Systematic NMR Analysis of Stable Isotope Labeled Metabolite Mixtures in Plant and Animal Systems: Coarse Grained Views of Metabolic Pathways

BACKGROUND: Metabolic phenotyping has become an important 'bird's-eye-view' technology which can be applied to higher organisms, such as model plant and animal systems in the post-genomics and proteomics era. Although genotyping technology has expanded greatly over the past decade, metabolic phenotyping has languished due to the difficulty of 'top-down' chemical analyses. Here, we describe a systematic NMR methodology for stable isotope-labeling and analysis of metabolite mixtures in plant and animal systems. METHODOLOGY/PRINCIPAL FINDINGS: The analysis method includes a stable isotope labeling technique for use in living organisms; a systematic method for simultaneously identifying a large number of metabolites by using a newly developed HSQC-based metabolite chemical shift database combined with heteronuclear multidimensional NMR spectroscopy; Principal Components Analysis; and a visualization method using a coarse-grained overview of the metabolic system. The database contains more than 1000 (1)H and (13)C chemical shifts corresponding to 142 metabolites measured under identical physicochemical conditions. Using the stable isotope labeling technique in Arabidopsis T87 cultured cells and Bombyx mori, we systematically detected >450 HSQC peaks in each (13)C-HSQC spectrum derived from model plant, Arabidopsis T87 cultured cells and the invertebrate animal model Bombyx mori. Furthermore, for the first time, efficient (13)C labeling has allowed reliable signal assignment using analytical separation techniques such as 3D HCCH-COSY spectra in higher organism extracts. CONCLUSIONS/SIGNIFICANCE: Overall physiological changes could be detected and categorized in relation to a critical developmental phase change in B. mori by coarse-grained representations in which the organization of metabolic pathways related to a specific developmental phase was visualized on the basis of constituent changes of 56 identified metabolites. Based on the observed intensities of (13)C atoms of given metabolites on development-dependent changes in the 56 identified (13)C-HSQC signals, we have determined the changes in metabolic networks that are associated with energy and nitrogen metabolism

Robust Models for Optic Flow Coding in Natural Scenes Inspired by Insect Biology

The extraction of accurate self-motion information from the visual world is a difficult problem that has been solved very efficiently by biological organisms utilizing non-linear processing. Previous bio-inspired models for motion detection based on a correlation mechanism have been dogged by issues that arise from their sensitivity to undesired properties of the image, such as contrast, which vary widely between images. Here we present a model with multiple levels of non-linear dynamic adaptive components based directly on the known or suspected responses of neurons within the visual motion pathway of the fly brain. By testing the model under realistic high-dynamic range conditions we show that the addition of these elements makes the motion detection model robust across a large variety of images, velocities and accelerations. Furthermore the performance of the entire system is more than the incremental improvements offered by the individual components, indicating beneficial non-linear interactions between processing stages. The algorithms underlying the model can be implemented in either digital or analog hardware, including neuromorphic analog VLSI, but defy an analytical solution due to their dynamic non-linear operation. The successful application of this algorithm has applications in the development of miniature autonomous systems in defense and civilian roles, including robotics, miniature unmanned aerial vehicles and collision avoidance sensors

Advancing microbial sciences by individual-based modelling

Remarkable technological advances have revealed ever more properties and behaviours of individual microorganisms, but the novel data generated by these techniques have not yet been fully exploited. In this Opinion article, we explain how individual-based models (IBMs) can be constructed based on the findings of such techniques and how they help to explore competitive and cooperative microbial interactions. Furthermore, we describe how IBMs have provided insights into self-organized spatial patterns from biofilms to the oceans of the world, phage-CRISPR dynamics and other emergent phenomena. Finally, we discuss how combining individual-based observations with IBMs can advance our understanding at both the individual and population levels, leading to the new approach of microbial individual-based ecology (μIBE)

Dimensionality Reduction Reveals Distinct Shapes of Normal and Malignant Hematopoietic Cell Populations

Abstract Abstract 1451 Simultaneously examining multiple epitopes in single cells has become increasingly useful as improvements are made to multi-parametric flow techniques. Increased parameterization has enabled subdivision of functionally distinct cell populations based on an increasing variety of physiological attributes. It has not only helped better define the landscape of “normal” hematopoiesis, but also has been clinically applied in detection of minimal residual disease (MRD) in hematopoietic malignancies. Detection of rare “abnormal” cells is the crux of MRD-based risk stratification where sporadic residual cancer cells are identified through multi-parameter flow cytometry. Current methods for the detection and characterization of cellular populations are generally reliant on manual examination and targeted gating techniques. This approach relies almost entirely on prior knowledge and leaves little room for discovery of novel populations. As the number of parameter per cell increases there is a rising need for dimensionality reduction (DR) methods to resolve high dimensional data “down” into a human-interpretable space. Classical DR, such as principle component analysis (PCA), fail to address the non-linear relationships in cellular phenotypes while newer approaches, such as spanning-tree progression of density normalized events (SPADE), have an inherent level of stochasticity that might adversely affect the robust separation of cellular phenotypes (i.e. discriminating healthy vs. diseased cells). Here, we present a novel algorithm that identifies and characterizes distinct cell populations, preserving the high dimensional information, but providing an interpretable visualization of their phenotypic relationships. This approach was applied to a cohort of normal human bone marrow (BM) specimens to discern a landscape of normal hematopoietic phenotypes. We then contrasted this to overlays of human leukemic bone marrow aspirates (AML and ALL) to understand the extent to which cancer corrupts the shape and form of the landscape. We illustrate the application for automated MRD detection in human leukemia (Figure 1). Method: Our approach, CellSNE, is an adaptation of t-Distributed Stochastic Neighbor Embedding (t-SNE), a non-linear dimensionality reduction algorithm. CellSNE finds a low dimensional mapping of cells that preserves their pairwise distances in a high dimensional space. A distance between each cell to every other cell in the dataset is calculated, based on a vector defined by the combined values of cellular parameters measure. An optimization algorithm then searches for a projection of the points into 2D, in such a way that maximizes the similarity in pairwise distances between the high-dimensional and two dimensional spaces. The resulting 2D projection organizes the sample into subpopulations that conserve the shape and relative distances between each cell. Results/Conclusion: Application of CellSNE to healthy BM clearly separated cells based on their known immune subtype and was confirmed by manual analysis (Figure 1A). The results are robust across data collected from different individuals on different days as well as in analyses conducted using low numbers of single cell parameters, suggesting that healthy BM generally maintains the same cellular population characteristics (or “shape”) across samples. When applied to leukemic BM from patients with AML and ALL CellSNE demonstrates a unique cancer landscape (“shape”) for each patient that is dramatically different from normal (Figure 1B). It is critical to note that despite the overwhelming infiltration by cancer cells, rare “normal” cell populations can still be discerned in the ALL BM. CellSNE succeeded in automatically identifying rare (&lt;1%) abnormal ALL cells (tracked using a CellSNE independent parameter) in an otherwise normal BM (Figure 1C). As such, CellSNE achieves in identifying and characterizing rare cellular populations that can be applied in both normal and malignant hematopoiesis. Thus, it provides opportunities for the automated analysis of both large cytometry datasets and clinical MRD detection. Disclosures: No relevant conflicts of interest to declare. </jats:sec

Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development

SummaryTissue regeneration is an orchestrated progression of cells from an immature state to a mature one, conventionally represented as distinctive cell subsets. A continuum of transitional cell states exists between these discrete stages. We combine the depth of single-cell mass cytometry and an algorithm developed to leverage this continuum by aligning single cells of a given lineage onto a unified trajectory that accurately predicts the developmental path de novo. Applied to human B cell lymphopoiesis, the algorithm (termed Wanderlust) constructed trajectories spanning from hematopoietic stem cells through to naive B cells. This trajectory revealed nascent fractions of B cell progenitors and aligned them with developmentally cued regulatory signaling including IL-7/STAT5 and cellular events such as immunoglobulin rearrangement, highlighting checkpoints across which regulatory signals are rewired paralleling changes in cellular state. This study provides a comprehensive analysis of human B lymphopoiesis, laying a foundation to apply this approach to other tissues and “corrupted” developmental processes including cancer

viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia

High-dimensional single-cell technologies are revolutionizing the way we understand biological systems. Technologies such as mass cytometry measure dozens of parameters simultaneously in individual cells, making interpretation daunting. We developed viSNE, a tool to map high-dimensional cytometry data onto 2D while conserving high-dimensional structure. We integrated mass cytometry with viSNE to map healthy and cancerous bone marrow samples. Healthy bone marrow maps into a canonical shape that separates between immune subtypes. In leukemia, however, the shape is malformed: the maps of cancer samples are distinct from the healthy map and from each other. viSNE highlights structure in the heterogeneity of surface phenotype expression in cancer, traverses the progression from diagnosis to relapse, and identifies a rare leukemia population in minimal residual disease settings. As several new technologies raise the number of simultaneously measured parameters in each cell to the hundreds, viSNE will become a mainstay in analyzing and interpreting such experiments