Single cell profiling of potentiated phospho-protein networks in cancer cells

PLCγ2 and PKC are important to myeloid lineage commitment triggered by M-SCF and G-CSF

Myeloid differentiation is a complex process whereby mature granulocytes or monocytes/macrophages are derived from a common myeloid progenitor through the coordinated action of hematopoietic cytokines. In this study, we explored the role of the Ca(2+)i signaling transduction pathway in the commitment of hematopoietic stem/progenitor cells to either the monocytic or granulocytic lineage in response to macrophage colony-stimulating factor (M-CSF) and granulocyte colony-stimulating factor (G-CSF). M-CSF and G-CSF induce cell expansion and monocyte or granulocyte differentiation, respectively, without affecting the percentage of hematopoietic progenitor cells. Colony-forming units (CFUs) and flow cytometry demonstrated the involvement of phospholipase Cγ (PLCγ) and protein kinase C (PKC) in monocyte/granulocyte commitment. In addition, using flow cytometry and RNA interference, we identified PLCγ2 as the PLCγ isoform that participates in this cell expansion and differentiation. Differences in signaling elicited by M-CSF and G-CSF were observed. The M-CSF-related effects were associated with the activation of ERK1/2 and nuclear factor of activated T-cells (NFAT); the inhibition of both molecules reduced the number of colonies in a CFU assay. In contrast, using flow cytometry and confocal evaluation, we demonstrated that G-CSF activated Jak-1 and STAT-3. Additionally, the effects induced by G-CSF were also related with the participation of Ca(2+) calmodulin kinase II and the transcription factor PU.1. STAT-3 activation and the increase of PU.1 expression were sensitive to PLC inhibition by U73122. These data show that PLCγ2 and PKC are important upstream signals that regulate myelopoiesis through cytokines, and differences in M-CSF and G-CSF downstream signaling were identified.

Single-cell RNA-seq: advances and future challenges

Phenotypically identical cells can dramatically vary with respect to behavior during their lifespan and this variation is reflected in their molecular composition such as the transcriptomic landscape. Single-cell transcriptomics using next-generation transcript sequencing (RNA-seq) is now emerging as a powerful tool to profile cell-to-cell variability on a genomic scale. Its application has already greatly impacted our conceptual understanding of diverse biological processes with broad implications for both basic and clinical research. Different single-cell RNA-seq protocols have been introduced and are reviewed here—each one with its own strengths and current limitations. We further provide an overview of the biological questions single-cell RNA-seq has been used to address, the major findings obtained from such studies, and current challenges and expected future developments in this booming field.

Inferring clonal composition from multiple sections of a breast cancer

Cancers arise from successive rounds of mutation and selection, generating clonal populations that vary in size, mutational content and drug responsiveness. Ascertaining the clonal composition of a tumor is therefore important both for prognosis and therapy. Mutation counts and frequencies resulting from next-generation sequencing (NGS) potentially reflect a tumor's clonal composition; however, deconvolving NGS data to infer a tumor's clonal structure presents a major challenge. We propose a generative model for NGS data derived from multiple subsections of a single tumor, and we describe an expectation-maximization procedure for estimating the clonal genotypes and relative frequencies using this model. We demonstrate, via simulation, the validity of the approach, and then use our algorithm to assess the clonal composition of a primary breast cancer and associated metastatic lymph node. After dividing the tumor into subsections, we perform exome sequencing for each subsection to assess mutational content, followed by deep sequencing to precisely count normal and variant alleles within each subsection. By quantifying the frequencies of 17 somatic variants, we demonstrate that our algorithm predicts clonal relationships that are both phylogenetically and spatially plausible. Applying this method to larger numbers of tumors should cast light on the clonal evolution of cancers in space and time.

Cancers arise from a series of mutations that occur over time. As a result, as a tumor grows each cell inherits a distinctive genotype, defined by the set of all somatic mutations that distinguish the tumor cell from normal cells. Acertaining these genotype patterns, and identifying which ones are associated with the growth of the cancer and its ability to metastasize, can potentially give clinicians insights into how to treat the cancer. In this work, we describe a method for inferring the predominant genotypes within a single tumor. The method requires that a tumor be sectioned and that each section be subjected to a high-throughput sequencing procedure. The resulting mutations and their associated frequencies within each tumor section are then used as input to a probabilistic model that infers the underlying genotypes and their relative frequencies within the tumor. We use simulated data to demonstrate the validity of the approach, and then we apply our algorithm to data from a primary breast cancer and associated metastatic lymph node. We demonstrate that our algorithm predicts genotypes that are consistent with an evolutionary model and with the physical topology of the tumor itself. Applying this method to larger numbers of tumors should cast light on the evolution of cancers in space and time.

Multiplexed high content screening assays create a systems cell biology approach to drug discovery

High content screening (HCS) has emerged as an important platform technology for early drug discovery from target identification through in vitro ADME/Tox. The focus is now on implementing multiplexed assays, developing and using advanced reagents and developing and harnessing more sophisticated informatics tools. Multiplexed HCS assays have the potential to dramatically improve the early drug discovery process by creating systems cell biology profiles on the activities of compounds. It is predicted that multiplexed HCS assays will accelerate the overall workflow and produce deeper functional knowledge, thereby permitting better decisions on what compounds to pursue.:

Phospho-specific flow cytometry in drug discovery

Here we present phospho-specific flow cytometry as a new tool for drug discovery with applications throughout the drug development pipeline, from target identification to library screening, disease model assessment and clinical screening and diagnostics. The single cell, multiparameter nature of flow cytometry generates high-content datasets, and current improvements in the technology are rapidly increasing its high-throughput capacity, making it a valuable platform in modern drug discovery.:

Quantitative measurement of alterations in DNA damage repair (DDR) pathways using single cell network profiling (SCNP)

Background

Homologous recombination repair (HRR) pathway deficiencies have significant implications for cancer predisposition and treatment strategies. Improved quantitative methods for functionally characterizing these deficiencies are required to accurately identify patients at risk of developing cancer and to identify mechanisms of drug resistance or sensitivity.

Methods

Flow cytometry-based single cell network profiling (SCNP) was used to measure drug-induced activation of DNA damage response (DDR) proteins in cell lines with defined HRR pathway mutations (including ATM-/-, ATM+/-, BRCA1+/-, BRCA2-/-) and in primary acute myeloid leukemia (AML) samples. Both non-homologous end joining (NHEJ) and HRR pathways were examined by measuring changes in intracellular readouts (including p-H2AX, p-ATM, p-DNA-PKcs, p-53BP1, p-RPA2/32, p-BRCA1, p-p53, and p21) in response to exposure to mechanistically distinct genotoxins. The cell cycle S/G2/M phase CyclinA2 marker was used to normalize for proliferation rates.

Results

Etoposide induced proliferation-independent DNA damage and activation of multiple DDR proteins in primary AML cells and ATM +/+but not ATM -/- cell lines. Treatment with the PARPi AZD2281 +/- temozolomide induced DNA damage in CyclinA2+ cells in both primary AML cells and cell lines and distngiushed cell lines deficient (BRCA2-/-) or impaired (BRCA1+/-) in HRR activity from BRCA1+/+ cell lines based on p-H2AX induction. Application of this assay to primary AML samples identified heterogeneous patterns of repair activity including muted or proficient activation of NHEJ and HRR pathways and predominant activation of NHEJ in a subset of samples.

Conclusions

SCNP identified functional DDR readouts in both NHEJ and HRR pathways, which can be applied to identify cells with BRCA1+/- haploinsuffiency and characterize differential DDR pathway functionality in primary clinical samples.

STAT signaling in the pathogenesis and treatment of myeloid malignancies

STAT transcription factors play a critical role in mediating the effects of cytokines on myeloid cells. As STAT target genes control key processes such as survival, proliferation and self-renewal, it is not surprising that constitutive activation of STATs, particularly STAT3 and STAT5, are common events in many myeloid tumors. STATs are activated both by mutant tyrosine kinases as well as other pathogenic events, and continued activation of STATs is common in the setting of resistance to kinase inhibitors. Thus, the targeting of STATs, alone or in combination with other drugs, will likely have increasing importance for cancer therapy.

A minimally invasive method for retrieving single adherent cells of different types from cultures

The field of single-cell analysis has gained a significant momentum over the last decade. Separation and isolation of individual cells is an indispensable step in almost all currently available single-cell analysis technologies. However, stress levels introduced by such manipulations remain largely unstudied. We present a method for minimally invasive retrieval of selected individual adherent cells of different types from cell cultures. The method is based on a combination of mechanical (shear flow) force and biochemical (trypsin digestion) treatment. We quantified alterations in the transcription levels of stress response genes in individual cells exposed to varying levels of shear flow and trypsinization. We report optimal temperature, RNA preservation reagents, shear force and trypsinization conditions necessary to minimize changes in the stress-related gene expression levels. The method and experimental findings are broadly applicable and can be used by a broad research community working in the field of single cell analysis.

Single-cell chemical proteomics with an activity-based probe: identification of low-copy membrane proteins on primary neurons

We propose a novel single-cell chemical proteomics (SCCP) strategy to profile low-abundance membrane proteins in single cells. In this approach, the membrane protein GB1 and its splicing variants were targeted on cultured cell lines and primary neurons using a specifically designed activity-based probe. The functionally labeled single cells were encapsulated in individual buffer droplets on a PDMS microwell array, and were further picked up one at a time and loaded into a capillary electrophoresis system for cell lysis, separation, and laser-induced fluorescence detection of the targeted proteins. The results revealed the expression of GB1 splicing variants in HEK and MEF cells, which was previously only suggested at the transcriptional level. We further applied this method to investigate single primary cells and observed significant heterogeneity among individual mouse cerebellar granule neurons. Interference experiments with GB1 antagonist and agonist validated this observation.

The Src homology-2 protein Shb modulates focal adhesion kinase signaling in a BCR-ABL myeloproliferative disorder causing accelerated progression of disease

Background

The Src homology-2 domain protein B (Shb) is an adapter protein operating downstream of several tyrosine kinase receptors and consequently Shb regulates various cellular responses. Absence of Shb was recently shown to reduce hematopoietic stem cell proliferation through activation of focal adhesion kinase (FAK) and thus we sought to investigate Shb’s role in the progression of leukemia.

Methods

Wild type and Shb knockout bone marrow cells were transformed with a retroviral BCR-ABL construct and subsequently transplanted to wild type or Shb knockout recipients. Disease latency, bone marrow and peripheral blood cell characteristics, cytokine expression, signaling characteristics and colony formation were determined by flow cytometry, qPCR, western blotting and methylcellulose colony forming assays.

Results

It was observed that Shb knockout BCR-ABL-transformed bone marrow cells produced a disease with death occurring at earlier time points compared with corresponding wild type controls due to elevated proliferation of transformed bone marrow cells. Moreover, significantly elevated interleukin-6 and granulocyte colony-stimulation factor mRNA levels were observed in Shb knockout c-Kit + leukemic bone marrow cells providing a plausible explanation for the concurrent peripheral blood neutrophilia. Shb knockout leukemic bone marrow cells also showed increased ability to form colonies in methylcellulose devoid of cytokines that was dependent on the concomitantly observed increased activity of FAK. Transplanting BCR-ABL-transformed Shb knockout bone marrow cells to Shb knockout recipients revealed decreased disease latency without neutrophilia, thus implicating the importance of niche-derived cues for the increase of blood granulocytes.

Conclusions

Absence of Shb accelerates disease progression by exerting dual roles in BCR-ABL-induced leukemia: increased cell expansion due to elevated FAK activity and neutrophilia in peripheral blood, the latter dependent on the genetic background of the leukemic niche.

Single cell kinase signaling assay using pinched flow coupled droplet microfluidics

Droplet-based microfluidics has shown potential in high throughput single cell assays by encapsulating individual cells in water-in-oil emulsions. Ordering cells in a micro-channel is necessary to encapsulate individual cells into droplets further enhancing the assay efficiency. This is typically limited due to the difficulty of preparing high-density cell solutions and maintaining them without cell aggregation in long channels (>5 cm). In this study, we developed a short pinched flow channel (5 mm) to separate cell aggregates and to form a uniform cell distribution in a droplet-generating platform that encapsulated single cells with >55% encapsulation efficiency beating Poisson encapsulation statistics. Using this platform and commercially available Sox substrates (8-hydroxy-5-(N,N-dimethylsulfonamido)-2-methylquinoline), we have demonstrated a high throughput dynamic single cell signaling assay to measure the activity of receptor tyrosine kinases (RTKs) in lung cancer cells triggered by cell surface ligand binding. The phosphorylation of the substrates resulted in fluorescent emission, showing a sigmoidal increase over a 12 h period. The result exhibited a heterogeneous signaling rate in individual cells and showed various levels of drug resistance when treated with the tyrosine kinase inhibitor, gefitinib.

Application of molecular technologies for phosphoproteomic analysis of clinical samples

The integration of small kinase inhibitors and monoclonal antibodies into oncological practice has opened a new paradigm for treating cancer patients. As proteins are the direct targets of the new generations of targeted therapeutics, many of which are kinase/enzymatic inhibitors, there is an increasing interest in developing technologies capable of monitoring post-translational changes of the human proteome for the identification of new predictive, prognostic and therapeutic biomarkers. It is well known that the vast majority of the activation/deactivation of these drug targets is driven by phosphorylation. This review provides a description of the main proteomic platforms (planar and bead array, reverse phase protein microarray, phosphoflow, AQUA and mass spectrometry) that have successfully been used for measuring changes in phosphorylation level of drug targets and downstream substrates using clinical specimens. Major emphasis was given to the strengths and weaknesses of the different platforms and to the major barriers that are associated with the analysis of the phosphoproteome. Finally, a number of examples of application of the above-mentioned technologies in the clinical setting are reported.

Interleukin-3-mediated regulation of β-catenin in myeloid transformation and acute myeloid leukemia

Aberrant activation of β-catenin is a common event in AML and is an independent predictor of poor prognosis. Although increased β-catenin signaling in AML has been associated with oncogenic translocation products and activating mutations in the FLT3R, the mechanisms that activate β-catenin in AML more broadly are still unclear. Here, we describe a novel link between IL-3 signaling and the regulation of β-catenin in myeloid transformation and AML. In a murine model of HoxB8 and IL-3 cooperation, we show that β-catenin protein levels are modulated by IL-3 and that Cre-induced deletion of β-catenin abolishes IL-3-dependent growth and colony formation. In IL-3-dependent leukemic TF-1.8 cells, we observed increased β-catenin protein levels and nuclear localization in response to IL-3, and this correlated with transcriptional induction of β-catenin target genes. Furthermore, IL-3 promoted β-catenin accumulation in a subset of AML patient samples, and gene-expression profiling of these cells revealed induction of WNT/β-catenin and TCF4 gene signatures in an IL-3-dependent manner. This study is the first to link β-catenin activation to IL-3 and suggests that targeting IL-3 signaling may be an effective approach for the inhibition of β-catenin activity in some patients with AML.

Adult B-cell acute lymphoblastic leukemia cells display decreased PTEN activity and constitutive hyperactivation of PI3K/Akt pathway despite high PTEN protein levels

Adult B-cell acute lymphoblastic leukemia remains a major therapeutic challenge, requiring a better characterization of the molecular determinants underlying disease progression and resistance to treatment. Here, using a phospho-flow cytometry approach we show that adult diagnostic B-cell acute lymphoblastic leukemia specimens display PI3K/Akt pathway hyperactivation, irrespective of their BCR-ABL status and despite paradoxically high basal expression of PTEN, the major negative regulator of the pathway. Protein kinase CK2 is known to phosphorylate PTEN thereby driving PTEN protein stabilization and concomitant PTEN functional inactivation. In agreement, we found that adult B-cell acute lymphoblastic leukemia samples show significantly higher CK2 kinase activity and lower PTEN lipid phosphatase activity than healthy controls. Moreover, the clinical-grade CK2 inhibitor CX-4945 (Silmitasertib) reversed PTEN levels in leukemia cells to those observed in healthy controls, and promoted leukemia cell death without significantly affecting normal bone marrow cells. Our studies indicate that CK2-mediated PTEN posttranslational inactivation, associated with PI3K/Akt pathway hyperactivation, are a common event in adult B-cell acute lymphoblastic leukemia and suggest that CK2 inhibition may constitute a valid, novel therapeutic tool in this malignancy.

Multidimensional single-cell analysis of BCR signaling reveals proximal activation defect as a hallmark of chronic lymphocytic leukemia B cells

Purpose

Chronic Lymphocytic Leukemia (CLL) is defined by a perturbed B-cell receptor-mediated signaling machinery. We aimed to model differential signaling behavior between B cells from CLL and healthy individuals to pinpoint modes of dysregulation.

Experimental Design

We developed an experimental methodology combining immunophenotyping, multiplexed phosphospecific flow cytometry, and multifactorial statistical modeling. Utilizing patterns of signaling network covariance, we modeled BCR signaling in 67 CLL patients using Partial Least Squares Regression (PLSR). Results from multidimensional modeling were validated using an independent test cohort of 38 patients.

Results

We identified a dynamic and variable imbalance between proximal (pSYK, pBTK) and distal (pPLCγ2, pBLNK, ppERK) phosphoresponses. PLSR identified the relationship between upstream tyrosine kinase SYK and its target, PLCγ2, as maximally predictive and sufficient to distinguish CLL from healthy samples, pointing to this juncture in the signaling pathway as a hallmark of CLL B cells. Specific BCR pathway signaling signatures that correlate with the disease and its degree of aggressiveness were identified. Heterogeneity in the PLSR response variable within the B cell population is both a characteristic mark of healthy samples and predictive of disease aggressiveness.

Conclusion

Single-cell multidimensional analysis of BCR signaling permitted focused analysis of the variability and heterogeneity of signaling behavior from patient-to-patient, and from cell-to-cell. Disruption of the pSYK/pPLCγ2 relationship is uncovered as a robust hallmark of CLL B cell signaling behavior. Together, these observations implicate novel elements of the BCR signal transduction as potential therapeutic targets.

Early gene expression of acute myeloid leukemia in response to chemotherapy

The use of gene expression arrays in the evaluation and classification of tumors is becoming increasingly important in a number of malignancies. This is a powerful technique able to disclose interpatient variance in gene expression. Such variation in gene expression may be the cause of different disease outcome and may reflect disease phenotypes or chemoresistance. Acute myeloid leukemia is a malignant disease of the bone marrow where overall long-term disease-free survival is less than 50%. The need for better disease classification and evaluation is consequently evident. Gene expression profiling in acute myeloid leukemia has, in recent years, proven able to distinguish acute myeloid leukemia subclasses and predict clinical outcome and is, as such, a promising technique for improved disease evaluation. The early detection of gene expression in response to chemotherapy may be a novel way of monitoring disease management. The immediate gene response may be an indication of whether the drug of choice is efficient in leukemic cell eradication and may early indicate the need for other therapeutic measures. Furthermore, these early alterations in gene expression could facilitate identification of new treatment targets, thereby enabling better patient care and follow-up in the future.

Challenges facing the development and use of protein chips to analyze the phosphoproteome

Recent advances in analytical methods, particularly in the area of protein microarrays, have brought the field of proteomics to the forefront of biological science. Protein arrays have shown to be useful for the multiplexed analysis of several hundreds of proteins in parallel. While much of the effort has focused on developing methods to identify expressed proteins, the identification of post-translational modifications is equally important for comprehensive proteome characterization. Protein phosphorylation constitutes a major type of post-translational modification that mobilizes a high number of genes, is involved in many crucial cell functions and largely contributes to the complexity of the proteome. One of the major challenges to analyze phosphoproteins using arrays is the availability of specific antibodies. Thus far, this has hampered the development of highly complex phosphoprotein arrays. This review discusses some of the recent progress made in the development of techniques and reagents to quantitatively determine sites of protein phosphorylation.

Cytobank: providing an analytics platform for community cytometry data analysis and collaboration

Cytometry is used extensively in clinical and laboratory settings to diagnose and track cell subsets in blood and tissue. High-throughput, single-cell approaches leveraging cytometry are developed and applied in the computational and systems biology communities by researchers, who seek to improve the diagnosis of human diseases, map the structures of cell signaling networks, and identify new cell types. Data analysis and management present a bottleneck in the flow of knowledge from bench to clinic. Multi-parameter flow and mass cytometry enable identification of signaling profiles of patient cell samples. Currently, this process is manual, requiring hours of work to summarize multi-dimensional data and translate these data for input into other analysis programs. In addition, the increase in the number and size of collaborative cytometry studies as well as the computational complexity of analytical tools require the ability to assemble sufficient and appropriately configured computing capacity on demand. There is a critical need for platforms that can be used by both clinical and basic researchers who routinely rely on cytometry. Recent advances provide a unique opportunity to facilitate collaboration and analysis and management of cytometry data. Specifically, advances in cloud computing and virtualization are enabling efficient use of large computing resources for analysis and backup. An example is Cytobank, a platform that allows researchers to annotate, analyze, and share results along with the underlying single-cell data.

Immune system functional pathway analysis using single cell network profiling (SCNP): a novel tool in cancer immunotherapy

The development of cancer immunotherapies has been ongoing for many years and has shown limited success. Novel biomarkers are needed to identify patients most likely to respond to anticancer immune-therapeutic approaches. Moreover, a systems-level approach is required for comprehensive understanding of the interconnected components, pathways, and cell types associated with an immune response. In this chapter, we describe single cell network profiling (SCNP), a novel method for assessing and measuring immune function/dysfunction at a systems level. SCNP is a multiparametric flow-cytometry-based analysis that can simultaneously measure, at the single cell level, both extracellular surface markers and changes in intracellular signaling proteins in response to extracellular modulators. Measuring changes in signaling proteins following the application of an external modulation informs on the functional capacity of the signaling network which cannot be assessed by the measurement of basal signaling alone. In addition, the simultaneous analysis of multiple pathways in multiple cell subsets can provide insight into the connectivity of both cell signaling networks and immune cell subtypes. The experimental steps associated with an SCNP assay are (1) pre-analytical sample preparation; (2) modulation for functional analysis; (3) staining with antibody cocktail; (4) data acquisition on flow cytometer; and (5) data analysis and metrics. Important considerations for each step of the assay will be discussed, and data demonstrating the utility of SCNP for immune monitoring applications will be summarized.

Microfluidics-based single-cell functional proteomics for fundamental and applied biomedical applications

We review an emerging microfluidics-based toolkit for single-cell functional proteomics. Functional proteins include, but are not limited to, the secreted signaling proteins that can reflect the biological behaviors of immune cells or the intracellular phosphoproteins associated with growth factor-stimulated signaling networks. Advantages of the microfluidics platforms are multiple. First, 20 or more functional proteins may be assayed simultaneously from statistical numbers of single cells. Second, cell behaviors (e.g., motility) may be correlated with protein assays. Third, extensions to quantized cell populations can permit measurements of cell-cell interactions. Fourth, rare cells can be functionally identified and then separated for further analysis or culturing. Finally, certain assay types can provide a conduit between biology and the physicochemical laws. We discuss the history and challenges of the field then review design concepts and uses of the microchip platforms that have been reported, with an eye toward biomedical applications. We then look to the future of the field.

High-dimensional single-cell cancer biology

Cancer cells are distinguished from each other and from healthy cells by features that drive clonal evolution and therapy resistance. New advances in high-dimensional flow cytometry make it possible to systematically measure mechanisms of tumor initiation, progression, and therapy resistance on millions of cells from human tumors. Here we describe flow cytometry techniques that enable a "single-cell " view of cancer. High-dimensional techniques like mass cytometry enable multiplexed single-cell analysis of cell identity, clinical biomarkers, signaling network phospho-proteins, transcription factors, and functional readouts of proliferation, cell cycle status, and apoptosis. This capability pairs well with a signaling profiles approach that dissects mechanism by systematically perturbing and measuring many nodes in a signaling network. Single-cell approaches enable study of cellular heterogeneity of primary tissues and turn cell subsets into experimental controls or opportunities for new discovery. Rare populations of stem cells or therapy-resistant cancer cells can be identified and compared to other types of cells within the same sample. In the long term, these techniques will enable tracking of minimal residual disease (MRD) and disease progression. By better understanding biological systems that control development and cell-cell interactions in healthy and diseased contexts, we can learn to program cells to become therapeutic agents or target malignant signaling events to specifically kill cancer cells. Single-cell approaches that provide deep insight into cell signaling and fate decisions will be critical to optimizing the next generation of cancer treatments combining targeted approaches and immunotherapy.

Detection of CREB phosphorylation via Zr (IV) ion mediated signal amplification

Phosphorylation of protein plays a vital regulatory role in a variety of biological processes. We herein report a novel method to assay the level of phosphorylated cAMP-response element binding protein (CREB) via Zr(4+) mediated signal amplification using gold nanoparticle/DNA/methylene blue (GNP/DNA/MB) nanocomposites. In this method the probe DNA immobilized at a gold electrode surface can specifically and efficiently recognize the phosphorylated target protein CREB. Then Zr(4+) links the phosphorylated CREB with GNP/DNA/MB nanocomposites by coordinating the phosphate groups on both CREB and the nanocomposites. Since the nanocomposites can provide high sensitivity (limit of detection: 0.25 nM) for the detection, efficient and highly sensitive bioanalysis of the expression level of phosphorylated protein CREB in human placenta tissues has also been conducted in this work. Our method is reported which shows acceptable stability, reproducibility for assaying of the protein phosphorylation states in real biosamples under physiological and pathological conditions with great potential for clinical applications in future.

JAK/STAT Signalling in Huntington's Disease Immune Cells

Huntington’s disease (HD) is an inherited neurodegenerative disorder caused by a CAG repeat expansion in the huntingtin (HTT) gene. Both central and peripheral innate immune activation have been described as features of the disease. Isolated human HD monocytes have been shown to produce more cytokines upon LPS stimulation compared to control monocytes. Understanding alterations in the signalling cascades responsible and activated by this increase in pro-inflammatory cytokine production is crucial in understanding the molecular basis of this phenomenon. Here we investigated the signalling cascade most commonly activated by pro-inflammatory cytokines such as IL-6 – the JAK/STAT signalling cascade. Using flow cytometry, we show that one out of three key transcription factors activated by JAK/STAT signalling is altered in primary human HD innate immune cells, suggesting that this pathway may only play a minor, additive role in the immune cell dysfunction in HD.

PLC-γ and PI3K link cytokines to ERK activation in hematopoietic cells with normal and oncogenic Kras

Oncogenic K-Ras proteins, such as K-Ras(G12D), accumulate in the active, guanosine triphosphate (GTP)-bound conformation and stimulate signaling through effector kinases. The presence of the K-Ras(G12D) oncoprotein at a similar abundance to that of endogenous wild-type K-Ras results in only minimal phosphorylation and activation of the canonical Raf-mitogen-activated or extracellular signal-regulated protein kinase kinase (MEK)-extracellular signal-regulated kinase (ERK) and phosphoinositide 3-kinase (PI3K)-Akt-mammalian target of rapamycin (mTOR) signaling cascades in primary hematopoietic cells, and these pathways remain dependent on growth factors for efficient activation. We showed that phospholipase C-γ (PLC-γ), PI3K, and their generated second messengers link activated cytokine receptors to Ras and ERK signaling in differentiated bone marrow cells and in a cell population enriched for leukemia stem cells. Cells expressing endogenous oncogenic K-Ras(G12D) remained dependent on the second messenger diacylglycerol for the efficient activation of Ras-ERK signaling. These data raise the unexpected possibility of therapeutically targeting proteins that function upstream of oncogenic Ras in cancer.

High-throughput, multiparameter analysis of single cells

Heterogeneity of cell populations in various biological systems has been widely recognized, and the highly heterogeneous nature of cancer cells has been emerging with clinical relevance. Single-cell analysis using a combination of high-throughput and multiparameter approaches is capable of reflecting cell-to-cell variability, and at the same time of unraveling the complexity and interdependence of cellular processes in the individual cells of a heterogeneous population. In this review, analytical methods and microfluidic tools commonly used for high-throughput, multiparameter single-cell analysis of DNA, RNA, and proteins are discussed. Applications and limitations of currently available technologies for cancer research and diagnostics are reviewed in the light of the ultimate goal to establish clinically applicable assays.

Towards defining biomarkers indicating resistances to targeted therapies

An impressive, but often short objective response was obtained in many tumor patients treated with different targeted therapies, but most of the patients develop resistances against these drugs. So far, a number of distinct mechanisms leading to intrinsic as well as acquired resistances have been identified in tumors of distinct origin. These can arise from genetic alterations, like mutations, truncations, and amplifications or due to deregulated expression of various proteins and signal transduction pathways, but also from cellular heterogeneity within tumors after an initial response. Therefore, biomarkers are urgently needed for cancer prognosis and personalized cancer medicine. The application of "ome"-based technologies including cancer (epi)genomics, next generation sequencing, cDNA microarrays and proteomics might led to the predictive or prognostic stratification of patients to categorize resistance mechanisms and to postulate combinations of treatment strategies. This review discusses the implementation of proteome-based analysis to identify markers of pathway (in)activation in tumors and the resistance mechanisms, which represent major clinical problems as a tool to optimize individually tailored therapies based on targeted drugs. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Validation of flow cytometric phospho-STAT5 as a diagnostic tool for juvenile myelomonocytic leukemia

To diagnose juvenile myelomonocytic leukemia (JMML) is sometimes challenging, because around 10% of patients lack molecular abnormalities affecting Ras-MAPK (mitogen-activated protein kinase) pathway and other diseases such as cytomegalovirus infection can mimic clinical signs of JMML. In order to validate a phospho-specific flow cytometry assay assessing phospho-signal transducer and activator of transcription factor 5 (p-STAT5) as a new diagnostic tool for JMML, we examined 22 samples from children with JMML and 47 controls. CD33+/CD34+ cells from 22 patients with JMML showed hyperphosphorylation of STAT5 induced by sub-saturating doses of granulocyte-macrophage colony-stimulating factor (GM-CSF). Using a training set of samples (11 JMML and 23 controls), we identified a threshold for p-STAT5-positive after stimulation with 0.1 ng/ml GM-CSF (17.17%) that discriminates JMML from controls. This threshold was validated in an independent series (11 JMML, 24 controls and 7 cases with diseases other than JMML) where we demonstrated that patients with JMML could be distinguished from other subjects with a sensitivity of 91% (confidence interval (CI) 59–100%) and a specificity of 87% (CI 70–96%). Positive and negative predictive values were 71% (CI 42–92%) and 96% (CI 82–100%), respectively. In conclusion, flow cytometric p-STAT5 profiling is a reliable diagnostic tool for identifying patients with JMML and can contribute to consistency of current diagnostic criteria.

Models of signalling networks - what cell biologists can gain from them and give to them

Computational models of cell signalling are perceived by many biologists to be prohibitively complicated. Why do math when you can simply do another experiment? Here, we explain how conceptual models, which have been formulated mathematically, have provided insights that directly advance experimental cell biology. In the past several years, models have influenced the way we talk about signalling networks, how we monitor them, and what we conclude when we perturb them. These insights required wet-lab experiments but would not have arisen without explicit computational modelling and quantitative analysis. Today, the best modellers are cross-trained investigators in experimental biology who work closely with collaborators but also undertake experimental work in their own laboratories. Biologists would benefit by becoming conversant in core principles of modelling in order to identify when a computational model could be a useful complement to their experiments. Although the mathematical foundations of a model are useful to appreciate its strengths and weaknesses, they are not required to test or generate a worthwhile biological hypothesis computationally.

Age-related changes of healthy bone marrow cell signaling in response to growth factors provide insight into low risk MDS

BACKGROUND

Single Cell Network Profiling (SCNP) is a multiparametric flow cytometry-based assay that quantifiably and simultaneously measures changes in intracellular signaling proteins in response to in vitro extracellular modulators at the single cell level. Myelodysplastic syndrome (MDS) is a heterogeneous clonal disorder of hematopoietic stem cells that occurs in elderly subjects and is characterized by dysplasia and ineffective hematopoiesis. The functional responsiveness of MDS bone marrow (BM) hematopoietic cells, including functionally distinct myeloid and erythroid precursor subsets, to hematopoietic growth factors (HGF) and the relationship of modulated signaling to disease characteristics is poorly understood.

METHODS

SCNP was used first to examine the effects of age on erythropoietin (EPO) and granulocyte colony stimulating factor (GCSF)-induced signaling in myeloid, nucleated red blood cells (nRBC), and CD34 expressing cell subsets in healthy BM (n = 15). SCNP was then used to map functional signaling profiles in low risk (LR) MDS (n = 7) for comparison to signaling in samples from healthy donors and to probe signaling associations within clinically defined subgroups.

RESULTS

In healthy BM samples, signaling responses to HGF were quite homogeneous (i.e., tightly regulated) with age-dependent effects observed in response to EPO but not to GCSF. Despite the relatively small number of samples assayed in the study, LR MDS could be classified into distinct subgroups based on both cell subset frequency and signaling profiles.

CONCLUSIONS

As a correlate of underlying genetic abnormalities, signal transduction analyses may provide a functional and potentially clinically relevant classification of MDS. Further evaluation in a larger cohort is warranted.

Using network biology to bridge pharmacokinetics and pharmacodynamics in oncology

If mathematical modeling is to be used effectively in cancer drug development, future models must take into account both the mechanistic details of cellular signal transduction networks and the pharmacokinetics (PK) of drugs used to inhibit their oncogenic activity. In this perspective, we present an approach to building multiscale models that capture systems-level architectural features of oncogenic signaling networks, and describe how these models can be used to design combination therapies and identify predictive biomarkers in silico.

Single-cell mass cytometry for analysis of immune system functional states

Mass cytometry facilitates high-dimensional, quantitative analysis of the effects of bioactive molecules on cell populations at single-cell resolution. Datasets are generated with panels of up to 45 antibodies. Each antibody is conjugated to a polymer chelated with a stable metal isotope, usually in the lanthanide series of the periodic table. Antibody panels recognize surface markers to delineate cell types simultaneously with intracellular signaling molecules to measure biological functions, such as metabolism, survival, DNA damage, cell cycle and apoptosis, to provide an overall determination of the network state of an individual cell. This review will cover the basics of mass cytometry as well as outline assays developed for the platform that enhance the immunologist's analytical arsenal.

Microchip platforms for multiplex single-cell functional proteomics with applications to immunology and cancer research

Single-cell functional proteomics assays can connect genomic information to biological function through quantitative and multiplex protein measurements. Tools for single-cell proteomics have developed rapidly over the past 5 years and are providing approaches for directly elucidating phosphoprotein signaling networks in cancer cells or for capturing high-resolution snapshots of immune system function in patients with various disease conditions. We discuss advances in single-cell proteomics platforms, with an emphasis on microchip methods. These methods can provide a direct correlation of morphological, functional and molecular signatures at the single-cell level. We also provide examples of how those platforms are being applied to both fundamental biology and clinical studies, focusing on immune-system monitoring and phosphoprotein signaling networks in cancer.

Antagonistic functional duality of cancer genes

Cancer evolution is a stochastic process both at the genome and gene levels. Most of tumors contain multiple genetic subclones, evolving in either succession or in parallel, either in a linear or branching manner, with heterogeneous genome and gene alterations, extensively rewired signaling networks, and addicted to multiple oncogenes easily switching with each other during cancer progression and medical intervention. Hundreds of discovered cancer genes are classified according to whether they function in a dominant (oncogenes) or recessive (tumor suppressor genes) manner in a cancer cell. However, there are many cancer "gene-chameleons", which behave distinctly in opposite way in the different experimental settings showing antagonistic duality. In contrast to the widely accepted view that mutant NADP(+)-dependent isocitrate dehydrogenases 1/2 (IDH1/2) and associated metabolite 2-hydroxyglutarate (R)-enantiomer are intrinsically "the drivers" of tumourigenesis, mutant IDH1/2 inhibited, promoted or had no effect on cell proliferation, growth and tumorigenicity in diverse experiments. Similar behavior was evidenced for dozens of cancer genes. Gene function is dependent on genetic network, which is defined by the genome context. The overall changes in karyotype can result in alterations of the role and function of the same genes and pathways. The diverse cell lines and tumor samples have been used in experiments for proving gene tumor promoting/suppressive activity. They all display heterogeneous individual karyotypes and disturbed signaling networks. Consequently, the effect and function of gene under investigation can be opposite and versatile in cells with different genomes that may explain antagonistic duality of cancer genes and the cell type- or the cellular genetic/context-dependent response to the same protein. Antagonistic duality of cancer genes might contribute to failure of chemotherapy. Instructive examples of unexpected activity of cancer genes and "paradoxical" effects of different anticancer drugs depending on the cellular genetic context/signaling network are discussed.

TrAp: a tree approach for fingerprinting subclonal tumor composition

Revealing the clonal composition of a single tumor is essential for identifying cell subpopulations with metastatic potential in primary tumors or with resistance to therapies in metastatic tumors. Sequencing technologies provide only an overview of the aggregate of numerous cells. Computational approaches to de-mix a collective signal composed of the aberrations of a mixed cell population of a tumor sample into its individual components are not available. We propose an evolutionary framework for deconvolving data from a single genome-wide experiment to infer the composition, abundance and evolutionary paths of the underlying cell subpopulations of a tumor. We have developed an algorithm (TrAp) for solving this mixture problem. In silico analyses show that TrAp correctly deconvolves mixed subpopulations when the number of subpopulations and the measurement errors are moderate. We demonstrate the applicability of the method using tumor karyotypes and somatic hypermutation data sets. We applied TrAp to Exome-Seq experiment of a renal cell carcinoma tumor sample and compared the mutational profile of the inferred subpopulations to the mutational profiles of single cells of the same tumor. Finally, we deconvolve sequencing data from eight acute myeloid leukemia patients and three distinct metastases of one melanoma patient to exhibit the evolutionary relationships of their subpopulations.

Diversity training for signal transduction: leveraging cell-to-cell variability to dissect cellular signaling, differentiation and death

Populations of 'identical' cells are rarely truly identical. Even when in the same state of differentiation, isogenic cells may vary in expression of key signaling regulators, activate signal transduction at different thresholds, and consequently respond heterogeneously to a given stimulus. Here, we review how new experimental and analytical techniques are suited to connect these different levels of variability, quantitatively mapping the effects of cell-to-cell variability on cellular decision-making. In particular, we summarize how this helps classify signaling regulators according to the impact of their variability on biological functions. We further discuss how variability can also be leveraged to shed light on the molecular mechanisms regulating cellular signaling, from the individual cell to the population of cells as a whole.

Deciphering the single-cell omic: innovative application for translational medicine

Traditional technologies to investigate system biology are limited by the detection of parameters resulting from the averages of large populations of cells, missing cells produced in small numbers, and attempting to uniform the heterogeneity. The advent of proteomics and genomics at a single-cell level has set the basis for an outstanding improvement in analytical technology and data acquisition. It has been well demonstrated that cellular heterogeneity is closely related to numerous stochastic transcriptional events leading to variations in patterns of expression among single genetically identical cells. The new-generation technology of single-cell analysis is able to better characterize a cell's population, identifying and differentiating outlier cells, in order to provide both a single-cell experiment and a corresponding bulk measurement, through the identification, quantification and characterization of all system biology aspects (genomics, transcriptomics, proteomics, metabolomics, degradomics and fluxomics). The movement of omics into single-cell analysis represents a significant and outstanding shift.

Future perspectives in melanoma research. Meeting report from the "Melanoma Bridge. Napoli, December 2nd-4th 2012"

Recent insights into the genetic and somatic aberrations have initiated a new era of rapidly evolving targeted and immune-based treatments for melanoma. After decades of unsuccessful attempts to finding a more effective cure in the treatment of melanoma now we have several drugs active in melanoma. The possibility to use these drugs in combination to improve responses to overcome the resistance, to potentiate the action of immune system with the new immunomodulating antibodies, and identification of biomarkers that can predict the response to a particular therapy represent new concepts and approaches in the clinical management of melanoma. The third "Melanoma Research: "A bridge from Naples to the World" meeting, shortened as "Bridge Melanoma Meeting" took place in Naples, December 2 to 4th, 2012. The four topics of discussion at this meeting were: advances in molecular profiling and novel biomarkers, combination therapies, novel concepts toward integrating biomarkers and therapies into contemporary clinical management of patients with melanoma across the entire spectrum of disease stage, and the knowledge gained from the biology of tumor microenvironment across different tumors as a bridge to impact on prognosis and response to therapy in melanoma. This international congress gathered more than 30 international faculty members who in an interactive atmosphere which stimulated discussion and exchange of their experience regarding the most recent advances in research and clinical management of melanoma patients.

Development and validation of a single-cell network profiling assay-based classifier to predict response to induction therapy in paediatric patients with de novo acute myeloid leukaemia: a report from the Children's Oncology Group

Single cell network profiling (SCNP) is a multi-parameter flow cytometry technique for simultaneous interrogation of intracellular signalling pathways. Diagnostic paediatric acute myeloid leukaemia (AML) bone marrow samples were used to develop a classifier for response to induction therapy in 53 samples and validated in an independent set of 68 samples. The area under the curve of a receiver operating characteristic curve (AUC(ROC)) was calculated to be 0·85 in the training set and after exclusion of induction deaths, the AUC(ROC) of the classifier was 0·70 (P = 0·02) and 0·67 (P = 0·04) in the validation set when induction deaths (intent to treat) were included. The highest predictive accuracy was noted in the cytogenetic intermediate risk patients (AUC(ROC) 0·88, P = 0·002), a subgroup that lacks prognostic/predictive biomarkers for induction response. Only white blood cell count and cytogenetic risk were associated with response to induction therapy in the validation set. After controlling for these variables, the SCNP classifier score was associated with complete remission (P = 0·017), indicating that the classifier provides information independent of other clinical variables that were jointly associated with response. This is the first validation of an SCNP classifier to predict response to induction chemotherapy. Herein we demonstrate the usefulness of quantitative SCNP under modulated conditions to provide independent information on AML disease biology and induction response.

viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia

New high-dimensional, single-cell technologies offer unprecedented resolution in the analysis of heterogeneous tissues. However, because these technologies can measure dozens of parameters simultaneously in individual cells, data interpretation can be challenging. Here we present viSNE, a tool that allows one to map high-dimensional cytometry data onto two dimensions, yet conserve the high-dimensional structure of the data. viSNE plots individual cells in a visual similar to a scatter plot, while using all pairwise distances in high dimension to determine each cell's location in the plot. We integrated mass cytometry with viSNE to map healthy and cancerous bone marrow samples. Healthy bone marrow automatically maps into a consistent shape, whereas leukemia samples map into malformed shapes that are distinct from healthy bone marrow and from each other. We also use viSNE and mass cytometry to compare leukemia diagnosis and relapse samples, and to identify a rare leukemia population reminiscent of minimal residual disease. viSNE can be applied to any multi-dimensional single-cell technology.

Paradoxical MAPK-activation in response to treatment with tyrosine kinase inhibitors in CML: flow cytometry loses track

BACKGROUND

Paradoxical activation of the MAP-kinases, ERK1, and ERK2 (ERK1/2) is observed in CML cell lines and primary CML patient cells treated with tyrosine kinase inhibitors (TKI) in vitro. The commonly accepted assumption is that activated ERK1/2 is key regulators of survival of leukemic cells treated with kinase inhibitors. Hence, paradoxical ERK1/2-activation may trigger resistance in vivo, which yet has to be shown. We therefore sought to establish a flow cytometric assay that enables us to measure paradoxical TKI-induced ERK1/2-activation on a single cell basis in primary CML cells.

METHODS

Side-by-side Western blot and intracellular flow cytometry (FCM) after in vitro exposure of cell lines and primary cells to nilotinib were performed. Detailed analysis of pre-analytical factors and the issue of compartmentalization of phosphorylated ERK1/2 by confocal laser scanning microscopy were performed.

RESULT

Results were conflicting in that pERK-activation was robustly detected in Western blot assays, but not when cells were analyzed by FCM despite well functioning positive and negative controls. This is in contrast to experiments on other targets such as phospho-CrkL, where also in our hands TKI-dependent inhibition of phosphorylation is trackable by both Western blot and FCM assays.

CONCLUSIONS

To our knowledge this is the first report of discordant results in phospho-protein analysis in TKI-treated cells analyzed by Western blot vs. FCM. We speculate that a substance specific interaction interferes with fluorescence dependent methods seeking to track phosphorylated ERK1/2 in TKI-treated cells.

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.

Early in vivo signaling profiles in MUC1-specific CD4+ T cells responding to two different MUC1-targeting vaccines in two different microenvironments

Vaccines are beginning to be explored for as measures to prevent cancer. Since determining the efficacy of vaccines by evaluating disease outcome requires a long time, there is an urgent need for early predictive biomarkers. To this end, immunological endpoints that can be assessed weeks or months post-vaccination are currently being evaluated. However, when multiple vaccines are available, waiting for the development of humoral and cellular immunity could still cause delays, whereas early assessments would allow for a timely shift to more effective prevention modalities. Applying the phospho-flow technique to primary T cells, we examined the phosphorylation status of various proteins that shape the activation, proliferation, and differentiation of mucin 1 (MUC1)-specific CD4+ T cells within the first 24 hours post-immunization. It is known that a vaccine composed of a MUC1-derived peptide loaded on dendritic cells is more effective in eliciting T-cell responses than a vaccine including the same peptide plus an adjuvant. Both these vaccines stimulate T cells more effectively in wild-type (WT) than in MUC1-transgenic mice. We examined if the signaling events downstream of the TCR or linked to various proliferative and survival pathways, monitored in two different hosts as early as 3, 6, 12 and 24 hours post-immunization, could predict the differential potential of these two MUC1-targeting vaccines. The signaling signatures that we obtained primarily reflect differences between the vaccines rather than between the hosts. We demonstrate the feasibility of using a phospho-flow-based approach to evaluate the potential of a given vaccine to elicit a desired immune response.