1. Immunology and Inflammation
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Biological controls for standardization and interpretation of adaptive immune receptor repertoire profiling

  1. Johannes Trück
  2. Anne Eugster
  3. Pierre Barennes
  4. Christopher M Tipton
  5. Eline T Luning Prak
  6. Davide Bagnara
  7. Cinque Soto
  8. Jacob S Sherkow
  9. Aimee S Payne
  10. Marie-Paule Lefranc
  11. Andrew Farmer
  12. The AIRR Community
  13. Magnolia Bostick  Is a corresponding author
  14. Encarnita Mariotti-Ferrandiz  Is a corresponding author
  1. University Children’s Hospital and the Children’s Research Center, University of Zurich, Switzerland
  2. CRTD Center for Regenerative Therapies Dresden, Faculty of Medicine, Technische Universität Dresden, Germany
  3. Sorbonne Université U959, Immunology-Immunopathology-Immunotherapy (i3), France
  4. AP-HP Hôpital Pitié-Salpêtrière, Biotherapy (CIC-BTi), France
  5. Lowance Center for Human Immunology, Emory University School of Medicine, United States
  6. Perelman School of Medicine, University of Pennsylvania, United States
  7. University of Genoa, Department of Experimental Medicine, Italy
  8. The Vanderbilt Vaccine Center, Vanderbilt University Medical Center, United States
  9. Department of Pediatrics, Vanderbilt University Medical Center, United States
  10. College of Law, University of Illinois, United States
  11. Center for Advanced Studies in Biomedical Innovation Law, University of Copenhagen Faculty of Law, Denmark
  12. Carl R. Woese Institute for Genomic Biology, University of Illinois, United States
  13. IMGT, The International ImMunoGeneTics Information System (IMGT), Laboratoire d'ImmunoGénétique Moléculaire (LIGM), Institut de Génétique Humaine (IGH), CNRS, University of Montpellier, France
  14. Laboratoire d'ImmunoGénétique Moléculaire (LIGM) CNRS, University of Montpellier, France
  15. Institut de Génétique Humaine (IGH), CNRS, University of Montpellier, France
  16. Takara Bio USA, Inc., United States
Review Article
Cite this article as: eLife 2021;10:e66274 doi: 10.7554/eLife.66274
3 figures and 2 tables

Figures

Figure 1 with 1 supplement
Geographic distribution of survey participants and their AIRR-seq research interests.

(A) Map with geographic distribution of survey participants. (B) Histogram showing the principal studied organisms among the participants. The ‘Other’ category includes rat, ferret, rabbit, goat, pig, canine, bovis, cattle, chicken, fish, teleost, salmon, zebrafish, other fish species, transgenic animals. (C) Venn diagram representing the percentage of participants according to their interest in AIRR template type. (D) Pie-chart representing the distribution of survey participants according to their research interest(s). Immune system diseases and other categories are described in more detail in the bar plots (right and left). Numbers of respondents for each category are shown next to the bars.

Figure 1—figure supplement 1
Heatmaps of the areas of study depending on the interest.

Each column is a participant group and the colors represent the area studied (blue: area studied, white: area not studied). For each heatmap, areas are ordered by decreasing frequency depending on the number of participants in each group. Information about the number of areas per participant and bioinformatic skills are added below (Chi2: p=0.0064).

Figure 2 with 1 supplement
Molecular approaches used in bulk sequencing.

(A) Venn diagram representing the most important molecular approaches used and their usage and sharing among participants. Numbers of respondents in each of the four main categories are shown in parentheses. (B) Bar plots representing biological material used and molecular barcoding proportion for the two major molecular biology approaches (multiplex PCR and template switching). Only the answers of respondents who used one technology exclusively are shown (Multiplex PCR: n = 29; Template switching: n = 10). UMI = unique molecular identifier.

Figure 2—figure supplement 1
Yearly number of PubMed entries referring to single-cell AIRR sequencing (left panel) and bioinformatic AIRR reconstruction from RNA-seq studies (right panel) 1980–2020 (via https://www.ncbi.nlm.nih.gov/pubmed/, accessed on 16 January 2020).

Search query (left panel): (‘single cell’[Title/Abstract] OR ‘10X’[Title/Abstract]) AND (‘VDJ’[Title/Abstract] OR ‘TCR’[Title/Abstract] OR ‘BCR’[Title/Abstract] OR ‘b cell receptor’[Title/Abstract] OR ‘t cell receptor’[Title/Abstract] OR ‘repertoire’[Title/Abstract]) Search query (right panel): (‘rna-seq’[Title/Abstract] OR ‘rna sequencing’[Title/Abstract]) AND (‘VDJ’[Title/Abstract] OR ‘TCR’[Title/Abstract] OR ‘BCR’[Title/Abstract] OR ‘b cell receptor’[Title/Abstract] OR ‘t cell receptor’[Title/Abstract] OR ‘repertoire’[Title/Abstract]).

Homebrew controls and their desired applications.

(A) Most frequently used homebrew controls (total n = 47). (B) Total frequencies of desired applications of homebrew controls for respondents currently using (gray bars; total n = 47) and currently not using controls (black bars; total n = 42).

Tables

Table 1
Current AIRR-seq methods and their typical use(s).

Bulk gDNA, bulk cDNA, and single-cell cDNA-based sequencing methods are compared with respect to their general features, uses, methods, and potential issues. Each is ranked using a semi-quantitative scale (from ‘+++” for best to ‘-” for worst or non-existent).

Bulk gDNA sequencingBulk cDNA sequencingSingle-cell cDNA sequencing
General FeaturesPCR methodMultiplexMultiplex and
5' RACE
Multiplex and
5' RACE
 Cell number102–106102–106102–103
 Sample throughputLow-highLow-moderateLow
 Length of receptor sequences100–600 bp150–600 bp700–800 bp
 Availability of commercial kits and service providers++++++
UsesGene usage+++++
 CDR3 length and properties+++++
 Somatic hypermutation (for IG)+++++
 Repertoire diversity+++++/-
 Clonal expansion++++++
 Clonal evolution+++++++
 Tracking of clonotypes++++++
 Clinical use (e.g., MRD detection)+++/--
 Unbiased detection of unproductive rearrangements++--
 Inference of germline++++/-
 Determination of constant gene-+++
 Structural annotation+/-+++
 Linkage of both antigen receptor chains+/-+/-++
Direct combination of AIRR-seq with single-cell immunophenotype
(e.g., transcriptome or cell surface protein expression)
--++
Characterization of clonotype full antigen receptor/Functional testing-+/-++
Rare clonotype detection+++++/-
MethodsSimplicity of workflow (library preparation)++++++
Cost for library preparation commercial kits
(per sample)
LowModerateHigh
Fidelity in sequencesModerateHighHigh
Molecular barcoding (correcting PCR/sequencing error)+/-++++
Potential IssuesV-gene amplification bias++++/-
 V-gene annotation issues++++
 PCR and sequencing error++++/-
 Difficulty with translation of copy number to cells+/-+++/-
 Degradation of template+++++
  1. bp = base pairs; CDR3 = complementarity determining region 3; MRD = minimal residual disease; RACE = rapid amplification of cDNA ends; V = variable.

Table 2
Concerns and expected errors introduced during AIRR-seq workflows and possible controls to detect them.

A typical workflow consists of 5 steps: Sample collection > Extraction > Amplification > Sequencing > Analysis.

ConcernMechanism(s)Example of potential controls
Sequence errorsEnzyme errors (RT, DNA polymerase); Sequencing errorsUMIs for bioinformatic error correction;
Spike-in controls with defined sequences to evaluate error rates
SensitivityEnzymatic inefficiencies (RT or PCR conditions/polymerase); Sample collection size (e.g., cell input number, purity); Sequencing depthSpike-in controls (synthetic or cellular) at known concentrations
SpecificityEnzyme bias (RT, DNA polymerase); Analysis pipelines (annotation, error correction)Spike-in controls with defined sequences to identify overall V/D/J gene amplification bias
Detection of contaminationBench-level cross contamination (sample mixing or PCR contamination) or barcode jumping during sequencingUnique spike-in (synthetic) for each sample; UDIs for sequencing barcode crosstalk
Sample quality controlSample collection or nucleic acid purificationIdentified by spectroscopy or agarose electrophoresis
Evaluate batch effectsSubtle differences introduced at all stages of the workflowSpike-in controls (synthetic or cellular); Parallel biological (clonal or complex) sample
Linearity/accuracy of clonotype quantificationEnzymatic inefficiencies (RT or PCR conditions); Analytical error correctionSpike-in controls (synthetic or cellular) at known concentrations
Reproducibility/Batch effectsAll stagesSpike-in controls (synthetic or cellular); Parallel biological (clonal or complex) sample; Comparison of replicate amplifications of the same sample; Comparison of sequences generated on the same sample in different sequencing runs
Data processingDatabase/annotation limitations; filtering; error correction; collapsing/consensus algorithmsSpike-in controls (synthetic or cellular); Parallel biological (clonal or complex) sample
  1. RT, reverse transcriptase; UMIs, unique molecular identifiers; UDIs, unique dual indices.

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