Biology subjects

Bodenmiller, B.

Publications and source records attributed to Bodenmiller, B..

6 recordsLinked to original sources

bioRxiv2018

Analysis of the human kinome and phosphatome reveals diseased signaling networks induced by overexpression

Kinase and phosphatase overexpression drives tumorigenesis and drug resistance in many cancer types. Signaling networks reprogrammed by protein overexpression remain largely uncharacterized, hindering discovery of paths to therapeutic intervention. We previously developed a single cell proteomics approach based on mass cytometry that enables quantitative assessment of overexpression effects on the signaling network. Here we applied this approach in a human kinome- and phosphatome-wide study to assess how 649 individually overexpressed proteins modulate the cancer-related signaling network in HEK293T cells. Based on these data we expanded the functional classification of human kinases and phosphatases and detected 208 novel signaling relationships. In the signaling dynamics analysis, we showed that increased ERK-specific phosphatases sustained proliferative signaling, and using a novel combinatorial overexpression approach, we confirmed this phosphatase-driven mechanism of cancer progression. Finally, we identified 54 proteins that caused ligand-independent ERK activation with potential as biomarkers for drug resistance in cells carrying BRAF mutations.

systems biology↗

bioRxiv2018

Modelling cell-cell interactions from spatial molecular data with spatial variance component analysis

Technological advances allow for assaying multiplexed spatially resolved RNA and protein expression profiling of individual cells, thereby capturing physiological tissue contexts of single cell variation. While methods for the high-throughput generation of spatial expression profiles are increasingly accessible, computational methods for studying the relevance of the spatial organization of tissues on cell-cell heterogeneity are only beginning to emerge. Here, we present spatial variance component analysis (SVCA), a computational framework for the analysis of spatial molecular data. SVCA enables quantifying the effect of cell-cell interactions, as well as environmental and intrinsic cell features on the expression levels of individual genes or proteins. In application to a breast cancer Imaging Mass Cytometry dataset, our model allows for robustly estimating spatial variance signatures, identifying cell-cell interactions as a major driver of expression heterogeneity. Finally, we apply SVCA to high-dimensional imaging-derived RNA data, where we identify molecular pathways that are linked to cell-cell interactions.

bioinformatics↗

bioRxiv2017

Channel crosstalk correction in suspension and imaging mass cytometry

Mass cytometry enables simultaneous analysis of over 40 proteins and their modifications in single cells through use of metal-tagged antibodies. Compared to fluorescent dyes, the use of pure metal isotopes strongly reduces spectral overlap among measurement channels. Crosstalk still exists, however, caused by isotopic impurity, oxide formation, and mass cytometer properties. Spillover effects can be minimized, but not avoided, by following a set of constraining rules when designing an antibody panel. Generation of such low crosstalk panels requires considerable expert knowledge, knowledge of the abundance of each marker and substantial experimental effort. Here we describe a novel bead-based compensation workflow that includes R-based software and a web tool, which enables correction for interference between channels. We demonstrate utility in suspension mass cytometry and show how this approach can be applied to imaging mass cytometry. Our approach greatly simplifies the development of new antibody panels, increases flexibility for antibody-metal pairing, improves overall data quality, thereby reducing the risk of reporting cell phenotype and function artifacts, and greatly facilitates analysis of complex samples for which antigen abundances are unknown.

bioinformatics↗

bioRxiv2017

Learning Edge Rewiring in EMT from Single Cell Data

Cellular regulatory networks are not static, but continuously reconfigure in response to stimuli via alterations in gene expression and protein confirmations. However, typical computational approaches treat them as static interaction networks derived from a single experimental time point. Here, we provide a method for learning the dynamic modulation, or rewiring of pairwise relationships (edges) from a static single-cell data. We use the epithelial-to-mesenchymal transition (EMT) in murine breast cancer cells as a model system, and measure mass cytometry data three days after induction of the transition by TGF{beta}. We take advantage of transitional rate variability between cells in the data by deriving a pseudo-time EMT trajectory. Then we propose methods for visualizing and quantifying time-varying edge behavior over the trajectory and use these methods: TIDES (Trajectory Imputed DREMI scores), and measure of edge dynamism (3DDREMI) to predict and validate the effect of drug perturbations on EMT.

systems biology↗

bioRxiv2017

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body, by undertaking a Human Cell Atlas Project as an international collaborative effort. The aim would be to define all human cell types in terms of distinctive molecular profiles (e.g., gene expression) and connect this information with classical cellular descriptions (e.g., location and morphology). A comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, as well as provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas.

cell biology↗

bioRxiv2017

Systematic analysis of cell phenotypes and cellular social networks in tissues using the multiplexed image cytometry analysis toolbox (miCAT)

Single-cell, spatially resolved omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source computational histology topography analysis toolbox (histoCAT) to enable the interactive, quantitative, and comprehensive exploration of phenotypes of individual cells, cell-to-cell interactions, microenvironments, and morphological structures within intact tissues. histoCAT will be useful in all areas of tissue-based research. We highlight the unique abilities of histoCAT by analysis of highly multiplexed mass cytometry images of human breast cancer tissues.\n\nTechnological advances in the multi-parametric analysis of single cells have revealed an unprecedented heterogeneity of cellular phenotypes and functional states that are concealed in population-based studies1-3. Each cellular phenotype is defined by the interplay of its internal state and the environment in which ...

systems biology↗