Multi-modal generative modeling for joint analysis of single-cell T cell receptor and gene expression data.
Nat Commun
; 15(1): 5577, 2024 Jul 03.
Article
em En
| MEDLINE
| ID: mdl-38956082
ABSTRACT
Recent advances in single-cell immune profiling have enabled the simultaneous measurement of transcriptome and T cell receptor (TCR) sequences, offering great potential for studying immune responses at the cellular level. However, integrating these diverse modalities across datasets is challenging due to their unique data characteristics and technical variations. Here, to address this, we develop the multimodal generative model mvTCR to fuse modality-specific information across transcriptome and TCR into a shared representation. Our analysis demonstrates the added value of multimodal over unimodal approaches to capture antigen specificity. Notably, we use mvTCR to distinguish T cell subpopulations binding to SARS-CoV-2 antigens from bystander cells. Furthermore, when combined with reference mapping approaches, mvTCR can map newly generated datasets to extensive T cell references, facilitating knowledge transfer. In summary, we envision mvTCR to enable a scalable analysis of multimodal immune profiling data and advance our understanding of immune responses.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Receptores de Antígenos de Linfócitos T
/
Análise de Célula Única
/
Transcriptoma
/
SARS-CoV-2
/
COVID-19
Limite:
Humans
Idioma:
En
Revista:
Nat Commun
/
Nature communications
Assunto da revista:
BIOLOGIA
/
CIENCIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Alemanha