A LINCS microenvironment perturbation resource for integrative assessment of ligand-mediated molecular and phenotypic responses

Gross, Sean M.; Dane, Mark; Smith, Rebecca; Devlin, Kaylyn L.; McLean, Ian C.; Derrick, Daniel S.; Mills, Caitlin E.; Subramanian, Kartik; London, Alexandra B.; Torre, Denis; Erdem, Cemal; Lyons, Nicholas; Natoli, Ted; Pessa, Sarah; Lü, Xiaodong; Mullahoo, James; Li, Jonathan; Adam, Miriam; Wassie, Brook T.; Liu, Moqing; Kilburn, David; Liby, Tiera; Bucher, Elmar; Sanchez-Aguila, Crystal; Daily, Kenneth; Omberg, Larsson; Wang, Yunguan; Jacobson, Connor A.; Yapp, Clarence; Chung, Mirra; Vidović, Dušica; Lu, Yiling; Schürer, Stephan C.; Lee, Albert; Pillai, Ajay S.; Subramanian, Aravind; Papanastasiou, Malvina; Fraenkel, Ernest; Feiler, Heidi S.; Mills, Gordon B.; Jaffe, Jacob D.; Ma’ayan, Avi; Birtwistle, Marc R.; Sorger, Peter K.; Korkola, James E.; Gray, Joe W.; Heiser, Laura M.

doi:10.1101/2021.08.06.455429

Cited by 6 publications

(23 citation statements)

References 104 publications

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“…An overview of the live-cell imaging trajectory embedding workflow is shown in Figure 1. The live-cell imaging assays we analyzed are part of the LINCS consortium 36,37 MCF10A project 38 . While lacking the molecular resolution of fluorescently labeled imaging, phase contrast microscopy has the benefit of minimally perturbing wild-type cell behavior, and the quantification of morphodynamical cell states demonstrates the biological information intrinsic to cellular trajectories in a broadly applicable and relatively simple imaging assay.…”

Section: Resultsmentioning

confidence: 99%

“…6 image stacks were collected for each ligand treatment. Experimental protocols can be found in detail at the publicly available Synapse database 38 .…”

Section: Methodsmentioning

confidence: 99%

“…All codes and scripts to perform the analysis in this work can be found at the project github repository. https://github.com/jcopperm/celltraj LINCS MCF10A Molecular Deep Dive data is available in some formats from the synapse database 38 and additional data is available upon request.…”

Section: Competing Interestmentioning

confidence: 99%

“…These ligands target canonical AKT/ERK, JAK/STAT, and SMAD signaling pathways and are implicated in regulating cell proliferation, differentiation state, and motility. The live-cell imaging is part of a broader data collection effort through the Library of Integrated Network-Based Cellular Signatures (LINCS) consortium 36,37 MCF10A project 38 where characterization of the molecular and phenotypic response to these ligand perturbations is explored. Molecular responses, as measured through bulk cell measurements (RNAseq, protein expression levels via RPPA, and chromatin state ATACseq) and cyclic immunofluorescence (cycIF) indicate that cellular responses under the different ligand treatments activate canonical cell signaling pathways leading to a unique cell response in each ligand condition 38 .…”

Section: Introductionmentioning

confidence: 99%

“…The live-cell imaging is part of a broader data collection effort through the Library of Integrated Network-Based Cellular Signatures (LINCS) consortium 36,37 MCF10A project 38 where characterization of the molecular and phenotypic response to these ligand perturbations is explored. Molecular responses, as measured through bulk cell measurements (RNAseq, protein expression levels via RPPA, and chromatin state ATACseq) and cyclic immunofluorescence (cycIF) indicate that cellular responses under the different ligand treatments activate canonical cell signaling pathways leading to a unique cell response in each ligand condition 38 . Our live-cell imaging cell-trajectory-based analysis was developed to characterize the morphodynamical changes accompanying these molecular responses.…”

Section: Introductionmentioning

confidence: 99%

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Morphodynamical cell state description via live-cell imaging trajectory embedding

Copperman

Gross

Chang

et al. 2021

Preprint

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Time-lapse imaging provides powerful insight into the dynamical response of cells to perturbation, but the quantitative analysis of morphological changes over time is a challenge. Here, we exploit the concept of "morphodynamical trajectories" to analyze cellular behavior using morphological feature trajectory histories, rather than the common practice of examining morphological feature time courses in the space of single-timepoint (snapshot) morphological features. Our morphodynamical trajectory embedding analysis yielded quantitative and descriptive models of future time points based on the extended history information of MCF10A mammary epithelial cells treated with a panel of ligands. The trajectory analysis constructs a shared morphodynamical cell-state landscape, where the response of MCF10A cells induced by various extracellular signals is characterized by ligand-specific regulation of state transitions. Additionally, we show that including trajectories in single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, and (ii) better separation of phenotypes and more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable for the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.

show abstract

Section: Resultsmentioning

confidence: 99%

“…6 image stacks were collected for each ligand treatment. Experimental protocols can be found in detail at the publicly available Synapse database 38 .…”

Section: Methodsmentioning

confidence: 99%

Section: Competing Interestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Morphodynamical cell state description via live-cell imaging trajectory embedding

Copperman

Gross

Chang

et al. 2021

Preprint

Self Cite

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A lineage tree-based hidden Markov model quantifies cellular heterogeneity and plasticity

et al. 2022

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Individual cells can assume a variety of molecular and phenotypic states and recent studies indicate that cells can rapidly adapt in response to therapeutic stress. Such phenotypic plasticity may confer resistance, but also presents opportunities to identify molecular programs that could be targeted for therapeutic benefit. Approaches to quantify tumor-drug responses typically focus on snapshot, population-level measurements. While informative, these methods lack lineage and temporal information, which are particularly critical for understanding dynamic processes such as cell state switching. As new technologies have become available to measure lineage relationships, modeling approaches will be needed to identify the forms of cell-to-cell heterogeneity present in these data. Here we apply a lineage tree-based adaptation of a hidden Markov model that employs single cell lineages as input to learn the characteristic patterns of phenotypic heterogeneity and state transitions. In benchmarking studies, we demonstrated that the model successfully classifies cells within experimentally-tractable dataset sizes. As an application, we analyzed experimental measurements in cancer and non-cancer cell populations under various treatments. We find evidence of multiple phenotypically distinct states, with considerable heterogeneity and unique drug responses. In total, this framework allows for the flexible modeling of single cell heterogeneity across lineages to quantify, understand, and control cell state switching.

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Morphodynamical cell state description via live-cell imaging trajectory embedding

et al. 2023

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Time-lapse imaging is a powerful approach to gain insight into the dynamic responses of cells, but the quantitative analysis of morphological changes over time remains challenging. Here, we exploit the concept of “trajectory embedding” to analyze cellular behavior using morphological feature trajectory histories—that is, multiple time points simultaneously, rather than the more common practice of examining morphological feature time courses in single timepoint (snapshot) morphological features. We apply this approach to analyze live-cell images of MCF10A mammary epithelial cells after treatment with a panel of microenvironmental perturbagens that strongly modulate cell motility, morphology, and cell cycle behavior. Our morphodynamical trajectory embedding analysis constructs a shared cell state landscape revealing ligand-specific regulation of cell state transitions and enables quantitative and descriptive models of single-cell trajectories. Additionally, we show that incorporation of trajectories into single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, (ii) better separation of phenotypes, and (iii) more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable to the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.

show abstract

A LINCS microenvironment perturbation resource for integrative assessment of ligand-mediated molecular and phenotypic responses

Cited by 6 publications

References 104 publications

Morphodynamical cell state description via live-cell imaging trajectory embedding

Morphodynamical cell state description via live-cell imaging trajectory embedding

A lineage tree-based hidden Markov model quantifies cellular heterogeneity and plasticity

Morphodynamical cell state description via live-cell imaging trajectory embedding

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