Integrated intracellular organization and its variations in human iPS cells.

Understanding how a subset of expressed genes dictates cellular phenotype is a considerable challenge owing to the large numbers of molecules involved, their combinatorics and the plethora of cellular behaviours that they determine1,2. Here we reduced this complexity by focusing on cellular organization-a key readout and driver of cell behaviour3,4-at the level of major cellular structures that represent distinct organelles and functional machines, and generated the WTC-11 hiPSC Single-Cell Image Dataset v1, which contains more than 200,000 live cells in 3D, spanning 25 key cellular structures. The scale and quality of this dataset permitted the creation of a generalizable analysis framework to convert raw image data of cells and their structures into dimensionally reduced, quantitative measurements that can be interpreted by humans, and to facilitate data exploration. This framework embraces the vast cell-to-cell variability that is observed within a normal population, facilitates the integration of cell-by-cell structural data and allows quantitative analyses of distinct, separable aspects of organization within and across different cell populations. We found that the integrated intracellular organization of interphase cells was robust to the wide range of variation in cell shape in the population; that the average locations of some structures became polarized in cells at the edges of colonies while maintaining the 'wiring' of their interactions with other structures; and that, by contrast, changes in the location of structures during early mitotic reorganization were accompanied by changes in their wiring.

A deep generative model of 3D single-cell organization.

We introduce a framework for end-to-end integrative modeling of 3D single-cell multi-channel fluorescent image data of diverse subcellular structures. We employ stacked conditional ß-variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent representation of subcellular structure localization which is conditioned on the learned cell morphology. Our model is flexible and can be trained on images of arbitrary subcellular structures and at varying degrees of sparsity and reconstruction fidelity. We train our full model on 3D cell image data and explore design trade-offs in the 2D setting. Once trained, our model can be used to predict plausible locations of structures in cells where these structures were not imaged. The trained model can also be used to quantify the variation in the location of subcellular structures by generating plausible instantiations of each structure in arbitrary cell geometries. We apply our trained model to a small drug perturbation screen to demonstrate its applicability to new data. We show how the latent representations of drugged cells differ from unperturbed cells as expected by on-target effects of the drugs.

Advance Care Planning and Treatment Intensity Before Death Among Black, Hispanic, and White Patients Hospitalized with COVID-19.

BACKGROUND: Black and Hispanic people are more likely to contract COVID-19, require hospitalization, and die than White people due to differences in exposures, comorbidity risk, and healthcare access. OBJECTIVE: To examine the association of race and ethnicity with treatment decisions and intensity for patients hospitalized for COVID-19. DESIGN: Retrospective cohort analysis of manually abstracted electronic medical records. PATIENTS: 7,997 patients (62% non-Hispanic White, 16% non-Black Hispanic, and 23% Black) hospitalized for COVID-19 at 135 community hospitals between March and June 2020 MAIN MEASURES: Advance care planning (ACP), do not resuscitate (DNR) orders, intensive care unit (ICU) admission, mechanical ventilation (MV), and in-hospital mortality. Among decedents, we classified the mode of death based on treatment intensity and code status as treatment limitation (no MV/DNR), treatment withdrawal (MV/DNR), maximal life support (MV/no DNR), or other (no MV/no DNR). KEY RESULTS: Adjusted in-hospital mortality was similar between White (8%) and Black patients (9%, OR=1.1, 95% CI=0.9-1.4, p=0.254), and lower among Hispanic patients (6%, OR=0.7, 95% CI=0.6-1.0, p=0.032). Black and Hispanic patients were significantly more likely to be treated in the ICU (White 23%, Hispanic 27%, Black 28%) and to receive mechanical ventilation (White 12%, Hispanic 17%, Black 16%). The groups had similar rates of ACP (White 12%, Hispanic 12%, Black 11%), but Black and Hispanic patients were less likely to have a DNR order (White 13%, Hispanic 8%, Black 7%). Among decedents, there were significant differences in mode of death by race/ethnicity (treatment limitation: White 39%, Hispanic 17% (p=0.001), Black 18% (p<0.0001); treatment withdrawal: White 26%, Hispanic 43% (p=0.002), Black 28% (p=0.542); and maximal life support: White 21%, Hispanic 26% (p=0.308), Black 36% (p<0.0001)). CONCLUSIONS: Hospitalized Black and Hispanic COVID-19 patients received greater treatment intensity than White patients. This may have simultaneously mitigated disparities in in-hospital mortality while increasing burdensome treatment near death.

Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy.

Understanding cells as integrated systems is central to modern biology. Although fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, is slow, and can damage cells. We present a label-free method for predicting three-dimensional fluorescence directly from transmitted-light images and demonstrate that it can be used to generate multi-structure, integrated images. The method can also predict immunofluorescence (IF) from electron micrograph (EM) inputs, extending the potential applications.

A method for characterizing phenotypic changes in highly variable cell populations and its application to high content screening of Arabidopsis thaliana protoplasts.

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 Cramér-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.

Synergistic Effects in Bimetallic Palladium-Copper Catalysts Improve Selectivity in Oxygenate Coupling Reactions.

Condensation reactions such as Guerbet and aldol are important since they allow for C-C bond formation and give higher molecular weight oxygenates. An initial study identified Pd-supported on hydrotalcite as an active catalyst for the transformation, although this catalyst showed extensive undesirable decarbonylation. A catalyst containing Pd and Cu in a 3:1 ratio dramatically decreased decarbonylation, while preserving the high catalytic rates seen with Pd-based catalysts. A combination of XRD, EXAFS, TEM, and CO chemisorption and TPD revealed the formation of CuPd bimetallic nanoparticles with a Cu-enriched surface. Finally, density functional theory studies suggest that the surface segregation of Cu atoms in the bimetallic alloy catalyst produces Cu sites with increased reactivity, while the Pd sites responsible for unselective decarbonylation pathways are selectively poisoned by CO.

Point process models for localization and interdependence of punctate cellular structures.

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.

Automated Learning of Subcellular Variation among Punctate Protein Patterns and a Generative Model of Their Relation to Microtubules.

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.

Cell states beyond transcriptomics: Integrating structural organization and gene expression in hiPSC-derived cardiomyocytes.

Although some cell types may be defined anatomically or by physiological function, a rigorous definition of cell state remains elusive. Here, we develop a quantitative, imaging-based platform for the systematic and automated classification of subcellular organization in single cells. We use this platform to quantify subcellular organization and gene expression in >30,000 individual human induced pluripotent stem cell-derived cardiomyocytes, producing a publicly available dataset that describes the population distributions of local and global sarcomere organization, mRNA abundance, and correlations between these traits. While the mRNA abundance of some phenotypically important genes correlates with subcellular organization (e.g., the beta-myosin heavy chain, MYH7), these two cellular metrics are heterogeneous and often uncorrelated, which suggests that gene expression alone is not sufficient to classify cell states. Instead, we posit that cell state should be defined by observing full distributions of quantitative, multidimensional traits in single cells that also account for space, time, and function.

Applications and Challenges of Machine Learning to Enable Realistic Cellular Simulations.

In this perspective, we examine three key aspects of an end-to-end pipeline for realistic cellular simulations: reconstruction and segmentation of cellular structures; generation of cellular structures; and mesh generation, simulation, and data analysis. We highlight some of the relevant prior work in these distinct but overlapping areas, with a particular emphasis on current use of machine learning technologies, as well as on future opportunities.

Image-derived models of cell organization changes during differentiation and drug treatments.

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.

Chromosome 8q24 markers: risk of early-onset and familial prostate cancer.

Recent admixture mapping and linkage/association studies have implicated an approximately 1 Mb region on chromosome 8q24 in prostate cancer susceptibility. In a subsequent follow-up investigation, Haiman et al. (Nat Genet 2007;39:638-44) observed significant, independent associations between 7 markers within this region and sporadic prostate cancer risk in a multi-ethnic sample. To clarify the risk associated with hereditary prostate cancer, we tested for prostate cancer association with 6 of these 7 markers in a sample of 1,015 non-Hispanic white men with and without prostate cancer from 403 familial and early-onset prostate cancer families. Single nucleotide polymorphisms (SNPs) rs6983561 and rs6983267 showed the strongest evidence of prostate cancer association. Using a family-based association test, the minor ("C") allele of rs6983561 and the major ("G") allele of rs6983267 were preferentially transmitted to affected men (p < 0.05), with estimated odds ratios (ORs) of 2.26 (95% confidence interval of 1.06-4.83) and 1.30 (95% confidence interval of 0.99-1.71), respectively, for an additive model. Notably, rs6983561 was significantly associated with prostate cancer among men diagnosed at an early (<50 years) but not later age (p = 0.03 versus p = 0.21). Similarly, the association with rs6983267 was (not) statistically significant among men with(out) clinically aggressive disease (p = 0.007 versus p = 0.34). Our results confirm the association of prostate cancer with several of the SNPs on chromosome 8q24 initially reported by Haiman et al. In addition, our results suggest that the increased risk associated with these SNPs is approximately doubled in individuals predisposed to develop early onset or clinically aggressive disease.

Common variation in the BRCA1 gene and prostate cancer risk.

Rare inactivating mutations in the BRCA1 gene seem to play a limited role in prostate cancer. To our knowledge, however, no study has comprehensively assessed the role of other BRCA1 sequence variations (e.g., missense mutations) in prostate cancer. In a study of 817 men with and without prostate cancer from 323 familial and early-onset prostate cancer families, we used family-based association tests and conditional logistic regression to investigate the association between prostate cancer and single nucleotide polymorphisms (SNPs) tagging common haplotype variation in a 200-kb region surrounding (and including) the BRCA1 gene. We also used the Genotype-Identity-by-Descent Sharing Test to determine whether our most strongly associated SNP could account for prostate cancer linkage to chromosome 17q21 in a sample of 154 families from our previous genome-wide linkage study. The strongest evidence for prostate cancer association was for a glutamine-to-arginine substitution at codon 356 (Gln(356)Arg) in exon 11 of the BRCA1 gene. The minor (Arg) allele was preferentially transmitted to affected men (P = 0.005 for a dominant model), with an estimated odds ratio of 2.25 (95% confidence interval, 1.21-4.20). Notably, BRCA1 Gln(356)Arg is not in strong linkage disequilibrium with other BRCA1 coding SNPs or any known HapMap SNP on chromosome 17. In addition, Genotype-Identity-by-Descent Sharing Test results suggest that Gln(356)Arg accounts (in part) for our prior evidence of prostate cancer linkage to chromosome 17q21 (P = 0.022). Thus, we have identified a common, nonsynonymous substitution in the BRCA1 gene that is associated with and linked to prostate cancer.

Degradation of protein translation machinery by amino acid starvation-induced macroautophagy.

Macroautophagy is regarded as a nonspecific bulk degradation process of cytoplasmic material within the lysosome. However, the process has mainly been studied by nonspecific bulk degradation assays using radiolabeling. In the present study we monitor protein turnover and degradation by global, unbiased approaches relying on quantitative mass spectrometry-based proteomics. Macroautophagy is induced by rapamycin treatment, and by amino acid and glucose starvation in differentially, metabolically labeled cells. Protein dynamics are linked to image-based models of autophagosome turnover. Depending on the inducing stimulus, protein as well as organelle turnover differ. Amino acid starvation-induced macroautophagy leads to selective degradation of proteins important for protein translation. Thus, protein dynamics reflect cellular conditions in the respective treatment indicating stimulus-specific pathways in stress-induced macroautophagy.

Ga[OSi(O(t)Bu)3]3·THF, a thermolytic molecular precursor for high surface area gallium-containing silica materials of controlled dispersion and stoichiometry.

The molecular precursor tris[(tri-tert-butoxy)siloxy]gallium, as the tetrahydrofuran adduct Ga[OSi(O(t)Bu)3]3·THF (), was synthesized via the salt metathesis reaction of gallium trichloride with NaOSi(O(t)Bu)3. This complex serves as a model for isolated gallium in a silica framework. Complex decomposes thermally in hydrocarbon solvent, eliminating isobutylene, water, and tert-butanol to generate high surface area gallium-containing silica at low temperatures. When thermal decomposition was performed in the presence of P-123 Pluronic as a templating agent the generated material displayed uniform vermicular pores. Textural mesoporosity was evident in untemplated material. Co-thermolysis of with HOSi(O(t)Bu)3 in the presence of P-123 Pluronic led to materials with Ga : Si ratios ranging from 1 : 3 to 1 : 50, denoted UCB1-GaSi3, UCB1-GaSi10, UCB1-GaSi20 and UCB1-GaSi50. After calcination at 500 °C these materials exhibited decreasing surface areas and broadening pore distributions with increasing silicon content, indicating a loss of template effects. The position and dispersion of the gallium in UCB1-GaSi materials was investigated using (71)Ga MAS-NMR, powder XRD, and STEM/EDS elemental mapping. The results indicate a high degree of gallium dispersion in all samples, with gallium oxide clusters or oligomers present at higher gallium content.

Correction: Ga[OSi(OtBu)3]3·THF, a thermolytic molecular precursor for high surface area gallium-containing silica materials of controlled dispersion and stoichiometry.

Correction for 'Ga[OSi(OtBu)3]3·THF, a thermolytic molecular precursor for high surface area gallium-containing silica materials of controlled dispersion and stoichiometry' by James P. Dombrowski et al., Dalton Trans., 2016, 45, 11025-11034.

Joint modeling of cell and nuclear shape variation.

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.

Non-Oxidative Dehydrogenation Pathways for the Conversion of C2 -C4 Alcohols to Carbonyl Compounds.

Gold nanoparticles (NPs) supported on hydrotalcite (Au/HT) are highly active and selective catalysts for the continuous, gas-phase, non-oxidative dehydrogenation of bioderived C2 -C4 alcohols. A sharp increase in turn over frequency (TOF) is noted when the size of Au NPs is less than 5 nm relating to the strong synergy between metallic Au NPs and the acid-base groups on the support surface. It is shown that catalytic activity depends critically on Au NP size, support composition, and support pretreatments. A reaction pathway elucidated from kinetic isotope effects suggests that the abstraction of ß-H by Au NPs (C-H activation) is the rate-determining step in the dehydrogenation of bioderived C2 -C4 alcohols.