Karen Sachs

Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum

Simultaneous measurement of more than 30 properties in individual human cells is used to characterize signaling in the immune system. 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.

Extracting a Cellular Hierarchy from High-dimensional Cytometry Data with SPADE

The ability to analyze multiple single-cell parameters is critical for understanding cellular heterogeneity. Despite recent advances in measurement technology, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system. To objectively uncover cellular heterogeneity from single-cell measurements, we present a versatile computational approach, spanning-tree progression analysis of density-normalized events (SPADE). We applied SPADE to flow cytometry data of mouse bone marrow and to mass cytometry data of human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. We demonstrate that SPADE is robust to measurement noise and to the choice of cellular markers. SPADE facilitates the analysis of cellular heterogeneity, the identification of cell types and comparison of functional markers in response to perturbations.

Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators

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.

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.

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.

Single-cell phospho-specific flow cytometric analysis demonstrates biochemical and functional heterogeneity in human hematopoietic stem and progenitor compartments

The low frequency of hematopoietic stem and progenitor cells (HSPCs) in human BM has precluded analysis of the direct biochemical effects elicited by cytokines in these populations, and their functional consequences. Here, single-cell phospho-specific flow cytometry was used to define the signaling networks active in 5 previously defined human HSPC subsets. This analysis revealed that the currently defined HSC compartment is composed of biochemically distinct subsets with the ability to respond rapidly and directly in vitro to a broader array of cytokines than previously appreciated, including G-CSF. The G-CSF response was physiologically relevant-driving cell-cycle entry and increased proliferation in a subset of single cells within the HSC compartment. The heterogeneity in the single-cell signaling and proliferation responses prompted subfractionation of the adult BM HSC compartment by expression of CD114 (G-CSF receptor). Xenotransplantation assays revealed that HSC activity is significantly enriched in the CD114(neg/lo) compartment, and almost completely absent in the CD114(pos) subfraction. The single-cell analyses used here can be adapted for further refinement of HSPC surface immunophenotypes, and for examining the direct regulatory effects of other factors on the homeostasis of stem and progenitor populations in normal or diseased states.

Distinct signaling programs control human hematopoietic stem cell survival and proliferation

Several growth factors (GFs) that together promote quiescent human hematopoietic stem cell (HSC) expansion ex vivo have been identified; however, the molecular mechanisms by which these GFs regulate the survival, proliferation. and differentiation of human HSCs remain poorly understood. We now describe experiments in which we used mass cytometry to simultaneously measure multiple surface markers, transcription factors, active signaling intermediates, viability, and cell-cycle indicators in single CD34+ cord blood cells before and up to 2 hours after their stimulation with stem cell factor, Fms-like tyrosine kinase 3 ligand, interleukin-3, interleukin-6, and granulocyte colony-stimulating factor (5 GFs) either alone or combined. Cells with a CD34+CD38-CD45RA-CD90+CD49f+ (CD49f+) phenotype (∼10% HSCs with >6-month repopulating activity in immunodeficient mice) displayed rapid increases in activated STAT1/3/5, extracellular signal-regulated kinase 1/2, AKT, CREB, and S6 by 1 or more of these GFs, and β-catenin only when the 5 GFs were combined. Certain minority subsets within the CD49f+ compartment were poorly GF-responsive and, among the more GF-responsive subsets of CD49f+ cells, different signaling intermediates correlated with the levels of the myeloid- and lymphoid-associated transcription factors measured. Phenotypically similar, but CD90-CD49f- cells (MPPs) contained lower baseline levels of multiple signaling intermediates than the CD90+CD49f+ cells, but showed similar response amplitudes to the same GFs. Importantly, we found activation or inhibition of AKT and β-catenin directly altered immediate CD49f+ cell survival and proliferation. These findings identify rapid signaling events that 5 GFs elicit directly in the most primitive human hematopoietic cell types to promote their survival and proliferation.

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.

NRASG12V oncogene facilitates 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 used a murine model that harbors Mll-AF9 and a tetracycline-repressible, activated NRAS (NRAS(G12V)). Using computational approaches to explore our gene-expression data sets, we found that NRAS(G12V) 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. NRAS(G12V) was required for leukemia self-renewal independent of its effects on growth and survival. Analysis of the gene-expression patterns of leukemic subpopulations revealed that the NRAS(G12V)-mediated leukemia self-renewal signature is preferentially expressed in the leukemia stem cell-enriched subpopulation. In a multiplexed analysis of RAS-dependent signaling, Mac-1(Low) cells, which harbor leukemia stem cells, were preferentially sensitive to NRAS(G12V) withdrawal. NRAS(G12V) 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.

LEARNING CYCLIC SIGNALING PATHWAY STRUCTURES WHILE MINIMIZING DATA REQUIREMENTS

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.