Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy

Bachireddy, Pavan; Azizi, Elham; Burdziak, Cassandra; Nguyen, Vinhkhang N.; Ennis, Christina; Maurer, Katie; Park, Cameron Y.; Choo, Zi-Ning; Li, Shuqiang; Gohil, Satyen; Ruthen, Neil; Ge, Zhongqi; Keskin, Derin B.; Cieri, Nicoletta; Livak, Kenneth J.; Kim, Haesook T.; Neuberg, Donna; Soiffer, Robert J.; Ritz, Jérôme; Alyea, Edwin P.; Pe’er, Dana; Wu, Catherine J.

doi:10.1016/j.celrep.2021.109992

Cited by 42 publications

(39 citation statements)

References 101 publications

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“…7a,b ). Integrated analysis of CD8+ T cells revealed infiltration of both stem-like, precursor exhausted, and terminally differentiated cells 16 ( Fig. 2h ) with corresponding diversification of clonotypes and contraction of pre-existing T cell clones over time ( Fig.…”

Section: Mainmentioning

confidence: 99%

Multi-modal single-cell and whole-genome sequencing of minute, frozen specimens to propel clinical applications

Wang

Fan

Melms

et al. 2022

Preprint

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Single-cell genomics are enabling technologies, but their broad clinical application remains challenging. We report an easily adaptable approach for single-cell transcriptome and T cell receptor (TCR)-sequencing, and matched whole-genome sequencing from tiny, frozen clinical specimens. We achieve similar quality and biological outputs while reducing artifactual signals compared to data from matched fresh tissue samples. Profiling sequentially collected melanoma samples from the KEYNOTE-001 trial, we resolve cellular, genomic, and clonotype dynamics that encapsulate molecular patterns of tumor evolution during anti-PD-1 therapy. To demonstrate applicability to banked biospecimens of rare diseases, we generate a large uveal melanoma liver metastasis single-cell and matched WGS atlas, which revealed niche-specific impairment of clonal T cell expansion. This study provides a foundational framework for propelling single-cell genomics to the clinical arena.

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Section: Mainmentioning

confidence: 99%

Multi-modal single-cell and whole-genome sequencing of minute, frozen specimens to propel clinical applications

Wang

Fan

Melms

et al. 2022

Preprint

Self Cite

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“…Finally, mechanisms that underly disease relapse following HSCT ( 75 , 100 , 144 ) and response to therapies that reinstate GvL through DLI ( 145 ) or checkpoint immune blockade ( 119 , 146 ) are increasingly characterized using single-cell sequencing. Lineage-tracing has many potential applications in these studies, for example finer dissection of intraclonal evolution ( 147 ) or tracking of exhausted T cells before and after immunomodulation, as has been demonstrated in a large-scale characterization of bone marrow-derived T cell states following effective DLI ( 145 , 148 ).…”

Section: Lineage-tracing In the Context Of Allogeneic Stem Cell Transplantationmentioning

confidence: 99%

Natural Barcodes for Longitudinal Single Cell Tracking of Leukemic and Immune Cell Dynamics

Penter

Gohil

2022

Front. Immunol.

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Blood malignancies provide unique opportunities for longitudinal tracking of disease evolution following therapeutic bottlenecks and for the monitoring of changes in anti-tumor immunity. The expanding development of multi-modal single-cell sequencing technologies affords newer platforms to elucidate the mechanisms underlying these processes at unprecedented resolution. Furthermore, the identification of molecular events that can serve as in-vivo barcodes now facilitate the tracking of the trajectories of malignant and of immune cell populations over time within primary human samples, as these permit unambiguous identification of the clonal lineage of cell populations within heterogeneous phenotypes. Here, we provide an overview of the potential for chromosomal copy number changes, somatic nuclear and mitochondrial DNA mutations, single nucleotide polymorphisms, and T and B cell receptor sequences to serve as personal natural barcodes and review technical implementations in single-cell analysis workflows. Applications of these methodologies include the study of acquired therapeutic resistance and the dissection of donor- and host cellular interactions in the context of allogeneic hematopoietic stem cell transplantation.

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“…The progress and development of experimental technologies 1–4 and computational tools 5–9 for single-cell genomics have enabled the construction of atlases with millions of cells serving as high-resolution coordinate systems 10 for biological and therapeutic discoveries 11–14 . However, leveraging existing atlases poses a computational challenge known as reference mapping.…”

Section: Introductionmentioning

confidence: 99%

Biologically informed deep learning to infer gene program activity in single cells

Lotfollahi

Rybakov

Hrovatin

et al. 2022

Preprint

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The increasing availability of large-scale single-cell datasets has enabled the detailed description of cell states across multiple biological conditions and perturbations. In parallel, recent advances in unsupervised machine learning, particularly in transfer learning, have enabled fast and scalable mapping of these new single-cell datasets onto reference atlases. The resulting large-scale machine learning models however often have millions of parameters, rendering interpretation of the newly mapped datasets challenging. Here, we propose expiMap, a deep learning model that enables interpretable reference mapping using biologically understandable entities, such as curated sets of genes and gene programs. The key concept is the substitution of the uninterpretable nodes in an autoencoder's bottleneck by labeled nodes mapping to interpretable lists of genes, such as gene ontologies, biological pathways, or curated gene sets, for which activities are learned as constraints during reconstruction. This is enabled by the incorporation of predefined gene programs into the reference model, and at the same time allowing the model to learn de novo new programs and refine existing programs durin reference mapping. We show that the model retains similar integration performance as existing methods while providing a biologically interpretable framework for understanding cellular behavior. We demonstrate the capabilities of expiMap by applying it to 15 datasets encompassing five different tissues and species. The interpretable nature of the mapping revealed unreported associations between interferon signaling via the RIG-I/MDA5 and GPCRs pathways, with differential behavior in CD8+ T cells and CD14+ monocytes in severe COVID-19, as well as the role of annexins in the cellular communications between lymphoid and myeloid compartments for explaining patient response to the applied drugs. Finally, expiMap enabled the direct comparison of a diverse set of pancreatic beta cells from multiple studies where we observed a strong, previously unreported correlation between the unfolded protein response and asparagine N-linked glycosylation. Altogether, expiMap enables the interpretable mapping of single cell transcriptome data sets across cohorts, disease states and other perturbations.

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Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy

Cited by 42 publications

References 101 publications

Multi-modal single-cell and whole-genome sequencing of minute, frozen specimens to propel clinical applications

Multi-modal single-cell and whole-genome sequencing of minute, frozen specimens to propel clinical applications

Natural Barcodes for Longitudinal Single Cell Tracking of Leukemic and Immune Cell Dynamics

Biologically informed deep learning to infer gene program activity in single cells

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