1. Computational and Systems Biology
  2. Immunology and Inflammation

Improved T cell receptor antigen pairing through data-driven filtering of sequencing information from single cells

  1. Helle Rus Povlsen  Is a corresponding author
  2. Amalie Kai Bentzen
  3. Mohammad Kadivar
  4. Leon Eyrich Jessen
  5. Sine Reker Hadrup
  6. Morten Nielsen  Is a corresponding author
  1. Department of Health Technology at Technical University of Denmark, Denmark
https://doi.org/10.7554/eLife.81810
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  1. Helle Rus Povlsen
  2. Amalie Kai Bentzen
  3. Mohammad Kadivar
  4. Leon Eyrich Jessen
  5. Sine Reker Hadrup
  6. Morten Nielsen
(2023)
Improved T cell receptor antigen pairing through data-driven filtering of sequencing information from single cells
eLife 12:e81810.
https://doi.org/10.7554/eLife.81810
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Abstract

Novel single-cell-based technologies hold the promise of matching T cell receptor (TCR) sequences with their cognate peptide-MHC recognition motif in a high-throughput manner. Parallel capture of TCR transcripts and peptide-MHC is enabled through the use of reagents labeled with DNA barcodes. However, analysis and annotation of such single-cell sequencing (SCseq) data are challenged by dropout, random noise, and other technical artifacts that must be carefully handled in the downstream processing steps. We here propose a rational, data-driven method termed ITRAP (improved T cell Receptor Antigen Paring) to deal with these challenges, filtering away likely artifacts, and enable the generation of large sets of TCR-pMHC sequence data with a high degree of specificity and sensitivity, thus outputting the most likely pMHC target per T cell. We have validated this approach across 10 different virus-specific T cell responses in 16 healthy donors. Across these samples, we have identified up to 1494 high-confident TCR-pMHC pairs derived from 4135 single cells.

Editor's evaluation

This paper is of interest to immunologists conducting single-cell analyses of T-cell recognition. It provides improved means of curating datasets to reduce noise and identify T cell-antigen pairs with greater confidence. Experimental data from human virus-specific TCRs are used to validate the methodology.

https://doi.org/10.7554/eLife.81810.sa0

Introduction

T cells are essential for immune protection and play a critical role in the immune response to pathogens or cancer, where they directly kill infected or malignant host cells or orchestrate the response of other immune cells. Recognition is mediated through the heterodimeric T-cell receptor (TCR) expressed on the surface of T cells, which engages specifically with a peptide antigen (p) displayed in the MHC. Accurate specificity and broad coverage of antigen recognition are obtained through somatic recombination of the genetic loci, V(D)J, that encodes the α (VJ) and β (VDJ) chains of TCR. The process creates an extensively variable and dynamic repertoire, with an estimated 107 distinct αβTCRs in an individual (Arstila et al., 1999; Davis and Bjorkman, 1988).

Due to this diversity, the individual TCR transcripts can be used as endogenous cellular barcodes inherited by the T cell progeny. This has been utilized for providing quantitative insight into TCR diversity (Robins et al., 2009), to trace lineage decisions of T cells (Gerlach et al., 2013) and to monitor the dynamics of T cells across immune-related diseases, such as infectious disease (Dziubianau et al., 2013; Hou et al., 2016), cancer (Kirsch et al., 2015; Sherwood, 2013; Zhang et al., 2018) and autoimmunity (Acha-Orbea et al., 1988; Madi et al., 2014). Most of such TCR repertoire studies have been confined to bulk experiments, tracing the TCR β chain because of its greater diversity (compared to the alpha chain) and because it is less ambiguous due to allelic exclusion (Bergman, 1999). However, accurate pairing of the variable TCR α and β regions is valuable for uncovering the biological function of a T cell and is generally lost in bulk experiments since the transcripts are separately encoded. Moreover, we and others have earlier demonstrated that paired α and β TCR data are essential for the characterization and learning of the relationship between the TCR sequence and specificity (Montemurro et al., 2021).

To accurately obtain TCR αβ-sequence-pair, single-cell sequencing platforms can be applied to simultaneously capture both TCR chains, while retaining cell origin information. To further assign specificity information to such TCRs, T cells can be stained with barcode-labeled pMHC multimers to simultaneously identify pMHC specificity and TCR sequence of individual cells (Bentzen et al., 2016; Zhang et al., 2018). Moreover, via DNA barcoded antibodies, the platform facilitates screening of surface proteins to distinguish cellular subtypes and enables cell hashing to trace origin of a given cell to, for example, a given donor, sample, or time point, which is highly valuable in patient studies.

Here we thus applied single-cell sequencing to describe the T cell specificities toward a set of viral-derived peptide-MHC (pMHC) complexes. The pMHCs were selected with the purpose of generating data to expand the current knowledge of TCR-pMHC interactions, and hence covered pMHCs with limited or no paired TCR coverage in the public-domain databases such as IEDB (Vita et al., 2019) and VDJdb (Bagaev et al., 2020). We deployed the droplet-based single-cell platform from 10x Genomics. Ideally a droplet contains a single cell with all its analytes and a gel-bead in emulsion (GEM). The gel-bead contains barcoded primers that ensures tracing of transcripts back to the cell of origin, referred to as GEMs. While the platform is highly promising, the sequence deconvolution is associated with substantial noise, and challenging to discriminate true from false signals. Common confounding factors include stochastic gene expression, cell cycle variations, apoptosis, and technical artifacts such as multiplet capture, contamination, dropout, and batch effects. Dropout and stochastic gene expression both result in zero-inflated gene counts and are typically insensitive to low expression levels (Buettner et al., 2015; Kharchenko et al., 2014; Yamawaki et al., 2021). Multiplet capture is the event of capturing two or more cells in a single GEM, and it is proportional to the capture rate of cells introduced to the system (Bloom, 2018; Zheng et al., 2017). The capture rate is determined by the rate of pulsing cells relative to the rate of gel-beads. Thus, to include even low-frequency cell populations, the capture rate must be high at the expense of introducing more multiplets. Contamination is particularly an issue when including analytes such as pMHC multimers, which may be dissolved in cell suspension (Gaublomme et al., 2019). The platform has no means of controlling how ambient analytes and their barcodes are partitioned with GEMs, which leads to GEMs that appear like multiplets or consist of ambiguous annotations from multiple analyte barcodes. The reverse issue arises from the risk that analytes may dissociate from the cell before capture. The listed confounders may result in both false-positive and false-negative discoveries.

The main concerns when screening for TCR specificity are nonspecific binding of pMHC and/or cell hashing analytes, incomplete TCR annotation, and T cell multiplets. Nonspecific binding and T cell multiplets may completely dilute the signal from actual interactions, while incomplete TCRs that are missing the annotation for either α- or β-chain render the single-cell setup superfluous. To ensure that a screening is fully exploited and interpreted correctly, we set out to develop a data-driven algorithm that facilitates a consistent and reproducible TCR categorization (clonotyping), peptide-MHC (pMHC) annotation, and antibody-based cell hashing referencing of the donors and their HLA profile.

We applied this algorithm to a dataset derived from screening PBMCs from 16 healthy donors for T cell recognition against common viruses. In total, we evaluated TCR recognition against 10 different pMHC multimers, each labeled with their unique barcode. We demonstrate that following the filtering steps described here we can obtain a confident pairing of pMHC specificity and TCR sequence. This strategy will open novel opportunities to evaluate the structural interplay and the sequence-driven signatures of pMHC recognition at large scale.

Results

Parallel capture of TCRɑβ sequences, peptide-MHC specificity, and sample origin from single cells

To obtain single-cell-derived triad information on TCR sequence, pMHC specificity, and sample origin, we stained peripheral blood mononuclear cells (PBMCs) from a total of 16 different healthy donors (Supplementary file 1). All samples were stained with the same panel composed of 10 different viral-derived pMHC multimers, each labeled with a unique barcode for that specificity and a common fluorescent label (allophycocyanin [APC]) (Figure 1; Supplementary file 2). We wished to enrich only for TCRs responsive to less commonly reported pMHCs, hence we co-stained the cells with the three most commonly reported viral-derived pMHC multimers (all A0201 restricted: GLC, GIL, NLV) bearing a different fluorochrome (phycoerythrin [PE]) and labeled with their own unique DNA barcode (Supplementary file 2). We sorted only the APC-labeled pMHC multimer binding T cells (and hence deselected the PE-labeled T cells) and included these in the downstream single-cell processing.

Figure 1 with 1 supplement see all
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Schematic of experimental design.

(A) Schematic of the experimental strategy. All samples are incubated with the same library of barcode-labeled pMHC multimers and subsequently with a sample-specific barcode-labeled hashing antibody to individually label cells derived from a given sample. Multimer-binding cells from all samples are sorted in bulk and processed through the 10x Chromium workflow. The sequencing output simultaneously captures the sample barcode, the pMHC barcode, and the TCR sequences, which are all matched to a single cell based on the 10x barcode. This also provides the means of retrospectively assigning each cell to their sample of origin via the sample-specific hashing barcode. (B) Example showing how the allophycocyanin (APC)-labeled pMHC multimers are sorted collectively from all samples into one tube that is further carried into the 10x workflow. The phycoerythrin (PE)-labeled pMHC multimers are not sorted and hence deselected. A total of 1800 APC-labeled cells are sorted from each donor. Here showing BC126 (large dotplot) and BC341 (small dotplot).

Prior to sorting, each sample was stained with a distinct hashing antibody to provide a sample identification barcode associated with the GEMs of the resultant single-cell data set. This is done to enable mixing of cells from different samples, while retaining the information of sample origin, and utilizing the capacity of capturing 6000–10,000 cells per lane in the 10x Genomics workflow. This is essential when capturing T cells based on their specificity since the MHC multimer-positive population is generally of low frequency (<1% of CD8 T cells). When several samples are mixed in the process of running the single-cell analysis, all mRNA and DNA barcodes (derived from hashing antibodies or the MHC multimers) associated with a given cell will be encoded with the same 10x barcode, proving the GEM association (Figure 1; Supplementary file 1).

Total data from simultaneous capture of cell, TCR, pMHC, and sample ID

The single-cell data is annotated using 10x Chromium Cellranger multi v6.1. This results in each GEM being quantified by a count of unique molecular identifiers (UMIs) (Kivioja et al., 2011) for the three components (TCR, pMHC, and sample hashing) based on transcripts of TCR α- and β-chains, barcodes co-attached to pMHC multimers and barcodes co-attached to cell hashing antibodies (Supplementary file 2).

To obtain the data presented here, a total of 1800 pMHC multimer-positive cells were sorted per donor irrespective of the frequency or the number of different antigen-specific T cell responses in a given sample, accumulating to a total of 28,800 cells sorted (Figure 1—figure supplement 1). All sorted cells were loaded into a single lane for 10x processing. Based on experience with pre-sorting of low frequent cell populations, this equals a total of 6000–9000 captured cells per lane after running the full 10x Genomics 5′ pipeline, and an acceptable doublet rate. This indicates that an appropriate proportion of cells are loaded on the Chromium. Initially, each GEM was annotated based on the most abundant transcripts from TCRαβ, pMHC, and cell hashing. However, this can lead to erroneous annotations as the noise level can differ substantially for the different reagents, resulting in different levels of UMIs.

Based on raw, unfiltered data, we found 6073 GEMs that contained all three components, that is, TCR, pMHC, and sample hashing, corresponding to 40% of the loaded cells (Figure 2a). A total of 716,069 GEMs only contained one or two of the components, with the majority containing only the cell hashing barcode (n = 677,502) and the second largest share containing cell hashing as well as pMHC barcodes (n = 37,277). This number vastly exceeds the number of cells in the assay (15,700 cells loaded) and indicates contamination from ambient barcodes in suspension. This is further supported by the observation that the sample hashing UMI count was significantly higher (p<0.0005, Mann–Whitney U) in the 6073 GEMs containing a TCR compared to the GEMs void of TCR (Figure 2b). A total of 43,455 GEMs captured a DNA barcode associated with the pMHC library and only 14% of these were completed with TCR transcripts and sample hashing barcodes. In the GEMs containing a TCR, 84% were completed with all three components, that is, included hashing and pMHC barcodes, while less than 0.05% of these GEMs were void of both sample hashing and pMHC barcodes. In the following, we will only consider the 6073 GEMs containing all three components, while taking into account that the high degree of noise also affects these seemingly completely mapped GEMs.

Figure 2
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Summary of raw data.

(a) Venn diagram of the content of all gel-beads in emulsion (GEMs) from 10x Chromium drop-seq. Each GEM is expected to contain three components: transcripts of TCR and DNA barcodes from the target pMHC multimer as well as the sample hashing antibody. The Venn diagram illustrates the extent of GEMs with complete capture (capture of all three components) in contrast to the GEMs with incomplete capture (capture of a subset of components). (b) Comparison of distributions of unique molecular identifier (UMI) counts of sample hashing barcode between GEMs that contain TCR transcripts (TCR-occupied GEMs) and GEMs that do not contain TCR transcripts (TCR-void GEMs) (p<0.0005, Mann–Whitney U). (c) Matrix of the distribution of pMHC singlets and multiplets across GEMs with TCRs either missing a chain, detected with multiple chains, or with a single, unique αβ-pair. The counts are given for each field and illustrated by a color. The lighter color represents higher counts. (d) Scatterplot of all detected pMHC barcodes (y-axis) within each of the 6073 GEMs (x-axis) that contain all three components: TCR, pMHC, and sample hashing. In each GEM, the most abundant pMHC is marked with green, while the remaining pMHCs in the GEM are gray. The marker size reports the UMI count of the given pMHC. The marker shape and color recount whether the HLA allele of the pMHC matches the HLA haplotype of the donor, which is deduced from sample hashing (yellow x: non-matching HLA). The fraction of HLA matches within the GEMs displaying a given specificity is annotated to the right of the plot. The first colorbar indicates the type of TCR chain annotation; whether the TCR has a unique αβ-pair, is missing a chain or consists of multiple chains. The second colorbar is a specificity check against the specificity databases IEDB and VDjdb. Colors highlight the GEMs where the CDR3αβ sequences are contained in the databases. The green color represents a match between the database pMHC and the detected pMHC, while red indicates a mismatch.

The GEMs are distributed across three categories of TCR and two categories of pMHC observations: GEMs either missing a TCR chain, contain multiple TCR chains, or contain a unique TCRαβ-pair and GEMs containing either a single or multiple pMHC barcodes (Figure 2c). Sample hashing multiplets constitute 100% of GEMs containing sample hashing barcodes, and there is both a large proportion of pMHC multiplets (65%) and GEMs missing either α- or β TCR-chain (39%), hence, multiplets of pMHC and sample hashing is the predominant issue. Few GEMs were detected with multiple TCR α- or β-chains (6%). This may be caused partly by naturally occurring multiplets of α-chain (4%) due to the incomplete gene restriction of the thymocyte during negative selection (Elliott and Altmann, 1995; Petrie et al., 1993) or due to experimental features of the 1ox platform causing an expected 6.9% of multiplets based on the number of cells loaded in our experiment.

Without further filtering, the pMHC-TCR pairing is subjected to extensive noise (Figure 2d), and we capture all the 10 DNA barcodes associated with the APC-labeled pMHCs in a varying number of GEMs. Importantly, the three negative control responses (GIL A0201, GLC A0201, and NLV A0201), which were present in the donors but not sorted, are only captured in a few GEMs; both as the most abundant pMHC (GIL: 4 GEMs/clonotypes; GLC: 17 GEMs/clonotypes; and NLV: 12 GEMs/clonotypes) and as presumed contamination (i.e., examples where the UMI count of the negative control(s) was not the most abundant). In this latter case, the vast majority (84%) of the negative control pMHCs had UMI counts of 1. Four of the abundant negative control responses matched known IEDB/VDJdb responses. This indicates that the cell isolation via sorting is effective in terms of capturing only the desired cells and relevant pMHC-associated barcode labels. The most frequently detected pMHC across all GEMs is RVR A0301, which is present with high UMI counts across all GEMs. Only RPH(10-mer) B0702-associated UMIs was consistently detected at low numbers per GEM. It was also evaluated whether the HLA allele of the pMHC matches the HLA haplotype of the donor(s) given via cell hashing (Figure 2d). Typically, the mismatches are found in GEMs where the most abundant pMHC is detected at low UMI counts while the matches consist of GEMs with higher pMHC UMI counts. Of the 65% GEMs containing pMHC multiplets (Figure 2c), 13% contained two or more pMHCs at the exact same UMI level (Supplementary file 3), which may either represent noise or true cross-binding events.

The detected specificities in our data have been cross-referenced with the IEDB (Vita et al., 2019) and VDJdb (Bagaev et al., 2020) databases (Figure 2d). Based on the unfiltered data, we found five TCR-pMHC matches (across nine GEMs) and one TCR (one GEM), which was annotated with a different pMHC (Figure 2d). This latter is a case of a GEM with multiple pMHCs present with almost equal number of UMIs, where the most abundant pMHC is RVR A0301 (11 UMIs) and the second most abundant pMHC is GLC A0201 (9 UMIs), which is the peptide registered as target in IEDB and VDJdb.

The data in Figure 2d suggests that most of the captured T cells interact with several of the screened pMHCs to a degree that exceeds the level expected from natural cross-recognition. Therefore, it is reasonable to assume that a large proportion of these multiplets are formed as a result of ambient pMHC leaking into GEMs.

A data-driven filtering approach

From these observations, it is clear that a substantial part of the data consists of noise, that is, GEMs with multiplets of pMHC and sample hashing, and that the data must be filtered for proper interpretation.

Clonotype annotation

The definition of T-cell clones (clonotypes) is fundamental for pairing a given TCR clonotype to its respective pMHC recognition. Initial clonotypes were called using 10x Genomics Cellranger, which defines a clonotype as a set of cells that share identical receptor sequences at the nucleotide level, spanning the entirety of the V(D)J-C genes as well as the junction segments. Assuming reliable gene and CDR3 sequence calls by 10x Cellranger, we redefine clonotypes based on TCR annotation. Subsequently, GEMs with no clonotype annotation from 10x were annotated to existing clonotypes conditioned on matching VJαβ-genes and CDR3αβ sequences or as novel clonotypes. Similarly, clonotypes with identical VJ-CDR3αβ were merged to form larger groups of theoretically identical TCRs (Figure 3—figure supplement 1). Merging GEMs of the same TCR is essential to make statistical inference based on those groupings, for example, determine expected pMHC target per clonotype. The outcome was a set of 2441 TCR clonotypes across the 6073 GEMs containing both TCR and pMHC. For the 337 GEMs containing TCR chain multiplets, the most abundant chain per GEM was for the subsequent analyses selected to represent the true TCR. Note that this annotation was made post the definition of clonotypes and was applied for the TCR inter- versus intra-similarity comparison.

Defining pMHC recognition for selected TCR clonotypes

As we have seen earlier, not all GEMs within a given clonotype support the same pMHC target, and defining the pMHC target of a TCR based on individual GEMs thus results in contradicting annotations. The key to identify the expected target for a clonotype is therefore to determine which pMHC identity represents the majority of UMIs across all GEMs within a given clonotype. Figure 3 illustrates an example from a pilot study that accentuates the importance of studying GEMs in ensemble rather than individually. Most GEMs are annotated with multiplets of pMHCs and across all GEMs the most abundant pMHC varies. While all pMHCs are found most abundant in at least one GEM, three pMHCs (TPR B0702, VTE A0101, and RAK B0801) are more often found most abundant (Figure 3a). Although TPR B0702 is detected in fewer GEMs (136) than VTE A0101 (260) and RAK B0801 (186), TPR B0702 is present at generally higher UMI counts (Figure 3b). It is evident that there is a difference in UMI distributions between the different pMHC within the GEMs of a given clonotype, and that TPR B0701 is the significantly most abundant pMHC across the ensemble of GEMs even though this pMHC is only present in a minority proportion of the GEM (Figure 3b). Based on these observations, we argue that the significantly most abundant pMHC should be annotated as the expected binder for the given clonotype rather than annotating based on the majority.

Figure 3 with 3 supplements see all
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An example of pMHC concordance in clonotype 1 (example from pilot study).

(a) All detected pMHC (y-axis) in each gel-bead in emulsion (GEM) (x-axis, n = 467) of clonotype 1. The marker size shows the unique molecular identifier (UMI) count for the particular pMHC in a given GEM, and the color indicates the pMHC with the highest UMI count, similar to what is shown in Figure 1d. If two pMHCs are equally abundant in a GEM, they are both colored. No marker means no detection of that pMHC in that given GEM. (b) The compiled distribution of UMI counts for each peptide (assigning 0 UMI when the pMHC is not detected in a GEM). The asterisk marks that a Wilcoxon test showed that the UMI counts of TPR B0702 were on average higher than for VTE A0101 UMI counts. (c) The specificity concordance across the GEMs of clonotype 1. Concordance is shown by a color gradient, that is, the larger the fraction of GEMs supporting a given specificity the darker the color.

Having annotated the expected pMHC of a given clonotype, one can next go back to the individual GEMs, and label GEMs where the most abundant pMHC corresponds to the expected binder, as ‘true,’ and all others as ‘false,’ and use these annotations to quantify the accuracy of the GEM annotations. Within each clonotype, one can compute a specificity concordance, that is, the fraction of GEMs detected with a certain specificity (defined by most abundant pMHC, i.e., highest pMHC UMI per GEM) (Figure 3c). In many cases across the full data set, the expected specificity for a clonotype coincides with the specificity, defined on a per-GEM level, resulting in high concordance. However, for some clonotypes, for example, clonotype 1, GEMs have diverging annotations and therefore lower concordance dispersed across multiple specificities (Figure 3). The clonotype visualized in Figure 3 is specifically chosen to exemplify how this lower concordance can affect the analysis. For clonotype 1, the fraction of GEMs that support VTE A0101 (0.33) is higher than the fraction of GEMs that supports TPR B0702 (0.26). This results in an overall low concordance, and only by considering the complete ensemble of clonotype 1 GEMs can the correct pMHC target be identified (Figure 3b).

Improving concordance between GEM and clonotype annotation based on grid search on UMI features

To rationally filter data, an accuracy metric was defined and optimized through the filtering process. For all specificities belonging to clonotypes with an assigned expected target, we calculated the overall accuracy as the proportion of GEMs where highest abundance pMHC annotation corresponds to the expected target of the clonotype. The raw unfiltered data yielded accuracy and average concordance scores of 69.6 and 83.8%, respectively. Next, we set out to investigate how different data-driven UMI filters could improve these performance values, removing noise and artifacts from the data. This removal would also reduce the number of included observations, hence the performance of different thresholds for filtering the data was evaluated based on a tradeoff between increased accuracy and discarded number of GEMs.

We tested various thresholds on UMI count and UMI ratios, that is, the ratio between the most abundant and second most abundant UMI feature, for pMHC and TCRαβ, respectively. The optimal thresholds were chosen to maximize the weighted average between accuracy and fraction of retained GEMs to favor increase in accuracy above losing some GEMs. This filtering analysis resulted in optimal thresholds of two pMHC UMI counts and a ratio pMHC UMI counts between top one and two >1. The latter results in the removal of GEMs where two pMHC were equally abundant for low UMI counts. The search did not result in thresholds imposing restrictions on neither TCR UMI counts nor TCR UMI ratio, which underpins that the TCRs with a missing chain as well as multiple chains also contribute to good performance. Imposing this filter yielded 4986 GEMs (82% of total), 1494 clonotypes (61% of total), and resulted in 95.3% accuracy, and a mean concordance of 90.6%.

Additional filters

Additional filters can be added to further clean the data. We investigated how an integrated filter in the 10x Genomics software, Cellranger, performed in removing potential noise from our data set (Figure 3—figure supplement 2). The filter (labeled ‘is cell’) evaluates whether a GEM has captured a cell based on full level of transcriptome data, when available, and otherwise solely on TCR transcript level. The filter was tested with both levels of transcript data, full level and TCR transcript level, which are respectively referred to as ‘is cell (GEX)’ and ‘is cell.’ Alternatively, viable cells are identified from the transcript data, independently of Cellranger, based on mitochondrial load and a minimum and maximum gene count per GEM. All three filterings are comparable (Figure 3—figure supplement 2) and taken into account in the further evaluations. It is worth noting that, while the filterings based on the full transcript data might remove slightly more noise, the economic costs associated could propose that this should only be applied when the transcript data is required for additional purposes.

Cell hashing is generally a much simpler task to resolve than pMHC multiplets because one hashing entry most often has much higher counts compared to the others (Figure 3—figure supplement 3). Moreover, due to the experimental design, where only one hashing antibody is added to each sample, it is expected that only a single hashing signal is associated with each GEM, that is, this does not mirror the complex nature of the pMHC data, where cross-reactivity could result in more than one pMHC be a true binder to a given TCR. Given this simplicity, we opted for utilizing the existing Seurat hashtag oligo (HTO) tool to demultiplex and annotate cell hashing (Stoeckius et al., 2018). In this setup, cell hashing also enables filtering based on matching HLA between the donor haplotypes and the HLA of the detected pMHC. Including this additional filter reduces the number of GEMs to 4135 (covering a set of 1494 clonotypes). Additionally, depending on the subsequent use of the data, retaining only complete TCRs containing both α and β may be desirable. Including only GEMs where the TCR-pMHC pair is observed more than once, that is, specificity multiplets, reduces the uncertainty described above. Below we investigate the impact of imposing such filters.

Impacts of filtering

Evaluating filters by comparing TCR similarity across specificity

To objectively evaluate the performance impact of the presented filters, we define a quantitative evaluation based on the hypothesis that T cells binding the same pMHC (intra-specificity) will share a higher sequence similarity compared to TCRs of different specificities (inter-specificity) (Figure 4). Thus, filtering away artifacts should increase intra-similarity while decreasing the inter-similarity. Here, the similarity score between two TCRs was calculated from the summed score of the pairwise α- and β-chain similarities calculated using a kernel method described in Shen et al., 2012 and applied in Chronister et al., 2021.

Figure 4
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Intra- and inter TCR similarity scores per peptide of the (a) total (unfiltered) data set and (b) the data filtered by the optimized threshold.

The similarity per peptide plots (a and b) illustrates the distribution of paired similarity scores for each clonotype (containing both α- and β-chain). For each pMHC, each clonotype is compared to the remaining clonotypes of the same specificity (intra) and across specificities (inter). The count of compared clonotypes is listed just to the right of the y-axis in both (a) and (b). (c) displays the pooled intra- and inter-scores across all peptides for each of the filtering methods: total (no filtering), optimal threshold, matching HLA, hashing singlets, complete TCRs, specificity multiplets, ‘is cell’ by cell-flagging, ‘is cell’ by cell-flagging when including GEX data, and viable cell from analyzing GEX data. An asterisk marks filters where intra-similarity is significantly larger than inter-similarity (Wilcoxon, α = 0.05). (d) displays the pooled intra- and inter-scores across all peptides for each of the filtering methods where each filtering is added cumulatively to the previously listed above it. An asterisk marks filters where intra-similarity is significantly larger than inter-similarity (Wilcoxon, α = 0.05). The count of compared clonotypes is listed just to the right of the y-axis in both (c) and (d).

Based on this kernel similarity metric, the filters were tested individually and cumulatively, that is, each filter was added to the previous set of filters. The general trend is that TCRs with the same specificity are more similar to each other than to TCRs of different specificities, when computing the intra- and inter-similarities per pMHC before and after filtering on the optimized UMI thresholds (Figure 4a and b). Before filtering, 9 out of 13 pMHCs displayed a higher mean intra-similarity than inter-similarity scores, whereas this number was 11 out 13 pMHCs when applying the UMI thresholds. The outliers before filtering were GIL A0201, VLE A0201, CLG A0201, and RPP B0702, while the outliers were reduced to VLE and RPP after filtering. Generally, the similarity scores often have a wide, overlapping range between the intra- and inter-categories. The three pMHCs that were deselected during sorting, GIL A0201, GLC A0201, and NLV A0201, are only detected in a few TCR binding events. To enhance the power of comparison, the intra- and inter-scores were pooled respectively across the individual pMHCs (Figure 4c and d). The results demonstrate that intra-similarity is significantly higher than inter-similarity at each filtering step, both individually and combined. Moreover, we observe that the differences between intra- and inter-similarity appear to increase as filters are cumulatively added and fewer observations are left (Figure 4d). Particularly, the median inter-similarity score is lowered, suggesting that the filtering steps predominantly remove false-positives.

Evaluating filters across selected performance metrics

To compare the effect of the filters, the similarity scores were converted to the performance metric: AUC (area under the receiver operating characteristic [ROC] curve). Here, intra-specificity comparisons are regarded as true-positive observations and inter-specificity comparisons as true-negatives. Based on these performance metric definitions, we quantify the effect of each filtering step (Figure 5) and find that the highest accuracy and highest average concordance are obtained by filtering on the optimal threshold (95.3 and 90.6%), while the highest AUC is obtained from filtering on specificity singlets (70.5%) (Figure 5a). Expectantly, the accuracy and average concordance increase when the filters are imposed cumulatively (Figure 5b). The accumulation of filters also results in drastic reduction of the GEMs, and it is evident that one must carefully weigh out the need for specificity over sensitivity when selecting the desired set of filters.

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Performance metrics for evaluating the filtering steps.

Performance is measured by number and ratio of retained gel-beads in emulsion (GEMs), accuracy defined by proportion of GEMs where most abundant pMHC matches the expected binder (accuracy), average binding concordance (avg. conc.), and AUC of similarity scores (AUC). The filtering steps consist of total (raw, unfiltered data), optimal threshold obtained from grid search, matching HLA, hashing singlets identified from Seurat HTO demultiplexing, complete TCRs with a unique set of α- and β-chain, specificity multiplets such that each TCR-pMHC pair must be observed in two or more GEMs, is cell defined by 10x Genomics Cellranger, is cell (GEX) defined by Cellranger where GEX data is included, and is viable cell defined by mitochondrial load and gene counts. (a) Presentation of the individual effect of each filter. (b) Presentation of the accumulated effects of the listed filters.

We conclude that the minimal filtering must include optimal threshold and matching HLA between pMHC and donor haplotype. Filtering on specificity multiplets would inherently result in more reliable observations, risking the removal of rare, low-avidity binding events. Generally, we did not find that including GEX data improved performance considerably. Finally, filtering on incomplete TCRs yields the second highest accuracy and average concordance. Unfortunately, the filter almost halves the number of GEMs. Hence, this filtering should be considered depending on future use of the data. An overview of the pipeline can be found in Figure 5—figure supplement 1.

Inspecting the filtered data

To determine the impact of the filtering steps, we have compiled the binding concordance for all clonotypes and applied three selected filtering steps: (1) the raw, unfiltered data, (2) filtering on optimal UMI thresholds and matching HLA, and (3) additionally filtering on complete TCRs (Figure 6). The raw, unfiltered data displays many clonotypes where the most abundant pMHC in GEMs of a given clonotype are dispersed across multiple of the screened pMHCs (Figure 6a). When imposing the recommended set of filters, optimal threshold, and HLA match, the outliers are greatly reduced, although not all low-concordance GEMs are removed (Figure 6b). By additionally filtering on complete TCRs, even fewer outliers are left (Figure 6c). Note again that we have purposely deselected T cells specific for GIL A0201, GLC A0201, and NLV A0201, explaining the few observations for these otherwise frequently recognized epitopes. An overview of gene usage for clonotypes specific for each of the 10 positive pMHC can be found in Figure 6—figure supplement 1.

Figure 6 with 1 supplement see all
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Specificity per clonotype.

The library peptides are listed on the y-axis and each clonotype is represented on the x-axis. Below the x-axis is annotated the total number of clonotypes and gel-beads in emulsion (GEMs) in the presented data. The marker size shows the number of GEMs supporting a given specificity. The color indicates the binding concordance that is calculated as the fraction of GEMs within a clonotype that support a given pMHC. The higher the concordance, the larger the fraction of supporting GEMs. The three plots illustrate the impact of three filtering criteria. (a) presents raw data with no filtering applied. (b) presents data filtered on optimal threshold and HLA matches. (c) presents data filtered as in (b) with the additional requirement of only complete TCRs (note that cell hasting filtering was not included here). A summary of the specificity singlet distribution for each subfigure is presented in Supplementary file 5.

Many of the remaining low-concordance GEMs still suggest the improbable event of cross-binding across HLA restriction. We suspect that these are artifacts that we have not successfully removed. When the most strict filtering is imposed (Figure 6c), there are 77 GEMs (out of 2833) with a binding concordance of 0.5 or lower, which will be referred to as outliers. Also, 72 of those GEMs contain pMHC multiplets. And 93% of the multiplet outliers actually do contain the pMHC, which defines the high-concordance GEMs, however, at a lower UMI count. In the GEMs with multiple pMHC annotations, the HLA is conserved across the pMHCs in 7% of the cases. In 82% of the cases, the HLAs are different, but still match the HLA haplotype of the donor given by the cell hashing. Of the 77 outliers, the most dominant pMHCs are RVR A0301 (38%) and TPR B0702 (30%). These values are in line with the overall contribution of these pMHCs in the GEMs with a binding concordance above 0.5 (34 and 21%). This suggests that the observed GEMs with low concordance are a result of noise and not a reflection of TCR cross-reactivity. Prior to filtering the data, six clonotypes were identified, which were already registered in IEDB and VDJdb, five with matching pMHC, and one with a different annotation than in our observation (Figure 2d). The five matching clonotypes (nine GEMs) were successfully retained, while the mismatching clonotype (one GEM) was filtered away. Four of these nine GEMs were negative control responses (two GLC and two NLV) and the remaining five GEMs (across two clonotypes) were all responses directed towards FLYALALLL A*02:01. All pMHCs were detected with UMI counts ranging from 17 to 46.

Comparing single-cell data with fluorescent-based pMHC multimer screening

Investigating dominant clones

Beyond mapping the landscape of known TCR-pMHC interactions, single-cell screening enables investigation of T cell repertoire diversity. The high resolution both reveals the specificity and the TCR clonality within the individual T cell populations, which is not possible to recover in classical stainings using fluorescent-labeled pMHC multimers (fluorescent multimers). The T cell diversity in the nine donors toward the set of analyzed pMHCs reveals a clear hierarchy with dominant responses in fluorescent multimer staining (Figure 7a); however, the clonality of each specificity is only available via single-cell data (Figure 7b). Here ITRAP represents data filtered by optimal UMI thresholds and matching HLA between pMHC and donor haplotype (given via cell hashing). Single-cell screening further enables comparison of the clonal distribution and the total clonal size per specificity. In this respect, the samples BC328 and BC62 are strikingly similar in their distribution of expanded clones. They both display a large and broad response toward RVR A0301 and two smaller responses toward RPP B0702 and TPS B0702, which are both dominated by a single clonotype. Further, most peptides elicit diverse relative responses between samples. For example, RPP B0702 is the dominant response in samples BC328 and BC62, but the minority response in sample BC314. Sample BC300 contains primarily small clones, that is, fewer cells in each clonotype; however, this sample is generally represented with low amounts of total data (46 GEMs). Of note, small clones might be a result of suboptimal single-cell capture or because high-frequency responses can potentially mask any lower frequency responses present in a given donor (Supplementary file 4) when only 1800 cells are sorted from each sample. Samples represented with many GEMs are expected to be fully covered and therefore may contain more different expanded clonotypes, as sample BC360.

Figure 7
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T cell diversity per peptide across the individual samples.

The nine samples, PBMCs from nine individual donors are represented on the x-axis. The marker size defines the distribution of T cells recognizing a given peptide, normalized per sample. (a) The T cell frequencies are visualized as the proportion of a given multimer-positive response within a donor. The black markers represent responses detected above the threshold, that is, ≥10 cells and ≥0.002% of total CD8 T cells, or ≤10 cells but ≥0.01% of total CD8 T cells. The gray dots represent detected specificities below threshold but represented by ≥2 cells. Summed frequencies of detected responses within a donor are given as % of total CD8 T cells and listed just above the x-axis. (b) The T cell frequencies are based on gel-bead in emulsion (GEM) counts normalized per sample from the single-cell data. Absolute GEM counts per sample are listed above the x-axis. The marker is colored by the fraction of GEMs within a specificity that originate from a given sample. Absolute GEM counts per peptide are listed to the right of the plot. The marker contains a donut diagram illustrating the distribution of clonotypes specific for the given peptide in the given sample. The wedge that represents the dominant clone is colored according to the center of the donut. Remaining clones (>1 GEM) are anthracite gray, and all clonotypes only supported by one GEM only are pooled and represented by a single light gray wedge. Comparing the sizes of the T cell populations for each specificity per donor between the two screening methods in (a) and (b) yielded the following Spearman correlations: BC126 (1.00, p<0.0005), BC328 (0.90, p=0.006), BC355 (0.74, p=0.02), BC360 (0.89, p=0.04), BC314 (0.90, p=0.04), and BC353 (1.00, p<0.0005). (c) Representative example showing the four different responses detected with fluorescent-labeled pMHC multimers in donor BC126. (d) Correlation between T cell responses detected by fluorescent-labeled MHC multimers (y-axis) and single-cell capturing (x-axis). Correlation is given by Pearson correlation coefficient 0.73 (p<0.0005). The responses from fluorescent-based screening are given as an adjusted number of cells based on the detected response frequency out of 1800 cells (see calculations in Supplementary file 4). The hollow markers represent responses below detection threshold as described in (a). The responses are colored by the donor-of-origin. BC mix corresponds to BC311, BC11, BC83, BC88, BC341, BC342, and BC76.

Evaluating ITRAP by ‘ground truth’ of fluorescent-based pMHC multimer screening

The net overlap of identified T cell responses between the two screenings (Figure 7a and b) is estimated to 0.63 by Matthew’s correlation coefficient (MCC). Most of the T cell populations detected by fluorescent multimers are also captured in the single-cell screening, reflected by a recall of 0.95. However, the single-cell capture of small T cell clones (Figure 7b) that were not detected using fluorescent multimers (Figure 7a) negatively impacts the precision, yielding a score of 0.71. For fluorescent-labeled MHC multimers, a detection threshold of 10 events is applied, which may account for the difference observed for the very rare clone, of which most were only represented by one GEM per clonotype (demonstrated by a light gray outer circle in Figure 7b). In only two cases, T cell populations were detected with fluorescent multimers but not captured in single cells: BC316/CLG A0201 and BC62/RPH(10mer) B0702 (Figure 7a). The large T cell population of BC316/CLG A0201 was likely a technical artifact related to the barcode-labeled pMHC multimers.

We calculated the number of antigen-specific T cells sorted per donor, based on the total number of sorted cells/donor (n = 1800) and the frequency of each T cell population (Figure 7c and Supplementary file 4). This number of sorted cells for a given specificity was strongly correlated with the numbers of single-cell GEMs assigned to the same specificity (Pearson correlation coefficient, PCC = 0.73, p<0.0005). Hence, even though many of the TCR-pMHC pairs are found across low numbers of GEMs, the strong correlation indicates that we can confidently assign pMHC specificity to the TCR sequence using ITRAP even in these cases. This is an essential feature of such analyses tool since individual antigen-specific T cell responses are often present at very low frequencies and patient material is often scarce. Hence, to conduct biologically relevant studies, strategies that enable the investigation of the breadth of antigen-specific T cell responses are required. We also fitted a linear regression for T cell populations sorted and assigned with at least one adjusted cell count or GEM in the log-log space (R2 = 0.56). The regression indicates that ~10% of sorted cells will be captured in a single-cell screening with TCR-pMHC information yielded by ITRAP.

Discussion

Here, we have described and validated ITRAP, a data-driven approach for Improved Pairing of T cell Receptor and Antigen. We have successfully filtered single-cell 10x Genomics data to identify reliable TCR-pMHC interactions of up to 1494 clonotypes. The method can be adapted to any single-cell immune profiling data set and is highly transparent in the steps taken, allowing the user to choose appropriate stringency of filtering.

Our recommended approach of cleaning data with minimal elimination of GEMs is obtained by two sets of filters: (1) the optimized data-driven UMI thresholds combined with (2) information on matching HLA specificity (as obtained from donor-specific hashing). Increasing filtering is naturally at the expense of the number of GEMs that might reflect the trade-off between specificity and sensitivity of the assay. However, any benchmarking or validation is made difficult without a golden standard. Our best attempt at quantifying the impact of filtering is based on three metrics: annotation accuracy, binding concordance, and AUC of clonotype similarity for which ITRAP yielded the scores 96.2, 92.3, and 66.7, respectively. Evaluation of ITRAP with responses from fluorescent pMHC multimer staining revealed strong correlation (PCC = 0.73, MCC = 0.63) between the number of sorted T cells and the number of detected GEMs across all specificities.

Accuracy of pMHC annotation was based on selected clonotypes where the expected target was statistically distinct and UMI thresholds were set to optimize the annotation accuracy. Rare clonotypes are not considered in this metric and clones are not expected to display cross-reactivity amongst the included pMHC multimers. The optimal UMI thresholds are intended to remove observations deviating from the expected target. The thresholds are based on the assumption that contamination will predominantly exist at lower UMI counts than actual binding events. This limits the sensitivity of the method in cases of low-affinity low-frequency interactions that otherwise might be of great scientific and clinical interest. It is, however, essential to underline that ITRAP does not explicitly exclude cross-reactivity, and cross-reactive events are maintained after threshold filtering if such GEMs exist with proper UMI count values. This is in contrast with other tools where cross-reactive events are explicitly excluded.

The binding concordance is a metric that highlights cross-reactive clonotypes. In assays where cross-reactivity is not an expected outcome, binding concordance can be useful to evaluate the clonotypes where an expected target could not be identified. On the contrary, for data where T cell cross-recognition is of particular interest, the binding concordance can be used to establish the relative TCR binding contribution of each of the attributed pMHC targets. Growing evidence points to the relevance of T cell cross-recognition in both infectious disease (Dowell et al., 2022) and cancer (Fluckiger et al., 2020). Hence, novel tools to interrogate this phenomena on a single T cell level are highly warranted.

The last evaluation metric, AUC of clonotype similarity, is based on the assumption that T cells sharing specificity have more similar TCR sequences than T cells of different specificities (Chronister et al., 2021). This approach showed increasing separation of intra-specificities and inter-specificities as filters were cumulatively added, indicating that nonspecific binders were effectively removed. To further increase the AUC, discarding clonotype singlets (i.e., TCR clonotypes represented by only one GEM) was the best single filtering step to improve the AUC of similarity scores (AUC = 70.5, Figure 5a). This likely reflects that a fraction of such clonotype singlets represents nonspecific binding events. However, removing these as a standard procedure of ITRAP results in a substantial loss of TCR capture, represented by all T cell specificities with a light gray outer circle in Figure 7b. Thus, when aiming for capture of very low-frequency T cell specificities, a balance should be made between including this more stringent filtering step, or including such events, as demonstrated here.

The UMI thresholds identified by ITRAP are data specific and cannot be universally applied but must be fitted for individual experiments. To investigate this and the robustness of ITRAP further, we have in a recent publication (Povlsen et al., 2023) applied the method to the large 10x Genomics data set available at https://support.10xgenomics.com/single-cell-vdj/datasets. In short, we find that the ITRAP method also for this data set was capable of accurately denoising the raw data. This accuracy was manifested not only through highly improved internal performance (concordance, accuracy, and AUC) metric values, but also in improved predictive power of a NetTCR (Montemurro et al., 2022) machine learning method trained on the denoised data. Due to the very different experimental settings in the two data sets, the obtained UMI-based filtering thresholds as expected varied between the two studies. This indicates the high strength of ITRAP being robust at being applied to data generated in very different biological and technical settings.

Other than investigating the robustness of ITRAP, the paper also benchmarked ITRAP performance against ICON (Integrative COntext-specific Normalization) (Zhang et al., 2021). ICON was similarly developed for denoising and discriminating true TCR-pMHC binding signal from nonspecific background noise in single-cell data. The results from this benchmark suggested an overall superior performance of the ITRAP method compared with the ICON method in terms of both data consistency and performance.

ICON was developed based on the public 10x Genomics data that includes 6 negative control pMHCs (Boutet et al., 2019) and 44 pMHC for positive selection of T cell populations. Comparing our method with ICON suggests that we present a more flexible and customizable approach. Where ICON yields a specific data set (Zhang et al., 2021), the ITRAP method allows varying yields, depending on the level of filtering applied. The ITRAP method requires haplotype information for optimal filtering, which ICON does not consider resulting in ~15% mismatched HLAs of their specificity annotations. Further, we believe that negative control pMHCs may provide false confidence as we do not know the limits to T cell specificity. For both methods, a particular awareness should be assigned to properly handle the range of affinity displayed by different clonotypes. One clonotype may display natural low affinity toward its cognate target, which might appear like noise in the comparison to other high-affinity clonotypes. This diversity in signals is challenging to handle in a one-fit-all filtering process, and for projects with specific interest in low-affinity cell interactions, a specific focus should be addressed not to lose such information.

Effective pairing of TCR and pMHC will open new avenues to interrogate T cell recognition and the role of different T cell populations in pathogenic processes. Intensive efforts have been made to identify antigen specificity based on the TCR sequence (Gielis et al., 2019; Montemurro et al., 2021; Moris et al., 2021; Sidhom et al., 2021; Weber et al., 2021; Zhang et al., 2021), and access to both TCR α- and β-chain is important to improve such prediction strategies (Montemurro et al., 2021).

The coveted data is ensured via the ITRAP framework for single-cell data of TCRs and associated barcodes. The perspectives of further exploiting the transcriptomic information, allowing in-depth tracking of specific T cell subsets based on the clonotypes, suggest that we are on the verge of achieving substantial novel insight to T cell involvement and behavior in health and disease.

In conclusion, we have demonstrated that ITRAP is a highly flexible tool for denoising sc-pMHC TCR data to identify the most likely pMHC-TCR pairs. The validation of the tool will benefit from future studies where the pMHC specificities of TCRs may be experimentally validated. Here, we provide a first important step toward this by enabling researchers identifying the most likely pair to proceed with also for very low frequent T cell populations.

Methods

All healthy donor material was collected from the central blood bank at the university hospital, Rigshospitalet, Copenhagen (under the agreement no. BC29). Collection was done under approval by the Scientific Ethics Committee of the Capital Region, Denmark, and written informed consent was obtained according to the Declaration of Helsinki. This material is fully anonymized.

Cell samples

PBMCs from healthy donors were isolated from whole blood by density centrifugation on Lymphoprep (Axis-Shield PoC) and cryopreserved at −150°C in FCS (Gibco) + 10% DMSO.

DNA barcodes and dextran conjugation

Oligonucleotides modified with a 5′ biotin tag were purchased from LCG Biosearch Technologies (Denmark). Read from 5′ to 3′, the oligonucleotides were designed with the 10x equivalent Read2N sequence, a 10 nt UMI, a distinct 15mer nucleotide sequences (extracted from Xu et al., 2019), a 9 nt UMI and ending in a 13 nt capture sequence complementary to the TSO of the 10x 5′ capture oligo (sequences are listed in Supplementary file 1). Barcodes were dissolved to 100 μM in RNAse-free water and stored at −20°C. For a working solution, the DNA barcodes were further diluted in PBS + 0.5% BSA + 1 mg/mL herring DNA + 2 mM EDTA to 2.17 μM and attached to PE- or APC- and streptavidin-conjugated dextran from FINA Biosolutions LCC (USA). The amount of DNA barcode attached to each new lot of dextran was titrated as described in Bentzen et al., 2016. DNA barcodes were attached by mixing with dextran-conjugate, followed by incubation, 30 min at 4°C. DNA barcode-assembled dextran-conjugates were stored for up to 24 hr at 4°C.

Peptides and MHC monomer production

Peptides were purchased from Pepscan (Pepscan Presto) and dissolved to 10 mM in DMSO. UV-sensitive ligands were synthesized as previously described (Bakker et al., 2008; Rodenko et al., 2006; Toebes et al., 2006). Recombinant HLA-A*0201, HLA-A*0301, and HLA-B*0702, heavy chains, and human β2 microglobulin light chain were produced in Escherichia coli. HLA heavy and light chains were refolded with UV-sensitive ligands and purified as described in Hadrup et al., 2009. Specific peptide-MHC complexes were generated by UV-mediated peptide MHC exchange (Chang et al., 2013; Frøsig et al., 2015; Rodenko et al., 2006; Toebes et al., 2006).

Generation of DNA barcode-labeled MHC multimer libraries

Unoccupied SA-binding sites on the DNA barcode-assembled dextran conjugates were used for the co-attachment of biotinylated pMHC molecules. 0.8 pmol pMHC monomer was mixed with 160× 10–15 mol DNA-barcoded dextran-conjugate and incubated 30 min at room temperature (RT). MHC multimers were diluted in PBS with 5.2 μM d-biotin (Avidity, Bio200) to 909 nM and incubated 20 min on ice. DNA-barcoded MHC multimers were stored for up 1 wk at −20°C (PBS supplemented with glycerol and BSA, final concentrations 5 and 0.5%, respectively). Immediately before staining barcode-labeled MHC multimers were thawed at 4°C, centrifuged (5 min at 3300 × g), and pooled (0.8 pmol of each pMHC/sample) to enable the detection of multiple T-cell responses in parallel. The pooled MHC multimers were centrifuged once more; 5 min at 3300 × g, to sediment aggregates before the volume of the reagent pool was reduced by ultrafiltration to obtain a final volume of ~80 μL of MHC multimers as described in Bentzen et al., 2016. Any aggregates in the MHC multimer reagent pool were sedimented by centrifugation, 5 min at 3300 × g before addition to the cell sample.

MHC multimer staining

Cryopreserved PBMCs were thawed and washed by sedimentation, 5 min, 390 × g, 4°C, in RPMI + 10% FCS. Cells were further washed in a barcode-cytometry buffer (PBS + 0.5% BSA). 5 × 106 cells were incubated, 60 min, 4°C, with pooled DNA-barcoded multimers in a total volume of 100 μL (final concentration of each distinct pMHC, 8 nM), and washed three times by sedimentation, 5 min, 390 × g, 4 °C. 5 µL of Human TruStain FcX Fc Blocking reagent was added to a total of 50 µL cell suspension, and incubated 10 min, 4°C. Hashing antibodies (BioLegend, TotalSeq-C0251 anti-human Hashtag 1-10 Antibodies) were centrifuged 10 min, 14,000 × g, 4°C, and 0.5 µL were added to each a distinct sample (Supplementary file 2), and incubated 15 min, 4°C. Next a 5× antibody mix composed of CD8-BV480 (BD 566121, clone RPA-T8) (final dilution 1/50), dump channel antibodies: CD4-FITC (BD 345768) (final dilution 1/80), CD14-FITC (BD 345784) (final dilution 1/32), CD19-FITC (BD 345776) (final dilution 1/16), CD40-FITC (Serotech MCA1590F) (final dilution 1/40), CD16-FITC (BD 335035) (final dilution 1/64), and a dead cell marker (LIVE/DEAD Fixable Near-IR; Invitrogen L10119) (final dilution 1/1000), was mixed for each sample. The antibody mix was added to cell samples and incubated 30 min, 4°C. Cells were washed three times in barcode-cytometry buffer and kept on ice until acquisition.

Cell sorting

Cells were sorted on a FACS Melody (BD) into tubes containing 100 μL of PBS + 0.5% BSA (tubes were saturated with PBS + 2% BSA in advance). Using FACS Chorus software, we gated on single, live, CD8-positive and ‘dump’ (CD4, 14, 16, 19, and 40)-negative lymphocytes and sorted only APC-positive (PE-negative) cells within this population (Figure 1—figure supplement 1 for gating strategy). Cells sorted from individual samples were collected into the same tube (Figure 1b). The sorted cells were centrifuged for 10 min at 390 × g, and the buffer was removed.

DNA barcode-labeled MHC multimer stained cells on 10x platform

We utilize the 10x 5′ v2 chemistry that allows the cell barcode to be appended at the 5′-end of transcripts, which is essential for capturing the CDR3 region of the V(D)J transcripts. In the 5′ chemistry, the template switch oligo (TSO) is encoded with a cell barcode, that is, one unique 10x barcode for every GEM. The TSO thus comprises the capture oligo, whereas the poly-dT primer is added in free suspension. Reverse transcription is initiated from binding of the poly-dT primer at the 3′-end, and mRNA is captured when the reverse transcriptase enzyme switches at the 5′-end of the transcript to the TSO. All DNA barcodes, partially complementary to the 10x Genomics 5′ TSO, are captured directly onto the GEMs. Annealing and extension during the reverse-transcription reaction associate the cell barcode and UMI from the gel-bead oligo with the pMHC and hashing antibody tags in parallel with the mRNAs in the same droplet.

Downstream processing of mRNA and DNA barcodes is performed according to the manufacturer’s instructions (Chromium Next GEM Single Cell 5' Reagent Kits v2 [Dual Index], with the Feature Barcode technology for Cell Surface Protein & Immune Receptor Mapping) (10x Genomics, USA). Approximately 15,700 cells were loaded (based on 55% recovery from 28,800 sorted cells) to yield a maximum of 9000 cells with an intermediate/high doublet rate (6,9%). Targeted amplification was performed for 13 cycles and the products were separated according to size into <400 bp (DNA barcode-tags) and >400 bp (the TCR sequences) using 0.6× SPRIselect beads (Beckman Coulter, B23318). Separate processing of the >400 bp bead-bound TCR sequences and the <400 bp in solution DNA barcodes was conducted according to the manufacturer’s instruction and the TCR amplification products were sequenced on a NovaSeq running a 150 paired-end program. DNA barcodes, TCR sequences, and mRNA were sequenced to a depth of 13,332, 12,503, and 18,398 mean reads per cell, respectively.

Bioinformatics

Processing of 10x single-cell data

Hashing barcode reads, peptide-MHC barcode reads, and T cell gene expression reads were provided in fastq format and were processed using 10x Genomics Cellranger multi v6.1.0 (10xGenomics, 2022b). The relevant outputs were the unfiltered count matrices of DNA barcodes and gene expression as well as clonotype annotations of each sequencing contig containing CDR3α/β sequences, V(D)J-C genes, and UMI counts.

Postprocessing 10x Cellranger clonotyping

The raw contig annotations from Cellranger were selected for downstream analysis with filtering on incomplete and unproductive receptor transcripts. Incomplete contigs are not full length, that is, do not span the V-gene start codon until the J-gene stop codon. Unproductive contigs contain a frameshift that either induces an early stop codon or completely removes the stop codon.

Clonotypes defined by 10x were merged when consisting of identical VJ-CDR3αβ, thus reducing functional duplicates.

Cellranger flags rare nucleotide transcripts as likely artifacts, meaning the GEMs are flagged as unlikely to contain a cell and are therefore not assigned a clonotype (10xGenomics, 2022a). Therefore, GEMs that were not annotated with a clonotype were imputed by searching the duplicate-reduced clonotype set. If no match, a new clonotype ID was annotated to the GEM.

Filtering based on gene expression

Filtering on gene expression data was performed as described in Zhang et al., 2021. Low-quality GEMs such as doublets may be removed by excluding GEMs with more than 2500 genes. Dead cells may be removed by excluding GEMs with fewer than 200 genes and a ratio of mitochondrial gene expression to the total gene expression above 0.2.

Demultiplexing samples via cell hashing

Cell Hashing uses oligo-tagged antibodies against ubiquitously expressed surface proteins to place a sample barcode on each single cell, enabling different samples to be multiplexed together and run in a single experiment. To demultiplex the samples, the method presented by Stoeckius et al. was implemented (Stoeckius et al., 2018). The method clusters the normalized count matrix using k-medoid clustering into k clusters, k=nsamples+1. For each barcode, a negative binomial distribution is fitted to the pool of all clusters except the cluster with the highest average expression for the given barcode. Each GEM is classified as positive if the barcode value exceeds a 0.99 quantile threshold for the negative distribution, and otherwise classified as negative. If GEMs contain multiple barcodes that pass the threshold, the GEM is annotated as a doublet (Stoeckius et al., 2018).

Defining the expected binder

The pMHC and cell hashing barcode annotations were merged with the T cell annotations on the GEMs that contained both TCR and pMHC attributes. Each clonotype is expected to have a preferred target within the pMHC library, thus each clonotype is evaluated to find the pMHC, which is most likely to be that target.

Each clonotype is evaluated to identify the expected target within the pMHC library. The pMHCs that are detected within the GEMs annotated to a given clonotype are compared by their UMI count distribution. The two pMHCs that have the highest mean UMI count are compared, testing the hypothesis that the expected binder will have a significantly higher mean UMI count than the other pMHC (Wilcoxon, α = 0.05). Clonotypes of less than 10 GEMs were not tested. The clonotypes where the mean UMI of the top two pMHCs was significantly different were collected as a training set. The pMHC that had significantly higher mean UMI was annotated as the expected target and specificity annotations of the GEMs were individually evaluated. True interactions were then defined as GEMs where the most abundant pMHC matched the identified expected target. The GEMs where the most abundant pMHC did not match the expected target were labeled as false interactions. The GEMs belonging to clonotypes where no expected target was identified were left out.

Defining specificity concordance

Concordance is an indirect measure of how cross-reactive a certain clonotype is. Specificity concordance is defined as the ratio of GEMs of a single clonotype that are annotated to bind a particular pMHC. The more GEMs in a clonotype annotated to the same pMHC the larger concordance. If a clonotype is only detected with one pMHC, the specificity concordance is 1.

Grid search on UMI features

Based on the labels of the training set, a performance metric, o, was defined to evaluate the accuracy at increasing thresholds for UMI count and UMI ratio of pMHC, α-chain, and β-chain. The UMI ratio measures multiplets and is defined as the ratio between the highest UMI count and the second highest UMI count in a GEM:

UMIratio=UMImaxUMIsec+0.25

A small number (0.25) was added in the denominator to avoid division by zero.

The performance metric, o, is a weighted average of accuracy and fraction of retained GEMs, given by the following equation:

o=2⋅acc + fretained GEMs3

The accuracy metric is defined by the ratio of training set GEMs that were labeled as true interactions over the total number of GEMs in the training set. The performance metric, o, was maximized by finding the set of filters that increase the accuracy without excluding too much data.

The thresholds for filtering were selected from a complete grid search. Each feature was tested in the range of 0 to the median value, determined ad hoc from the experience that thresholds never approached the median value.

Comparing TCR similarity

Effects of filtering were also evaluated through a comparison of TCR similarity. The similarity score is based on the kernel similarity score underlying the TCRmatch method between CDR3 sequences (Chronister et al., 2021). This score can be calculated for CDR3s of variable length and takes a value between 0–1, with the value of 1 representing identical pairs. As both the α- and β-chain partake in the pMHC interaction, TCRs will be compared based on the summed similarity between the α- and β-chains, and GEMs missing a chain will be excluded to avoid bias. Two similarity scores are computed for each unique TCRαβ-pair (per clonotype): an intra-score and an inter-score. The intra-score is based on the maximum similarity of the given clonotype to all other clonotypes sharing its pMHC target (intra-specificity). The inter-score is based on the maximum similarity of the given clonotype to an equal-sized set of clonotypes specific to other pMHC targets (inter-specificity). The computation is done peptide-wise, such that clonotypes with maximum concordance for a given peptide will, for that peptide, be included in an intra-similarity score, but for another peptide be included in an inter-similarity score. Clonotypes consisting of GEMs causing diverging specificities were limited to the expected target pMHC or, if nonexisting, to the specificity of highest concordance to avoid potential overlaps from ‘cross-reactive’ clonotypes in the computation.

The similarity difference between intra- and inter-specificity clonotypes was tested for the hypothesis that intra-similarity is greater than inter similarity (Wilcoxon, α = 0.05). The similarity test was performed on all filtering methods described in the paper.

Validating single-cell capture against fluorescent multimer staining responses

The 16 donors were known to respond to the panel of peptides used in the screening. Response proportions of sorted CD8+ T cells were detected by fluorescent multimer staining, as described previously. A total of 1800 cells were selected from each donor and, based on the detected response proportions, an adjusted count of cells could be computed. Cells were selected based on two criteria: ≥10 cells and ≥0.002% of total CD8 T cells, or ≤10 cells but ≥0.01% of total CD8 T cells. The multimer responses were compared to GEMs filtered on UMI thresholds and matching HLA. To visually compare the two screening methods, the responses were normalized within each sample and plotted side-by-side. The methods were also quantitatively compared, both in absolute counts of responses and as binary classes with multimer responses as true labels and single-cell responses as query labels.

The correlation in Figure 7d was also fitted via linear regression on log10 transformed data, resulting in the following equation:

log10(y)=0.86⋅log10(x)+1.18, R2=0.56

The equation was used to estimate the yield of single-cell captured cells relative to multimer screening. Three examples were computed to estimate an approximate 10% yield.

log10(10)=0.86⋅log10(0.6)+1.18
log10(100)=0.86⋅log10(9.0)+1.18
log10(1000)=0.86⋅log10(131.2)+1.18

Data availability

The different data sets analyzed and generated in this study are available at https://doi.org/10.11583/DTU.22645342.v1. These data includes the raw data file (raw.csv), the data filtered by the optimized UMI count thresholds (opt_thr.csv), the data filtered by the UMI thresholds and HLA matching (hla_match.csv), and final filtered data including only GEMs with complete TCR annotation. The ITRAP code for optimal UMI count threshold identification, and subsequent filtering is available at https://github.com/mnielLab/itrap (copy archived at Povlsen and Nielsen, 2023).

The following data sets were generated
    1. Nielsen M
    2. Povlsen HR
    3. Bentzen AK
    4. Kadivar M
    5. Jessen LE
    6. Hadrup SR
    (2023) Technical University of Denmark
    Data for: Accurate T cell Receptor Antigen Pairing through data-driven filtering of sequencing information from single-cells.
    https://doi.org/10.11583/DTU.22645342.v1

References

Decision letter

  1. K Christopher Garcia
    Reviewing Editor; Stanford University, United States
  2. Tadatsugu Taniguchi
    Senior Editor; University of Tokyo, Japan
  3. Michael E Birnbaum
    Reviewer; Massachusetts Institute of Technology, United States

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "ATRAP – Accurate T cell Receptor Antigen Pairing through data–driven filtering of sequencing information from single–cells" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Tadatsugu Taniguchi as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Michael E Birnbaum (Reviewer #1).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

This paper is of interest to immunologists conducting single–cell analyses of T–cell recognition. It provides a means of curating datasets to ensure T cell–antigen pairs are identified. However, the data generated through this method often suffers from a relatively high background. The authors present a computational approach to enhance signal–to–noise of this type of analysis. However, from the reviewer's comments, it is unclear if the thresholds and filtering steps described by the authors can be generally applied to other datasets of different qualities than the one used here. The reviewers make suggestions to better stress–test the robustness of the method. Overall this is a potentially valuable contribution but requires additional benchmarking and more clarity on the limitations of the approach.

Essential revisions:

1) The manuscript is more suitable for an eLife Resource given that it is entirely methodological and does not shed new biological insight.

2) There is a need for benchmarking the data against other datasets to assess the robustness and optimal threshold selection.

3) Ensure that the code and data used in the manuscript are publicly available so others can use the method.

4) Address reviewer comments about other technical concerns.

5) Consider the limitations of the study in the revised manuscript and title.

Reviewer #1 (Recommendations for the authors):

1. It will be helpful for the figures in the manuscript to be of higher quality (vectorized) for publication – there are areas where figure quality made data interpretation difficult.

2. Line 34 – MHC should be defined.

3. Line 55 – a and b should be changed to α and β.

4. In figure 2, it is difficult to distinguish between HLA match True and False. May it be worth having the two in different colors?

5. Lines 116 – 121: It would help to clarify the use of the PE–multimers a bit more, than by using them while sorting for APC–only, it ensures that any PE–based signal is purely due to solution–based noise rather than cellular contamination. This was something I only fully understood after reading through the paper once.

6. In lines 160–168, the authors state that 28000 cells were sorted and 45% of the cells were lost in the loading process. It would be helpful if the authors could clarify how were these numbers generated. Were 28000 cells a proxy for 28000 sorted events? How did the authors know that 45% of cells were lost in the process and only 15,700 cells were loaded on the 10X?

7. Based on the observations and discussion in lines 288 through 298, it might be helpful if the authors explicitly stated that they are defining true binders = expected binder=significantly most abundant pMHC for a given clonotype, rather than as defined through orthogonal means.

8. On line 204 the authors mention that the three negative control pMHCs were only present in a few GEMs. However, are these barcodes were captured as ambient contamination or were they captured with distinct clonotypes?

9. Line 274–275: The authors state that for GEMs with TCR multiplets, they take the most abundant chain for analyses. It would help if this were clarified. Is this the most abundant UMI per GEM (so if a given GEM has ⍺1 with 10 UMIs and ⍺2 with 8 UMIs, it counts as ⍺1 ), or the most abundant call per clonotype? (so if there are 5 GEMs with ⍺1 with the most UMIs and 3 GEMs with ⍺2 with the most UMIs, the entire clonotype is called for ⍺1 )? Does this analysis already remove any ⍺ transcripts that are recombined but out of frame or contain stop codons?

10. Line 479–482: The percentages for HLA match and mismatch are provided – what would the probabilities be expected if by chance?

11. Lines 531–535: The authors should further clarify why differences in fluorescence signal may account for differences in analysis for the single–cell sequencing vs FACS, especially given the fact that the sequenced cells are also sorted via FACS before 10x analysis. Is there a difference in avidity and/or concentration between the two staining reagents used?

Reviewer #2 (Recommendations for the authors):

1) Please explain what was the motivation for doing the experiment. Are the donors seropositive/seronegative for CMV/EBV/Flu? Why were these particular epitopes selected? What was the phenotype of sorted cells? What was the hypothesis?

2) Please make the raw data, processed data, and code available. The main strength of the paper is the robust code which could be potentially used to clean up other datasets of the same kind. The link to github returns 404 error.

3) It seems that the approach is not robust in the presence of cross–reactivity. If there are two different pMHC complexes loaded with highly similar peptides recognized by the same clone (and thus two pMHCs with different barcodes bound to the same cell), how the specificity will be assigned (and how will it influence UMI threshold selection, lines 344–345)?

4) It seems that TCR similarity metrics (both for inter and intra–similarity) are defined as maximal similarity values across all the comparisons within the same peptide assignment, or others, lines 888–891. This value should be systematically biased by the sample size (the more pairwise comparisons we do, the more extreme similarity we will find, even if the underlying sequence distance distribution is the same). It is not clear to me, how authors normalize this effect (do they downsample to the minimal number of unique clonotypes across all epitopes)?

5) Figure S1 shows sharing of VJCDRab between GEMs. How will this plot look if we consider sharing of a single chain nucleotide sequence (VJCDR3a_nucleotide or VJCDR3bnucleotide) between GEMs? If a clone has two alphas, will the proposed pipeline split it into two different clonotypes?

6) Please discuss and compare the data analysis strategy to the one from the following recent manuscripts:

https://www.science.org/doi/10.1126/sciimmunol.abk3070

https://www.nature.com/articles/s41590–022–01184–4

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184244/

7) Line 318 mentions Figure 1d (probably instead of 2d)

Reviewer #3 (Recommendations for the authors):

– It will be clearer if the authors could provide a workflow of ATRAP that describes the key steps in the bioinformatic process.

– The cell number loaded on the 10X Chromium for each donor shown in supplementary table 4 indicated the GEM counts for each donor were less than 264. It would be important for the authors to comment on cell numbers and what threshold would be sufficient to perform this method. This would enable future users of the technique to have more guidance in decision–making.

– For experimental control, the authors only use three additional pMHC multimers bearing a different fluorochrome (PE). However, it would be more interesting to see if the authors could include negative pMHC multimers bearing the same fluorochrome (APC) to estimate the background binding noise for each donor and to check if the ATRAP could successfully remove those negative pMHC multimers.

– In this study, the author only used IEDB (Vita et al., 2019) and VDJ (Bagaev et al., 2020) databases to prove the detected specificities in their data have been cross–referenced on only five clonotypes. However, the author did not provide experimental evidence showing the pMHC selected by the ATRAP is a real target of a specific clonotype and that those pMHCs removed by the ATRAP are not the target of that clonotype. This may be a rather intensive set of experiments to show this, but the authors could consider it or at least make some statements/caveats if they choose not to do such additional validation.

– At lines 654–659, the authors write that low–avidity clonotypes might appear like noise, but this method is only able to detect the binding affinity, not the avidity. The binding affinity of TCR is not always correlated with avidity. I wonder if the information in their dataset really provides avidity measurements.

– At lines 344–345, it said that "This filtering analysis resulted in optimal thresholds of 2 pMHC UMI counts and a ratio pMHC UMI counts between top one and two >1". Is it possible if the sequence results are deeper that it might result in more noise or background pMHC UMI counts, in such a case, how would one adjust the optimal thresholds?

– In general, I think readers would find the V and J gene usage, along with other immune repertoire information interesting for all pMHC binders. The authors should consider this, perhaps as supplementary data.

https://doi.org/10.7554/eLife.81810.sa1

Author response

Essential revisions:

1) The manuscript is more suitable for an eLife Resource given that it is entirely methodological and does not shed new biological insight.

We have no objections to this if the editor agrees.

2) There is a need for benchmarking the data against other datasets to assess the robustness and optimal threshold selection.

We have described such benchmark comparisons in a separate manuscript submitted for publication and currently available in BioRxiv doi.org/10.1101/2023.02.01.526310. Here ITRAP was compared to ICON the, to the best of our knowledge, only other currently available method for denoising of single cell obtained TCR-pMHC data on the large set of data generated from 10X Genomics. We have added a short section to the manuscript Discussion section describing the outcome of this benchmark and how the results compare to the result described in the current manuscript.

3) Ensure that the code and data used in the manuscript are publicly available so others can use the method.

We have made all data from the manuscript publicly available at https://services.healthtech.dtu.dk/suppl/immunology/ITRAP. Further, we have made the code for the ITRAP threshold filtering step available for download at https://services.healthtech.dtu.dk/suppl/immunology/ITRAP/.

4) Address reviewer comments about other technical concerns.

We believe to have carefully addressed all the reviewers comments related technical concerns in the responses below.

5) Consider the limitations of the study in the revised manuscript and title.

We have discussed in detail the limitations of the study in the current manuscript, and have updated the title and method acronym to ITRAP (Improved T cell Receptor Antigen Pairing) replacing the word “accurate”. We are naturally very happy that eLife finds our work of interest. Regarding the specific question related to if the thresholds and filtering steps described in the current can be applied to other data sets, we have, as described above, conducted an analysis on the 10X Genomics data set in a separate manuscript. Here, we demonstrate that the method is both generally applicable to any single cell data set with UMI count information related to pMHC and TCR. We also demonstrated how the denoising can be highly improved if information about donor origin is included in terms of cell-hashing. Further, we find that the impact of the different filtering steps contained in ITRAP aligns well between the two studies. In one aspect the results however are somewhat different. This is with respect to the actual thresholds identified for the UMI filtering. Here, the values from the 10X data as expected were different compared to the values found in the current manuscript (see Author response table 1). These differences however in our view indicate the high strength of ITRAP being robust at being applied to data generated in very different biological and technical settings. We have extended the manuscript with a section on this independent data set analysis and a discussion of the results.

Author response table 1
Overview of UMI thresholds given by ITRAP.

The table provides the UMI thresholds for each component (pMHC, TRA, and TRB) identified using the accuracy optimizing approach of ITRAP. Beyond direct UMI thresholds, data may also be filtered based on the UMI ratio between the two largest UMI measurements within a GEM (per component). Two sets of thresholds are given, as the thresholds are dependent on the dataset: first set is based on the data of this publication, the second set is based on the publicly available data by 10x Genomics presented in Povlsen & Montemurro, 2023 (DOI 10.1101/2023.02.01.526310).

VariableCurrent manuscript10x benchmark
UMI pMHC2.05.0000
UMI ratio pMHC1.01.1684
UMI TRA0.01.0000
UMI ratio TRA0.00.7579
UMI TRB0.00.0
UMI ratio TRB0.00.7579

with “improved”.

Reviewer #1 (Recommendations for the authors):

1. It will be helpful for the figures in the manuscript to be of higher quality (vectorized) for publication – there are areas where figure quality made data interpretation difficult.

We are sorry about this, and have updated the figure quality in the revised manuscript.

2. Line 34 – MHC should be defined.

Resolved.

3. Line 55 – a and b should be changed to α and β.

Resolved.

4. In figure 2, it is difficult to distinguish between HLA match True and False. May it be worth having the two in different colors?

Resolved.

5. Lines 116 – 121: It would help to clarify the use of the PE–multimers a bit more, than by using them while sorting for APC–only, it ensures that any PE–based signal is purely due to solution–based noise rather than cellular contamination. This was something I only fully understood after reading through the paper once.

We understand why this can be confusing. We have updated the text to give a more thorough description for the motivation behind the experimental design, which should make the use of the PE labeled multimers more clear.

6. In lines 160–168, the authors state that 28000 cells were sorted and 45% of the cells were lost in the loading process. It would be helpful if the authors could clarify how were these numbers generated. Were 28000 cells a proxy for 28000 sorted events? How did the authors know that 45% of cells were lost in the process and only 15,700 cells were loaded on the 10X?

We have updated the text to make it evident that this number of sorted cells is indeed a proxy for an appropriate number of cells to be loaded on the 10x Chromium, taking into account our specific experiences with the number of cells lost in and immediately after the sorting process.

7. Based on the observations and discussion in lines 288 through 298, it might be helpful if the authors explicitly stated that they are defining true binders = expected binder=significantly most abundant pMHC for a given clonotype, rather than as defined through orthogonal means.

We agree and have updated the wording in line 313 to:

“Having annotated the “expected” pMHC of a given clonotype”.

in the revised manuscript. We believe this also help clarify the subsequent text in line 314:

“where the most abundant pMHC corresponds to the expected binder, as “true”, and all others as “false”, and use these annotations to quantify the accuracy of the GEM annotations”.

8. On line 204 the authors mention that the three negative control pMHCs were only present in a few GEMs. However, are these barcodes were captured as ambient contamination or were they captured with distinct clonotypes?

Note first that the “negative control” pMHC are not truly negative. They are highly dominant pMHC specificities deselected in the sorting prior to the 10x single cell sequencing. It is hence expected that a small proportion of true positive TCRs towards these pMHCs can be found in the data. This is indeed the case, as 4 of the identified TCRs against the control peptides had identical matches within the IEDB/VDJdb database. As stated in the text, the three negative controls were only captured in a few GEMs. We have added the actual numbers (GIL:4 GEMs/Clonotypes, GLC: 17 GEMs/Clonotypes, and NLV: 12 GEMs/Clonotypes) to the manuscript to better underline this. Note, further from these numbers, that all negative control clonotypes are singletons, i.e one GEM per clonotype. -We also observe 107 cases where these pMHCs appear like contamination (i.e. with an UMI count lower than the top one pMHC). In 91 (85%) of these GEMs the negative control was present with 1 UMI, while the UMI counts range from 2-20 in the remaining GEMs. In comparison, a similar proportion of ambient contamination was observed for the CLG peptides. This peptide was found as a “contaminant” in 618 GEMs of which 523 (85%) had an UMI count of 1. These observations strongly suggest that the background of control pMHC barcodes is due to ambient contamination.

9. Line 274–275: The authors state that for GEMs with TCR multiplets, they take the most abundant chain for analyses. It would help if this were clarified. Is this the most abundant UMI per GEM (so if a given GEM has ⍺1 with 10 UMIs and ⍺2 with 8 UMIs, it counts as ⍺1 ), or the most abundant call per clonotype? (so if there are 5 GEMs with ⍺1 with the most UMIs and 3 GEMs with ⍺2 with the most UMIs, the entire clonotype is called for ⍺1 )? Does this analysis already remove any ⍺ transcripts that are recombined but out of frame or contain stop codons?

This is a detailed observation, and a very good question. We assume the reviewer is referring to the text in lines 269-270 of the original submission. Clonotypes are defined from the TCRs present in the GEMs. This means that individual GEMs within a given clonotype can be TCR multiplets and have ambiguous TCR associations. The ITRAP pipeline does not resolve such ambiguities. This is done by the user and any given point in the filtering pipeline. In our case, this was done prior to conducting the similarity analysis. Here TCR annotations for individual GEMs were generated from the highest UMI count values. We have updated the text to clarify this in the revised manuscript.

10. Line 479–482: The percentages for HLA match and mismatch are provided – what would the probabilities be expected if by chance?

This is a valid question. Here rather than addressing the random distribution, we have calculated the pMHC distribution across the 2777 non-outlier GEMs. Here we find RVR A0301 and TPR B0702 to be present in 34% and 21% of the data, respectively. We have updated the text to include and comment on this information.

11. Lines 531–535: The authors should further clarify why differences in fluorescence signal may account for differences in analysis for the single–cell sequencing vs FACS, especially given the fact that the sequenced cells are also sorted via FACS before 10x analysis. Is there a difference in avidity and/or concentration between the two staining reagents used?

We appreciate the reviewers comment on this important topic.

For the T cell sorting prior to the single-cell analyses we apply dextran-conjugated multimers while for the fluorescent-labeled combinatorial-encoded we applied tetramers. We have previous compared detection using these different types of pMHC reagents, and did not observe any differences for T cells recognizing viral epitopes (Bentzen, Nat Biot 2016). However, the two measures were runned on two different flow cytometers, and used different fluorophores, which can impact the fluorescent separation and cell identification. Furthermore, a threshold of 10 events was applied to the T cell detections strategy using the combinatorial-encoded tetramers, while in the single cell platform, all GEM that passed our filtering process was included. This discrepancy has been clarified in the manuscript (lines 564-567).

Moreover, we would here again like to refer to figure 7, where we demonstrate a very high concordance between the two approaches, reflected by the vast majority of the responses detected by fluorescent multimer staining also being captured in the single-cell screening, (recall of 0.95).

Reviewer #2 (Recommendations for the authors):

1) Please explain what was the motivation for doing the experiment. Are the donors seropositive/seronegative for CMV/EBV/Flu? Why were these particular epitopes selected? What was the phenotype of sorted cells? What was the hypothesis?

Please refer to the text above related to the selection of donors and epitopes. It was not essential for us to know the phenotype of the cells in this study, hence we have not investigated this parameter. However, this could be explored looking more into the transcriptomic data which is available and in future experiments by including TotalSeq antibodies for simultaneous staining of surface markers.

2) Please make the raw data, processed data, and code available. The main strength of the paper is the robust code which could be potentially used to clean up other datasets of the same kind. The link to github returns 404 error.

All data and code has been made available at https://services.healthtech.dtu.dk/suppl/immunology/ITRAP

3) It seems that the approach is not robust in the presence of cross–reactivity. If there are two different pMHC complexes loaded with highly similar peptides recognized by the same clone (and thus two pMHCs with different barcodes bound to the same cell), how the specificity will be assigned (and how will it influence UMI threshold selection, lines 344–345)?

This is a very important question. As described above, ITRAP does not exclude cross-reactivity. The filtering thresholds are defined from a subset of clonetypes with well-defined single specificities. While one can argue that a minor limitation of ITRAP is that such well-defined single specificity clonotypes are needed, we believe this will most often be the case. And if not, a limited set of prevalent pMHC targets can be included to ensure this. Once these thresholds are defined, clonotypes with multiple specificities can trivially be identified and analyzed. Note that this is in contrast to what can be archived with for instance the ICON method.

4) It seems that TCR similarity metrics (both for inter and intra–similarity) are defined as maximal similarity values across all the comparisons within the same peptide assignment, or others, lines 888–891. This value should be systematically biased by the sample size (the more pairwise comparisons we do, the more extreme similarity we will find, even if the underlying sequence distance distribution is the same). It is not clear to me, how authors normalize this effect (do they downsample to the minimal number of unique clonotypes across all epitopes)?

This is an important and insightful observation. In the inter versus intra similarity analysis, all clonotypes within a given plateau (with a given annotated peptide specificity) are compared against all other clonotypes within the plateau and against the same number of clonotypes randomly sampled from all other plateaus. It is clear that this sampling approach could result in biases associated with the size of the given plateau. It is however not to us trivially clear whether this bias would be in favor of larger or short plateaus having a significant difference between inter and intra similarity scores. It is trivially clear that the intra similarity score will be biased towards higher values for larger plateaus, but the same is the case for the inter similarity score (since we here also are comparing against a larger set of TCRs from other plateaus). Given this we believe our current simple approach is fair and suitable for capturing the proposed signal.

5) Figure S1 shows sharing of VJCDRab between GEMs. How will this plot look if we consider sharing of a single chain nucleotide sequence (VJCDR3a_nucleotide or VJCDR3bnucleotide) between GEMs? If a clone has two alphas, will the proposed pipeline split it into two different clonotypes?

The pipeline does as such not refine clonotypes. Clonotypes are defined from the TCR annotations of 10X CellRanger. The only modifications are the cases listed in the section “Clonotype annotation” and covers “we redefine clonotypes based on TCR annotation. Subsequently, GEMs with no clonotype annotation from 10x were annotated to existing clonotypes conditioned on matching VJαβ-genes and CDR3αβ sequences or as novel clonotypes. Similarly, clonotypes with identical VJ-CDR3αβ were merged to form larger groups of theoretically identical TCRs”.

While we agree that a further analysis of the sharing of VJCDR between GEMs is interesting, we believe that such an additional analysis split on VJCDR3a_nucleotide or VJCDR3b_nucleotide is beyond the scope of the current paper.

6) Please discuss and compare the data analysis strategy to the one from the following recent manuscripts:

https://www.science.org/doi/10.1126/sciimmunol.abk3070

https://www.nature.com/articles/s41590–022–01184–4

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184244/

All these studies are related to data generation rather than data denoising. As stated above, we are currently in the process of analyzing these data using the ITRAP framework. We however believe these analyses are beyond the scope of the current publication. Note also that we, as stated above, have conducted a benchmark experiment of the data set provided by 10x Genomics. The results is this study described in an separate publication [BioRxiv doi.org/10.1101/2023.02.01.526310]

7) Line 318 mentions Figure 1d (probably instead of 2d)

Resolved.

Reviewer #3 (Recommendations for the authors):

– It will be clearer if the authors could provide a workflow of ATRAP that describes the key steps in the bioinformatic process.

We thank the reviewer for this suggestion and have added a workflow of ITRAP to the revised manuscript (new Supplementary figure 6).

– The cell number loaded on the 10X Chromium for each donor shown in supplementary table 4 indicated the GEM counts for each donor were less than 264. It would be important for the authors to comment on cell numbers and what threshold would be sufficient to perform this method. This would enable future users of the technique to have more guidance in decision–making.

The number of GEMs captured in a single 10X run is expected to be of the order 8000-10000. Of these only a proportion will end up containing signals for both pMHC and TCR (in our case ~6000). Upon filtering this number is further reduced to ~4000. Splitting these responses among the individual donors (hashing) results in ~200 GEMs per donor (note that hashing entry 10 covers multiple donors, and hence take up a higher proportion of the GEMS). The observed GEM count of 264 or less per donor is hence what should be expected when conducting an experiment with a complexity like the one performed here. Note however, that even with these low GEMs counts per donor, we were able to detect responses that strongly align both qualitatively and quantitatively with what has been observed in the same donors using conventional fluorescent multimer staining. We have elaborated briefly on this in the text, by indicating that the number of captured GEMs with a given TCR-pMHC pair is directly proportional with the input. Hence low numbers of cells included from one sample will result in lower numbers of GEMs for any given pMHC-TCR pair.

We have updated the manuscript to include a discussion of this (lines 579-585).

– For experimental control, the authors only use three additional pMHC multimers bearing a different fluorochrome (PE). However, it would be more interesting to see if the authors could include negative pMHC multimers bearing the same fluorochrome (APC) to estimate the background binding noise for each donor and to check if the ATRAP could successfully remove those negative pMHC multimers.

The PE labeled pMHC multimers were only included as internal controls for us to get a first qualitative estimation of the specificity of the 10X technology. Also refer to previous response for the rationale for including the PE labeled pMHCs, i.e. to actively deselect certain specificities.

We actively chose not to be dependent on experimental controls in the ITRAP method. We believe this is an advantage of the ITRAP approach over for instance ICON where negative controls are explicitly needed in order to define background noise. This requirement imposes an additional cost for barcode MHC-multimer reagents. We agree with the reviewer that it can be valuable for users to investigate their data broadly to “estimate” background signals. However, in most experimental setups where several samples are screened in parallel (including the one included in this manuscript) indirect negative controls will be included. This includes HLA mismatches for the samples, and the notion that no sample is expected to recognise all pMHCs (based on previous screens). Our experience is that these might be more relevant as negative controls, since we can in fact not be sure that a given epitope is not recognised due to cross-recognition events or binding of T cells from the naive repertoire.

– In this study, the author only used IEDB (Vita et al., 2019) and VDJ (Bagaev et al., 2020) databases to prove the detected specificities in their data have been cross–referenced on only five clonotypes. However, the author did not provide experimental evidence showing the pMHC selected by the ATRAP is a real target of a specific clonotype and that those pMHCs removed by the ATRAP are not the target of that clonotype. This may be a rather intensive set of experiments to show this, but the authors could consider it or at least make some statements/caveats if they choose not to do such additional validation.

We agree that further experimental validation is needed to fully confirm the specificities of the TCRs identified/removed by ITRAP. However, we believe it is beyond the scope of the current manuscript to conduct such experiments. As stated earlier, we have however in a separate publication demonstrated that applying ITRAP to denoise SC pMHC-TCR data, results in high predictive power on TCR-specificity predictors compared to training on the raw unfiltered data. We have expanded the discussion to underline this, and clarify that additional validations are needed to fully confirm the identified TCR specificities.

– At lines 654–659, the authors write that low–avidity clonotypes might appear like noise, but this method is only able to detect the binding affinity, not the avidity. The binding affinity of TCR is not always correlated with avidity. I wonder if the information in their dataset really provides avidity measurements.

We agree that the use of the word “avidity” is misleading. We have updated the manuscript and replaced this work with affinity.

– At lines 344–345, it said that "This filtering analysis resulted in optimal thresholds of 2 pMHC UMI counts and a ratio pMHC UMI counts between top one and two >1". Is it possible if the sequence results are deeper that it might result in more noise or background pMHC UMI counts, in such a case, how would one adjust the optimal thresholds?

It is clear that the optimal filtering thresholds are somewhat dependent on the setup applied in the given experiment (see further above). This setup includes the sequencing approach and depth. However, as described in the responses to reviewer 2, one of the powers of ITRAP is the step where the optimal filtering thresholds are defined. The step makes the method flexible with respect to such experimental variations. We have demonstrated this in a benchmark study based on the publicly available 10x data, where we applied ITRAP for denoising of the data. Here slightly different filtering threshold values were identified reflecting the differences in the experimental setup between the two studies. We have included a section to the revised manuscript briefly summarizing these results.

– In general, I think readers would find the V and J gene usage, along with other immune repertoire information interesting for all pMHC binders. The authors should consider this, perhaps as supplementary data.

While this is a very interesting suggestion, we believe such an analysis will add limited value to the current study (where denoising is the prime focus). We have nonetheless made the analysis from the data included in figure 6C (i.e. filtered by UMI thresholds, HLA match, and complete TCRs). The result is shown in Figure 6—figure supplement 1. Here, the gene usage for each positive pMHC multimer is displayed as a Sankey diagram for the 10 most frequent gene-pairs. We suggest including such analyses in future studies where a biological interpretation of the denoised data is of interest.

https://doi.org/10.7554/eLife.81810.sa2

Article and author information

Author details

  1. Helle Rus Povlsen

    Department of Health Technology at Technical University of Denmark, Kongens Lyngby, Denmark
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Amalie Kai Bentzen
    For correspondence
    herpov@dtu.dk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5687-4386

  2. Amalie Kai Bentzen

    Department of Health Technology at Technical University of Denmark, Kongens Lyngby, Denmark
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Helle Rus Povlsen
    Competing interests
    AKB and SRH are co-inventors on a patent covering the use of DNA barcode labeled MHC multimers (WO2015185067 and WO2015188839), which is licensed to Immudex

  3. Mohammad Kadivar

    Department of Health Technology at Technical University of Denmark, Kongens Lyngby, Denmark
    Contribution
    Conceptualization, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1499-191X

  4. Leon Eyrich Jessen

    Department of Health Technology at Technical University of Denmark, Kongens Lyngby, Denmark
    Contribution
    Supervision, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2879-2559

  5. Sine Reker Hadrup

    Department of Health Technology at Technical University of Denmark, Kongens Lyngby, Denmark
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Project administration, Writing – review and editing
    Contributed equally with
    Morten Nielsen
    Competing interests
    AKB and SRH are co-inventors on a patent covering the use of DNA barcode labeled MHC multimers (WO2015185067 and WO2015188839), which is licensed to Immudex

  6. Morten Nielsen

    Department of Health Technology at Technical University of Denmark, Kongens Lyngby, Denmark
    Contribution
    Resources, Software, Formal analysis, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing
    Contributed equally with
    Sine Reker Hadrup
    For correspondence
    morni@dtu.dk
    Competing interests
    No competing interests declared

Funding

Danmarks Frie Forskningsfond (7014-00055)

  • Sine Reker Hadrup

Lundbeckfonden (R322-2019-2445)

  • Amalie Kai Bentzen

Lundbeckfonden (R324-2019-1671)

  • Amalie Kai Bentzen

Lundbeckfonden (R190-2014-4178)

  • Sine Reker Hadrup

European Research Council (StG 677268 NextDART)

  • Sine Reker Hadrup

HORIZON EUROPE Marie Sklodowska-Curie Actions (713683)

  • Helle Rus Povlsen

National Institute of Allergy and Infectious Diseases (75N93019C00001)

  • Helle Rus Povlsen

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We would like to thank all healthy donors contributing material to this study. This research was funded in part through the Independent Research Fund Denmark (DFF 7014-00055 to SRH and MN), the Lundbeck Foundation (R322-2019-2445 and R324-2019-1671 to AKB and R190-2014-4178 to SRH), the European Research Council, StG 677268 NextDART to SRH, the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 713683 (COFUNDfellowsDTU), and National Institute of Allergy and Infectious Diseases (NIAID), under award number 75N93019C00001 to HRP and MN.

Ethics

Human subjects: All healthy donor material was collected from the central blood bank at the university hospital, Rigshospitalet, Copenhagen (under the agreement no. BC29). Collection was done under approval by the Scientific Ethics Committee of the Capital Region, Denmark, and written informed consent was obtained according to the Declaration of Helsinki. This material is fully anonymized.

Senior Editor

  1. Tadatsugu Taniguchi, University of Tokyo, Japan

Reviewing Editor

  1. K Christopher Garcia, Stanford University, United States

Reviewer

  1. Michael E Birnbaum, Massachusetts Institute of Technology, United States

Version history

  1. Received: July 12, 2022
  2. Preprint posted: September 2, 2022 (view preprint)
  3. Accepted: March 13, 2023
  4. Version of Record published: May 3, 2023 (version 1)

Copyright

© 2023, Povlsen, Bentzen et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Helle Rus Povlsen
  2. Amalie Kai Bentzen
  3. Mohammad Kadivar
  4. Leon Eyrich Jessen
  5. Sine Reker Hadrup
  6. Morten Nielsen
(2023)
Improved T cell receptor antigen pairing through data-driven filtering of sequencing information from single cells
eLife 12:e81810.
https://doi.org/10.7554/eLife.81810

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