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Glioblastoma Multiforme (GBM) remains a particularly difficult cancer to treat, and survival outcomes remain poor. In addition to the lack of dedicated drug discovery programs for GBM, extensive intratumor heterogeneity and epigenetic plasticity related to cell-state transitions are major roadblocks to successful drug therapy in GBM. To study these phenomenon, publicly available snRNAseq and bulk RNAseq data from patient samples were used to categorize cells from patients into four cell states (i.e., phenotypes), namely: (i) neural progenitor-like (NPC-like), (ii) oligodendrocyte progenitor-like (OPC-like), (iii) astrocyte-like (AC-like), and (iv) mesenchymal-like (MES-like). Patients were subsequently grouped into subpopulations based on which cell-state was the most dominant in their respective tumor. By incorporating phosphoproteomic measurements from the same patients, a protein-protein interaction network (PPIN) was constructed for each cell state. These four-cell state PPINs were pooled to form a single Boolean network that was used for in silico protein knockout simulations to investigate mechanisms that either promote or prevent cell state transitions. Simulation results were input into a boosted tree machine learning model which predicted the cell states or phenotypes of GBM patients from an independent public data source, the Glioma Longitudinal Analysis (GLASS) Consortium. Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. For example, the transcription factor TFAP2A can be seen to promote a transition from the NPC-like to the MES-like state. Such protein nodes and the associated signaling pathways provide potential drug targets that can be further tested in vitro and support cell state-directed (CSD) therapy., Competing Interests: Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Developing messenger RNA (mRNA) vaccines for COVID-19 renewed and intensified the interest in using mRNA for disease prevention and treatment. Despite their efficacy, linear mRNA molecules are short-lived in the human body, primarily due to enzymatic degradation at the free ends. In contrast, circular RNA (circRNA) exhibits enhanced stability and resistance to exonuclease degradation. However, this stability depends highly on purity. Unfortunately, the in vitro transcription/self-splicing reaction products contain a mixture of circular and linear RNAs. Yet, practical methods for purifying circRNA from solutions containing linear RNA contaminants are lacking. In this study, we explored the feasibility of using ultrafiltration to purify protein-encoding circRNA produced by the self-splicing of a precursor RNA (preRNA) during in vitro transcription (IVT). We measured the sieving coefficients, a separation metric, of circRNA, linear precursor RNA, and nicked RNA conformers using polyethersulfone membranes with molecular weight cutoffs from 30 to 300 kDa, analyzing performance as a function of permeate flux. We also estimated the RNA critical fluxes and determined suitable operating conditions for purification. We achieved a purity of 86% with a yield above 50%. By comparison, the purity achieved by size-exclusion high-performance liquid chromatography (SE-HPLC), the leading alterative separation technology, was 41% with a yield of 45%. These findings highlight ultrafiltration as a superior method for purifying circRNA at the research scale. Its scalability suggests that it could play a critical role in enabling the large-scale manufacturing of circRNA-based therapeutics.

Western blotting is a stalwart technique for analyzing specific proteins and/or their post-translational modifications. However, it remains challenging to accommodate more than ~10 samples per experiment without substantial departure from trusted, established protocols involving accessible instrumentation. Here, we describe a 96-sample western blot that conforms to standard 96-well plate dimensional constraints and has little operational deviation from standard western blotting. The main differences are that (i) submerged polyacrylamide gel electrophoresis is operated horizontally (similar to agarose gels) as opposed to vertically, and (ii) a 6 mm thick gel is used, with 2 mm most relevant for membrane transfer (vs ~1 mm typical). Results demonstrate both wet and semi-dry transfer are compatible with this gel thickness. The major tradeoff is reduced molecular weight resolution, due primarily to less available migration distance per sample. We demonstrate proof-of-principle using gels loaded with molecular weight ladder, recombinant protein, and cell lysates. We expect the 96-well western blot will increase reproducibility, efficiency (cost and time ~8-fold), and capacity for biological characterization relative to established western blots.

Genetically-encoded, single-cell barcodes are broadly useful for experimental tasks such as lineage tracing or genetic screens. For such applications, a barcode library would ideally have high diversity (many unique barcodes), non-destructive identification (repeated measurements in the same cells or population), and fast, inexpensive readout (many cells and conditions). Current nucleic acid barcoding methods generate high diversity but require destructive and slow/expensive readout, and current fluorescence barcoding methods are non-destructive, fast, and inexpensive to readout but lack high diversity. We recently proposed theory for how fluorescent protein combinations may generate a high-diversity barcode library with non-destructive, fast and inexpensive identification. Here, we present an initial experimental proof-of-concept by generating a library of ~150 barcodes from two-way combinations of 18 fluorescent proteins. We use a pooled cloning strategy to generate a barcode library that is validated to contain every possible combination of the 18 fluorescent proteins. Experimental results using single mammalian cells and spectral flow cytometry demonstrate excellent classification performance of individual fluorescent proteins, with the exception of mTFP1, and of most evaluated barcodes, with many true positive rates >99%. The library is compatible with genetic screening for hundreds of genes (or gene pairs) and lineage tracing hundreds of clones. This work lays a foundation for greater diversity libraries (potentially ~10 5 and more) generated from hundreds of spectrally-resolvable tandem fluorescent protein probes.

Drug-induced gene expression profiles can identify potential mechanisms of toxicity. We focus on obtaining signatures for cardiotoxicity of FDA-approved tyrosine kinase inhibitors (TKIs) in human induced-pluripotent-stem-cell-derived cardiomyocytes, using bulk transcriptomic profiles. We use singular value decomposition to identify drug-selective patterns across cell lines obtained from multiple healthy human subjects. Cellular pathways affected by cardiotoxic TKIs include energy metabolism, contractile, and extracellular matrix dynamics. Projecting these pathways to published single cell expression profiles indicates that TKI responses can be evoked in both cardiomyocytes and fibroblasts. Integration of transcriptomic outlier analysis with whole genomic sequencing of our six cell lines enables us to correctly reidentify a genomic variant causally linked to anthracycline-induced cardiotoxicity and predict genomic variants potentially associated with TKI-induced cardiotoxicity. We conclude that mRNA expression profiles when integrated with publicly available genomic, pathway, and single cell transcriptomic datasets, provide multiscale signatures for cardiotoxicity that could be used for drug development and patient stratification., (© 2024. The Author(s).)

Data from cell viability assays, which measure cumulative division and death events in a population and reflect substantial cellular heterogeneity, are widely available. However, interpreting such data with mechanistic computational models is hindered because direct model/data comparison is often muddled. We developed an algorithm that tracks simulated division and death events in mechanistically detailed single-cell lineages to enable such a model/data comparison and suggest causes of cell-cell drug response variability. Using our previously developed model of mammalian single-cell proliferation and death signaling, we simulated drug dose response experiments for four targeted anti-cancer drugs (alpelisib, neratinib, trametinib and palbociclib) and compared them to experimental data. Simulations are consistent with data for strong growth inhibition by trametinib (MEK inhibitor) and overall lack of efficacy for alpelisib (PI-3K inhibitor), but are inconsistent with data for palbociclib (CDK4/6 inhibitor) and neratinib (EGFR inhibitor). Model/data inconsistencies suggest (i) the importance of CDK4/6 for driving the cell cycle may be overestimated, and (ii) that the cellular balance between basal (tonic) and ligand-induced signaling is a critical determinant of receptor inhibitor response. Simulations show subpopulations of rapidly and slowly dividing cells in both control and drug-treated conditions. Variations in mother cells prior to drug treatment all impinging on ERK pathway activity are associated with the rapidly dividing phenotype and trametinib resistance. This work lays a foundation for the application of mechanistic modeling to large-scale cell viability assay datasets and better understanding determinants of cellular heterogeneity in drug response.

Full-spectrum flow cytometry has increased antibody-based multiplexing, yet further increases remain potentially impactful. We recently proposed how fluorescence multiplexing using spectral imaging and combinatorics (MuSIC) could do so using tandem dyes and an oligo-based antibody labeling method. In this work, we found that such labeled antibodies had significantly lower signal intensities than conventionally labeled antibodies in human cell experiments. To improve signal intensity, we tested moving the fluorophores from the original external (ext.) 5' or 3' end-labeled orientation to internal (int.) fluorophore modifications. Cell-free spectrophotometer measurements showed a ∼6-fold signal intensity increase of the new int. configuration compared to the previous ext. configuration. Time-resolved fluorescence and fluorescence correlation spectroscopy showed that the ∼3-fold brightness difference is due to static quenching most likely by the oligo or solution in the ext. configuration. Spectral flow cytometry experiments using peripheral blood mononuclear cells show int. MuSIC probe-labeled antibodies (i) retained increased signal intensity while having no significant difference in the estimated % of CD8+ lymphocytes and (ii) labeled with Atto488, Atto647, and Atto488/647 combinations can be demultiplexed in triple-stained samples. The antibody labeling approach is general and can be broadly applied to many biological and diagnostic applications where spectral detection is available.

Understanding the dynamics of intracellular signaling pathways, such as ERK1/2 (ERK) and Akt1/2 (Akt), in the context of cell fate decisions is important for advancing our knowledge of cellular processes and diseases, particularly cancer. While previous studies have established associations between ERK and Akt activities and proliferative cell fate, the heterogeneity of single-cell responses adds complexity to this understanding. This study employed a data-driven approach to address this challenge, developing machine learning models trained on a dataset of growth factor-induced ERK and Akt activity time courses in single cells, to predict cell division events. The most predictive models were developed by applying discrete wavelet transforms (DWTs) to extract low-frequency features from the time courses, followed by using Ensemble Integration, a data integration and predictive modeling framework. The results demonstrated that these models effectively predicted cell division events in MCF10A cells (F-measure=0.524, AUC=0.726). ERK dynamics were found to be more predictive than Akt, but the combination of both measurements further enhanced predictive performance. The ERK model`s performance also generalized to predicting division events in RPE cells, indicating the potential applicability of these models and our data-driven methodology for predicting cell division across different biological contexts. Interpretation of these models suggested that ERK dynamics throughout the cell cycle, rather than immediately after growth factor stimulation, were associated with the likelihood of cell division. Overall, this work contributes insights into the predictive power of intra-cellular signaling dynamics for cell fate decisions, and highlights the potential of machine learning approaches in unraveling complex cellular behaviors., (© 2024. The Author(s).)

Glioblastoma Multiforme (GBM) remains a particularly difficult cancer to treat, and survival outcomes remain poor. In addition to the lack of dedicated drug discovery programs for GBM, extensive intratumor heterogeneity and epigenetic plasticity related to cell-state transitions are major roadblocks to successful drug therapy in GBM. To study these phenomenon, publicly available snRNAseq and bulk RNAseq data from patient samples were used to categorize cells from patients into four cell states (i.e. phenotypes), namely: (i) neural progenitor-like (NPC-like), (ii) oligodendrocyte progenitor-like (OPC-like), (iii) astrocyte- like (AC-like), and (iv) mesenchymal-like (MES-like). Patients were subsequently grouped into subpopulations based on which cell-state was the most dominant in their respective tumor. By incorporating phosphoproteomic measurements from the same patients, a protein-protein interaction network (PPIN) was constructed for each cell state. These four-cell state PPINs were pooled to form a single Boolean network that was used for in silico protein knockout simulations to investigate mechanisms that either promote or prevent cell state transitions. Simulation results were input into a boosted tree machine learning model which predicted the cell states or phenotypes of GBM patients from an independent public data source, the Glioma Longitudinal Analysis (GLASS) Consortium. Combining the simulation results and the machine learning predictions, we generated hypotheses for clinically relevant causal mechanisms of cell state transitions. For example, the transcription factor TFAP2A can be seen to promote a transition from the NPC-like to the MES-like state. Such protein nodes and the associated signaling pathways provide potential drug targets that can be further tested in vitro and support cell state-directed (CSD) therapy., Competing Interests: Competing interests All authors declare no financial or non-financial competing interests.

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 ∼10 5 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.

Robust identification of context-specific network features that control cellular phenotypes remains a challenge. We here introduce MOBILE (Multi-Omics Binary Integration via Lasso Ensembles) to nominate molecular features associated with cellular phenotypes and pathways. First, we use MOBILE to nominate mechanisms of interferon-γ (IFNγ) regulated PD-L1 expression. Our analyses suggest that IFNγ-controlled PD-L1 expression involves BST2, CLIC2, FAM83D, ACSL5, and HIST2H2AA3 genes, which were supported by prior literature. We also compare networks activated by related family members transforming growth factor-beta 1 (TGFβ1) and bone morphogenetic protein 2 (BMP2) and find that differences in ligand-induced changes in cell size and clustering properties are related to differences in laminin/collagen pathway activity. Finally, we demonstrate the broad applicability and adaptability of MOBILE by analyzing publicly available molecular datasets to investigate breast cancer subtype specific networks. Given the ever-growing availability of multi-omics datasets, we envision that MOBILE will be broadly useful for identification of context-specific molecular features and pathways., (© 2023. The Author(s).)

Cancer chemotherapy combines multiple drugs, but predicting the effects of drug combinations on cancer cell proliferation remains challenging, even for simple in vitro systems. We hypothesized that by combining knowledge of single drug dose responses and cell state transition network dynamics, we could predict how a population of cancer cells will respond to drug combinations. We tested this hypothesis here using three targeted inhibitors of different cell cycle states in two different cell lines in vitro. We formulated a Markov model to capture temporal cell state transitions between different cell cycle phases, with single drug data constraining how drug doses affect transition rates. This model was able to predict the landscape of all three different pairwise drug combinations across all dose ranges for both cell lines with no additional data. While further application to different cell lines, more drugs, additional cell state networks, and more complex co-culture or in vivo systems remain, this work demonstrates how currently available or attainable information could be sufficient for prediction of drug combination response for single cell lines in vitro., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Sarmah et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Shim, Xiong, Dhanan, Dariolli, Azeloglu, Hu, Jayaraman, Schaniel, Birtwistle, Iyengar, Dubois and Sobie.)

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)., (© The Author(s) 2023. Published by Oxford University Press.)

Two things Tunde loved were dynamics and probability. The work described herein combined them both, which explains why Tunde invariably asked me each time we talked how this work was proceeding. However, as I've come to appreciate as reminiscent of a surprisingly large amount of work in almost any researcher's career, I did not complete a peer-reviewed article on the matter while he could see it. We were broadly motivated by analysis of data for total DNA content in single cells, across thousands of cells. From such data one can estimate the proportions of cells in different phases of the cell cycle by fitting a mixture model for subpopulations of G 0 /G 1 phase cells (1 relative copy of the genome), S phase cells (between 1 and 2 relative copies of the genome), and G 2 /M phase cells (2 relative copies of the genome). Given an asynchronously cycling population, Gaussian models are reasonable for the G 0 /G 1 and G 2 /M subpopulations, but an appropriate functional form for the S-phase subpopulation was unclear. Since the probability of observing an S-phase cell is intimately related to the dynamics of DNA replication, we worked to derive a model for DNA replication dynamics from first principles, resulting in a closed-form, analytic expression for the dynamics of DNA synthesis. While quite arguably a somewhat superfluous effort, there is a certain satisfaction and academic beauty to modeling systems from a first-principles approach, and it can sometimes lead to unexpected scientific insights. Yet, while mathematically elegant, there was a fundamental issue with a key assumption that the so-called inter-origin distance distribution (distances between DNA replication initiation sites) was time-invariant. First, I present the model as developed previously. Then, to address the time-invariant inter-origin distance distribution issue, I provide a treatment of time-varying inter-origin distance distributions that, while mathematically simple, provides (i) mechanistic predictions for how all the DNA in a fertilized frog egg can be replicated "on time" despite some inter-origin distances initially exceeding the corresponding amount of allowable time and (ii) evidence that, based only on data from DNA content versus time and average inter-origin distances, somatic cell DNA is parsed into distinct regions whose replication is temporally separate.

Computational models that can explain and predict complex sub-cellular, cellular, and tissue-level drug response mechanisms could speed drug discovery and prioritize patient-specific treatments (i.e., precision medicine). Some models are mechanistic with detailed equations describing known (or supposed) physicochemical processes, while some are statistical or machine learning-based approaches, that explain datasets but have no mechanistic or causal guarantees. These two types of modeling are rarely combined, missing the opportunity to explore possibly causal but data-driven new knowledge while explaining what is already known. Here, we explore combining machine learned associations with mechanistic models to develop computational models that could more fully represent cellular behavior. In this proposed MEMMAL (MEchanistic Modeling with MAchine Learning) framework, machine learning/statistical models built using omics datasets provide predictions for new interactions between genes and proteins where there is physicochemical uncertainty. These interactions are used as a basis for new reactions in mechanistic models. As a test case, we focused on incorporating novel IFNγ/PD-L1 related associations into a large-scale mechanistic model for cell proliferation and death to better recapitulate the recently released NIH LINCS Consortium MCF10A dataset and enable description of the cellular response to checkpoint inhibitor immunotherapies. This work is a template for combining big-data-inferred interactions with mechanistic models, which could be more broadly applicable for building multi-scale precision medicine and whole cell models., Competing Interests: Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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., (© 2022. The Author(s).)

Biochemical correlates of stochastic single-cell fates have been elusive, even for the well-studied mammalian cell cycle. 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 h). 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. Simple metrics of central tendency in this time window are associated with subsequent cell division fates. ERK activity was more strongly associated with division fates than Akt activity, suggesting Akt activity dynamics may contribute less 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 may be more important than Akt activity dynamics for driving cell division in this non-transformed context., (© 2022. The Author(s).)

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. 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. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis., (© 2022. The Author(s).)

Multiangle 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 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.

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., Competing Interests: The authors declare the following competing financial interest(s): Authors Dr. Marc Birtwistle, Cameron Zadeh, and Jonah Huggins are co-founders of Blotting Innovations LLC., (© 2022 The Authors. Published by American Chemical Society.)

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., Competing Interests: Declaration of Competing Interest AVDV and ACW were employed by F. Hoffmann-La Roche Ltd. at time of submission of this manuscript and ACW is stockholder of F. Hoffmann-La Roche Ltd. The Krogan Laboratory has received research support from Vir Biotechnology and F. Hoffmann-La Roche. NJK has consulting agreements with the Icahn School of Medicine at Mount Sinai, New York, Maze Therapeutics and Interline Therapeutics. He is a shareholder in Tenaya Therapeutics, Maze Therapeutics and Interline Therapeutics and is a financially compensated Scientific Advisory Board Member for GEn1E Lifesciences, Inc. MB is a compensated scientific advisor for GEn1E Lifesciences, Inc., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Mechanistic models of how single cells respond to different perturbations 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. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models., (© 2022. The Author(s).)

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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., (© 2022. The Author(s).)

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., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Competing interest statement: R.R. and A.S. are co-founders of Aichemy Inc.

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.

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., (© 2019 F. Hoffmann-La Roche Ltd. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of the American Society for Clinical Pharmacology and Therapeutics.)

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.

Evidence that some high-impact biomedical results cannot be repeated has stimulated interest in practices that generate findable, accessible, interoperable, and reusable (FAIR) data. Multiple papers have identified specific examples of irreproducibility, but practical ways to make data more reproducible have not been widely studied. Here, five research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cellular networks, and patient stratification. While many experimental and computational factors impact intra- and inter-center reproducibility, the factors most difficult to identify and control are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways to identify such context-sensitive factors, thereby improving both the theory and practice of reproducible cell-based assays., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

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.

Ultraviolet-to-infrared fluorescence is a versatile and accessible assay modality but is notoriously hard to multiplex due to overlap of wide emission spectra. We present an approach for fluorescence called multiplexing using spectral imaging and combinatorics (MuSIC). MuSIC consists of creating new independent probes from covalently linked combinations of individual fluorophores, leveraging the wide palette of currently available probes with the mathematical power of combinatorics. Probe levels in a mixture can be inferred from spectral emission scanning data. Theory and simulations suggest MuSIC can increase fluorescence multiplexing ∼4-5 fold using currently available dyes and measurement tools. Experimental proof-of-principle demonstrates robust demultiplexing of nine solution-based probes using ∼25% of the available excitation wavelength window (380-480 nm), consistent with theory. The increasing prevalence of white lasers, angle filter-based wavelength scanning, and large, sensitive multianode photomultiplier tubes make acquisition of such MuSIC-compatible data sets increasingly attainable.

Fluorescence-based western blots are quantitative in principal, but require determining linear range for each antibody. Here, we use microwestern array to rapidly evaluate suitable conditions for quantitative western blotting, with up to 192 antibody/dilution/replicate combinations on a single standard size gel with a seven-point, two-fold lysate dilution series (~100-fold range). Pilot experiments demonstrate a high proportion of investigated antibodies (17/24) are suitable for quantitative use; however this sample of antibodies is not yet comprehensive across companies, molecular weights, and other important antibody properties, so the ubiquity of this property cannot yet be determined. In some cases microwestern struggled with higher molecular weight membrane proteins, so the technique may not be uniformly applicable to all validation tasks. Linear range for all validated antibodies is at least 8-fold, and up to two orders of magnitude. Phospho-specific and total antibodies do not have discernable trend differences in linear range or limit of detection. Total antibodies generally required higher working concentrations, but more comprehensive antibody panels are required to better establish whether this trend is general or not. Importantly, we demonstrate that results from microwestern analyses scale to normal "macro" western for a subset of antibodies.

Most cancer cells harbor multiple drivers whose epistasis and interactions with expression context clouds drug and drug combination sensitivity prediction. We constructed a mechanistic computational model that is context-tailored by omics data to capture regulation of stochastic proliferation and death by pan-cancer driver pathways. Simulations and experiments explore how the coordinated dynamics of RAF/MEK/ERK and PI-3K/AKT kinase activities in response to synergistic mitogen or drug combinations control cell fate in a specific cellular context. In this MCF10A cell context, simulations suggest that synergistic ERK and AKT inhibitor-induced death is likely mediated by BIM rather than BAD, which is supported by prior experimental studies. AKT dynamics explain S-phase entry synergy between EGF and insulin, but simulations suggest that stochastic ERK, and not AKT, dynamics seem to drive cell-to-cell proliferation variability, which in simulations is predictable from pre-stimulus fluctuations in C-Raf/B-Raf levels. Simulations suggest MEK alteration negligibly influences transformation, consistent with clinical data. Tailoring the model to an alternate cell expression and mutation context, a glioma cell line, allows prediction of increased sensitivity of cell death to AKT inhibition. Our model mechanistically interprets context-specific landscapes between driver pathways and cell fates, providing a framework for designing more rational cancer combination therapy.

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., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Current treatments for glioblastoma multiforme (GBM)-an aggressive form of brain cancer-are minimally effective and yield a median survival of 14.6 months and a two-year survival rate of 30%. Given the severity of GBM and the limitations of its treatment, there is a need for the discovery of novel drug targets for GBM and more personalized treatment approaches based on the characteristics of an individual's tumor. Most receptor tyrosine kinases-such as EGFR-act as oncogenes, but publicly available data from the Cancer Cell Line Encyclopedia (CCLE) indicates copy number loss in the ERBB4 RTK gene across dozens of GBM cell lines, suggesting a potential tumor suppressor role. This loss is mutually exclusive with loss of its cognate ligand NRG1 in CCLE as well, more strongly suggesting a functional role. The availability of higher resolution copy number data from clinical GBM patients in The Cancer Genome Atlas (TCGA) revealed that a region in Intron 1 of the ERBB4 gene was deleted in 69.1% of tumor samples harboring ERBB4 copy number loss; however, it was also found to be deleted in the matched normal tissue samples from these GBM patients (n = 81). Using the DECIPHER Genome Browser, we also discovered that this mutation occurs at approximately the same frequency in the general population as it does in the disease population. We conclude from these results that this loss in Intron 1 of the ERBB4 gene is neither a de novo driver mutation nor a predisposing factor to GBM, despite the indications from CCLE. A biological role of this significantly occurring genetic alteration is still unknown. While this is a negative result, the broader conclusion is that while copy number data from large cell line-based data repositories may yield compelling hypotheses, careful follow up with higher resolution copy number assays, patient data, and general population analyses are essential to codify initial hypotheses prior to investing experimental resources.

Monotherapy clinical trials with mutation-targeted kinase inhibitors, despite some success in other cancers, have yet to impact glioblastoma (GBM). Besides insufficient blood-brain barrier penetration, combinations are key to overcoming obstacles such as intratumoral heterogeneity, adaptive resistance, and the epistatic nature of tumor genomics that cause mutation-targeted therapies to fail. With now hundreds of potential drugs, exploring the combination space clinically and preclinically is daunting. We are building a simulation-based approach that integrates patient-specific data with a mechanistic computational model of pan-cancer driver pathways (receptor tyrosine kinases, RAS/RAF/ERK, PI3K/AKT/mTOR, cell cycle, apoptosis, and DNA damage) to prioritize drug combinations by their simulated effects on tumor cell proliferation and death. Here we illustrate a first step, tailoring the model to 14 GBM patients from The Cancer Genome Atlas defined by an mRNA-seq transcriptome, and then simulating responses to three promiscuous FDA-approved kinase inhibitors (bosutinib, ibrutinib, and cabozantinib) with evidence for blood-brain barrier penetration. The model captures binding of the drug to primary targets and off-targets based on published affinity data and simulates responses of 100 heterogeneous tumor cells within a patient. Single drugs are marginally effective or even counterproductive. Common copy number alterations (PTEN loss, EGFR amplification, and NF1 loss) have a negligible correlation with single-drug or combination efficacy, reinforcing the importance of postgenetic approaches that account for kinase inhibitor promiscuity to match drugs to patients. Drug combinations tend to be either cytostatic or cytotoxic, but seldom both, highlighting the need for considering targeted and nontargeted therapy. Although we focus on GBM, the approach is generally applicable.

Creating a cDNA library for deep mRNA sequencing (mRNAseq) is generally done by random priming, creating multiple sequencing fragments along each transcript. A 3'-end-focused library approach cannot detect differential splicing, but has potentially higher throughput at a lower cost, along with the ability to improve quantification by using transcript molecule counting with unique molecular identifiers (UMI) that correct PCR bias. Here, we compare an implementation of such a 3'-digital gene expression (3'-DGE) approach with "conventional" random primed mRNAseq. Given our particular datasets on cultured human cardiomyocyte cell lines, we find that, while conventional mRNAseq detects ~15% more genes and needs ~500,000 fewer reads per sample for equivalent statistical power, the resulting differentially expressed genes, biological conclusions, and gene signatures are highly concordant between two techniques. We also find good quantitative agreement at the level of individual genes between two techniques for both read counts and fold changes between given conditions. We conclude that, for high-throughput applications, the potential cost savings associated with 3'-DGE approach are likely a reasonable tradeoff for modest reduction in sensitivity and inability to observe alternative splicing, and should enable many larger scale studies focusing on not only differential expression analysis, but also quantitative transcriptome profiling.

Tyrosine kinase inhibitors (TKIs) are highly potent cancer therapeutics that have been linked with serious cardiotoxicity, including left ventricular dysfunction, heart failure, and QT prolongation. TKI-induced cardiotoxicity is thought to result from interference with tyrosine kinase activity in cardiomyocytes, where these signaling pathways help to control critical processes such as survival signaling, energy homeostasis, and excitation-contraction coupling. However, mechanistic understanding is limited at present due to the complexities of tyrosine kinase signaling, and the wide range of targets inhibited by TKIs. Here, we review the use of TKIs in cancer and the cardiotoxicities that have been reported, discuss potential mechanisms underlying cardiotoxicity, and describe recent progress in achieving a more systematic understanding of cardiotoxicity via the use of mechanistic models. In particular, we argue that future advances are likely to be enabled by studies that combine large-scale experimental measurements with Quantitative Systems Pharmacology (QSP) models describing biological mechanisms and dynamics. As such approaches have proven extremely valuable for understanding and predicting other drug toxicities, it is likely that QSP modeling can be successfully applied to cardiotoxicity induced by TKIs. We conclude by discussing a potential strategy for integrating genome-wide expression measurements with models, illustrate initial advances in applying this approach to cardiotoxicity, and describe challenges that must be overcome to truly develop a mechanistic and systematic understanding of cardiotoxicity caused by TKIs.

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 (OsO 4 ) 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 OsO 4 ; mass cytometry analysis demonstrates that the two staining intensities correlate well with one another. We conclude that both WGA and OsO 4 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., (© 2016 International Society for Advancement of Cytometry.)

A main application for mRNA sequencing (mRNAseq) is determining lists of differentially-expressed genes (DEGs) between two or more conditions. Several software packages exist to produce DEGs from mRNAseq data, but they typically yield different DEGs, sometimes markedly so. The underlying probability model used to describe mRNAseq data is central to deriving DEGs, and not surprisingly most softwares use different models and assumptions to analyze mRNAseq data. Here, we propose a mechanistic justification to model mRNAseq as a binomial process, with data from technical replicates given by a binomial distribution, and data from biological replicates well-described by a beta-binomial distribution. We demonstrate good agreement of this model with two large datasets. We show that an emergent feature of the beta-binomial distribution, given parameter regimes typical for mRNAseq experiments, is the well-known quadratic polynomial scaling of variance with the mean. The so-called dispersion parameter controls this scaling, and our analysis suggests that the dispersion parameter is a continually decreasing function of the mean, as opposed to current approaches that impose an asymptotic value to the dispersion parameter at moderate mean read counts. We show how this leads to current approaches overestimating variance for moderately to highly expressed genes, which inflates false negative rates. Describing mRNAseq data with a beta-binomial distribution thus may be preferred since its parameters are relatable to the mechanistic underpinnings of the technique and may improve the consistency of DEG analysis across softwares, particularly for moderately to highly expressed genes.

Fluorescently-labeled antibodies are central to many biochemical assays, but they are not easy to multiplex beyond 3-4 colors. A long-term hypothesis of ours is that labeling antibodies with multiple fluorophores, in a way such that fluorescence resonance energy transfer (FRET) occurs, may provide a way to increase fluorescence multiplexing ability by creating a rich variety of complex emission spectra that could be deconvolved via spectral methods. However, it is not yet clear how one can effectively label antibodies with multiple fluorophores that exhibit FRET. Here, we show how to use Mix-n-Stain antibody labeling kits from Biotium to label antibodies with multiple fluorophores that exhibit FRET. Key to our approach is the use of Fab fragments, as opposed to full IgG molecules, since the full IgG molecules are generally too large to allow the fluorophore proximity necessary for observable FRET. We show that our approach works with two different sets of FRET-capable fluorophore combinations: CF405M/CF488A and CF568/CF640R. These results form the basis for continued development of approaches for increased multiplexing of fluorescent antibody measurements.

Dynamic interactions between RhoA and Rac1, members of the Rho small GTPase family, play a vital role in the control of cell migration. Using predictive mathematical modeling, mass spectrometry-based quantitation of network components, and experimental validation in MDA-MB-231 mesenchymal breast cancer cells, we show that a network containing Rac1, RhoA, and PAK family kinases can produce bistable, switch-like responses to a graded PAK inhibition. Using a small chemical inhibitor of PAK, we demonstrate that cellular RhoA and Rac1 activation levels respond in a history-dependent, bistable manner to PAK inhibition. Consequently, we show that downstream signaling, actin dynamics, and cell migration also behave in a bistable fashion, displaying switches and hysteresis in response to PAK inhibition. Our results demonstrate that PAK is a critical component in the Rac1-RhoA inhibitory crosstalk that governs bistable GTPase activity, cell morphology, and cell migration switches., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)