Single-cell systems-level analysis of human Toll-like receptor activation defines a chemokine signature in patients with systemic lupus erythematosus
JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY
2015; 136 (5): 1326-1336
Activation of Toll-like receptors (TLRs) induces inflammatory responses involved in immunity to pathogens and autoimmune pathogenesis, such as in patients with systemic lupus erythematosus (SLE). Although TLRs are differentially expressed across the immune system, a comprehensive analysis of how multiple immune cell subsets respond in a system-wide manner has not been described.We sought to characterize TLR activation across multiple immune cell subsets and subjects, with the goal of establishing a reference framework against which to compare pathologic processes.Peripheral whole-blood samples were stimulated with TLR ligands and analyzed by means of mass cytometry simultaneously for surface marker expression, activation states of intracellular signaling proteins, and cytokine production. We developed a novel data visualization tool to provide an integrated view of TLR signaling networks with single-cell resolution. We studied 17 healthy volunteer donors and 8 patients with newly diagnosed and untreated SLE.Our data revealed the diversity of TLR-induced responses within cell types, with TLR ligand specificity. Subsets of natural killer cells and T cells selectively induced nuclear factor κ light chain enhancer of activated B cells in response to TLR2 ligands. CD14(hi) monocytes exhibited the most polyfunctional cytokine expression patterns, with more than 80 distinct cytokine combinations. Monocytic TLR-induced cytokine patterns were shared among a group of healthy donors, with minimal intraindividual and interindividual variability. Furthermore, autoimmune disease altered baseline cytokine production; newly diagnosed untreated SLE patients shared a distinct monocytic chemokine signature, despite clinical heterogeneity.Mass cytometry defined a systems-level reference framework for human TLR activation, which can be applied to study perturbations in patients with inflammatory diseases, such as SLE.
View details for DOI 10.1016/j.jaci.2015.04.008
View details for Web of Science ID 000364787200023
View details for PubMedID 26037552
Making Storytelling Personal: Finding Your User in Your Story
Ninth International Conference on Tangible, Embedded, and Embodied Interaction (TEI)
Association for Computing Machinery (ACM). 2015: 485-488
View details for DOI 10.1145/2677199.2683587
A Multidisciplinary Care Team Perspective on Children's Emotional Experience in Isolation for Stem Cell Transplantation
2015 BMT Tandem Meetings
View details for DOI 10.1016/j.bbmt.2014.11.269
Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers
JOURNAL OF DIGITAL IMAGING
2013; 26 (4): 630-641
A widening array of novel imaging biomarkers is being developed using ever more powerful clinical and preclinical imaging modalities. These biomarkers have demonstrated effectiveness in quantifying biological processes as they occur in vivo and in the early prediction of therapeutic outcomes. However, quantitative imaging biomarker data and knowledge are not standardized, representing a critical barrier to accumulating medical knowledge based on quantitative imaging data. We use an ontology to represent, integrate, and harmonize heterogeneous knowledge across the domain of imaging biomarkers. This advances the goal of developing applications to (1) improve precision and recall of storage and retrieval of quantitative imaging-related data using standardized terminology; (2) streamline the discovery and development of novel imaging biomarkers by normalizing knowledge across heterogeneous resources; (3) effectively annotate imaging experiments thus aiding comprehension, re-use, and reproducibility; and (4) provide validation frameworks through rigorous specification as a basis for testable hypotheses and compliance tests. We have developed the Quantitative Imaging Biomarker Ontology (QIBO), which currently consists of 488 terms spanning the following upper classes: experimental subject, biological intervention, imaging agent, imaging instrument, image post-processing algorithm, biological target, indicated biology, and biomarker application. We have demonstrated that QIBO can be used to annotate imaging experiments with standardized terms in the ontology and to generate hypotheses for novel imaging biomarker-disease associations. Our results established the utility of QIBO in enabling integrated analysis of quantitative imaging data.
View details for DOI 10.1007/s10278-013-9599-2
View details for Web of Science ID 000322434700006
View details for PubMedID 23589184
2013; 10 (7): 595-?
View details for PubMedID 23967480
Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators
2012; 30 (9): 858-U89
Mass cytometry facilitates high-dimensional, quantitative analysis of the effects of bioactive molecules on human samples at single-cell resolution, but instruments process only one sample at a time. Here we describe mass-tag cellular barcoding (MCB), which increases mass cytometry throughput by using n metal ion tags to multiplex up to 2n samples. We used seven tags to multiplex an entire 96-well plate, and applied MCB to characterize human peripheral blood mononuclear cell (PBMC) signaling dynamics and cell-to-cell communication, signaling variability between PBMCs from eight human donors, and the effects of 27 inhibitors on this system. For each inhibitor, we measured 14 phosphorylation sites in 14 PBMC types at 96 conditions, resulting in 18,816 quantified phosphorylation levels from each multiplexed sample. This high-dimensional, systems-level inquiry allowed analysis across cell-type and signaling space, reclassified inhibitors and revealed off-target effects. High-content, high-throughput screening with MCB should be useful for drug discovery, preclinical testing and mechanistic investigation of human disease.
View details for DOI 10.1038/nbt.2317
View details for Web of Science ID 000308705700020
View details for PubMedID 22902532