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Paul I, Bolzan D, Youssef A, Gagnon KA, Hook H, Karemore G, Oliphant MUJ, Lin W, Liu Q, Phanse S, White C, Padhorny D, Kotelnikov S, Chen CS, Hu P, Denis GV, Kozakov D, Raught B, Siggers T, Wuchty S, Muthuswamy SK, Emili A. Parallelized multidimensional analytic framework applied to mammary epithelial cells uncovers regulatory principles in EMT. Nat Commun 2023; 14:688. [PMID: 36755019 PMCID: PMC9908882 DOI: 10.1038/s41467-023-36122-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 01/17/2023] [Indexed: 02/10/2023] Open
Abstract
A proper understanding of disease etiology will require longitudinal systems-scale reconstruction of the multitiered architecture of eukaryotic signaling. Here we combine state-of-the-art data acquisition platforms and bioinformatics tools to devise PAMAF, a workflow that simultaneously examines twelve omics modalities, i.e., protein abundance from whole-cells, nucleus, exosomes, secretome and membrane; N-glycosylation, phosphorylation; metabolites; mRNA, miRNA; and, in parallel, single-cell transcriptomes. We apply PAMAF in an established in vitro model of TGFβ-induced epithelial to mesenchymal transition (EMT) to quantify >61,000 molecules from 12 omics and 10 timepoints over 12 days. Bioinformatics analysis of this EMT-ExMap resource allowed us to identify; -topological coupling between omics, -four distinct cell states during EMT, -omics-specific kinetic paths, -stage-specific multi-omics characteristics, -distinct regulatory classes of genes, -ligand-receptor mediated intercellular crosstalk by integrating scRNAseq and subcellular proteomics, and -combinatorial drug targets (e.g., Hedgehog signaling and CAMK-II) to inhibit EMT, which we validate using a 3D mammary duct-on-a-chip platform. Overall, this study provides a resource on TGFβ signaling and EMT.
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Affiliation(s)
- Indranil Paul
- Department of Biochemistry, Boston University School of Medicine, Boston University, 71 East Concord Street, Boston, MA, 02118, USA
| | - Dante Bolzan
- Department of Computer Science, University of Miami, 1356 Memorial Drive, Coral Gables, FL, 33146, USA
| | - Ahmed Youssef
- Graduate Program in Bioinformatics, Boston University, 24 Cummington Mall, Boston, MA, 02215, USA
| | - Keith A Gagnon
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA, 02215, USA
| | - Heather Hook
- Department of Biology, Boston University, 24 Cummington Mall, Boston, MA, 02115, USA
- Biological Design Center, Boston University, 610 Commonwealth Avenue, Boston, MA, 02215, USA
| | - Gopal Karemore
- Advanced Analytics, Novo Nordisk A/S, 2760, Måløv, Denmark
| | - Michael U J Oliphant
- Cancer Research Institute, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA
| | - Weiwei Lin
- Department of Biochemistry, Boston University School of Medicine, Boston University, 71 East Concord Street, Boston, MA, 02118, USA
| | - Qian Liu
- Department of Biochemistry and Medical Genetics, University of Manitoba, Winnipeg, Manitoba, R3E 0J9, Canada
| | - Sadhna Phanse
- Department of Biochemistry, Boston University School of Medicine, Boston University, 71 East Concord Street, Boston, MA, 02118, USA
| | - Carl White
- Department of Biochemistry, Boston University School of Medicine, Boston University, 71 East Concord Street, Boston, MA, 02118, USA
| | - Dzmitry Padhorny
- Department of Applied Mathematics and Statistics, Stony Brook University, 11794, Stony Brook, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Sergei Kotelnikov
- Department of Applied Mathematics and Statistics, Stony Brook University, 11794, Stony Brook, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Christopher S Chen
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, MA, 02215, USA
- Wyss Institute for Biologically Inspired Engineering, Harvard University, 3 Blackfan Circle, Boston, MA, 02115, USA
| | - Pingzhao Hu
- Department of Biochemistry, Western University, London, ON, N6A 5C1, Canada
| | - Gerald V Denis
- Boston Medical Center Cancer Center, Boston University, Boston University, 72 East Concord Street, Boston, MA, 02118, USA
| | - Dima Kozakov
- Department of Applied Mathematics and Statistics, Stony Brook University, 11794, Stony Brook, NY, USA
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, 11794, USA
| | - Brian Raught
- Discovery Tower (TMDT), 101 College St, Rm. 9-701A, University of Toronto, Toronto, ON, M5G 1L7, Canada
| | - Trevor Siggers
- Department of Biology, Boston University, 24 Cummington Mall, Boston, MA, 02115, USA
- Biological Design Center, Boston University, 610 Commonwealth Avenue, Boston, MA, 02215, USA
| | - Stefan Wuchty
- Department of Computer Science, University of Miami, 1356 Memorial Drive, Coral Gables, FL, 33146, USA
| | - Senthil K Muthuswamy
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Andrew Emili
- Department of Biochemistry, Boston University School of Medicine, Boston University, 71 East Concord Street, Boston, MA, 02118, USA.
- Department of Biology, Charles River Campus, Boston University, Life Science & Engineering (LSEB-602), 24 Cummington Mall, Boston, MA, 02215, USA.
- Division of Oncological Sciences, Knight Cancer Institute, Oregon Health and Science University, Portland, USA.
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Struck-Lewicka W, Karpińska B, Rodzaj W, Nasal A, Wielgomas B, Markuszewski MJ, Siluk D. Development of the thin film solid phase microextraction (TF-SPME) method for metabolomics profiling of steroidal hormones from urine samples using LC-QTOF/MS. Front Mol Biosci 2023; 10:1074263. [PMID: 36950525 PMCID: PMC10025495 DOI: 10.3389/fmolb.2023.1074263] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 02/22/2023] [Indexed: 03/08/2023] Open
Abstract
In the present study, the development and optimization of a thin film solid phase microextraction method (TF-SPME) was conducted for metabolomics profiling of eight steroid compounds (androsterone, dihydrotestosterone, dihydroepiandrosterone, estradiol, hydroxyprogesterone, pregnenolone, progesterone and testosterone) from urine samples. For optimization of extraction method, two extraction sorbents (PAN-C18 and PS-DVB) were used as they are known to be effective for isolation of low-polarity analytes. The stages of sample extraction and analyte desorption were considered as the most crucial steps in the process. Regarding the selection of the most suitable desorption solution, six different mixtures were analyzed. As a result, the mixture of ACN: MeOH (1:1, v/v) was chosen in terms of the highest analytes' abundances that were achieved using the chosen solvent. Besides other factors were examined such as the volume of desorption solvent and the time of both extraction and desorption processes. The analytical determination was carried out using the ultra-high performance liquid chromatography coupled with high resolution tandem mass spectrometry detection in electrospray ionization and positive polarity in a scan mode (UHPLC-ESI-QTOF/MS). The developed and optimized TF-SPME method was validated in terms of such parameters as extraction efficiency, recovery as well as matrix effect. As a result, the extraction efficiency and recovery were in a range from 79.3% to 99.2% and from 88.8% to 111.8%, respectively. Matrix effect, calculated as coefficient of variation was less than 15% and was in a range from 1.4% to 11.1%. The values of both validation parameters (recovery and matrix effect) were acceptable in terms of EMA criteria. The proposed TF-SPME method was used successfully for isolation of steroids hormones from pooled urine samples before and after enzymatic hydrolysis of analytes.
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Affiliation(s)
- Wiktoria Struck-Lewicka
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gdańsk, Poland
| | - Beata Karpińska
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gdańsk, Poland
| | - Wojciech Rodzaj
- Department of Toxicology, Medical University of Gdańsk, Gdańsk, Poland
| | - Antoni Nasal
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gdańsk, Poland
| | - Bartosz Wielgomas
- Department of Toxicology, Medical University of Gdańsk, Gdańsk, Poland
| | - Michał Jan Markuszewski
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gdańsk, Poland
| | - Danuta Siluk
- Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gdańsk, Poland
- *Correspondence: Danuta Siluk,
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Metabolomics and modelling approaches for systems metabolic engineering. Metab Eng Commun 2022; 15:e00209. [PMID: 36281261 PMCID: PMC9587336 DOI: 10.1016/j.mec.2022.e00209] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/21/2022] Open
Abstract
Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.
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Kanu AB. Recent developments in sample preparation techniques combined with high-performance liquid chromatography: A critical review. J Chromatogr A 2021; 1654:462444. [PMID: 34380070 DOI: 10.1016/j.chroma.2021.462444] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/21/2021] [Accepted: 07/24/2021] [Indexed: 12/29/2022]
Abstract
This review article compares and contrasts sample preparation techniques coupled with high-performance liquid chromatography (HPLC) and describes applications developed in biomedical, forensics, and environmental/industrial hygiene in the last two decades. The proper sample preparation technique can offer valued data for a targeted application when coupled to HPLC and a suitable detector. Improvements in sample preparation techniques in the last two decades have resulted in efficient extraction, cleanup, and preconcentration in a single step, thus providing a pathway to tackle complex matrix applications. Applications such as biological therapeutics, proteomics, lipidomics, metabolomics, environmental/industrial hygiene, forensics, glycan cleanup, etc., have been significantly enhanced due to improved sample preparation techniques. This review looks at the early sample preparation techniques. Further, it describes eight sample preparation technique coupled to HPLC that has gained prominence in the last two decades. They are (1) solid-phase extraction (SPE), (2) liquid-liquid extraction (LLE), (3) gel permeation chromatography (GPC), (4) Quick Easy Cheap Effective Rugged, Safe (QuEChERS), (5) solid-phase microextraction (SPME), (6) ultrasonic-assisted solvent extraction (UASE), and (7) microwave-assisted solvent extraction (MWASE). SPE, LLE, GPC, QuEChERS, and SPME can be used offline and online with HPLC. UASE and MWASE can be used offline with HPLC but have also been combined with the online automated techniques of SPE, LLE, GPC, or QuEChERS for targeted analysis. Three application areas of biomedical, forensics, and environmental/industrial hygiene are reviewed for the eight sample preparation techniques. Three hundred and twenty references on the eight sample preparation techniques published over the last two decades (2001-2021) are provided. Other older references were included to illustrate the historical development of sample preparation techniques.
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Affiliation(s)
- A Bakarr Kanu
- Department of Chemistry, Winston-Salem State University, Winston-Salem, NC 27110, United States.
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Tinte MM, Chele KH, van der Hooft JJJ, Tugizimana F. Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview. Metabolites 2021; 11:445. [PMID: 34357339 PMCID: PMC8305945 DOI: 10.3390/metabo11070445] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/30/2021] [Accepted: 07/03/2021] [Indexed: 12/27/2022] Open
Abstract
Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.
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Affiliation(s)
- Morena M. Tinte
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
| | - Kekeletso H. Chele
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
| | | | - Fidele Tugizimana
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa; (M.M.T.); (K.H.C.)
- International Research and Development Division, Omnia Group, Ltd., Johannesburg 2021, South Africa
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Łuczykowski K, Warmuzińska N, Operacz S, Stryjak I, Bogusiewicz J, Jacyna J, Wawrzyniak R, Struck-Lewicka W, Markuszewski MJ, Bojko B. Metabolic Evaluation of Urine from Patients Diagnosed with High Grade (HG) Bladder Cancer by SPME-LC-MS Method. Molecules 2021; 26:2194. [PMID: 33920347 PMCID: PMC8068997 DOI: 10.3390/molecules26082194] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 04/04/2021] [Accepted: 04/07/2021] [Indexed: 02/06/2023] Open
Abstract
Bladder cancer (BC) is a common malignancy of the urinary system and a leading cause of death worldwide. In this work, untargeted metabolomic profiling of biological fluids is presented as a non-invasive tool for bladder cancer biomarker discovery as a first step towards developing superior methods for detection, treatment, and prevention well as to further our current understanding of this disease. In this study, urine samples from 24 healthy volunteers and 24 BC patients were subjected to metabolomic profiling using high throughput solid-phase microextraction (SPME) in thin-film format and reversed-phase high-performance liquid chromatography coupled with a Q Exactive Focus Orbitrap mass spectrometer. The chemometric analysis enabled the selection of metabolites contributing to the observed separation of BC patients from the control group. Relevant differences were demonstrated for phenylalanine metabolism compounds, i.e., benzoic acid, hippuric acid, and 4-hydroxycinnamic acid. Furthermore, compounds involved in the metabolism of histidine, beta-alanine, and glycerophospholipids were also identified. Thin-film SPME can be efficiently used as an alternative approach to other traditional urine sample preparation methods, demonstrating the SPME technique as a simple and efficient tool for urinary metabolomics research. Moreover, this study's results may support a better understanding of bladder cancer development and progression mechanisms.
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Affiliation(s)
- Kamil Łuczykowski
- Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, 85-089 Bydgoszcz, Poland; (K.Ł.); (N.W.); (S.O.); (I.S.); (J.B.)
| | - Natalia Warmuzińska
- Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, 85-089 Bydgoszcz, Poland; (K.Ł.); (N.W.); (S.O.); (I.S.); (J.B.)
| | - Sylwia Operacz
- Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, 85-089 Bydgoszcz, Poland; (K.Ł.); (N.W.); (S.O.); (I.S.); (J.B.)
| | - Iga Stryjak
- Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, 85-089 Bydgoszcz, Poland; (K.Ł.); (N.W.); (S.O.); (I.S.); (J.B.)
| | - Joanna Bogusiewicz
- Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, 85-089 Bydgoszcz, Poland; (K.Ł.); (N.W.); (S.O.); (I.S.); (J.B.)
| | - Julia Jacyna
- Department of Biopharmacy and Pharmacodynamics, Faculty of Pharmacy, Medical University of Gdańsk, 80-416 Gdańsk, Poland; (J.J.); (R.W.); (W.S.-L.); (M.J.M.)
| | - Renata Wawrzyniak
- Department of Biopharmacy and Pharmacodynamics, Faculty of Pharmacy, Medical University of Gdańsk, 80-416 Gdańsk, Poland; (J.J.); (R.W.); (W.S.-L.); (M.J.M.)
| | - Wiktoria Struck-Lewicka
- Department of Biopharmacy and Pharmacodynamics, Faculty of Pharmacy, Medical University of Gdańsk, 80-416 Gdańsk, Poland; (J.J.); (R.W.); (W.S.-L.); (M.J.M.)
| | - Michał J. Markuszewski
- Department of Biopharmacy and Pharmacodynamics, Faculty of Pharmacy, Medical University of Gdańsk, 80-416 Gdańsk, Poland; (J.J.); (R.W.); (W.S.-L.); (M.J.M.)
| | - Barbara Bojko
- Department of Pharmacodynamics and Molecular Pharmacology, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, 85-089 Bydgoszcz, Poland; (K.Ł.); (N.W.); (S.O.); (I.S.); (J.B.)
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