Immunoglobulin chains (IG) and T-cell receptor chains (TR) are generated by DNA recombination, a process of somatic rearrangement of variable (V), diversity (D), and joining (J) genes (Tonegawa, 1983). The diversity of the resulting rearranged genes (referred as V-J and V-D-J) is very high, due to not only the combination of different germline V, D, and J genes, but also to the deletion and addition of templated (P) nucleotides and the addition of non-templated (N) nucleotides at the junctions between the rearranged genes, and somatic hypermutation of expressed IG (Papavasiliou and Schatz, 2002; Lefranc and Lefranc, 2020). The total number of potential expressed rearranged IG and TR sequences in an individual is referred to as the adaptive immune receptor repertoire (AIRR). The adaptive immune repertoire is very diverse in a healthy individual, with the theoretically possible number of clonotypes reaching more than 10¹⁹ different TR (Bradley and Thomas, 2019) and 10¹¹ IG (Glanville et al., 2009), far exceeds the number of B and T cells in a given individual (Davis and Bjorkman, 1988; Freeman et al., 2009; Elhanati et al., 2015). Thanks to next-generation sequencing (NGS), the AIRR can be sampled with sufficient depth for some of its complexity to be studied (Weinstein et al., 2009; Six et al., 2013). AIRR sequencing (AIRR-seq) provides insights into the immune status of an individual at steady-state or in altered conditions such as malignancy, autoimmune disease, immunodeficiency, infectious disease, or vaccination, and allows comparison of B- and T-cell populations between individuals and time points (Benichou et al., 2012; Kirsch et al., 2015; Dziubianau et al., 2013; Hou et al., 2016; Ghraichy et al., 2018). AIRR-seq permits the description and quantification of global diversity and characteristics of AIRR, the identification of clonal expansions, the tracking of particular clonotypes, and the prediction of their specificities (Miho et al., 2018; Zvyagin et al., 2020; Sidhom et al., 2018; Glanville et al., 2017; Huang et al., 2020; Jokinen et al., 2021; Akbar et al., 2021; Hayashi et al., 2021) as well as the antibody selection through phage display (Rouet et al., 2018; Ravn et al., 2013), thereby providing opportunities for new biomarker identification (Gittelman, 2021; Dines, 2020), therapeutic antibody discovery (Akbar et al., 2021; Richardson et al., 2021), CAR-T cell bioengineering (Sheih et al., 2020), vaccine development, cancer diagnostics and treatment (Linette et al., 2019; Zhang et al., 2018; Lu et al., 2018), including neoantigen discovery (Chiou et al., 2021; Richters et al., 2019) and immune intervention monitoring in diverse pathologies, such as stem cell transplantation (Robinson, 2015; Fink, 2019; Jiang et al., 2019; Jacobsen et al., 2017; Theil, 2017; Link-Rachner et al., 2019; Rubelt et al., 2017; Parola et al., 2018; Georgiou et al., 2014; Arnaout et al., 2021; Anand et al., 2021).

NGS-based approaches and methods have multiplied, now including high-throughput bulk sequencing of IG or TR starting from genomic DNA (gDNA) or mRNA (as cDNA), which typically provides information on one receptor chain only, and more recently to the sequencing of the two IG or TR chains expressed in a single cell, which provides information on the antigen-specific receptor. These approaches are increasingly applied, mostly to human AIRRs, but also to study AIRRs from other organisms (Chaudhary and Wesemann, 2018; Minervina et al., 2019). Molecular protocols to amplify IG or TR chains typically rely on Polymerase Chain Reaction (PCR), such as multiplex-PCR or RACE-PCR (Robins et al., 2009; Wang et al., 2010; DeKosky et al., 2013; Eugster et al., 2013; Heather et al., 2015; Mamedov et al., 2013).

To obtain reliable and comparable AIRR-seq data, the methods for performing AIRR-seq need to fulfill a number of requirements. First, the data generated by AIRR-seq must reflect the composition and diversity of the ‘real cellular repertoire.’ The cell subset(s), the cell sample size, and the sequencing depth used in an AIRR-seq experiment all influence the downstream data and should therefore be carefully adapted to the experimental question (Rosenfeld et al., 2018). PCR amplification of AIRR-seq libraries can introduce bias by preferentially targeting certain genes, by missing certain alleles (in the case of multiplex PCR with primers anchored in the V or J genes), and by overamplifying targets of certain genes and length (in multiplex PCR and RACE-PCR) (Calis and Rosenberg, 2014; Alamyar et al., 2012; Gadala-Maria et al., 2015; Primi et al., 1986; Barennes et al., 2021). All these parameters will influence how accurately AIRR profiling reflects the true abundance and diversity of clones in the immune repertoire. Second, AIRR V and J genes (and constant (C) genes for IG) must be identified in an unambiguous and unbiased manner as knowledge of the complementarity determining region 3 (CDR3; the site of V, (D), and J recombination in an IG or TR, and hence its greatest sequence diversity), but also of the CDR1 and CDR2 (and that of the C gene in IG) is critical to assess the physicochemical constraints that define specificity, affinity, and function of the TR or the IG (Li et al., 2013; Rossjohn et al., 2015). Third, AIRR-seq data should be as free from sequencing error as possible. The CDR3, key for assigning a sequence to a clonotype, is by definition unknown, and in the case of IG sequences it is important to be able to distinguish bona fide somatic hypermutation from artifactual mutations introduced by PCR or by sequencing errors all along the rearranged molecule, as the latter can generate falsely elevated inter- and intra-clonal variation. Further complicating the matter is the fact that the germline V genes from the same subgroup (e.g., IGHV4-31 vs. IGHV4-30-4 for IG) and V alleles or polymorphic variants of a given gene (e.g., TRAV14/DV4 for TR) may have very few nucleotide differences (Lefranc, 2014; Lefranc and Lefranc, 2001). Therefore, distinguishing errors from true biological variants can be a major challenge. Finally, samples for AIRR-seq should be free from cross-sample contamination, to which AIRR-seq experiments are prone as multiple samples are often processed in parallel and sequenced on a single lane of a sequencing run.

In the past decade, multiple molecular biology protocols and approaches have been developed by academic and industrial investigators, rendering comparisons among studies difficult. Moreover, experimental and analytical protocols are highly complex and therefore prone to intra- and inter-experimental variability (Barennes et al., 2021; Liu et al., 2016; Rosati et al., 2017; Bashford-Rogers et al., 2014). AIRR-seq can be performed either on gDNA or cDNA from multiple T/B cell populations (bulk sequencing) or on individual cells (single-cell sequencing). The starting material and sequencing method used depend on the application, as each has advantages and disadvantages (Table 1). The availability of commercial kits can be helpful, since they are produced following standards and rigorous quality control, thus offering standardized reagents and protocols across laboratories. Currently, available commercial kits include gDNA-based methods (e.g., Adaptive Biotechnologies, iRepertoire) as well as mRNA-based methods that use cDNA (e.g., Illumina, Takara Bio, iRepertoire) for bulk sequencing. Mainly mRNA-based commercial methods (e.g., 10X Genomics, Takara Bio, HiFiBio) are used for single-cell analysis, which can provide sequences for full receptors or antibodies. This is an important consideration as the determination of the isotype for IG requires a full or partial sequence of the constant region. Single-cell approaches are further helping in the detection of clonotypes because they provide both paired chains and potentially allow full-length cDNA sequencing (Stubbington et al., 2017). The fidelity of sequence is an additional factor to consider. Unique molecular identifiers (UMI) consisting of random stretches of 8–12 nucleotides are incorporated into oligonucleotides that are used to generate cDNA from mRNA, such that statistically each cDNA molecule contains a unique sequence. Analysis of sequences that share the same UMI is used to generate a consensus sequence, greatly reducing sequencing errors (Shugay et al., 2014). In contrast, multiplex PCR approaches can be associated with artifacts arising from primer competition or off-target primer binding. Although this favors RACE-PCR when considering mRNA-based methods, gDNA-based multiplex PCR may offer higher fidelity since it does not rely on reverse transcription (reverse transcriptase enzymes have higher error rates than DNA polymerases [Ellefson et al., 2016; Holland et al., 1982]). Finally, cost may influence the choice of a particular protocol. There are many factors that contribute to the cost of AIRR-seq data generation. For example, the cost of sequencing, the sequencing depth, and the number of cells analyzed per sample are variable; also, the choice between commercial kits and ‘homebrew’ methods will influence costs. In general, gDNA analysis is the most cost-effective, because it requires the lowest sequencing depth with the largest representation of cells per sample, whereas single-cell analysis is on the opposite end of the scale, with bulk cDNA sequencing in the middle.

Several considerations should be taken into account when designing and planning an AIRR-seq experiment. In addition to the large number of different methods and protocols, other factors including budgetary constraints, timelines, sample types, and processing are also important. Given the diversity of AIRR-seq workflows, comparisons between different data sets are challenging or even impossible. Standards and controls are needed for optimal AIRR-seq data harmonization, interpretation, and sharing (Rubelt et al., 2017; Breden et al., 2017; Vander Heiden et al., 2018). This need led to the formation in 2015 of a grassroots community of scientists and other interested parties, known as the AIRR Community (https://www.antibodysociety.org/the-airr-community/). The objective of the Biological Resources Working Group (WG) within the AIRR Community is to coordinate the assessment and development of AIRR-seq controls, ultimately providing the scientific community with controls and standards for the generation, harmonization, and rigorous comparison and interpretation of AIRR-seq data. In order to recommend biological standards that are needed and to prioritize their development, a written survey was developed asking participants about the use of and need for controls for AIRR-seq experiments. The survey gathered information on participants’ research interests, sample types, sequencing methodologies, currently available controls, and desired controls. In addition to the survey results, different AIRR-seq methods and controls were also gleaned from the literature, and finally, the WG also invited scientists with unpublished research on controls for their input. These three sources of data were then used to provide a comprehensive overview of sequencing methods, technical issues, current standards, and potential priorities for the development of future standards. Here we describe the progress of the Biological Resources WG of the AIRR Community, its information collection and proposed strategies to define, develop, and use AIRR-seq standards.

Biological controls in AIRR-seq experiments

AIRR Community survey: Overview and respondent demographics

To address the use, needs, and requirements for AIRR-seq controls and standards, we designed and disseminated a questionnaire to researchers in the AIRR Community, as well as to users of IMGT, the international ImMunoGeneTics information system (http://www.imgt.org) (Lefranc, 2014). The questionnaire was composed of 4 sections and included 28 questions, with tick-box predefined answers and free-text options allowing for participants’ personalized answers (Supplementary material). After 6 months, 105 responses were recorded, including one incomplete response from a participant who neither produces nor analyzes AIRR-seq data. Three respondents participated twice, with consistent answers and same name and contact information, therefore only one completed form was considered for each. Answers from 101 remaining participants originated mainly from North America and Europe (Figure 1A) and were further analyzed. At the time of the survey, 96% of respondents were involved in AIRR-seq studies and 4% had plans to perform AIRR-seq studies in the future. Of the respondents, 92% were engaged in human studies, 48% in mouse studies, and 38% in the use of AIRR-seq to study other species or synthetic molecules (e.g., from phage-displayed antibody libraries; Figure 1B). Approximately half of the respondents focused exclusively on IG while a quarter each studied TR and IG or TR alone (Figure 1C). Several respondents were interested in many different topics (Figure 1D and Figure 1—figure supplement 1), with their fields of interest dominated by ‘immune system diseases’ including infection, autoimmune disease, and cancer. Furthermore, the survey results clearly indicate an interest among the majority of respondents in developing bioinformatic tools for the analysis of AIRR-seq data, followed by major interests in other research areas such as vaccinology, immune repertoire homeostasis, immunotherapy, antibody engineering, hematology, and aging (Figure 1D). In addition, respondents with bioinformatic skills tended to be those who had broader research interests (Figure 1—figure supplement 1). Finally, 89/101 of survey respondents indicated an interest in using AIRR-seq to either track clonotypes over time or across samples, whereas 88/101 were interested in identifying highly expanded clonotypes, 87/101 on analyzing diversity, and 83/101 on studying clonal selection. In conclusion, participants in this survey came from diverse backgrounds, had wet and dry bench expertise, and had a breadth of research interests covering different aspects of AIRR-seq studies.

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Survey results on sequencing methodologies used

Whereas 91% (92/101) questionnaire respondents commonly perform bulk sequencing experiments, 67% (68/101) combine bulk sequencing with the use of single-cell technologies. Only 8% of respondents focused exclusively on single-cell sequencing or phage display technologies only. With respect to the input biological material used in bulk sequencing, the majority of participants (83%) preferred to sequence long amplicons that covered the entire (or almost the entire) V-(D)-J region and part of the C region despite the associated higher sequencing cost per read and restrictions on the type of compatible sequencer. AIRR-seq researchers mainly used mRNA for cDNA sequencing (69%), while both mRNA and gDNA were used by 25%, and gDNA alone by 6% of respondents. Figure 2A shows that the majority of survey respondents performing bulk sequencing used multiplex PCR or the template-switching approach with a considerable number of AIRR-seq researchers using both methods. For those using either approach, mRNA was still the preferred starting material. In addition, UMIs were more commonly used with template switching than multiplex PCR approaches (Figure 2B). The association of UMIs with template switching methods is likely related to the template choice, since UMIs are not easy to use with gDNA-based templates, due to the incorporation of the UMI into the amplified products; nevertheless, two participants in the survey reported using UMIs with gDNA (Figure 2B).

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Altogether, these initial results suggest that among survey respondents bulk sequencing on mRNA and gDNA are the most frequently used methods. However, the literature reflects increasing use of single-cell approaches, including the computational construction of IG or TR from RNA-sequencing (RNA-seq) libraries (Figure 2—figure supplement 1) and the combined use of target capture plus single-cell RNA-seq (e.g., 10X Genomics’ 5′ end kits, etc.).

To gain insight into which standards should be prioritized for development and sharing with the scientific community, we asked survey participants about the standards they currently use and about the types of standards they would like to use. Based on the results, we concentrate below on potential controls for bulk analysis, as this approach is being used by the majority of the respondents (91%) and because the development of standards for single-cell applications is somewhat distinct.

Survey results on controls used and desired controls

Most respondents (88%) were interested in using standards or controls in AIRR-seq experiments. The 47 respondents who already use controls (n = 47) did so for protocol development (12/47 = 26%), everyday use (7/47 = 15%), or for both (15/47 = 32%), with the remaining (13/47 = 28%) not indicating their specific application. Commercial controls were used by 11, and homebrew controls were used by 23 participants, with 3 people using both and 16 not specifying their source. Figure 3A indicates that the most commonly used homebrew controls were cell lines or pooled-cell preparations. Respondents who did not use controls (black bars) were also asked how they might want to use controls. Figure 3B depicts the community survey responses to these questions.

Figure 3

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In summary, the survey, as well as further discussions within the AIRR Biological Resources WG, identified major concerns arising from different steps in the AIRR-seq workflow (Table 2, left and middle columns). Additionally, based on these identified issues, the right column of Table 2 describes potential controls to address them. These controls are described in more detail in the subsequent sections of the manuscript.

Table 2

Concern	Mechanism(s)	Example of potential controls
Sequence errors	Enzyme errors (RT, DNA polymerase); Sequencing errors	UMIs for bioinformatic error correction; Spike-in controls with defined sequences to evaluate error rates
Sensitivity	Enzymatic inefficiencies (RT or PCR conditions/polymerase); Sample collection size (e.g., cell input number, purity); Sequencing depth	Spike-in controls (synthetic or cellular) at known concentrations
Specificity	Enzyme 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 contamination	Bench-level cross contamination (sample mixing or PCR contamination) or barcode jumping during sequencing	Unique spike-in (synthetic) for each sample; UDIs for sequencing barcode crosstalk
Sample quality control	Sample collection or nucleic acid purification	Identified by spectroscopy or agarose electrophoresis
Evaluate batch effects	Subtle differences introduced at all stages of the workflow	Spike-in controls (synthetic or cellular); Parallel biological (clonal or complex) sample
Linearity/accuracy of clonotype quantification	Enzymatic inefficiencies (RT or PCR conditions); Analytical error correction	Spike-in controls (synthetic or cellular) at known concentrations
Reproducibility/Batch effects	All stages	Spike-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 processing	Database/annotation limitations; filtering; error correction; collapsing/consensus algorithms	Spike-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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Author details

1. Johannes Trück

   University Children’s Hospital and the Children’s Research Center, University of Zurich, Zurich, Switzerland

   Contribution

   Conceptualization, Formal analysis, Writing - original draft, Writing - review and editing

   Contributed equally with

   Anne Eugster and Pierre Barennes

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0418-7381

2. Anne Eugster

   CRTD Center for Regenerative Therapies Dresden, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany

   Contribution

   Conceptualization, Writing - original draft, Writing - review and editing

   Contributed equally with

   Johannes Trück and Pierre Barennes

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8009-5959

3. Pierre Barennes

   1. Sorbonne Université U959, Immunology-Immunopathology-Immunotherapy (i3), Paris, France
   2. AP-HP Hôpital Pitié-Salpêtrière, Biotherapy (CIC-BTi), Paris, France

   Contribution

   Data curation, Formal analysis, Writing - original draft

   Contributed equally with

   Johannes Trück and Anne Eugster

   Competing interests

   No competing interests declared

4. Christopher M Tipton

   Lowance Center for Human Immunology, Emory University School of Medicine, Atlanta, United States

   Contribution

   Writing - original draft

   Competing interests

   No competing interests declared

5. Eline T Luning Prak

   Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Conceptualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   is consulting or is an advisor for Roche Diagnostics Corporation, Enpicom, The Antibody Society, The American Autoimmune Related Diseases Association and IEDB.

   "This ORCID iD identifies the author of this article:" 0000-0002-9478-9211

6. Davide Bagnara

   University of Genoa, Department of Experimental Medicine, Genoa, Italy

   Contribution

   Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

7. Cinque Soto

   1. The Vanderbilt Vaccine Center, Vanderbilt University Medical Center, Nashville, United States
   2. Department of Pediatrics, Vanderbilt University Medical Center, Nashville, United States

   Contribution

   Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3997-6217

8. Jacob S Sherkow

   1. College of Law, University of Illinois, Champaign, United States
   2. Center for Advanced Studies in Biomedical Innovation Law, University of Copenhagen Faculty of Law, Copenhagen, Denmark
   3. Carl R. Woese Institute for Genomic Biology, University of Illinois, Urbana, Illinois, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9724-9261

9. Aimee S Payne

   Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States

   Contribution

   Writing - review and editing

   Competing interests

   No competing interests declared

10. Marie-Paule Lefranc

    1. IMGT, The International ImMunoGeneTics Information System (IMGT), Laboratoire d'ImmunoGénétique Moléculaire (LIGM), Institut de Génétique Humaine (IGH), CNRS, University of Montpellier, Montpellier, France
    2. Laboratoire d'ImmunoGénétique Moléculaire (LIGM) CNRS, University of Montpellier, Montpellier, France
    3. Institut de Génétique Humaine (IGH), CNRS, University of Montpellier, Montpellier, France

    Contribution

    Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0116-9353

11. Andrew Farmer

    Takara Bio USA, Inc., Mountain View, United States

    Contribution

    Writing - review and editing

    Competing interests

    works for Takara Bio USA, but has no ownership or stock in the company

12. The AIRR Community

    Contribution

    Validation

    Competing interests

    No competing interests declared

13. Magnolia Bostick

    Takara Bio USA, Inc., Mountain View, United States

    Present address

    Process Development, PACTPharma, Inc, South San Francisco, United States

    Contribution

    Conceptualization, Supervision, Investigation, Writing - original draft, Project administration, Writing - review and editing

    Contributed equally with

    Encarnita Mariotti-Ferrandiz

    For correspondence

    magnolia.bostick@gmail.com

    Competing interests

    During the writing of the manuscript, Magnolia Bostick was employed by Takara Bio USA, but has no ownership or stock in the company.

14. Encarnita Mariotti-Ferrandiz

    Sorbonne Université U959, Immunology-Immunopathology-Immunotherapy (i3), Paris, France

    Contribution

    Conceptualization, Supervision, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Contributed equally with

    Magnolia Bostick

    For correspondence

    encarnita.mariotti@sorbonne-universite.fr

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8770-0186

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