Patterns of Early p21 Dynamics Determine Proliferation-Senescence Cell Fate after Chemotherapy.

Chemotherapy is designed to induce cell death. However, at non-lethal doses, cancer cells can choose to remain proliferative or become senescent. The slow development of senescence makes studying this decision challenging. Here, by analyzing single-cell p21 dynamics before, during, and days after drug treatment, we link three distinct patterns of early p21 dynamics to final cell fate. Surprisingly, while high p21 expression is classically associated with senescence, we find the opposite at early times during drug treatment: most senescence-fated cells express much lower p21 levels than proliferation-fated cells. We demonstrate that these dynamics lead to a p21 "Goldilocks zone" for proliferation, in which modest increases of p21 expression can lead to an undesirable increase of cancer cell proliferation. Our study identifies a counter-intuitive role for early p21 dynamics in the cell-fate decision and pinpoints a source of proliferative cancer cells that can emerge after exposure to non-lethal doses of chemotherapy.

The Developmental Rules of Neural Superposition in Drosophila.

Complicated neuronal circuits can be genetically encoded, but the underlying developmental algorithms remain largely unknown. Here, we describe a developmental algorithm for the specification of synaptic partner cells through axonal sorting in the Drosophila visual map. Our approach combines intravital imaging of growth cone dynamics in developing brains of intact pupae and data-driven computational modeling. These analyses suggest that three simple rules are sufficient to generate the seemingly complex neural superposition wiring of the fly visual map without an elaborate molecular matchmaking code. Our computational model explains robust and precise wiring in a crowded brain region despite extensive growth cone overlaps and provides a framework for matching molecular mechanisms with the rules they execute. Finally, ordered geometric axon terminal arrangements that are not required for neural superposition are a side product of the developmental algorithm, thus elucidating neural circuit connectivity that remained unexplained based on adult structure and function alone.

Network crosstalk dynamically changes during neutrophil polarization.

How complex signaling networks shape highly coordinated, multistep cellular responses is poorly understood. Here, we made use of a network-perturbation approach to investigate causal influences, or "crosstalk," among signaling modules involved in the cytoskeletal response of neutrophils to chemoattractant. We quantified the intensity and polarity of cytoskeletal marker proteins over time to characterize stereotyped cellular responses. Analyzing the effects of network disruptions revealed that, not only does crosstalk evolve rapidly during polarization, but also that intensity and polarity responses are influenced by different patterns of crosstalk. Interestingly, persistent crosstalk is arranged in a surprisingly simple circuit: a linear cascade from front to back to microtubules influences intensities, and a feed-forward network in the reverse direction influences polarity. Our approach provided a rational strategy for decomposing a complex, dynamically evolving signaling system and revealed evolving paths of causal influence that shape the neutrophil polarization response.

Identification of F-actin as the dynamic hub in a microbial-induced GTPase polarity circuit.

Polarity in mammalian cells emerges from the assembly of signaling molecules into extensive biochemical interaction networks. Despite their complexity, bacterial pathogens have evolved parsimonious mechanisms to hijack these systems. Here, we develop a tractable experimental and theoretical model to uncover fundamental operating principles, in both mammalian cell polarity and bacterial pathogenesis. Using synthetic derivatives of the enteropathogenic Escherichia coli guanine-nucleotide exchange factor (GEF) Map, we discover that Cdc42 GTPase signal transduction is controlled by the interaction between Map and F-actin. Mathematical modeling reveals how actin dynamics coupled to a Map-dependent positive feedback loop spontaneously polarizes Cdc42 on the plasma membrane. By rewiring the pathogenic signaling circuit to operate through ß-integrin stimulation, we further show how Cdc42 is polarized in response to an extracellular spatial cue. Thus, a molecular pathway of polarity is proposed, centered on the interaction between GEFs and F-actin, which is likely to function in diverse biological systems.

Membrane tension maintains cell polarity by confining signals to the leading edge during neutrophil migration.

Little is known about how neutrophils and other cells establish a single zone of actin assembly during migration. A widespread assumption is that the leading edge prevents formation of additional fronts by generating long-range diffusible inhibitors or by sequestering essential polarity components. We use morphological perturbations, cell-severing experiments, and computational simulations to show that diffusion-based mechanisms are not sufficient for long-range inhibition by the pseudopod. Instead, plasma membrane tension could serve as a long-range inhibitor in neutrophils. We find that membrane tension doubles during leading-edge protrusion, and increasing tension is sufficient for long-range inhibition of actin assembly and Rac activation. Furthermore, reducing membrane tension causes uniform actin assembly. We suggest that tension, rather than diffusible molecules generated or sequestered at the leading edge, is the dominant source of long-range inhibition that constrains the spread of the existing front and prevents the formation of secondary fronts.

Cellular heterogeneity: do differences make a difference?

A central challenge of biology is to understand how individual cells process information and respond to perturbations. Much of our knowledge is based on ensemble measurements. However, cell-to-cell differences are always present to some degree in any cell population, and the ensemble behaviors of a population may not represent the behaviors of any individual cell. Here, we discuss examples of when heterogeneity cannot be ignored and describe practical strategies for analyzing and interpreting cellular heterogeneity.

Only two ways to achieve perfection.

The functional repertoire of a network is determined by its topology. Ma et al. (2009) analyze enzyme networks with three nodes and take a reverse-engineering approach to ask how many core network topologies can establish perfect adaptation, the ability to reset after perturbation. Surprisingly, the answer is just two.

Analysis of growth cone extension in standardized coordinates highlights self-organization rules during wiring of the Drosophila visual system.

A fascinating question in neuroscience is how ensembles of neurons, originating from different locations, extend to the proper place and by the right time to create precise circuits. Here, we investigate this question in the Drosophila visual system, where photoreceptors re-sort in the lamina to form the crystalline-like neural superposition circuit. The repeated nature of this circuit allowed us to establish a data-driven, standardized coordinate system for quantitative comparison of sparsely perturbed growth cones within and across specimens. Using this common frame of reference, we investigated the extension of the R3 and R4 photoreceptors, which is the only pair of symmetrically arranged photoreceptors with asymmetric target choices. Specifically, we found that extension speeds of the R3 and R4 growth cones are inherent to their cell identities. The ability to parameterize local regularity in tissue organization facilitated the characterization of ensemble cellular behaviors and dissection of mechanisms governing neural circuit formation.

Scalable analysis of cell-type composition from single-cell transcriptomics using deep recurrent learning.

Recent advances in large-scale single-cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states in heterogeneous tissues. We present scScope, a scalable deep-learning-based approach that can accurately and rapidly identify cell-type composition from millions of noisy single-cell gene-expression profiles.

Sampling strategies to capture single-cell heterogeneity.

Advances in single-cell technologies have highlighted the prevalence and biological significance of cellular heterogeneity. A critical question researchers face is how to design experiments that faithfully capture the true range of heterogeneity from samples of cellular populations. Here we develop a data-driven approach, illustrated in the context of image data, that estimates the sampling depth required for prospective investigations of single-cell heterogeneity from an existing collection of samples.

Membrane Tension Acts Through PLD2 and mTORC2 to Limit Actin Network Assembly During Neutrophil Migration.

For efficient polarity and migration, cells need to regulate the magnitude and spatial distribution of actin assembly. This process is coordinated by reciprocal interactions between the actin cytoskeleton and mechanical forces. Actin polymerization-based protrusion increases tension in the plasma membrane, which in turn acts as a long-range inhibitor of actin assembly. These interactions form a negative feedback circuit that limits the magnitude of membrane tension in neutrophils and prevents expansion of the existing front and the formation of secondary fronts. It has been suggested that the plasma membrane directly inhibits actin assembly by serving as a physical barrier that opposes protrusion. Here we show that efficient control of actin polymerization-based protrusion requires an additional mechanosensory feedback cascade that indirectly links membrane tension with actin assembly. Specifically, elevated membrane tension acts through phospholipase D2 (PLD2) and the mammalian target of rapamycin complex 2 (mTORC2) to limit actin nucleation. In the absence of this pathway, neutrophils exhibit larger leading edges, higher membrane tension, and profoundly defective chemotaxis. Mathematical modeling suggests roles for both the direct (mechanical) and indirect (biochemical via PLD2 and mTORC2) feedback loops in organizing cell polarity and motility-the indirect loop is better suited to enable competition between fronts, whereas the direct loop helps spatially organize actin nucleation for efficient leading edge formation and cell movement. This circuit is essential for polarity, motility, and the control of membrane tension.

Examining Crosstalk among Transforming Growth Factor ß, Bone Morphogenetic Protein, and Wnt Pathways.

The integration of morphogenic signals by cells is not well understood. A growing body of literature suggests increasingly complex coupling among classically defined pathways. Given this apparent complexity, it is difficult to predict where, when, or even whether crosstalk occurs. Here, we investigated pairs of morphogenic pathways, previously reported to have multiple points of crosstalk, which either do not share (TGFß and Wnt/ß-catenin) or share (TGFß and bone morphogenetic protein (BMP)) core signaling components. Crosstalk was measured by the ability of one morphogenic pathway to cross-activate core transcription factors and/or target genes of another morphogenic pathway. In contrast to previous studies, we found a surprising absence of crosstalk between TGFß and Wnt/ß-catenin. Further, we did not observe expected cross-pathway inhibition in between TGFß and BMP, despite the fact that both use (or could compete) for the shared component SMAD4. Critical to our assays was a separation of timescales, which helped separate crosstalk due to initial signal transduction from subsequent post-transcriptional feedback events. Our study revealed fewer (and different) inter-morphogenic pathway crosstalk connections than expected; even pathways that share components can be insulated from one another.

PHOCOS: inferring multi-feature phenotypic crosstalk networks.

MOTIVATION: Quantification of cellular changes to perturbations can provide a powerful approach to infer crosstalk among molecular components in biological networks. Existing crosstalk inference methods conduct network-structure learning based on a single phenotypic feature (e.g. abundance) of a biomarker. These approaches are insufficient for analyzing perturbation data that can contain information about multiple features (e.g. abundance, activity or localization) of each biomarker. RESULTS: We propose a computational framework for inferring phenotypic crosstalk (PHOCOS) that is suitable for high-content microscopy or other modalities that capture multiple phenotypes per biomarker. PHOCOS uses a robust graph-learning paradigm to predict direct effects from potential indirect effects and identify errors owing to noise or missing links. The result is a multi-feature, sparse network that parsimoniously captures direct and strong interactions across phenotypic attributes of multiple biomarkers. We use simulated and biological data to demonstrate the ability of PHOCOS to recover multi-attribute crosstalk networks from cellular perturbation assays. AVAILABILITY AND IMPLEMENTATION: PHOCOS is available in open source at https://github.com/AltschulerWu-Lab/PHOCOS CONTACT: steven.altschuler@ucsf.edu or lani.wu@ucsf.edu.

GSK-3 modulates cellular responses to a broad spectrum of kinase inhibitors.

A fundamental challenge in treating disease is identifying molecular states that affect cellular responses to drugs. Here, we focus on glycogen synthase kinase 3 (GSK-3), a key regulator for many of the hallmark behaviors of cancer cells. We alter GSK-3 activity in colon epithelial cells to test its role in modulating drug response. We find that GSK-3 activity broadly affects the cellular sensitivities to a panel of oncology drugs and kinase inhibitors. Specifically, inhibition of GSK-3 activity can strongly desensitize or sensitize cells to kinase inhibitors (for example, mTOR or PLK1 inhibitors, respectively). Additionally, colorectal cancer cell lines, in which GSK-3 function is commonly suppressed, are resistant to mTOR inhibitors and yet highly sensitive to PLK1 inhibitors, and this is further exacerbated by additional GSK-3 inhibition. Finally, by conducting a kinome-wide RNAi screen, we find that GSK-3 modulates the cell proliferative phenotype of a large fraction (∼35%) of the kinome, which includes ∼50% of current, clinically relevant kinase-targeted drugs. Our results highlight an underappreciated interplay of GSK-3 with therapeutically important kinases and suggest strategies for identifying disease-specific molecular profiles that can guide optimal selection of drug treatment.

On comparing heterogeneity across biomarkers.

Microscopy reveals complex patterns of cellular heterogeneity that can be biologically informative. However, a limitation of microscopy is that only a small number of biomarkers can typically be monitored simultaneously. Thus, a natural question is whether additional biomarkers provide a deeper characterization of the distribution of cellular states in a population. How much information about a cell's phenotypic state in one biomarker is gained by knowing its state in another biomarker? Here, we describe a framework for comparing phenotypic states across biomarkers. Our approach overcomes the current limitation of microscopy by not requiring costaining biomarkers on the same cells; instead, we require staining of biomarkers (possibly separately) on a common collection of phenotypically diverse cell lines. We evaluate our approach on two image datasets: 33 oncogenically diverse lung cancer cell lines stained with 7 biomarkers, and 49 less diverse subclones of one lung cancer cell line stained with 12 biomarkers. We first validate our method by comparing it to the "gold standard" of costaining. We then apply our approach to all pairs of biomarkers and use it to identify biomarkers that yield similar patterns of heterogeneity. The results presented in this work suggest that many biomarkers provide redundant information about heterogeneity. Thus, our approach provides a practical guide for selecting independently informative biomarkers and, more generally, will yield insights into both the connectivity of biological networks and the complexity of the state space of biological systems.

The evolution and development of neural superposition.

Visual systems have a rich history as model systems for the discovery and understanding of basic principles underlying neuronal connectivity. The compound eyes of insects consist of up to thousands of small unit eyes that are connected by photoreceptor axons to set up a visual map in the brain. The photoreceptor axon terminals thereby represent neighboring points seen in the environment in neighboring synaptic units in the brain. Neural superposition is a special case of such a wiring principle, where photoreceptors from different unit eyes that receive the same input converge upon the same synaptic units in the brain. This wiring principle is remarkable, because each photoreceptor in a single unit eye receives different input and each individual axon, among thousands others in the brain, must be sorted together with those few axons that have the same input. Key aspects of neural superposition have been described as early as 1907. Since then neuroscientists, evolutionary and developmental biologists have been fascinated by how such a complicated wiring principle could evolve, how it is genetically encoded, and how it is developmentally realized. In this review article, we will discuss current ideas about the evolutionary origin and developmental program of neural superposition. Our goal is to identify in what way the special case of neural superposition can help us answer more general questions about the evolution and development of genetically "hard-wired" synaptic connectivity in the brain.

On the spontaneous emergence of cell polarity.

Diverse cell polarity networks require positive feedback for locally amplifying distributions of signalling molecules at the plasma membrane. Additional mechanisms, such as directed transport or coupled inhibitors, have been proposed to be required for reinforcing a unique axis of polarity. Here we analyse a simple model of positive feedback, with strong analogy to the 'stepping stone' model of population genetics, in which a single species of diffusible, membrane-bound signalling molecules can self-recruit from a cytoplasmic pool. We identify an intrinsic stochastic mechanism through which positive feedback alone is sufficient to account for the spontaneous establishment of a single site of polarity. We find that the polarization frequency has an inverse dependence on the number of signalling molecules: the frequency of polarization decreases as the number of molecules becomes large. Experimental observation of polarizing Cdc42 in budding yeast is consistent with this prediction. Our work suggests that positive feedback can work alone or with additional mechanisms to create robust cell polarity.

Small molecule in situ resin capture provides a compound first approach to natural product discovery.

Culture-based microbial natural product discovery strategies fail to realize the extraordinary biosynthetic potential detected across earth's microbiomes. Here we introduce Small Molecule In situ Resin Capture (SMIRC), a culture-independent method to obtain natural products directly from the environments in which they are produced. We use SMIRC to capture numerous compounds including two new carbon skeletons that were characterized using NMR and contain structural features that are, to the best of our knowledge, unprecedented among natural products. Applications across diverse marine habitats reveal biome-specific metabolomic signatures and levels of chemical diversity in concordance with sequence-based predictions. Expanded deployments, in situ cultivation, and metagenomics facilitate compound discovery, enhance yields, and link compounds to candidate producing organisms, although microbial community complexity creates challenges for the later. This compound-first approach to natural product discovery provides access to poorly explored chemical space and has implications for drug discovery and the detection of chemically mediated biotic interactions.

Identifying FUS amyotrophic lateral sclerosis disease signatures in patient dermal fibroblasts.

Amyotrophic lateral sclerosis (ALS) is a rapidly progressing, highly heterogeneous neurodegenerative disease, underscoring the importance of obtaining information to personalize clinical decisions quickly after diagnosis. Here, we investigated whether ALS-relevant signatures can be detected directly from biopsied patient fibroblasts. We profiled familial ALS (fALS) fibroblasts, representing a range of mutations in the fused in sarcoma (FUS) gene and ages of onset. To differentiate FUS fALS and healthy control fibroblasts, machine-learning classifiers were trained separately on high-content imaging and transcriptional profiles. "Molecular ALS phenotype" scores, derived from these classifiers, captured a spectrum from disease to health. Interestingly, these scores negatively correlated with age of onset, identified several pre-symptomatic individuals and sporadic ALS (sALS) patients with FUS-like fibroblasts, and quantified "movement" of FUS fALS and "FUS-like" sALS toward health upon FUS ASO treatment. Taken together, these findings provide evidence that non-neuronal patient fibroblasts can be used for rapid, personalized assessment in ALS.

Large-scale prediction of Saccharomyces cerevisiae gene function using overlapping transcriptional clusters.

Genome sequencing has led to the discovery of tens of thousands of potential new genes. Six years after the sequencing of the well-studied yeast Saccharomyces cerevisiae and the discovery that its genome encodes approximately 6,000 predicted proteins, more than 2,000 have not yet been characterized experimentally, and determining their functions seems far from a trivial task. One crucial constraint is the generation of useful hypotheses about protein function. Using a new approach to interpret microarray data, we assign likely cellular functions with confidence values to these new yeast proteins. We perform extensive genome-wide validations of our predictions and offer visualization methods for exploration of the large numbers of functional predictions. We identify potential new members of many existing functional categories including 285 candidate proteins involved in transcription, processing and transport of non-coding RNA molecules. We present experimental validation confirming the involvement of several of these proteins in ribosomal RNA processing. Our methodology can be applied to a variety of genomics data types and organisms.