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Delgadillo D, Burch JE, Kim LJ, de Moraes LS, Niwa K, Williams J, Tang MJ, Lavallo VG, Khatri Chhetri B, Jones CG, Rodriguez IH, Signore JA, Marquez L, Bhanushali R, Woo S, Kubanek J, Quave C, Tang Y, Nelson HM. High-Throughput Identification of Crystalline Natural Products from Crude Extracts Enabled by Microarray Technology and microED. ACS CENTRAL SCIENCE 2024; 10:176-183. [PMID: 38292598 PMCID: PMC10823509 DOI: 10.1021/acscentsci.3c01365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/04/2023] [Accepted: 12/04/2023] [Indexed: 02/01/2024]
Abstract
The structural determination of natural products (NPs) can be arduous because of sample heterogeneity. This often demands iterative purification processes and characterization of complex molecules that may be available only in miniscule quantities. Microcrystal electron diffraction (microED) has recently shown promise as a method to solve crystal structures of NPs from nanogram quantities of analyte. However, its implementation in NP discovery remains hampered by sample throughput and purity requirements, akin to traditional NP-discovery workflows. In the methods described herein, we leverage the resolving power of transmission electron microscopy (TEM) and the miniaturization capabilities of deoxyribonucleic acid (DNA) microarray technology to address these challenges through the establishment of an NP screening platform, array electron diffraction (ArrayED). In this workflow, an array of high-performance liquid chromatography (HPLC) fractions taken from crude extracts was deposited onto TEM grids in picoliter-sized droplets. This multiplexing of analytes on TEM grids enables 1200 or more unique samples to be simultaneously inserted into a TEM instrument equipped with an autoloader. Selected area electron diffraction analysis of these microarrayed grids allows for the rapid identification of crystalline metabolites. In this study, ArrayED enabled structural characterization of 14 natural products, including four novel crystal structures and two novel polymorphs, from 20 crude extracts. Moreover, we identify several chemical species that would not be detected by standard mass spectrometry (MS) or ultraviolet-visible (UV/vis) spectroscopy and crystal forms that would not be characterized using traditional methods.
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Affiliation(s)
- David
A. Delgadillo
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Jessica E. Burch
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Lee Joon Kim
- Department of Chemistry
and Biochemistry, and Department of Chemical and Biomolecular
Engineering, University of California, Los
Angeles, Los Angeles, California 90095, United States
| | - Lygia S. de Moraes
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Kanji Niwa
- Department of Chemistry
and Biochemistry, and Department of Chemical and Biomolecular
Engineering, University of California, Los
Angeles, Los Angeles, California 90095, United States
| | - Jason Williams
- Department of Chemistry
and Biochemistry, and Department of Chemical and Biomolecular
Engineering, University of California, Los
Angeles, Los Angeles, California 90095, United States
| | - Melody J. Tang
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Vincent G. Lavallo
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Bhuwan Khatri Chhetri
- School
of Biological Sciences, School of Chemistry
and Biochemistry, and Neuroscience Program, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Christopher G. Jones
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Isabel Hernandez Rodriguez
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Joshua A. Signore
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
| | - Lewis Marquez
- Molecular
and Systems Pharmacology, Laney Graduate School, Emory University, Atlanta, Georgia 30322, United States
| | - Riya Bhanushali
- School
of Biological Sciences, School of Chemistry
and Biochemistry, and Neuroscience Program, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Sunmin Woo
- Center
for the Study of Human Health, Emory University, Atlanta, Georgia 30322, United States
| | - Julia Kubanek
- School
of Biological Sciences, School of Chemistry
and Biochemistry, and Neuroscience Program, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Cassandra Quave
- Molecular
and Systems Pharmacology, Laney Graduate School, Emory University, Atlanta, Georgia 30322, United States
- Center
for the Study of Human Health, Emory University, Atlanta, Georgia 30322, United States
- Department
of Dermatology, Emory University School
of Medicine, Atlanta, Georgia 30322, United
States
| | - Yi Tang
- Department of Chemistry
and Biochemistry, and Department of Chemical and Biomolecular
Engineering, University of California, Los
Angeles, Los Angeles, California 90095, United States
| | - Hosea M. Nelson
- Division
of Chemistry and Chemical Engineering, California
Institute of Technology, Pasadena, California 91125, United States
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Wang X, Li J, Ha HD, Dahl JC, Ondry JC, Moreno-Hernandez I, Head-Gordon T, Alivisatos AP. AutoDetect-mNP: An Unsupervised Machine Learning Algorithm for Automated Analysis of Transmission Electron Microscope Images of Metal Nanoparticles. JACS AU 2021; 1:316-327. [PMID: 33778811 PMCID: PMC7988451 DOI: 10.1021/jacsau.0c00030] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Indexed: 05/27/2023]
Abstract
The synthesis quality of artificial inorganic nanocrystals is most often assessed by transmission electron microscopy (TEM) for which high-throughput advances have dramatically increased both the quantity and information richness of metal nanoparticle (mNP) characterization. Existing automated data analysis algorithms of TEM mNP images generally adopt a supervised approach, requiring a significant effort in human preparation of labeled data that reduces objectivity, efficiency, and generalizability. We have developed an unsupervised algorithm AutoDetect-mNP for automated analysis of TEM images that objectively extracts morphological information on convex mNPs from TEM images based on their shape attributes, requiring little to no human input in the process. The performance of AutoDetect-mNP is tested on two data sets of bright field TEM images of Au nanoparticles with different shapes and further extended to palladium nanocubes and cadmium selenide quantum dots, demonstrating that the algorithm is quantitatively reliable and can thus serve as a generalizable measure of the morphology distributions of any mNP synthesis. The AutoDetect-mNP algorithm will aid in future developments of high-throughput characterization of mNPs and the future advent of time-resolved TEM studies that can investigate reaction mechanisms of mNP synthesis and reactivity.
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Affiliation(s)
- Xingzhi Wang
- Department
of Chemistry, University of California, Berkeley, California 94720, United States
- Materials
Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
| | - Jie Li
- Department
of Chemistry, University of California, Berkeley, California 94720, United States
- Kenneth
S. Pitzer Theory Center, University of California, Berkeley, California 94720, United States
| | - Hyun Dong Ha
- Department
of Chemistry, University of California, Berkeley, California 94720, United States
- Materials
Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
| | - Jakob C. Dahl
- Department
of Chemistry, University of California, Berkeley, California 94720, United States
- Materials
Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
| | - Justin C. Ondry
- Department
of Chemistry, University of California, Berkeley, California 94720, United States
- Kavli
Energy NanoScience Institute, Berkeley, California 94720, United States
| | - Ivan Moreno-Hernandez
- Department
of Chemistry, University of California, Berkeley, California 94720, United States
| | - Teresa Head-Gordon
- Department
of Chemistry, University of California, Berkeley, California 94720, United States
- Kenneth
S. Pitzer Theory Center, University of California, Berkeley, California 94720, United States
- Chemical
Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
- Departments
of Bioengineering and Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, United States
| | - A. Paul Alivisatos
- Department
of Chemistry, University of California, Berkeley, California 94720, United States
- Materials
Sciences Division, Lawrence Berkeley National
Laboratory, Berkeley, California 94720, United States
- Kavli
Energy NanoScience Institute, Berkeley, California 94720, United States
- Department
of Materials Science and Engineering, University
of California, Berkeley, California 94720, United States
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4
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Arnold SA, Müller SA, Schmidli C, Syntychaki A, Rima L, Chami M, Stahlberg H, Goldie KN, Braun T. Miniaturizing EM Sample Preparation: Opportunities, Challenges, and “Visual Proteomics”. Proteomics 2018; 18:e1700176. [DOI: 10.1002/pmic.201700176] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 01/15/2018] [Indexed: 01/31/2023]
Affiliation(s)
- Stefan A. Arnold
- Center for Cellular Imaging and NanoAnalytics (C-CINA); Biozentrum; University of Basel; Basel Switzerland
- Swiss Nanoscience Institute; University of Basel; Basel Switzerland
| | - Shirley A. Müller
- Center for Cellular Imaging and NanoAnalytics (C-CINA); Biozentrum; University of Basel; Basel Switzerland
| | - Claudio Schmidli
- Center for Cellular Imaging and NanoAnalytics (C-CINA); Biozentrum; University of Basel; Basel Switzerland
- Swiss Nanoscience Institute; University of Basel; Basel Switzerland
| | - Anastasia Syntychaki
- Center for Cellular Imaging and NanoAnalytics (C-CINA); Biozentrum; University of Basel; Basel Switzerland
| | - Luca Rima
- Center for Cellular Imaging and NanoAnalytics (C-CINA); Biozentrum; University of Basel; Basel Switzerland
| | - Mohamed Chami
- BioEM Lab; Biozentrum; University of Basel; Basel Switzerland
| | - Henning Stahlberg
- Center for Cellular Imaging and NanoAnalytics (C-CINA); Biozentrum; University of Basel; Basel Switzerland
| | - Kenneth N. Goldie
- Center for Cellular Imaging and NanoAnalytics (C-CINA); Biozentrum; University of Basel; Basel Switzerland
| | - Thomas Braun
- Center for Cellular Imaging and NanoAnalytics (C-CINA); Biozentrum; University of Basel; Basel Switzerland
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Tan YZ, Cheng A, Potter CS, Carragher B. Automated data collection in single particle electron microscopy. Microscopy (Oxf) 2015; 65:43-56. [PMID: 26671944 DOI: 10.1093/jmicro/dfv369] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Accepted: 11/06/2015] [Indexed: 11/12/2022] Open
Abstract
Automated data collection is an integral part of modern workflows in single particle electron microscopy (EM) research. This review surveys the software packages available for automated single particle EM data collection. The degree of automation at each stage of data collection is evaluated, and the capabilities of the software packages are described. Finally, future trends in automation are discussed.
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Affiliation(s)
- Yong Zi Tan
- The National Resource for Automated Molecular Microscopy, New York Structural Biology Center, New York, NY 10027, USA Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
| | - Anchi Cheng
- The National Resource for Automated Molecular Microscopy, New York Structural Biology Center, New York, NY 10027, USA Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA
| | - Clinton S Potter
- The National Resource for Automated Molecular Microscopy, New York Structural Biology Center, New York, NY 10027, USA Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
| | - Bridget Carragher
- The National Resource for Automated Molecular Microscopy, New York Structural Biology Center, New York, NY 10027, USA Simons Electron Microscopy Center, New York Structural Biology Center, 89 Convent Ave, New York, NY 10027, USA Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA
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