Chemical Science Facility Core [CSFC]
The CSFC specializes in analysis and characterization of toxicants and their responses.
About this Core
The CSFC provides TiCER scientists with access to and training in advanced analytical and characterization tools for quantifying agents of exposure, as well as for identifying biological markers established by the effects of exposure in experimental model systems. This ability to quantitatively measure exposure and the biological effects of exposure will allow investigators to develop cause-and-effect relationships and predictive models. The CSFC is comprised of three foundational components: analysis and quantification of potential environmental toxicants; various ‘omics and compositional analyses of biological samples; and metabolomic capabilities for identifying and quantifying metabolite signatures. In addition, the CSFC works in conjunction with the DSFC on an integrative component connecting microbiota composition, microbiota and tissue metabolite profiles, and transcriptomic data. Thus the CSFC provides TiCER scientists with unparalleled analytical capabilities to comprehensively characterize toxicants and their responses, spanning from host genomics to microbial metabolites.
The CSFC Components
The Chem Science facility core (CSFC) comprises three foundational components that provide Center investigators analytical abilities to characterize exposure and response to environmental and associated toxicants. Specifically, the CSFC integrates technical expertise, analytical capabilities, and instrumentation across the Texas A&M campus under a single umbrella. The three components that serve as the foundation for the CSFC are:
Quantification of Potential Environmental Toxicants Through GERG
The exposure assessment component of the CSFC provides a suite of analytical services to Center investigators for quantitative targeted and untargeted analyses of samples using a range of LC/MS/MS and GC/MS/MS instruments, expertise, and standardized methods. The instumentation available through the CSFC includes:
- Agilent Ion mobility GTOF LC/MS/MS GC/MS with autosamplers (6)
- Agilent GC/MS/MS (1)
- Shimadzu LC/MS (1)
- Agilent 6470 LC/MS/MS (1)
- Agilent Ion Mobility GTOF LC/MS (1)
- Agilent Rapidfire 365 high through MS system (1)
- GC FID (4)
- GC ECD (2)
- HPLC (2)
- Leca GC/GC/TOFMS
- Hg analyzer (1)
- Nutrient analyzer (1)
- ASE systems (2)
- Thermal Desorber, Head space GC/MS (1)
The list of toxicants that can be analyzed include, but not limited to:
- polycyclic aromatic hydrocarbons (PAHs)
- perchloroethylene (PCE)
- polychlorinated biphenyls (PCBs)
- polybrominated di-ethyl ethers
- volatile organic compounds (VOCs)
The CSFC will not only use existing methodologies but also advance the development of new high-throughput methods for the detection of pollutants in environmental and biological samples. Partnering with the Community Engagement Core, these activities will also support dissemination of exposure-related findings to stakeholders and engaged communities.
The CSFC leverages the resources and expertise at the GERG (directed by the co-investigator Dr. Knap) for providing these resources to the Center members. GERG has been performing these analyses for more than 30 years and has documented expertise in all the relevant methodologies.
Environmental Sample Extraction
GERG has developed, validated, and applied methods for analyses of known contaminants, including PCBs, PAHs, dioxins/furans, metals, PCE, slightly volatile organic compounds (SVOCs), and VOCs. GERG analyzes thousands of samples each year for these contaminants. Standardized methods for extraction and processing of environmental samples are available for several environmental contaminants of concern.
Samples will be extracted with an Accelerated Solvent Extractor (ASE) after adding surrogate compounds used to quantitate components in the sample. The extracts will then be concentrated by evaporating solvent to a smaller volume and for sediments treated with copper to remove sulfur. Next, chromatography (silica/alumina, size exclusion, etc.) will be used to purify the extracts before analyses by high-resolution GC/high-resolution MS. Aliquots of component extracts will be split to provide samples for targeted GC-MS and LC-MS analysis.
Analysis of Environmental and Laboratory Samples for Known Compounds
All samples will be logged in and assigned a unique file number. Samples will be analyzed in batches of 20 samples or less. For each batch of samples, a procedural blank, duplicate matrix spike, and when available, a standard reference material (e.g., National Institute of Technology NIST SRM 1944) will be included. This will provide quality control for the analytical method and validate data acquisition.
For metal analyses, the same quality assurance samples will be analyzed with each sample batch. Samples for metal analyses will be acid digested followed by cold vapor or Inductively Coupled Plasma MS analyses. Routine analyses will include 57 PAHs, 209 PCBs, the 17 toxic dioxins/furans, 122 SVOCs, 70 VOCs, 38 pesticides, and 10 metals. These analytes are routinely measured at GERG in many diverse matrices.
Methods will also be developed for “newly discovered” environmental contaminants as needed by Center investigators. Contaminants currently measured and their detection limits are presented in Table 2. This panel will be expanded as new investigator projects are initiated.
Increasing Analyte Libraries for Targeted Analysis
Depending on the need for identification of new contaminants or groups of contaminants om Center investigator projects, new analytical techniques will be developed for routine analyses. GERG’s extensive experience in developing new analytical methods, for example, for the analyses of PAH metabolites, tributyltin (TBT), planar PCB, and PBDE, will ensure responsiveness to project needs for new methods. We will follow an established and successful approach to the development of new analytical techniques for emerging contaminants of concern.3 Physical properties will guide method development and choice of isolation, separation, concentration, and analytical techniques (e.g., GC/MS vs. high-performance LC/MS).
Genomic and Compositional Analysis of Biological Samples Through TIGSS
Genomic information is becoming increasingly important in a variety of research fields. Many methods have been developed to assess genetic makeup, gene expression, and genomic architecture. The rise of next-generation sequencing has seen the cost per giga-base decrease while the amount of data produced increases.
Although ability to acquire data has increased, there is now a significant need for expertise in genomic methodologies to assist in experimental design and implementation. The advances in genomic technologies presents unparalleled opportunity to understand how genes and genomes are affected by environmental stimuli. The ability to assay whole genome, transcriptome, and microbiomes have allowed investigators unprecedented insight into the complex nature of these interactions.
One of the goals of the CSFC is to provide Center members the infrastructure and technical expertise in genomic methods and analysis.
Specifically, the core provides standardized workflows for profiling, identifying, and quantifying genomic and epigenetic data from different tissue and sample types (tissue, cell populations, single-cells, fecal material, and water samples) through the TIGSS Molecular Genomics Workspace. In addition, the CSFC also offers Center members state-of-the-art services for microbiome research (microbial metagenomic, whole genome sequencing, or bacterial 16s rRNA profiling), thus enabling Center investigators to address a number of questions on host, microbe, and environment interactions.
The CSFC also provides study design consultation, standardized genomic analyses workflows, and advanced bioinformatics support in conjunction with the DSFC.
Instruments available through the CSFC for genomic analysis include:
- Illumina MiSeq Sequencer (1)
- Illumina NextSeq 500 Sequencer (1)
- Illumina NovaSeq 6000 Sequencer (2 as part of Texas Genomics Core Alliance)
- Oxford Nanopore GridION Mk1 Sequencer (1)
- Oxford Nanopore MInION Sequencer (1)
- Illumina iScan Array Reader (1)
- Fluidigm C1 Single Cell Autoprep System (1)
- Fluidigm BioMark HD (1)
- 10X Genomics Chromium System (1)
- Eppendorf EpMotion 5075 (2)
- Bio-Rad QX200 Droplet Digital System (1)
- Agilent Tape Station (1)
- BioTek Cytation 5 (1)
Every genomic study is unique in the sense that it has specific features that can impact downstream analysis. Therefore, one of the primary objectives of the CSFC is to provide Center members with the expertise for effective experimental design.
A key feature of the core is to work with Center members to provide input and ensure experiments are designed in such a way as to maximize significant data along with incorporating appropriate controls. Furthermore, the Center has access to the specialized equipment necessary to prepare samples for genomic studies or to validate the results from previously generated data.
The goal is to address the needs of Center members over a spectrum of topics ranging from experimental design to sample processing to sequence analysis and beyond.
The TIGSS Molecular Genomics workspace has the expertise and equipment necessary for a variety of experiments. With two different sequencers from Illumina (MiSeq and NextSeq500, the facility can generate genomic data from a variety of samples. The Illumina MiSeq provides the longest reads available from an Illumina sequencer and is ideal for sequencing small genomes or for targeted sequencing. Furthermore, the TIGSS Molecular Genomics workspace offers long read, nanopore-based sequencing utilizing the Oxford Nanopore minIon desktop sequencer to help with large genome assembly generation.
There are two complementary resources available for single-cell genomic studies. The Fluidigm C1 system is available for medium- to high-throughput single-cell mRNA sequencing or whole genome sequencing preparation. The Chromium system from 10x Genomics is ideal for high-throughput analysis of single-cell gene expression. To complement these technologies, the Illumina NextSeq 500 is an ideal machine for rapid transcriptional profiling for small- to medium-scale projects and generates transcriptome data in less than 24 hours.
For all of these technologies, the TIGSS Molecular Genomics Workspace has standardized workflows in place for nucleic acid isolation, library preparation, and sequence generation for whole genome or transcriptome profiling.
A standard method to assess differences in bacterial populations is to analyze the variable region of the bacterial 16S ribosomal RNA (rRNA) gene. TIGSS currently has an automated workflow for sample preparation to assess the V3-V4 region of bacterial 16S rRNA genes. DNA isolated from several sources (feces, skin, surface swabs, etc.) can be tested with these methods. Sequencing libraries are prepared using an automated fluid-handling robot and then sequenced on the Illumina MiSeq instrument. The resultant data is analyzed using an existing data analysis pipeline to determine the number of operational taxonomic units (OTUs) in each sample. These methods can be easily adapted to investigate other variable regions as experiments dictate.
Metabolomic Capabilities for Identifying and Quantifying Metabolite Signatures Through IMAC
Increasing experimental evidence suggests that small molecules or secondary metabolites produced by different cells, tissues, and organs in the body are important for understanding the host response to different environmental exposures. While gene expression and proteomics methodologies provide information on the expression of different genes and their products (proteins), they do not provide information on the actual molecules that are produced as end products of different cellular pathways.
Thus, identifying and quantifying the complete panel of produced metabolites (i.e., the metabolome) provides a measure of the functional output of the different biochemical pathways in cells, tissues, or hosts. Moreover, this data also complements the information provided by genomics and proteomics.Since environmental exposure causes pronounced changes in metabolism, the identification and quantification of metabolites that represent these changes is crucial to understanding the adverse effects of environmental toxicants and identifying biomarkers for exposures.
Challenges that limit the application of metabolomic technologies for the identification of biomarkers include:
- detection of low-abundance metabolites
- the need for multiple metabolomic techniques for the same sample due to the diversity in metabolite properties
- lack of predictive tools for metabolomic analysis
Therefore, establishing state-of-the-art metabolomics capabilities is integral to achieving the Center’s goals. The CSFC provides Center members study design consultation, standardized LC/MS/MS and GC/MS/MS metabolomic analyses workflows, and advanced bioinformatics support through leveraging the resources and expertise at the Integrated Metabolomic Analysis Core (IMAC).
IMAC is a state-of-the-art metabolomics facility that is equipped with several high-end mass spectrometers for metabolomic studies. The instruments available to Center members through IMAC and its partner cores in the Texas A&M Chemical & Biological Mass Spectrometry Facility include:
- Thermo Exactive Orbitrap MS with HPLC
- Thermo QExactive Plus Orbitrap MS with UHPLC (2)
- Thermo Quantiva triple quadrupole LC/MS (1)
- Thermo Altis triple quadrupole (1)
- Thermo EVO 8000 triple quadrupole GC/MS system
- Thermo Fusion Orbitrap LC/MS
- Bruker FT-ICR MS
- Sciex 4000 QTrap LC/MS
- Applied Biosystems MALDI-TOF MS (2)
Study Design and Sample Preparation
For studies that are in inception stages, CSFC staff will work with the investigators to provide input on statistical design of experiments required for achieving significance in metabolomics readouts. In addition, we will consult with the investigators to help design experiments and incorporate appropriate controls to meet our standard operating procedure requirements. This study design consultation will also help investigators choose the appropriate analytical procedure for the scientific question being asked. For example, short chain fatty acids (SCFAs) are more amenable to GC/MS/MS than LC/MS/MS, whereas other metabolites like bile acids may be analyzed using either method. An important aspect of this stage would be to determine the appropriate analytical method (e.g., hydrophobic or hydrophilic column) and solvents (methanol/chloroform or methanol/water) that should be used for the type of sample and abundance of the metabolites being tested. As required, the DSFC will be involved to account for statistical considerations.
Two types of metabolomics analysis will be offered through the CSFC: targeted and untargeted.
A discovery workflow for global untargeted metabolomic analysis will be available on an ultra-high performance liquid chromatography electrospray ionization MS platform using a QExactive Plus instrument (Thermo). This workflow can be customized based on whether the user is primarily interested in hydrophilic (e.g., amino acids, amines, cationic metabolites) and/or hydrophobic (e.g., some bile acids, free fatty acids) metabolites by using HILIC (hydrophilic) and/or C18 reverse phase (hydrophobic) columns. This workflow will also leverage several key features of the mass spectrometer, including high-resolution accurate mass (HRAM), and fast acquisition time, to perform data-independent acquisition (DIA) experiments. Initially, a full scan (m/z 67 – 1000) will be carried out at resolution of 70,000, followed by data-dependent MS/MS scans for metabolite identification.
If a Center member wants to utilize a method or workflow that is currently established in the facility and has a small number of samples, the CSFC staff will carry out the sample processing, data acquisition, and routine bioinformatics analysis. If the member wants to analyze a class of molecules not previously studied in the Core, a new method that is specific to the user’s project will be developed by the technical staff. In this mode, it will also be possible for students and postdoctoral fellows to gain hands-on experience after suitable training.
A targeted metabolomics platform on triple quadrupole mass spectrometers (Altis & Quantiva, Thermo) will be used to quantify specific classes of metabolites for Center members. For example, IMAC has developed optimal methods and specific transitions for simultaneous quantification of a panel of ~35 tryptophan-derived microbiota metabolites (e.g., indole, indole-3-aldehyde, tryptamine) using multiple reaction monitoring (MRM). This and other such workflows will be made available to Center members. Metabolite identification will be based on retention time of pure standards and parent/fragment ion mass spectra in databases. As additional metabolites are identified by Center members and become commercially available, they will be added to the methods database. GC/MS/MS workflows will also be provided to identify metabolites such as SCFAs and medium chain fatty acids (MCFAs) that are more amenable to GC-MS analysis than LC-MS. Metabolite extraction and GC-MS analysis will be carried using standard derivatization protocols on a TSQ EVO8000 instrument.
The CSFC will also provide in silico analysis tools for metabolomics. For example, the identification of intermediates and metabolites that can be produced in the body from an environmental toxicant is extremely important for understanding the effects of the toxicant on human health. However, it is not straightforward to identify such metabolites as they can be at very low abundance or be consumed as intermediates in other host or microbiota reactions. Dr. Jayaraman’s group has recently developed a probabilistic prediction algorithm that addresses this problem. The model was used to predict that 58 metabolites could be produced from aromatic amino acids exclusively by the microbiota, and model predictions were validated using conventionally raised and germ-free mice and LC/MS/MS metabolomics. This algorithm and other custom modifications (e.g., metabolites arising from microbiota‒host co-metabolism) will be made available to Center members through the CSFC.
Bioinformatics and Statistical Analysis Pipeline
Raw data from untargeted LC/MS projects processed for background subtraction, peak alignment, and deconvolution using the Progenesis QI (Waters) software. Putative compound identification will be carried out using two independent methods. First, mass spectra will be compared against four publicly available metabolite databases – HMDB, KEGG, and NIST. To account for discrepancies in metabolite identification from multiple databases, a positive match in at least two databases will be required for putative identification. If a metabolite is identified only in a single database, secondary in silico validation will be carried out using MetFrag44. As an independent measure, putative chemical identities will be assigned to the features based on accurate mass (m/z) and MS/MS data using a recently developed automated annotation tool and implemented in our laboratory. Statistical analysis including normalization, biomarker identification between experimental groups, and pathway analysis will be conducted using Metaboanalyst. As information on additional metabolites with association to exposures become available in the literature, we will use data-independent acquisition to mine the full scan data and identify additional metabolites. For GC/MS data, automated mass spectral deconvolution and identification software (NIST) will be used to process chromatograms for background subtraction and peak integration.45 The sample mass spectra will be compared to the NIST library of standards to identify metabolites.46 All bioinformatics software will be stored in the cloud with multiple user licenses so that users can access data and carry out analyses from remote locations without blocking access to instruments.
Statistical analysis of the results will be customized per the needs of the investigator in collaboration with the DSFC using multivariate data analysis and ordination methods.47-49 The data from untargeted mass spectrometry is usually complex as well as difficult to interpret and the use of multivariate statistical analysis will aid in reducing data dimensions, differentiating similar spectra, and building predictive models.50,51 Our data analysis workflow will start by looking at the overall structuring of the data using ordination methods like Principal Component analysis and then use linear/non-linear models to identify key markers that are important in the dataset.49
In collaboration with the DSFC, we also propose to build integrative models utilizing different omics datasets. For example, microbiome composition and metabolome data from the same experiment can be correlated to identify the specific bacterial genera or species responsible for the observed changes. Center members Drs. Chapkin and Ivanov have pioneered such systems biology methods for integrating host gene expression and the gut microbiota metagenome in human subjects. We propose to make this capability available to Center investigators so that different combinations of omics data from the host and microbiota can be integrated to develop novel predictive computational models of exposure. By including the multivariate nature of metagenome, metabolome, and transcriptome data sets, richer information content will be generated compared to analyses focusing on a single data set only (e.g., only metagenomics data) or single variable testing (e.g., gene by gene differential expression).
- Dynamic Gene Expression Profiling Using a Microfabricated Living Cell Array
- Environmental Genome Shotgun Sequencing of the Sargasso Sea
- Indole is an inter-species biofilm signal mediated by SdiA
- Differential Effects of Epinephrine, Norepinephrine, and Indole on Escherichia coli O157:H7 Chemotaxis, Colonization, and Gene Expression
- Corrosion inhibition by aerobic biofilms on SAE 1018 steel
- Enterohemorrhagic Escherichia coli Biofilms Are Inhibited by 7-Hydroxyindole and Stimulated by Isatin
- Ecological Effects of a Major Oil Spill on Panamanian Coastal Marine Communities
- Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry
- SNP genotyping reveals major QTLs for plant architectural traits between A-genome peanut wild species
- A Gnotobiotic Mouse Model Demonstrates That Dietary Fiber Protects against Colorectal Tumorigenesis in a Microbiota- and Butyrate-Dependent Manner
- Quantitative trait loci in a bacterially induced model of inflammatory bowel disease
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