CD69 marks a subpopulation of acute myeloid leukemia with enhanced colony forming capacity and a unique signaling activation state

In acute myeloid leukemia (AML), leukemia stem cells (LSCs) have self-renewal potential and are responsible for relapse. We previously showed that, in Mll-AF9/NRASG12V murine AML, CD69 expression marks an LSC-enriched subpopulation with enhanced in vivo self-renewal capacity. Here, we used CyTOF to define activated signaling pathways in LSC subpopulations in Mll-AF9/NRASG12V AML. Furthermore, we compared the signaling activation states of CD69High and CD36High subsets of primary human AML. The human CD69High subset expresses low levels of Ki67 and high levels of NFκB and pMAPKAPKII. Additionally, the human CD69High AML subset also has enhanced colony-forming capacity. We applied Bayesian network modeling to compare the global signaling network within the human AML subsets. We find that distinct signaling states, distinguished by NFκB and pMAPKAPKII levels, correlate with divergent functional subsets, defined by CD69 and CD36 expression, in human AML. Targeting NFκB with proteasome inhibition diminished colony formation.

Optimal adjustment sets for causal query estimation in partially observed biomolecular networks

Causal query estimation in biomolecular networks commonly selects a 'valid adjustment set', i.e. a subset of network variables that eliminates the bias of the estimator. A same query may have multiple valid adjustment sets, each with a different variance. When networks are partially observed, current methods use graph-based criteria to find an adjustment set that minimizes asymptotic variance. Unfortunately, many models that share the same graph topology, and therefore same functional dependencies, may differ in the processes that generate the observational data. In these cases, the topology-based criteria fail to distinguish the variances of the adjustment sets. This deficiency can lead to sub-optimal adjustment sets, and to miss-characterization of the effect of the intervention. We propose an approach for deriving 'optimal adjustment sets' that takes into account the nature of the data, bias and finite-sample variance of the estimator, and cost. It empirically learns the data generating processes from historical experimental data, and characterizes the properties of the estimators by simulation. We demonstrate the utility of the proposed approach in four biomolecular Case studies with different topologies and different data generation processes. The implementation and reproducible Case studies are at https://github.com/srtaheri/OptimalAdjustmentSet.

Peripheral T-Cells, B-Cells, and Monocytes from Multiple Sclerosis Patients Supplemented with High-Dose Vitamin D Show Distinct Changes in Gene Expression Profiles

Vitamin D is a steroid hormone that has been widely studied as a potential therapy for multiple sclerosis and other inflammatory disorders. Pre-clinical studies have implicated vitamin D in the transcription of thousands of genes, but its influence may vary by cell type. A handful of clinical studies have failed to identify an in vivo gene expression signature when using bulk analysis of all peripheral immune cells. We hypothesized that vitamin D's gene signature would vary by immune cell type, requiring the analysis of distinct cell types. Multiple sclerosis patients (n = 18) were given high-dose vitamin D (10,400 IU/day) for six months as part of a prospective clinical trial (NCT01024777). We collected peripheral blood mononuclear cells from participants at baseline and again after six months of treatment. We used flow cytometry to isolate three immune cell types (CD4⁺ T-cells, CD19⁺ B-cells, CD14⁺ monocytes) for RNA microarray analysis and compared the expression profiles between baseline and six months. We identified distinct sets of differentially expressed genes and enriched pathways between baseline and six months for each cell type. Vitamin D's in vivo gene expression profile in the immune system likely differs by cell type. Future clinical studies should consider techniques that allow for a similar cell-type resolution.

Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data

The clinical presentation of amyotrophic lateral sclerosis (ALS), a fatal neurodegenerative disease, varies widely across patients, making it challenging to determine if potential therapeutics slow progression. We sought to determine whether there were common patterns of disease progression that could aid in the design and analysis of clinical trials. We developed an approach based on a mixture of Gaussian processes to identify clusters of patients sharing similar disease progression patterns, modeling their average trajectories and the variability in each cluster. We show that ALS progression is frequently nonlinear, with periods of stable disease preceded or followed by rapid decline. We also show that our approach can be extended to Alzheimer's and Parkinson's diseases. Our results advance the characterization of disease progression of ALS and provide a flexible modeling approach that can be applied to other progressive diseases.

Do-calculus enables estimation of causal effects in partially observed biomolecular pathways

MOTIVATION

Estimating causal queries, such as changes in protein abundance in response to a perturbation, is a fundamental task in the analysis of biomolecular pathways. The estimation requires experimental measurements on the pathway components. However, in practice many pathway components are left unobserved (latent) because they are either unknown, or difficult to measure. Latent variable models (LVMs) are well-suited for such estimation. Unfortunately, LVM-based estimation of causal queries can be inaccurate when parameters of the latent variables are not uniquely identified, or when the number of latent variables is misspecified. This has limited the use of LVMs for causal inference in biomolecular pathways.

RESULTS

In this article, we propose a general and practical approach for LVM-based estimation of causal queries. We prove that, despite the challenges above, LVM-based estimators of causal queries are accurate if the queries are identifiable according to Pearl's do-calculus and describe an algorithm for its estimation. We illustrate the breadth and the practical utility of this approach for estimating causal queries in four synthetic and two experimental case studies, where structures of biomolecular pathways challenge the existing methods for causal query estimation.

AVAILABILITY AND IMPLEMENTATION

The code and the data documenting all the case studies are available at https://github.com/srtaheri/LVMwithDoCalculus.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Variation of Immune Cell Responses in Humans Reveals Sex-Specific Coordinated Signaling Across Cell Types

Assessing the health and competence of the immune system is central to evaluating vaccination responses, autoimmune conditions, cancer prognosis, and treatment. With an increasing number of studies examining immune dysregulation, there is a growing need for a curated reference of variation in immune parameters in healthy individuals. We used mass cytometry (CyTOF) to profile blood from 86 humans in response to 15 ex vivo immune stimuli. We present reference ranges for cell-specific immune markers and highlight differences that appear across sex and age. We identified modules of immune features that suggest there exists an underlying structure to the immune system based on signaling pathway responses across cell types. We observed increased MAPK signaling in inflammatory pathways in innate immune cells and greater overall coordination of immune cell responses in females. In contrast, males exhibited stronger pSTAT1 and pTBK1 responses. These reference data are publicly available as a resource for immune profiling studies.

Answer ALS, a large-scale resource for sporadic and familial ALS combining clinical and multi-omics data from induced pluripotent cell lines

Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical-molecular-biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics.

An integrated multi-omic analysis of iPSC-derived motor neurons from C9ORF72 ALS patients

Neurodegenerative diseases are challenging for systems biology because of the lack of reliable animal models or patient samples at early disease stages. Induced pluripotent stem cells (iPSCs) could address these challenges. We investigated DNA, RNA, epigenetics, and proteins in iPSC-derived motor neurons from patients with ALS carrying hexanucleotide expansions in C9ORF72. Using integrative computational methods combining all omics datasets, we identified novel and known dysregulated pathways. We used a C9ORF72 Drosophila model to distinguish pathways contributing to disease phenotypes from compensatory ones and confirmed alterations in some pathways in postmortem spinal cord tissue of patients with ALS. A different differentiation protocol was used to derive a separate set of C9ORF72 and control motor neurons. Many individual -omics differed by protocol, but some core dysregulated pathways were consistent. This strategy of analyzing patient-specific neurons provides disease-related outcomes with small numbers of heterogeneous lines and reduces variation from single-omics to elucidate network-based signatures.

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Multi-omic analysis of differentiated C9ORF72 iPSC-derived motor neurons

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Network-based integrative computational analysis

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Pathogenic versus compensatory pathways elucidated using C9ORF72 Drosophila model

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Pathways confirmed with alternative differentiation protocol and postmortem data

High-grade serous ovarian tumor cells modulate NK cell function to create an immune-tolerant microenvironment

Tubo-ovarian high-grade serous carcinoma (HGSC) is unresponsive to immune checkpoint blockade despite significant frequencies of exhausted T cells. Here we apply mass cytometry and uncover decidual-like natural killer (dl-NK) cell subpopulations (CD56+CD9+CXCR3+KIR+CD3-CD16-) in newly diagnosed HGSC samples that correlate with both tumor and transitioning epithelial-mesenchymal cell abundance. We show different combinatorial expression patterns of ligands for activating and inhibitory NK receptors within three HGSC tumor compartments: epithelial (E), transitioning epithelial-mesenchymal (EV), and mesenchymal (vimentin expressing [V]), with a more inhibitory ligand phenotype in V cells. In cocultures, NK-92 natural killer cells acquire CD9 from HGSC tumor cells by trogocytosis, resulting in reduced anti-tumor cytokine production and cytotoxicity. Cytotoxicity in these cocultures is restored with a CD9-blocking antibody or CD9 CRISPR knockout, thereby identifying mechanisms of immune suppression in HGSC. CD9 is widely expressed in HGSC tumors and so represents an important new therapeutic target with immediate relevance for NK immunotherapy.

Abstract PR08: High-grade serous ovarian tumor cells modulate natural killer cells to create an immune-tolerant microenvironment

Although the immunogenicity of HGSOC is well documented, responses to immunotherapy for HGSOC have been disappointing. Therefore, a deeper understanding of the cell types within the HGSOC tumor immune microenvironment could assist in identifying predictive mechanistic biomarkers to select patients likely to gain the most benefit from immunotherapy. Since their discovery in 1975, natural killer (NK) cells, an innate immune cell type, have been recognized to possess potent antitumor activity. NK cells are mechanistically distinct from T lymphocytes in that their killing activity is not mediated by high-resolution antigen specificity but through intracellular signaling by multiple germline cell surface receptors with both killing and inhibitory activities. These dual effector functions endow NK cells with roles in both immune surveillance to eradicate tumor cells and conversely with the creation of an immune tolerant microenvironment facilitating tumor progression. The balance between activating and inhibitory cell surface NK receptors determines which of these functions dominates. We recently published the first mass cytometry (aka CyTOF) study of newly diagnosed HGSOC tumors that focused on the tumor cells. We have now analyzed the CyTOF data of the immune cell infiltrate for the same set of tumors. To examine relationships between tumor and immune cell types, we performed extensive pairwise correlation analyses. In addition to correlations between tumor cell types and exhausted T-cell subpopulations, we observed strong positive correlations between different NK cell subpopulations with both overall tumor cell abundance and with tumor cells coexpressing E-cadherin and vimentin (EV cells). The latter are likely undergoing epithelial-to-mesenchymal transition and contributing to tumor progression. Intriguingly, the phenotype of the NK cell subpopulations resembled decidual NK cells. Decidual NK cells play a critical role in early pregnancy by conferring immune tolerance toward the hemi-allogeneic fetus. Our analysis suggests that the same features of immune tolerance could be subverted for HGSOC tumor maintenance and/or progression from the epithelial to metastatic state. Herein, we identify the phenotypes of the NK cells infiltrating HGSOC tumors and integrate these data with our CyTOF analysis of NK receptor ligands expressed by the tumor cells. Furthermore, CyTOF analysis of cocultures between HGSOC tumor and NK cell lines provides mechanistic insight as to how HGSOC tumor cells might direct NK cell function to create an immunosuppressive environment favoring tumor survival. NK cells are now at the center of a variety of immunotherapeutic approaches to exploit their tumor cell-killing activity. The data from this study could provide critical information about unappreciated adversity within the tumor immune microenvironment, which needs to be overcome for optimizing NK cell-based immunotherapy.

This abstract is also being presented as Poster B57.

Citation Format: Veronica D. Gonzalez, Shih-Yu Chen, Ying-Wen Huang, Antonio Delgado, Karen Sachs, Garry P. Nolan, Wendy J. Fantl. High-grade serous ovarian tumor cells modulate natural killer cells to create an immune-tolerant microenvironment [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr PR08.

Single-Cell Gene Expression Analyses Reveal Distinct Self-Renewing and Proliferating Subsets in the Leukemia Stem Cell Compartment in Acute Myeloid Leukemia

Standard chemotherapy for acute myeloid leukemia (AML) targets proliferative cells and efficiently induces complete remission; however, many patients relapse and die of their disease. Relapse is caused by leukemia stem cells (LSC), the cells with self-renewal capacity. Self-renewal and proliferation are separate functions in normal hematopoietic stem cells (HSC) in steady-state conditions. If these functions are also separate functions in LSCs, then antiproliferative therapies may fail to target self-renewal, allowing for relapse. We investigated whether proliferation and self-renewal are separate functions in LSCs as they often are in HSCs. Distinct transcriptional profiles within LSCs of Mll-AF9/NRAS^G12V murine AML were identified using single-cell RNA sequencing. Single-cell qPCR revealed that these genes were also differentially expressed in primary human LSCs and normal human HSPCs. A smaller subset of these genes was upregulated in LSCs relative to HSPCs; this subset of genes constitutes "LSC-specific" genes in human AML. To assess the differences between these profiles, we identified cell surface markers, CD69 and CD36, whose genes were differentially expressed between these profiles. In vivo mouse reconstitution assays resealed that only CD69^High LSCs were capable of self-renewal and were poorly proliferative. In contrast, CD36^High LSCs were unable to transplant leukemia but were highly proliferative. These data demonstrate that the transcriptional foundations of self-renewal and proliferation are distinct in LSCs as they often are in normal stem cells and suggest that therapeutic strategies that target self-renewal, in addition to proliferation, are critical to prevent relapse and improve survival in AML. SIGNIFICANCE: These findings define and functionally validate a self-renewal gene profile of leukemia stem cells at the single-cell level and demonstrate that self-renewal and proliferation are distinct in AML. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/3/458/F1.large.jpg.

GateFinder: projection-based gating strategy optimization for flow and mass cytometry

Motivation

High-parameter single-cell technologies can reveal novel cell populations of interest, but studying or validating these populations using lower-parameter methods remains challenging.

Results

Here, we present GateFinder, an algorithm that enriches high-dimensional cell types with simple, stepwise polygon gates requiring only two markers at a time. A series of case studies of complex cell types illustrates how simplified enrichment strategies can enable more efficient assays, reveal novel biomarkers and clarify underlying biology.

Availability and implementation

The GateFinder algorithm is implemented as a free and open-source package for BioConductor: https://nalab.stanford.edu/gatefinder.

Supplementary information

Supplementary data are available at Bioinformatics online.

A Bayesian Active Learning Experimental Design for Inferring Signaling Networks

Machine learning methods for learning network structure are applied to quantitative proteomics experiments and reverse-engineer intracellular signal transduction networks. They provide insight into the rewiring of signaling within the context of a disease or a phenotype. To learn the causal patterns of influence between proteins in the network, the methods require experiments that include targeted interventions that fix the activity of specific proteins. However, the interventions are costly and add experimental complexity. We describe an active learning strategy for selecting optimal interventions. Our approach takes as inputs pathway databases and historic data sets, expresses them in form of prior probability distributions on network structures, and selects interventions that maximize their expected contribution to structure learning. Evaluations on simulated and real data show that the strategy reduces the detection error of validated edges as compared with an unguided choice of interventions and avoids redundant interventions, thereby increasing the effectiveness of the experiment.

The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

Abstract PR09: Single-cell transcriptional profiling of acute myeloid leukemia identifies self-renewing stem cells

Acute myeloid leukemia (AML) is a lethal cancer with a survival of less than 50%. Standard cytotoxic therapies frequently induce complete remission, but patients frequently relapse and die of their disease. Leukemia stem cells (LSCs) are the leukemia cells with self-renewal potential and ability to recapitulate the disease. Most anticancer therapies are designed to inhibit proliferation. Yet, in hematopoietic stem cells, the mechanisms of proliferation are distinct from self-renewal (Li et al. Nature 2013). Consequently, targeting proliferation may explain the failure of traditional chemotherapy to target LSCs and eradicate AML. Our goal is define the self-renewing LSCs in order to develop therapeutics that target them and eliminate AML relapse. We previously showed that activated NRAS (NRASG12V) facilitates self-renewal in the LSC-enriched subpopulation of a transgenic mouse model of AML (Mll-AF9/NRASG12V) (Sachs et al. Blood 2014; Kim et al. Blood 2009). We hypothesize that self-renewal capacity and the NRAS-activated pathways required for self-renewal are limited to a subpopulation of LSCs.

We used single-cell RNA sequencing to identify the self-renewing cells among the LSC-enriched subgroup in this model (Mac1LowKit+Sca1+, “MKS”). We identified three discrete transcriptional profiles among the LSC-enriched subpopulation and found that that two of these profiles (Profile 1 and Profile 2) are NRASG12V-dependent. These two profiles can be differentiated by CD36 and CD69 expression. We sorted the MKS LSCs based on CD36 and CD69 expression. Sorted LSC subsets were transplanted into recipient mice to compare their ability to transfer leukemia as a measure of their self-renewal capacity. We found that MKS-CD36-CD69+ cells (consistent with Profile 1) rapidly transferred leukemia with high penetrance in 20 of 22 mice. In contrast, MKS-CD36+CD69- cells (Profile 2) failed transfer leukemia in most mice; only 2 of 25 of these mice developed AML (p < 0.004).

In our previous work, we demonstrated that the NRASG12V-activated self-renewal gene expression profile that we identified in our murine model was expressed in human AML, suggesting that the gene expression behavior of LSCs from this model may recapitulate the gene expression behavior of human LSCs (Sachs et al. Blood 2014). In order to determine if the single-cell transcriptional profiles of our murine AML can be found in primary human AML precursors, we performed single-cell RNA sequencing on CD34+ human AML cells obtained from a diagnostic bone marrow specimen. Analogous to our murine model, we found that these human AML cells express 2 distinct single-cell transcriptional profiles and they differentially express RAS-activated gene expression profiles and profiles of hematopoietic differentiation. Next, we used our murine single-cell self-renewal transcriptional profile to define a 96-gene panel consisting of 88 genes from this profile and 8 housekeeping genes. We sorted primary, diagnostic human AML cells for leukemia stem and progenitor cells (CD34+CD38-) and performed single-cell qPCR on these cells using our 96-gene panel. We found that a subset of these cells preferentially expresses Profile 1, the self-renewal gene expression profile that we identified in our murine model, and another subset preferentially expresses Profile 2 (the profile associated with no leukemia-reconstituting capacity).

In these experiments, we use a murine model of AML to define the LSC self-renewal gene expression profile at the single-cell level and functionally validate this profile in vivo. Analogous to the murine model, a subset of human AML stem and progenitor cells expresses this LSC self-renewal gene expression profile at the single-cell level. These data suggest that single-cell gene expression profiling can delineate leukemia cells with true self-renewal capacity.

This abstract is also being presented as Poster 43.

Citation Format: Klara E. Noble-Orcutt, Karen Sachs, Connor Navis, Alexandria Hillesheim, Ian Nykaza, Rebecca S. LaRue, Conner Hansen, Ngoc Ha, Michael A. Linden, David A. Largaespada, Zohar Sachs. Single-cell transcriptional profiling of acute myeloid leukemia identifies self-renewing stem cells [abstract]. In: Proceedings of the Second AACR Conference on Hematologic Malignancies: Translating Discoveries to Novel Therapies; May 6-9, 2017; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(24_Suppl):Abstract nr PR09.

From Correlation to Causality: Statistical Approaches to Learning Regulatory Relationships in Large-Scale Biomolecular Investigations

Causal inference, the task of uncovering regulatory relationships between components of biomolecular pathways and networks, is a primary goal of many high-throughput investigations. Statistical associations between observed protein concentrations can suggest an enticing number of hypotheses regarding the underlying causal interactions, but when do such associations reflect the underlying causal biomolecular mechanisms? The goal of this perspective is to provide suggestions for causal inference in large-scale experiments, which utilize high-throughput technologies such as mass-spectrometry-based proteomics. We describe in nontechnical terms the pitfalls of inference in large data sets and suggest methods to overcome these pitfalls and reliably find regulatory associations.

Abstract B15: NRASG12V oncogene mediates self-renewal in a murine model of acute myelogenous leukemia

Mutant RAS oncoproteins activate signaling molecules that drive oncogenesis in multiple human tumors including acute myelogenous leukemia (AML). However, the specific functions of these pathways in AML are unclear, thwarting the rational application of targeted therapeutics. To elucidate the downstream functions of activated NRAS in AML, we employed a murine model that harbors Mll-AF9 and a tetracycline repressible, activated NRAS (NRASG12V). We performed gene expression microarray and RNA sequencing of our AML cells in the presence and absence of NRASG12V. By employing computational approaches to explore NRASG12V-responsive genes in our model, we found that NRASG12V enforced the leukemia self-renewal gene expression signature and was required to maintain an MLL-AF9 and Myb-dependent leukemia self-renewal gene expression program. In functional assays, NRASG12V was required for leukemia self-renewal independently of its effects on growth and survival. We used CyTOF (mass cytometry) for a multiplexed analysis of RAS-dependent signaling intermediates, and found that Mac-1Low cells, which harbor leukemia stem cells, were preferentially sensitive to NRASG12V withdrawal. Using RAS-pathway inhibitors, we found NRASG12V maintained leukemia self-renewal through mTor and Mek pathway activation, implicating these pathways as potential targets for cancer stem cell-specific therapies. Together, these experimental results define a RAS oncogene-driven function that is critical for leukemia maintenance and represents a novel mechanism of oncogene addiction. Recent work has shown that NRASG12V has bimodal effects in hematopoietic stem cells (Li et al. Nature 2013). To understand the mechanism of these bimodal effects, we have performed single cell RNA sequencing on our AML model. We expect that these analyses will reveal the cell-type specific NRASG12V –mediated mechanisms of leukemia self renewal.

Citation Format: Zohar Sachs, Rebecca S. LaRue, Hanh T. Nguyen, Karen Sachs, Nurul Azyan Mohd Hassan, Ernesto Diaz-Flores, Susan K. Rathe, Aaron L. Sarver, Sean C. Bendall, Ngoc A. Ha, Miechaleen D. Diers, Garry P. Nolan, Kevin M. Shannon, David A. Largaespada. NRASG12V oncogene mediates self-renewal in a murine model of acute myelogenous leukemia. [abstract]. In: Proceedings of the AACR Special Conference on RAS Oncogenes: From Biology to Therapy; Feb 24-27, 2014; Lake Buena Vista, FL. Philadelphia (PA): AACR; Mol Cancer Res 2014;12(12 Suppl):Abstract nr B15. doi: 10.1158/1557-3125.RASONC14-B15

Single timepoint models of dynamic systems

Many interesting studies aimed at elucidating the connectivity structure of biomolecular pathways make use of abundance measurements, and employ statistical and information theoretic approaches to assess connectivities. These studies often do not address the effects of the dynamics of the underlying biological system, yet dynamics give rise to impactful issues such as timepoint selection and its effect on structure recovery. In this work, we study conditions for reliable retrieval of the connectivity structure of a dynamic system, and the impact of dynamics on structure-learning efforts. We encounter an unexpected problem not previously described in elucidating connectivity structure from dynamic systems, show how this confounds structure learning of the system and discuss possible approaches to overcome the confounding effect. Finally, we test our hypotheses on an accurate dynamic model of the IGF signalling pathway. We use two structure-learning methods at four time points to contrast the performance and robustness of those methods in terms of recovering correct connectivity.

Normalization of mass cytometry data with bead standards

Mass cytometry uses atomic mass spectrometry combined with isotopically pure reporter elements to currently measure as many as 40 parameters per single cell. As with any quantitative technology, there is a fundamental need for quality assurance and normalization protocols. In the case of mass cytometry, the signal variation over time due to changes in instrument performance combined with intervals between scheduled maintenance must be accounted for and then normalized. Here, samples were mixed with polystyrene beads embedded with metal lanthanides, allowing monitoring of mass cytometry instrument performance over multiple days of data acquisition. The protocol described here includes simultaneous measurements of beads and cells on the mass cytometer, subsequent extraction of the bead-based signature, and the application of an algorithm enabling correction of both short- and long-term signal fluctuations. The variation in the intensity of the beads that remains after normalization may also be used to determine data quality. Application of the algorithm to a one-month longitudinal analysis of a human peripheral blood sample reduced the range of median signal fluctuation from 4.9-fold to 1.3-fold.

Search for charged Higgs bosons in e+e- collisions at [Formula: see text]

A search is made for charged Higgs bosons predicted by Two-Higgs-Doublet extensions of the Standard Model (2HDM) using electron-positron collision data collected by the OPAL experiment at [Formula: see text], corresponding to an integrated luminosity of approximately 600 pb-1. Charged Higgs bosons are assumed to be pair-produced and to decay into [Formula: see text], τντ or AW±. No signal is observed. Model-independent limits on the charged Higgs-boson production cross section are derived by combining these results with previous searches at lower energies. Under the assumption [Formula: see text], motivated by general 2HDM type II models, excluded areas on the [Formula: see text] plane are presented and charged Higgs bosons are excluded up to a mass of 76.3 GeV at 95 % confidence level, independent of the branching ratio BR(H±→τντ ). A scan of the 2HDM type I model parameter space is performed and limits on the Higgs-boson masses [Formula: see text] and mA are presented for different choices of tanβ.

Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum

Flow cytometry is an essential tool for dissecting the functional complexity of hematopoiesis. We used single-cell "mass cytometry" to examine healthy human bone marrow, measuring 34 parameters simultaneously in single cells (binding of 31 antibodies, viability, DNA content, and relative cell size). The signaling behavior of cell subsets spanning a defined hematopoietic hierarchy was monitored with 18 simultaneous markers of functional signaling states perturbed by a set of ex vivo stimuli and inhibitors. The data set allowed for an algorithmically driven assembly of related cell types defined by surface antigen expression, providing a superimposable map of cell signaling responses in combination with drug inhibition. Visualized in this manner, the analysis revealed previously unappreciated instances of both precise signaling responses that were bounded within conventionally defined cell subsets and more continuous phosphorylation responses that crossed cell population boundaries in unexpected manners yet tracked closely with cellular phenotype. Collectively, such single-cell analyses provide system-wide views of immune signaling in healthy human hematopoiesis, against which drug action and disease can be compared for mechanistic studies and pharmacologic intervention.

Characterization of patient specific signaling via augmentation of Bayesian networks with disease and patient state nodes

Characterization of patient-specific disease features at a molecular level is an important emerging field. Patients may be characterized by differences in the level and activity of relevant biomolecules in diseased cells. When high throughput, high dimensional data is available, it becomes possible to characterize differences not only in the level of the biomolecules, but also in the molecular interactions among them. We propose here a novel approach to characterize patient specific signaling, which augments high throughput single cell data with state nodes corresponding to patient and disease states, and learns a Bayesian network based on this data. Features distinguishing individual patients emerge as downstream nodes in the network. We illustrate this approach with a six phospho-protein, 30,000 cell-per-patient dataset characterizing three comparably diagnosed follicular lymphoma, and show that our approach elucidates signaling differences among them.

Learning signaling network structures with sparsely distributed data

Flow cytometric measurement of signaling protein abundances has proved particularly useful for elucidation of signaling pathway structure. The single cell nature of the data ensures a very large dataset size, providing a statistically robust dataset for structure learning. Moreover, the approach is easily scaled to many conditions in high throughput. However, the technology suffers from a dimensionality constraint: at the cutting edge, only about 12 protein species can be measured per cell, far from sufficient for most signaling pathways. Because the structure learning algorithm (in practice) requires that all variables be measured together simultaneously, this restricts structure learning to the number of variables that constitute the flow cytometer's upper dimensionality limit. To address this problem, we present here an algorithm that enables structure learning for sparsely distributed data, allowing structure learning beyond the measurement technology's upper dimensionality limit for simultaneously measurable variables. The algorithm assesses pairwise (or n-wise) dependencies, constructs "Markov neighborhoods" for each variable based on these dependencies, measures each variable in the context of its neighborhood, and performs structure learning using a constrained search.

Causal protein-signaling networks derived from multiparameter single-cell data

Machine learning was applied for the automated derivation of causal influences in cellular signaling networks. This derivation relied on the simultaneous measurement of multiple phosphorylated protein and phospholipid components in thousands of individual primary human immune system cells. Perturbing these cells with molecular interventions drove the ordering of connections between pathway components, wherein Bayesian network computational methods automatically elucidated most of the traditionally reported signaling relationships and predicted novel interpathway network causalities, which we verified experimentally. Reconstruction of network models from physiologically relevant primary single cells might be applied to understanding native-state tissue signaling biology, complex drug actions, and dysfunctional signaling in diseased cells.

Measurement of spin-transfer observables in p p-->Lambda Lambda at 1.637 GeV/c

Spin-transfer observables for p p-->Lambda Lambda have been measured using a transversely polarized frozen-spin target and a beam momentum of 1.637 GeV/c. Current models of the reaction near threshold are in good agreement with existing measurements performed with unpolarized particles in the initial state but produce conflicting predictions for the spin-transfer observables Dnn and Knn (the normal-to-normal depolarization and polarization transfer), which are measurable only with polarized target or beam. Measurements of Dnn and Knn presented here are found to be in disagreement with predictions from these models.

Bayesian network approach to cell signaling pathway modeling

The modeling of cellular signaling pathways is an emerging field. Sachs et al. illustrate the application of Bayesian networks to an example cellular pathway involving the activation of focal adhesion kinase (FAK) and extracellular signal-regulated kinase (ERK) in response to fibronectin binding to an integrin. They describe how to use the analysis to select from among proposed models, formulate hypotheses regarding component interactions, and uncover potential dynamic changes in the interactions between these components. Although the data sets currently available for this example problem are too small to definitively point to a particular model, the approach and results provide a glimpse into the power that these methods will achieve once the technology for obtaining the necessary data becomes readily available.

Assays for activation of opsin by all-trans-retinal

The data collected with the techniques discussed in this chapter suggest significant differences between the active conformation(s) of the opsin/atr complex, which are reversibly formed in the dark, and the active conformation (R*) of the meta-II photoproduct. First, there is good evidence for noncovalent opsin/atr complexes with considerable activity (although covalent binding of atr is found in mutant opsins. Even more intriguing, all-trans-retinal in an amount that saturates the activity of the opsin/atr complex toward Gt does not measurably inhibit the access of 11-cis-retinal to the light-sensitive binding site during regeneration (Fig. 2C). On the other hand, forced protonation at or near Glu-134 appears to be an integral mechanism for both the meta-II and the opsin-like activities (Fig. 4). Thus, it is not inconceivable that these two activities of the receptor arise from two fundamentally different conformations, one meta-II-like and one opsin-like. They would be similar with respect to the Gt (or RK) protein-protein interaction but different in their mode of retinal-protein interaction.

Diffusible ligand all-trans-retinal activates opsin via a palmitoylation-dependent mechanism

In rhodopsin's function as a photoreceptor, 11-cis-retinal is covalently bound to Lys(296) via a protonated Schiff base. 11-cis/all-trans photoisomerization and relaxation through intermediates lead to the metarhodopsin II photoproduct, which couples to transducin (G(t)). Here we have analyzed a different signaling state that arises from noncovalent binding of all-trans-retinal (atr) to the aporeceptor opsin and enhances the very low opsin activity by several orders of magnitude. Like with metarhodopsin II, coupling of G(t) to opsin-atr is sensitive to competition by synthetic peptides from the COOH termini of both G(t)alpha and G(t)gamma. However, atr does not compete with 11-cis-retinal incorporation into the Lys(296) binding site and formation of the light-sensitive pigment. Blue light illumination fails to photorevert opsin-atr to the ground state. Thus noncovalently bound atr has no access to the light-dependent binding site and reaction pathway. Moreover, in contrast to light-dependent signaling, removal of the palmitoyl anchors at Cys(322) and Cys(323) in the rhodopsin COOH terminus impairs the atr-stimulated activity. Repalmitoylation by autoacylation with palmitoyl-coenzyme A restores most of the original activity. We hypothesize that the palmitoyl moieties are part of a second binding pocket for the chromophore, mediating hydrophobic interactions that can activate a large part of the catalytic receptor/G-protein interface.

Palmitoylation of rhodopsin with S-protein acyltransferase: enzyme catalyzed reaction versus autocatalytic acylation

Protein palmitoylation in vitro was studied using bovine rhodopsin as the substrate and a partially purified acylating enzymatic activity (PAT) from placental membranes. PAT incorporates fatty acid into rhodopsin with higher efficiency (10 times higher initial rate), as compared to autoacylation. The activity is sensitive to heat and trypsin, indicating a protein-mediated enzymatic process and requires the native conformation of rhodopsin. The presence of deacylated, free cysteine residues in dark-adapted rhodopsin increases palmitoylation via PAT. The sites for non-enzymatic and enzymatic palmitoylation could not be distinguished by peptide mapping. The reversible palmitoylation described here will provide a tool for the study of the role of palmitoylation in photoreceptor function.

Use of cisplatin, cyclophosphamide, vincristine, and doxorubicin for the treatment of non-small cell lung cancer

Forty-four evaluable patients with non-small cell carcinoma of the lung were treated with cisplatin, cyclophosphamide, vincristine, and doxorubicin. The overall response rate was 57%. The 16% who had complete response had a median survival of 81 weeks.

The athletic polydrug abuse phenomenon. A case report

A case of a power weight lifter who is ingesting large doses of anabolic steroids plus other drugs to counteract their short-term side effects is presented. This type of polydrug abuse phenomenon which is unique to the competitive athlete is widespread despite the lack of convincing evidence that anabolic steroids increase muscular strength. The vast extent of this drug abuse problem is poorly appreciated by the general medical community. The potential complications of the long-term usage of these drugs such as liver failure, hepatocellular carcinoma, and peliosis hepatitis make these drugs extremely dangerous.