles
Text-fig. 2. Light micrographs of Pinus spp. cuticles prepared with the modified, gentle bleaching procedure. a: Cuticle 1, Pinus sp. 1. Nearly the entire width of the leaf has been preserved. Five parallel rows of stomata are visible. b: Cuticle 1, close-up of (a). Two guard cells are visible around each stoma. c: Cuticle 1, close-up of eight stomata. Two guard cells and eight subsidiary cells are visible around each stoma. d: Cuticle 2, Pinus sp. 2. Some folding of the cuticle occurred during preparation, but many parallel rows of stomata on both sides of a thin, central midvein are evident. e: Cuticle 2, close-up of (d). Pairs of guard cells surround each stoma. f: Cuticle 2, close-up of (e). Subsidiary and epithelial cells can be observed around the stomata.
Germinal Center and Cultured B cell expression libraries
<p>Comparison of gene expression profiles between in vitro mitogen-activated spleen B lymphocytes and FACS-sorted Germinal Center B lymphocytes isolated from intestinal PeyerPatches of C57BL/6 mice. In order to explore gene expression signatures that may be orchestrating somatic hypermutation (SHM, also known as affinity maturation) during antigen-induced B lymphocyte differentiation in Germinal Centers (GC), we profiled whole transcriptome gene expression of ex vivo, commensal microflora-activated GC B-cells that exhibit efficient SHM, with in vitro activated B-cell blasts, which fail to undergo SHM. In In particular, we examine our hypothesis that differential expression or the DNA endonucleases apex1 and apex2 regulate SHM. Using Templated Oligo Sequencing (TempO-Seq®) in conjunction with conventional intracellular FACS staining and B lymphocyte cell sorting, we compare gene expression profiles of in vitro mitogen-activated (LPS and anti-IgD-dextran), short-term (48h) cultured B cell blasts with Germinal Center B-cells isolated from intestinal Peyer Patches (PP, identified by surface staining B220+, CD95+, GL7+), compared to each other and to naive PP B-cells (B220+, CD95-, GL7-). We find that unique, distinct transcriptional signatures dominate the DNA repair processes in GC B-cells or in vitro B-cell blast, shedding light on genomic processes that orchestrate efficient affinity maturation of B-cells during GC differentiation. We use Templated Oligo Sequencing (TempO-Seq), a RNA motif-specific short tandem Oligo DNA hybridization technique to quantitatively measure whole transcriptome mRNA in conjunction with conventional surface membrane, fixable live/dead discriminator dye, and intracytoplasmic fluorescence antibody staining followed by FACS to isolate B cell populations of interest.</p>
spindle cell variant diffuse large B-cell lymphoma (NGS annotation file; high confidence calls) hematolrep-2136295
<p>Diffuse large B-cell lymphoma with spindle cell morphology is a rare variant. We present the case of a 74-year-old male who initially presented with a right supraclavicular (lymph) node enlargement. Histological analysis showed a proliferation of spindle-shaped cells with narrow cytoplasms. An immunohistochemical panel was used to exclude other tumors, such as melanoma, carcinoma, and sarcoma. The lymphoma was characterized by a cell-of-origin subtype of germinal center B-cell-like (GCB) based on Hans’ classifier (CD10-negative, BCL6-positive, and MUM1-negative); EBER negativity, and the absence of BCL2, BCL6, and MYC rearrangements. Mutational profiling using a custom panel of 168 genes associated with aggressive B-cell lymphomas confirmed mutations in ACTB, ARID1B, DUSP2, DTX1, HLA-B, PTEN, and TNFRSF14. Based on the LymphGen 1.0 classification tool, this case had an ST2 subtype prediction. The immune microenvironment was characterized by moderate infiltration of M2-like tumor-associated macrophages (TMAs) with positivity of CD163, CSF1R, CD85A (LILRB3), and PD-L1; moderate PD-1 positive T cells, and low FOXP3 regulatory T lymphocytes (Tregs). Immunohistochemical expression of PTX3 and TNFRSF14 was absent. Interestingly, the lymphoma cells were positive for HLA-DP-DR, IL-10, and RGS1, which are markers associated with poor prognosis in DLBCL. The patient was treated with R-CHOP therapy, and achieved a metabolically complete response.</p> <p>Carreras J, Kikuti YY, Miyaoka M, Hiraiwa S, Tomita S, Ikoma H, Kondo Y, Ito A, Nagase S, Miura H, Roncador G, Colomo L, Hamoudi R, Campo E, Nakamura N. Mutational Profile and Pathological Features of a Case of Interleukin-10 and RGS1-Positive Spindle Cell Variant Diffuse Large B-Cell Lymphoma. <em>Hematology Reports</em>. 2023; 15(1):188-200. https://doi.org/10.3390/hematolrep15010020</p>
spindle cell variant diffuse large B-cell lymphoma (hematolrep-2136295)
<p>Diffuse large B-cell lymphoma with spindle cell morphology is a rare variant. We present the case of a 74-year-old male who initially presented with a right supraclavicular (lymph) node enlargement. Histological analysis showed a proliferation of spindle-shaped cells with narrow cytoplasms. An immunohistochemical panel was used to exclude other tumors, such as melanoma, carcinoma, and sarcoma. The lymphoma was characterized by a cell-of-origin subtype of germinal center B-cell-like (GCB) based on Hans’ classifier (CD10-negative, BCL6-positive, and MUM1-negative); EBER negativity, and the absence of BCL2, BCL6, and MYC rearrangements. Mutational profiling using a custom panel of 168 genes associated with aggressive B-cell lymphomas confirmed mutations in ACTB, ARID1B, DUSP2, DTX1, HLA-B, PTEN, and TNFRSF14. Based on the LymphGen 1.0 classification tool, this case had an ST2 subtype prediction. The immune microenvironment was characterized by moderate infiltration of M2-like tumor-associated macrophages (TMAs) with positivity of CD163, CSF1R, CD85A (LILRB3), and PD-L1; moderate PD-1 positive T cells, and low FOXP3 regulatory T lymphocytes (Tregs). Immunohistochemical expression of PTX3 and TNFRSF14 was absent. Interestingly, the lymphoma cells were positive for HLA-DP-DR, IL-10, and RGS1, which are markers associated with poor prognosis in DLBCL. The patient was treated with R-CHOP therapy, and achieved a metabolically complete response.</p> <p>Carreras J, Kikuti YY, Miyaoka M, Hiraiwa S, Tomita S, Ikoma H, Kondo Y, Ito A, Nagase S, Miura H, Roncador G, Colomo L, Hamoudi R, Campo E, Nakamura N. Mutational Profile and Pathological Features of a Case of Interleukin-10 and RGS1-Positive Spindle Cell Variant Diffuse Large B-Cell Lymphoma. <em>Hematology Reports</em>. 2023; 15(1):188-200. https://doi.org/10.3390/hematolrep15010020</p>
SARS-CoV-2 Omicron Boosting Induces De Novo B Cell Response in Humans
<p>These are the<strong> processed</strong> BCR repertoire and transcriptomics data described in <a href="https://doi.org/10.1038/s41586-023-06025-4">Alsoussi & Malladi et al., <em>Nature</em>, 2023</a>. The <strong>raw</strong> sequencing data new to this study are available on SRA under BioProject <a href="https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA800176">PRJNA800176</a>. This study also used BCR repertoire data from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., <em>Nature</em>, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">PRJNA731610</a>), <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., <em>Immunity</em>, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA741267">PRJNA741267</a>), and <a href="https://doi.org/10.1038/s41586-022-04527-1">Kim & Zhou et al., <em>Nature</em>, 2022</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA777934/">PRJNA777934</a>).</p> <p> </p> <p><strong>Code</strong></p> <p>Code along with Docker containers for reproducing the NGS data-based figures and analyses in the published paper can be <a href="https://github.com/julianqz/wustl_published/tree/main/nature_2023">found on GitHub</a>.</p> <p> </p> <p><strong>Metadata</strong></p> <p>File: WU382_alsoussi_et_al_nature_2023_meta.tsv.gz</p> <p>Notes:</p> <ul> <li>181 samples in total, including: <ul> <li>78 new</li> <li>90 from <a href="https://doi.org/10.1038/s41586-022-04527-1">Kim & Zhou et al., <em>Nature</em>, 2022</a></li> <li>8 from <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., <em>Immunity</em>, 2021</a></li> <li>5 from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., <em>Nature</em>, 2021</a></li> </ul> </li> <li>Participant IDs: 6 participants who were in previous studies and who continued in the new study were referenced by new participant IDs. Correspondence with previous participant IDs is as follows: <ul> <li>382-01 = 368-22</li> <li>382-02 = 368-20</li> <li>382-07 = 368-02a</li> <li>382-08 = 368-04</li> <li>382-13 = 368-01a</li> <li>382-15 = 368-10</li> </ul> </li> <li>Sample collection time was originally recorded in days in the `timepoint` column. Values in parentheses indicate variations in which the BCR data was coded. Timepoints were mainly referenced in weeks in the manuscript, as shown in the `timepoint_ms` column. </li> <li>Pre-3rd dose ("pre-boost") samples were coded `b0` in the `booster_num` column; post-3rd dose ("post-boost") samples were coded `b1`.</li> <li>The `booster_type` column records the 3rd dose ("booster") variant. <ul> <li>`regular` = mRNA-1273 (WA1/2020)</li> <li>`beta_delta` = mRNA-1273.213 (Beta & Delta)</li> <li>`v1.1.529` = mRNA-1273.529 (Omicron)</li> </ul> </li> <li>382-02/07/08 received mRNA-1273; 382-01/13/15 received mRNA-1273.213; 382-53/54/55 received mRNA-1273.529.</li> <li>The `seq_type` column indicates the platform from which sequences originated. <ul> <li>`bulk` = bulk BCR sequencing</li> <li>`tgx` = 10x Genomics single-cell VDJ + 5' gene expression</li> <li>`mab`, `mab_1`, `mab_2`: single-cell sorted mAb synthesis. The suffixes were purely for the convenience of distinguishing originating studies.</li> </ul> </li> </ul> <p>Abbreviations:</p> <ul> <li>LN = lymph node</li> <li>BM = bone marrow</li> <li>PB = plasmablast</li> <li>GC = germinal centre</li> <li>LLPC = long-lived plasma cell</li> <li>NS = no sorting</li> <li>mAb = monoclonal antibody</li> </ul> <p> </p> <p><strong>[Beta & Delta booster] Processed BCR data - heavy chains</strong></p> <p>File: WU382_alsoussi_et_al_nature_2023_betaDelta_bcr_heavy.tsv.gz</p> <p><em>Analysis was based on heavy chain-based clonal inference.</em></p> <p>Notes on columns:</p> <p>The columns largely follow the <a href="https://changeo.readthedocs.io/en/stable/standard.html">AIRR-C Rearrangement format</a>. The main deviation is that CDR3s were used, as opposed to IMGT-defined "junctions". Nonetheless, junction-related columns are included here as some repositories such as <a href="https://gateway.ireceptor.org/login"><em>iReceptor</em></a> use these. Non-standard columns are noted below.</p> <ul> <li>`cell_id`: Only sequences from single-cell samples and synthesized mAbs have cell IDs. 10x sequences follow the format `[donor]_[sample]@[id]`. `NA` for bulk sequences.</li> <li>`sequence_id`: Sequence IDs follow the format `[donor]_[sample]@[id]`.</li> <li>`v_call_genotyped`: V gene annotation reassigned after individualized genotyping by <a href="https://tigger.readthedocs.io/en/stable/">TIgGER</a>.</li> <li>`germline_[vdj]_call`: Clonal consensus germline calls after corresponding clonal consensus sequences were reconstructed via <a href="https://changeo.readthedocs.io/en/stable/methods/germlines.html">`CreateGermlines.py --cloned` from Change-O</a>.</li> <li>`collapse_count`: Number of duplicate IMGT-aligned V(D)J sequences that were collapsed by <a href="https://alakazam.readthedocs.io/en/stable/topics/collapseDuplicates/">`alakazam::collapseDuplicates`</a>.</li> <li>`timepoint`: Timepoints follow the format `b[01]_d*`, where `b0` and `b1` correspond to pre-3rd dose ("pre-boost") and post-3rd dose ("post-boost") respectively, and `d*` indicates the timepoint in days. There's one exception: `b0_m6or9` for pre-3rd dose d201 or d280 (m6or9 = 6 or 9 months).</li> <li>`gex_anno`: Cell type identity annotation based on transcriptomic profiles. Mapped from `anno_leiden_0.35` from WU382_alsoussi_et_al_nature_2023_betaDelta_gex_b_cells.h5ad.</li> <li>`compartment`: B cell compartment</li> <li>`clone_id`: B cell clonal lineage IDs follow the format `[donor]@[id]`.</li> <li>`s_pos_clone`: `TRUE` if a sequence belonged to a B cell clone that was designated as S-binding by virtue of containing one of the recombinant mAbs that tested positive via ELISA.</li> <li>`expressed_id`: mAb IDs of mAbs from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., <em>Nature</em>, 2021</a> and the current study; and of recombinant mAbs generated based on 10x BCRs from <a href="https://doi.org/10.1038/s41586-022-04527-1">Kim & Zhou et al., <em>Nature</em>, 2022</a>. `NA` for everything else.</li> <li>`elisa`: ELISA results for binding of recombinant mAbs to SARS-CoV-2 S. `TRUE` if positive (WA1+); `FALSE` if negaive; `NA` if not tested or test failed.</li> <li>`nuc_RS_19_312`: number of replacement and silent mutations between IMGT-numbered nucleotide positions 19-312 along IGHV sequences, calculated by <a href="https://shazam.readthedocs.io/en/stable/topics/calcObservedMutations/">`shazam::calcObservedMutations`</a>.</li> <li>`nuc_denom_19_312`: number of informative nucleotide positions for counting mutations, excluding non-A/T/G/C positions (such as "N", "-", ".").</li> <li>`nuc_RS_freq_19_312`: nucleotide-level mutation frequency (= nuc_RS_19_312 / nuc_denom_19_312).</li> </ul> <p> </p> <p><strong>[Beta & Delta booster] Processed BCR data - light chains</strong></p> <p>File: WU382_alsoussi_et_al_nature_2023_betaDelta_bcr_light.tsv.gz</p> <p><em>Light chains were not used for heavy chain-based clonal inference or analysis.</em></p> <p> </p> <p><strong>[Beta & Delta booster] Processed transcriptomics data</strong></p> <p>Files: </p> <ul> <li>WU382_alsoussi_et_al_nature_2023_betaDelta_gex_all_cells.h5ad</li> <li>WU382_alsoussi_et_al_nature_2023_betaDelta_gex_b_cells.h5ad</li> <li>WU382_alsoussi_et_al_nature_2023_betaDelta_gex_b_cell_umap.tsv.gz</li> </ul> <p>Notes on the `h5ad` files:</p> <ul> <li>These files can be imported into <a href="https://scanpy.readthedocs.io/en/stable/index.html">Scanpy</a> as an <a href="https://scanpy.readthedocs.io/en/stable/usage-principles.html#anndata">AnnData object</a>.</li> <li>Each `AnnData` object has 3 `.layers`, each representing a version of the count matrix. <ul> <li>`raw_counts`: Imported from `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` output by `scanpy.read_10x_mtx`.</li> <li>`log_norm`: Log-noramlized expression values outputted by `scanpy.pp.normalize_total` followed by `scanpy.pp.log1p`.</li> <li>`scaled`: The `log_norm` layer scaled to unit variance and zero mean by `scanpy.pp.scale`. </li> </ul> </li> <li>The `gene_name` and `biotype` columns in `.var` were extracted from GENCODE v32 GTF.</li> <li>Columns in `.obs` (each row corresponds to a cell) <ul> <li>`n_feature`: The `n_genes_by_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The number of genes expressed. This is before subsetting the genes.</li> <li>`n_umi`: The `total_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The total UMI counts in a cell.</li> <li>`pct_mt`: The `pct_counts_mt` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The percentage of counts in mitochondrial genes.</li> <li>`n_hkg`: The number of housekeeping genes for which expression was detected.</li> <li>`n_gene_expressed`: The total number of genes for which expression was detected. This is after subsetting the genes.</li> <li>`pre_qc_bcr`: `TRUE` if a cell also had paired BCR data available. Produced by cross-referencing the cellular barcodes in `cell_barcodes.json` outputted by `cellranger vdj`. At this point the BCR data had not gone through the QC process in the BCR processing pipeline (hence `pre_qc`). </li> <li>`leiden_[resolution]`: Cluster assignment by `scanpy.tl.leiden`.</li> <li>`anno_leiden_[resolution]`: Cell type identity annotations based on transcriptomic profiles. This was mapped onto the `gex_anno` column in the processed heavy chain BCR data.</li> </ul> </li> <li>UMAP coordinates can be found in `.obsm["X_umap"]`.</li> <li>`.X` has been set to `None` in order to reduce file size.</li> </ul> <p>Note on the `tsv.gz` file: This file was derived from WU382_alsoussi_et_al_nature_2023_betaDelta_gex_b_cells.h5ad. It contains UMAP coordinates and select attributes of the cells, including their log-normalized expression values of XBP1 (`ln_XBP1`). For analysis and visualization in conjunction with BCR data.</p> <p>In addition, the preprocessed count matrix outputted by `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` is available from <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE227562">GEO under BioProject PRJNA800176</a>.</p> <p> </p> <p><strong>[Omicron booster] Processed BCR data - heavy chains</strong></p> <p>File: WU382_alsoussi_et_al_nature_2023_omicron_bcr_heavy.tsv.gz</p> <p>Notes on columns:</p> <ul> <li>`elisa`: ELISA results for mAbs, with values being one of `WA1+`, `BA1+WA1-`, or `negative`. `NA` for bulk sequences.</li> <li>`clone_type`: If a sequence was in an S-binding B cell clone (`TRUE` for `s_pos_clone`), its `clone_type` was based on the `elisa` value of the S-binding mAb in that clone -- either `WA1+` or `BA1+WA1-`; otherwise `NA`.</li> </ul>
Hepatitis B surface antigen reduction is associated with hepatitis B core-specific CD8+ T cell quality
<p>The file containing post QC, count matrix, containing 6 samples as following.</p> <p>(S01:CHBN001, S02:CHBN002, S03:CHBN003, S04:CHBN004, S12:CHBN005, S14:CHBN006.)</p> <p>scRNAseq Libraries generated by 10xGenomics 5'-kit were read by NovaSeq 6000 platform. </p> <p>After sequencing, raw reads were mapped to human generated by cellranger 6.1.2, then generated count matrix were subjected to QC according to Seurat manual (mitochondrial genes <10%, ribosomal genes > 0.05%), then SCT-transformed and integrated with 3000 features. Detail of QC/integration will be described in our manuscript.</p>
Inferring B cell phylogenies from paired heavy and light chain BCR sequences with Dowser
<p>In our publication, we created simulations of paired heavy and light chain BCR sequences. Uploaded here is all the data needed to rerun the simulations, as well as the output of the simulations we ran. The output of BCR phylo included here are the lineage trees, unpickled and put into one file (true_trees.tsv), and the fasta files ('starting_fastas' folder). The naive BCR sequences we used as a starting point are found in the 'naive_data' folder. The post-simulation data for all 20 iterations (combined heavy and light chain data through both simulation frameworks) can be found in the 'simulation_data' folder.</p>
Zanubrutinib, in Combination With Lenalidomide, With or Without Rituximab in Participants With Relapsed/Refractory Diffuse Large B-Cell Lymphoma
ClinicalTrials.gov study NCT04436107. IPD Sharing: YES. Countries: 1. Publications: 0.
Tisagenlecleucel in Adult Patients With Aggressive B-cell Non-Hodgkin Lymphoma
ClinicalTrials.gov study NCT03570892. IPD Sharing: YES. Countries: 18. Publications: 3.
Tailoring Maintenance Therapy to Cluster of Differentiation 5 Positive (CD5+) Regulatory B Cell Recovery in ANCA Vasculitis
ClinicalTrials.gov study NCT03906227. IPD Sharing: YES. Countries: 1. Publications: 1.
Treatment of CD79B Mutant Relapsed/Refractory Diffuse Large B-Cell Lymphoma With Bruton Tyrosine Kinase Inhibitor Zanubrutinib
ClinicalTrials.gov study NCT05068440. IPD Sharing: YES. Countries: 1. Publications: 0.
Ofatumumab Bendamustine Combination Compared With Bendamustine Monotherapy in Indolent B-cell NHL Unresponsive to Rituxtherapy
ClinicalTrials.gov study NCT01077518. IPD Sharing: YES. Countries: 17. Publications: 1.
Phase III Study of RAD001 Adjuvant Therapy in Poor Risk Patients With Diffuse Large B-Cell Lymphoma (DLBCL) of RAD001 Versus Matching Placebo After Patients Have Achieved Complete Response With First-
ClinicalTrials.gov study NCT00790036. IPD Sharing: UNDECIDED. Countries: 34. Publications: 1.
Data for: Antigen footprint governs activation of the B cell receptor
Open the record for dataset details and reuse information.
Intravital quantification reveals dynamic calcium concentration changes across B cell differentiation stages
Open the record for dataset details and reuse information.
Pre-processed B cell receptor repertoire sequencing data from BioProject PRJNA527941
<p><strong>Data Processing</strong></p> <p> </p> <p>Samples were demultiplexed via their Illumina indices, and processed using the Immcantation toolkit(1,2). Raw fastq files were filtered based on a quality score threshold of 20. Paired reads were joined if they had a minimum length of 10 nt, maximum error rate of 0.3 and a significance threshold of 0.0001. Reads with identical UMI were collapsed to a consensus sequence. Reads with identical full-length sequence and identical constant primer but differing UMI were further collapsed. Sequences were then submitted to IgBlast (3) for VDJ assignment and sequence annotation. Constant region sequences were mapped to germline using Stampy(4). The number and type of V gene mutations was calculated using the shazam R package.(2)</p> <p> </p> <p><strong>software_versions</strong> pRESTO:0.5.3,Change-O:0.3.4,IgBlast 1.6.1, stampy1.0.21. shazam0.1.8</p> <p><strong>quality_thresholds</strong> FilterSeq.py pRESTO Q>20</p> <p><strong>paired_reads_assembly</strong> AssemblePairs.py pRESTO minlen 10 maxerror 0.3 alpha 0.0001</p> <p><strong>primer_match_cutoffs</strong> MaskPrimers.py pRESTO C primer & V primer maxerror 0.2</p> <p><strong>consensus_building</strong> BuildConsensus.py pRESTO maxerror 0.1 maxgap 0.5</p> <p><strong>collapsing_method</strong> CollapseSeq.py pRESTO</p> <p><strong>germline_database </strong>IMGT</p> <p> </p> <p><strong>Format</strong></p> <p> </p> <p>Processed sequences are provided in a tab delimited file format, including the following annotations:</p> <p> </p> <p><strong>C_CALL </strong>Isotype subclass</p> <p><strong>SEQUENCE_ID </strong>Sequence identifier</p> <p><strong>V_CALL </strong>V segment gene and allele</p> <p><strong>D_CALL </strong>D segment gene and allele</p> <p><strong>J_CALL </strong>J segment gene and allele</p> <p><strong>JUNCTION_LENGTH </strong>Junction length</p> <p><strong>CONSCOUNT </strong>Raw read count from which UMI consensus sequences were generated, summed over all UMIs for the given unique sequence.</p> <p><strong>DUPCOUNT </strong>UMI count for the given unique sequence</p> <p><strong>ISOTYPE </strong>Constant region primer (isotype)</p> <p><strong>MU_COUNT_CDR_R </strong>Number of replacement mutations in CDR region</p> <p><strong>MU_COUNT_CDR_S </strong>Number of silent mutations in CDR region</p> <p><strong>MU_COUNT_FWR_R </strong>Number of replacement mutations in FWR region</p> <p><strong>MU_COUNT_FWR_S </strong>Number of silent mutations in FWR region</p> <p><strong>MUT_TOTAL </strong>Total number of mutations in V gene </p> <p><strong>SEQUENCE_INPUT </strong>Full length sequence</p> <p><strong>SEQUENCE_IMGT </strong>Gapped IMGT sequence</p> <p><strong>V_GERM_START_VDJ </strong>position of the first nucleotide in ungapped V germline sequence alignment</p> <p><strong>JUNCTION </strong>Junction nucleotide sequence</p> <p><strong>GERMLINE_IMGT_D_MASK </strong>IMGT-gapped germline nucleotide sequence with ns masking the NP1-D-NP2 regions</p> <p><strong>Run </strong>ID of sequencing run</p> <p><strong>Sample_type </strong>The tissue sampled (e.g Peripheral Blood, bone marrow, ..)</p> <p><strong>Sex </strong>Sex of the Subject</p> <p><strong>Age </strong>Age of the subject</p> <p><strong>UNIQUE_ID </strong>Subject identifier </p> <p><strong>SAMPLE_ID </strong>Sample identifier, linking back to raw data</p> <p><strong>Subset </strong>Defined B cell subset </p> <p><strong>Repertoire </strong>Defined B cell repertoire (Naive, Memory IgM/IgD, IgA, IgG)</p> <p><strong>R_SCDR </strong>R/S ratio in CDR region</p> <p><strong>R_SFWR </strong>R/S ratio in FWR region</p> <p><strong>V_FAM </strong>V family gene</p> <p><strong>V_GENE </strong>V segment gene</p> <p><strong>D_GENE </strong>D segment gene</p> <p><strong>J_GENE </strong>J segment gene</p> <p><strong>Clust_Rank </strong>Cluster rank</p> <p><strong>Clust_REPRES </strong>Cluster representative</p> <p><strong>Clust_SIZE </strong>Cluster size</p> <p><strong>Clust_MAXFREQ </strong>Cluster maximum frequency</p> <p><strong>Clust_SHAREDNESS </strong>Cluster sharedness</p> <p><strong>CDR3_AA_GRAVY </strong>CDR3 hydrophobicity index</p> <p><strong>CDR3_AA_CHARGE </strong>CDR3 charge</p> <p><strong>CDRH3PDB </strong>CDRH3 PDB (Structure) code</p> <p><strong>H1Canon </strong>H1 Canonical class</p> <p><strong>H2Canon </strong>H2 Canonical class</p> <p><strong>H1_GERMLINE </strong>H1 Germline Canonical class</p> <p><strong>H2_GERMLINE </strong>H2 Germline Canonical class</p> <p> </p> <p><strong>References</strong></p> <p>1. Vander Heiden, J. A., G. Yaari, M. Uduman, J. N. H. Stern, K. C. O’Connor, D. A. Hafler, F. Vigneault, and S. H. Kleinstein. 2014. PRESTO: A toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. <em>Bioinformatics</em>30: 1930–1932.</p> <p>2. Gupta, N. T., J. A. Vander Heiden, M. Uduman, D. Gadala-Maria, G. Yaari, and S. H. Kleinstein. 2015. Change-O: A toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. <em>Bioinformatics</em>31: 3356–3358.</p> <p>3. Ye, J., N. Ma, T. L. Madden, and J. M. Ostell. 2013. IgBLAST: an immunoglobulin variable domain sequence analysis tool. <em>Nucleic Acids Res.</em>41.</p> <p>4. Lunter, G., and M. Goodson. 2011. Stampy: A statistical algorithm for sensitive and fast mapping of Illumina sequence reads. <em>Genome Res.</em>21: 936–939.</p>
A Bayesian Phylogenetic Hidden Markov Model for B Cell Receptor Sequence Analysis
<p>simulation and PC64/VRC01 input/output data files</p>
Dataset used for "Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data"
<p>Each simulated dataset was generated using the AbSim R package (version 0.2.6) in a B cell single-lineage fashion. Each B cell clone simulation begins with a random selection from sets of IGHV, IGHD, and IGHJ germline sequences to produce a unique V(D)J recombination event. Then, clones are made by introducing mutations using a local nucleotide context-dependent model (S5F model) along a phylogenetic tree in which branching events occur stochastically. </p>
Pre-processed B cell receptor sequences from BioProject PRJNA349143
<p>Processed sequencing data from BioProject PRJNA349143.</p> <p><strong>Study Design</strong></p> <p>Samples were collected from human volunteers as described in Laserson and Vigneault et al, 2014 (1). Briefly, blood samples were collected from three individuals both pre- and post-vaccination for seasonal influenza. Samples were collected for sequencing at time points -8 days, -2 days, -1 hour, +1 hour, +1 day, +3 days, +7 days, +14 days, +21 days and +28 days relative to injection with seasonal influenza vaccine.</p> <p><strong>Library Preparation and Sequencing</strong></p> <p>The original samples from Laserson and Vigneault et al, 2014 (1) were re-sequenced as described in Gupta et al, 2017 (2). Briefly, sequencing libraries were prepared from mRNA using 5'RACE with addition of 17-nucleotide unique molecular identifiers (UMIs). Amplification was performed using constant region primers specific to IGHA, IGHD, IGHE, IGHG, IGHM, IGKC and IGLC. Sequencing was conducted on the Illumina MiSeq platform using the 600 cycle kit with 325 cycles for read 1 and 275 cycles for read 2. A 10% PhiX spike-in was added for sequencing.</p> <p><strong>Data Processing</strong></p> <p>Sequences were processed using the pRESTO (3) and Change-O (4) toolkits as described in Gupta et al, 2017 (2).</p> <p>Note, the provided data has been filtered significantly, including the removal of sequences that fail V(D)J alignment and the exclusion of non-functional sequences.</p> <p><strong>Format</strong></p> <p>Processed sequences are provided in FASTA format annotated using the pRESTO scheme.</p> <p>Annotations included are as follows:</p> <ul> <li><strong>CONSCOUNT:</strong> Raw read count from which UMI consensus sequences were generated, summed over all UMIs for the given unique sequence.</li> <li><strong>DUPCOUNT:</strong> UMI count for the given unique sequence.</li> <li><strong>PRCONS:</strong> Constant region primer (isotype).</li> <li><strong>SUBJECT:</strong> Subject identifier.</li> <li><strong>TIME_POINT:</strong> Time point label.</li> </ul> <p><strong>Citations</strong></p> <ol> <li>Laserson U and Vigneault F, et al. High-resolution antibody dynamics of vaccine-induced immune responses. Proc Natl Acad Sci USA 111, 4928-33 (2014).</li> <li>Gupta NT, et al. Hierarchical Clustering Can Identify B Cell Clones with High Confidence in Ig Repertoire Sequencing Data. J Immunol 1601850 (2017).</li> <li>Vander Heiden JA and Yaari G, et al. pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. Bioinformatics 30, 1930–2 (2014).</li> <li>Gupta NT and Vander Heiden JA, et al. Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics 31, 3356–8 (2015).</li> </ol>
Sustained liver HBsAg loss and clonal T and B cell expansion upon therapeutic DNA vaccination require low HBsAg levels
<p>C57BL/6 mice samples transduced with AAV-HBV, followed by treatment with siRNA+TxTv, control siRNA+TxVx or control siRNA+empty plasmid. 10x genomics (VDJ) was performed in liver IHIC.</p><p>An rds object with the raw and normalised counts, the annotations is available.</p>
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