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1. BayReL: Bayesian Relational Learning for Multi-omics Data Integration

   Hasanzadeh, Arman; Hajiramezanali, Ehsan; Duffield, Nick; Narayanan, Krishna; Zhou, Mingyuan; Qian, Xiaoning (October 2020, Advances in neural information processing systems)

   null (Ed.)

   High-throughput molecular profiling technologies have produced high-dimensional multi-omics data, enabling systematic understanding of living systems at the genome scale. Studying molecular interactions across different data types helps reveal signal transduction mechanisms across different classes of molecules. In this paper, we develop a novel Bayesian representation learning method that infers the relational interactions across multi-omics data types. Our method, Bayesian Relational Learning (BayReL) for multi-omics data integration, takes advantage of a priori known relationships among the same class of molecules, modeled as a graph at each corresponding view, to learn view-specific latent variables as well as a multi-partite graph that encodes the interactions across views. Our experiments on several real-world datasets demonstrate enhanced performance of BayReL in inferring meaningful interactions compared to existing baselines. 

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2. Dynamic mass spectrometry probe for electrospray ionization mass spectrometry monitoring of bioreactors for therapeutic cell manufacturing

   https://doi.org/10.1002/bit.26832  

   Chilmonczyk, Mason_A; Kottke, Peter_A; Stevens, Hazel_Y; Guldberg, Robert_E; Fedorov, Andrei_G (November 2018, Biotechnology and Bioengineering)

   Abstract Large‐scale manufacturing of therapeutic cells requires bioreactor technologies with online feedback control enabled by monitoring of secreted biomolecular critical quality attributes (CQAs). Electrospray ionization mass spectrometry (ESI‐MS) is a highly sensitive label‐free method to detect and identify biomolecules, but requires extensive sample preparation before analysis, making online application of ESI‐MS challenging. We present a microfabricated, monolithically integrated device capable of continuous sample collection, treatment, and direct infusion for ESI‐MS detection of biomolecules in high‐salt solutions. The dynamic mass spectrometry probe (DMSP) uses a microfluidic mass exchanger to rapidly condition samples for online MS analysis by removing interfering salts, while concurrently introducing MS signal enhancers to the sample for sensitive biomolecular detection. Exploiting this active conditioning capability increases MS signal intensity and signal‐to‐noise ratio. As a result, sensitivity for low‐concentration biomolecules is significantly improved, and multiple proteins can be detected from chemically complex samples. Thus, the DMSP has significant potential to serve as an enabling portion of a novel analytical tool for discovery and monitoring of CQAs relevant to therapeutic cell manufacturing. 

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3. Integration of Meta-Multi-Omics Data Using Probabilistic Graphs and External Knowledge

   https://doi.org/10.3390/cells12151998  

   Can, Handan; Chanumolu, Sree K.; Nielsen, Barbara D.; Alvarez, Sophie; Naldrett, Michael J.; Ünlü, Gülhan; Otu, Hasan H. (August 2023, Cells)

   Multi-omics has the promise to provide a detailed molecular picture of biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimal structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to have a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30 °C and 37 °C and obtained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites, suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofaciens at 37 °C. 

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4. Laser capture of tomato pericarp tissues for microscale carotenoid analysis by supercritical fluid chromatography

   https://doi.org/10.1016/bs.mie.2022.01.014  

   Ramsey J, Fish T (January 2022, Methods in enzymology)

   Plant organs and tissues are comprised of an array of cell types often superimposed on a gradient of developmental stages. As a result, the ability to analyze and understand the synthesis, metabolism, and accumulation of plant biomolecules requires improved methods for cell- and tissue-specific analysis. Tomato (Solanum lycopersicum) is the world’s most valuable fruit crop and is an important source of health-promoting dietary compounds, including carotenoids. Furthermore, tomato possesses unique genetic activities at the cell and tissue levels, making it an ideal system for tissue- and cell-type analysis of important biochemicals. A sample preparation workflow was developed for cell-type-specific carotenoid analysis in tomato fruit samples. Protocols for hyperspectral imaging of tomato fruit samples, cryoembedding and sectioning of pericarp tissue, laser microdissection of specific cell types, metabolite extraction using cell wall digestion enzymes and pressure cycling, and carotenoid quantification by supercritical fluid chromatography were optimized and integrated into a working protocol. The workflow was applied to quantify carotenoids in the cuticle and noncuticle component of the tomato pericarp during fruit development from the initial ripening to full ripe stages. Carotenoids were extracted and quantified from cell volumes less than 10 nL. This workflow for cell-type-specific metabolite extraction and quantification can be adapted for the analysis of diverse metabolites, cell types, and organisms 

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5. Identification of DIR1-Dependant Cellular Responses in Guard Cell Systemic Acquired Resistance

   https://doi.org/10.3389/fmolb.2021.746523  

   David, Lisa; Kang, Jianing; Nicklay, Josh; Dufresne, Craig; Chen, Sixue (December 2021, Frontiers in Molecular Biosciences)

   After localized invasion by bacterial pathogens, systemic acquired resistance (SAR) is induced in uninfected plant tissues, resulting in enhanced defense against a broad range of pathogens. Although SAR requires mobilization of signaling molecules via the plant vasculature, the specific molecular mechanisms remain elusive. The lipid transfer protein defective in induced resistance 1 (DIR1) was identified in Arabidopsis thaliana by screening for mutants that were defective in SAR. Here, we demonstrate that stomatal response to pathogens is altered in systemic leaves by SAR, and this guard cell SAR defense requires DIR1. Using a multi-omics approach, we have determined potential SAR signaling mechanisms specific for guard cells in systemic leaves by profiling metabolite, lipid, and protein differences between guard cells in the wild type and dir1-1 mutant during SAR. We identified two long-chain 18 C and 22 C fatty acids and two 16 C wax esters as putative SAR-related molecules dependent on DIR1. Proteins and metabolites related to amino acid biosynthesis and response to stimulus were also changed in guard cells of dir1-1 compared to the wild type. Identification of guard cell-specific SAR-related molecules may lead to new avenues of genetic modification/molecular breeding for disease-resistant plants. 

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