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    Below is an overview of the experiment from TCR/BCR nucleotides to reads to clonotypes. Yellow tabs represent information on the pipeline which includes the Cellecta DriverMap Adaptive Immune Receptor (AIR) which provides a description of the DriverMap AIR assay and Bioinformatics Workflow which provides an in-depth description of the computational workflow.

    The tabs in green represent the experimental data and data analysis. This includes information on DriveMap Adaptive Immune Receptor TCR/BCR Profiling assay which is described in the Experimental Description tab. This tab contain the details on the sample collection all the way to library generation. The Sequencing & Alignment Quality tab contain details on the initial processing of the sequences and quality of reads and success of read alignment to TCR/BCR genes. The Clonotype Summary tab provides an overview of the identified clonotypes within each sample. The analysis of these clonotypes are further broken down into chains (IGH, IGK, IGL, TRAD, TRB and TRG). For each chain relevant to the experiment, a variety of metrics are calculated provide an overall picture of the repertoire characteristics. This includes repertoire statistics, the top clonotypes, clonotype overlaps between samples, gene usage, gene usage overlaps between samples, diversity metrics and kmer analysis.


DriverMap Adaptive Immune Receptor Repertoire Profiling


    The DriverMap Adaptive Immune Receptor (AIR) Repertoire Profiling Service from Cellecta provides you with a profile of all TCR and BCR CDR3 or full-length variable regions in blood, cell, or RNA samples. With the DriverMap AIR TCR-BCR Profiling Service, you get a larger complement of clonotypes than other similar assays, reproducible and comprehensive coverage from a range of immune sample inputs, including total RNA from whole blood and Rapid, 1-month turnaround from sample submission to an extensive analysis report

    Since T- and B-cells work synergistically in the adaptive immune response, Cellecta has designed an assay that profiles both T-cell receptor (TCR) and B-cell receptor (BCR) repertoires in a single convenient reaction. Separate assays specific for T- or B-cell chains are also available. The DriverMap AIR-RNA assay quantifies T-cell and B-cell receptor transcripts. It is designed to specifically amplify only functional RNA molecules from human or mouse TCR and BCR cells, avoiding non-functional pseudogenes with similar structures or full-length variable regions from human RNA molecules enables highly sensitive detection of low-frequency, rare TCR and BCR clonotypes and more comprehensive profiling when working with small samples and limited numbers of cells. The DriverMap AIR-DNA assay amplifies receptor genes directly from genomic DNA. The AIR-DNA assay provides a more quantitative measurement of the genetic copies for each CDR3-specific clonotype which correlates to the number of cells with that clonotype in that sample. This data enables the measurement of clonal expansion in T and B cells. Combining data obtained from both the AIR-DNA and AIR-RNA assays enables assessment of both the transcriptional activation and number of cells with a particular clonotype. The ability to differentiate these two effects provides a quantitative basis to assess antigen-activated clonotypes

    Applications of BCR sequencing: Identify broadly neutralizing antibodies (BNAbs) and map Ig-seq datasets to known antibody structures for antibody and vaccine development, Track B-cell migration and development patterns, Find markers of autoimmune diseases such as multiple sclerosis, rheumatoid arthritis and cancers (e.g. B-cell lymphoma), and Contrast naïve and antigenically challenged datasets to understand antibody maturation.

    Applications of TCR sequencing: Track T-cell clonality and diversity for insights into mechanisms of action of immune checkpoint inhibitors for immunotherapies, Assess TCR overlap between repertoires to define spatial and temporal heterogeneity of the anti-tumoral immune response, and Analyze TCR sequence and structure to annotate antigenic specificity for developing personalized cellular immunotherapies


How is the DriverMap AIR Assay Different from other AIR Assays?

    DriverMap™ Multiplex PCR technology uses gene-specific primers which significantly reduce the level of non-specific binding and primer-dimer amplification products, and are designed to target only TCR/BCR isoforms. Unique Molecular Identifiers (UMIs) facilitate accurate quantitation of the copy number of cDNA or DNA molecules in amplification steps, as well as detection of low abundance clonotypes and correction of amplification biases and sequencing errors. Dual-index amplicon labeling strategy minimizes index hopping during NGS allowing for comprehensive readouts. Full profiles of the antigen-recognition CDR3 region enable assessment of CDR3 length distribution, V(D)J segment usage, isotype composition for BCRs, somatic mutations, and similar characteristics with immune receptor profiling software such as MiXCR (MiLabs).

DriverMap Adaptive Imumune Repertoire (AIR) profiling Assay workflow is as follow:

Pipeline


    Below is an overview of the bioinformatics analysis pipeline used on DriverMap Adaptive Immune Receptor (AIR) Sequencing data. MiXCR (Bolotin et al., 2015) is used to align the sequencing reads and identify clonotypes and their abundances. The MiXCR Cellecta DNA or RNA Preset is used to perform the read alignment. After alignment, a variety of repetoire metrics can be calculated from the resulting clonotype abundances. This includes repertoire statistics, the top clonotypes, clonotype overlaps between samples, gene usage, gene usage overlaps between samples, diversity metrics and kmer analysis. This is performed mainly using the Immunarch package alongside a variety of R packages for data visualization.


Experimental Details


    The details of the library generation is described below. The Protocol section describes the sample collection, the profiling assay, PCR amplification, PCR yields, and primer sequences. The PCR Amplification Results section contains the gel image from the PCR. Sample Description section contains the list of samples in the analysis including relevant metadata.



5-20-24. Repeat of 306 and FC1, Immunization-Rx_Alex Chenchik(AC)> AIR-CDR3 vs AIR-CDR1-2-3 profiling in whole blood.

38 Samples – flow cells (#350) > 300-n paired-end read (high-throughput)> NextSeq500

Sample Description > please find in the attached Excel File

Experiment description: This is fourth experiment (repeat of FC298, 306 and FC1) using reduced starting amount of RNA (25 ng vs old 100ng), reduced cycle numbers for Samples 7 (Im1)>4 cycle less, only immunization 1. Goal is to compare CDR1-2-3 vs CDR3 profiling, as previous data show less variability in controls for CDR1-2-3 profiling (FC1, Dongfang data) .

AIR profiling of Whole Blood RNA samples isolated from AC before and after following treatments:

  1. Immunization 1: PPSV23(pneumococcal polysaccharide)plus Td (tetanus+diphteria) vaccine > 4/27/22

  2. Rx treatment against H.Pilory (clarithromycion+larisolorazole+amoxicillin) > 6/21/22

  3. Immunization 2: Shingrix (against shingles, based on recombinant VZV glycoprotein E antigen herpes zoster virus)> 7/15/22

Before and after each treatment we collected blood in 2 Tempus test tubes (3ml). From Tempus test tubes we purified both total RNA (R-T name in the Sample list) and DNA. Please, note that Tempus and AXgene are two main test tubes used for stabilization/collection of whole blood for RNA/DNA purification.

E.g. for most samples we have duplicates (D1 and D2) for the most time points:

1) 4/1/22 > control before any treatment

2) 4/6/22 > control before any treatment

3) 4/26/22 > control before any treatment

- 4/27/22 > Immunization 1

5) 4/29/22 > 2 days after Im1

6) 5/2/22 > 5 days after Im1

7) 5/5/22 > 8 days after Im1 (T/B cell fraction sorting)

8) 5/12/22 > 15 days after Im1

9) 5/19/22 > 22 days after Im1

10) 6/17/22 > control before Rx

Additional “control” AC whole blood samples (first 3 from Alex Chenchik) collected before:

C_R-T_BP > 3/20/22 (back pain condition)

C1_R-T > 1/12/22

C2_R-T > 1/27/22

Observation and Data quality: Yield of amplified NGS PCR products was +/-2-fold for all samples (e.g. it equal to 16x activation for 7) after appr. one week after Im1t > decided to combine all amplified products in equal amount except sample 7 (4x more) in order to “compensate” loss of reads for activated clonotypes and all CDR1-2-3 will 1.5x more than CDR3 (considering differences in amplicon sizes 500bp vs 350bp). No significant background, smear or primer dimers in any samples.

Protocol:

Step 2- RevGSP binding to mRNA. Total RNA (50ng for WB for all AC samples was incubated with mix of Reverse AIR TCR+BCR GSPs (set10 of 6/23/22, final primer concentration is appr. 10nM of each primer) in 20ul of 1xHyb buffer at 70C, 5 min, 60C for 60 min, cool down to 25C. Hybridized RNA-RevGSP products were purified with 1.2 volume (24 ul) of SPRI beads. The bind to beads RNA-RevGSP hybrid was washed by 2x80% ethanol.

Step 2- Rev GSP extension > cDNA synthesis. The washed magnetic beads were resuspended in 45 ul of 1xRT-Ext buffer, dNTP (0.5 mM), reverse transcriptase (RTscript) and RT hot-start aptamer, collect 41 ul without beads and incubated at 50C for 30 min, and 72C for 10min.

Step 3. Fwd GSP extension. cDNA product (in 40 ul) was splitted for two test tubes and extended by adding 20 ul of master mix with pool of Forward AIR CDR1-2-3 TCR+BCR or AIR CDR3 TCR+BCR GSPs (10 nM final concentration of the each primer)and incubated 98C, 1min, 68C, 10 min and treated with 2 ul of ExoI, 37C, 20min, 95C,5min.

Step 4-1st PCR. FwdGSP-extended cDNA was diluted in PCR master mix (60ul), and anchored cDNA fragments were amplified in 100-ul of Multiplex DNA polymerase reaction mix with universal anchor PCR primers for 19 (most samples) or 15 (sample 7) cycles.

Step 5-2nd PCR. 2ul aliquotes of 1st PCR were added in 96-well plate with 50ul master mix for 2nd PCR step (each well has unique Dual Nextera Index primers) and amplified using unique combination of Nextera Fwd-P5-Index and Rev-P7-Index for 8 cycles, treated with ExoI (1-ul) at 37C for 30min. PCR products were analyzed in Fragment analyzer (see attached file), combined at equal amount, except sample 7 (4x) and all CDR1-2-3 were 1.5x more than CDR3, purified using AMPpure magnetic beads (1.5X volume). The purified cDNA products were quantitated by Qubit fluorescence measurement, and diluted to 10 nM (2.1 ng/ul) for next-generation sequencing using NextSeq500.

Program:

Read1:eSeqDNA-Fwd>148c; Ind1:eSeqIND-Fwd>10c; Ind2:eSeqIND-Rev>10c;Read2: eSeqDNA-Rev>148c.

DriverMap AIR assay Amplicon Structure

eSeqDNA-Fwd

FP5  UDPIndex10 AGCAGCAGCACCGACCAGCAGACA F ACGGCGACCACCGAGATCTACACNNNNNNNNNNAGCAGCAGCACCGACCAGCAGACA-GSP-DNA-

TGCCGCTGGTGGCTCTAGATGTGNNNNNNNNNNTCGTCGTCGTGGCTGGTCGTCTGT-GSP-DNA-

TCGTCGTCGTGGCTGGTCGTCTGT

eSeqIND-Rev

eSeqIND-Fwd

UMI14 TCTGTGCTGGTCGGTGCTCGTCGT

-DNA-GSP-NNNNNNNNNNNNNN-TCTGTGCTGGTCGGTGCTCGTCGTNNNNNNNNNNTATCTCGTATGCCGTCTTCTGCT

-DNA-GSP-NNNNNNNNNNNNNN-AGACACGACCAGCCACGAGCAGCANNNNNNNNNNATAGAGCATACGGCAGAAGACGA

R AGACACGACCAGCCACGAGCAGCA UDPIndex10 RP7

eSeqDNA-Rev





The following samples are included in the analysis.

Samples Sample_Source Preset Experiment Species Condition
Control_1 Control_1 cellecta-human-rna-xcr-umi-drivermap-air CDR3 hsa Control
Control_2 Control_2 cellecta-human-rna-xcr-umi-drivermap-air CDR3 hsa Control
Control_3 Control_3 cellecta-human-rna-xcr-umi-drivermap-air CDR3 hsa Control
Immunized_Day5_1 Immunized_Day5_1 cellecta-human-rna-xcr-umi-drivermap-air CDR3 hsa PPSV23+Td_Immunized
Immunized_Day5_2 Immunized_Day5_2 cellecta-human-rna-xcr-umi-drivermap-air CDR3 hsa PPSV23+Td_Immunized
Immunized_Day8_1 Immunized_Day8_1 cellecta-human-rna-xcr-umi-drivermap-air CDR3 hsa PPSV23+Td_Immunized
Immunized_Day8_2 Immunized_Day8_2 cellecta-human-rna-xcr-umi-drivermap-air CDR3 hsa PPSV23+Td_Immunized


Sequencing and Alignment Quality


    This section contains an overview of the quality of read sequencing and alignment. The sequencing section outlines the total number of sequences in each sample, as well as FastQC metrics which are relevant to DriverMap AIR libraries. The alignment section outlines the alignment of the samples to the reference genome. Highlighted in this section are successful alignments, non-TCR/IG alignments, reads with no V and/or J hits, reads with no CDR3 regions, reads with no barcodes, etc. Lastly, the reads per UMI filter section shows the histogram generated by MiXCR which shows the number of reads per UMI. A cut-off threshold marked in a red dotted line is used to filter out erroneous UMI prior to downstream analysis.

    fastqc is used to determine the quality of the sequences. A variety of metrics are curated by the software to determine whether the samples are suited for downstream bioinformatic analysis. Each metric is deemed to PASS, WARN or FAIL indicating the success, requirement for some concern, or that the sample needs to be evaluated more carefully. Note that the quality metrics were designed for generic purposes. For further explanation on each metric, see the following article (external source):


mixcr_qc_report.knit


Sequencing Statistics

sample pct.gc tot.seq seq.length
Control_1_S20_R1_001 58 8952881 308
Control_1_S20_R2_001 57 8952881 308
Control_2_S21_R1_001 57 7328409 308
Control_2_S21_R2_001 57 7328409 308
Control_3_S28_R1_001 58 7205548 308
Control_3_S28_R2_001 57 7205548 308
Immunized_Day5_1_S24_R1_001 57 11659613 308
Immunized_Day5_1_S24_R2_001 57 11659613 308
Immunized_Day5_2_S31_R1_001 57 10644001 308
Immunized_Day5_2_S31_R2_001 57 10644001 308
Immunized_Day8_1_S25_R1_001 58 27375362 308
Immunized_Day8_1_S25_R2_001 57 27375362 308
Immunized_Day8_2_S32_R1_001 57 25610902 308
Immunized_Day8_2_S32_R2_001 57 25610902 308


pct.gc = GC Percentage
tot.seq = Total Number of Reads
seq.length = Sequencing Length (NT)

Poor quality samples

sample nb_problems module
Control_1_S20_R1_001 1 Adapter Content
Control_1_S20_R2_001 1 Adapter Content
Control_2_S21_R1_001 1 Adapter Content
Control_2_S21_R2_001 1 Adapter Content
Control_3_S28_R1_001 1 Adapter Content
Control_3_S28_R2_001 1 Adapter Content
Immunized_Day5_1_S24_R1_001 1 Adapter Content
Immunized_Day5_1_S24_R2_001 1 Adapter Content
Immunized_Day5_2_S31_R1_001 1 Adapter Content
Immunized_Day5_2_S31_R2_001 1 Adapter Content
Immunized_Day8_1_S25_R1_001 1 Adapter Content
Immunized_Day8_1_S25_R2_001 1 Adapter Content
Immunized_Day8_2_S32_R1_001 1 Adapter Content
Immunized_Day8_2_S32_R2_001 1 Adapter Content


nb_problems = Number of criteria that failed
module = List of criteria that failed

Summary of FastQC Calls

Control_1_S20_R1_001 Control_1_S20_R2_001 Control_2_S21_R1_001 Control_2_S21_R2_001 Control_3_S28_R1_001 Control_3_S28_R2_001 Immunized_Day5_1_S24_R1_001 Immunized_Day5_1_S24_R2_001 Immunized_Day5_2_S31_R1_001 Immunized_Day5_2_S31_R2_001 Immunized_Day8_1_S25_R1_001 Immunized_Day8_1_S25_R2_001 Immunized_Day8_2_S32_R1_001 Immunized_Day8_2_S32_R2_001
Basic Statistics PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS
Per base sequence quality PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS
Per tile sequence quality WARN WARN WARN WARN WARN WARN WARN WARN WARN WARN WARN WARN WARN WARN
Per sequence quality scores PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS
Per base N content PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS
Sequence Length Distribution PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS PASS
Adapter Content FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL FAIL



MiXCR Alignment Calls

Control_1 Control_2 Control_3 Immunized_Day5_1 Immunized_Day5_2 Immunized_Day8_1 Immunized_Day8_2
Successfully aligned reads: OK OK OK OK OK OK OK
Off target (non TCR/IG) reads: OK OK OK OK OK OK OK
Reads with no V or J hits: OK OK OK OK OK OK OK
Reads with no barcode: OK OK OK OK OK OK OK
Overlapped paired-end reads: OK OK OK OK OK OK OK
Alignments that do not cover VDJRegion: NA NA NA NA NA NA NA
Tag groups that do not cover VDJRegion: NA NA NA NA NA NA NA
Barcode collisions in clonotype assembly: OK OK OK OK OK ALERT ALERT
Unassigned alignments in clonotype assembly: OK OK OK OK OK OK OK
Reads used in clonotypes: OK OK OK OK OK WARN WARN
Alignments dropped due to low sequence quality: OK OK OK OK OK OK OK
Alignments clustered in PCR error correction: NA NA NA NA NA NA NA
Clonotypes clustered in PCR error correction: NA NA NA NA NA NA NA
Clones dropped in post-filtering: OK OK OK OK OK OK OK
Alignments dropped in clones post-filtering: OK OK OK OK OK OK OK
Reads dropped in tags error correction and filtering: OK OK OK OK OK WARN ALERT
UMIs artificial diversity eliminated: OK OK WARN OK OK OK OK
Reads dropped in UMI error correction and whitelist: OK OK OK OK OK OK OK
Reads dropped in tags filtering: OK OK OK OK OK WARN ALERT


MiXCR Alignment Statistics

Control_1 Control_2 Control_3 Immunized_Day5_1 Immunized_Day5_2 Immunized_Day8_1 Immunized_Day8_2
Successfully aligned reads: 99.15% 99.01% 99.0% 99.21% 99.22% 99.55% 99.56%
Off target (non TCR/IG) reads: 0.11% 0.12% 0.11% 0.11% 0.1% 0.11% 0.1%
Reads with no V or J hits: 0.73% 0.86% 0.88% 0.67% 0.68% 0.33% 0.33%
Reads with no barcode: 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Overlapped paired-end reads: 99.5% 99.55% 99.51% 99.51% 99.52% 99.47% 99.47%
Alignments that do not cover VDJRegion: NA NA NA NA NA NA NA
Tag groups that do not cover VDJRegion: NA NA NA NA NA NA NA
Barcode collisions in clonotype assembly: 0.28% 0.28% 0.24% 0.6% 0.72% 14.98% 15.81%
Unassigned alignments in clonotype assembly: 0.45% 0.41% 0.44% 0.53% 0.52% 2.6% 2.66%
Reads used in clonotypes: 97.59% 97.3% 97.32% 97.4% 96.81% 87.79% 85.0%
Alignments dropped due to low sequence quality: 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Alignments clustered in PCR error correction: NA NA NA NA NA NA NA
Clonotypes clustered in PCR error correction: NA NA NA NA NA NA NA
Clones dropped in post-filtering: 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Alignments dropped in clones post-filtering: 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%
Reads dropped in tags error correction and filtering: 0.74% 0.92% 0.86% 0.96% 1.56% 9.19% 11.0%
UMIs artificial diversity eliminated: 28.45% 26.29% 31.44% 25.36% 21.65% 8.1% 7.81%
Reads dropped in UMI error correction and whitelist: 0.27% 0.28% 0.26% 0.25% 0.3% 0.53% 0.59%
Reads dropped in tags filtering: 0.47% 0.64% 0.59% 0.71% 1.26% 8.66% 10.41%


Alignment Percentages



MiXCR automatically sets a filter to identify UMIs that attain a sufficient number of reads to be called real. Shown below are the number of samples with a given number of reads per UMI. In the dotted red line is the filter applied for that particular sample.

Sample: Control_1