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CD4+ and CD8+ T cell β antigen receptors have different
and predictable V and J gene usage and CDR3 lengths
Ryan Emerson, Cindy Desmarais, Annie Sherwood, Harlan
1,2
Robins
Fred Hutchinson Cancer Research Center, Seattle, WA1, Adaptive TCR Technologies, Seattle, WA2
Introduction
Results: TCRB sequence data
Results: in silico validation
The human cellular adaptive immune system is mediated by
two primary types of T cells; killer T cells and helper T cells.
Killer T cells, marked by the surface expression of CD8,
recognize short peptides (~8-10 amino acids) presented on
the surface of cells by human leukocyte antigen (HLA) Class I
molecules(1).
Helper T cells, marked by the surface
expression of CD4, recognize longer peptides (~12-16
nucleotides) presented on the surface of cells by HLA Class II
molecules(2). Both of these T cell types derive from a
common progenitor cell type. During the development of T
cells in the thymus, the DNA loci coding for the α and β
chains of the Y-like T cell receptors (TCR) rearrange in a
pseudo-random process to form an enormous variety of
TCRs(3). TCR sequence diversity is primarily contained in
the complementarity determining region 3 (CDR3) loops of
the α and β chains, which bind to the peptide antigen,
conveying specificity. The nucleotide sequences that encode
the CDR3 loops are generated by V(D)J recombination:
variable (Vβ), diversity (Dβ) and joining (Jβ) genes in the
genome are rearranged to form a β chain, while Vα and Jα
genes rearrange to form an α chain(3).
While it’s known that the CD8 and CD4 proteins determine
which MHC class the T cells bind, it is unknown if the antigen
receptor sequences vary between the two populations. The
diversity of possible receptors is huge and until recently this
diversity precluded the possibility of capturing the antigen
receptor repertoire. Adaptive Biotechnologies has developed
a novel method that amplifies rearranged T cell receptor b
(TCRB) CDR3 sequences and uses high throughput
sequencing to sequence millions of TCRB CDR3 chains. We
identified sequence features that differentiate the TCRB
CDR3 chains from CD4+ and CD8+ T cells. We exploited
these differences to develop a likelihood model to estimate
the proportion of CD4+ and CD8+ T cells in a mixed
population.
1. Identify TCRB CDR3 chain sequence features that
differ between CD4+ and CD8+ T cells.
2. Build and test a likelihood model to differentiate these
two cell types using in silico data.
3. Validate model using TCRB sequences obtained from
in vitro mixtures of CD4+ and CD8+ T cells.
CD4+ and CD8+ T cells have
significantly different TCRβV gene
usage (Fig. 2).
CD4+ and CD8+ T cells have
significantly different TCRβJ gene
usage (Fig. 3).
CDR3 lengths by V gene usage
differs between CD4+ and CD8+ T
cells (Fig. 4).
Model accurately differentiates resampled pure populations of CD4+
and CD8+ T cells (Fig. 5).
Model can accurately estimate a
samples proportion of CD4+ and
CD8+ T cells with 1000 TCRB
sequences (Fig. 6C).
Fig. 2: Mean frequency across 42 subjects of each V gene
segment in CD4+ and CD8+ TCRb sequences. Error bars
represent the standard error of the mean and significance is
marked: *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Fig 3: J gene usage in CD4+ and CD8+ T
cells:
PCR and Sequencing:
TCRB chains from all samples were sequenced using the
TCRB immunoSEQ assay (Fig. 1).
Fig. 1:
Assay
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Fig. 5 in silico estimation of the proportion of CD4+ T cells on
flow-sorted training data using 118 CD4+ or CD8+ samples
from 17 healthy subjects and 25 subjects with Multiple
Sclerosis. Results are presented as a box-and-whisker plot:
the open box for each sample represents the second and
third quartiles of estimated CD4+ proportion. Top whisker
represents the maximum and bottom whisker represents the
minimum.
Fig 6: CD4: CD8 estimation: effect of sample
size:
Fig. 3: Mean frequency across 42 subjects of each J gene
segment in CD4+ and CD8+ TCRb sequences. Error bars
represent the standard error of the mean and significance is
marked: *, p < 0.05; **, p < 0.01; ***, p < 0.001.
Fig 4: CDR3 length distribution in CD4+ and
CD8+ T cells:
Analysis:
1. Using the pure populations from the training set,
we calculated the P(V|CD4), P(J|CD4),
P(CDR3|V,CD4), P(CDR3|J,CD4).
2. The likelihood of any one TCRB CDR3 sequence
being from a CD4+ T cell was calculated as [p *
P(V|CD4) * P(CDR3|V, CD4) * P(J|CD4) *
P(CDR3|J, CD4)] + [(1-p) * P(V|CD8) * P(CDR3|V,
CD8) * P(J|CD8) * P(CDR3|J, CD8)]
3. 15 populations with known CD4:CD8 ratio were
sequenced and the proportion of CD4:CD8 T cells
was estimated using sequence data. We
compared the observed and expected data.
Fig 4: Distribution of CDR3 length among between CD4+
(black) and CD8+ (red) T cells averaged over 42 subjects A)
across all productive CDR3 TCRb chains, B) across only
productive CDR3 TCRb chains that use TRBV6-4, and C) as
deviation from mode CDR3 TCRb length give V segment
usage.
For additional information about immunoSEQ assays and the
immunoSEQ Analyzer suite of bioninformatics applications at
Adaptive TCR Technologies, visit our booth or contact us on the
web at www.adaptivetcr.com and www.immunoseq.com.
Fig 7: CD4: CD8 estimation: in vitro validation
results
CD4:CD8 Estimation, Cross-validation
Materials and Methods
Samples:
1. 118 Training Samples: PBMC from 17 health controls
was individually sorted into CD4+/CD45RA+/CD62L+,
CD4+/CD45RA-/CD45RO+, CD8+/CD45RA+/CD62L+,
and CD8+/CD45RA-/CD45RO+ T cell populations (4
samples per subject), PBMC from 25 patients with Multiple
Sclerosis was individually sorted into CD4+ and CD8+ T
cells(2 samples per subject).
2. 15 sample in vitro test: Sequencing libraries from 5 of
the Multiple Sclerosis subjects described above were
mixed into three ratios: 1:1, 1:3 and 3:1 CD4:CD8 T cells.
 Estimate of CD4: CD8 T cell ratio
correlated with expected ratio (R2 =
0.93) (Fig. 7)
Fig. 5: In silico CD4:CD8 estimation, training
and test results:
Estimated proportion CD4+
Fig. 2: V gene usage in CD4+ and CD8+ T
cells:
Results: in vitro validation
Fig 6: Each panel shows the results of 1,000 independent
experiments. The datasets used for each experiment are a
randomly assembled set of TCR sequence data. Each
experiment represents the result of the likelihood models
predicted ratio of CD4: CD8 T cells plotted against the
expected ratio. To elucidate the effect of sample size on our
model’s ability to estimate the CD4+:CD8+ ratio, each panel
represents the results of different sized data sets; A) 1000
datasets with 10 simulated TCR sequences each, B) 1000
datasets with 100 simulated TCR sequences each, C) 1000
datasets with 1000 simulated TCR sequences each, and D)
1000 datasets with 10,0000 simulated TCR sequences each.
Fig. 6 Shown above are the results of 15 experiments in
which sorted CD4+ and CD8+ T cell DNA samples from five
subjects with Multiple Sclerosis were mixed to produce
samples with nominal proportions of CD4+ sequences of
25%, 50%, or 75%; however, sequencing protocols allowed
us to determine the exact proportion CD4+ in each mixed
sample. Samples were sequenced, and then assessed
computationally to predict the proportion CD4+ T cell
sequences in each sample. Our model correctly estimates the
proportion of CD4+ T cells in samples assembled in vitro from
sorted CD4+ and CD8+ TCR sequences.
Conclusions:
1. CD4+ and CD8+ T cells have
quantifiably different TCRB CDR3
chain characteristics based on V
gene usage and and V gene usage
by CDR3 length (Fig 2,3,4).
2. These differences can be utilized
to predict T cell population source
(Fig 5/6)
3. These differences can predict the
proportion of CD4:CD8 T cells in a
mixed population (Fig 7). Potential
applications include identifying
proportion of CD4:CD8 T cells in
solid tumors based on TCR
sequencing data.
Work cited
1. E. Pamer, P. Cresswell, MECHANISMS OF MHC CLASS
I–RESTRICTED ANTIGEN PROCESSING. Annual Review of
Immunology 16, 323 (1998).
2. S. A. Leddon, A. J. Sant, Generation of MHC class II-peptide ligands for
CD4 T-cell allorecognition of MHC class II molecules. Curr Opin Organ
Transplant 15, 505 (Aug, 2010).
3. M. M. Davis, P. J. Bjorkman, T-cell antigen receptor genes and T-cell
recognition. Nature 334, 395 (1988).
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