Predicting antigen specificity of single T cells based on TCR CDR3 regions

Abstract It has recently become possible to simultaneously assay T‐cell specificity with respect to large sets of antigens and the T‐cell receptor sequence in high‐throughput single‐cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectur...
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Autor*in:	Fischer, David S [verfasserIn] Wu, Yihan [verfasserIn] Schubert, Benjamin [verfasserIn] Theis, Fabian J [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	antigen specificity multimodal single cell supervised learning T‐cell receptors

Anmerkung:	© The Author(s) 2020

Übergeordnetes Werk:	Enthalten in: Molecular Systems Biology - Nature Publishing Group UK, 2023, 16(2020), 8 vom: 11. Aug.
Übergeordnetes Werk:	volume:16 ; year:2020 ; number:8 ; day:11 ; month:08

Links:	Volltext

DOI / URN:	10.15252/msb.20199416

Katalog-ID:	SPR058094318

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520			\|a Abstract It has recently become possible to simultaneously assay T‐cell specificity with respect to large sets of antigens and the T‐cell receptor sequence in high‐throughput single‐cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T‐cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single‐cell immune repertoire screens can be mitigated by modeling cell‐specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell‐to‐dextramer binding strength and receptor‐to‐pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single‐cell RNA‐seq studies on T cells without the need for MHC staining.
520			\|a Synopsis TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. pMHC measurements are predicted in a large single‐cell data set with > 100,000 cells, additionally using TCR‐antigen pairs from IEDB and VDJdb.Benchmarking categorical models of antigens with antigen‐embedding models indicates that categorical models are often preferable.The study highlights the need to measure TCR specificity for a larger repertoire of antigens to generalize models to unseen antigens.
520			\|a Graphical Abstract TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties.
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allfields	10.15252/msb.20199416 doi (DE-627)SPR058094318 (SPR)msb.20199416-e DE-627 ger DE-627 rakwb eng Fischer, David S verfasserin (orcid)0000-0002-1293-7656 aut Predicting antigen specificity of single T cells based on TCR CDR3 regions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract It has recently become possible to simultaneously assay T‐cell specificity with respect to large sets of antigens and the T‐cell receptor sequence in high‐throughput single‐cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T‐cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single‐cell immune repertoire screens can be mitigated by modeling cell‐specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell‐to‐dextramer binding strength and receptor‐to‐pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single‐cell RNA‐seq studies on T cells without the need for MHC staining. Synopsis TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. pMHC measurements are predicted in a large single‐cell data set with > 100,000 cells, additionally using TCR‐antigen pairs from IEDB and VDJdb.Benchmarking categorical models of antigens with antigen‐embedding models indicates that categorical models are often preferable.The study highlights the need to measure TCR specificity for a larger repertoire of antigens to generalize models to unseen antigens. Graphical Abstract TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. antigen specificity (dpeaa)DE-He213 multimodal (dpeaa)DE-He213 single cell (dpeaa)DE-He213 supervised learning (dpeaa)DE-He213 T‐cell receptors (dpeaa)DE-He213 Wu, Yihan verfasserin (orcid)0000-0003-2718-8704 aut Schubert, Benjamin verfasserin (orcid)0000-0003-3412-1102 aut Theis, Fabian J verfasserin (orcid)0000-0002-2419-1943 aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 16(2020), 8 vom: 11. Aug. (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:16 year:2020 number:8 day:11 month:08 https://dx.doi.org/10.15252/msb.20199416 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 16 2020 8 11 08
spelling	10.15252/msb.20199416 doi (DE-627)SPR058094318 (SPR)msb.20199416-e DE-627 ger DE-627 rakwb eng Fischer, David S verfasserin (orcid)0000-0002-1293-7656 aut Predicting antigen specificity of single T cells based on TCR CDR3 regions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract It has recently become possible to simultaneously assay T‐cell specificity with respect to large sets of antigens and the T‐cell receptor sequence in high‐throughput single‐cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T‐cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single‐cell immune repertoire screens can be mitigated by modeling cell‐specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell‐to‐dextramer binding strength and receptor‐to‐pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single‐cell RNA‐seq studies on T cells without the need for MHC staining. Synopsis TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. pMHC measurements are predicted in a large single‐cell data set with > 100,000 cells, additionally using TCR‐antigen pairs from IEDB and VDJdb.Benchmarking categorical models of antigens with antigen‐embedding models indicates that categorical models are often preferable.The study highlights the need to measure TCR specificity for a larger repertoire of antigens to generalize models to unseen antigens. Graphical Abstract TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. antigen specificity (dpeaa)DE-He213 multimodal (dpeaa)DE-He213 single cell (dpeaa)DE-He213 supervised learning (dpeaa)DE-He213 T‐cell receptors (dpeaa)DE-He213 Wu, Yihan verfasserin (orcid)0000-0003-2718-8704 aut Schubert, Benjamin verfasserin (orcid)0000-0003-3412-1102 aut Theis, Fabian J verfasserin (orcid)0000-0002-2419-1943 aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 16(2020), 8 vom: 11. Aug. (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:16 year:2020 number:8 day:11 month:08 https://dx.doi.org/10.15252/msb.20199416 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 16 2020 8 11 08
allfields_unstemmed	10.15252/msb.20199416 doi (DE-627)SPR058094318 (SPR)msb.20199416-e DE-627 ger DE-627 rakwb eng Fischer, David S verfasserin (orcid)0000-0002-1293-7656 aut Predicting antigen specificity of single T cells based on TCR CDR3 regions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract It has recently become possible to simultaneously assay T‐cell specificity with respect to large sets of antigens and the T‐cell receptor sequence in high‐throughput single‐cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T‐cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single‐cell immune repertoire screens can be mitigated by modeling cell‐specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell‐to‐dextramer binding strength and receptor‐to‐pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single‐cell RNA‐seq studies on T cells without the need for MHC staining. Synopsis TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. pMHC measurements are predicted in a large single‐cell data set with > 100,000 cells, additionally using TCR‐antigen pairs from IEDB and VDJdb.Benchmarking categorical models of antigens with antigen‐embedding models indicates that categorical models are often preferable.The study highlights the need to measure TCR specificity for a larger repertoire of antigens to generalize models to unseen antigens. Graphical Abstract TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. antigen specificity (dpeaa)DE-He213 multimodal (dpeaa)DE-He213 single cell (dpeaa)DE-He213 supervised learning (dpeaa)DE-He213 T‐cell receptors (dpeaa)DE-He213 Wu, Yihan verfasserin (orcid)0000-0003-2718-8704 aut Schubert, Benjamin verfasserin (orcid)0000-0003-3412-1102 aut Theis, Fabian J verfasserin (orcid)0000-0002-2419-1943 aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 16(2020), 8 vom: 11. Aug. (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:16 year:2020 number:8 day:11 month:08 https://dx.doi.org/10.15252/msb.20199416 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 16 2020 8 11 08
allfieldsGer	10.15252/msb.20199416 doi (DE-627)SPR058094318 (SPR)msb.20199416-e DE-627 ger DE-627 rakwb eng Fischer, David S verfasserin (orcid)0000-0002-1293-7656 aut Predicting antigen specificity of single T cells based on TCR CDR3 regions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract It has recently become possible to simultaneously assay T‐cell specificity with respect to large sets of antigens and the T‐cell receptor sequence in high‐throughput single‐cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T‐cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single‐cell immune repertoire screens can be mitigated by modeling cell‐specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell‐to‐dextramer binding strength and receptor‐to‐pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single‐cell RNA‐seq studies on T cells without the need for MHC staining. Synopsis TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. pMHC measurements are predicted in a large single‐cell data set with > 100,000 cells, additionally using TCR‐antigen pairs from IEDB and VDJdb.Benchmarking categorical models of antigens with antigen‐embedding models indicates that categorical models are often preferable.The study highlights the need to measure TCR specificity for a larger repertoire of antigens to generalize models to unseen antigens. Graphical Abstract TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. antigen specificity (dpeaa)DE-He213 multimodal (dpeaa)DE-He213 single cell (dpeaa)DE-He213 supervised learning (dpeaa)DE-He213 T‐cell receptors (dpeaa)DE-He213 Wu, Yihan verfasserin (orcid)0000-0003-2718-8704 aut Schubert, Benjamin verfasserin (orcid)0000-0003-3412-1102 aut Theis, Fabian J verfasserin (orcid)0000-0002-2419-1943 aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 16(2020), 8 vom: 11. Aug. (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:16 year:2020 number:8 day:11 month:08 https://dx.doi.org/10.15252/msb.20199416 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 16 2020 8 11 08
allfieldsSound	10.15252/msb.20199416 doi (DE-627)SPR058094318 (SPR)msb.20199416-e DE-627 ger DE-627 rakwb eng Fischer, David S verfasserin (orcid)0000-0002-1293-7656 aut Predicting antigen specificity of single T cells based on TCR CDR3 regions 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2020 Abstract It has recently become possible to simultaneously assay T‐cell specificity with respect to large sets of antigens and the T‐cell receptor sequence in high‐throughput single‐cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T‐cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single‐cell immune repertoire screens can be mitigated by modeling cell‐specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell‐to‐dextramer binding strength and receptor‐to‐pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single‐cell RNA‐seq studies on T cells without the need for MHC staining. Synopsis TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. pMHC measurements are predicted in a large single‐cell data set with > 100,000 cells, additionally using TCR‐antigen pairs from IEDB and VDJdb.Benchmarking categorical models of antigens with antigen‐embedding models indicates that categorical models are often preferable.The study highlights the need to measure TCR specificity for a larger repertoire of antigens to generalize models to unseen antigens. Graphical Abstract TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. antigen specificity (dpeaa)DE-He213 multimodal (dpeaa)DE-He213 single cell (dpeaa)DE-He213 supervised learning (dpeaa)DE-He213 T‐cell receptors (dpeaa)DE-He213 Wu, Yihan verfasserin (orcid)0000-0003-2718-8704 aut Schubert, Benjamin verfasserin (orcid)0000-0003-3412-1102 aut Theis, Fabian J verfasserin (orcid)0000-0002-2419-1943 aut Enthalten in Molecular Systems Biology Nature Publishing Group UK, 2023 16(2020), 8 vom: 11. Aug. (DE-627)490536905 (DE-600)2193510-5 1744-4292 nnns volume:16 year:2020 number:8 day:11 month:08 https://dx.doi.org/10.15252/msb.20199416 X:SPRINGER Resolving-System kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_72 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4116 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4155 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4314 GBV_ILN_4315 GBV_ILN_4318 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4598 GBV_ILN_4700 AR 16 2020 8 11 08
language	English
source	Enthalten in Molecular Systems Biology 16(2020), 8 vom: 11. Aug. volume:16 year:2020 number:8 day:11 month:08
sourceStr	Enthalten in Molecular Systems Biology 16(2020), 8 vom: 11. Aug. volume:16 year:2020 number:8 day:11 month:08
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	antigen specificity multimodal single cell supervised learning T‐cell receptors
isfreeaccess_bool	true
container_title	Molecular Systems Biology
authorswithroles_txt_mv	Fischer, David S @@aut@@ Wu, Yihan @@aut@@ Schubert, Benjamin @@aut@@ Theis, Fabian J @@aut@@
publishDateDaySort_date	2020-08-11T00:00:00Z
hierarchy_top_id	490536905
id	SPR058094318
language_de	englisch
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author	Fischer, David S
spellingShingle	Fischer, David S misc antigen specificity misc multimodal misc single cell misc supervised learning misc T‐cell receptors Predicting antigen specificity of single T cells based on TCR CDR3 regions
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topic_title	Predicting antigen specificity of single T cells based on TCR CDR3 regions antigen specificity (dpeaa)DE-He213 multimodal (dpeaa)DE-He213 single cell (dpeaa)DE-He213 supervised learning (dpeaa)DE-He213 T‐cell receptors (dpeaa)DE-He213
topic	misc antigen specificity misc multimodal misc single cell misc supervised learning misc T‐cell receptors
topic_unstemmed	misc antigen specificity misc multimodal misc single cell misc supervised learning misc T‐cell receptors
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format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	Predicting antigen specificity of single T cells based on TCR CDR3 regions
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title_full	Predicting antigen specificity of single T cells based on TCR CDR3 regions
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author_browse	Fischer, David S Wu, Yihan Schubert, Benjamin Theis, Fabian J
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author-letter	Fischer, David S
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title_sort	predicting antigen specificity of single t cells based on tcr cdr3 regions
title_auth	Predicting antigen specificity of single T cells based on TCR CDR3 regions
abstract	Abstract It has recently become possible to simultaneously assay T‐cell specificity with respect to large sets of antigens and the T‐cell receptor sequence in high‐throughput single‐cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T‐cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single‐cell immune repertoire screens can be mitigated by modeling cell‐specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell‐to‐dextramer binding strength and receptor‐to‐pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single‐cell RNA‐seq studies on T cells without the need for MHC staining. Synopsis TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. pMHC measurements are predicted in a large single‐cell data set with > 100,000 cells, additionally using TCR‐antigen pairs from IEDB and VDJdb.Benchmarking categorical models of antigens with antigen‐embedding models indicates that categorical models are often preferable.The study highlights the need to measure TCR specificity for a larger repertoire of antigens to generalize models to unseen antigens. Graphical Abstract TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. © The Author(s) 2020
abstractGer	Abstract It has recently become possible to simultaneously assay T‐cell specificity with respect to large sets of antigens and the T‐cell receptor sequence in high‐throughput single‐cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T‐cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single‐cell immune repertoire screens can be mitigated by modeling cell‐specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell‐to‐dextramer binding strength and receptor‐to‐pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single‐cell RNA‐seq studies on T cells without the need for MHC staining. Synopsis TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. pMHC measurements are predicted in a large single‐cell data set with > 100,000 cells, additionally using TCR‐antigen pairs from IEDB and VDJdb.Benchmarking categorical models of antigens with antigen‐embedding models indicates that categorical models are often preferable.The study highlights the need to measure TCR specificity for a larger repertoire of antigens to generalize models to unseen antigens. Graphical Abstract TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. © The Author(s) 2020
abstract_unstemmed	Abstract It has recently become possible to simultaneously assay T‐cell specificity with respect to large sets of antigens and the T‐cell receptor sequence in high‐throughput single‐cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T‐cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single‐cell immune repertoire screens can be mitigated by modeling cell‐specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell‐to‐dextramer binding strength and receptor‐to‐pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single‐cell RNA‐seq studies on T cells without the need for MHC staining. Synopsis TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. pMHC measurements are predicted in a large single‐cell data set with > 100,000 cells, additionally using TCR‐antigen pairs from IEDB and VDJdb.Benchmarking categorical models of antigens with antigen‐embedding models indicates that categorical models are often preferable.The study highlights the need to measure TCR specificity for a larger repertoire of antigens to generalize models to unseen antigens. Graphical Abstract TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. © The Author(s) 2020
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title_short	Predicting antigen specificity of single T cells based on TCR CDR3 regions
url	https://dx.doi.org/10.15252/msb.20199416
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author2	Wu, Yihan Schubert, Benjamin Theis, Fabian J
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up_date	2024-10-25T04:55:56.841Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR058094318</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20241025065217.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">241025s2020 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.15252/msb.20199416</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR058094318</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)msb.20199416-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Fischer, David S</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-1293-7656</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Predicting antigen specificity of single T cells based on TCR CDR3 regions</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2020</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract It has recently become possible to simultaneously assay T‐cell specificity with respect to large sets of antigens and the T‐cell receptor sequence in high‐throughput single‐cell experiments. Leveraging this new type of data, we propose and benchmark a collection of deep learning architectures to model T‐cell specificity in single cells. In agreement with previous results, we found that models that treat antigens as categorical outcome variables outperform those that model the TCR and antigen sequence jointly. Moreover, we show that variability in single‐cell immune repertoire screens can be mitigated by modeling cell‐specific covariates. Lastly, we demonstrate that the number of bound pMHC complexes can be predicted in a continuous fashion providing a gateway to disentangle cell‐to‐dextramer binding strength and receptor‐to‐pMHC affinity. We provide these models in the Python package TcellMatch to allow imputation of antigen specificities in single‐cell RNA‐seq studies on T cells without the need for MHC staining.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Synopsis TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties. pMHC measurements are predicted in a large single‐cell data set with > 100,000 cells, additionally using TCR‐antigen pairs from IEDB and VDJdb.Benchmarking categorical models of antigens with antigen‐embedding models indicates that categorical models are often preferable.The study highlights the need to measure TCR specificity for a larger repertoire of antigens to generalize models to unseen antigens.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Graphical Abstract TcellMatch is a deep‐learning based algorithm that predicts the antigen specificity of single T cells based on multimodal single‐cell experiments that measure pMHC binding and T‐cell receptor sequences among other properties.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">antigen specificity</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">multimodal</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">single cell</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">supervised learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">T‐cell receptors</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Yihan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-2718-8704</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Schubert, Benjamin</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-3412-1102</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Theis, Fabian J</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-2419-1943</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Molecular Systems Biology</subfield><subfield code="d">Nature Publishing Group UK, 2023</subfield><subfield code="g">16(2020), 8 vom: 11. 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Predicting antigen specificity of single T cells based on TCR CDR3 regions

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