Instructions
Upload a FASTQ (or FASTA) file with the raw sequencing reads.
Upload a tab-delimited file (with no headers), where each row is of the format:
<key> <tab> <sample name> <tab> <experiment>
Example:
A-Key_2OS_F1_01 sampleA 1
A-Key_2OS_F1_02 sampleB 1
A-Key_2OS_F1_03 sampleX 2
A-Key_2OS_F1_04 sampleY 2
A-Key_2OS_F1_05 input 1
A-Key_2OS_F1_06 input 1
A-Key_2OS_F1_07 input 2
A-Key_2OS_F1_08 input 2
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Importantly, key should match the sequence names in the FASTA file uploaded in the field sample identification tag.
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Equally important, the sample name field should be "input" (all non-capital letters) for those samples that are used for controls.
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The sample name field will be used to annotate results and figures.
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The experiment field is used to denote that the data should be treated as multiple experiments. The samples will be analysed in the groups defined by experiment. In the above example there are two experiments: sample A and B will be analysed together with the two first input samples. Sample X and Y will be analysed together with the two last input samples.
If all samples are to be analysed as one experiment, this field should simply contain the same value in all the rows (eg. "1").
Upload a tab-delimited file with user-specified annotations for the DNA barcodes. The file should have headers, but there are no specific requirements for the headers.
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The first column should contain the AxBy name of each barcode made by combining Oligos A with Oligos B, e.g. "A1B2".
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There can be any number of additional columns with information about these barcodes, such as the peptide they are linked to, and this data will be appended to the output where possible (mainly in excel sheets.)
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Make sure there are no special symbols in the annotation file (such as greek letters, url links etc). You can easily remove such symbols with online copy-paste tools, such as this one.
Example:
Barcode HLA allele Peptide Sequence
A7B1 HLA-A0201 707-AP RVAALARDAP
A7B2 HLA-A0201 ATIC (AICRT) RLDFNLIRV
A7B3 HLA-A0201 ATIC (AICRT) MVYDLYKTL
Optionally, upload an excel file that describes how the DNA barcodes were layed out on 384-well plates in the experimental setup. This will be used to make figures of your results, where the values (read counts, log fold change, p-values) are layed out on a heatmap-type plot to mimic the 384-well plate. This will hopefully aid in detecting experimental errors and biases, such as spill-over between wells, or mix-up of barcodes on the plates.
Example of excel sheet:
5. Submit the job
Click on the
"Submit" button. The status of your job (either 'queued'
or 'running') will be displayed and constantly updated until it terminates and
the server output appears in the browser window.
At any time during the wait you may enter your e-mail address and simply leave
the window. Your job will continue; you will be notified by e-mail when it has
terminated. The e-mail message will contain the URL under which the results are
stored; they will remain on the server for 24 hours for you to collect them.
Please cite
Large-scale detection of antigen-specific T cells using peptide-MHC-I multimers labeled with DNA barcodes
Amalie Kai Bentzen, Andrea Marion Marquard, Rikke Lyngaa, Sunil Kumar Saini, Sofie Ramskov, Marco Donia, Lina Such, Andrew J S Furness, Nicholas McGranahan, Rachel Rosenthal, Per thor Straten, Zoltan Szallasi, Inge Marie Svane, Charles Swanton, Sergio A Quezada, Søren Nyboe Jakobsen, Aron Charles Eklund & Sine Reker Hadrup
https://www.ncbi.nlm.nih.gov/pubmed/27571370
Abstract
Identification of the peptides recognized by individual T cells is important for understanding and treating immune-related diseases. Current cytometry-based approaches are limited to the simultaneous screening of 10-100 distinct T-cell specificities in one sample. Here we use peptide-major histocompatibility complex (MHC) multimers labeled with individual DNA barcodes to screen >1,000 peptide specificities in a single sample, and detect low-frequency CD8 T cells specific for virus- or cancer-restricted antigens. When analyzing T-cell recognition of shared melanoma antigens before and after adoptive cell therapy in melanoma patients, we observe a greater number of melanoma-specific T-cell populations compared with cytometry-based approaches. Furthermore, we detect neoepitope-specific T cells in tumor-infiltrating lymphocytes and peripheral blood from patients with non-small cell lung cancer. Barcode-labeled pMHC multimers enable the combination of functional T-cell analysis with large-scale epitope recognition profiling for the characterization of T-cell recognition in various diseases, including in small clinical samples.
