Showing papers by "Garry P. Nolan published in 2008"

MIFlowCyt: the minimum information about a Flow Cytometry Experiment.

Abstract: A fundamental tenet of scientific research is that published results are open to independent validation and refutation. Minimum data standards aid data providers, users, and publishers by providing a specification of what is required to unambiguously interpret experimental findings. Here, we present the Minimum Information about a Flow Cytometry Experiment (MIFlowCyt) standard, stating the minimum information required to report flow cytometry (FCM) experiments. We brought together a cross-disciplinary international collaborative group of bioinformaticians, computational statisticians, software developers, instrument manufacturers, and clinical and basic research scientists to develop the standard. The standard was subsequently vetted by the International Society for Advancement of Cytometry (ISAC) Data Standards Task Force, Standards Committee, membership, and Council. The MIFlowCyt standard includes recommendations about descriptions of the specimens and reagents included in the FCM experiment, the configuration of the instrument used to perform the assays, and the data processing approaches used to interpret the primary output data. MIFlowCyt has been adopted as a standard by ISAC, representing the FCM scientific community including scientists as well as software and hardware manufacturers. Adoptionof MIFlowCyt by the scientific and publishing communities will facilitate third-party understanding and reuse of FCM data.

High-content single-cell drug screening with phosphospecific flow cytometry

Abstract: Drug screening is often limited to cell-free assays involving purified enzymes, but it is arguably best applied against systems that represent disease states or complex physiological cellular networks. Here, we describe a high-content, cell-based drug discovery platform based on phosphospecific flow cytometry, or phosphoflow, that enabled screening for inhibitors against multiple endogenous kinase signaling pathways in heterogeneous primary cell populations at the single-cell level. From a library of small-molecule natural products, we identified pathway-selective inhibitors of Jak-Stat and MAP kinase signaling. Dose-response experiments in primary cells confirmed pathway selectivity, but importantly also revealed differential inhibition of cell types and new druggability trends across multiple compounds. Lead compound selectivity was confirmed in vivo in mice. Phosphoflow therefore provides a unique platform that can be applied throughout the drug discovery process, from early compound screening to in vivo testing and clinical monitoring of drug efficacy.

Genomic and Proteomic Analysis Reveals a Threshold Level of MYC Required for Tumor Maintenance

Abstract: MYC overexpression has been implicated in the pathogenesis of most types of human cancers. MYC is likely to contribute to tumorigenesis by its effects on global gene expression. Previously, we have shown that the loss of MYC overexpression is sufficient to reverse tumorigenesis. Here, we show that there is a precise threshold level of MYC expression required for maintaining the tumor phenotype, whereupon there is a switch from a gene expression program of proliferation to a state of proliferative arrest and apoptosis. Oligonucleotide microarray analysis and quantitative PCR were used to identify changes in expression in 3,921 genes, of which 2,348 were down-regulated and 1,573 were up-regulated. Critical changes in gene expression occurred at or near the MYC threshold, including genes implicated in the regulation of the G1-S and G2-M cell cycle checkpoints and death receptor/apoptosis signaling. Using two-dimensional protein analysis followed by mass spectrometry, phospho-flow fluorescence-activated cell sorting, and antibody arrays, we also identified changes at the protein level that contributed to MYC-dependent tumor regression. Proteins involved in mRNA translation decreased below threshold levels of MYC. Thus, at the MYC threshold, there is a loss of its ability to maintain tumorigenesis, with associated shifts in gene and protein expression that reestablish cell cycle checkpoints, halt protein translation, and promote apoptosis. [Cancer Res 2008;68(13):5132–42]

Methods and compositions for risk stratification

Abstract: The present invention provides an approach for the simultaneous determination of the activation states of a plurality of proteins in single cells. This approach permits the rapid detection of heterogeneity in a complex cell population based on activation states, and the identification of cellular subsets that exhibit correlated changes in activation within the cell population. Moreover, this approach allows the correlation of cellular activities or properties. In addition, the use of potentiators of cellular activation allows for characterization of such pathways and cell populations.

Multiplex Cellular Assays Using Detectable Cell Barcodes

Abstract: We describe herein a cell-based multiplexing technique called detectable cell barcoding (DCB). In DCB, each individual sample is labeled with a different DCB signature that distinguishes each sample by one or both of detected intensity or type of detection characteristic. The samples are then combined and analyzed for a detectable characteristic of interest (e.g., presence of an analyte). By employing multiple distinct DCB labels at varying concentrations, one can perform multiplex analyses on up to hundreds or thousands (or more) of cell samples in a single reaction tube. DCB reduces reagent consumption by factors of 100-fold or more, significantly reduces data acquisition times and allows for stringent control sample analysis.

Structure learning in causal cyclic networks

Abstract: Cyclic graphical models are unnecessary for accurate representation of joint probability distributions, but are often indispensable when a causal representation of variable relationships is desired. For variables with a cyclic causal dependence structure, DAGs are guaranteed not to recover the correct causal structure, and therefore may yield false predictions about the outcomes of perturbations (and even inference.) In this paper, we introduce an approach to generalize Bayesian Network structure learning to structures with cyclic dependence. We introduce a structure learning algorithm, prove its performance given reasonable assumptions, and use simulated data to compare its results to the results of standard Bayesian network structure learning. We then propose a modified, heuristic algorithm with more modest data requirements, and test its performance on a real-life dataset from molecular biology, containing causal, cyclic dependencies.

Single-cell, phosphoepitope-specific analysis demonstrates cell type- and pathway-specific dysregulation of Jak/STAT and MAPK signaling associated with in vivo human immunodeficiency virus type 1 infection.

Abstract: Despite extensive evidence of cell signaling alterations induced by human immunodeficiency virus type 1 (HIV-1) in vitro, the relevance of these changes to the clinical and/or immunologic status of HIV-1-infected individuals is often unclear. As such, mapping the details of cell type-specific degradation of immune function as a consequence of changes to signaling network responses has not been readily accessible. We used a flow cytometric-based assay of signaling to determine Janus kinase/signal transducers and activators of transcription (Jak/STAT) signaling changes at the single-cell level within distinct cell subsets from the primary immune cells of HIV-1-infected donors. We identified a specific defect in granulocyte-macrophage colony-stimulating factor (GM-CSF)-driven Stat5 phosphorylation in the monocytes of HIV-1+ donors. This inhibition was statistically significant in a cohort of treated and untreated individuals. Ex vivo Stat5 phosphorylation levels varied among HIV-1+ donors but did not correlate with CD4+ T-cell counts or HIV-1 plasma viral load. Low Stat5 activation occurred in HIV-1-infected donors despite normal GM-CSF receptor levels. Investigation of mitogen-activated protein kinase (MAPK) pathways, also stimulated by GM-CSF, led to the observation that lipopolysaccharide-stimulated extracellular signal-regulated kinase phosphorylation is enhanced in monocytes. Thus, we have identified a specific, imbalanced monocyte signaling profile, with inhibition of STAT and enhancement of MAPK signaling, associated with HIV-1 infection. This understanding of altered monocyte signaling responses that contribute to defective antigen presentation during HIV-1 infection could lead to immunotherapeutic approaches that compensate for the deficiency.

Learning cyclic signaling pathway structures while minimizing data requirements

Abstract: Bayesian network structure learning is a useful tool for elucidation of regulatory structures of biomolecular pathways. The approach however is limited by its acyclicity constraint, a problematic one in the cycle-containing biological domain. Here, we introduce a novel method for modeling cyclic pathways in biology, by employing our newly introduced Generalized Bayesian Networks (GBNs). Our novel algorithm enables cyclic structure learning while employing biologically relevant data, as it extends our cycle-learning algorithm to permit learning with singly perturbed samples. We present theoretical arguments as well as structure learning results from realistic, simulated data of a biological system. We also present results from a real world dataset, involving signaling pathways in T-cells.

Electron Microscopy Characterization of Composite Organic-Inorganic Nanoparticles (COINs) as Raman Labels for Extra-cellular Analyses

Abstract: Extended abstract of a paper presented at Microscopy and Microanalysis 2008 in Albuquerque, New Mexico, USA, August 3 – August 7, 2008

Detection of surface molecules and phosphorylation events by surface enhanced raman scattering (SERS) using composite organic-inorganic nanoparticles (COINs) in single cells.

Abstract: 4742 To better understand the processes occurring in abnormal cells compared to normal cells, there is an urgent need to improve the technology for simultaneous detection of multiple events in a single cell. To better characterize several different events that occur in a single cell at a given time, we need to be able to accurately detect and analyze several nodes concurrently. Currently, immuno-detecton assays are used with fluorescence tags. Fluorescent molecules have broad and overlapping spectra. We have developed “Composite Organic-Inorganic Nanoparticles” (COINs) Raman nanoparticles for immuno-detection assays of extra- and intra-cellular proteins in single cells. COINs are Surface-Enhanced Raman Scattering (SERS) silver particle clusters with incorporated Raman labels. Each COIN has a unique Raman spectral fingerprint, which allows higher levels of multiplexing. To measure Raman spectra in single cells we have built an Integrated Raman BioAnalyzer (IRBA). The IRBA is a low noise, high sensitivity, automated and compact Raman Spectrometer for biomedical applications. We tested the ability of antibody-conjugated COINs to detect surface antigens by recognizing the CD54 antigen that is expressed on U937 monocytic leukemia cells compared to the CD8 antigen that is not expressed on U937 cells in single cell immunoassays. We determined the cell specific detection of the CD54 antigen on U937 expressing cells compared to non-expressing H82 small cell lung cancer cells. To evaluate the COIN Raman technology for detecting specific populations in primary human cells we identified CD8 expressing T-cells among a heterogeneous population of primary human peripheral blood mononuclear cells (PBMC). Finally, we used COINs to measure intracellular phosphorylation in cells. We treated U937 cells with IFNγ and IL-4 cytokines and measured changes in Stat1 (Y701) and Stat6 (Y641) phosphorylation. We found a six-fold increase in Stat1 phosphorylation and a three-fold increase in Stat6 phosphorylation with COINs; both are comparable to the fluorescence signals measured by flow cytometry. We confirmed the utility of COINs in a multiplex assay by simultaneous detection of pStat1 (BFU) and pStat6 (AOH) in single cells. We have demonstrated the feasibility and practicality of using COINs for cell surface marker analysis and for measuring changes in intracellular phosphorylation. The COIN Raman nanoparticles offer new possibilities to expand on the current fluorescent technology used for immunoassays in single cells.

Structure learning for biomolecular pathways containing cycles

Abstract: Bayesian network structure learning is a useful tool for elucidation of regulatory structures of biomolecular pathways. The approach however is limited by its acyclicity constraint, a problematic one in the cycle-containing biological domain. Here, we introduce a novel method for modeling cyclic pathways in biology, by employing our newly introduced Generalized Bayesian Networks (GBNs) and proposing a structure learning algorithm suitable for the biological domain. This algorithm relies on data and perturbations which are feasible for collection in an experimental setting, such as perturbations affecting either the abundance or activity of a molecule. We present theoretical arguments as well as structure learning results from simulated data. We also present results from a small real world dataset, involving genes from the galactose system in S. cerevisiae.