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Western blotting is a widely used technique for molecular-weight-resolved analysis of proteins and their posttranslational modifications, but high-throughput implementations of the standard slab gel arrangement are scarce. The previously developed Microwestern requires a piezoelectric pipetting instrument, which is not available for many labs. Here, we report the Mesowestern blot, which uses a 3D-printable gel casting mold to enable high-throughput Western blotting without piezoelectric pipetting and is compatible with the standard sample preparation and small (∼1 μL) sample sizes. The main tradeoffs are reduced molecular weight resolution and higher sample-to-sample CV, making it suitable for qualitative screening applications. The casted polyacrylamide gel contains 336, ∼0.5 μL micropipette-loadable sample wells arranged within a standard microplate footprint. Polyacrylamide % can be altered to change molecular weight resolution profiles. Proof-of-concept experiments using both infrared-fluorescent molecular weight protein ladder and cell lysate (RIPA buffer) demonstrate that the protein loaded in Mesowestern gels is amenable to the standard Western blotting steps. The main difference between Mesowestern and traditional Western is that semidry horizontal instead of immersed vertical gel electrophoresis is used. The linear range of detection is at least 32-fold, and at least ∼500 attomols of β-actin can be detected (∼29 ng of total protein from mammalian cell lysates: ∼100-300 cells). Because the gel mold is 3D-printable, users with access to additive manufacturing cores have significant design freedom for custom layouts. We expect that the technique could be easily adopted by any typical cell and molecular biology laboratory already performing Western blots.

Multi-angle light scattering (MALS) was used to determine the absolute molar mass of fluorescent macromolecules. It is standard protocol to install bandwidth filters before MALS detectors to suppress detection of fluorescent emissions. Fluorescence can introduce tremendous error in light scattering measurements and is a formidable challenge in accurately characterizing fluorescent macromolecules and particles. However, we show that for some systems, bandwidth filters alone are insufficient for blocking fluorescence in molar mass determinations. For these systems, we have devised a post-collection correction procedure to calculate the amount of fluorescence interference in the filtered signal. By determining the intensity of fluorescent emission not blocked by the bandwidth filters, we can correct the filtered signal accordingly and accurately determine the true molar mass. The transmission rates are calculated before MALS experimentation using emission data from standard fluorimetry techniques, allowing for the characterization of unknown samples. To validate the correction procedure, we synthesized fluorescent dye-conjugated proteins using an IR800CW (LI-COR) fluorophore and Bovine Serum Albumin protein. We successfully eliminated fluorescence interference in MALS measurements using this approach. This correction procedure has potential application toward more accurate molar mass characterizations of macromolecules with intrinsic fluorescence, such as lignins, fluorescent proteins, fluorescence-tagged proteins, and optically active nanoparticles.

Introduction: Tyrosine kinase inhibitor drugs (TKIs) are highly effective cancer drugs, yet many TKIs are associated with various forms of cardiotoxicity. The mechanisms underlying these drug-induced adverse events remain poorly understood. We studied mechanisms of TKI-induced cardiotoxicity by integrating several complementary approaches, including comprehensive transcriptomics, mechanistic mathematical modeling, and physiological assays in cultured human cardiac myocytes.Methods: Induced pluripotent stem cells (iPSCs) from two healthy donors were differentiated into cardiac myocytes (iPSC-CMs), and cells were treated with a panel of 26 FDA-approved TKIs. Drug-induced changes in gene expression were quantified using mRNA-seq, changes in gene expression were integrated into a mechanistic mathematical model of electrophysiology and contraction, and simulation results were used to predict physiological outcomes.Results: Experimental recordings of action potentials, intracellular calcium, and contraction in iPSC-CMs demonstrated that modeling predictions were accurate, with 81% of modeling predictions across the two cell lines confirmed experimentally. Surprisingly, simulations of how TKI-treated iPSC-CMs would respond to an additional arrhythmogenic insult, namely, hypokalemia, predicted dramatic differences between cell lines in how drugs affected arrhythmia susceptibility, and these predictions were confirmed experimentally. Computational analysis revealed that differences between cell lines in the upregulation or downregulation of particular ion channels could explain how TKI-treated cells responded differently to hypokalemia.Discussion: Overall, the study identifies transcriptional mechanisms underlying cardiotoxicity caused by TKIs, and illustrates a novel approach for integrating transcriptomics with mechanistic mathematical models to generate experimentally testable, individual-specific predictions of adverse event risk.

XLSX file - 5764K, S1. TP53 Codon 72 Polymorphism Genotype Frequency. S2. RNA-Seq Data. S3. TP53 Dependent Genes. S4. TP53 Codon 72 Dependent Genes. S5. Deep Sequencing. S6. TP53 Dependent shRNA. S7. Arg Dependent shRNA. S8. Pro Dependent shRNA.

African Americans are disproportionately affected by early-onset, high-grade malignancies. A fraction of this cancer health disparity can be explained by genetic differences between individuals of African or European descent. Here the wild-type Pro/Pro genotype at the TP53Pro72Arg (P72R) polymorphism (SNP: rs1042522) is more frequent in African Americans with cancer than in African Americans without cancer (51% vs. 37%), and is associated with a significant increase in the rates of cancer diagnosis in African Americans. To test the hypothesis that Tp53 allele–specific gene expression may contribute to African American cancer disparities, TP53 hemizygous knockout variants were generated and characterized in the RKO colon carcinoma cell line, which is wild type for TP53 and heterozygous at the TP53Pro72Arg locus. Transcriptome profiling, using RNAseq, in response to the DNA-damaging agent etoposide revealed a large number of Tp53-regulated transcripts, but also a subset of transcripts that were TP53Pro72Arg allele specific. In addition, a shRNA-library suppressor screen for Tp53 allele–specific escape from Tp53-induced arrest was performed. Several novel RNAi suppressors of Tp53 were identified, one of which, PRDM1β (BLIMP-1), was confirmed to be an Arg-specific transcript. Prdm1β silences target genes by recruiting H3K9 trimethyl (H3K9me3) repressive chromatin marks, and is necessary for stem cell differentiation. These results reveal a novel model for African American cancer disparity, in which the TP53 codon 72 allele influences lifetime cancer risk by driving damaged cells to differentiation through an epigenetic mechanism involving gene silencing.Implications:TP53 P72R polymorphism significantly contributes to increased African American cancer disparity. Mol Cancer Res; 12(7); 1029–41. ©2014 AACR.

Summary Large-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc.). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed-up simulation by ∼4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations. Fully deterministic speed-up facilitates model initialization, parameter estimation and sensitivity analysis tasks. Availability and implementation Source code is freely available at https://github.com/birtwistlelab/SPARCED/releases/tag/v1.3.0 implemented in python, and supported on Linux, Windows and MacOS (via Docker).

Summary A library of well-characterized human induced pluripotent stem cell (hiPSC) lines from clinically healthy human subjects could serve as a useful resource of normal controls for in vitro human development, disease modeling, genotype-phenotype association studies, and drug response evaluation. We report generation and extensive characterization of a gender-balanced, racially/ethnically diverse library of hiPSC lines from 40 clinically healthy human individuals who range in age from 22 to 61 years. The hiPSCs match the karyotype and short tandem repeat identities of their parental fibroblasts, and have a transcription profile characteristic of pluripotent stem cells. We provide whole-genome sequencing data for one hiPSC clone from each individual, genomic ancestry determination, and analysis of mendelian disease genes and risks. We document similar transcriptomic profiles, single-cell RNA-sequencing-derived cell clusters, and physiology of cardiomyocytes differentiated from multiple independent hiPSC lines. This extensive characterization makes this hiPSC library a valuable resource for many studies on human biology., Highlights • A library of induced pluripotent stem cells from 40 healthy human subjects • Racially/ethnically diverse subjects of clinically well-characterized health • Whole-genome sequencing identifies variants of mild common phenotypes or incomplete penetrance • Similar physiology of cardiomyocytes from independent hiPSC clones and individuals, In this resource, Schaniel et al. report the generation and characterization of a gender-balanced, racially/ethnically diverse library of hiPSC lines from 40 clinically well-characterized healthy human individuals. The authors provide whole-genome sequencing data, ancestry determination, and analysis of mendelian disease genes and risks. Furthermore, similar physiology of cardiomyocytes differentiated from independent hiPSC clones derived from two individuals is documented.

Background Glioblastoma (GBM) remains a largely incurable disease as current therapy fails to target the invasive nature of glioma growth in disease progression and recurrence. Here, we use the FDA-approved drug and small molecule Hippo inhibitor Verteporfin (VP) to target YAP-TEAD activity, known to mediate convergent aspects of tumor invasion/metastasis, and assess the drug’s efficacy and survival benefit in GBM models. Methods Up to 8 low-passage patient-derived GBM cell lines with distinct genomic drivers, including 3 primary/recurrent pairs, were treated with VP or vehicle (VEH) to assess in vitro effects on proliferation, migration, invasion, YAP-TEAD activity, and transcriptomics. Patient-derived orthotopic xenograft (PDX) models were used to assess VP’s brain penetrance and effects on tumor burden and survival. Results VP treatment disturbed YAP/TAZ-TEAD activity; disrupted transcriptome signatures related to invasion, epithelial-to-mesenchymal, and proneural-to-mesenchymal transition, phenocopying TEAD1-knockout effects; and impaired tumor migration/invasion dynamics across primary and recurrent GBM lines. In an aggressive orthotopic PDX GBM model, short-term VP treatment consistently diminished core and infiltrative tumor burden, which was associated with decreased tumor expression of Ki67, nuclear YAP, TEAD1, and TEAD-associated targets EGFR, CDH2, and ITGB1. Finally, long-term VP treatment appeared nontoxic and conferred survival benefit compared to VEH in 2 PDX models: as monotherapy in primary (de novo) GBM and in combination with Temozolomide chemoradiation in recurrent GBM, where VP treatment associated with increased MGMT methylation. Conclusions We demonstrate combined anti-invasive and anti-proliferative efficacy for VP with survival benefit in preclinical GBM models, indicating potential therapeutic value of this already FDA-approved drug if repurposed for GBM patients.

SummaryLarge-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed up simulation by ~4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations.Availability and ImplementationSource code is freely available athttps://github.com/birtwistlelab/SPARCED/releases/tag/v1.1.0implemented in python, and supported on Linux, Windows, and MacOS (via Docker).ContactMarc Birtwistlembirtwi@clemson.eduSupplementary informationN/A

Systematic, genome-scale genetic screens have been instrumental for elucidating genotype-phenotype relationships, but approaches for probing genetic interactions have been limited to at most ∼100 pre-selected gene combinations in mammalian cells. Here, we introduce a theory for high-throughput genetic interaction screens. The theory extends our recently developed Multiplexing using Spectral Imaging and Combinatorics (MuSIC) approach to propose ∼105 spectrally unique, genetically-encoded MuSIC barcodes from 18 currently available fluorescent proteins. Simulation studies based on constraints imposed by spectral flow cytometry equipment suggest that genetic interaction screens at the human genome-scale may be possible if MuSIC barcodes can be paired to guide RNAs. While experimental testing of this theory awaits, it offers transformative potential for genetic perturbation technology and knowledge of genetic function. More broadly, the availability of a genome-scale spectral barcode library for non-destructive identification of single-cells could find more widespread applications such as traditional genetic screening and high-dimensional lineage tracing.

Cell phenotypes are dictated by both extra- and intra-cellular contexts, and robust identification of context-specific network features that control phenotypes remains challenging. Here, we developed a multi-omics data integration strategy called MOBILE (Multi-Omics Binary Integration via Lasso Ensembles) to nominate molecular features associated with specific cellular phenotypes. We applied this method to chromatin accessibility, mRNA, protein, and phospho-protein time course datasets and focus on two illustrative use cases after we show MOBILE could recover known biology. First, MOBILE nominated new mechanisms of interferon-γ (IFNγ) regulated PD-L1 expression, where analyses suggested, and literature supported that IFNγ-controlled PD-L1 expression involves BST2, CLIC2, FAM83D, ACSL5, and HIST2H2AA3 genes. Second, we explored differences between the highly similar transforming growth factor-beta 1 (TGFβ1) and bone morphogenetic protein 2 (BMP2) and showed that differential cell size and clustering properties induced by TGFβ1, but not BMP2, were related to the laminin/collagen pathway activity. Given the ever-growing availability of multi-omics datasets, we envision that MOBILE will be broadly applicable to identify context-specific molecular features associated with cellular phenotypes.Graphical SummaryMulti-Omics Binary Integration via Lasso Ensembles (MOBILE) pipeline yields statistically robust, context-specific association networksThe MOBILE pipeline integrates omics datasets in a data-driven, biologically-structured manner.The pipeline outputs are gene-level, contextspecific association networks.These association networks nominate differentially enriched pathways, subnetworks, and new connections.Broadly applicable to find condition specific networks using multi-omics datasets.

Networks underlie much of biology from subcellular to ecological scales. Yet, understanding what experimental data are needed and how to use them for unambiguously identifying the structure of even small networks remains a broad challenge. Here, we integrate a dynamic least squares framework into established modular response analysis (DL-MRA), that specifies sufficient experimental perturbation time course data to robustly infer arbitrary two and three node networks. DL-MRA considers important network properties that current methods often struggle to capture: (i) edge sign and directionality; (ii) cycles with feedback or feedforward loops including self-regulation; (iii) dynamic network behavior; (iv) edges external to the network; and (v) robust performance with experimental noise. We evaluate the performance of and the extent to which the approach applies to cell state transition networks, intracellular signaling networks, and gene regulatory networks. Although signaling networks are often an application of network reconstruction methods, the results suggest that only under quite restricted conditions can they be robustly inferred. For gene regulatory networks, the results suggest that incomplete knockdown is often more informative than full knockout perturbation, which may change experimental strategies for gene regulatory network reconstruction. Overall, the results give a rational basis to experimental data requirements for network reconstruction and can be applied to any such problem where perturbation time course experiments are possible.

Reliably predicting in vivo efficacy from in vitro data would facilitate drug development by reducing animal usage and guiding drug dosing in human clinical trials. However, such prediction remains challenging. Here, we built a quantitative pharmacokinetic/pharmacodynamic (PK/PD) mathematical model capable of predicting in vivo efficacy in animal xenograft models of tumor growth while trained almost exclusively on in vitro cell culture data sets. We studied a chemical inhibitor of LSD1 (ORY‐1001), a lysine‐specific histone demethylase enzyme with epigenetic function, and drug‐induced regulation of target engagement, biomarker levels, and tumor cell growth across multiple doses administered in a pulsed and continuous fashion. A PK model of unbound plasma drug concentration was linked to the in vitro PD model, which enabled the prediction of in vivo tumor growth dynamics across a range of drug doses and regimens. Remarkably, only a change in a single parameter—the one controlling intrinsic cell/tumor growth in the absence of drug—was needed to scale the PD model from the in vitro to in vivo setting. These findings create a framework for using in vitro data to predict in vivo drug efficacy with clear benefits to reducing animal usage while enabling the collection of dense time course and dose response data in a highly controlled in vitro environment.

Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is one of the centers for the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Its key aim is to generate proteomic and transcriptomic signatures that can predict cardiotoxic adverse effects of kinase inhibitors approved by the Food and Drug Administration. Towards this goal, high throughput shotgun proteomics experiments (308 cell line/drug combinations +64 control lysates) have been conducted. Using computational network analyses, these proteomic data can be integrated with transcriptomic signatures, generated in tandem, to identify cellular signatures of cardiotoxicity that may predict kinase inhibitor-induced toxicity and enable possible mitigation. Both raw and processed proteomics data have passed several quality control steps and been made publicly available on the PRIDE database. This broad protein kinase inhibitor-stimulated human cardiomyocyte proteomic data and signature set is valuable for prediction of drug toxicities., Measurement(s)protein expression profilingTechnology Type(s)mass spectrometry assayFactor Type(s)Individual proteome • Kinase inhibitorsSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.16990033

Dysregulated epigenetic processes can lead to altered gene expression and give rise to malignant transformation and tumorigenesis. Epigenetic drugs aim to revert the phenotype of cancer cells to normally functioning cells, and are developed and applied to treat both hematological and solid cancers. Despite this promising therapeutic avenue, the successful development of epigenetic modulators has been challenging. We argue that besides identifying the right responder patient population, the selection of an optimized dosing regimen is equally important. For the majority of epigenetic modulators, hematological adverse effects such as thrombocytopenia, anemia or neutropenia are frequently observed and may limit their therapeutic potential. Therefore, one of the key challenges is to identify a dosing regimen that maximizes drug efficacy and minimizes toxicity. This requires a good understanding of the quantitative relationship between the administered dose, the drug exposure and the magnitude and duration of drug response related to safety and efficacy. With case examples, we highlight how modeling and simulation has been successfully applied to address those questions. As an outlook, we suggest the combination of efficacy and safety prediction models that capture the quantitative, mechanistic relationships governing the balance between their safety and efficacy dynamics. A stepwise approach for its implementation is presented. Utilizing in silico explorations, the impact of dosing regimen on the therapeutic window can be explored. This will serve as a basis to select the most promising dosing regimen that maximizes efficacy while minimizing adverse effects and to increase the probability of success for the given epigenetic drug.

Western blotting is a widely-used technique for molecular-weight-resolved analysis of proteins and their post-translational modifications, but has been refractory to affordable scale-up. Here, we report the Mesowestern blot, which uses a 3D-printable gel-casting mold to enable affordable, high-throughput Western blotting with standard sample preparation and small (

Drug-induced gene expression profiles are an important source for the characterization of drug-specific mechanisms of action that might also indicate potential mechanisms of drug toxicity. Unfortunately, drug-induced transcriptomic signatures are often the sum of multiple responses, such as cell line or cell-type specific responses, and can include false positive results. Both issues complicate the identification of drug-specific effects. To unmask drug-specific effects in drug-induced transcriptomic signatures, we use singular value decomposition to characterize shared transcriptomic responses induced in multiple cell lines treated with the same drug. Six different cardiomyocyte cell lines that were generated from induced pluripotent stem cells obtained from six healthy human subjects were stimulated with 25 protein kinase inhibitors, 4 monoclonal antibodies against protein kinases and 25 other cardiac- and non-cardiac acting drugs. Searching for drug-specific subspaces that characterize drug-specific responses in the original data, we could identify highly similar drug responses for multiple drugs. Pathway enrichment analysis of those responses predicts reasonable effects for multiple drugs. Revealing drug-specific responses from transcriptomic signatures might allow an easier characterization of drug-induced toxicity, such as cardiotoxic side effects induced by multiple protein kinase inhibitors and monoclonal antibodies against protein kinases.

Predictive determinants of stochastic single-cell fates have been elusive, even for the well-studied mammalian cell cycle. What drives proliferation decisions of single cells at any given time? We monitored single-cell dynamics of the ERK and Akt pathways, critical cell cycle progression hubs and anti-cancer drug targets, and paired them to division events in the same single cells using the non-transformed MCF10A epithelial line. Following growth factor treatment, in cells that divide both ERK and Akt activities are significantly higher within the S-G2 time window (∼8.5-40 hours). Such differences were much smaller in the pre-S-phase, restriction point window which is traditionally associated with ERK and Akt activity dependence, suggesting unappreciated roles for ERK and Akt in S through G2. Machine learning algorithms show that simple metrics of central tendency in this time window are most predictive for subsequent cell division; median ERK and Akt activities classify individual division events with an AUC=0.76. Surprisingly, ERK dynamics alone predict division in individual cells with an AUC=0.74, suggesting Akt activity dynamics contribute little to the decision driving cell division in this context. We also find that ERK and Akt activities are less correlated with each other in cells that divide. Network reconstruction experiments demonstrated that this correlation behavior was likely not due to crosstalk, as ERK and Akt do not interact in this context, in contrast to other transformed cell types. Overall, our findings support roles for ERK and Akt activity throughout the cell cycle as opposed to just before the restriction point, and suggest ERK activity dynamics are substantially more important than Akt activity dynamics for driving cell division in this non-transformed context. Single cell imaging along with machine learning algorithms provide a better basis to understand cell cycle progression on the single cell level.

SUMMARYThe phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods (synapse.org/LINCS_MCF10A). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes.

Summary: Computational models of biological systems can exploit on a broad range of rapidly developing approaches, including novel experimental approaches, bioinformatics data analysis, emerging modelling paradigms, data standards, and algorithms. A discussion about the most recent advances among experts from various domains is crucial to foster data-driven computational modelling and its growing use in assessing and predicting the behaviour of biological systems. Intending to encourage the development of tools, approaches, and predictive models, and to deepen our understanding of biological systems, the Community of Special Interest (COSI) was launched in Computational Modelling of Biological Systems (SysMod) in 2016. SysMod’s main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, which brings together computer scientists, biologists, mathematicians, engineers, computational and systems biologists. In the five years since its inception, SysMod has evolved into a dynamic and expanding community as the increasing number of contributions and participants illustrate. SysMod maintains several online resources to facilitate interaction among the community members, including an online forum, a calendar of relevant meetings, and a YouTube channel with talks and lectures of interest for the modelling community. Since more than half a decade, the growing interest in computational systems modelling and multi-scale data integration has continued to inspire and support the SysMod community. Its members get progressively more involved and actively contribute not only to the annual COSI meeting but also to several related community workshops and meetings, which focus on specific topics, including particular techniques for computational modelling or standardisation efforts. Contact: sysmod-coord@googlegroups.com., This article appeared in the journal Bioinformatics at https://doi.org/10.1093/bioinformatics/btab229.

Fluorescent antibodies are a workhorse of biomedical science, but fluorescence multiplexing has been notoriously difficult due to spectral overlap between fluorophores. We recently established proof-of-principal for fluorescence Multiplexing using Spectral Imaging and Combinatorics (MuSIC), which uses combinations of existing fluorophores to create unique spectral signatures for increased multiplexing. However, a method for labeling antibodies with MuSIC probes has not yet been developed. Here, we present a method for labeling antibodies with MuSIC probes. We conjugate a DBCO-Peg5-NHS ester linker to antibodies and a single-stranded DNA "docking strand" to the linker and, finally, hybridize two MuSIC-compatible, fluorescently labeled oligos to the docking strand. We validate the labeling protocol with spin-column purification and absorbance measurements. We demonstrate the approach using (i) Cy3, (ii) Tex615, and (iii) a Cy3-Tex615 combination as three different MuSIC probes attached to three separate batches of antibodies. We created single-, double-, and triple-positive beads that are analogous to single cells by incubating MuSIC probe-labeled antibodies with protein A beads. Spectral flow cytometry experiments demonstrate that each MuSIC probe can be uniquely distinguished, and the fraction of beads in a mixture with different staining patterns are accurately inferred. The approach is general and might be more broadly applied to cell-type profiling or tissue heterogeneity studies in clinical, biomedical, and drug discovery research.

Gene expression signatures (GES) connect phenotypes to differential messenger RNA (mRNA) expression of genes, providing a powerful approach to define cellular identity, function, and the effects of perturbations. The use of GES has suffered from vague assessment criteria and limited reproducibility. Because the structure of proteins defines the functional capability of genes, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from multiple levels of protein structure (e.g., domain and fold) encoded by the mRNAs in GES. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), the all RNA-seq and ChIP-seq sample and signature search (ARCHS4) database, and mRNA expression of drug effects on cardiomyocytes show that sGES are useful for characterizing biological phenomena. sGES enable phenotypic characterization across experimental platforms, facilitates interoperability of expression datasets, and describe drug action on cells.

Under physiological conditions, strength and persistence of memory must be regulated in order to produce behavioral flexibility. In fact, impairments in memory flexibility are associated with pathologies such as post-traumatic stress disorder or autism; however, the underlying mechanisms that enable memory flexibility are still poorly understood. Here, we identify transcriptional repressor Wilm’s Tumor 1 (WT1) as a critical synaptic plasticity regulator that decreases memory strength, promoting memory flexibility. WT1 is activated in the hippocampus following induction of long-term potentiation (LTP) or learning. WT1 knockdown enhances CA1 neuronal excitability, LTP and long-term memory whereas its overexpression weakens memory retention. Moreover, forebrain WT1-deficient mice show deficits in both reversal, sequential learning tasks and contextual fear extinction, exhibiting impaired memory flexibility. We conclude that WT1 limits memory strength or promotes memory weakening, thus enabling memory flexibility, a process that is critical for learning from new experiences., Impairments in memory flexibility are associated with neuropsychiatric disorders such as PTSD and autism. Here, the authors report that the transcriptional repressor Wilm's Tumor 1 regulates synaptic plasticity leading to weakening of memory strength and enabling memory flexibility.

There is a contemporary push to map tissues and their disease states quantitatively at single-cell and spatial resolution, but standard assays to do so, such as immunohistochemistry, have been historically lowly multiplexed (2–4 measurements). This push has driven the development of several new multiplexed techniques for quantitative tissue imaging, which we review here. Standard multiplexed imaging is primarily limited by fluorophore spectral overlap. Innovations increasing multiplexing capacity include iterative cycles of staining/bleaching/imaging, imaging mass spectrometry with metal-conjugated antibodies, leveraging fluorophore combinatorics, and coupling to sequencing-based methods. Recent progress has increased image-based multiplexing roughly 10-fold and, in some cases of nucleic acid analytes, to genome scale. This has given unprecedented biological and disease knowledge, but there is still substantial work to achieve genome scale across all types of analytes, as well as spatial scales greater than ~millimeters. Concomitantly, challenges in data storage, retrieval, and analysis will need to be solved moving forward.

Individual cells in clonal populations often respond differently to environmental changes; for binary phenotypes, such as cell death, this can be measured as a fractional response. These types of responses have been attributed to cell-intrinsic stochastic processes and variable abundances of biochemical constituents, such as proteins, but the influence of organelles is still under investigation. We use the response to TNF-related apoptosis inducing ligand (TRAIL) and a new statistical framework for determining parameter influence on cell-to-cell variability through the inference of variance explained, DEPICTIVE, to demonstrate that variable mitochondria abundance correlates with cell survival and determines the fractional cell death response. By quantitative data analysis and modeling we attribute this effect to variable effective concentrations at the mitochondria surface of the pro-apoptotic proteins Bax/Bak. Further, our study suggests that inhibitors of anti-apoptotic Bcl-2 family proteins, used in cancer treatment, may increase the diversity of cellular responses, enhancing resistance to treatment., Phenotypic cell-to-cell variability contributes to fractional killing, but the mechanisms are incompletely understood. Here the authors show that mitochondrial density correlates with cell survival in response to TRAIL, and that variable effective concentrations of Bax/Bak contribute to the effect.

Mechanistic models of how single cells respond to different perturbagens can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Our lab previously constructed one of the largest mechanistic models for single mammalian cell regulation of proliferation and death (774 species, 141 genes, 8 ligands, 2400 reactions). However, this, as many other large-scale models, was written using licensed software (MATLAB) with intricate programming structure, impeding alteration, expansion, and sharing. Here, we generated a new foundation for this model, which includes a python-based creation and simulation pipeline converting a few structured text files into an SBML-compatible format. This new open-source model (named SPARCED) is high-performance- and cloud-computing compatible and enables the study of virtual cell population responses at the single-cell level. We applied this new model to a subset of the LINCS MCF10A Data Cube, which observed that IFNγ acts as an anti-proliferative factor, but the reasons why were unknown. After expanding the SPARCED model with an IFNγ signaling module (to 950 species, 150 genes, 9 ligands, 2500 reactions), we ran stochastic single-cell simulations for two different putative crosstalk mechanisms and looked at the number of cycling cells in each case. Our model-based analysis suggested, and experiments support that these observations are better explained by IFNγ-induced SOCS1 expression sequestering activated EGF receptors, thereby downregulating AKT activity, as opposed to direct IFNγ-induced upregulation of p21 expression. This work forms a foundation for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically predictive mechanistic models.

Fluorescent antibodies are a workhorse of biomedical science, but fluorescence multiplexing has been notoriously difficult due to spectral overlap between fluorophores. We recently established proof-of-principal for fluorescence Multiplexing using Spectral Imaging and Combinatorics (MuSIC), which uses combinations of existing fluorophores to create unique spectral signatures for increased multiplexing. However, a method for labeling antibodies with MuSIC probes has not yet been developed. Here, we present a method for labeling antibodies with MuSIC probes. We conjugate a DBCO-Peg5-NHS ester linker to antibodies, a single stranded DNA “docking strand” to the linker, and finally, hybridize two MuSIC-compatible, fluorescently-labeled oligos to the docking strand. We validate the labeling protocol with spin-column purification and absorbance measurements, which show a degree of labeling of ~9.66 linker molecules / antibody. We demonstrate the approach using (i) Cy3, (ii) Tex615, and (iii) a Cy3-Tex615 combination as three different MuSIC probes attached to three separate batches of antibodies. We incubated MuSIC probe-labeled antibodies with protein A beads to create single and double positive beads that are analogous to single cells. Spectral flow cytometry experiments demonstrate that each MuSIC probe can be uniquely distinguished, and the fraction of beads in a mixture with different staining patterns is accurately measured. The approach is general and might be more broadly applied to cell type profiling or tissue heterogeneity studies in clinical, biomedical, and drug discovery research.

Kinase inhibitors (KIs) represent an important class of anti-cancer drugs. Although cardiotoxicity is a serious adverse event associated with several KIs, the reasons remain poorly understood, and its prediction remains challenging. We obtain transcriptional profiles of human heart-derived primary cardiomyocyte like cell lines treated with a panel of 26 FDA-approved KIs and classify their effects on subcellular pathways and processes. Individual cardiotoxicity patient reports for these KIs, obtained from the FDA Adverse Event Reporting System, are used to compute relative risk scores. These are then combined with the cell line-derived transcriptomic datasets through elastic net regression analysis to identify a gene signature that can predict risk of cardiotoxicity. We also identify relationships between cardiotoxicity risk and structural/binding profiles of individual KIs. We conclude that acute transcriptomic changes in cell-based assays combined with drug substructures are predictive of KI-induced cardiotoxicity risk, and that they can be informative for future drug discovery., Cardiotoxic adverse events associated with kinase inhibitors are a growing concern in clinical oncology. Here the authors use cellular transcriptomic responses of human cardiomyocytes treated with protein kinase inhibitors and the associated drug structural signatures to determine an integrated predictive signature of cardiotoxicity.

Gene expression signatures (GES) connect phenotypes to mRNA expression patterns, providing a powerful approach to define cellular identity, function, and the effects of perturbations. However, the use of GES has suffered from vague assessment criteria and limited reproducibility. The structure of proteins defines the functional capability of genes, and hence, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from various levels of protein structure (e.g. domain, fold) encoded by the transcribed genes in GES, to describe cellular phenotypes. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), ARCHS4, and mRNA expression of drug effects on cardiomyocytes show that structural GES (sGES) are useful for identifying robust signatures of biological phenomena. sGES also enables the characterization of signatures across experimental platforms, facilitates the interoperability of expression datasets, and can describe drug action on cells.

The Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is one of the centers of the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. A key aim of DToxS is to generate both proteomic and transcriptomic signatures that can predict adverse effects, especially cardiotoxicity, of kinase inhibitors approved by the Food and Drug Administration. Towards this goal, high throughput shot-gun proteomics experiments (317 cell line/drug combinations + 64 control lysates) have been conducted at the Center for Advanced Proteomics Research at Rutgers University - New Jersey Medical School. Using computational network analyses, these proteomic data can be integrated with transcriptomic signatures generated in tandem to identify cellular signatures of cardiotoxicity that may predict kinase inhibitor-induced toxicity and possible mitigation. Both raw and processed proteomics data have been carefully screened for quality and made publicly available via the PRIDE database. As such, this broad protein kinase inhibitor-stimulated cardiomyocyte proteomic data and signature set is valuable for the prediction of drug toxicities.Links to: Metadata Tables

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

Mass cytometry offers the advantage of allowing the simultaneous measurement of a greater number parameters than conventional flow cytometry. However, to date, mass cytometry has lacked a reliable alternative to the light scatter properties that are commonly used as a cell size metric in flow cytometry (forward scatter intensity-FSC). Here, we report the development of two plasma membrane staining assays to evaluate mammalian cell size in mass cytometry experiments. One is based on wheat germ agglutinin (WGA) staining and the other on Osmium tetroxide (OsO4 ) staining, both of which have preferential affinity for cell membranes. We first perform imaging and flow cytometry experiments to establish a relationship between WGA staining intensity and traditional measures of cell size. We then incorporate WGA staining in mass cytometry analysis of human whole blood and show that WGA staining intensity has reproducible patterns within and across immune cell subsets that have distinct cell sizes. Lastly, we stain PBMCs or dissociated lung tissue with both WGA and OsO4 ; mass cytometry analysis demonstrates that the two staining intensities correlate well with one another. We conclude that both WGA and OsO4 may be used to acquire cell size-related parameters in mass cytometry experiments, and expect these stains to be broadly useful in expanding the range of parameters that can be measured in mass cytometry experiments. © 2016 International Society for Advancement of Cytometry.

We propose a method for discriminating among competing models for biological systems. Our approach is based on learning temporal logic formulas from data obtained by simulating the models. We apply this method to find dynamic features of epidermal growth factor induced extracellular signal-regulated kinase (ERK) activation that are strictly unique to positive versus negative feedback models. We first search for a temporal logic formula from a training set that can eliminate ERK dynamics observed with both models and then identify the ERK dynamics that are unique to each model. The obtained formulas are tested with a validation sample set and the decision rates and classification rates are estimated using the Chernoff bound. The results can be used in guiding and optimizing the design of experiments for model discrimination.

Identifying the network of biochemical interactions that underpin disease pathophysiology is a key hurdle in drug discovery. While many components involved in these biological processes are identified, how components organize differently in health and disease remains unclear. In chemical engineering, mechanistic modeling provides a quantitative framework to capture our understanding of a reactive system and test this knowledge against data. Here, we describe an emerging approach to test this knowledge against data that leverages concepts from probability, Bayesian statistics, and chemical kinetics by focusing on two related inverse problems. The first problem is to identify the causal structure of the reaction network, given uncertainty as to how the reactive components interact. The second problem is to identify the values of the model parameters, when a network is known a priori.

The diffusely infiltrative growth and spread, complex molecular and oncogenic signaling aberrations, and inter-and intra-tumoral heterogeneity in glioblastoma (GBM) impedes gross-total resection and chemoradiation and highlight the need for improved tumor-specific brain-penetrant therapies that inhibit cell migration in addition to proliferation. Invasive GBM cells undergo an epithelial-mesenchymal transition (EMT) phenotypic switch, expressing higher levels of genes involved in EMT remodeling, survival, and immune response compared to non-invasive GBM tumor cells. Recently, our lab defined a regulatory chromatin accessibility signature centered around the TEAD transcriptional family, which relates specifically to tumor migration in uncultured, patient-derived GBM stem cell populations, and we functionally validated TEAD1 as a driver of GBM migration and EMT, both in vitro and in vivo. The TEAD family of transcription factors, along with their co-activators YAP/TAZ, are the main downstream effectors of the Hippo pathway, a regulator of tissue growth and cell fate whose dysregulation has been implicated in tumor invasion, metastasis, and chemoresistance in other solid tumors. Here we explored the therapeutic efficacy of Verteporfin (VP), an FDA-approved macular degeneration therapy, and a small-molecule inhibitor of the YAP/TEAD complex, in patient-derived GBM cells and orthotopic xenotransplant mouse models (PDX), assessing VP's impact on GBM proliferation and migration both in vitro and in vivo. VP treatment across three different cell lines inhibited not only glioma growth but also significantly impaired tumor migration in three different in vitro assays (live cell tracking, transwell invasion, and spheroid dispersion) in a dose-dependent manner. At the protein and whole transcriptomic levels, VP-treated cells showed dose-dependent downregulation of TEAD-target activity and significant downregulation of EMT and cell migration functional gene sets, compared to vehicle. In several independent in vivo experiments, intraperitoneal administration of VP in mice with aggressive PDX glioblastoma showed consistent drug penetration into the brain parenchyma without systemic toxicity, resulted in lower tumor burden both at the tumor core and the leading infiltrative edge (n=22 VP, n=21 VEH, p=0.02, each), showed decreased number of individual distal migratory tumor cells (n=6 VP, n=5 VEH, p=0.0001 / n=6 VP, n=5 VEH, p=0.04), and, notably, conferred a survival benefit in combination with TMZ and radiation (n=10 per condition, p=0.02). Furthermore, in PDX mice transplanted with a more typical GBM, daily administration of VP for up to 280 days conferred survival benefit even as monotherapy (n=8 per condition, p=0.02), in the absence of systemic toxicity. The inhibitory effect of Verteporfin on downstream YAP/TEAD signaling and GBM migration, and its brain penetrance at non-toxic levels, underscore potential future therapeutic value and repurposing of this drug in GBM patients. Citation Format: Anne Marie Barrette, Alexandros Bouras, German Nudelman, Dominique Bozec, Noshin Shakawat, Elena Zaslavsky, Constantinos Hadjipanayis, Marc R. Birtwistle, Nadejda M. Tsankova. Verteporfin inhibits GBM growth and migration and confers survival benefit in xenograft models [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1111.