Dataset Information

SCOTv2: Single-Cell Multiomic Alignment with Disproportionate Cell-Type Representation.

ABSTRACT: Multiomic single-cell data allow us to perform integrated analysis to understand genomic regulation of biological processes. However, most single-cell sequencing assays are performed on separately sampled cell populations, as applying them to the same single-cell is challenging. Existing unsupervised single-cell alignment algorithms have been primarily benchmarked on coassay experiments. Our investigation revealed that these methods do not perform well for noncoassay single-cell experiments when there is disproportionate cell-type representation across measurement domains. Therefore, we extend our previous work-Single Cell alignment using Optimal Transport (SCOT)-by using unbalanced Gromov-Wasserstein optimal transport to handle disproportionate cell-type representation and differing sample sizes across single-cell measurements. Our method, SCOTv2, gives state-of-the-art alignment performance across five non-coassay data sets (simulated and real world). It can also integrate multiple (M≥2) single-cell measurements while preserving the self-tuning capabilities and computational tractability of its original version.

SUBMITTER: Demetci P

PROVIDER: S-EPMC9805876 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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SCOTv2: Single-Cell Multiomic Alignment with Disproportionate Cell-Type Representation.

Demetci Pinar P Santorella Rebecca R Chakravarthy Manav M Sandstede Bjorn B Singh Ritambhara R

Journal of computational biology : a journal of computational molecular cell biology 20221012 11

Multiomic single-cell data allow us to perform integrated analysis to understand genomic regulation of biological processes. However, most single-cell sequencing assays are performed on separately sampled cell populations, as applying them to the same single-cell is challenging. Existing unsupervised single-cell alignment algorithms have been primarily benchmarked on coassay experiments. Our investigation revealed that these methods do not perform well for noncoassay single-cell experiments when ...[more]

PMID: 36251763

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Project description:T-cell activation is a key step in the amplification of an immune response. Over the course of an immune response, cells may be chronically stimulated, with some proportion becoming exhausted; an enormous number of molecules are involved in this process. There remain a number of questions about the process, namely: (1) what degree of heterogeneity and plasticity do T-cells exhibit during stimulation? (2) how many unique cell states define chronic stimulation? and (3) what markers discriminate activated from exhausted cells? We addressed these questions by performing single-cell multiomic analysis to simultaneously measure expression of 38 proteins and 399 genes in human T cells expanded in vitro. This approach allowed us to study -with unprecedented depth-how T cells change over the course of chronic stimulation. Comprehensive immunophenotypic and transcriptomic analysis at day 0 enabled a refined characterization of T-cell maturational states and the identification of a donor-specific subset of terminally differentiated T-cells that would have been otherwise overlooked using canonical cell classification schema. As expected, activation downregulated naïve-cell markers and upregulated effector molecules, proliferation regulators, co-inhibitory and co-stimulatory receptors. Our deep kinetic analysis further revealed clusters of proteins and genes identifying unique states of activation, defined by markers temporarily expressed upon 3 days of stimulation (PD-1, CD69, LTA), markers constitutively expressed throughout chronic activation (CD25, GITR, LGALS1), and markers uniquely up-regulated upon 14 days of stimulation (CD39, ENTPD1, TNFDF10); expression of these markers could be associated with the emergence of short-lived cell types. Notably, different ratios of cells expressing activation or exhaustion markers were measured at each time point. These data reveal the high heterogeneity and plasticity of chronically stimulated T cells. Our study demonstrates the power of a single-cell multiomic approach to comprehensively characterize T-cells and to precisely monitor changes in differentiation, activation, and exhaustion signatures during cell stimulation.

Dataset Information

SCOTv2: Single-Cell Multiomic Alignment with Disproportionate Cell-Type Representation.

Publications

SCOTv2: Single-Cell Multiomic Alignment with Disproportionate Cell-Type Representation.

Similar Datasets

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