From 681f6d610045485fd35ee29e624ac90be2fabdd5 Mon Sep 17 00:00:00 2001 From: Thomas Sandmann Date: Sun, 29 Dec 2024 11:46:04 -0800 Subject: [PATCH] Added unfinished draft --- posts/lee_2021/index.qmd | 338 +++++++++++++++++++++++++++++++ posts/lee_2021/lee_figure_1A.jpg | Bin 0 -> 102146 bytes 2 files changed, 338 insertions(+) create mode 100644 posts/lee_2021/index.qmd create mode 100644 posts/lee_2021/lee_figure_1A.jpg diff --git a/posts/lee_2021/index.qmd b/posts/lee_2021/index.qmd new file mode 100644 index 0000000..c666878 --- /dev/null +++ b/posts/lee_2021/index.qmd @@ -0,0 +1,338 @@ +--- +title: "CITE-seq analysis of B-cells" +subtitle: "Reanalysis of data published by Lee et al, 2021" +author: "Thomas Sandmann" +date: "2024-09-02" +draft: true +freeze: true +categories: [TIL, NGS] +editor: + markdown: + wrap: 72 +--- + +## tl;dr + +This week I learned how to analyze CITE-seq data, e.g. transcriptome and +surface-protein levels measured simultaneously for the same single cells. + +To learn how gene expression and protein markers can be combined to identify +cell sub-types, I reanalyzed a dataset published by +[Lee et al, Nature Communications, 2021](https://www.nature.com/articles/s41467-021-27232-5), +starting with raw sequencing reads. + +## Overview + +In 2021, +[Lee et al](https://www.nature.com/articles/s41467-021-27232-5) +characterized the gene expression landscape of mouse B-cells, performing +[CITE-seq](https://en.wikipedia.org/wiki/CITE-Seq) +on B cells collected from bone marrow of two wildtype C57BL/6 mice. + +First, the authors enriched B-cells using fluorescent activated cell sorting +(FACS) with antibodies detecting the B220 of the CD45 protein (encoded by the +[PTPRC gene](https://en.wikipedia.org/wiki/PTPRC)), a pan B-cell marker in mice +[^1]. To capture both naive and mature cells, they retained equal numbers of +B-cells positive or negative for CD43 staining [^2] + +[^1]: The field of immunology has identified a large number of cell type +specific markers, which are often used in combination to define granular +cell subtypes. Confusingly, despite having the same names in humans and mice, +the cell type distribution of many markers differs between species. See +[Figure 3 by Weisel et al, Nat Immunology, 2022 ](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8712407/figure/F3/) +for a helpful overview of human and mouse B-cell markers. + +[^2]: In mice, CD43 (aka sialophorin) is expressed on pro–B cells, plasma cells, +peritoneal and spleen CD5+ B cells (B-1 cells), but not on resting, conventional +peripheral B cells. + +B-cells from each mouse were first incubated with different +oligonucleotide-tagged "cell hashing" antibodies, and then combined into a +single pool. Afterwards, the cells were stained with additional oligo-tagged +"feature detection" antibodies recognizing B-cell surface markers. Finally, +both the intracellular transcriptome and the presence of the antibody markers +were read out using single-cell RNA-seq capture on the 10X Genomics +microfluidics platform. + +![Figure 1A by Lee et al, 2021](lee_figure_1A.jpg){width=80%, fig-alt="Schematic of the experimental setup."} + +::: {.callout-note collapse="false"} + +### Cell staining - details + +Bone marrow cells from two 8-week-old wild-type mice were each stained +with + +- 2 different hashtag antibodies (one for each mouse) +- 5 CITE-seq antibodies (targeting surface proteins) +- 2 fluorescently labelled antibodies (B220/CD45R and CD43) used for + FACS enrichment of B-cells. + - Both B220+,CD43+ and B220+/CD43- populations were captured and mixed at a + 1:1 ratio to enrich for all early progenitor B-cell subsets. + +This strategy captures the vast majority of developing B cells in the bone +marrow but does exclude a small fraction of developing CD19+ B cells that +express CD11c, Ly6G, or NK1.1. + +::: + +## Data availability + +The authors made all of the raw sequencing data and processed count matrices +available via NCBI's GEO repository as +[serices GSE168158](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE168158). + +They performed their own analysis with 10X Genomics' `cellranger` software +(version 3.1.0), aligning the reads to the `mm10` version of the mouse genome. +Downstream processing was performed using various R/Bioconductor packages. + +Here, I am performing a re-analysis of the raw data with the latest available +version of `cellranger` (version 8.0.1) and the latest reference genome (GRCm39, +Ensembl version 110). + +## Retrieving the raw data + +The +[ENA repository](https://www.ebi.ac.uk/ena/browser/home) +mirrors data available from the SRA - and it offers FASTQ files for download via +FTP. +(SRA also has cloud delivery options for FASTQ files, but I prefer a simple FTP +download). + +The download links are shown in the table at the bottom of +[ENA's browser page for this project](https://www.ebi.ac.uk/ena/browser/view/PRJNA706373). + +I really like to use +[rclone](https://rclone.org/) +as my "swiss army knife" to transfer files between remote and / or local sources. + +First, I set up a new `ena` _remote_ for the ENA ftp server +(`ftp.sra.ebi.ac.uk`) with the interactive `rclone config` command (username: +`anonymous`, password: `anonymous`). Next, I copy the folders for each of the +three runs (each with 2 FASTQ files - R1 and R2) to my local machine. + +Finally, because `cellranger` expects filename with a specific pattern, I rename +them afterwards. + +```bash +mkdir -p fastq +pushd fastq + +# interactively configure FTP remote for ftp.sra.ebi.ac.uk called `ena` +# rclone config + +# transfer the files for the three runs +rclone copy -v ena://vol1/fastq/SRR138/085/SRR13847285/ . +rclone copy -v ena://vol1/fastq/SRR138/086/SRR13847286/ . +rclone copy -v ena://vol1/fastq/SRR138/087/SRR13847287/ . + +# rename the files according to cellranger's expectations +mv SRR13847285_1.fastq.gz SRR13847285_S1_L001_R1_001.fastq.gz # gene expression (47 Gb) +mv SRR13847285_2.fastq.gz SRR13847285_S1_L001_R2_001.fastq.gz # gene expression (114 Gb) +mv SRR13847286_1.fastq.gz SRR13847286_S1_L001_R1_001.fastq.gz # antibody capture +mv SRR13847286_2.fastq.gz SRR13847286_S1_L001_R2_001.fastq.gz # antibody capture +mv SRR13847287_1.fastq.gz SRR13847287_S1_L001_R1_001.fastq.gz # multiplexing capture +mv SRR13847287_2.fastq.gz SRR13847287_S1_L001_R2_001.fastq.gz # multiplexing capture + +popd +``` + +To process the raw data from this study, I need to consider both the hashtag +antibodies (identifying each cell's animal of origin) and the CITE-seq +antibodies (identifying the detected surface proteins). (The fluorescently +labelled antibodies are not captured in the sequencing library; they determined +which cells were included in the experiment in the first place.) + +### Cell hashing antibodies + +Lee et al used two hashing antibodies to label cells from two animals: + +- [TotalSeq A0301](https://www.biolegend.com/en-gb/products/totalseq-a0301-anti-mouse-hashtag-1-antibody-16103) + - A mixture of two antibodies recognizing mouse CD45 and MHC class I + - Barcode sequence: `ACCCACCAGTAAGAC` +- [TotalSeq A0302](https://www.biolegend.com/en-gb/products/totalseq-a0302-anti-mouse-hashtag-2-antibody-16104) + - The same mixture of antibodies recognizing mouse CD45 and MHC class I, but + with a different barcode. + - barcode `GGTCGAGAGCATTCA` + +Interestingly, in the method section they also mention: + +>Four other hashtag samples were present in this experiment and were excluded to derive +only the wildtype B cells. + +So they _actually_ combined cells from six different animals and super-loaded a +single 10X genomics lane. (This also explains why the FASTQ files of gene +expression library are so large - they contain 1.2 billion reads.) +Unfortunately, they don't specify what the barcodes of these other four hashing +antibodies were. For a first pass analysis, I will assume that they used the +TotalSeq anti-mouse hashtags 3-6 for the remaining samples, as they used +hashtags 1 and 2 for the two samples of interest. + +::: {.callout-note collapse="true"} + +The hashtag barcodes are the first 15 nucleotides of the R2 read. This baroque +bash pipe will list the top 6 most frequent barcodes among the first 100,000 +reads - and confirms that the authors indeed used Biolegends hashing antibodies +A0301-A0306. + +``` +gzip -cd fastq/SRR13847287_S1_L001_R2_001.fastq.gz | \ + grep -A 1 "^@" | \ + grep -v "@" | \ + grep -v "-" | \ + cut -c 1-15 | \ + head -n 100000 | \ + sort | \ + uniq -c | \ + sort -n | \ + tail -n 6 +``` +::: + +#### Cell hashing reference file + +Cellranger was designed to demultiplex cells labelled with +cholesterol-modified oligonucleotides (CMOs), defined in a +[CMO reference file](https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/multi#cmoreference). + +To decode the cell hashing antibodies used by Lee et al (the two barcodes +associated with the relevant samples, as well as the four barcodes assigned to +_mystery_ samples not further described in the paper) I provide an analogous +`hashing_reference.csv` configuration file: + +``` +id,name,read,pattern,sequence,feature_type +A0301,A0301,R2,5P(BC),ACCCACCAGTAAGAC,Multiplexing Capture +A0302,A0302,R2,5P(BC),GGTCGAGAGCATTCA,Multiplexing Capture +A0303,A0303,R2,5P(BC),CTTGCCGCATGTCAT,Multiplexing Capture +A0304,A0304,R2,5P(BC),AAAGCATTCTTCACG,Multiplexing Capture +A0305,A0305,R2,5P(BC),CTTTGTCTTTGTGAG,Multiplexing Capture +A0306,A0306,R2,5P(BC),TATGCTGCCACGGTA,Multiplexing Capture +``` + +### CITE-Seq antibodies + +In addition to the hashing antibodies, the authors also labelled the cells with +oligo-tagged antibodies recognizing the following surface proteins that are +expressed on B-cells: + +- anti-B220 / CD45R [TotalSeq A0103](https://www.biolegend.com/en-us/punchout/punchout-products/product-detail/totalseq-a0103-anti-mouse-human-cd45r-b220antibody-15925?GroupID=GROUP658) + - Clone RA3-6B2 + - The B220/CD45R isoform of CD45 is a pan B-cell marker in mice. + - Barcode sequence: `CCTACACCTCATAAT` +- anti-CD19 [TotalSeq A0093](https://www.biolegend.com/en-us/punchout/punchout-products/product-detail/totalseq-a0093-anti-mouse-cd19-antibody-16199) + - Clone 6D5 + - CD19 is expressed on all pro-B to mature B cells (during development) and + follicular dendritic cells. + - Barcode sequence: `ATCAGCCATGTCAGT` +- anti-CD93 / C1QR1 [TotalSeq A0113](https://www.biolegend.com/en-us/punchout/punchout-products/product-detail/totalseq-a0113-anti-mouse-cd93-aa4-1-early-b-lineage-antibody-16416) + - Clone AA4.1 + - CD93 / C1QR1 is expressed on B cell precursors until the T2 stage, as well + as a wide variety of cells such as platelets, monocytes, microglia and + endothelial cells. + - Barcode sequence: `GGTATTTCCTGTGGT` +- anti-CD25/ IL-2Rα [TotalSeq A0097](https://www.biolegend.com/en-gb/products/totalseq-a0097-anti-mouse-cd25-antibody-16233) + - Clone PC61 + - IL-2Rα is expressed on activated T and B cells, thymocyte subsets, pre-B + cells, and T regulatory cells + - Barcode sequence: `ACCATGAGACACAGT` +- anti-IgM [TotalSeq A0450](https://www.biolegend.com/en-gb/products/totalseq-a0450-anti-mouse-igm-antibody-16772) + - Clone RMM-1 + - Surface IgM is expressed on the majority of mature B cells. + - Barcode sequence: `AGCTACGCATTCAAT` + +#### Feature reference file + +The barcode information is provided to `cellranger multi` in the +`feature_reference.csv` +[file](https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/using/feature-bc-analysis#feature-ref): + +``` +id,name,read,pattern,sequence,feature_type +CD45R,CD45R_TotalA,R2,^(BC),CCTACACCTCATAAT,Antibody Capture +CD19,CD19_TotalA,R2,^(BC),ATCAGCCATGTCAGT,Antibody Capture +CD93,CD93_TotalA,R2,^(BC),GGTATTTCCTGTGGT,Antibody Capture +CD25,CD25_TotalA,R2,^(BC),ACCATGAGACACAGT,Antibody Capture +IgM ,IgM_TotalA,R2,^(BC),AGCTACGCATTCAAT,Antibody Capture +``` + +## Running cellranger multi + +The current version (8.0.1) of the +[cellranger multi pipeline](https://www.10xgenomics.com/support/software/cell-ranger/latest/analysis/running-pipelines/cr-3p-multi) +can process the three libraries (gene expression, hashtag oligos and CITE-seq oligos) +together, and automatically demultiplexes hashed samples. +The `cellranger multi` command requires a set of +[configuration options](https://www.10xgenomics.com/support/software/cell-ranger/latest/advanced/cr-multi-config-csv-opts), +provided in a CSV file (alongside the hashtag- and feature-configuration files defined +above). + +Instructions for analyzing antibody hash tags alongside surface antibody protein +capture data +[are here](https://kb.10xgenomics.com/hc/en-us/articles/4407386498957-I-used-antibody-tags-for-cell-surface-protein-capture-and-cell-hashing-with-Single-Cell-3-chemistry-How-can-I-use-Cell-Ranger-to-analyze-my-data). + +I will use the following `config.csv` file, together with the +`refdata-gex-GRCm39-2024-A` mouse genome reference (and STAR index) +[supplied by 10X Genomics](https://www.10xgenomics.com/support/software/cell-ranger/latest/release-notes/cr-reference-release-notes). + +Interestingly, the R1 reads for the gene expression library (SRR13847285) are 26 +bp long, but the R1 reads for the hashing and CITE-seq libraries are 28 bp long. +To avoid a `cellranger` error I set the `r1-length` parameter to 26 for both +gene expression and feature libraries. + +::: {.callout-note collapse="true"} + +Cellranger 8.0.1 returned the following error message due to the mismatched +R1 read lenghts: + +>We detected a mixture of different R1 lengths ([26-28]), which breaks +assumptions in how UMIs are tabulated and corrected. To process these data, +you will need to truncate to the shortest observed R1 length by providing 26 +to the --r1-length argument if are running count/vdj, or via the r1-length +parameter in each of the following tables of your multi config CSV if you are +running multi: [gene-expression], [feature], [feature] + +::: + +Note: Only _absolute_ paths are accepted in the `config.csv` file, e.g. the +working directory (`/data/bcells`) needs to be specified in full. (Symbolic +links are not followed, either.) + +``` +[gene-expression] +reference,/data/bcells/refdata-gex-GRCm39-2024-A +no-secondary,false +create-bam,false +min-assignment-confidence,0.9 +cmo-set,/data/bcells/hashing_reference.csv +r1-length,26 + +[feature] +reference,/data/bcells/feature_reference.csv +r1-length,26 + +[libraries] +fastq_id,fastqs,lanes,feature_types,subsample_rate +SRR13847285,/data/bcells/fastq,any,Gene Expression,1 +SRR13847286,/data/bcells/fastq,any,Antibody Capture,1 +SRR13847287,/data/bcells/fastq,any,Multiplexing Capture,1 + +[samples] +sample_id,cmo_ids,description +A0301,A0301,Animal 1 (WT) +A0302,A0302,Animal 2 (WT) +A0303,A0303,Animal 3 (unknown) +A0304,A0304,Animal 4 (unknown) +A0305,A0305,Animal 5 (unknown) +A0306,A0306,Animal 6 (unknown) +``` + +Now I can start the analysis pipeline with `cellranger` (version 8.0.1) with the +following command: + +```bash +cellranger multi --id=B_cells --csv=config.csv +``` + +## Examining the results + diff --git 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