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MotivationCells contain dozens of major organelles and thousands of other structures, many of which vary extensively in their number, size, shape and spatial distribution. This complexity and variation dramatically complicates the use of both traditional and deep learning methods to build accurate models of cell organization. Most cellular organelles are distinct objects with defined boundaries that do not overlap, while the pixel resolution of most imaging methods is not sufficient to resolve these boundaries. Thus while cell organization is conceptually object-based, most current methods are pixel-based. Using extensive image collections in which particular organelles were fluorescently-labeled, deep learning methods can be used to build conditional autoencoder models for particular organelles. A major advance occurred with the use of a U-net approach to make multiple models all conditional upon a common reference, unlabeled image, allowing the relationships between different organelles to be at least partially inferred.ResultsWe have developed improved GAN-based approaches for learning these models and have also developed novel criteria for evaluating how well synthetic cell images reflect the properties of real images. The first set of criteria measure how well models preserve the expected property that organelles do not overlap. We also developed a modified loss function that allows retraining of the models to minimize that overlap. The second set of criteria uses object-based modeling to compare object shape and spatial distribution between synthetic and real images. Our work provides the first demonstration that, at least for some organelles, deep learning models can capture object-level properties of cell images.AvailabilityA Reproducible Research Archive containing all source code, generated images and analysis results will be made available at http://murphylab.cbd.cmu.edu/Software upon publication.Contactmurphy@cmu.eduSupplementary informationSupplementary data are available at Bioinformatics online.

An automated image analysis pipeline to map cell geometry and basal body organization in the ciliate Tetrahymena thermophila is described. Generative modeling was used to computationally reconstruct cell cycle progression from images of cells at single time points. The results reveal novel aspects of basal body distribution and replication.

Dose Levels

Purpose: Our preclinical studies showed that the PARP inhibitor, olaparib, prior to carboplatin attenuated carboplatin cytotoxicity. We evaluated sequence-specific pharmacokinetic and pharmacodynamic effects, safety, and activity of the combination.Experimental Design: Eligible patients had metastatic or recurrent women's cancer. Olaparib tablets were introduced (100 or 200 mg twice daily, days 1–7) in a 3 + 3 dose escalation with carboplatin AUC4 or 5 every 21 days, up to eight cycles, followed by olaparib 300 mg twice daily maintenance. Patients were randomly assigned to starting schedule: cohort A (olaparib days 1–7, carboplatin on day 8) or B (carboplatin on day 1, olaparib days 2–8) during cycle 1. Patients received the reversed scheme in cycle 2. Blood was collected for olaparib pharmacokinetics, platinum–DNA adducts, comet assay, and PAR concentrations. The primary objectives were to examine schedule-dependent effects on olaparib pharmacokinetics and platinum–DNA adducts.Results: A total of 77 (60 ovarian, 14 breast, and 3 uterine cancer) patients were treated. Dose-limiting toxicity was thrombocytopenia and neutropenia, defining olaparib 200 mg twice daily + carboplatin AUC4 as the MTD. Olaparib clearance was increased approximately 50% when carboplatin was given 24 hours before olaparib. In vitro experiments demonstrated carboplatin preexposure increased olaparib clearance due to intracellular olaparib uptake. Quantities of platinum–DNA adducts were not different as a function of the order of drug administration. Responses included 2 CRs and 31 PRs (46%) with a higher RR in BRCA mutation carriers compared with nonmutation carriers (68% vs. 19%).Conclusions: Tablet olaparib with carboplatin is a safe and active combination. Carboplatin preexposure causes intracellular olaparib accumulation reducing bioavailable olaparib, suggesting carboplatin should be administered prior to olaparib. Clin Cancer Res; 23(6); 1397–406. ©2016 AACR.

Supplementary method 1. DNA platinum-adducts measurement

While many biological processes can be modeled by abstracting away the space in which those processes occur, some modeling (particularly at the cellular level) requires space itself to be modeled, with processes happening not in well-mixed compartments, but spatially-defined compartments. The SBML Level 3 Core specification does not include an explicit mechanism to encode geometries and spatial processes in a model, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactic constructs. The SBML Spatial Processes package for SBML Level 3 adds the necessary features to allow models to encode geometries and other spatial information about the elements and processes it describes.

A major challenge for protein databases is reconciling information from diverse sources. This is especially difficult when some information consists of secondary, human-interpreted rather than primary data. For example, the Swiss-Prot database contains curated annotations of subcellular location that are based on predictions from protein sequence, statements in scientific articles, and published experimental evidence. The Human Protein Atlas (HPA) consists of millions of high-resolution microscopic images that show protein spatial distribution on a cellular and subcellular level. These images are manually annotated with protein subcellular locations by trained experts. The image annotations in HPA can capture the variation of subcellular location across different cell lines, tissues, or tissue states. Systematic investigation of the consistency between HPA and Swiss-Prot assignments of subcellular location, which is important for understanding and utilizing protein location data from the two databases, has not been described previously. In this paper, we quantitatively evaluate the consistency of subcellular location annotations between HPA and Swiss-Prot at multiple levels, as well as variation of protein locations across cell lines and tissues. Our results show that annotations of these two databases differ significantly in many cases, leading to proposed procedures for deriving and integrating the protein subcellular location data. We also find that proteins having highly variable locations are more likely to be biomarkers of diseases, providing support for incorporating analysis of subcellular location in protein biomarker identification and screening.

PC12 cells are a popular model system to study changes driving and accompanying neuronal differentiation. While attention has been paid to changes in transcriptional regulation and protein signaling, much less is known about the changes in organization that accompany PC12 differentiation. Fluorescence microscopy can provide extensive information about these changes, although it is difficult to continuously observe changes over many days of differentiation. We describe a generative model of differentiation-associated changes in cell and nuclear shape and their relationship to mitochondrial distribution constructed from images of different cells at discrete time points. We show that the model accurately represents complex cell and nuclear shapes and learn a regression model that relates cell and nuclear shape to mitochondrial distribution; the predictive accuracy of the model increases during differentiation. Most importantly, we propose a method, based on cell matching and interpolation, to produce realistic simulations of the dynamics of cell differentiation from only static images. We also found that the distribution of cell shapes is hollow: most shapes are very different from the average shape. Finally, we show how the method can be used to model nuclear shape changes of human-induced pluripotent stem cells resulting from drug treatments.

Cell segmentation is a cornerstone of many bioimage informatics studies and inaccurate segmentation introduces error in downstream analysis. Evaluating segmentation results is thus a necessary step for developing segmentation methods as well as for choosing the most appropriate method for a particular type of sample. The evaluation process has typically involved comparison of segmentations to those generated by humans, which can be expensive and subject to unknown bias. We present here an approach to evaluating cell segmentation methods without relying upon comparison to results from humans. For this, we defined a number of segmentation quality metrics that can be applied to multichannel fluorescence images. We calculated these metrics for 14 previously-described segmentation methods applied to datasets from 4 multiplexed microscope modalities covering 5 tissues. Using principal component analysis to combine the metrics we defined an overall cell segmentation quality score and ranked the segmentation methods. We found that two deep learning-based methods performed the best overall, but that results for all methods could be significantly improved by postprocessing to ensure proper matching of cell and nuclear masks. Our evaluation tool is available as open source and all code and data are available in a Reproducible Research Archive.

Introduction: Surgical Site Infection (SSI) is a rare but serious complication following Posterior Spinal Fusion (PSF) for pediatric scoliosis. Several studies have identified patient risk factors for SSI following PSF, including high Body Mass Index (BMI). The purpose of this study was to determine whether Radiographic Skin-to-spine Distance (RSD) can be used as a surrogate for BMI when evaluating the pediatric patient’s risk of developing a SSI after PSF. Methods: This study was performed retrospectively and included patients ages 0-18 who underwent PSF for scoliosis over a four-year period. RSD, defined as the distance from the L5 spinous process to the skin, was measured using preoperative lateral radiographs. Results: Of the 176 included patients, we identified eight (4.5%) who suffered from surgical site infection following PSF. Logistic regression analysis revealed no statistically significant predictors for SSI. However, there was a strong, statistically significant linear regression prediction between RSD and BMI (r = 0.71, p=0.001). Discussion: RSD was not shown to be predictive of SSI in this patient cohort. However, the strong correlation found between RSD and BMI suggests that when assessing risk of SSI, RSD may be useful as a substitute for BMI when BMI cannot be easily ascertained.

Displaced pediatric forearm fractures often are treated with closed reduction and immobilization. Recent literature demonstrates no difference in maintaining alignment or needing repeat intervention in patients immobilized with either a single sugar-tong splint or a long-arm cast, but most series include patients with distal fractures. This study included patients 3 to 15 years old who underwent closed reduction and immobilization for displaced midshaft or proximal forearm fractures. Radiographs from the time of injury, after reduction, and at 4-week follow-up were reviewed for coronal and sagittal plane angular alignment. Secondary interventions also were recorded. A total of 121 patients (70 long-arm cast, 51 simple sugar-tong splint) met inclusion criteria. Groups were matched in terms of age ( P =.95), sex ( P =.41), body mass index ( P =.12), and angular deformity prior to reduction in the sagittal ( P =.78) and coronal ( P =.83) planes. Following closed reduction, sagittal ( P =.003) and coronal ( P =.002) alignment improved significantly in all patients. At 4-week follow-up, there were no significant differences in sagittal ( P =.15) or coronal ( P =.68) alignment between the 2 groups. Nine patients underwent a secondary intervention after the index reduction (long-arm cast, n=7; simple sugar-tong splint, n=2), with no statistically significant difference between groups ( P =.30). There were no statistically significant differences between patients managed with long-arm cast or simple sugar-tong splint regarding residual sagittal or coronal plane deformity at 4-week follow-up or incidence of secondary intervention. These findings indicate simple sugar-tong splint and long-arm cast appear to be acceptable and equivalent methods of immobilization for these injuries. [ Orthopedics . 2021;44(2):e178–e182.]

Most of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multi-molecular structures embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computer simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However, it is often difficult to reconcile conflicting computational results that use different approaches to describe the same phenomenon. To address this issue systematically, we have defined a series of computational test cases ranging from very simple to moderately complex, varying key features of dimensionality, reaction type, reaction speed, crowding, and cell size. We then quantified how explicit spatial and/or stochastic implementations alter outcomes, even when all methods use the same reaction network, rates, and concentrations. For simple cases we generally find minor differences in solutions of the same problem. However, we observe increasing discordance as the effects of localization, dimensionality reduction, and irreversible enzymatic reactions are combined. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision-making by researchers developing new models. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.

Surgical management of Early Onset Spinal Deformity with magnetically controlled growing rods (MCGR) is a common intervention. Multiple complications of the use of these devices have been reported in the literature. To date, failure of the device to lengthen due to the physical distance of the rods from the external controller due to patient body habitus has not been reported. Consideration of the resultant distance from the external remote controller to the rods should be part of preoperative planning and intraoperative decision-making.

The killing of tumor cells by CD8(+) T cells is suppressed by the tumor microenvironment, and increased expression of inhibitory receptors, including programed cell death protein-1 (PD-1), is associated with tumor-mediated suppression of T cells. To discover cellular defects triggered by tumor exposure and associated PD-1 signaling, we established an ex vivo imaging approach to investigate the response of antigen-specific, activated effector CD8(+) tumor infiltrating lymphocytes (TILs) after interaction with target tumor cells. Although TIL–tumor cell couples readily formed, couple stability deteriorated within minutes. This was associated with impaired F-actin clearing from the center of the cellular interface, reduced Ca(2+) signaling, increased TIL locomotion, and impaired tumor cell killing. The interaction of CD8(+) T lymphocytes with tumor cell spheroids in vitro induced a similar phenotype, supporting a critical role of direct T cell–tumor cell contact. Diminished engagement of PD-1 within the tumor, but not acute ex vivo blockade, partially restored cell couple maintenance and killing. PD-1 thus contributes to the suppression of TIL function by inducing a state of impaired subcellular organization.

Motivation High throughput and high content screening are extensively used to determine the effect of small molecule compounds and other potential therapeutics upon particular targets as part of the early drug development process. However, screening is typically used to find compounds that have a desired effect but not to identify potential undesirable side effects. This is because the size of the search space precludes measuring the potential effect of all compounds on all targets. Active machine learning has been proposed as a solution to this problem. Results In this article, we describe an improved imputation method, Impute by Committee, for completion of matrices containing categorical values. We compare this method to existing approaches in the context of modeling the effects of many compounds on many targets using latent similarities between compounds and conditions. We also compare these methods for the task of driving active learning in well-characterized settings for synthetic and real datasets. Our new approach performed the best overall both in the accuracy of matrix completion itself and in the number of experiments needed to train an accurate predictive model compared to random selection of experiments. We further improved upon the performance of our new method by developing an adaptive switching strategy for active learning that iteratively chooses between different matrix completion methods. Availability and implementation A Reproducible Research Archive containing all data and code is available at http://murphylab.cbd.cmu.edu/software. Supplementary information Supplementary data are available at Bioinformatics online.

Most of the fascinating phenomena studied in cell biology emerge from interactions among highly organized multi-molecular structures and rapidly propagating molecular signals embedded into complex and frequently dynamic cellular morphologies. For the exploration of such systems, computational simulation has proved to be an invaluable tool, and many researchers in this field have developed sophisticated computational models for application to specific cell biological questions. However it is often difficult to reconcile conflicting computational results that use different simulation approaches (for example partial differential equations versus particle-based stochastic methods) to describe the same phenomenon. Moreover, the details of the computational implementation of any particular algorithm may give rise to quantitatively or even qualitatively different results for the same set of starting assumptions and parameters. In an effort to address this issue systematically, we have defined a series of computational test cases ranging from very simple (bimolecular binding in solution) to moderately complex (spatial and temporal oscillations generated by proteins binding to membranes) that represent building blocks for comprehensive three-dimensional models of cellular function. Having used two or more distinct computational approaches to solve each of these test cases with consistent parameter sets, we generally find modest but measurable differences in the solutions of the same problem, and a few cases where significant deviations arise. We discuss the strengths and limitations of commonly used computational approaches for exploring cell biological questions and provide a framework for decision-making by researchers wishing to develop new models for cell biology. As computational power and speed continue to increase at a remarkable rate, the dream of a fully comprehensive computational model of a living cell may be drawing closer to reality, but our analysis demonstrates that it will be crucial to evaluate the accuracy of such models critically and systematically.

A key step in understanding the spatial organization of cells and tissues is the ability to construct generative models that accurately reflect that organization. In this paper, we focus on building generative models of electron microscope (EM) images in which the positions of cell membranes and mitochondria have been densely annotated, and propose a two-stage procedure that produces realistic images using Generative Adversarial Networks (or GANs) in a supervised way. In the first stage, we synthesize a label "image" given a noise "image" as input, which then provides supervision for EM image synthesis in the second stage. The full model naturally generates label-image pairs. We show that accurate synthetic EM images are produced using assessment via (1) shape features and global statistics, (2) segmentation accuracies, and (3) user studies. We also demonstrate further improvements by enforcing a reconstruction loss on intermediate synthetic labels and thus unifying the two stages into one single end-to-end framework., Comment: Accepted at WACV-18

Patients with developmental dysplasia of the hip (DDH) who undergo periacetabular osteotomy (PAO) often have labral tears. The objective of this retrospective study was to compare PAO alone with PAO combined with arthrotomy or arthroscopy in DDH patients who had a full-thickness labral tear on magnetic resonance imaging. In total, 47 hips in the PAO group (PAO) were compared with 60 hips in the PAO with concomitant arthrotomy or arthroscopy (PAO-A) with respect to Hip Disability and Osteoarthritis Outcome Score (HOOS), modified Harris Hip Score (mHHS), Visual Analog Scale (VAS), clinical and radiographic outcomes at a median of 29 months. Reoperation rate and complications were compared between two groups of treatment. The PAO group was younger than the PAO-A group (25.2 ± 9.7 versus 31.3 ± 8.3). The PAO group was more likely to have worse dysplasia: lateral center edge angle (7.6°±9.63° versus 10.8°±6.85°) and anterior center edge angle (4°±12.92° versus 10.8°±9.92°). The PAO group had a higher preoperative mHHS (65.2 ± 15.3 versus 57.8 ± 14.8) and HOOS (66.3 ± 17.5 versus 55.8 ± 20.1). There were no significant differences in final functional outcome scores across treatment groups: mHHS (PAO; 86.8 ± 12.4 versus PAO-A, 83.3 ± 17.2), HOOS (86.5 ± 13.3 versus 82.5 ± 16.8) and VAS (2.5 ± 2.8 versus 2.5 ± 3.1). There was no difference in reoperation rate between two groups (6.4% versus 11.6%, P = 0.51). The overall complication rate was lower in the PAO group (26% versus 68%), but major complications were comparable. On the basis of our data, we were not able to conclusively demonstrate a clear benefit for the routine treatment of all labral tears; however, arthrotomy or arthroscopy may play a role in some conditions.

Upstream open reading frames (uORFs), located in transcript leaders (5′ UTRs), are potent cis-acting regulators of translation and mRNA turnover. Recent genome-wide ribosome profiling studies suggest that thousands of uORFs initiate with non-AUG start codons. Although intriguing, these non-AUG uORF predictions have been made without statistical control or validation; thus, the importance of these elements remains to be demonstrated. To address this, we took a comparative genomics approach to study AUG and non-AUG uORFs. We mapped transcription leaders in multiple Saccharomyces yeast species and applied a novel machine learning algorithm (uORF-seqr) to ribosome profiling data to identify statistically significant uORFs. We found that AUG and non-AUG uORFs are both frequently found in Saccharomyces yeasts. Although most non-AUG uORFs are found in only one species, hundreds have either conserved sequence or position within Saccharomyces. uORFs initiating with UUG are particularly common and are shared between species at rates similar to that of AUG uORFs. However, non-AUG uORFs are translated less efficiently than AUG-uORFs and are less subject to removal via alternative transcription initiation under normal growth conditions. These results suggest that a subset of non-AUG uORFs may play important roles in regulating gene expression.

Purpose: Our preclinical studies showed that the PARP inhibitor, olaparib, prior to carboplatin attenuated carboplatin cytotoxicity. We evaluated sequence-specific pharmacokinetic and pharmacodynamic effects, safety, and activity of the combination. Experimental Design: Eligible patients had metastatic or recurrent women's cancer. Olaparib tablets were introduced (100 or 200 mg twice daily, days 1–7) in a 3 + 3 dose escalation with carboplatin AUC4 or 5 every 21 days, up to eight cycles, followed by olaparib 300 mg twice daily maintenance. Patients were randomly assigned to starting schedule: cohort A (olaparib days 1–7, carboplatin on day 8) or B (carboplatin on day 1, olaparib days 2–8) during cycle 1. Patients received the reversed scheme in cycle 2. Blood was collected for olaparib pharmacokinetics, platinum–DNA adducts, comet assay, and PAR concentrations. The primary objectives were to examine schedule-dependent effects on olaparib pharmacokinetics and platinum–DNA adducts. Results: A total of 77 (60 ovarian, 14 breast, and 3 uterine cancer) patients were treated. Dose-limiting toxicity was thrombocytopenia and neutropenia, defining olaparib 200 mg twice daily + carboplatin AUC4 as the MTD. Olaparib clearance was increased approximately 50% when carboplatin was given 24 hours before olaparib. In vitro experiments demonstrated carboplatin preexposure increased olaparib clearance due to intracellular olaparib uptake. Quantities of platinum–DNA adducts were not different as a function of the order of drug administration. Responses included 2 CRs and 31 PRs (46%) with a higher RR in BRCA mutation carriers compared with nonmutation carriers (68% vs. 19%). Conclusions: Tablet olaparib with carboplatin is a safe and active combination. Carboplatin preexposure causes intracellular olaparib accumulation reducing bioavailable olaparib, suggesting carboplatin should be administered prior to olaparib. Clin Cancer Res; 23(6); 1397–406. ©2016 AACR.

Quantitative image analysis procedures are necessary for the automated discovery of effects of drug treatment in large collections of fluorescent micrographs. When compared to their mammalian counterparts, the effects of drug conditions on protein localization in plant species are poorly understood and underexplored. To investigate this relationship, we generated a large collection of images of single plant cells after various drug treatments. For this, protoplasts were isolated from six transgenic lines of A. thaliana expressing fluorescently tagged proteins. Eight drugs at three concentrations were applied to protoplast cultures followed by automated image acquisition. For image analysis, we developed a cell segmentation protocol for detecting drug effects using a Hough transform-based region of interest detector and a novel cross-channel texture feature descriptor. In order to determine treatment effects, we summarized differences between treated and untreated experiments with an L1 Cramer-von Mises statistic. The distribution of these statistics across all pairs of treated and untreated replicates was compared to the variation within control replicates to determine the statistical significance of observed effects. Using this pipeline, we report the dose dependent drug effects in the first high-content Arabidopsis thaliana drug screen of its kind. These results can function as a baseline for comparison to other protein organization modeling approaches in plant cells. © 2017 International Society for Advancement of Cytometry.

As a central element within the RAS/ERK pathway, the serine/threonine kinase BRAF plays a key role in development and homeostasis and represents the most frequently mutated kinase in tumors. Consequently, it has emerged as an important therapeutic target in various malignancies. Nevertheless, the BRAF activation cycle still raises many mechanistic questions as illustrated by the paradoxical action and side effects of RAF inhibitors. By applying SEC‐PCP‐SILAC, we analyzed protein–protein interactions of hyperactive BRAFV600E and wild‐type BRAF (BRAFWT). We identified two macromolecular, cytosolic BRAF complexes of distinct molecular composition and phosphorylation status. Hyperactive BRAFV600E resides in large complexes of higher molecular mass and activity, while BRAFWT is confined to smaller, slightly less active complexes. However, expression of oncogenic K‐RasG12V, either by itself or in combination with RAF dimer promoting inhibitors, induces the incorporation of BRAFWT into large, active complexes, whereas pharmacological inhibition of BRAFV600E has the opposite effect. Thus, the quaternary structure of BRAF complexes is shaped by its activation status, the conformation of its kinase domain, and clinically relevant inhibitors. ![][1] The serine/threonine kinase BRAF plays a key role in development and homeostasis and represents the most frequently mutated kinase in tumors. We characterize two macromolecular, cytosolic BRAF complexes of distinct molecular composition and phosphorylation status. [1]: /embed/graphic-1.gif

Supramolecular signaling assemblies are of interest for their unique signaling properties. A µm scale signaling assembly, the central supramolecular signaling cluster (cSMAC), forms at the center of the interface of T cells activated by antigen-presenting cells. We have determined that it is composed of multiple complexes of a supramolecular volume of up to 0.5 µm3 and associated with extensive membrane undulations. To determine cSMAC function, we have systematically manipulated the localization of three adaptor proteins, LAT, SLP-76, and Grb2. cSMAC localization varied between the adaptors and was diminished upon blockade of the costimulatory receptor CD28 and deficiency of the signal amplifying kinase Itk. Reconstitution of cSMAC localization restored IL-2 secretion which is a key T cell effector function as dependent on reconstitution dynamics. Our data suggest that the cSMAC enhances early signaling by facilitating signaling interactions and attenuates signaling thereafter through sequestration of a more limited set of signaling intermediates., eLife digest Cells receive dozens of signals at different times and in different places. Integrating incoming information and deciding how to respond is no easy task. Signaling molecules on the cell surface pass messages inwards using chemical messengers that interact in complicated networks within the cell. One way to unravel the complexity of these networks is to look at specific groups of signaling molecules in test tubes to see how they interact. But the interior of a living cell is a very different environment. Molecules inside cells are tightly packed and, under certain conditions, they interact with each other by the thousands. They form structures known as ‘supramolecular complexes’, which changes their behavior. One such supramolecular complex is the ‘central supramolecular activation cluster’, or cSMAC for short. It forms under the surface of immune cells called T cells when they are getting ready to fight an infection. Under the microscope, the cSMAC looks like the bullseye of a dartboard, forming a crowd of signaling molecules at the center of the interface between the T cell and another cell. Its exact role is not clear, but evidence suggests it helps to start and stop the signals that switch T cells on. The cSMAC contains two key protein adaptors called LAT and SLP-76 that help to hold the structure together. So, to find out what the cSMAC does, Clark et al. genetically modified these adaptors to gain control over when the cSMAC forms. Clark et al. examined mouse T cells using super-resolution microscopy and electron microscopy, watching as other immune cells delivered the signal to switch on. As the T cells started to activate, the composition of the cSMAC changed. In the first two minutes after the cells started activating, the cSMAC included a large number of different components. This made T cell activation more efficient, possibly because the supramolecular complex was helping the network of signals to interact. Later, the cSMAC started to lose many of these components. Separating components may have helped to stop the activation signals. Understanding how T cells activate could lead to the possibility of turning them on or off in immune-related diseases. But these findings are not just relevant to immune cells. Other cells also use supramolecular complexes to control their signaling. Investigating how these complexes change over time could help us to understand how other cell types make decisions.

Protein complexes play a significant role in the core functionality of cells. These complexes are typically identified by detecting densely connected subgraphs in protein-protein interaction (PPI) networks. Recently, multiple large-scale mass spectrometry-based experiments have significantly increased the availability of PPI data in order to further expand the set of known complexes. However, high-throughput experimental data generally are incomplete, show limited agreement between experiments, and show frequent false positive interactions. There is a need for computational approaches that can address these limitations in order to improve the coverage and accuracy of human protein complexes. Here, we present a new method that integrates data from multiple heterogeneous experiments and sources in order to increase the reliability and coverage of predicted protein complexes. We first fused the heterogeneous data into a feature matrix and trained classifiers to score pairwise protein interactions. We next used graph based methods to combine pairwise interactions into predicted protein complexes. Our approach improves the accuracy and coverage of protein pairwise interactions, accurately identifies known complexes, and suggests both novel additions to known complexes and entirely new complexes. Our results suggest that integration of heterogeneous experimental data helps improve the reliability and coverage of diverse high-throughput mass-spectrometry experiments, leading to an improved global map of human protein complexes.

This chapter describes the procedures necessary to create generative models of the spatial organization of cells directly from microscope images and use them to automatically provide geometries for spatial simulations of cell processes and behaviors. Such models capture the statistical variation in the overall cell architecture as well as the number, shape, size, and spatial distribution of organelles and other structures. The different steps described include preparing images, learning models, evaluating model quality, creating sampled cell geometries by various methods, and combining those geometries with biochemical model specifications to enable simulations.

PURPOSE OF REVIEW: Spinal deformity is a common issue in pediatric patients with an underlying neurological diagnosis or syndrome. Management of neuromuscular scoliosis (NMS) is a major part of the orthopedic care of such patients, as the deformity is often progressive, and may affect gait, seating and positioning. In addition, untreated large spinal deformities may be associated with pain and/or cardiopulmonary issues over time. RECENT FINDINGS: Recent changes in medical management of the underlying disease process appears to alter the natural history of certain neuromuscular conditions, and in the case of patients with Duchenne’s muscular dystrophy significantly diminish the incidence of spinal deformity. In the most common diagnosis associated with NMS, cerebral palsy, there is evidence that despite a high complication rate, surgical management of spinal deformity is associated with measurable improvements in validated health-related quality-of-life measures. SUMMARY: Spinal deformity is a common finding in patients with neurological diagnoses. It is important for those involved in the care of these patients to understand the natural history of NMS, as well as the potential risks and benefits to the patient and caregivers, of surgical and non-surgical interventions.

Cellular differentiation is a complex process requiring the coordination of many cellular components. PC12 cells are a popular model system to study changes driving and accompanying neuronal differentiation. While significant attention has been paid to changes in transcriptional regulation and protein signaling, much less is known about the changes in cell organization that accompany PC12 differentiation. Fluorescence microscopy can provide extensive information about this, although photobleaching and phototoxicity frequently limit the ability to continuously observe changes in single cells over the many days that differentiation occurs. Here we describe a generative model of differentiation-associated changes in cell and nuclear shape and their relationship to mitochondrial distribution constructed from images of different cells at discrete time points. We show that our spherical harmonic-based model can accurately represent cell and nuclear shapes by measuring reconstruction errors. We then learn a regression model that relates cell and nuclear shape and mitochondrial distribution and observe that the predictive accuracy generally increases during differentiation. Most importantly, we propose a method, based on cell matching and linear interpolation in the shape space, to model the dynamics of cell differentiation using only static images. Without any prior knowledge, the method produces a realistic shape evolution process.Author SummaryCellular differentiation is an important process that is challenging to study due to the number of organizational changes it includes and the different time scales over which it occurs. Fluorescent microscopy is widely used to study cell dynamics and differentiation, but photobleaching and phototoxicity often make it infeasible to continuously observe a single cell undergoing differentiation for several days. In this work, we described a method to model aspects of the dynamics of PC12 cell differentiation without continuous imaging. We constructed accurate representations of cell and nuclear shapes and quantified the relationships between shapes and mitochondrial distributions. We used these to construct a generative model and combined it with a matching process to infer likely sequences of the changes in single cells undergoing differentiation.

Complications following spine fusion for adolescent idiopathic scoliosis can be characterized as either intra-operative or post-operative. The most serious and feared complication is neurologic injury, both in the intra- and post-operative period. Other intra-operative complications include dural tears and ophthalmologic or peripheral nerve deficits, which may be related to positioning. Among the most common post-operative complications are surgical site infection, venous thromboembolism, gastrointestinal complications, and implant-related complications. Significant blood loss requiring transfusion, traditionally considered a known sequelae of spine fusion, is now being recognized as a “complication” in large national databases. Pediatric spine surgeons who care for patients with AIS must be thoroughly familiar with all potential complications and their management.

Accurate representations of cellular organization for multiple eukaryotic cell types are required for creating predictive models of dynamic cellular function. To this end, we have previously developed the CellOrganizer platform, an open source system for generative modeling of cellular components from microscopy images. CellOrganizer models capture the inherent heterogeneity in the spatial distribution, size, and quantity of different components among a cell population. Furthermore, CellOrganizer can generate quantitatively realistic synthetic images that reflect the underlying cell population. A current focus of the project is to model the complex, interdependent nature of organelle localization. We built upon previous work on developing multiple non-parametric models of organelles or structures that show punctate patterns. The previous models described the relationships between the subcellular localization of puncta and the positions of cell and nuclear membranes and microtubules. We extend these models to consider the relationship to the endoplasmic reticulum (ER), and to consider the relationship between the positions of different puncta of the same type. Our results do not suggest that the punctate patterns we examined are dependent on ER position or inter- and intra-class proximity. With these results, we built classifiers to update previous assignments of proteins to one of 11 patterns in three distinct cell lines. Our generative models demonstrate the ability to construct statistically accurate representations of puncta localization from simple cellular markers in distinct cell types, capturing the complex phenomena of cellular structure interaction with little human input. This protocol represents a novel approach to vesicular protein annotation, a field that is often neglected in high-throughput microscopy. These results suggest that spatial point process models provide useful insight with respect to the spatial dependence between cellular structures. © 2016 International Society for Advancement of Cytometry.

The use of fluorescence microscopy has undergone a major revolution over the past twenty years, both with the development of dramatic new technologies and with the widespread adoption of image analysis and machine learning methods. Many open source software tools provide the ability to use these methods in a wide range of studies, and many molecular and cellular phenotypes can now be automatically distinguished. This article presents the next major challenge in microscopy automation, the creation of accurate models of cell organization directly from images, and reviews the progress that has been made towards this challenge.

Motivation Cell shape provides both geometry for, and a reflection of, cell function. Numerous methods for describing and modeling cell shape have been described, but previous evaluation of these methods in terms of the accuracy of generative models has been limited. Results Here we compare traditional methods and deep autoencoders to build generative models for cell shapes in terms of the accuracy with which shapes can be reconstructed from models. We evaluated the methods on different collections of 2D and 3D cell images, and found that none of the methods gave accurate reconstructions using low dimensional encodings. As expected, much higher accuracies were observed using high dimensional encodings, with outline-based methods significantly outperforming image-based autoencoders. The latter tended to encode all cells as having smooth shapes, even for high dimensions. For complex 3D cell shapes, we developed a significant improvement of a method based on the spherical harmonic transform that performs significantly better than other methods. We obtained similar results for the joint modeling of cell and nuclear shape. Finally, we evaluated the modeling of shape dynamics by interpolation in the shape space. We found that our modified method provided lower deformation energies along linear interpolation paths than other methods. This allows practical shape evolution in high dimensional shape spaces. We conclude that our improved spherical harmonic based methods are preferable for cell and nuclear shape modeling, providing better representations, higher computational efficiency and requiring fewer training images than deep learning methods. Availability and implementation All software and data is available at http://murphylab.cbd.cmu.edu/software. Supplementary information Supplementary data are available at Bioinformatics online.

Supramolecular signaling assemblies are of interest for their unique signaling properties. A µm scale signaling assembly, the central supramolecular signaling cluster (cSMAC), forms at the center of the interface of T cells activated by antigen presenting cells. We have determined that it is composed of multiple complexes of a supramolecular volume of up to 0.5µm3 and associated with extensive membrane undulations. To determine cSMAC function, we have systematically manipulated the localization of three adaptor proteins, LAT, SLP-76, and Grb2. cSMAC localization varied between the adaptors and was diminished upon blockade of the costimulatory receptor CD28 and deficiency of the signal amplifying kinase Itk. Reconstitution of cSMAC localization restored IL-2 secretion which is a key T cell effector function as dependent on reconstitution dynamics. Our data suggest that the cSMAC enhances early signaling by facilitating signaling interactions and attenuates signaling thereafter through sequestration of a more limited set of signaling intermediates.

Background Based on pre-clinical and clinical activity in adult refractory tumors, and absence of significant neuro-, nephro-, or oto-toxicity, we conducted a pediatric phase 1 trial to determine the toxicities, maximum tolerated dose (MTD), and pharmacokinetics of satraplatin, an oral platinum analogue, in children and young adults with refractory solid tumors. Procedure Satraplatin was administered orally once daily on days 1–5 of a 28-day cycle at dose level (DL) 1 (60 mg/m2/dose), and DL2 (80 mg/m2/dose). Toxicities, responses, satraplatin pharmacokinetics, and pharmacogenomic expression of specific DNA repair genes were evaluated. Results Nine patients received 1–15 cycles (median = 2). The MTD was exceeded at DL2 with delayed prolonged myelosuppression as dose-limiting toxicity (DLT) in 2/4 patients. At DL1, 0/5 patients had DLTs. Common non-DLTs included myelosuppression, gastrointestinal toxicities, fatigue, headache, liver enzyme elevation, and electrolyte abnormalities. No significant neuro-, nephro-, or oto-toxicity was observed. No objective responses were observed but 2 patients experienced prolonged disease stabilization (­­­6–15 cycles). Satraplatin exposure (day 1 plasma ultrafiltrate area under the curve) was similar at DL1 and DL2. A strong correlation between estimated creatinine clearance and satraplatin pharmacokinetic parameters (clearance, area under the curve, and peak concentration) was observed. Conclusions The MTD of oral satraplatin in children with solid tumors was 60 mg/m2/dose daily ×5 days every 28 days, which is lower than the adult recommended dose of 80–120 mg/m2/dose. The toxicity profile was similar to adults and delayed myelosuppression was the DLT. No significant neuro-, nephro- or oto-toxicities were observed. Pediatr Blood Cancer 2015;62:603–610. © 2015 Wiley Periodicals, Inc.

It is shown for the first time that cell shape can be accurately predicted from nuclear shape (and vice versa) for three different cell lines. This correlation is reduced by altering protein C1QBP or various drugs. In addition, a generative model is given for the kinetics of shape change. The software is available in the open-source CellOrganizer system., Modeling cell shape variation is critical to our understanding of cell biology. Previous work has demonstrated the utility of nonrigid image registration methods for the construction of nonparametric nuclear shape models in which pairwise deformation distances are measured between all shapes and are embedded into a low-dimensional shape space. Using these methods, we explore the relationship between cell shape and nuclear shape. We find that these are frequently dependent on each other and use this as the motivation for the development of combined cell and nuclear shape space models, extending nonparametric cell representations to multiple-component three-dimensional cellular shapes and identifying modes of joint shape variation. We learn a first-order dynamics model to predict cell and nuclear shapes, given shapes at a previous time point. We use this to determine the effects of endogenous protein tags or drugs on the shape dynamics of cell lines and show that tagged C1QBP reduces the correlation between cell and nuclear shape. To reduce the computational cost of learning these models, we demonstrate the ability to reconstruct shape spaces using a fraction of computed pairwise distances. The open-source tools provide a powerful basis for future studies of the molecular basis of cell organization.