Astrocytic trans-Differentiation Completes a Multicellular Paracrine Feedback Loop Required for Medulloblastoma Tumor Growth.

The tumor microenvironment (TME) is critical for tumor progression. However, the establishment and function of the TME remain obscure because of its complex cellular composition. Using a mouse genetic system called mosaic analysis with double markers (MADMs), we delineated TME evolution at single-cell resolution in sonic hedgehog (SHH)-activated medulloblastomas that originate from unipotent granule neuron progenitors in the brain. First, we found that astrocytes within the TME (TuAstrocytes) were trans-differentiated from tumor granule neuron precursors (GNPs), which normally never differentiate into astrocytes. Second, we identified that TME-derived IGF1 promotes tumor progression. Third, we uncovered that insulin-like growth factor 1 (IGF1) is produced by tumor-associated microglia in response to interleukin-4 (IL-4) stimulation. Finally, we found that IL-4 is secreted by TuAstrocytes. Collectively, our studies reveal an evolutionary process that produces a multi-lateral network within the TME of medulloblastoma: a fraction of tumor cells trans-differentiate into TuAstrocytes, which, in turn, produce IL-4 that stimulates microglia to produce IGF1 to promote tumor progression.

Cell-to-Cell Transcript Variability: Seeing Signal in the Noise.

How stochastic is gene expression in mammalian cells? Not very, according to Battich et al., who report that single-cell variability in cytoplasmic mRNAs is remarkably predictable given measurements of a cell's phenotypic state and microenvironment. The noise from transcriptional bursts is buffered by a hallmark of eukaryotes-the nucleus.

The receptor tyrosine kinase Ror is required for dendrite regeneration in Drosophila neurons.

While many regulators of axon regeneration have been identified, very little is known about mechanisms that allow dendrites to regenerate after injury. Using a Drosophila model of dendrite regeneration, we performed a candidate screen of receptor tyrosine kinases (RTKs) and found a requirement for RTK-like orphan receptor (Ror). We confirmed that Ror was required for regeneration in two different neuron types using RNA interference (RNAi) and mutants. Ror was not required for axon regeneration or normal dendrite development, suggesting a specific role in dendrite regeneration. Ror can act as a Wnt coreceptor with frizzleds (fzs) in other contexts, so we tested the involvement of Wnt signaling proteins in dendrite regeneration. We found that knockdown of fz, dishevelled (dsh), Axin, and gilgamesh (gish) also reduced dendrite regeneration. Moreover, Ror was required to position dsh and Axin in dendrites. We recently found that Wnt signaling proteins, including dsh and Axin, localize microtubule nucleation machinery in dendrites. We therefore hypothesized that Ror may act by regulating microtubule nucleation at baseline and during dendrite regeneration. Consistent with this hypothesis, localization of the core nucleation protein γTubulin was reduced in Ror RNAi neurons, and this effect was strongest during dendrite regeneration. In addition, dendrite regeneration was sensitive to partial reduction of γTubulin. We conclude that Ror promotes dendrite regeneration as part of a Wnt signaling pathway that regulates dendritic microtubule nucleation.

Cytokine-induced signaling networks prioritize dynamic range over signal strength.

Signaling networks respond to diverse stimuli, but how the state of the signaling network is relayed to downstream cellular responses is unclear. We modeled how incremental activation of signaling molecules is transmitted to control apoptosis as a function of signal strength and dynamic range. A linear relationship between signal input and response output, with the dynamic range of signaling molecules uniformly distributed across activation states, most accurately predicted cellular responses. When nonlinearized signals with compressed dynamic range relay network activation to apoptosis, we observe catastrophic, stimulus-specific prediction failures. We develop a general computational technique, "model-breakpoint analysis," to analyze the mechanism of these failures, identifying new time- and stimulus-specific roles for Akt, ERK, and MK2 kinase activity in apoptosis, which were experimentally verified. Dynamic range is rarely measured in signal-transduction studies, but our experiments using model-breakpoint analysis suggest it may be a greater determinant of cell fate than measured signal strength.

A cell-nonautonomous mechanism of yeast chronological aging regulated by caloric restriction and one-carbon metabolism.

Caloric restriction (CR) improves health span and life span of organisms ranging from yeast to mammals. Understanding the mechanisms involved will uncover future interventions for aging-associated diseases. In budding yeast, Saccharomyces cerevisiae, CR is commonly defined by reduced glucose in the growth medium, which extends both replicative and chronological life span (CLS). We found that conditioned media collected from stationary-phase CR cultures extended CLS when supplemented into nonrestricted (NR) cultures, suggesting a potential cell-nonautonomous mechanism of CR-induced life span regulation. Chromatography and untargeted metabolomics of the conditioned media, as well as transcriptional responses associated with the longevity effect, pointed to specific amino acids enriched in the CR conditioned media (CRCM) as functional molecules, with L-serine being a particularly strong candidate. Indeed, supplementing L-serine into NR cultures extended CLS through a mechanism dependent on the one-carbon metabolism pathway, thus implicating this conserved and central metabolic hub in life span regulation.

CIRCOAST: a statistical hypothesis test for cellular colocalization with network structures.

Motivation: Colocalization of structures in biomedical images can lead to insights into biological behaviors. One class of colocalization problems is examining an annular structure (disk-shaped such as a cell, vesicle or molecule) interacting with a network structure (vascular, neuronal, cytoskeletal, organellar). Examining colocalization events across conditions is often complicated by changes in density of both structure types, confounding traditional statistical approaches since colocalization cannot be normalized to the density of both structure types simultaneously. We have developed a technique to measure colocalization independent of structure density and applied it to characterizing intercellular colocation with blood vessel networks. This technique could be used to analyze colocalization of any annular structure with an arbitrarily shaped network structure. Results: We present the circular colocalization affinity with network structures test (CIRCOAST), a novel statistical hypothesis test to probe for enriched network colocalization in 2D z-projected multichannel images by using agent-based Monte Carlo modeling and image processing to generate the pseudo-null distribution of random cell placement unique to each image. This hypothesis test was validated by confirming that adipose-derived stem cells (ASCs) exhibit enriched colocalization with endothelial cells forming arborized networks in culture and then applied to show that locally delivered ASCs have enriched colocalization with murine retinal microvasculature in a model of diabetic retinopathy. We demonstrate that the CIRCOAST test provides superior power and type I error rates in characterizing intercellular colocalization compared to generic approaches that are confounded by changes in cell or vessel density. Availability and implementation: CIRCOAST source code available at: https://github.com/uva-peirce-cottler-lab/ARCAS. Supplementary information: Supplementary data are available at Bioinformatics online.

Profiling Subcellular Protein Phosphatase Responses to Coxsackievirus B3 Infection of Cardiomyocytes.

Cellular responses to stimuli involve dynamic and localized changes in protein kinases and phosphatases. Here, we report a generalized functional assay for high-throughput profiling of multiple protein phosphatases with subcellular resolution and apply it to analyze coxsackievirus B3 (CVB3) infection counteracted by interferon signaling. Using on-plate cell fractionation optimized for adherent cells, we isolate protein extracts containing active endogenous phosphatases from cell membranes, the cytoplasm, and the nucleus. The extracts contain all major classes of protein phosphatases and catalyze dephosphorylation of plate-bound phosphosubstrates in a microtiter format, with cellular activity quantified at the end point by phosphospecific ELISA. The platform is optimized for six phosphosubstrates (ERK2, JNK1, p38α, MK2, CREB, and STAT1) and measures specific activities from extracts of fewer than 50,000 cells. The assay was exploited to examine viral and antiviral signaling in AC16 cardiomyocytes, which we show can be engineered to serve as susceptible and permissive hosts for CVB3. Phosphatase responses were profiled in these cells by completing a full-factorial experiment for CVB3 infection and type I/II interferon signaling. Over 850 functional measurements revealed several independent, subcellular changes in specific phosphatase activities. During CVB3 infection, we found that type I interferon signaling increases subcellular JNK1 phosphatase activity, inhibiting nuclear JNK1 activity that otherwise promotes viral protein synthesis in the infected host cell. Our assay provides a high-throughput way to capture perturbations in important negative regulators of intracellular signal-transduction networks.

Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles.

Regulated changes in gene expression underlie many biological processes, but globally profiling cell-to-cell variations in transcriptional regulation is problematic when measuring single cells. Transcriptome-wide identification of regulatory heterogeneities can be robustly achieved by randomly collecting small numbers of cells followed by statistical analysis. However, this stochastic-profiling approach blurs out the expression states of the individual cells in each pooled sample. Here, we show that the underlying distribution of single-cell regulatory states can be deconvolved from stochastic-profiling data through maximum-likelihood inference. Guided by the mechanisms of transcriptional regulation, we formulated plausible mixture models for cell-to-cell regulatory heterogeneity and maximized the resulting likelihood functions to infer model parameters. Inferences were validated both computationally and experimentally for different mixture models, which included regulatory states for multicellular function that were occupied by as few as 1 in 40 cells of the population. Importantly, when the method was extended to programs of heterogeneously coexpressed transcripts, we found that population-level inferences were much more accurate with pooled samples than with one-cell samples when the extent of sampling was limited. Our deconvolution method provides a means to quantify the heterogeneous regulation of molecular states efficiently and gain a deeper understanding of the heterogeneous execution of cell decisions.

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.

A high-throughput assay for phosphoprotein-specific phosphatase activity in cellular extracts.

Protein phosphatases undo the post-translational modifications of kinase-signaling networks, but phosphatase activation in cells is difficult to measure and interpret. Here, we report the design of a quantitative and high-throughput assay platform for monitoring cellular phosphatase activity toward specific phosphoprotein targets. Protein substrates of interest are purified recombinantly, phosphorylated in vitro using the upstream kinase, and adsorbed to 96-well plates. Total phosphatase extracts from cells are then added to trigger a solid-phase dephosphorylation reaction. After stopping the reaction, phosphoprotein levels are quantified by ELISA with a phospho-specific antibody, and the loss of phospho-specific immunoreactivity is used as the readout of phosphatase activity. We illustrate the generality of the method by developing specific phosphatase-activity assays for the three canonical mitogen-activated protein phospho-kinases: ERK, JNK, and p38. The assays capture changes in activity with a dynamic range of 25-100-fold and are sensitive to a limit of detection below 25,000 cells. When applied to cytokine-induced signaling, the assays revealed complex and dynamic regulation of phosphatases suggesting cross-communication and a means for cellular memory. Our assay platform should be beneficial for phosphoproteomic surveys and computational-systems models of signaling, where phosphatases are known to be important but their activities are rarely measured.

Intersection of FOXO- and RUNX1-mediated gene expression programs in single breast epithelial cells during morphogenesis and tumor progression.

Gene expression networks are complicated by the assortment of regulatory factors that bind DNA and modulate transcription combinatorially. Single-cell measurements can reveal biological mechanisms hidden by population averages, but their value has not been fully explored in the context of mRNA regulation. Here, we adapted a single-cell expression profiling technique to examine the gene expression program downstream of Forkhead box O (FOXO) transcription factors during 3D breast epithelial acinar morphogenesis. By analyzing patterns of mRNA fluctuations among individual matrix-attached epithelial cells, we found that a subset of FOXO target genes was jointly regulated by the transcription factor Runt-related transcription factor 1 (RUNX1). Knockdown of RUNX1 causes hyperproliferation and abnormal morphogenesis, both of which require normal FOXO function. Down-regulating RUNX1 and FOXOs simultaneously causes widespread oxidative stress, which arrests proliferation and restores normal acinar morphology. In hormone-negative breast cancers lacking human epidermal growth factor receptor 2 (HER2) amplification, we find that RUNX1 down-regulation is strongly associated with up-regulation of FOXO1, which may be required to support growth of RUNX1-negative tumors. The coordinate function of these two tumor suppressors may provide a failsafe mechanism that inhibits cancer progression.

Three Modes of Viral Adaption by the Heart.

Viruses elicit long-term adaptive responses in the tissues they infect. Understanding viral adaptions in humans is difficult in organs such as the heart, where primary infected material is not routinely collected. In search of asymptomatic infections with accompanying host adaptions, we mined for cardio-pathogenic viruses in the unaligned reads of nearly one thousand human hearts profiled by RNA sequencing. Among virus-positive cases (~20%), we identified three robust adaptions in the host transcriptome related to inflammatory NFκB signaling and post-transcriptional regulation by the p38-MK2 pathway. The adaptions are not determined by the infecting virus, and they recur in infections of human or animal hearts and cultured cardiomyocytes. Adaptions switch states when NFκB or p38-MK2 are perturbed in cells engineered for chronic infection by the cardio-pathogenic virus, coxsackievirus B3. Stratifying viral responses into reversible adaptions adds a targetable systems-level simplification for infections of the heart and perhaps other organs.

Patient-derived response estimates from zero-passage organoids of luminal breast cancer.

Background: Primary luminal breast cancer cells lose their identity rapidly in standard tissue culture, which is problematic for testing hormone interventions and molecular pathways specific to the luminal subtype. Breast cancer organoids are thought to retain tumor characteristics better, but long-term viability of luminal-subtype cases is a persistent challenge. Our goal was to adapt short-term organoids of luminal breast cancer for parallel testing of genetic and pharmacologic perturbations. Methods: We freshly isolated patient-derived cells from luminal tumor scrapes, miniaturized the organoid format into 5 µl replicates for increased throughput, and set an endpoint of 14 days to minimize drift. Therapeutic hormone targeting was mimicked in these "zero-passage" organoids by withdrawing ß-estradiol and adding 4-hydroxytamoxifen. We also examined sulforaphane as an electrophilic stress and commercial neutraceutical with reported anti-cancer properties. Downstream mechanisms were tested genetically by lentiviral transduction of two complementary sgRNAs and Cas9 stabilization for the first week of organoid culture. Transcriptional changes were measured by RT-qPCR or RNA sequencing, and organoid phenotypes were quantified by serial brightfield imaging, digital image segmentation, and regression modeling of cellular doubling times. Results: We achieved >50% success in initiating luminal breast cancer organoids from tumor scrapes and maintaining them to the 14-day zero-passage endpoint. Success was mostly independent of clinical parameters, supporting general applicability of the approach. Abundance of ESR1 and PGR in zero-passage organoids consistently remained within the range of patient variability at the endpoint. However, responsiveness to hormone withdrawal and blockade was highly variable among luminal breast cancer cases tested. Combining sulforaphane with knockout of NQO1 (a phase II antioxidant response gene and downstream effector of sulforaphane) also yielded a breadth of organoid growth phenotypes, including growth inhibition with sulforaphane, growth promotion with NQO1 knockout, and growth antagonism when combined. Conclusions: Zero-passage organoids are a rapid and scalable way to interrogate properties of luminal breast cancer cells from patient-derived material. This includes testing drug mechanisms of action in different clinical cohorts. A future goal is to relate inter-patient variability of zero-passage organoids to long-term outcomes.

Identifying single-cell molecular programs by stochastic profiling.

Cells in tissues can be morphologically indistinguishable yet show molecular expression patterns that are remarkably heterogeneous. Here we describe an approach to comprehensively identify co-regulated, heterogeneously expressed genes among cells that otherwise appear identical. The technique, called stochastic profiling, involves repeated, random selection of very small cell populations via laser-capture microdissection followed by a customized single-cell amplification procedure and transcriptional profiling. Fluctuations in the resulting gene-expression measurements are then analyzed statistically to identify transcripts that are heterogeneously coexpressed. We stochastically profiled matrix-attached human epithelial cells in a three-dimensional culture model of mammary-acinar morphogenesis. Of 4,557 transcripts, we identified 547 genes with strong cell-to-cell expression differences. Clustering of this heterogeneous subset revealed several molecular 'programs' implicated in protein biosynthesis, oxidative-stress responses and NF-kappaB signaling, which we independently confirmed by RNA fluorescence in situ hybridization. Thus, stochastic profiling can reveal single-cell heterogeneities without the need to measure expression in individual cells.

Common effector processing mediates cell-specific responses to stimuli.

The fundamental components of many signalling pathways are common to all cells. However, stimulating or perturbing the intracellular network often causes distinct phenotypes that are specific to a given cell type. This 'cell specificity' presents a challenge in understanding how intracellular networks regulate cell behaviour and an obstacle to developing drugs that treat signalling dysfunctions. Here we apply a systems-modelling approach to investigate how cell-specific signalling events are integrated through effector proteins to cause cell-specific outcomes. We focus on the synergy between tumour necrosis factor and an adenoviral vector as a therapeutically relevant stimulus that induces cell-specific responses. By constructing models that estimate how kinase-signalling events are processed into phenotypes through effector substrates, we find that accurate predictions of cell specificity are possible when different cell types share a common 'effector-processing' mechanism. Partial-least-squares regression models based on common effector processing accurately predict cell-specific apoptosis, chemokine release, gene induction, and drug sensitivity across divergent epithelial cell lines. We conclude that cell specificity originates from the differential activation of kinases and other upstream transducers, which together enable different cell types to use common effectors to generate diverse outcomes. The common processing of network signals by downstream effectors points towards an important cell biological principle, which can be applied to the understanding of cell-specific responses to targeted drug therapies.

Pairwise network mechanisms in the host signaling response to coxsackievirus B3 infection.

Signal transduction networks can be perturbed biochemically, genetically, and pharmacologically to unravel their functions. But at the systems level, it is not clear how such perturbations are best implemented to extract molecular mechanisms that underlie network function. Here, we combined pairwise perturbations with multiparameter phosphorylation measurements to reveal causal mechanisms within the signaling network response of cardiomyocytes to coxsackievirus B3 (CVB3) infection. Using all possible pairs of six kinase inhibitors, we assembled a dynamic nine-protein phosphorylation signature of perturbed CVB3 infectivity. Cluster analysis of the resulting dataset showed repeatedly that paired inhibitor data were required for accurate data-driven predictions of kinase substrate links in the host network. With pairwise data, we also derived a high-confidence network based on partial correlations, which identified phospho-IκBα as a central "hub" in the measured phosphorylation signature. The reconstructed network helped to connect phospho-IκBα with an autocrine feedback circuit in host cells involving the proinflammatory cytokines, TNF and IL-1. Autocrine blockade substantially inhibited CVB3 progeny release and improved host cell viability, implicating TNF and IL-1 as cell autonomous components of CVB3-induced myocardial damage. We conclude that pairwise perturbations, when combined with network-level intracellular measurements, enrich for mechanisms that would be overlooked by single perturbants.

Proteome-wide copy-number estimation from transcriptomics.

Protein copy numbers constrain systems-level properties of regulatory networks, but absolute proteomic data remain scarce compared to transcriptomics obtained by RNA sequencing. We addressed this persistent gap by relating mRNA to protein statistically using best-available data from quantitative proteomics-transcriptomics for 4366 genes in 369 cell lines. The approach starts with a central estimate of protein copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model that links mRNAs to protein. For dozens of independent cell lines and primary prostate samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, and empirical protein-to-mRNA ratios. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein interaction complexes, suggesting mechanistic relationships are embedded. We use the method to estimate viral-receptor abundances of CD55-CXADR from human heart transcriptomes and build 1489 systems-biology models of coxsackievirus B3 infection susceptibility. When applied to 796 RNA sequencing profiles of breast cancer from The Cancer Genome Atlas, inferred copy-number estimates collectively reclassify 26% of Luminal A and 29% of Luminal B tumors. Protein-based reassignments strongly involve a pharmacologic target for luminal breast cancer (CDK4) and an α-catenin that is often undetectable at the mRNA level (CTTNA2). Thus, by adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility limits of contemporary proteomics. The collection of gene-specific models is assembled as a web tool for users seeking mRNA-guided predictions of absolute protein abundance (http://janeslab.shinyapps.io/Pinferna).

Nucleocytoplasmic transport of active HER2 causes fractional escape from the DCIS-like state.

Activation of HER2/ErbB2 coincides with escape from ductal carcinoma in situ (DCIS) premalignancy and disrupts 3D organization of cultured breast-epithelial spheroids. The 3D phenotype is infrequent, however, and mechanisms for its incomplete penetrance have been elusive. Using inducible HER2/ErbB2-EGFR/ErbB1 heterodimers, we match phenotype penetrance to the frequency of co-occurring transcriptomic changes and uncover a reconfiguration in the karyopherin network regulating ErbB nucleocytoplasmic transport. Induction of the exportin CSE1L inhibits nuclear accumulation of ErbBs, whereas nuclear ErbBs silence the importin KPNA1 by inducing miR-205. When these negative feedbacks are incorporated into a validated systems model of nucleocytoplasmic transport, steady-state localization of ErbB cargo becomes ultrasensitive to initial CSE1L abundance. Erbb2-driven carcinomas with Cse1l deficiency outgrow less irregularly from mammary ducts, and NLS-attenuating mutants or variants of HER2 favor escape in 3D culture. We conclude here that adaptive nucleocytoplasmic relocalization of HER2 creates a systems-level molecular switch at the premalignant-to-malignant transition.