High-dimension single-cell analysis applied to cancer.
ABSTRACT: High-dimension single-cell technology is transforming our ability to study and understand cancer. Numerous studies and reviews have reported advances in technology development. The biological insights gleaned from single-cell technology about cancer biology are less reviewed. Here we focus on research studies that illustrate novel aspects of cancer biology that bulk analysis could not achieve, and discuss the fresh insights gained from the application of single-cell technology across basic and clinical cancer studies.
Project description:Tumour metastasis is a dynamic and systemic process. It is no longer seen as a tumour cell-autonomous program but as a multifaceted and complex series of events, which is influenced by the intrinsic cellular mutational burden of cancer cells and the numerous bidirectional interactions between malignant and non-malignant cells and fine-tuned by the various extrinsic cues of the extracellular matrix. In cancer biology, metastasis as a process is one of the most technically challenging aspects of cancer biology to study. As a result, new platforms and technologies are continually being developed to better understand this process. In this review, we discuss some of the recent advances in metastasis and how the information gleaned is re-shaping our understanding of metastatic dissemination.
Project description:It has been posited that animal development evolved from pre-existing mechanisms for regulating cell differentiation in the single celled and colonial ancestors of animals. Although the progenitors of animals cannot be studied directly, insights into their cell biology may be gleaned from comparisons between animals and their closest living relatives, the choanoflagellates. We report here on the life history, cell differentiation and intercellular interactions in the colony-forming choanoflagellate Salpingoeca rosetta. In response to diverse environmental cues, S. rosetta differentiates into at least five distinct cell types, including three solitary cell types (slow swimmers, fast swimmers, and thecate cells) and two colonial forms (rosettes and chains). Electron microscopy reveals that cells within colonies are held together by a combination of fine intercellular bridges, a shared extracellular matrix, and filopodia. In addition, we have discovered that the carbohydrate-binding protein wheat germ agglutinin specifically stains colonies and the slow swimmers from which they form, showing that molecular differentiation precedes multicellular development. Together, these results help establish S. rosetta as a model system for studying simple multicellularity in choanoflagellates and provide an experimental framework for investigating the origin of animal multicellularity and development.
Project description:Pancreatic ductal adenocarcinoma co-opts multiple cellular and extracellular mechanisms to create a complex cancer organ with an unusual proclivity for metastasis and resistance to therapy. Cell-autonomous events are essential for the initiation and maintenance of pancreatic ductal adenocarcinoma, but recent studies have implicated critical non-cell autonomous processes within the robust desmoplastic stroma that promote disease pathogenesis and resistance. Thus, non-malignant cells and associated factors are culprits in tumor growth, immunosuppression and invasion. However, even this increasing awareness of non-cell autonomous contributions to disease progression is tempered by the conflicting roles stromal elements can play. A greater understanding of stromal complexity and complicity has been aided in part by studies in highly faithful genetically engineered mouse models of pancreatic ductal adenocarcinoma. Insights gleaned from such studies are spurring the development of therapies designed to reengineer the pancreas cancer stroma and render it permissive to agents targeting cell-autonomous events or to reinstate immunosurveillance. Integrating conventional and immunological treatments in the context of stromal targeting may provide the key to a durable clinical impact on this formidable disease.
Project description:Our goal in developing Microphysiological Systems (MPS) technology is to provide an improved approach for more predictive preclinical drug discovery via a highly integrated experimental/computational paradigm. Success will require quantitative characterization of MPSs and mechanistic analysis of experimental findings sufficient to translate resulting insights from in vitro to in vivo. We describe herein a systems pharmacology approach to MPS development and utilization that incorporates more mechanistic detail than traditional pharmacokinetic/pharmacodynamic (PK/PD) models. A series of studies illustrates diverse facets of our approach. First, we demonstrate two case studies: a PK data analysis and an inflammation response--focused on a single MPS, the liver/immune MPS. Building on the single MPS modeling, a theoretical investigation of a four-MPS interactome then provides a quantitative way to consider several pharmacological concepts such as absorption, distribution, metabolism, and excretion in the design of multi-MPS interactome operation and experiments.
Project description:The cloning of green fluorescent protein (GFP) 15 years ago revolutionized cell biology by permitting visualization of a wide range of molecular mechanisms within living cells. Though initially used to make largely qualitative assessments of protein levels and localizations, fluorescence microscopy has since evolved to become highly quantitative and high-throughput. Computational image analysis has catalyzed this evolution, enabling rapid and automated processing of large datasets. Here, we review studies that combine time-lapse fluorescence microscopy and automated image analysis to investigate dynamic events at the single-cell level. We highlight examples where single-cell analysis provides unique mechanistic insights into cellular processes that cannot be otherwise resolved in bulk assays. Additionally, we discuss studies where quantitative microscopy facilitates the assembly of detailed 4D lineages in developing organisms. Finally, we describe recent advances in imaging technology, focusing especially on platforms that allow the simultaneous perturbation and quantitative monitoring of biological systems.
Project description:Much of our understanding of the biological mechanisms that underlie cellular functions, such as migration, differentiation and force-sensing has been garnered from studying cells cultured on two-dimensional (2D) glass or plastic surfaces. However, more recently the cell biology field has come to appreciate the dissimilarity between these flat surfaces and the topographically complex, three-dimensional (3D) extracellular environments in which cells routinely operate in vivo. This has spurred substantial efforts towards the development of in vitro 3D biomimetic environments and has encouraged much cross-disciplinary work among biologists, material scientists and tissue engineers. As we move towards more-physiological culture systems for studying fundamental cellular processes, it is crucial to define exactly which factors are operative in 3D microenvironments. Thus, the focus of this Commentary will be on identifying and describing the fundamental features of 3D cell culture systems that influence cell structure, adhesion, mechanotransduction and signaling in response to soluble factors, which - in turn - regulate overall cellular function in ways that depart dramatically from traditional 2D culture formats. Additionally, we will describe experimental scenarios in which 3D culture is particularly relevant, highlight recent advances in materials engineering for studying cell biology, and discuss examples where studying cells in a 3D context provided insights that would not have been observed in traditional 2D systems.
Project description:Extracellular signaling is commonly mediated through post-translational protein modifications that propagate messages from membrane-bound receptors to ultimately regulate gene expression. Signaling cascades are ubiquitously intertwined, and a full understanding of function can only be gleaned by observing dynamics across multiple key signaling nodes. Importantly, targets within signaling cascades often represent opportunities for therapeutic development or can serve as diagnostic biomarkers. Protein phosphorylation is a particularly important post-translational modification that controls many essential cellular signaling pathways. Not surprisingly, aberrant phosphorylation is found in many human diseases, including cancer, and phosphoprotein-based biomarker signatures hold unrealized promise for disease monitoring. Moreover, phosphoprotein analysis has wide-ranging applications across fundamental chemical biology, as many drug discovery efforts seek to target nodes within kinase signaling pathways. For both fundamental and translational applications, the analysis of phosphoprotein biomarker targets is limited by a reliance on labor-intensive and/or technically challenging methods, particularly when considering the simultaneous monitoring of multiplexed panels of phosphoprotein biomarkers. We have developed a technology based upon arrays of silicon photonic microring resonator sensors that fills this void, facilitating the rapid and automated analysis of multiple phosphoprotein levels from both cell lines and primary human tumor samples requiring only minimal sample preparation.
Project description:During the formation of breast cancer, many genes become altered as cells evolve progressively from normal to a pre-malignant to a malignant state of growth. How mutations in genes lead to specific subtypes of human breast cancer is only partially understood. Here we review how initial genetic or epigenetic alterations within mammary epithelial cells (MECs) can alter cell fate decisions and put pre-malignant cells on a path towards cancer development with specific phenotypes. Understanding the early stages of breast cancer initiation and progression and how normal developmental processes are hijacked during transformation has significant implications for improving early detection and prevention of breast cancer. In addition, insights gleaned from this understanding may also be important for developing subtype-specific treatment options.
Project description:Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity. Capitalizing on these developments, various public efforts such as The Cancer Genome Atlas (TCGA) generate disparate omic data for large patient cohorts. As demonstrated by recent studies, these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression. However, these insights are limited by the vast search space and as a result low statistical power to make new discoveries. In this paper, we propose methods for integrating disparate omic data using molecular interaction networks, with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity. Namely, we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression, and that network-based integration of mutation and differential expression data can reveal these "silent players". For this purpose, we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution. We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes, but have an essential role in tumor development and patient outcome. We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA. Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data, providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation.
Project description:Immunotherapies, including vaccines, represent a potent tool to prevent or contain disease with high morbidity or mortality such as infections and cancer. However, despite their widespread use, we still have a limited understanding of the mechanisms underlying the induction of protective immune responses.Immunity is made of a multifaceted set of integrated responses involving a dynamic interaction of thousands of molecules; among those is a growing appreciation for the role the innate immunity (i.e. pathogen recognition receptors - PRRs) plays in determining the nature and duration (immune memory) of adaptive T and B cell immunity. The complex network of interactions between immune manipulation of the host (immunotherapy) on one side and innate and adaptive responses on the other might be fully understood only employing the global level of investigation provided by systems biology. In this framework, the advancement of high-throughput technologies, together with the extensive identification of new genes, proteins and other biomolecules in the "omics" era, facilitate large-scale biological measurements. Moreover, recent development of new computational tools enables the comprehensive and quantitative analysis of the interactions between all of the components of immunity over time. Here, we review recent progress in using systems biology to study and evaluate immunotherapy and vaccine strategies for infectious and neoplastic diseases. Multi-parametric data provide novel and often unsuspected mechanistic insights while enabling the identification of common immune signatures relevant to human investigation such as the prediction of immune responsiveness that could lead to the improvement of the design of future immunotherapy trials. Thus, the paradigm switch from "empirical" to "knowledge-based" conduct of medicine and immunotherapy in particular, leading to patient-tailored treatment.