Kinome-wide decoding of network-attacking mutations rewiring cancer signaling.

Cancer cells acquire pathological phenotypes through accumulation of mutations that perturb signaling networks. However, global analysis of these events is currently limited. Here, we identify six types of network-attacking mutations (NAMs), including changes in kinase and SH2 modulation, network rewiring, and the genesis and extinction of phosphorylation sites. We developed a computational platform (ReKINect) to identify NAMs and systematically interpreted the exomes and quantitative (phospho-)proteomes of five ovarian cancer cell lines and the global cancer genome repository. We identified and experimentally validated several NAMs, including PKCγ M501I and PKD1 D665N, which encode specificity switches analogous to the appearance of kinases de novo within the kinome. We discover mutant molecular logic gates, a drift toward phospho-threonine signaling, weakening of phosphorylation motifs, and kinase-inactivating hotspots in cancer. Our method pinpoints functional NAMs, scales with the complexity of cancer genomes and cell signaling, and may enhance our capability to therapeutically target tumor-specific networks.

Uncovering axes of variation among single-cell cancer specimens.

While several tools have been developed to map axes of variation among individual cells, no analogous approaches exist for identifying axes of variation among multicellular biospecimens profiled at single-cell resolution. For this purpose, we developed 'phenotypic earth mover's distance' (PhEMD). PhEMD is a general method for embedding a 'manifold of manifolds', in which each datapoint in the higher-level manifold (of biospecimens) represents a collection of points that span a lower-level manifold (of cells). We apply PhEMD to a newly generated drug-screen dataset and demonstrate that PhEMD uncovers axes of cell subpopulational variation among a large set of perturbation conditions. Moreover, we show that PhEMD can be used to infer the phenotypes of biospecimens not directly profiled. Applied to clinical datasets, PhEMD generates a map of the patient-state space that highlights sources of patient-to-patient variation. PhEMD is scalable, compatible with leading batch-effect correction techniques and generalizable to multiple experimental designs.

Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry.

Mass cytometry enables high-dimensional, single-cell analysis of cell type and state. In mass cytometry, rare earth metals are used as reporters on antibodies. Analysis of metal abundances using the mass cytometer allows determination of marker expression in individual cells. Mass cytometry has previously been applied only to cell suspensions. To gain spatial information, we have coupled immunohistochemical and immunocytochemical methods with high-resolution laser ablation to CyTOF mass cytometry. This approach enables the simultaneous imaging of 32 proteins and protein modifications at subcellular resolution; with the availability of additional isotopes, measurement of over 100 markers will be possible. We applied imaging mass cytometry to human breast cancer samples, allowing delineation of cell subpopulations and cell-cell interactions and highlighting tumor heterogeneity. Imaging mass cytometry complements existing imaging approaches. It will enable basic studies of tissue heterogeneity and function and support the transition of medicine toward individualized molecularly targeted diagnosis and therapies.

Enhanced multiplexing in mass cytometry using osmium and ruthenium tetroxide species.

Mass cytometry facilitates high-dimensional, quantitative, single-cell analysis. The method for sample multiplexing in mass cytometry, called mass-tag cellular barcoding (MCB), relies on the covalent reaction of bifunctional metal chelators with intracellular proteins. Here, we describe the use of osmium and ruthenium tetroxides (OsO4 and RuO4 ) that bind covalently with fatty acids in the cellular membranes and aromatic amino acids in proteins. Both OsO4 and RuO4 rapidly reacted and allowed for MCB with live cells, crosslinked cells, and permeabilized cells. Given the covalent nature of the labeling reaction, isotope leaching was not observed. OsO4 and RuO4 were used in a 20-sample barcoding protocol together with palladium isotopes. As mass channels occupied by osmium and ruthenium are not used for antibody detection the number of masses effectively utilized in a single experiment is expanded. OsO4 and RuO4 can therefore be used as MCB reagents for a wide range of mass cytometry workflows. © 2016 International Society for Advancement of Cytometry.

A practical guide to multiplexed mass cytometry.

Recent advances in inductively coupled plasma mass spectrometry (ICP-MS) as applied in mass cytometry, enabled its broad applicability to life science research. Mass cytometry enables the high-dimensional characterization of cellular systems by simultaneously measuring dozens of metal isotope reporter labeled antibodies bound to cell components. With the ability to simultaneously interrogate an unprecedented number of molecular components on a per cell basis, it offers the possibility to gain better understanding of single cell biology in heterogeneous samples. To upscale this single cell information to screening approaches by mass cytometry, a cell-based multiplexing technique, called mass-tag cellular barcoding (MCB), was developed. MCB enables the simultaneous analysis of multiple cell samples by using n metal ion tags to multiplex up to 2 (n) samples. Different mass tag combinations are used to label individual cell samples with a unique mass barcode that allows multiple samples to be combined and immunostained together for a single analysis on the mass cytometer. Taken together, MCB enables increased sample throughput, reduces antibody consumption, and increases the overall data quality. In this chapter, we describe the MCB to array the samples in a 96-well format that allows for medium-scale profiling/screening experiments to be run on a standard mass cytometer.

Single-cell immune profiling reveals markers of emergency myelopoiesis that distinguish severe from mild respiratory syncytial virus disease in infants.

Whereas most infants infected with respiratory syncytial virus (RSV) show no or only mild symptoms, an estimated 3 million children under five are hospitalized annually due to RSV disease. This study aimed to investigate biological mechanisms and associated biomarkers underlying RSV disease heterogeneity in young infants, enabling the potential to objectively categorize RSV-infected infants according to their medical needs. Immunophenotypic and functional profiling demonstrated the emergence of immature and progenitor-like neutrophils, proliferative monocytes (HLA-DRLow , Ki67+), impaired antigen-presenting function, downregulation of T cell response and low abundance of HLA-DRLow B cells in severe RSV disease. HLA-DRLow monocytes were found as a hallmark of RSV-infected infants requiring hospitalization. Complementary transcriptomics identified genes associated with disease severity and pointed to the emergency myelopoiesis response. These results shed new light on mechanisms underlying the pathogenesis and development of severe RSV disease and identified potential new candidate biomarkers for patient stratification.

Mechanistic Model of Signaling Dynamics Across an Epithelial Mesenchymal Transition.

Intracellular signaling pathways are at the core of cellular information processing. The states of these pathways and their inputs determine signaling dynamics and drive cell function. Within a cancerous tumor, many combinations of cell states and microenvironments can lead to dramatic variations in responses to treatment. Network rewiring has been thought to underlie these context-dependent differences in signaling; however, from a biochemical standpoint, rewiring of signaling networks should not be a prerequisite for heterogeneity in responses to stimuli. Here we address this conundrum by analyzing an in vitro model of the epithelial mesenchymal transition (EMT), a biological program implicated in increased tumor invasiveness, heterogeneity, and drug resistance. We used mass cytometry to measure EGF signaling dynamics in the ERK and AKT signaling pathways before and after induction of EMT in Py2T murine breast cancer cells. Analysis of the data with standard network inference methods suggested EMT-dependent network rewiring. In contrast, use of a modeling approach that adequately accounts for single-cell variation demonstrated that a single reaction-based pathway model with constant structure and near-constant parameters is sufficient to represent differences in EGF signaling across EMT. This result indicates that rewiring of the signaling network is not necessary for heterogeneous responses to a signal and that unifying reaction-based models should be employed for characterization of signaling in heterogeneous environments, such as cancer.

Learning time-varying information flow from single-cell epithelial to mesenchymal transition data.

Cellular regulatory networks are not static, but continuously reconfigure in response to stimuli via alterations in protein abundance and confirmation. However, typical computational approaches treat them as static interaction networks derived from a single time point. Here, we provide methods for learning the dynamic modulation of relationships between proteins from static single-cell data. We demonstrate our approach using TGFß induced epithelial-to-mesenchymal transition (EMT) in murine breast cancer cell line, profiled with mass cytometry. We take advantage of the asynchronous rate of transition to EMT in the data and derive a pseudotime EMT trajectory. We propose methods for visualizing and quantifying time-varying edge behavior over the trajectory, and a metric of edge dynamism to predict the effect of drug perturbations on EMT.