Elucidating the spectrum of epithelial-mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) states in clinical samples promises insights on cancer progression and drug resistance. ...Using mass cytometry time-course analysis, we resolve lung cancer EMT states through TGFβ-treatment and identify, through TGFβ-withdrawal, a distinct MET state. We demonstrate significant differences between EMT and MET trajectories using a computational tool (TRACER) for reconstructing trajectories between cell states. In addition, we construct a lung cancer reference map of EMT and MET states referred to as the EMT-MET PHENOtypic STAte MaP (PHENOSTAMP). Using a neural net algorithm, we project clinical samples onto the EMT-MET PHENOSTAMP to characterize their phenotypic profile with single-cell resolution in terms of our in vitro EMT-MET analysis. In summary, we provide a framework to phenotypically characterize clinical samples in the context of in vitro EMT-MET findings which could help assess clinical relevance of EMT in cancer in future studies.
B cells are capable of a wide range of effector functions including antibody secretion, antigen presentation, cytokine production, and generation of immunological memory. A consistent strategy for ...classifying human B cells by using surface molecules is essential to harness this functional diversity for clinical translation. We developed a highly multiplexed screen to quantify the co-expression of 351 surface molecules on millions of human B cells. We identified differentially expressed molecules and aligned their variance with isotype usage, VDJ sequence, metabolic profile, biosynthesis activity, and signaling response. Based on these analyses, we propose a classification scheme to segregate B cells from four lymphoid tissues into twelve unique subsets, including a CD45RB+CD27− early memory population, a class-switched CD39+ tonsil-resident population, and a CD19hiCD11c+ memory population that potently responds to immune activation. This classification framework and underlying datasets provide a resource for further investigations of human B cell identity and function.
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•A mass cytometry screen reveals 98 surface molecules expressed by human B cells•High-dimensional analysis identifies twelve B cell subsets across four tissues•CD45RB, CD11c, CD39, CD73, and CD95 define subsets of antigen-experienced B cells•Isotype usage, signaling, and metabolism vary in accordance with cell surface phenotype
To identify molecules that distinguish functionally distinct subsets of human B cells, Glass et al. screened the expression of 351 surface molecules by mass cytometry. By combining identified molecules with functional readouts, they propose a new classification scheme to segregate B cells from four lymphoid tissues into twelve unique subsets.
Cellular products derived from the activity of DNA, RNA, and protein synthesis collectively control cell identity and function. Yet there is little information on how these three biosynthesis ...activities are coordinated during transient and sparse cellular processes, such as activation and differentiation. Here, we describe Simultaneous Overview of tri-Molecule Biosynthesis (SOM
B), a molecular labeling and simultaneous detection strategy to quantify DNA, RNA, and protein synthesis in individual cells. Comprehensive interrogation of biosynthesis activities during transient cell states, such as progression through cell cycle or cellular differentiation, is achieved by partnering SOM
B with parallel quantification of select biomolecules with conjugated antibody reagents. Here, we investigate differential de novo DNA, RNA, and protein synthesis dynamics in transformed human cell lines, primary activated human immune cells, and across the healthy human hematopoietic continuum, all at a single-cell resolution.
Mesoderm induction begins during gastrulation. Recent evidence from several vertebrate species indicates that mesoderm induction continues after gastrulation in neuromesodermal progenitors (NMPs) ...within the posteriormost embryonic structure, the tailbud. It is unclear to what extent the molecular mechanisms of mesoderm induction are conserved between gastrula and post-gastrula stages of development. Fibroblast growth factor (FGF) signaling is required for mesoderm induction during gastrulation through positive transcriptional regulation of the T-box transcription factor
We find in zebrafish that FGF is continuously required for paraxial mesoderm (PM) induction in post-gastrula NMPs. FGF signaling represses the NMP markers
(
) and
through regulation of
and
, thereby committing cells to a PM fate. FGF-mediated PM induction in NMPs functions in tight coordination with canonical Wnt signaling during the epithelial to mesenchymal transition (EMT) from NMP to mesodermal progenitor. Wnt signaling initiates EMT, whereas FGF signaling terminates this event. Our results indicate that germ layer induction in the zebrafish tailbud is not a simple continuation of gastrulation events.
Subunits from the chromatin remodelers mammalian SWItch/Sucrose Non-Fermentable (mSWI/SNF) are mutated, deleted, or amplified in more than 40% of cancers. Understanding their functions in normal ...cells and the consequences of cancerous alterations will provide insight into developing new targeted therapies. Here we examined whether mSWI/SNF mutations increase cellular sensitivity to specific drugs. Taking advantage of the DepMap studies, we demonstrate that cancer cells harboring mutations of specific mSWI/SNF subunits exhibit a genetic dependency on translation factors and are sensitive to translation pathway inhibitors. Furthermore, mSWI/SNF subunits were present in the cytoplasm and interacted with the translation initiation machinery, and short-term inhibition and depletion of specific subunits decreased global translation, implicating a direct role for these factors in translation. Depletion of specific mSWI/SNF subunits also increased sensitivity to mTOR-PI3K inhibitors. In patient-derived breast cancer samples, mSWI/SNF subunits expression in both the nucleus and the cytoplasm was substantially altered. In conclusion, an unexpected cytoplasmic role for mSWI/SNF complexes in translation suggests potential new therapeutic opportunities for patients afflicted by cancers demonstrating alterations in their subunits.
This work establishes direct functions for mSWI/SNF in translation and demonstrates that alterations in mSWI/SNF confer a therapeutic vulnerability to translation pathway inhibitors in cancer cells.
Innate pattern recognition receptor agonists, including Toll-like receptors (TLRs), alter the tumor microenvironment and prime adaptive antitumor immunity. However, TLR agonists present toxicities ...associated with widespread immune activation after systemic administration. To design a TLR-based therapeutic suitable for systemic delivery and capable of safely eliciting tumor-targeted responses, we developed immune-stimulating antibody conjugates (ISACs) comprising a TLR7/8 dual agonist conjugated to tumor-targeting antibodies. Systemically administered human epidermal growth factor receptor 2 (HER2)-targeted ISACs were well tolerated and triggered a localized immune response in the tumor microenvironment that resulted in tumor clearance and immunological memory. Mechanistically, ISACs required tumor antigen recognition, Fcγ-receptor-dependent phagocytosis and TLR-mediated activation to drive tumor killing by myeloid cells and subsequent T-cell-mediated antitumor immunity. ISAC-mediated immunological memory was not limited to the HER2 ISAC target antigen since ISAC-treated mice were protected from rechallenge with the HER2
parental tumor. These results provide a strong rationale for the clinical development of ISACs.
Single-cell technologies generate large, high-dimensional datasets encompassing a diversity of omics. Dimensionality reduction captures the structure and heterogeneity of the original dataset, ...creating low-dimensional visualizations that contribute to the human understanding of data. Existing algorithms are typically unsupervised, using measured features to generate manifolds, disregarding known biological labels such as cell type or experimental time point. We repurpose the classification algorithm, linear discriminant analysis (LDA), for supervised dimensionality reduction of single-cell data. LDA identifies linear combinations of predictors that optimally separate a priori classes, enabling the study of specific aspects of cellular heterogeneity. We implement feature selection by hybrid subset selection (HSS) and demonstrate that this computationally efficient approach generates non-stochastic, interpretable axes amenable to diverse biological processes such as differentiation over time and cell cycle. We benchmark HSS-LDA against several popular dimensionality-reduction algorithms and illustrate its utility and versatility for the exploration of single-cell mass cytometry, transcriptomics, and chromatin accessibility data.
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•LDA is repurposed for supervised dimensionality reduction of single-cell data•HSS provides additional feature selection functionality•Performance is benchmarked against other dimensionality-reduction methods•LDA can be applied to any single-cell omics data
Dimensionality reduction enables the visualization of high-dimensional single-cell datasets. Typically, these algorithms are unsupervised, disregarding known biological labels such as cell type or experimental time point. LDA identifies linear combinations of predictors that optimally separate these a priori labels. Combined with HSS for feature selection, LDA generates interpretable axes for visualization and analysis of single-cell data. LDA is extensible to unseen data and can also be used as an input to other methods, for example, to integrate multi-label data for UMAP visualization.
LDA enables the exploration of different aspects of cellular heterogeneity, even within the same dataset, by tailoring axes to separate specific, user-defined labels. More generally, this work introduces an alternative approach to visualize single-cell data and highlights the abundance of experimental metadata and cellular features that can be exploited for supervised analysis.
Amouzgar et al. repurpose the classification algorithm, LDA, for supervised dimensionality reduction of single-cell data. Paired with HSS for feature selection, LDA creates intuitive visualizations, separating cells from user-defined labels such as experimental time point and cell-cycle phase. LD axes can be extended to unseen data and used as inputs for other algorithms and methods. HSS-LDA is benchmarked against several popular dimensionality-reduction methods and is applied to a diversity of biological problems and single-cell omics.
Abstract
The role of EMT in cancer has been well reported and has been shown to prime cells for invasion and metastasis. EMT can be adopted or reversed (i.e. mesenchymal to epithelial transition, ...MET) by cells, revealing plasticity that can also lead to stemness and drug resistance. Although it is appreciated that EMT is not a binary process of two extremes but instead a continuum of intermediate states of partial EMT phenotypes, these are poorly defined. Given that intermediate EMT cancer states are viewed as critical for understanding and clinically targeting EMT processes, our aim was to dynamically capture and characterize intermediate EMT states in TGF beta treated lung cancer cells and clinical specimens. With single cell analysis we identified 4 distinct transition states. These states patterned an EMT axis featuring: (1) an epithelial, (2) a partial EMT and (3) a mesenchymal state that branches off to (4) a subset of phenotypically stem-like cells. Transition was reflected by gradual changes in E Cadherin, Vimentin, CD44 and CD24 levels. “True” mesenchymal state (E Cad- Vim+) was isolated to the stem-like cells (CD44hiCD24lo), which were negative for Twist protein expression. To interrogate the dynamism of EMT and MET processes, we performed TGF beta withdrawal experiments, which showed that most cells were able to reverse their behavior. Simultaneous analysis of 30 parameters with CyTOF confirmed the 4-state transition, offering deep dynamic views of a variety of cell surface, signaling, cell cycle, and transcriptional markers. We then proceeded to computationally identify and define additional states of the EMT/MET spectrum that are visited by transitioning cells in a step-wise manner, in order to construct a lung cancer EMT/MET proteomic landscape. To tackle this, we applied CCAST, an algorithm that employs decision trees to identify and discretize homogeneous cell subpopulations among heterogeneous single cell data. When we assembled the high dimensional data in a timely order, we were able to visualize the emerging states that cells visited during their transitions. This revealed the existence of more than one possible EMT/MET trajectories as well as the existence of a transient subpopulation of cells with a distinct phenotype (Twist+, CD34+, E Cad-, Cytokeratin 7+, pEGFR-). Finally, CyTOF analysis and projection of 2 lung adenocarcinoma specimens on the constructed EMT/MET landscape confirmed the existence of states observed in our cell line studies, including the aforementioned Twist+ transient subpopulation. In summary, we provide a lung cancer cell proteomic map that dissects EMT phenotypic plasticity. Clinically, this type of EMT/MET proteomic mapping could help identify, predict and target EMT mechanisms known to have a role in cancer progression, drug resistance and disease recurrence.
Citation Format: Loukia G. Karacosta, Benedict Anchang, Samuel Kimmey, Matt van de Rijn, Joseph B. Shrager, Sean C. Bendall, Sylvia K. Plevritis. Identifying dynamic EMT states and constructing a proteomic EMT landscape of lung cancer using single cell multidimensional analysis abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4997.
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