We show that, despite being selected according to the stringent clinical and biological criteria, patients with stable graft function display heterogeneous usage of their T-cell repertoire, ranging from unbiased to highly selected profiles.

We confirm that the TcL pattern reveals immunological differences between TOL and CHR patients. Furthermore, a positive correlation between peripheral T-cell repertoire profiles and Banff grade is demonstrated. Altogether, these data suggest that the shape of the T-cell repertoire could constitute a valuable parameter which could be used to assess graft outcome, this website guide the medical management of patient with chronic rejection and indicate the necessity of the long-term follow-up of those stable patients who have an altered T-cell repertoire. Evaluating the complexity of the TCR repertoire from spectratyping data, as produced by the TcL technology, needs appropriate statistical method 12. An unsupervised analysis was conducted to explore both the qualitative (Kurtosis of CDR3-length distribution (CDR3-LD) and the quantitative (amount of Vβ transcripts) diversity of the TCR repertoire of the 209 patients with stable graft function (stable for an average of 9 years; range 1.9–22.9 years) on immunosuppressants (mycophenolate

mofetil or azathioprine) plus calcineurin inhibitors (STA). Principal component analysis (PCA), a statistical method used to reduce the complexity of data sets, was adapted FDA-approved Drug Library to TcL data. A factorial map, where a patient’s TcL location reflects its overall TCR repertoire diversity was produced (Fig. 1). Eigenvalue decomposition of the covariance matrix shows that PCA C1 and PCA C2 account for a significant amount of the variability

(Supporting Information Fig. 1). The widespread location of the patient’s TcL in the factorial map highlights the heterogeneity of their T-cell repertoire. As shown in Fig. 1, TcL patterns stemmed from Gaussian repertoire to highly selected TCR usage O-methylated flavonoid (low and high PCA C1 values, respectively). To analyze this heterogeneity, a K-means clustering algorithm was applied to the distribution of the C1 coordinate values of the 209 STA patients and four classes of TcL shapes were defined by C1 boundary values of −0.032, 0.008 and 0.071 (dotted lines Fig. 2A). A representative TcL for each of the four classes is shown in Fig. 2B. TcL pattern 1 is composed of “Gaussian-like” Vβ CDR3-LD (Kurtosis KGr1 median=0.10, inter-quartile range (IQR)=0.60). TcL patterns 2 and 3 exhibit an increased level of Vβ CDR3-LD alterations (increased Kurtosis) compared with pattern 1 (Kurtosis KGr2 median=0.89, IQR=0.57; Kurtosis KGr3 median=2.24, IQR=0.73). Pattern 4 characterizes altered TcL with distinct oligoclonal Vβ CDR3-LD (Kurtosis KGr4 median=3.22, IQR=1.05). Multiple group comparisons on Kurtosis show that the four TcL classes are significantly different.