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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.

Within influenza virus infected cells, viral genomic RNA are selectively packed into progeny virions, which predominantly contain a single copy of 8 viral RNA segments. Intersegmental RNA-RNA interactions are thought to mediate selective packaging of each viral ribonucleoprotein complex (vRNP). Clear evidence of a specific interaction network culminating in the full genomic set has yet to be identified. Using multi-color fluorescence in situ hybridization to visualize four vRNP segments within a single cell, we developed image-based models of vRNP-vRNP spatial dependence. These models were used to construct likely sequences of vRNP associations resulting in the full genomic set. Our results support the notion that selective packaging occurs during cytoplasmic transport and identifies the formation of multiple distinct vRNP sub-complexes that likely form as intermediate steps toward full genomic inclusion into a progeny virion. The methods employed demonstrate a statistically driven, model based approach applicable to other interaction and assembly problems.

Introduction: Guidelines regarding postsurgical dental care have been common in adult total joint arthroplasty patients despite limited supporting evidence in the literature. There is anecdotal evidence that similar precautions are implemented frequently in postoperative pediatric orthopaedic patients without any published evidence to guide clinicians. Methods: A survey of the Active Members of the Pediatric Orthopaedic Society of North America (POSNA) was performed to assess the use of postsurgical guidelines regarding dental care (procedures and prophylaxis) in pediatric patients. A 20-question survey regarding this subject was developed with the input of, and approved by, the Evidence-Based Practice Committee of POSNA, and a link to participate was distributed to the Active Membership. Results: One hundred seventy-three POSNA Members opened the link to the survey. One hundred sixty-three respondents self-identified as Active, for a response rate of 19.8%. One hundred fifty-seven (96.3%) reported direct involvement in surgical and postsurgical care of pediatric orthopaedic patients. Sixty-two (39.4%) indicated use of a postoperative protocol regarding dental interventions. Forty percent of those indicating use of guidelines were uncertain of the specifics of the protocol(s) used at their institution. Eighty-five percent of those utilizing protocols felt that the use of guidelines was a valuable topic for investigation. Conclusions: These results demonstrate that many of the active Members of POSNA utilize postsurgical guidelines for dental care, particularly for those patients who have undergone spinal surgery with permanent implants. This is despite the fact that there is limited evidence to support their use in adult total joint patients and no literature regarding use of such guidelines in pediatric orthopaedic patients. Key Concepts: (1) There is limited and contradictory evidence that postsurgical dental guidelines are indicated in adult orthopaedic patients. (2) There is no published evidence supporting postsurgical dental guidelines in pediatric orthopaedic patients. (3) Almost 40% of active Pediatric Orthopaedic Society of North America Members responding to a survey appear to utilize postsurgical dental care guidelines in pediatric patients, particularly after spinal procedures utilizing implants. Level of Evidence: Level V

A new generation of science curriculum materials is being developed and made widely available across K–12. These materials, along with their associated professional learning, are expected to play a pivotal role in transforming science instruction to meet the vision of A Framework for K–12 Science Education and the performance expectations of the Next Generation Science Standards. This study investigated one of the first widely available middle school curriculum programs designed for the NGSS, Amplify Science 6–8. The study team conducted a randomized experiment to examine the implementation and efficacy of the middle school curriculum program (materials plus teacher professional learning) for improving seventh grade students’ learning in physical science. Findings show that students in schools where the program was implemented significantly outperformed peers in the comparison condition on an NGSS-focused physical science assessment. These findings underscore the importance of conducting additional research on NGSS-designed curriculum programs across other grade levels and within different science domains.

The long-term goal of connecting scales in biological simulation can be facilitated by scale-agnostic methods. We demonstrate that the weighted ensemble (WE) strategy, initially developed for molecular simulations, applies effectively to spatially resolved cell-scale simulations. The WE approach runs an ensemble of parallel trajectories with assigned weights and uses a statistical resampling strategy of replicating and pruning trajectories to focus computational effort on difficult-to-sample regions. The method can also generate unbiased estimates of non-equilibrium and equilibrium observables, sometimes with significantly less aggregate computing time than would be possible using standard parallelization. Here, we use WE to orchestrate particle-based kinetic Monte Carlo simulations, which include spatial geometry (e.g., of organelles, plasma membrane) and biochemical interactions among mobile molecular species. We study a series of models exhibiting spatial, temporal and biochemical complexity and show that although WE has important limitations, it can achieve performance significantly exceeding standard parallel simulation--by orders of magnitude for some observables.

High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance.

Characterizing the spatial distribution of proteins directly from microscopy images is a difficult problem with numerous applications in cell biology (e.g. identifying motor-related proteins) and clinical research (e.g. identification of cancer biomarkers). Here we describe the design of a system that provides automated analysis of punctate protein patterns in microscope images, including quantification of their relationships to microtubules. We constructed the system using confocal immunofluorescence microscopy images from the Human Protein Atlas project for 11 punctate proteins in three cultured cell lines. These proteins have previously been characterized as being primarily located in punctate structures, but their images had all been annotated by visual examination as being simply "vesicular". We were able to show that these patterns could be distinguished from each other with high accuracy, and we were able to assign to one of these subclasses hundreds of proteins whose subcellular localization had not previously been well defined. In addition to providing these novel annotations, we built a generative approach to modeling of punctate distributions that captures the essential characteristics of the distinct patterns. Such models are expected to be valuable for representing and summarizing each pattern and for constructing systems biology simulations of cell behaviors.

Detection of neuronal cell differentiation is essential to study cell fate decisions under various stimuli and/or environmental conditions. Many tools exist that quantify differentiation by neurite length measurements of single cells. However, quantification of differentiation in whole cell populations remains elusive so far. Because such populations can consist of both proliferating and differentiating cells, the task to assess the overall differentiation status is not trivial and requires a high-throughput, fully automated approach to analyze sufficient data for a statistically significant discrimination to determine cell differentiation. We address the problem of detecting differentiation in a mixed population of proliferating and differentiating cells over time by supervised classification. Using nerve growth factor induced differentiation of PC12 cells, we monitor the changes in cell morphology over 6 days by phase-contrast live-cell imaging. For general applicability, the classification procedure starts out with many features to identify those that maximize discrimination of differentiated and undifferentiated cells and to eliminate features sensitive to systematic measurement artifacts. The resulting image analysis determines the optimal post treatment day for training and achieves a near perfect classification of differentiation, which we confirmed in technically and biologically independent as well as differently designed experiments. Our approach allows to monitor neuronal cell populations repeatedly over days without any interference. It requires only an initial calibration and training step and is thereafter capable to discriminate further experiments. In conclusion, this enables long-term, large-scale studies of cell populations with minimized costs and efforts for detecting effects of external manipulation of neuronal cell differentiation.

High throughput and high content screening involve determination of the effect of many compounds on a given target. As currently practiced, screening for each new target typically makes little use of information from screens of prior targets. Further, choices of compounds to advance to drug development are made without significant screening against off-target effects. The overall drug development process could be made more effective, as well as less expensive and time consuming, if potential effects of all compounds on all possible targets could be considered, yet the cost of such full experimentation would be prohibitive. In this paper, we describe a potential solution: probabilistic models that can be used to predict results for unmeasured combinations, and active learning algorithms for efficiently selecting which experiments to perform in order to build those models and determining when to stop. Using simulated and experimental data, we show that our approaches can produce powerful predictive models without exhaustive experimentation and can learn them much faster than by selecting experiments at random.

Microtubules are filamentous structures that are involved in several important cellular processes, including cell division, cellular structure and mechanics, and intracellular transportation. Little is known about potential differences in microtubule distributions within and across cell lines. Here we describe a method to estimate information pertaining to 3D microtubule distributions from 2D fluorescence images. Our method allows for quantitative comparisons of microtubule distribution parameters (number of microtubules, mean length) between different cell lines. Among eleven cell lines compared, some showed differences that could be accounted for by differences in the total amount of tubulin per cell while others showed statistically significant differences in the balance between number and length of microtubules. We also observed that some cell lines that visually appear different in their microtubule distributions are quite similar when the model parameters are considered. The method is expected to be generally useful for comparing microtubule distributions between cell lines and for a given cell line after various perturbations. The results are also expected to enable analysis of the differences in gene expression underlying the observed differences in microtubule distributions among cell types.

The Human Protein Atlas contains immunofluorescence images showing subcellular locations for thousands of proteins. These are currently annotated by visual inspection. In this paper, we describe automated approaches to analyze the images and their use to improve annotation. We began by training classifiers to recognize the annotated patterns. By ranking proteins according to the confidence of the classifier, we generated a list of proteins that were strong candidates for reexamination. In parallel, we applied hierarchical clustering to group proteins and identified proteins whose annotations were inconsistent with the remainder of the proteins in their cluster. These proteins were reexamined by the original annotators, and a significant fraction had their annotations changed. The results demonstrate that automated approaches can provide an important complement to visual annotation.

T cells can express multiple inhibitory receptors. Upon induction of T cell exhaustion in response to a persistent antigen, prominently in the anti-tumor immune response, many are expressed simultaneously. Key inhibitory receptors are CTLA-4, PD-1, LAG3, TIM3, and TIGIT, as investigated here. These receptors are important as central therapeutic targets in cancer immunotherapy. Inhibitory receptors are not constitutively expressed on the cell surface, but substantial fractions reside in intracellular vesicular structures. It remains unresolved to which extent the subcellular localization of different inhibitory receptors is distinct. Using quantitative imaging of subcellular distributions and plasma membrane insertion as complemented by proximity proteomics and biochemical analysis of the association of the inhibitory receptors with trafficking adaptors, the subcellular distributions of the five inhibitory receptors were discrete. The distribution of CTLA-4 was most distinct, with preferential association with lysosomal-derived vesicles and the sorting nexin 1/2/5/6 transport machinery. With a lack of evidence for the existence of specific vesicle subtypes to explain divergent inhibitory receptor distributions, we suggest that such distributions are driven by divergent trafficking through an overlapping joint set of vesicular structures. This extensive characterization of the subcellular localization of five inhibitory receptors in relation to each other lays the foundation for the molecular investigation of their trafficking and its therapeutic exploitation.