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High-content microscopy offers a scalable approach to screen against multiple targets in a single pass. Prior work has focused on methods to select “optimal” cellular readouts in microscopy screens. However, methods to select optimal cell line models have garnered much less attention. Here, we provide a roadmap for how to select the cell line or lines that are best suited to identify bioactive compounds and their mechanism of action (MOA). We test our approach on compounds targeting cancer-relevant pathways, ranking cell lines in two tasks: detecting compound activity (“phenoactivity”) and grouping compounds with similar MOA by similar phenotype (“phenosimilarity”). Evaluating six cell lines across 3214 well-annotated compounds, we show that optimal cell line selection depends on both the task of interest (e.g. detecting phenoactivity vs. inferring phenosimilarity) and distribution of MOAs within the compound library. Given a task of interest and set of compounds, we provide a systematic framework for choosing optimal cell line(s). Our framework can be used to reduce the number of cell lines required to identify hits within a compound library and help accelerate the pace of early drug discovery.

Legends for the supplementary figures.

This file shows all the supplementary figures as follows: Supplementary Figure 1: Clonal variability introduced by the TP53, KRAS, and MYC manipulations Supplementary Figure 2: Quantitation of signaling alterations for beta-CATENIN, P65, FOXO1 and p-ERK1/2 Supplementary Figure 3: Comparison between parental HBEC and HBECPKM cells for their sensitivity to the MEK inhibitor AZD6244 Supplementary Figure 4: Similar distributions of SMAD2/3 signaling with total or phospho antibodies Supplementary Figure 5: Alterations of p-STAT3 signaling in high EGF growth condition Supplementary Figure 6: Identification of lower EGF concentration (

Oncogene-specific changes in cellular signaling have been widely observed in lung cancer. Here, we investigated how these alterations could affect signaling heterogeneity and suggest novel therapeutic strategies. We compared signaling changes across six human bronchial epithelial cell (HBEC) strains that were systematically transformed with various combinations of TP53, KRAS, and MYC—oncogenic alterations commonly found in non–small cell lung cancer (NSCLC). We interrogated at single-cell resolution how these alterations could affect classic readouts (β-CATENIN, SMAD2/3, phospho-STAT3, P65, FOXO1, and phospho-ERK1/2) of key pathways commonly affected in NSCLC. All three oncogenic alterations were required concurrently to observe significant signaling changes, and significant heterogeneity arose in this condition. Unexpectedly, we found two mutually exclusive altered subpopulations: one with STAT3 upregulation and another with SMAD2/3 downregulation. Treatment with a STAT3 inhibitor eliminated the upregulated STAT3 subpopulation, but left a large surviving subpopulation with downregulated SMAD2/3. A bioinformatics search identified BCL6, a gene downstream of SMAD2/3, as a novel pharmacologically accessible target of our transformed HBECs. Combination treatment with STAT3 and BCL6 inhibitors across a panel of NSCLC cell lines and in xenografted tumors significantly reduced tumor cell growth. We conclude that BCL6 is a new therapeutic target in NSCLC and combination therapy that targets multiple vulnerabilities (STAT3 and BCL6) downstream of common oncogenes, and tumor suppressors may provide a potent way to defeat intratumor heterogeneity. Cancer Res; 77(11); 3070–81. ©2017 AACR.

Precision medicine can significantly improve outcomes for cancer patients, but implementation requires comprehensive characterization of tumor cells to identify therapeutically exploitable vulnerabilities. Here we describe somatic biallelic TET2 mutations in an elderly patient with acute myeloid leukemia (AML) that was chemoresistant to anthracycline and cytarabine (Ara-C), but acutely sensitive to 5'-azacitidine (5'-Aza) hypomethylating monotherapy resulting in long-term morphological remission. Given the role of TET2 as a regulator of genomic methylation, we hypothesized that mutant TET2 allele dosage affects response to 5'-Aza. Using an isogenic cell model system and an orthotopic mouse xenograft, we demonstrate that biallelic TET2 mutations confer sensitivity to 5'-Aza compared to cells with monoallelic mutation. Our data argue in favor of using hypomethylating agents for chemoresistant disease or as first line therapy in patients with biallelic TET2-mutated AML and demonstrate the importance of considering mutant allele dosage in the implementation of precision medicine for cancer patients.

Parkinson’s disease-causing LRRK2 mutations lead to varying degrees of Rab GTPase hyperphosphorylation. Puzzlingly, LRRK2 GTPase-inactivating mutations—which do not affect intrinsic kinase activity—lead to higher levels of cellular Rab phosphorylation than kinase-activating mutations. Here, we investigated whether mutation-dependent differences in LRRK2 cellular localization could explain this discrepancy. We discovered that blocking endosomal maturation leads to the rapid formation of mutant LRRK2+endosomes on which LRRK2 phosphorylates substrate Rabs. LRRK2+endosomes are maintained through positive feedback, which mutually reinforces membrane localization of LRRK2 and phosphorylated Rab substrates. Furthermore, across a panel of mutants, cells expressing GTPase-inactivating mutants formed strikingly more LRRK2+endosomes than cells expressing kinase-activating mutants, resulting in higher total cellular levels of phosphorylated Rabs. Our study suggests that an increased probability of LRRK2 GTPase-inactivating mutants to be retained on intracellular membranes over the kinase-activating mutants leads to higher substrate phosphorylation.

Mechanisms that prevent accidental degradation of healthy mitochondria by the PINK1/Parkin mitophagy pathway are poorly understood. On the surface of damaged mitochondria, PINK1 accumulates and acts as the input signal to a positive feedback loop of Parkin recruitment, which in turn promotes mitochondrial degradation via mitophagy. However, PINK1 is also present on healthy mitochondria where it could errantly recruit Parkin and thereby activate this positive feedback loop. Here, we quantitatively mapped the relationship between PINK1 input levels and Parkin recruitment dynamics using live-cell microscopy and mathematical modeling. We found that Parkin is recruited to the mitochondria only if PINK1 levels exceed a threshold and only after a delay that is inversely proportional to PINK1 levels. The threshold and delay provide a “two-factor authentication” step for PINK1/Parkin activation. These properties arise from the PINK1/Parkin circuit topology and provide a mechanism for cells to assess damage signals before committing to mitophagy.

Cancer persister cells are able to survive otherwise lethal doses of drugs through nongenetic mechanisms, which can lead to cancer regrowth and drug resistance. The broad spectrum of molecular differences observed between persisters and their treatment-naïve counterparts makes it challenging to identify causal mechanisms underlying persistence. Here, we modulate environmental signals to identify cellular mechanisms that promote the emergence of persisters and to pinpoint actionable vulnerabilities that eliminate them. We found that interferon-γ (IFNγ) can induce a pro-persistence signal that can be specifically eliminated by inhibition of type I protein arginine methyltransferase (PRMT) (PRMTi). Mechanistic investigation revealed that signal transducer and activator of transcription 1 (STAT1) is a key component connecting IFNγ’s pro-persistence and PRMTi’s antipersistence effects, suggesting a previously unknown application of PRMTi to target persisters in settings with high STAT1 expression. Modulating environmental signals can accelerate the identification of mechanisms that promote and eliminate cancer persistence.

Recent advances in large-scale single cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states within 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.

Renewing tissues have the remarkable ability to continually produce both proliferative progenitor and specialized differentiated cell types. How are complex milieus of microenvironmental signals interpreted to coordinate tissue-cell-type composition? Here, we investigate the responses of intestinal epithelium to individual and paired perturbations across eight epithelial signaling pathways. Using a high-throughput approach that combines enteroid monolayers and quantitative imaging, we identified conditions that enrich for specific cell types as well as interactions between pathways. Importantly, we found that modulation of transit-amplifying cell proliferation changes the ratio of differentiated secretory to absorptive cell types. These observations highlight an underappreciated role for transit-amplifying cells in the tuning of differentiated cell-type composition.

Advances in spatial transcriptomics technologies enable simultaneous profiling of morphological and transcriptional modalities from the same cells or regions within tissues. Here, we present multi-modal structured embedding (MUSE), an approach to deeply characterize tissue heterogeneity through analysis of combined image and transcriptional measurements. We demonstrate that MUSE can discover tissue subpopulations missed by either modality as well as compensate for modality-specific noise. We apply MUSE to diverse datasets containing spatial transcriptomics (seqFISH+, STARmap or ST) and imaging (H&E or fluorescence microscopy) modalities. MUSE identified biologically meaningful tissue subpopulations and stereotyped spatial patterning within heterogeneous mouse cortex, human cancer tissue and mouse brain sections with Alzheimer’s disease. MUSE provides a framework for combining multi-modal data to reveal deeper insights into the states, functions and organizations of cells in complex biological tissues.

During development, neurons extend in search of synaptic partners. Precise control of axon extension velocity can therefore be crucial to ensuring proper circuit formation. How velocity is regulated – particularly by the extending axons themselves – remains poorly understood. Here, we investigate this question in theDrosophilavisual system, where photoreceptors make precise connections with a specific set of synaptic partners that together create a circuit underpinning neural superposition (NSP). We used a combination of genetic perturbations and quantitative image analysis to investigate the influence of cell identity on growth cone velocity and subsequent spatial-temporal coincidence of presynaptic and postsynaptic neurons. Our study provides a case study of how cell autonomous properties of presynaptic axons play a pivotal role in controlling the dynamics of growing axons and determining the formation of a precise neuronal circuit.

Spatial transcriptomics enables the simultaneous measurement of morphological features and transcriptional profiles of the same cells or regions in tissues. Here we present multi-modal structured embedding (MUSE), an approach to characterize cells and tissue regions by integrating morphological and spatially resolved transcriptional data. We demonstrate that MUSE can discover tissue subpopulations missed by either modality as well as compensate for modality-specific noise. We apply MUSE to diverse datasets containing spatial transcriptomics (seqFISH+, STARmap or Visium) and imaging (hematoxylin and eosin or fluorescence microscopy) modalities. MUSE identified biologically meaningful tissue subpopulations and stereotyped spatial patterning in healthy brain cortex and intestinal tissues. In diseased tissues, MUSE revealed gene biomarkers for proximity to tumor region and heterogeneity of amyloid precursor protein processing across Alzheimer brain regions. MUSE enables the integration of multi-modal data to provide insights into the states, functions and organization of cells in complex biological tissues.

Advances in spatial transcriptomics technologies enable optical profiling of morphological and transcriptional modalities from the same cells within tissues. Here, we present multi-modal structured embedding (MUSE), an approach to deeply characterize tissue heterogeneity through analysis of combined image and transcriptional single-cell measurements. We demonstrate that MUSE can discover cellular subpopulations missed by either modality as well as compensate for modality-specific noise. MUSE identified biologically meaningful cellular subpopulations and stereotyped spatial patterning within heterogeneous mouse cortex brain tissues, profiled by seqFISH+ or STARmap technologies. MUSE provides a framework for combining multi-modal single-cell data to reveal deeper insights into the states, functions and organization of cells in complex biological tissues.

The intestinal epithelium is a single layer of cells that plays a critical role in digestion, absorbs nutrients from food, and coordinates the delicate interplay between microbes in the gut lumen and the immune system. Epithelial homeostasis is crucial for maintaining health; disruption of homeostasis results in disorders including inflammatory bowel disease and cancer. The advent of 3D intestinal epithelial organoids has greatly advanced our understanding of the molecular underpinnings of epithelial homeostasis and disease. Recently, we developed an enteroid monolayer (2D) culture system that recapitulates important features of 3D organoids and the in vivo intestinal epithelium such as tissue renewal, representation of diverse epithelial cell types, self-organization, and apical-basolateral polarization. Enteroid monolayers are cultured in microtiter plates, enabling high-throughput experiments. Furthermore, their 2D nature makes it easier to distinguish individual cells by fluorescent microscopy, enabling quantitative analysis of single cell behaviors within the epithelial tissue.Here we describe experimental methods for generating enteroid monolayers and computational methods for analyzing immunofluorescence images of enteroid monolayers. We outline experimental methods for generating enteroid monolayers from freshly isolated intestinal crypts, frozen intestinal crypts, and 3D organoids. Fresh crypts are easily obtained from murine or human intestinal samples, and the ability to derive enteroid monolayers from both frozen crypts and 3D organoids enables genetic modification and/or biobanking of patient samples for future studies. We outline computational methods for identifying distinct epithelial cell types (goblet, stem, EdU+) in immunofluorescence images of enteroid monolayers and, importantly, individual nuclei, enabling truly single cell measurements of epithelial cell behaviors to be made. Taken together, these methods will enable detailed studies of epithelial homeostasis and intestinal disease.

SummaryA persistent puzzle in cancer biology is how mutations, which neither alter canonical growth signaling pathways nor directly interfere with drug mechanism, can still recur and persist in tumors. One notable example is the loss-of-function mutation of the DNA demethylase Tet2 in acute myeloid leukemias (AMLs) that frequently persists from diagnosis through remission and relapse (Rothenberg-Thurley et al., 2018; Corces-Zimmerman et al., 2014; Nibourel et al., 2010), but whose fitness advantage in the setting of anti-leukemic chemotherapy is unclear. Here we use paired isogenic human AML cell lines to show that Tet2 loss-of-function alters the dynamics of transitions between differentiated and stem-like states. Mathematical modeling and experimental validation reveal that these altered cell-state dynamics can benefit the cell population by slowing population decay during drug treatment and lowering the number of survivor cells needed to re-establish the initial population. These studies shed light on the functional and phenotypic effects of a Tet2 loss-of-function in AML, illustrate how a single gene mutation can alter a cells’ phenotypic plasticity, and open up new avenues in the development of strategies to combat AML relapse.

The impact of different metabolic environments on cancer cell behavior is poorly understood. Here, we systematically altered nutrient composition of cell culture media and examined the impact on three phenotypes—drug-treatment survival, cell migration, and lactate overflow—that are frequently studied in cancer cells. These perturbations across diverse metabolic environments revealed simple relationships between cell growth rate and drug-treatment survival or migration. In contrast, lactate overflow was highly sensitive to changes in sugar availability but largely insensitive to changes in amino acid availability, regardless of the growth rate. Further investigation suggested that the degree of lactate overflow across metabolic environments is largely determined by the cells’ ability to maintain high rates of sugar uptake. This study enabled us to elucidate quantitative relationships between metabolic environment and cancer cell phenotypes, which echo empirical growth laws discovered to govern analogous phenotypes in microbes.

The intestinal epithelium is a single layer of cells that plays a critical role in digestion, absorbs nutrients from food, and coordinates the delicate interplay between microbes in the gut lumen and the immune system. Epithelial homeostasis is crucial for maintaining health; disruption of homeostasis results in disorders including inflammatory bowel disease and cancer. The advent of 3D intestinal epithelial organoids has greatly advanced our understanding of the molecular underpinnings of epithelial homeostasis and disease. Recently, we developed an enteroid monolayer (2D) culture system that recapitulates important features of 3D organoids and the in vivo intestinal epithelium such as tissue renewal, representation of diverse epithelial cell types, self-organization, and apical-basolateral polarization. Enteroid monolayers are cultured in microtiter plates, enabling high-throughput experiments. Furthermore, their 2D nature makes it easier to distinguish individual cells by fluorescent microscopy, enabling quantitative analysis of single cell behaviors within the epithelial tissue.Here we describe experimental methods for generating enteroid monolayers and computational methods for analyzing immunofluorescence images of enteroid monolayers. We outline experimental methods for generating enteroid monolayers from freshly isolated intestinal crypts, frozen intestinal crypts, and 3D organoids. Fresh crypts are easily obtained from murine or human intestinal samples, and the ability to derive enteroid monolayers from both frozen crypts and 3D organoids enables genetic modification and/or biobanking of patient samples for future studies. We outline computational methods for identifying distinct epithelial cell types (goblet, stem, EdU+) in immunofluorescence images of enteroid monolayers and, importantly, individual nuclei, enabling truly single cell measurements of epithelial cell behaviors to be made. Taken together, these methods will enable detailed studies of epithelial homeostasis and intestinal disease.

Drug-insensitive tumor subpopulations remain a significant barrier to effective cancer treatment. Recent works suggest that within isogenic drug-sensitive cancer populations, subsets of cells can enter a ‘persister’ state allowing them to survive prolonged drug treatment. Such persisters are well-described in antibiotic-treated bacterial populations. In this review, we compare mechanisms of drug persistence in bacteria and cancer. Both bacterial and cancer persisters are associated with slow-growing phenotypes, are metabolically distinct from non-persisters, and depend on the activation of specific regulatory programs. Moreover, evidence suggests that bacterial and cancer persisters are an important reservoir for the emergence of drug-resistant mutants. The emerging parallels between persistence in bacteria and cancer can guide efforts to untangle mechanistic links between growth, metabolism, and cellular regulation, and reveal exploitable therapeutic vulnerabilities.

Renewing tissues have the remarkable ability to continually produce both proliferative progenitor and specialized differentiated cell-types. How are complex milieus of microenvironmental signals interpreted to coordinate tissue cell-type composition? Here, we develop a high-throughput approach that combines organoid technology and quantitative imaging to address this question in the context of the intestinal epithelium. Using this approach, we comprehensively survey enteroid responses to individual and paired perturbations to eight epithelial signaling pathways. We uncover culture conditions that enrich for specific cell-types, including Lgr5+ stem and enteroendocrine cells. We analyze interactions between perturbations and dissect mechanisms underlying an unexpected mutual antagonism between EGFR and IL-4 signals. Finally, we show that, across diverse perturbations, modulating proliferation of transit-amplifying cells also consistently changes the composition of differentiated secretory and absorptive cell-types. This property is conserved in vivo and can arise from differential amplification of secretory and absorptive progenitor cells. Taken together, the observations highlight an underappreciated role for transit-amplifying cells in which proliferation of these short-lived progenitors provides a lineage-based mechanism for tuning differentiated cell-type composition.

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., Author summary One of the most fascinating questions in neuroscience is how complex neural circuits form. Here, we investigate self-organization principles during neuronal development in the visual circuit of fruit flies. We utilized the regularity of the tissue environment to establish a developmental coordinate system during wiring, which allowed us to uncover a role for cell identity in governing neuronal growth cone extension speed. Our study highlights the importance of velocity control in the precise formation of complex neuronal circuits, as well as the use of developmental coordinates in identifying ensemble behaviors of tissue patterning.

Patient-derived xenografts (PDXs) are an essential pre-clinical resource for investigating tumor biology. However, cellular heterogeneity within and across PDX tumors can strongly impact the interpretation of PDX studies. Here, we generated a multi-modal, large-scale dataset to investigate PDX heterogeneity in metastatic colorectal cancer (CRC) across tumor models, spatial scales and genomic, transcriptomic, proteomic and imaging assay modalities. To showcase this dataset, we present analysis to assess sources of PDX variation, including anatomical orientation within the implanted tumor, mouse contribution, and differences between replicate PDX tumors. A unique aspect of our dataset is deep characterization of intra-tumor heterogeneity via immunofluorescence imaging, which enables investigation of variation across multiple spatial scales, from subcellular to whole tumor levels. Our study provides a benchmark data resource to investigate PDX models of metastatic CRC and serves as a template for future, quantitative investigations of spatial heterogeneity within and across PDX tumor models., Measurement(s)transcription profiling assay • gene mutation • protein profiling assay • neoplastic cell • hematoxylin and eosin stainTechnology Type(s)RNA sequencing • DNA sequencing • RPPA protein profiling assay • immunofluorescence microscopy assay • brightfield microscopySample Characteristic - OrganismHomo sapiens/Mus musculus xenograft Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9892316

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.

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

High-content, image-based screens enable the identification of compounds that induce cellular responses similar to those of known drugs but through different chemical structures or targets. A central challenge in designing phenotypic screens is choosing suitable imaging biomarkers. Here we present a method for systematically identifying optimal reporter cell lines for annotating compound libraries (ORACLs), whose phenotypic profiles most accurately classify a training set of known drugs. We generate a library of fluorescently tagged reporter cell lines, and let analytical criteria determine which among them--the ORACL--best classifies compounds into multiple, diverse drug classes. We demonstrate that an ORACL can functionally annotate large compound libraries across diverse drug classes in a single-pass screen and confirm high prediction accuracy by means of orthogonal, secondary validation assays. Our approach will increase the efficiency, scale and accuracy of phenotypic screens by maximizing their discriminatory power.

Standard and targeted cancer therapies for late-stage cancer patients almost universally fail due to tumor heterogeneity/plasticity and intrinsic or acquired drug resistance. We used the telomerase substrate nucleoside precursor, 6-thio-2'-deoxyguanosine (6-thio-dG), to target telomerase-expressing non-small cell lung cancer cells resistant to EGFR-inhibitors and commonly used chemotherapy combinations. Colony formation assays, human xenografts as well as syngeneic and genetically engineered immune competent mouse models of lung cancer were used to test the effect of 6-thio-dG on targeted therapy- and chemotherapy-resistant lung cancer human cells and mouse models. We observed that erlotinib-, paclitaxel/carboplatin-, and gemcitabine/cisplatin-resistant cells were highly sensitive to 6-thio-dG in cell culture and in mouse models. 6-thio-dG, with a known mechanism of action, is a potential novel therapeutic approach to prolong disease control of therapy-resistant lung cancer patients with minimal toxicities.

Recent advances in large-scale single cell RNA-seq enable fine-grained characterization of phenotypically distinct cellular states within 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.

SUMMARY Cell-to-cell communication networks have critical roles in coordinating diverse organismal processes, such as tissue development or immune cell response. However, compared with intracellular signal transduction networks, the function and engineering principles of cell-to-cell communication networks are far less understood. Major complications include: cells are themselves regulated by complex intracellular signaling networks; individual cells are heterogeneous; and output of any one cell can recursively become an additional input signal to other cells. Here, we make use of a framework that treats intracellular signal transduction networks as “black boxes” with characterized input-to-output response relationships. We study simple cell-to-cell communication circuit motifs and find conditions that generate bimodal responses in time, as well as mechanisms for independently controlling synchronization and delay of cell-population responses. We apply our modeling approach to explain otherwise puzzling data on cytokine secretion onset times in T cells. Our approach can be used to predict communication network structure using experimentally accessible input-to-output measurements and without detailed knowledge of intermediate steps., In Brief Interacting cellular communities have critical roles in biological functions such as tissue development or immune responses. Cell-to-cell communication networks comprise both intra- and intercellular processes, making detailed mathematical models intractable. Here, we develop a scalable framework for modeling extra-cellular communication networks that treats intracellular signal transduction networks as “black boxes” with characterized input-to-output response relationships. We discover that a range of dynamic cell-population behaviors, including cellular synchronization, delays, and bimodal responses, can emerge from simple cell-to-cell communication networks.

Summary 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. PaperClip

Most chemoattractants rely on activation of the heterotrimeric G-protein Gαi to regulate directional cell migration, but few links from Gαi to chemotactic effectors are known. Homer3 is a novel Gαi2-interacting protein that spatially organizes actin assembly to support efficient polarity and motility in neutrophils., Most chemoattractants rely on activation of the heterotrimeric G-protein Gαi to regulate directional cell migration, but few links from Gαi to chemotactic effectors are known. Through affinity chromatography using primary neutrophil lysate, we identify Homer3 as a novel Gαi2-binding protein. RNA interference–mediated knockdown of Homer3 in neutrophil-like HL-60 cells impairs chemotaxis and the establishment of polarity of phosphatidylinositol 3,4,5-triphosphate (PIP3) and the actin cytoskeleton, as well as the persistence of the WAVE2 complex. Most previously characterized proteins that are required for cell polarity are needed for actin assembly or activation of core chemotactic effectors such as the Rac GTPase. In contrast, Homer3-knockdown cells show normal magnitude and kinetics of chemoattractant-induced activation of phosphoinositide 3-kinase and Rac effectors. Chemoattractant-stimulated Homer3-knockdown cells also exhibit a normal initial magnitude of actin polymerization but fail to polarize actin assembly and intracellular PIP3 and are defective in the initiation of cell polarity and motility. Our data suggest that Homer3 acts as a scaffold that spatially organizes actin assembly to support neutrophil polarity and motility downstream of GPCR activation.

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 co-staining 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 co-staining. 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.

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.

Advances in single-cell technologies have highlighted the prevalence and biological significance of cellular heterogeneity. A critical question 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. This dataset contains the tissue image data needed to reproduce Fig.1., This is supplementary data associated with the paper "Sampling strategies to capture single-cell heterogeneity" to appear in Nature Methods on Sep 4th 2017.