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Singh A, Sen S, Iter M, Adelaja A, Luecke S, Guo X, Hoffmann A. Stimulus-response signaling dynamics characterize macrophage polarization states. Cell Syst 2024; 15:563-577.e6. [PMID: 38843840 DOI: 10.1016/j.cels.2024.05.002] [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: 05/13/2022] [Revised: 12/03/2023] [Accepted: 05/10/2024] [Indexed: 06/22/2024]
Abstract
The functional state of cells is dependent on their microenvironmental context. Prior studies described how polarizing cytokines alter macrophage transcriptomes and epigenomes. Here, we characterized the functional responses of 6 differentially polarized macrophage populations by measuring the dynamics of transcription factor nuclear factor κB (NF-κB) in response to 8 stimuli. The resulting dataset of single-cell NF-κB trajectories was analyzed by three approaches: (1) machine learning on time-series data revealed losses of stimulus distinguishability with polarization, reflecting canalized effector functions. (2) Informative trajectory features driving stimulus distinguishability ("signaling codons") were identified and used for mapping a cell state landscape that could then locate macrophages conditioned by an unrelated condition. (3) Kinetic parameters, inferred using a mechanistic NF-κB network model, provided an alternative mapping of cell states and correctly predicted biochemical findings. Together, this work demonstrates that a single analyte's dynamic trajectories may distinguish the functional states of single cells and molecular network states underlying them. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Apeksha Singh
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, and Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Supriya Sen
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, and Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Michael Iter
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, and Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Adewunmi Adelaja
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, and Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Stefanie Luecke
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, and Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Xiaolu Guo
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, and Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Alexander Hoffmann
- Signaling Systems Laboratory, Department of Microbiology, Immunology, and Molecular Genetics, and Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, Los Angeles, CA 90095, USA.
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Kinnunen PC, Humphries BA, Luker GD, Luker KE, Linderman JJ. Characterizing heterogeneous single-cell dose responses computationally and experimentally using threshold inhibition surfaces and dose-titration assays. NPJ Syst Biol Appl 2024; 10:42. [PMID: 38637530 PMCID: PMC11026493 DOI: 10.1038/s41540-024-00369-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 04/08/2024] [Indexed: 04/20/2024] Open
Abstract
Single cancer cells within a tumor exhibit variable levels of resistance to drugs, ultimately leading to treatment failures. While tumor heterogeneity is recognized as a major obstacle to cancer therapy, standard dose-response measurements for the potency of targeted kinase inhibitors aggregate populations of cells, obscuring intercellular variations in responses. In this work, we develop an analytical and experimental framework to quantify and model dose responses of individual cancer cells to drugs. We first explore the connection between population and single-cell dose responses using a computational model, revealing that multiple heterogeneous populations can yield nearly identical population dose responses. We demonstrate that a single-cell analysis method, which we term a threshold inhibition surface, can differentiate among these populations. To demonstrate the applicability of this method, we develop a dose-titration assay to measure dose responses in single cells. We apply this assay to breast cancer cells responding to phosphatidylinositol-3-kinase inhibition (PI3Ki), using clinically relevant PI3Kis on breast cancer cell lines expressing fluorescent biosensors for kinase activity. We demonstrate that MCF-7 breast cancer cells exhibit heterogeneous dose responses with some cells requiring over ten-fold higher concentrations than the population average to achieve inhibition. Our work reimagines dose-response relationships for cancer drugs in an emerging paradigm of single-cell tumor heterogeneity.
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Affiliation(s)
- Patrick C Kinnunen
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Brock A Humphries
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Gary D Luker
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Kathryn E Luker
- Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jennifer J Linderman
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.
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Maltz E, Wollman R. Quantifying the phenotypic information in mRNA abundance. Mol Syst Biol 2022; 18:e11001. [PMID: 35965452 PMCID: PMC9376724 DOI: 10.15252/msb.202211001] [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: 02/24/2022] [Revised: 07/12/2022] [Accepted: 07/14/2022] [Indexed: 11/09/2022] Open
Abstract
Quantifying the dependency between mRNA abundance and downstream cellular phenotypes is a fundamental open problem in biology. Advances in multimodal single‐cell measurement technologies provide an opportunity to apply new computational frameworks to dissect the contribution of individual genes and gene combinations to a given phenotype. Using an information theory approach, we analyzed multimodal data of the expression of 83 genes in the Ca2+ signaling network and the dynamic Ca2+ response in the same cell. We found that the overall expression levels of these 83 genes explain approximately 60% of Ca2+ signal entropy. The average contribution of each single gene was 17%, revealing a large degree of redundancy between genes. Using different heuristics, we estimated the dependency between the size of a gene set and its information content, revealing that on average, a set of 53 genes contains 54% of the information about Ca2+ signaling. Our results provide the first direct quantification of information content about complex cellular phenotype that exists in mRNA abundance measurements.
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Affiliation(s)
- Evan Maltz
- Department of Chemistry and Biochemistry, UCLA, Los Angeles, CA, USA.,Institute of Quantitative and Computational Bioscience, UCLA, Los Angeles, CA, USA
| | - Roy Wollman
- Department of Chemistry and Biochemistry, UCLA, Los Angeles, CA, USA.,Institute of Quantitative and Computational Bioscience, UCLA, Los Angeles, CA, USA.,Department of Integrative Biology and Physiology, UCLA, Los Angeles, CA, USA
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Brink DP, Borgström C, Persson VC, Ofuji Osiro K, Gorwa-Grauslund MF. D-Xylose Sensing in Saccharomyces cerevisiae: Insights from D-Glucose Signaling and Native D-Xylose Utilizers. Int J Mol Sci 2021; 22:12410. [PMID: 34830296 PMCID: PMC8625115 DOI: 10.3390/ijms222212410] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/11/2021] [Accepted: 11/12/2021] [Indexed: 11/17/2022] Open
Abstract
Extension of the substrate range is among one of the metabolic engineering goals for microorganisms used in biotechnological processes because it enables the use of a wide range of raw materials as substrates. One of the most prominent examples is the engineering of baker's yeast Saccharomyces cerevisiae for the utilization of d-xylose, a five-carbon sugar found in high abundance in lignocellulosic biomass and a key substrate to achieve good process economy in chemical production from renewable and non-edible plant feedstocks. Despite many excellent engineering strategies that have allowed recombinant S. cerevisiae to ferment d-xylose to ethanol at high yields, the consumption rate of d-xylose is still significantly lower than that of its preferred sugar d-glucose. In mixed d-glucose/d-xylose cultivations, d-xylose is only utilized after d-glucose depletion, which leads to prolonged process times and added costs. Due to this limitation, the response on d-xylose in the native sugar signaling pathways has emerged as a promising next-level engineering target. Here we review the current status of the knowledge of the response of S. cerevisiae signaling pathways to d-xylose. To do this, we first summarize the response of the native sensing and signaling pathways in S. cerevisiae to d-glucose (the preferred sugar of the yeast). Using the d-glucose case as a point of reference, we then proceed to discuss the known signaling response to d-xylose in S. cerevisiae and current attempts of improving the response by signaling engineering using native targets and synthetic (non-native) regulatory circuits.
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Affiliation(s)
- Daniel P. Brink
- Applied Microbiology, Department of Chemistry, Lund University, P.O. Box 124, SE-221 00 Lund, Sweden; (C.B.); (V.C.P.); (K.O.O.)
| | - Celina Borgström
- Applied Microbiology, Department of Chemistry, Lund University, P.O. Box 124, SE-221 00 Lund, Sweden; (C.B.); (V.C.P.); (K.O.O.)
- BioZone Centre for Applied Bioscience and Bioengineering, Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College St., Toronto, ON M5S 3E5, Canada
| | - Viktor C. Persson
- Applied Microbiology, Department of Chemistry, Lund University, P.O. Box 124, SE-221 00 Lund, Sweden; (C.B.); (V.C.P.); (K.O.O.)
| | - Karen Ofuji Osiro
- Applied Microbiology, Department of Chemistry, Lund University, P.O. Box 124, SE-221 00 Lund, Sweden; (C.B.); (V.C.P.); (K.O.O.)
- Genetics and Biotechnology Laboratory, Embrapa Agroenergy, Brasília 70770-901, DF, Brazil
| | - Marie F. Gorwa-Grauslund
- Applied Microbiology, Department of Chemistry, Lund University, P.O. Box 124, SE-221 00 Lund, Sweden; (C.B.); (V.C.P.); (K.O.O.)
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