Group of Immunosequencing Algorithms

The Group main focus lies in developing computational methods and bioinformatic solutions dedicated to pre-processing, analysis and interpretation of deep immune repertoire sequencing data. We aim at transforming the readout of millions of T- and B-cell receptor sequences into immunologically relevant features, such as the overall immune repertoire diversity, the magnitude of selection pressure coming from self and foreign antigens, and, ultimately, the ability of certain T- and B-cells to mount an effective response against specific pathogens.

The Group collaborates with Immunosequencing methods laboratory (Prof Chudakov DM), Structural organization of T-cell immunity group (Dr Britanova OV), Laboratory of Comparative and Functional Genomics (Prof Lebedev YuB, Dr Mamedov IZ, Dr Zvyagin IV), Prof. Can Kesmir, Utrecht University, Prof. Andrew Sewell, Cardiff University, Prof. David Price, Cardiff University.

The group has branched from the Prof Chudakov's Genomics of Adaptive Immunity Lab at 2017.

  • Developing algorithms for comprehensive analysis of T- and B-cell receptor sequencing data. These algorithms include basic summary statistics such as rearrangement probabilities for certain germline segments, physicochemical characteristics of hypervariable CDR3 regions, somatic hypermutation profiles and lineage tracing for B-cells.
  • Developing and maintaining the database of T-cell receptors (TCRs) with known antigen specificities (VDJdb, vdjdb.cdr3.net). The database contains T-cell receptor sequences encoding receptors that were experimentally validated to recognize known epitopes in a given HLA context.
  • Developing and maintaining the database of TCR:peptide:MHC structures. Developing TCR specificity prediction algorithms that integrate structural data with the TCR specificity database to predict TCR recognition based on linear amino acid sequences of TCR, peptide and MHC.
  • HLA binding prediction. Inferring the effect of certain HLA alleles on the structure of T-cell repertoire and the set of so-called "public" clonotypes. Using HLA binding prediction and cognate TCR repertoire data to infer antigen immunogenicity.

All publications (show selected)

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Mikhail Shugay

SARS-CoV-2 epitopes are recognized by a public and diverse repertoire of human T cell receptors

Understanding the hallmarks of the immune response to SARS-CoV-2 is critical for fighting the COVID-19 pandemic. We assessed antibody and T cell reactivity in convalescent COVID-19 patients and healthy donors sampled both prior to and during the pandemic. Healthy donors examined during the pandemic exhibited increased numbers of SARS-CoV-2-specific T cells, but no humoral response. Their probable exposure to the virus resulted in either asymptomatic infection without antibody secretion, or activation of pre-existing immunity. In convalescent patients, we observed a public and diverse T cell response to SARS-CoV-2 epitopes, revealing T cell receptor (TCR) motifs with germline-encoded features. Bulk CD4+ and CD8+ T cell responses to the spike glycoprotein were mediated by groups of homologous TCRs, some of them shared across multiple donors. Overall, our results demonstrate that the T cell response to SARS-CoV-2, including the identified set of TCRs, can serve as a useful biomarker for surveying antiviral immunity.

- two functionally distinct subpopulations of CD8 + memory stem cells described.

- a new approach developed to assess the clonal heterogeneity of tumor-infiltrating T-lymphocytes.

-  stably clonal T cell response was shown to be associated with a response to anti-PD1 immunotherapy.

- the T-cell repertoire of nickel-specific CD4 + T-lymphocytes was characterized.

- the repertoire of gamma-delta T lymphocytes involved in the antitumor response was investigated.

- a comparative analysis of the repertoire of follicular helper T-lymphocytes was carried out.

- a deep comparative study of methods for analyzing the repertoire of T-cell receptors was carried out.

Publications

  1. Galletti G, De Simone G, Mazza EMC, Puccio S, Mezzanotte C, Bi TM, Davydov AN, Metsger M, Scamardella E, Alvisi G, De Paoli F, Zanon V, Scarpa A, Camisa B, Colombo FS, Anselmo A, Peano C, Polletti S, Mavilio D, Gattinoni L, Boi SK, Youngblood BA, Jones RE, Baird DM, Gostick E, Llewellyn-Lacey S, Ladell K, Price DA, Chudakov DM, Newell EW, Casucci M, Lugli E (2020). Two subsets of stem-like CD8+ memory T cell progenitors with distinct fate commitments in humans. Nat Immunol ,
  2. Yuzhakova DV, Volchkova LN, Pogorelyy MV, Serebrovskaya EO, Shagina IA, Bryushkova EA, Nakonechnaya TO, Izosimova AV, Zavyalova DS, Karabut MM, Izraelson M, Samoylenko IV, Zagainov VE, Chudakov DM, Zagaynova EV, Sharonov GV (2020). Measuring Intratumoral Heterogeneity of Immune Repertoires. Front Oncol 10, 512
  3. Zhigalova EA, Izosimova AI, Yuzhakova DV, Volchkova LN, Shagina IA, Turchaninova MA, Serebrovskaya EO, Zagaynova EV, Chudakov DM, Sharonov GV (2020). RNA-Seq-Based TCR Profiling Reveals Persistently Increased Intratumoral Clonality in Responders to Anti-PD-1 Therapy. Front Oncol 10, 385
  4. Aparicio-Soto M, Riedel F, Leddermann M, Bacher P, Scheffold A, Kuhl H, Timmermann B, Chudakov DM, Molin S, Worm M, Heine G, Thierse HJ, Luch A, Siewert K (2020). TCRs with segment TRAV9-2 or a CDR3 histidine are overrepresented among nickel-specific CD4+ T cells. Allergy 75 (10), 2574–2586
  5. Janssen A, Villacorta Hidalgo J, Beringer DX, van Dooremalen S, Fernando F, van Diest E, Terrizi AR, Bronsert P, Kock S, Schmitt-Gräff A, Werner M, Heise K, Follo M, Straetemans T, Sebestyen Z, Chudakov DM, Kasatskaya SA, Frenkel FE, Ravens S, Spierings E, Prinz I, Küppers R, Malkovsky M, Fisch P, Kuball J (2020). γδ T-cell Receptors Derived from Breast Cancer-Infiltrating T Lymphocytes Mediate Antitumor Reactivity. Cancer Immunol Res 8 (4), 530–543
  6. Brenna E, Davydov AN, Ladell K, McLaren JE, Bonaiuti P, Metsger M, Ramsden JD, Gilbert SC, Lambe T, Price DA, Campion SL, Chudakov DM, Borrow P, McMichael AJ (2020). CD4 T Follicular Helper Cells in Human Tonsils and Blood Are Clonally Convergent but Divergent from Non-Tfh CD4 Cells. Cell Rep 30 (1), 137–152.e5
  7. Barennes P, Quiniou V, Shugay M, Egorov ES, Davydov AN, Chudakov DM, Uddin I, Ismail M, Oakes T, Chain B, Eugster A, Kashofer K, Rainer PP, Darko S, Ransier A, Douek DC, Klatzmann D, Mariotti-Ferrandiz E (2020). Benchmarking of T cell receptor repertoire profiling methods reveals large systematic biases. Nat Biotechnol 39 (2), 236–245
  8. De Simone G, Mazza EMC, Cassotta A, Davydov AN, Kuka M, Zanon V, De Paoli F, Scamardella E, Metsger M, Roberto A, Pilipow K, Colombo FS, Tenedini E, Tagliafico E, Gattinoni L, Mavilio D, Peano C, Price DA, Singh SP, Farber JM, Serra V, Cucca F, Ferrari F, Orrù V, Fiorillo E, Iannacone M, Chudakov DM, Sallusto F, Lugli E (2019). CXCR3 Identifies Human Naive CD8 T Cells with Enhanced Effector Differentiation Potential. J Immunol 203 (12), 3179–3189

A new approach to the functional analysis of sequencing data for T-lymphocyte repertoires

In collaboration with Laboratory of immunosequencing methods,  Laboratory of comparative and functional genomics

In order to extract clinically relevant information from large high-throughput sequencing of TCR repertoires we create a new statistical approach - Antigen-specific Lymphocyte Identification by Clustering of Expanded sequences (ALICE) /fig a/. We applied our algorithm to distinguish naïve from the effector memory cells in available TCR beta repertoires /fig b/, to identify reactive T-cell clones in mixed lymphocyte reaction (MLR) assay /fig c, d/, to fractionate TCR repertoires of patients with autoimmune disease or ones being under cancer immunotherapy, or subject to an acute viral infection. In summary, implementation of ALICE facilitate the identification of TCR variants associated with diseases and conditions, which can be used for diagnostics and rational vaccine design.