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Ramos Zapatero M, Tong A, Opzoomer JW, O'Sullivan R, Cardoso Rodriguez F, Sufi J, Vlckova P, Nattress C, Qin X, Claus J, Hochhauser D, Krishnaswamy S, Tape CJ. Trellis tree-based analysis reveals stromal regulation of patient-derived organoid drug responses. Cell 2023; 186:5606-5619.e24. [PMID: 38065081 DOI: 10.1016/j.cell.2023.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 07/27/2023] [Accepted: 11/02/2023] [Indexed: 12/18/2023]
Abstract
Patient-derived organoids (PDOs) can model personalized therapy responses; however, current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly multiplexed mass cytometry platform to measure post-translational modification (PTM) signaling, DNA damage, cell-cycle activity, and apoptosis in >2,500 colorectal cancer (CRC) PDOs and cancer-associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment-specific drug responses in thousands of single-cell datasets, we developed "Trellis"-a highly scalable, tree-based treatment effect analysis method. Trellis single-cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rarer, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate PDO plasticity-shifting proliferative colonic stem cells (proCSCs) to slow-cycling revival colonic stem cells (revCSCs) to protect cancer cells from chemotherapy.
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Affiliation(s)
- María Ramos Zapatero
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Alexander Tong
- Department of Computer Science, Yale University, New Haven, CT, USA; Department of Computer Science and Operations Research, Université de Montréal, Montreal, QC, Canada; Mila - Quebec AI Institute, Montréal, QC, Canada
| | - James W Opzoomer
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Rhianna O'Sullivan
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Ferran Cardoso Rodriguez
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Jahangir Sufi
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Petra Vlckova
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Callum Nattress
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Xiao Qin
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Jeroen Claus
- Phospho Biomedical Animation, The Greenhouse Studio 6, London N17 9QU, UK
| | - Daniel Hochhauser
- Drug-DNA Interactions Group, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK
| | - Smita Krishnaswamy
- Department of Computer Science, Yale University, New Haven, CT, USA; Department of Genetics, Yale University, New Haven, CT, USA; Program for Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA; Program for Applied Math, Yale University, New Haven, CT, USA; Wu-Tsai Institute, Yale University, New Haven, CT, USA.
| | - Christopher J Tape
- Cell Communication Lab, Department of Oncology, University College London Cancer Institute, London WC1E 6DD, UK.
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Senra D, Guisoni N, Diambra L. Cell annotation using scRNA-seq data: A protein-protein interaction network approach. MethodsX 2023; 10:102179. [PMID: 37128282 PMCID: PMC10148184 DOI: 10.1016/j.mex.2023.102179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 04/09/2023] [Indexed: 05/03/2023] Open
Abstract
Pathway analysis is an important step in the interpretation of single cell transcriptomic data, as it provides powerful information to detect which cellular processes are active in each individual cell. We have recently developed a protein-protein interaction network-based framework to quantify pluripotency associated pathways from scRNA-seq data. On this occasion, we extend this approach to quantify the activity of a pathway associated with any biological process, or even any list of genes. A systems-level characterization of pathway activities across multiple cell types provides a broadly applicable tool for the analysis of pathways in both healthy and disease conditions. Dysregulated cellular functions are a hallmark of a wide spectrum of human disorders, including cancer and autoimmune diseases. Here, we illustrate our method by analyzing various biological processes in healthy and cancer breast samples. Using this approach we found that tumor breast cells, even when they form a single group in the UMAP space, keep diverse biological programs active in a differentiated manner within the cluster.•We implement a protein-protein interaction network-based approach to quantify the activity of different biological processes.•The methodology can be used for cell annotation in scRNA-seq studies and is freely available as R package.
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