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BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings. This resource describes a collection of neurons from a variety of light microscopy-based datasets, which can serve as a gold standard for testing automated tracing algorithms, as shown by comparison of the performance of 35 algorithms.

Table ST1: Glossary of terms used in the paper Figure S1. Genetic modification and expansion of CAR+ T cells. Figure S2. In vitro expansion of CAR+ T cells. Figure S3. High-throughput cytotoxicity assay for monitoring T-cell target cell interactions in nanowell grids. Figure S4. CD19 expression on NALM-6 tumor cells or CD19+EL4 target cells as determined by immunofluoresecent staining. Figure S5. Composite micrographs illustrating representative examples of the interactions between single CAR+ T cells (E) and one or more NALM-6 tumor (T) cells. Figure S6. Increased probability of killing by individual CAR+ T cells at higher target cell densities. Figure S7. Donut plots summarizing the outcomes of the interaction between individual CAR8 cells and 1-3 CD19+-NALM-6 tumor cells. Figure S8. Timelapse Imaging Microscopy In Nanowell Grids (TIMING). Figure S9. At an E:T ratio of 1:1, identification of subgroups of killer CAR8 cells based on their motility and contact behavior with tumor cells. Figure S10. At an E:T of 1:1, the total duration of conjugation prior to NALM-6 tumor cell killing is no different for the CAR8 cells in the different subgroups. Figure S11. At an E:T ratio of 1:1, CAR8 cells in the S1 subgroup, demonstrate: (A) drop in motility, and (B) increased circularization upon conjugation to NALM-6 tumor cell. Figure S12. At an E:T ratio of 1:1, CAR8 cells in the different subgroups demonstrate different frequencies and kinetics of AICD subsequent to the interactions with NALM-6 cells. Figure S13. At an E:T ratio of 1:2-5, multi-killer CAR8 cells demonstrate no significant differences in their duration of conjugation prior to killing multiple NALM-6 tumor cells. Figure S14. Despite the increased crowding because of higher tumor cell density, multi-killer CAR8 cells displayed greater motility when conjugated to tumor cell in comparison to single-killer CAR8 cells that encountered only a single tumor cell. Figure S15. At an E:T ratio of 1:1, identification of subgroups of killer CAR4 cells based on their motility and contact behavior with tumor cells. Figure S16. At an E:T of 1:1, the total duration of conjugation prior to NALM-6 tumor cell killing is significantly longer for CAR4 cells in S2 subgroup in comparison to subgroups S1 and S3. Figure S17. At an E:T of 1:1, CAR4 cells in S2 subgroup induce apoptosis in tumor cells with delayed kinetics in comparison to CAR8 cells in the S2 subgroup. Figure S18. At an E:T ratio of 1:2-5, multi-killer CAR4 cells demonstrate increased circularization upon contact with one or more NALM-6 tumor cells. Figure S19. The ability of individual CAR4 cells to simultaneously conjugate to multiple NALM-6 tumor cells increases as the number of tumor cells within the nanowell increases. Figure S20. Comparison of the killing efficiency of individual single killer CAR+ T cells (E:T 1:1) with multi-killer CAR+ T cells (E:T 1:2-5) that killed multiple NALM-6 tumor cells.

T cells genetically modified to express a CD19-specific chimeric antigen receptor (CAR) for the investigational treatment of B-cell malignancies comprise a heterogeneous population, and their ability to persist and participate in serial killing of tumor cells is a predictor of therapeutic success. We implemented Timelapse Imaging Microscopy in Nanowell Grids (TIMING) to provide direct evidence that CD4+CAR+ T cells (CAR4 cells) can engage in multikilling via simultaneous conjugation to multiple tumor cells. Comparisons of the CAR4 cells and CD8+CAR+ T cells (CAR8 cells) demonstrate that, although CAR4 cells can participate in killing and multikilling, they do so at slower rates, likely due to the lower granzyme B content. Significantly, in both sets of T cells, a minor subpopulation of individual T cells identified by their high motility demonstrated efficient killing of single tumor cells. A comparison of the multikiller and single-killer CAR+ T cells revealed that the propensity and kinetics of T-cell apoptosis were modulated by the number of functional conjugations. T cells underwent rapid apoptosis, and at higher frequencies, when conjugated to single tumor cells in isolation, and this effect was more pronounced on CAR8 cells. Our results suggest that the ability of CAR+ T cells to participate in multikilling should be evaluated in the context of their ability to resist activation-induced cell death. We anticipate that TIMING may be used to rapidly determine the potency of T-cell populations and may facilitate the design and manufacture of next-generation CAR+ T cells with improved efficacy. Cancer Immunol Res; 3(5); 473–82. ©2015 AACR.See related commentary by June, p. 470

Movie M4. Representative example of a CAR8 cell that kills multiple tumor cells.

Movie M1. Representative example of a CAR8 cell from the S1 subgroup.

Movie M2. Representative example of a CAR8 cell from the S2 subgroup.

Movie M3. Representative example of a CAR8 cell from the S3 subgroup.

Movie M5. Representative example of a CAR4 cell that kills multiple tumor cells.

Ligand inducible proteins that enable precise and reversible control of nuclear translocation of passenger proteins have broad applications ranging from genetic studies in mammals to therapeutics that target diseases such as cancer and diabetes. One of the drawbacks of the current translocation systems is that the ligands used to control nuclear localization are either toxic or prone to crosstalk with endogenous protein cascades within live animals. We sought to take advantage of salicylic acid (SA), a small molecule that has been extensively used in humans. In plants, SA functions as a hormone that can mediate immunity and is sensed by the non-expressor of pathogenesis-related (NPR) proteins. Although it is well recognized that nuclear translocation of NPR1 is essential to promoting immunity in plants, the exact subdomain of Arabidopsis thaliana NPR1 (AtNPR1) essential for SA mediated nuclear translocation is controversial. Here, we utilized the fluorescent protein mCherry as the reporter to investigate the ability of SA to induce nuclear translocation of the full-length NPR1 protein or its C-terminal transactivation (TAD) domain using HEK293 cells as a heterologous system. HEK293 cells lack accessory plant proteins including NPR3/NPR4 and are thus ideally suited for studying the impact of SA-induced changes in NPR1. Our results obtained using a stable expression system show that the TAD of AtNPR1 is sufficient to enable the reversible SA mediated nuclear translocation of mCherry. Our studies advance a basic understanding of nuclear translocation mediated by the TAD of AtNPR1 and uncover a biotechnological tool for SA mediated nuclear localization. This article is protected by copyright. All rights reserved.

Mapping biological processes in brain tissues requires piecing together numerous histological observations of multiple tissue samples. We present a direct method that generates readouts for a comprehensive panel of biomarkers from serial whole-brain slices, characterizing all major brain cell types, at scales ranging from subcellular compartments, individual cells, local multi-cellular niches, to whole-brain regions from each slice. We use iterative cycles of optimized 10-plex immunostaining with 10-color epifluorescence imaging to accumulate highly enriched image datasets from individual whole-brain slices, from which seamless signal-corrected mosaics are reconstructed. Specific fluorescent signals of interest are isolated computationally, rejecting autofluorescence, imaging noise, cross-channel bleed-through, and cross-labeling. Reliable large-scale cell detection and segmentation are achieved using deep neural networks. Cell phenotyping is performed by analyzing unique biomarker combinations over appropriate subcellular compartments. This approach can accelerate pre-clinical drug evaluation and system-level brain histology studies by simultaneously profiling multiple biological processes in their native anatomical context., It is challenging to map complex processes in brain tissue. Here the authors report a toolkit enabling large-scale multiplexed IHC and automated cell classification whereby they use a conventional epifluorescence microscope and deep neural networks to phenotype all major cell classes of the brain.

BigNeuron is an open community bench-testing platform combining the expertise of neuroscientists and computer scientists toward the goal of setting open standards for accurate and fast automatic neuron reconstruction. The project gathered a diverse set of image volumes across several species representative of the data obtained in most neuroscience laboratories interested in neuron reconstruction. Here we report generated gold standard manual annotations for a selected subset of the available imaging datasets and quantified reconstruction quality for 35 automatic reconstruction algorithms. Together with image quality features, the data were pooled in an interactive web application that allows users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and reconstruction data, and benchmarking of automatic reconstruction algorithms in user-defined data subsets. Our results show that image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. By benchmarking automatic reconstruction algorithms, we observed that diverse algorithms can provide complementary information toward obtaining accurate results and developed a novel algorithm to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms. Finally, to aid users in predicting the most accurate automatic reconstruction results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic reconstructions.

Background: Hippocampal place cells play an integral role in generating spatial maps. Impaired spatial memory is a characteristic pathology of Alzheimer’s disease (AD), yet it remains unclear how AD influences the properties of hippocampal place cells. Objective: To record electrophysiological activity in hippocampal CA1 neurons in freely-moving 18-month-old male TgF344-AD and age-matched wild-type (WT) littermates to examine place cell properties. Methods: We implanted 32-channel electrode arrays into the CA1 subfield of 18-month-old male WT and TgF344-AD (n = 6/group) rats. Ten days after implantation, single unit activity in an open field arena was recorded across days. The spatial information content, in-field firing rate, and stability of each place cell was compared across groups. Pathology was assessed by immunohistochemical staining, and a deep neural network approach was used to count cell profiles. Results: Aged TgF344-AD rats exhibited hippocampal amyloid-β deposition, and a significant increase in Iba1 immunoreactivity and microglia cell counts. Place cells from WT and TgF344-AD rat showed equivalent spatial information, in-field firing rates, and place field stability when initially exposed to the arena. However, by day 3, the place cells in aged WT rats showed characteristic spatial tuning as evidenced by higher spatial information content, stability, and in-field firing rates, an effect not seen in TgF344-AD rats. Conclusion: These findings support the notion that altered electrophysiological properties of place cells may contribute to the learning and memory deficits observed in AD.

Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist's ability to extract information on diagnosis, prognosis, and therapy responsiveness from native or transplant kidney biopsies. Although AI can be used to analyze a wide variety of biopsy-related data, this review focuses on whole slide images traditionally used in nephropathology. AI applications in nephropathology have recently become available through several advancing technologies, including (i) widespread introduction of glass slide scanners, (ii) data servers in pathology departments worldwide, and (iii) through greatly improved computer hardware to enable AI training. In this review, we explain how AI can enhance the reproducibility of nephropathology results for certain parameters in the context of precision medicine using advanced architectures, such as convolutional neural networks, that are currently the state of the art in machine learning software for this task. Because AI applications in nephropathology are still in their infancy, we show the power and potential of AI applications mostly in the example of oncopathology. Moreover, we discuss the technological obstacles as well as the current stakeholder and regulatory concerns about developing AI applications in nephropathology from the perspective of nephropathologists and the wider nephrology community. We expect the gradual introduction of these technologies into routine diagnostics and research for selective tasks, suggesting that this technology will enhance the performance of nephropathologists rather than making them redundant.