Recent advances in single-cell RNA sequencing technologies enable deep insights into cellular development, gene regulation, and phenotypic diversity by measuring gene expression for thousands of ...cells in a single experiment. While these technologies hold great potential for improving our understanding of cellular states and progression, they also pose new challenges and require advanced mathematical and algorithmic tools to extract underlying biological signals. In this review, we cover one of the most promising avenues of research into unlocking the potential of scRNA-seq data: the field of manifold learning, and the related manifold assumption in data analysis. Manifold learning provides a powerful structure for algorithmic approaches to process the data, extract its dynamics, and infer patterns in it. In particular, we cover manifold learning-based methods for denoising the data, revealing gene interactions, extracting pseudotime progressions with model fitting, visualizing the cellular state space via dimensionality reduction, and clustering the data.
Various riboswitch classes are being discovered that precisely monitor the status of important biological processes, including metabolic pathway function, signaling for physiological adaptations, and ...responses to toxic agents. Biochemical components for some of these processes might make excellent targets for the development of novel antibacterial molecules, which can be broadly sought by using phenotypic drug discovery (PDD) methods. However, PDD data do not normally provide clues regarding the target for each hit compound. We have developed and validated a robust fluorescent reporter system based on a ZTP riboswitch that identifies numerous folate biosynthesis inhibitors with high sensitivity and precision. The utility of the riboswitch-based PDD strategy was evaluated using Escherichia coli bacteria by conducting a 128 310-compound high-throughput screen, which identified 78 sulfanilamide derivatives among the many initial hits. Similarly, representatives of other riboswitch classes could be employed to rapidly match antibacterial hits with the biological processes they target.
We developed a hyperspectral imaging tool based on surface-enhanced Raman spectroscopy (SERS) probes to determine the expression level and visualize the distribution of PD-L1 in individual cells. ...Electron-microscopic analysis of PD-L1 antibody - gold nanorod conjugates demonstrated binding the cell surface and internalization into endosomal vesicles. Stimulation of cells with IFN-γ or metformin was used to confirm the ability of SERS probes to report treatment-induced changes. The multivariate curve resolution-alternating least squares (MCR-ALS) analysis of spectra provided a greater signal-noise ratio than single peak mapping. However, single peak mapping allowed a systematic subtraction of background and the removal of non-specific binding and endocytic SERS signals. The mean or maximum peak height in the cell or the mean peak height in the area of specific PD-L1 positive pixels was used to estimate the PD-L1 expression levels in single cells. The PD-L1 levels were significantly up-regulated by IFN-γ and inhibited by metformin in human lung cancer cells from the A549 cell line. In conclusion, the method of analyzing hyperspectral SERS imaging data together with systematic and comprehensive removal of non-specific signals allows SERS imaging to be a quantitative tool in the detection of the cancer biomarker, PD-L1.
To identify genetic factors associated with risk of stroke among survivors of childhood cancer treated with cranial radiotherapy (CRT).
We analyzed whole-genome sequencing (36.8-fold) data of 686 ...childhood cancer survivors of European ancestry median (range), 40.4 (12.4-64.7) years old; 54% male from the St. Jude Lifetime Cohort study treated with CRT, of whom 116 (17%) had clinically diagnosed stroke. Association analyses (single-variant and Burden/SKAT tests) were performed, adjusting for demographic characteristics and childhood cancer treatment exposures.
We identified a genome-wide significant association between 5p15.33 locus and stroke rs112896372: HR = 2.55;
= 1.42 × 10
, with a stronger association (HR = 3.68) among survivors treated with CRT dose 25-50 Gray (Gy) and weaker associations among those treated with CRT doses <20 or 20-25 or >50 Gy (HRs = 2.14, 2.40, and 2.28). The association was replicated in 90 CRT-exposed African survivors (HR = 3.05;
= 0.034). In CRT-exposed Europeans, rs112896372 significantly (
< 0.001) improved predictive ability (AUC = 0.717) for determining stroke risk than nongenetic factors alone (AUC = 0.663) at 30 years since diagnosis, with significant improvement among African survivors (
= 0.047). SNP rs112896372 was further evaluated in three independent datasets including 1,641 European (HR = 1.54;
= 0.055) and 316 African survivors (HR = 1.88;
= 0.283) not treated with CRT, and 166,988 males in the UK Biobank (OR = 1.0012;
= 0.042).
A novel locus 5p15.33 is associated with stroke risk among childhood cancer survivors, with a possible CRT dose-specific effect. The locus is of potential clinical utility in characterizing individuals who may benefit from surveillance and intervention strategies.
Bounding the best achievable error probability for binary classification problems is relevant to many applications including machine learning, signal processing, and information theory. Many bounds ...on the Bayes binary classification error rate depend on information divergences between the pair of class distributions. Recently, the Henze–Penrose (HP) divergence has been proposed for bounding classification error probability. We consider the problem of empirically estimating the HP-divergence from random samples. We derive a bound on the convergence rate for the Friedman–Rafsky (FR) estimator of the HP-divergence, which is related to a multivariate runs statistic for testing between two distributions. The FR estimator is derived from a multicolored Euclidean minimal spanning tree (MST) that spans the merged samples. We obtain a concentration inequality for the Friedman–Rafsky estimator of the Henze–Penrose divergence. We validate our results experimentally and illustrate their application to real datasets.
The densities of eutectic (LiF)2–BeF2 and mixtures of this salt (FLiBe) with LaF3 were measured by dilatometry and by neutron attenuation from 673 K to 1,073 K. Because LaF3 has a limited solubility ...in FLiBe, it was necessary to determine the amount of LaF3 in solution before the density could be determined. The FLiBe density determination was favorably benchmarked against the literature data. A simple comparison was not available for the LaF3–FLiBe mixtures, so extrapolation of published data was necessary based on analysis using the Molten Salt Thermal Properties Database-Thermochemistry, or MSTDB-TC, developed by the US Department of Energy. Solubilities for LaF3 in FLiBe ranged from 1 to 4 mol % over 673 to 1,073 K. The salt system was heated and cooled over 24 h to evaluate potential changes in composition and hysteresis during the measurement. Changes in the meniscus were observed, and these were included in the correction for density determinations. Salt surface tension may have led to supersaturation of LaF3 in the salt because the solubility curve was nonlinear with respect to the inverse temperature, as would be expected for an ideal system. Surface tension measurements are currently underway to test this hypothesis.
ATPase inhibitory factor 1 (IF1) is an ATP synthase‐interacting protein that suppresses the hydrolysis activity of ATP synthase. In this study, we observed that the expression of IF1 was up‐regulated ...in response to electrical pulse stimulation of skeletal muscle cells and in exercized mice and healthy men. IF1 stimulates glucose uptake via AMPK in skeletal muscle cells and primary cultured myoblasts. Reactive oxygen species and Rac family small GTPase 1 (Rac1) function in the upstream and downstream of AMPK, respectively, in IF1‐mediated glucose uptake. In diabetic animal models, the administration of recombinant IF1 improved glucose tolerance and down‐regulated blood glucose level. In addition, IF1 inhibits ATP hydrolysis by β‐F1‐ATPase in plasma membrane, thereby increasing extracellular ATP and activating the protein kinase B (Akt) pathway, ultimately leading to glucose uptake. Thus, we suggest that IF1 is a novel myokine and propose a mechanism by which AMPK and Akt contribute independently to IF1‐mediated improvement of glucose tolerance impairment. These results demonstrate the importance of IF1 as a potential antidiabetic agent.—Lee, H. J., Moon, J., Chung, I., Chung, J. H., Park, C., Lee, J. O., Han, J. A., Kang, M. J., Yoo, E. H., Kwak, S.‐Y., Jo, G., Park, W., Park, J., Kim, K. M., Lim, S., Ngoei, K. R. W., Ling, N. X. Y., Oakhill, J. S., Galic, S., Murray‐Segal, L., Kemp, B. E., Mantzoros, C. S., Krauss, R. M., Shin, M.‐J., Kim, H. S. ATP synthase inhibitory factor 1 (IF1), a novel myokine, regulates glucose metabolism by AMPK and Akt dual pathways. FASEB J. 33, 14825‐14840 (2019). www.fasebj.org
High frequency oscillations (HFOs) are a promising biomarker of epileptic brain tissue and activity. HFOs additionally serve as a prototypical example of challenges in the analysis of discrete events ...in high-temporal resolution, intracranial EEG data. Two primary challenges are (1) dimensionality reduction, and (2) assessing feasibility of classification. Dimensionality reduction assumes that the data lie on a manifold with dimension less than that of the features space. However, previous HFO analysis have assumed a linear manifold, global across time, space (i.e. recording electrode/channel), and individual patients. Instead, we assess both (a) whether linear methods are appropriate and (b) the consistency of the manifold across time, space, and patients. We also estimate bounds on the Bayes classification error to quantify the distinction between two classes of HFOs (those occurring during seizures and those occurring due to other processes). This analysis provides the foundation for future clinical use of HFO features and guides the analysis for other discrete events, such as individual action potentials or multi-unit activity.
The behavior of fission gases in molten fuel salt reactors governs activity transport from the reactor and can also affect the performance of the reactor itself. The gas solubility can be described ...thermodynamically by Henry’s law. However, the coupling of the condensed and gas phases depends on the interfacial area, which is difficult to measure or even to estimate. Surfaces of materials in the reactor will include disperse phases in the salt and porosity within the structural materials, covering a range of compositions and sizes. These attributes can affect measurements of fundamental properties such as gas solubility. Methods to obtain gas solubility, surface tension, interfacial energies, and bubble gas transport are reviewed. Recent data from manometric experiments are interpreted based on xenon sorption onto salt-wetted quartz.
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