Optimal transport for single-cell and spatial omics

Bunne, Charlotte; Schiebinger, Geoffrey; Krause, Andreas; Regev, Aviv; Cuturi, Marco

doi:10.1038/s43586-024-00334-2

Nat Rev Methods Primers

2024

DOI: 10.1038/s43586-024-00334-2

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Optimal transport for single-cell and spatial omics

Charlotte Bunne,

Geoffrey Schiebinger,

Andreas Krause

et al.

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2024

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Cited by 3 publications

References 144 publications

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Holotomographic microscopy reveals label-free quantitative dynamics of endothelial cells during endothelialization

Leineweber,

Munares,

Leycam

et al. 2024

Preprint

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Holotomograhic microscopy (HTM) has emerged as a non-invasive imaging technique that offers high-resolution, quantitative 3D imaging of biological samples. This study explores the application of HTM in examining endothelial cells (ECs). HTM overcomes the limitations of traditional microscopy methods in capturing the real-time dynamics of ECs by leveraging the refractive index (RI) to map 3D distributions label-free. This work demonstrates the utility of HTM in visualizing key cellular processes during endothelialization, wherein ECs anchor, adhere, migrate, and proliferate. Leveraging the high resolution and quantitative power of HTM, we show that lipid droplets and mitochondria are readily visualized, enabling more comprehensive studies on their respective roles during endothelialization. The study highlights how HTM can uncover novel insights into EC behavior, offering potential applications in medical diagnostics and research, particularly in developing treatments for cardiovascular diseases. This advanced imaging technique not only enhances our understanding of EC biology but also presents a significant step forward in the study of cardiovascular diseases, providing a robust platform for future research and therapeutic development.

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Holotomographic microscopy reveals label-free quantitative dynamics of endothelial cells during endothelialization

Leineweber,

Munares,

Leycam

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases

Chernigovskaya,

Lê Quý,

Stensland

et al. 2024

Preprint

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The circulating antibody repertoire is crucial for immune protection, holding significant immunological and biotechnological value. While bottom-up mass spectrometry (MS) is the most widely used proteomics technique for profiling the sequence diversity of circulating antibodies (Ab-seq), it has not been thoroughly benchmarked. We quantified the replicability and robustness of Ab-seq using six monoclonal antibodies with known protein sequences in 70 different combinations of concentration and oligoclonality, both with and without polyclonal serum IgG background. Each combination underwent four protease treatments and was analyzed across four experimental and three technical replicates, totaling 3,360 LC-MS/MS runs. We quantified the dependence of MS-based Ab-seq identification on antibody sequence, concentration, protease, background signal diversity, and bioinformatics setups. Integrating the data from experimental replicates, proteases, and bioinformatics tools enhanced antibody identification. De novo peptide sequencing showed similar performance to database-dependent methods for higher antibody concentrations, but de novo antibody reconstruction remains challenging. Our work provides a foundational resource for the field of MS-based antibody profiling.

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Modeling CAR Response at the Single-Cell Level Using Conditional Optimal Transport

Driessen,

Born,

Castellanos Rueda

et al. 2024

Preprint

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Chimeric Antigen Receptor (CAR) T cell therapy is a promising area of cancer immunotherapy. However, many challenges such as loss of persistence, T cell exhaustion, and therapy associated toxicities hamper further advancement of CAR T cell therapy. Therefore, recent efforts have focused on designing improved CARs that show better therapeutic characteristics. However, it is unfeasible to test all CAR variants in lab-based assays as CARs consist of multiple intracellular signalling domains. This results in over 100'000 possible variants. We leverage computational modeling to navigate this vast combinatorial space by learning the relationship between CAR design and T cell functionality, thereby proposing promising CAR T cell designs. CAR T cells expressing different variants can be viewed as cells that underwent different perturbations. Neural Optimal Transport is an upcoming field that can model single cell perturbations and predict unseen cells and conditions. In this work we leverage the conditional Monge Gap to model the response to CAR expression at a single-cell level and generate gene expression of cells that express an unseen CAR design. We show that CAR OT (CAROT) significantly outperforms the baseline for gene expression prediction for in-distribution CAR variants, with distinct gene expression patterns per CAR that capture biological characteristics. When predicting unseen CAR variants, we demonstrate promising results in terms of gene expression prediction and show the model learns gene expression patterns linked to domains in the training set. This work demonstrates that optimal transport may support discovery and development of new CAR T cell designs.

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Optimal transport for single-cell and spatial omics

Cited by 3 publications

References 144 publications

Holotomographic microscopy reveals label-free quantitative dynamics of endothelial cells during endothelialization

Holotomographic microscopy reveals label-free quantitative dynamics of endothelial cells during endothelialization

Systematic benchmarking of mass spectrometry-based antibody sequencing reveals methodological biases

Modeling CAR Response at the Single-Cell Level Using Conditional Optimal Transport

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