Katherine Louie

OpenMSI: A High-Performance Web-Based Platform for Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) enables researchers to directly probe endogenous molecules directly within the architecture of the biological matrix. Unfortunately, efficient access, management, and analysis of the data generated by MSI approaches remain major challenges to this rapidly developing field. Despite the availability of numerous dedicated file formats and software packages, it is a widely held viewpoint that the biggest challenge is simply opening, sharing, and analyzing a file without loss of information. Here we present OpenMSI, a software framework and platform that addresses these challenges via an advanced, high-performance, extensible file format and Web API for remote data access (http://openmsi.nersc.gov). The OpenMSI file format supports storage of raw MSI data, metadata, and derived analyses in a single, self-describing format based on HDF5 and is supported by a large range of analysis software (e.g., Matlab and R) and programming languages (e.g., C++, Fortran, and Python). Careful optimization of the storage layout of MSI data sets using chunking, compression, and data replication accelerates common, selective data access operations while minimizing data storage requirements and are critical enablers of rapid data I/O. The OpenMSI file format has shown to provide >2000-fold improvement for image access operations, enabling spectrum and image retrieval in less than 0.3 s across the Internet even for 50 GB MSI data sets. To make remote high-performance compute resources accessible for analysis and to facilitate data sharing and collaboration, we describe an easy-to-use yet powerful Web API, enabling fast and convenient access to MSI data, metadata, and derived analysis results stored remotely to facilitate high-performance data analysis and enable implementation of Web based data sharing, visualization, and analysis.

Mass spectrometry imaging for in situ kinetic histochemistry

Tissues are composed of diverse cell subpopulations each with distinct metabolic characteristics that influence overall behavior. Unfortunately, traditional histopathology imaging techniques are ‘blind’ to the spatially ordered metabolic dynamics within tissue. While mass spectrometry imaging enables spatial mapping of molecular composition, resulting images are only a static snapshot in time of molecules involved in highly dynamic processes; kinetic information of flux through metabolic pathways is lacking. To address this limitation, we developed kinetic mass spectrometry imaging (kMSI), a novel technique integrating soft desorption/ionization mass spectrometry with clinically accepted in vivo metabolic labeling of tissue with deuterium to generate images of kinetic information of biological processes. Applied to a tumor, kMSI revealed heterogeneous spatial distributions of newly synthesized versus pre-existing lipids, with altered lipid synthesis patterns distinguishing region-specific intratumor subpopulations. Images also enabled identification and correlation of metabolic activity of specific lipids found in tumor regions of varying grade.

Acoustic deposition with NIMS as a high-throughput enzyme activity assay

Mass spectrometry (MS)-based enzyme assay has been shown to be a useful tool for screening enzymatic activities from environmental samples. Recently, reported approaches for high-specificity multiplexed characterization of enzymatic activities allow for providing detailed information on the range of enzymatic products and monitoring multiple enzymatic reactions. However, the throughput has been limited by the slow liquid–liquid handling and manual analysis. This rapid communication demonstrates the integration of acoustic sample deposition with nanostructure initiator mass spectrometry (NIMS) imaging to provide reproducible measurements of multiple enzymatic reactions at a throughput that is tenfold to 100-fold faster than conventional MS-based enzyme assay. It also provides a simple means for the visualization of multiple reactions and reaction pathways.

Lineage-specific chromatin signatures reveal a regulator of lipid metabolism in microalgae

Alga-derived lipids represent an attractive potential source of biofuels. However, lipid accumulation in algae is a stress response tightly coupled to growth arrest, thereby imposing a major limitation on productivity. To identify transcriptional regulators of lipid accumulation, we performed an integrative chromatin signature and transcriptomic analysis to decipher the regulation of lipid biosynthesis in the alga Chlamydomonas reinhardtii. Genome-wide histone modification profiling revealed remarkable differences in functional chromatin states between the algae and higher eukaryotes and uncovered regulatory components at the core of lipid accumulation pathways. We identified the transcription factor, PSR1, as a pivotal switch that triggers cytosolic lipid accumulation. Dissection of the PSR1-induced lipid profiles corroborates its role in coordinating multiple lipid-inducing stress responses. The comprehensive maps of functional chromatin signatures in a major clade of eukaryotic life and the discovery of a transcriptional regulator of algal lipid metabolism will facilitate targeted engineering strategies to mediate high lipid production in microalgae.

A robust gene-stacking method utilizing yeast assembly for plant synthetic biology

The advent and growth of synthetic biology has demonstrated its potential as a promising avenue of research to address many societal needs. However, plant synthetic biology efforts have been hampered by a dearth of DNA part libraries, versatile transformation vectors and efficient assembly strategies. Here, we describe a versatile system (named jStack) utilizing yeast homologous recombination to efficiently assemble DNA into plant transformation vectors. We demonstrate how this method can facilitate pathway engineering of molecules of pharmaceutical interest, production of potential biofuels and shuffling of disease-resistance traits between crop species. Our approach provides a powerful alternative to conventional strategies for stacking genes and traits to address many impending environmental and agricultural challenges.

Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly

Like all higher organisms, plants have evolved in the context of a microbial world, shaping both their evolution and their contemporary ecology. Interactions between plant roots and soil microorganisms are critical for plant fitness in natural environments. Given this co-evolution and the pivotal importance of plant–microbial interactions, it has been hypothesized, and a growing body of literature suggests, that plants may regulate the composition of their rhizosphere to promote the growth of microorganisms that improve plant fitness in a given ecosystem. Here, using a combination of comparative genomics and exometabolomics, we show that pre-programmed developmental processes in plants (Avena barbata) result in consistent patterns in the chemical composition of root exudates. This chemical succession in the rhizosphere interacts with microbial metabolite substrate preferences that are predictable from genome sequences. Specifically, we observed a preference by rhizosphere bacteria for consumption of aromatic organic acids exuded by plants (nicotinic, shikimic, salicylic, cinnamic and indole-3-acetic). The combination of these plant exudation traits and microbial substrate uptake traits interact to yield the patterns of microbial community assembly observed in the rhizosphere of an annual grass. This discovery provides a mechanistic underpinning for the process of rhizosphere microbial community assembly and provides an attractive direction for the manipulation of the rhizosphere microbiome for beneficial outcomes.Using comparative genomics and exometabolomics, the authors characterize the chemical composition of plant root exudates and show that this chemical succession is a likely driver of microbial community assembly in the rhizosphere.

Metabolic Imaging Using Nanostructure-Initiator Mass Spectrometry (NIMS)

Nanostructure-initiator mass spectrometry (NIMS) imaging using soft laser desorption/ionization has proven to be a powerful tool in localizing the spatial distribution of intact biomolecules. NIMS specifically has been demonstrated to have high sensitivity and low background, particularly in the low mass range <1,000 Da, making this technique well suited for metabolic imaging studies. Here, we describe NIMS imaging for direct analysis of metabolite composition across a sectioned biospecimen.

New insight into the role of MMP14 in metabolic balance

Membrane-anchored matrix metalloproteinase 14 (MMP14) is involved broadly in organ development through both its proteolytic and signal-transducing functions. Knockout of Mmp14 (KO) in mice results in a dramatic reduction of body size and wasting followed by premature death, the mechanism of which is poorly understood. Since the mammary gland develops after birth and is thus dependent for its functional progression on systemic and local cues, we chose it as an organ model for understanding why KO mice fail to thrive. A global analysis of the mammary glands’ proteome in the wild type (WT) and KO mice provided insight into an unexpected role of MMP14 in maintaining metabolism and homeostasis. We performed mass spectrometry and quantitative proteomics to determine the protein signatures of mammary glands from 7 to 11 days old WT and KO mice and found that KO rudiments had a significantly higher level of rate-limiting enzymes involved in catabolic pathways. Glycogen and lipid levels in KO rudiments were reduced, and the circulating levels of triglycerides and glucose were lower. Analysis of the ultrastructure of mammary glands imaged by electron microscopy revealed a significant increase in autophagy signatures in KO mice. Finally, Mmp14 silenced mammary epithelial cells displayed enhanced autophagy. Applied to a systemic level, these findings indicate that MMP14 is a crucial regulator of tissue homeostasis. If operative on a systemic level, these findings could explain how Mmp14KO litter fail to thrive due to disorder in metabolism.

MAGI: A Bayesian-like method for metabolite, annotation, and gene integration

Metabolomics is a widely used technology for obtaining direct measures of metabolic activities from diverse biological systems. However, it is limited by ambiguous metabolite identifications. Furthermore, interpretation is limited by incomplete and inaccurate genome-based predictions of enzyme activities (i.e. gene annotations). Metabolite, Annotation, and Gene Integration (MAGI) addresses these challenges by generating metabolite-gene associations via biochemical reactions based on a score between probable metabolite identifications and probable gene annotations. This is calculated by a Bayesian-like method and emphasizes consensus between metabolites and genes. We applied MAGI to sequence data and metabolomics data collected from Streptomyces coelicolor A3(2), an extensively characterized bacterium that produces diverse secondary metabolites. We found that coupling metabolomics and genomics data by scoring consensus between the two increases the quality of both metabolite identifications and gene annotations. Moreover, MAGI was found to make correct biochemical predictions for poorly annotated genes that were readily validated by literature searches. As metabolomics data become more widely available for sequenced organisms, this approach has the potential to improve our understanding of microbial metabolism while also providing testable hypotheses for specific biochemical functions. MAGI is freely available for academic use both as an online tool at https://magi.nersc.gov and with source code available at https://github.com/biorack/magi

Localizing metabolic synthesis in microbial cultures with kinetic mass spectrometry imaging (kMSI)

Mass spectrometry imaging (MSI) has emerged as a powerful technique enabling spatially defined imaging of metabolites within microbial biofilms. Here, we extend this approach to enable differentiation of newly synthesized versus pre-existing metabolites across a co-culture. This is accomplished by MS imaging two soil microbes, Shewanella oneidensis MR1 and Pseudomonas stutzeri RCH2, that were administered heavy water (D2O) during growth on agar plates. For two species-specific diglyceride (DG) lipids, isotopic analysis was performed on each spectra collected across the co-culture to determine the relative amount of newly synthesized versus pre-existing lipid. Here, highest levels of new synthesis of RCH2 lipid was localized to border regions adjacent to S. oneidensis MR1, while the MR1 lipid showed highest levels in regions further from RCH2. Interestingly, regions of high lipid abundance did not correspond to the regions with highest new lipid biosynthesis. Given the simplicity and generality of using D2O as a stable isotopic probe combined with the accessibility of kMSI to a range of MSI instrumentation, this approach has broad application for improving our understanding of how microbial interactions influence metabolite biosynthesis.