Biology subjects

Bodenmiller, B.

Publications and source records attributed to Bodenmiller, B..

At least 37 records · Page 2Linked to original sources

bioRxiv2021

Optimizing multiplexed imaging experimental design through tissue spatial segregation estimation

Recent advances in multiplexed imaging methods allow simultaneous detection of dozens of proteins and hundreds of RNAs enabling deep spatial characterization of both healthy and diseased tissues. Parameters for design of optimal sequencing-based experiments have been established, but such parameters, especially those estimating how much area has to be imaged to capture all cell phenotype clusters, are lacking for multiplex imaging studies. Here, using a spatial transcriptomic atlas of healthy and tumor human tissues, we developed a new statistical framework that determines the number and area of fields of view necessary to accurately identify all cell types that are part of a tissue. Using this strategy on imaging mass cytometry data, we identified a measurement of tissue spatial segregation that enables optimal experimental design. This strategy will enable significantly improved design of multiplexed imaging studies.

bioinformatics↗

bioRxiv2021

An end-to-end workflow for multiplexed image processing and analysis

Simultaneous profiling of the spatial distributions of multiple biological molecules at single-cell resolution has recently been enabled by the development of highly multiplexed imaging technologies. Extracting and analyzing biologically relevant information contained in complex imaging data requires the use of a diverse set of computational tools and algorithms. Here, we report the development of a user-friendly, customizable, and interoperable workflow for processing and analyzing data generated by highly multiplexed imaging technologies. The steinbock framework supports image pre-processing, segmentation, feature extraction, and standardized data export. Each step is performed in a reproducible fashion. The imcRtools R/Bioconductor package forms the bridge between image processing and single-cell analysis by directly importing data generated by steinbock. The package further supports spatial data analysis and integrates with tools developed within the Bio-conductor project. Together, the tools described in this workflow facilitate analyses of multiplexed imaging raw data at the single-cell and spatial level.

bioinformatics↗

bioRxiv2021

Multiplexed Imaging Mass Cytometry of Chemokine Milieus in Metastatic Melanoma Characterizes Features of Response to Immunotherapy

Intratumoral immune cells are crucial for tumor control and anti-tumor responses during immunotherapy. Immune cell trafficking into tumors is mediated by chemokines, which are expressed and secreted upon various stimuli and interact with specific receptors. To broadly characterize chemokine expression and function in tumors, we have used multiplex mass cytometry-based imaging of protein markers and RNA transcripts to analyze the chemokine landscape and immune infiltration in metastatic melanoma samples. Tumors that lacked immune infiltration were devoid of most chemokines and exhibited particularly low levels of antigen presentation and inflammation. Infiltrated tumors were characterized by expression of multiple chemokines. CXCL9 and CXCL10 were often localized in patches associated with dysfunctional T cells expressing CXCL13 which was strongly associated with B cell patches and follicles. TCF7+ naive-like T cells, which predict response to immunotherapy, were enriched in the vicinity of B cell patches and follicles. Our data highlight the strength of RNA and protein co-detection which was critical to deconvolve specialized immune microenvironments in inflamed tumors based on chemokine expression. Our findings further suggest that the formation of tertiary lymphoid structures is accompanied by naive and naive- like T cell recruitment, which ultimately boosts anti-tumor activity. One sentence summaryInflammatory chemokine milieus in metastatic melanoma are hotspots of T cell dysfunction and CXCL13 expression, which likely guide the recruitment of B cells and the formation of B cell follicles responsible for anti-tumor immunity.

cancer biology↗

bioRxiv2021

Exploration and analysis of molecularly annotated, 3D models of breast cancer at single-cell resolution using virtual reality

A set of increasingly powerful approaches are enabling spatially resolved measurements of growing numbers of molecular features in biological samples. While important insights can be derived from the two-dimensional data that many of these technologies generate, it is clear that extending these approaches into the third and fourth dimensions will magnify their impact. Realizing biological insights from datasets where thousands to millions of cells are annotated with tens to hundreds of parameters in space will require the development of new computational and visualization strategies. Here, we describe Theia, a virtual reality-based platform, which enables exploration and analysis of either volumetric or segmented, molecularly-annotated, three-dimensional datasets, with the option to extend the analysis to time-series data. We also describe our pipeline for generating annotated 3D models of breast cancer and supply several datasets to enable users to explore the utility of Theia for understanding cancer biology in three dimensions.

cancer biology↗

bioRxiv2021

The Imaging and Molecular Annotation of Xenografts and Tumours (IMAXT) High Throughput Data and Analysis Infrastructure

With the aim of producing a 3D representation of tumours, IMAXT uses a large variety of modalities in order to acquire tumour samples and produce a map of every cell in the tumour and its host environment. With the large volume and variety of data produced in the project we develop automatic data workflows and analysis pipelines and introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located instead of bringing data to their local computers. Here we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumour models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT.

cancer biology↗

bioRxiv2021

Mass Cytometric and Transcriptomic Profiling of Epithelial-Mesenchymal Transitions in Human Mammary Cell Lines

Epithelial-mesenchymal transition (EMT) equips breast cancer cells for metastasis and treatment resistance. Inhibition and elimination of EMT-undergoing cells are therefore promising therapy approaches. However, detecting EMT-undergoing cells is challenging due to the intrinsic heterogeneity of cancer cells and the phenotypic diversity of EMT programs. Here, we profiled EMT transition phenotypes in four non-cancerous human mammary epithelial cell lines using a FACS surface marker screen, RNA sequencing, and mass cytometry. EMT was induced in the HMLE and MCF10A cell lines and in the HMLE-Twist-ER and HMLE-Snail-ER cell lines by chronic exposure to TGF{beta}1 or 4-hydroxytamoxifen, respectively. We observed a spectrum of EMT transition phenotypes in each cell line and the spectrum varied across the time course. Our data provide multiparametric insights at single-cell level into the phenotypic diversity of EMT at different time points and in four human cellular models. These insights are valuable to better understand the complexity of EMT, to compare EMT transitions between the cellular models used herein, and for the design of EMT time course experiments. O_TBL View this table: org.highwire.dtl.DTLVardef@10d2b87org.highwire.dtl.DTLVardef@1a4cc67org.highwire.dtl.DTLVardef@67bc95org.highwire.dtl.DTLVardef@8df40dorg.highwire.dtl.DTLVardef@1919e7c_HPS_FORMAT_FIGEXP M_TBL C_TBL Mendeley Data: DOI: 10.17632/pt3gmyk5r2.1 ArrayExpress Data: Accession number E-MTAB-9365

cell biology↗

bioRxiv2021

Cell-to-cell and type-to-type heterogeneity of signaling networks: Insights from the crowd

Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell-types, with important implications to understand and treat diseases such as cancer. These technologies are however limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organised the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry data set, covering 36 markers in over 4,000 conditions totalling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=58 SRC="FIGDIR/small/436603v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@a95337org.highwire.dtl.DTLVardef@966d5aorg.highwire.dtl.DTLVardef@1e55b18org.highwire.dtl.DTLVardef@bfb5d7_HPS_FORMAT_FIGEXP M_FIG C_FIG Key pointsO_LIOver 80 million single-cell multiplexed measurements across 67 cell lines, 54 conditions and 10 time points to benchmark predictive models of single cell signaling C_LIO_LI73 approaches from 27 teams for predicting response to kinase inhibitors on single cell level, and dynamic response from unperturbed basal omics data C_LIO_LIPredictions of single marker models correlate with measurements with a correlation coefficient of 0.76 C_LIO_LITop models of whole signaling response models perform almost as well as a biological replicate C_LIO_LICell-line specific variation in dynamics can be predicted from basal omics C_LI

bioinformatics↗

bioRxiv2021

Single-Cell Proteomics Defines the Cellular Heterogeneity of Localized Prostate Cancer

Localized prostate cancer exhibits multiple genomic alterations and heterogeneity at the proteomic level. Single-cell technologies capture important cell-to-cell variability responsible for heterogeneity in biomarker expression that may be overlooked when molecular alterations are based on bulk tissue samples. The aim of this study was to identify novel prognostic biomarkers and describe the heterogeneity of prostate cancer and the associated immune cell infiltrates by simultaneously quantifying 36 proteins using single-cell mass cytometry analysis of over 1,6 million cells from 58 men with localized prostate cancer. To perform this task, we proposed a novel computational pipeline, Franken, which showed unprecedented combination of performance, sensitivity and scalability for high dimensional clustering compared to state of the art methods. We were able to describe subpopulations of immune, stromal, and prostate cells, including unique changes occurring in tumor tissues and high grade disease providing insights into the coordinated progression of prostate cancer. Our results further indicated that men with localized disease already harbor rare subpopulations that typically occur in castration-resistant and metastatic disease, which were confirmed through imaging. Our methodology could be used to discover novel prognostic biomarkers to personalize treatment and improve outcomes.

cancer biology↗

bioRxiv2020

Multidimensional single-cell modeling of cellular signaling

Cell-to-cell differences in signaling components can lead to qualitatively different responses to stimuli. Understanding this heterogeneity in signaling response is limited by the inability of time-lapse methods to measure multiple pathway components simultaneously in situ. Here, we present Distribution-Independent Single-Cell ODE modeling (DISCO), a computational method for inference of continuous single-cell signaling dynamics from multiplexed snapshot data. We used DISCO to analyze signaling in the MAPK/ERK pathway of HEK293T cells stimulated with the growth factor EGF. Our model recapitulates known features of the ERK signaling response and enables the detection of hidden cell-to-cell variation in seemingly homogeneous samples. Further, DISCO analysis suggested that the MAPK/ERK pathway transmits signal duration rather than amplitude, and that cell-to-cell variation in MAPK/ERK signaling response depends primarily on initial cell states. Finally, we applied an extended version of DISCO to explain changes in signaling kinetics due to overexpression of a disease-relevant protein. Overall, DISCO enables a deeper understanding of how single-cell variation affects cellular responses in complex signaling systems.

systems biology↗

bioRxiv2020

cytomapper: an R/Bioconductor package for visualisation of highly multiplexed imaging data

Highly multiplexed imaging technologies enable spatial profiling of dozens of biomarkers in situ. Standard data processing pipelines quantify cell-specific features and generate object segmentation masks as well as multi-channel images. Therefore, multiplexed imaging data can be visualised across two layers of information: pixel-intensities represent the spatial expression of biomarkers across an image while segmented objects visualise cellular morphology, interactions and cell phenotypes in their microenvironment. Here we describe cytomapper, a computational tool that enables visualisation of pixel- and cell-level information obtained by multiplexed imaging. The package is written in the statistical programming language R, integrates with the image and single-cell analysis infrastructure of the Bioconductor project, and allows visualisation of single to hundreds of images in parallel. Using cytomapper, expression of multiple markers is displayed as composite images, segmentation masks are coloured based on cellular features, and selected cells can be outlined in images based on their cell type, among other functions. We illustrate the utility of cytomapper by analysing 100 images obtained by imaging mass cytometry from a cohort of type 1 diabetes patients and healthy individuals. In addition, cytomapper includes a Shiny application that allows hierarchical gating of cells based on marker expression and visualisation of selected cells in corresponding images. Together, cytomapper offers tools for diverse image and single-cell visualisation approaches and supports robust cell phenotyping via gating.

bioinformatics↗

bioRxiv2020

A distinct innate immune signature marks progression from mild to severe COVID-19

Coronavirus disease 2019 (COVID-19) manifests with a range of severities, but immune signatures of mild and severe disease are still not fully understood. Excessive inflammation has been postulated to be a major factor in the pathogenesis of severe COVID-19 and innate immune mechanisms are likely to be central in the inflammatory response. We used 40-plex mass cytometry and targeted serum proteomics to profile innate immune cell populations from peripheral blood of patients with mild or severe COVID-19 and healthy controls. Sampling at different stages of COVID-19 allowed us to reconstruct a pseudo-temporal trajectory of the innate immune response. Despite the expected patient heterogeneity, we identified consistent changes during the course of the infection. A rapid and early surge of CD169+ monocytes associated with an IFN{gamma}+MCP-2+ signature quickly followed symptom onset; at symptom onset, patients with mild and severe COVID-19 had a similar signature, but over the course of the disease, the differences between patients with mild and severe disease increased. Later in the disease course, we observed a more pronounced re-appearance of intermediate/non-classical monocytes and mounting systemic CCL3 and CCL4 levels in patients with severe disease. Our data provide new insights into the dynamic nature of the early inflammatory response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and identifies sustained pathological innate immune responses as a likely key mechanism in severe COVID-19, further supporting investigation of targeted anti-inflammatory interventions in severe COVID-19.

immunology↗

bioRxiv2020

Lymphopenia-induced T cell proliferation is a hallmark of severe COVID-19

Coronavirus disease 2019 (COVID-19), caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has a broad clinical presentation ranging from asymptomatic infection to fatal disease. Different features associated with the immune response to SARS-CoV-2, such as hyperinflammation and reduction of peripheral CD8+ T cell counts are strongly associated with severe disease. Here, we confirm the reduction in peripheral CD8+ T cells both in relative and absolute terms and identify T cell apoptosis and migration into inflamed tissues as possible mechanisms driving peripheral T cell lymphopenia. Furthermore, we find evidence of elevated serum interleukin-7, thus indicating systemic T cell paucity and signs of increased T cell proliferation in patients with severe lymphopenia. Following T cell lymphopenia in our pseudo-longitudinal time course, we observed expansion and recovery of poly-specific antiviral T cells, thus arguing for lymphopenia-induced T cell proliferation. In summary, this study suggests that extensive T cell loss and subsequent T cell proliferation are characteristic of severe COVID-19.

immunology↗

bioRxiv2020

A quantitative analysis of the interplay of environment, neighborhood and cell state in 3D spheroids

1Cells react to their microenvironment by integrating external stimuli into phenotypic decisions via an intracellular signaling network. Even cells with deregulated signaling can adapt to their environment. To analyze the interplay of environment, neighborhood, and cell state on phenotypic variability, we developed an experimental approach that enables multiplexed mass cytometric imaging to analyze up to 240 pooled spheroid microtissues. This system allowed us to quantify the contributions of environment, neighborhood, and intracellular state to phenotypic variability in spheroid cells. A linear model explained on average more than half of the variability of 34 markers across four cell lines and six growth conditions. We found that the contributions of cell-intrinsic and environmental factors are hierarchically interdependent. By overexpression of 51 signaling protein constructs in subsets of cells, we identified proteins that have cell-intrinsic and extrinsic effects, exemplifying how cell states depend on the cellular neighborhood in spheroid culture. Our study deconvolves factors influencing cellular phenotype in a 3D tissue and provides a scalable experimental system, analytical principles, and rich multiplexed imaging datasets for future studies.

systems biology↗

bioRxiv2020

Highly multiplexed molecular and cellular mapping of breast cancer tissue in three dimensions using mass tomography.

A holistic understanding of tissue and organ structures and their functions requires the detection of molecular constituents in their original three-dimensional (3D) context. Imaging mass cytometry (IMC) makes possible the detection of up to 40 antigens and specific nucleic acids simultaneously using metal-tagged antibodies or nucleic acid probes, respectively, but has so far been restricted to two-dimensional imaging. To enable use of IMC for 3D tissue analyses, we developed mass tomography, which combines quasi deformation-free serial sectioning with novel computational methods. We utilized mass tomography to analyze a breast cancer sample. The resulting 3D representation reveals spatial and cellular heterogeneity, preferential cell-to-cell interactions, detailed tissue-architecture motifs, and the unique microenvironment of a micro-invasion, where micro-metastases clonality is examined, showing that cells arising from the same invasive area, displaying very distinct phenotypes, are all able to produce initial invasive lesions. Mass tomography will provide invaluable insights into the tissue microenvironment, cellular neighborhoods, and tissue organization.

cell biology↗

bioRxiv2020

Deciphering the Signaling Network Landscape of Breast Cancer Improves Drug Sensitivity Prediction

Although genetic and epigenetic abnormalities in breast cancer have been extensively studied, it remains difficult to identify those patients who will respond to particular therapies. This is due in part to our lack of understanding of how the variability of cellular signaling affects drug sensitivity. Here, we used mass cytometry to characterize the single-cell signaling landscapes of 62 breast cancer cell lines and five lines from healthy tissue. We quantified 34 markers in each cell line upon stimulation by the growth factor EGF in the presence or absence of five kinase inhibitors. These data - on more than 80 million single cells from 4,000 conditions - were used to fit mechanistic signaling network models that provide unprecedented insights into the biological principles of how cancer cells process information. Our dynamic single-cell-based models more accurately predicted drug sensitivity than static bulk measurements for drugs targeting the PI3K-MTOR signaling pathway. Finally, we identified genomic features associated with drug sensitivity by using signaling phenotypes as proxies, including a missense mutation in DDIT3 predictive of PI3K-inhibition sensitivity. This provides proof of principle that single-cell measurements and modeling could inform matching of patients with appropriate treatments in the future. One-linerSingle-cell proteomics coupled to perturbations improves accuracy of breast tumor drug sensitivity predictions and reveals mechanisms of sensitivity and resistance. HIGHLIGHTSO_LIMass cytometry study of signaling responses of 62 breast cancer cell lines and five lines from healthy tissue to EGF stimulation with or without perturbation with five kinase inhibitors. C_LIO_LISingle-cell signaling features and mechanistic signaling network models predicted drug sensitivity. C_LIO_LIMechanistic signaling network models deepen the understanding of drug resistance and sensitivity mechanisms. C_LIO_LIWe identify drug sensitivity-predictive genomic features via proxy signaling phenotypes. C_LI

systems biology↗

bioRxiv2018

Embedding the single-cell sample manifold to reveal insights into cancer pathogenesis and disease heterogeneity

Previously, the effect of a drug on a cell population was measured based on simple metrics such as cell viability. However, as single-cell technologies are becoming more advanced, drug screen experiments can now be conducted with more complex readouts such as gene expression profiles of individual cells. The increasing complexity of measurements from these multi-sample experiments calls for more sophisticated analytical approaches than are currently available. We developed a novel method called PhEMD (Phenotypic Earth Movers Distance) and show that it can be used to embed the space of drug perturbations on the basis of the drugs effects on cell populations. When testing PhEMD on a newly-generated, 300-sample CyTOF kinase inhibition screen experiment, we find that the state space of the perturbation conditions is surprisingly low-dimensional and that the network of drugs demonstrates manifold structure. We show that because of the fairly simple manifold geometry of the 300 samples, we can accurately capture the full range of drug effects using a dictionary of only 30 experimental conditions. We also show that new drugs can be added to our PhEMD embedding using similarities inferred from other characterizations of drugs using a technique called Nystrom extension. Our findings suggest that large-scale drug screens can be conducted by measuring only a small fraction of the drugs using the most expensive high-throughput single-cell technologies--the effects of other drugs may be inferred by mapping and extending the perturbation space. We additionally show that PhEMD can be useful for analyzing other types of single-cell samples, such as patient tumor biopsies, by mapping the patient state space in a similar way as the drug state space. We demonstrate that PhEMD is scalable, compatible with leading batch effect correction techniques, and generalizable to multiple experimental designs. Altogether, our analyses suggest that PhEMD may facilitate drug discovery efforts and help uncover the network geometry of a collection of single-cell samples.

bioinformatics↗

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↗