1
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Kuhn TM, Paulsen M, Cuylen-Haering S. Accessible high-speed image-activated cell sorting. Trends Cell Biol 2024; 34:657-670. [PMID: 38789300 DOI: 10.1016/j.tcb.2024.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 04/15/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024]
Abstract
Over the past six decades, fluorescence-activated cell sorting (FACS) has become an essential technology for basic and clinical research by enabling the isolation of cells of interest in high throughput. Recent technological advancements have started a new era of flow cytometry. By combining the spatial resolution of microscopy with high-speed cell sorting, new instruments allow cell sorting based on simple image-derived parameters or sophisticated image analysis algorithms, thereby greatly expanding the scope of applications. In this review, we discuss the systems that are commercially available or have been described in enough methodological and engineering detail to allow their replication. We summarize their strengths and limitations and highlight applications that have the potential to transform various fields in basic life science research and clinical settings.
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Affiliation(s)
- Terra M Kuhn
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Malte Paulsen
- Novo Nordisk Foundation Center for Stem Cell Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Sara Cuylen-Haering
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
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2
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Dutta T, Vlassakis J. Microscale measurements of protein complexes from single cells. Curr Opin Struct Biol 2024; 87:102860. [PMID: 38848654 DOI: 10.1016/j.sbi.2024.102860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 05/07/2024] [Accepted: 05/14/2024] [Indexed: 06/09/2024]
Abstract
Proteins execute numerous cell functions in concert with one another in protein-protein interactions (PPI). While essential in each cell, such interactions are not identical from cell to cell. Instead, PPI heterogeneity contributes to cellular phenotypic heterogeneity in health and diseases such as cancer. Understanding cellular phenotypic heterogeneity thus requires measurements of properties of PPIs such as abundance, stoichiometry, and kinetics at the single-cell level. Here, we review recent, exciting progress in single-cell PPI measurements. Novel technology in this area is enabled by microscale and microfluidic approaches that control analyte concentration in timescales needed to outpace PPI disassembly kinetics. We describe microscale innovations, needed technical capabilities, and methods poised to be adapted for single-cell analysis in the near future.
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Affiliation(s)
- Tanushree Dutta
- Department of Bioengineering, Rice University, Houston, TX 77005, USA. https://twitter.com/duttatanu1717
| | - Julea Vlassakis
- Department of Bioengineering, Rice University, Houston, TX 77005, USA.
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3
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Iványi GT, Nemes B, Gróf I, Fekete T, Kubacková J, Tomori Z, Bánó G, Vizsnyiczai G, Kelemen L. Optically Actuated Soft Microrobot Family for Single-Cell Manipulation. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2401115. [PMID: 38814436 DOI: 10.1002/adma.202401115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 05/17/2024] [Indexed: 05/31/2024]
Abstract
Precisely controlled manipulation of nonadherent single cells is often a pre-requisite for their detailed investigation. Optical trapping provides a versatile means for positioning cells with submicrometer precision or measuring forces with femto-Newton resolution. A variant of the technique, called indirect optical trapping, enables single-cell manipulation with no photodamage and superior spatial control and stability by relying on optically trapped microtools biochemically bound to the cell. High-resolution 3D lithography enables to prepare such cell manipulators with any predefined shape, greatly extending the number of achievable manipulation tasks. Here, it is presented for the first time a novel family of cell manipulators that are deformable by optical tweezers and rely on their elasticity to hold cells. This provides a more straightforward approach to indirect optical trapping by avoiding biochemical functionalization for cell attachment, and consequently by enabling the manipulated cells to be released at any time. Using the photoresist Ormocomp, the deformations achievable with optical forces in the tens of pN range and present three modes of single-cell manipulation as examples to showcase the possible applications such soft microrobotic tools can offer are characterized. The applications describe here include cell collection, 3D cell imaging, and spatially and temporally controlled cell-cell interaction.
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Affiliation(s)
- Gergely T Iványi
- HUN-REN Biological Research Centre, Szeged Institute of Biophysics, Temesvári krt. 62, Szeged, 6726, Hungary
- Doctoral School of Multidisciplinary Medical Sciences, University of Szeged, Szeged, 6720, Hungary
| | - Botond Nemes
- HUN-REN Biological Research Centre, Szeged Institute of Biophysics, Temesvári krt. 62, Szeged, 6726, Hungary
| | - Ilona Gróf
- HUN-REN Biological Research Centre, Szeged Institute of Biophysics, Temesvári krt. 62, Szeged, 6726, Hungary
| | - Tamás Fekete
- HUN-REN Biological Research Centre, Szeged Institute of Biophysics, Temesvári krt. 62, Szeged, 6726, Hungary
| | - Jana Kubacková
- Department of Biophysics, Institute of Experimental Physics SAS, Watsonova 47, Košice, 04001, Slovakia
| | - Zoltán Tomori
- Department of Biophysics, Institute of Experimental Physics SAS, Watsonova 47, Košice, 04001, Slovakia
| | - Gregor Bánó
- Department of Biophysics, Faculty of Science, P. J. Šafárik University in Košice, Jesenná 5, Košice, 04154, Slovakia
| | - Gaszton Vizsnyiczai
- HUN-REN Biological Research Centre, Szeged Institute of Biophysics, Temesvári krt. 62, Szeged, 6726, Hungary
- Department of Biotechnology, University of Szeged, Szeged, 6720, Hungary
| | - Lóránd Kelemen
- HUN-REN Biological Research Centre, Szeged Institute of Biophysics, Temesvári krt. 62, Szeged, 6726, Hungary
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4
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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5
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Zhang Y, Chang K, Ogunlade B, Herndon L, Tadesse LF, Kirane AR, Dionne JA. From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis. ACS NANO 2024; 18:18101-18117. [PMID: 38950145 DOI: 10.1021/acsnano.4c04282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.
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Affiliation(s)
- Yirui Zhang
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States
| | - Kai Chang
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, United States
| | - Babatunde Ogunlade
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States
| | - Liam Herndon
- Department of Chemical Engineering, Stanford University, Stanford, California 94305, United States
| | - Loza F Tadesse
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Ragon Institute of MGH, MIT and Harvard, Cambridge, Massachusetts 02139, United States
- Jameel Clinic for AI & Healthcare, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Amanda R Kirane
- Department of Surgery, Stanford University, Stanford, California 94305, United States
| | - Jennifer A Dionne
- Department of Materials Science and Engineering, Stanford University, Stanford, California 94305, United States
- Department of Radiology, Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, California 94305, United States
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6
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Weng L, Yan G, Liu W, Tai Q, Gao M, Zhang X. Picoliter Single-Cell Reactor for Proteome Profiling by In Situ Cell Lysis, Protein Immobilization, Digestion, and Droplet Transfer. J Proteome Res 2024; 23:2441-2451. [PMID: 38833655 DOI: 10.1021/acs.jproteome.4c00117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024]
Abstract
Global profiling of single-cell proteomes can reveal cellular heterogeneity, thus benefiting precision medicine. However, current mass spectrometry (MS)-based single-cell proteomic sample processing still faces technical challenges associated with processing efficiency and protein recovery. Herein, we present an innovative sample processing platform based on a picoliter single-cell reactor (picoSCR) for single-cell proteome profiling, which involves in situ protein immobilization and sample transfer. PicoSCR helped minimize surface adsorptive losses by downscaling the processing volume to 400 pL with a contact area of less than 0.4 mm2. Besides, picoSCR reached highly efficient cell lysis and digestion within 30 min, benefiting from optimal reagent and high reactant concentrations. Using the picoSCR-nanoLC-MS system, over 1400 proteins were identified from an individual HeLa cell using data-dependent acquisition mode. Proteins with copy number below 1000 were identified, demonstrating this system with a detection limit of 1.7 zmol. Furthermore, we profiled the proteome of circulating tumor cells (CTCs). Data are available via ProteomeXchange with the identifier PXD051468. Proteins associated with epithelial-mesenchymal transition and neutrophil extracellular traps formation (which are both related to tumor metastasis) were observed in all CTCs. The cellular heterogeneity was revealed by differences in signaling pathways within individual cells. These results highlighted the potential of the picoSCR platform to help discover new biomarkers and explore differences in biological processes between cells.
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Affiliation(s)
- Lingxiao Weng
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200438, China
| | - Guoquan Yan
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200438, China
| | - Wei Liu
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200438, China
| | - Qunfei Tai
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200438, China
| | - Mingxia Gao
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200438, China
- Pharmacy Department, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Shanghai 201399, China
| | - Xiangmin Zhang
- Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 200438, China
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7
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Colón Rosado J, Sun L. Solid-Phase Microextraction-Aided Capillary Zone Electrophoresis-Mass Spectrometry: Toward Bottom-Up Proteomics of Single Human Cells. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:1120-1127. [PMID: 38514245 PMCID: PMC11157658 DOI: 10.1021/jasms.3c00429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/02/2024] [Accepted: 03/13/2024] [Indexed: 03/23/2024]
Abstract
Capillary zone electrophoresis-mass spectrometry (CZE-MS) has been recognized as a valuable technique for the proteomics of mass-limited biological samples (i.e., single cells). However, its broad adoption for single cell proteomics (SCP) of human cells has been impeded by the low sample loading capacity of CZE, only allowing us to use less than 5% of the available peptide material for each measurement. Here we present a reversed-phase-based solid-phase microextraction (RP-SPME)-CZE-MS platform to solve the issue, paving the way for SCP of human cells using CZE-MS. The RP-SPME-CZE system was constructed in one fused silica capillary with zero dead volume for connection via in situ synthesis of a frit, followed by packing C8 beads into the capillary to form a roughly 2 mm long SPME section. Peptides captured by SPME were eluted with a buffer containing 30% (v/v) acetonitrile and 50 mM ammonium acetate (pH 6.5), followed by dynamic pH junction-based CZE-MS. The SPME-CZE-MS enabled the injection of nearly 40% of the available peptide sample for each measurement. The system identified 257 ± 24 proteins and 523 ± 69 peptides (N = 2) using a Q-Exactive HF mass spectrometer when only 0.25 ng of a commercial HeLa cell digest was available in the sample vial and 0.1 ng of the sample was injected. The amount of available peptide is equivalent to the protein mass of one HeLa cell. The data indicate that SPME-CZE-MS is ready for SCP of human cells.
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Affiliation(s)
- Jorge
A. Colón Rosado
- Department of Chemistry, Michigan
State University, 578 S Shaw Lane, East Lansing, Michigan 48824, United States
| | - Liangliang Sun
- Department of Chemistry, Michigan
State University, 578 S Shaw Lane, East Lansing, Michigan 48824, United States
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8
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Giansanti V, Giannese F, Botrugno OA, Gandolfi G, Balestrieri C, Antoniotti M, Tonon G, Cittaro D. Scalable integration of multiomic single-cell data using generative adversarial networks. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae300. [PMID: 38696763 DOI: 10.1093/bioinformatics/btae300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/22/2024] [Accepted: 04/30/2024] [Indexed: 05/04/2024]
Abstract
MOTIVATION Single-cell profiling has become a common practice to investigate the complexity of tissues, organs, and organisms. Recent technological advances are expanding our capabilities to profile various molecular layers beyond the transcriptome such as, but not limited to, the genome, the epigenome, and the proteome. Depending on the experimental procedure, these data can be obtained from separate assays or the very same cells. Yet, integration of more than two assays is currently not supported by the majority of the computational frameworks avaiable. RESULTS We here propose a Multi-Omic data integration framework based on Wasserstein Generative Adversarial Networks suitable for the analysis of paired or unpaired data with a high number of modalities (>2). At the core of our strategy is a single network trained on all modalities together, limiting the computational burden when many molecular layers are evaluated. AVAILABILITY AND IMPLEMENTATION Source code of our framework is available at https://github.com/vgiansanti/MOWGAN.
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Affiliation(s)
- Valentina Giansanti
- Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, 20125, Italy
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
| | - Francesca Giannese
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
| | - Oronza A Botrugno
- Functional Genomics of Cancer Unit, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
- Università Vita-Salute San Raffaele, Milan, 20132, Italy
| | - Giorgia Gandolfi
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
| | - Chiara Balestrieri
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
- Experimental Hematology Unit, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
| | - Marco Antoniotti
- Department of Informatics, Systems and Communication, Università degli Studi di Milano-Bicocca, Milan, 20125, Italy
- Bicocca Bioinformatics Biostatistics and Bioimaging Centre-B4, Università degli Studi di Milano-Bicocca, Milan, 20125, Italy
- Istituto di Bioimmagini e Fisiologia Molecolare, Consiglio Nazionale delle Ricerche (CNR), Milan, 20090, Italy
| | - Giovanni Tonon
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
- Functional Genomics of Cancer Unit, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
- Università Vita-Salute San Raffaele, Milan, 20132, Italy
| | - Davide Cittaro
- Center for Omics Sciences, IRCCS San Raffaele Scientific Institute, Milan, 20132, Italy
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9
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Yu SH, Chen SC, Wu PS, Kuo PI, Chen TA, Lee HY, Lin MH. Quantification Quality Control Emerges as a Crucial Factor to Enhance Single-Cell Proteomics Data Analysis. Mol Cell Proteomics 2024; 23:100768. [PMID: 38621647 PMCID: PMC11103571 DOI: 10.1016/j.mcpro.2024.100768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 03/12/2024] [Accepted: 04/11/2024] [Indexed: 04/17/2024] Open
Abstract
Mass spectrometry (MS)-based single-cell proteomics (SCP) provides us the opportunity to unbiasedly explore biological variability within cells without the limitation of antibody availability. This field is rapidly developed with the main focuses on instrument advancement, sample preparation refinement, and signal boosting methods; however, the optimal data processing and analysis are rarely investigated which holds an arduous challenge because of the high proportion of missing values and batch effect. Here, we introduced a quantification quality control to intensify the identification of differentially expressed proteins (DEPs) by considering both within and across SCP data. Combining quantification quality control with isobaric matching between runs (IMBR) and PSM-level normalization, an additional 12% and 19% of proteins and peptides, with more than 90% of proteins/peptides containing valid values, were quantified. Clearly, quantification quality control was able to reduce quantification variations and q-values with the more apparent cell type separations. In addition, we found that PSM-level normalization performed similar to other protein-level normalizations but kept the original data profiles without the additional requirement of data manipulation. In proof of concept of our refined pipeline, six uniquely identified DEPs exhibiting varied fold-changes and playing critical roles for melanoma and monocyte functionalities were selected for validation using immunoblotting. Five out of six validated DEPs showed an identical trend with the SCP dataset, emphasizing the feasibility of combining the IMBR, cell quality control, and PSM-level normalization in SCP analysis, which is beneficial for future SCP studies.
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Affiliation(s)
- Sung-Huan Yu
- Institute of Precision Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan; School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Shiau-Ching Chen
- Institute of Precision Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Pei-Shan Wu
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Pei-I Kuo
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Ting-An Chen
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Hsiang-Ying Lee
- Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; Department of Urology, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Miao-Hsia Lin
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan.
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10
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Xu J, Huang D, Zhang X. scmFormer Integrates Large-Scale Single-Cell Proteomics and Transcriptomics Data by Multi-Task Transformer. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307835. [PMID: 38483032 PMCID: PMC11109621 DOI: 10.1002/advs.202307835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/24/2024] [Indexed: 05/23/2024]
Abstract
Transformer-based models have revolutionized single cell RNA-seq (scRNA-seq) data analysis. However, their applicability is challenged by the complexity and scale of single-cell multi-omics data. Here a novel single-cell multi-modal/multi-task transformer (scmFormer) is proposed to fill up the existing blank of integrating single-cell proteomics with other omics data. Through systematic benchmarking, it is demonstrated that scmFormer excels in integrating large-scale single-cell multimodal data and heterogeneous multi-batch paired multi-omics data, while preserving shared information across batchs and distinct biological information. scmFormer achieves 54.5% higher average F1 score compared to the second method in transferring cell-type labels from single-cell transcriptomics to proteomics data. Using COVID-19 datasets, it is presented that scmFormer successfully integrates over 1.48 million cells on a personal computer. Moreover, it is also proved that scmFormer performs better than existing methods on generating the unmeasured modality and is well-suited for spatial multi-omic data. Thus, scmFormer is a powerful and comprehensive tool for analyzing single-cell multi-omics data.
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Affiliation(s)
- Jing Xu
- Key Laboratory of Plant Germplasm Enhancement and Specialty AgricultureWuhan Botanical GardenChinese Academy of SciencesWuhan430074China
- University of Chinese Academy of SciencesBeijing100049China
| | - De‐Shuang Huang
- Eastern Institute for Advanced StudyEastern Institute of TechnologyNingbo315200China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty AgricultureWuhan Botanical GardenChinese Academy of SciencesWuhan430074China
- Center of Economic BotanyCore Botanical GardensChinese Academy of SciencesWuhan430074China
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11
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Wang W, Yang L, Sun H, Peng X, Yuan J, Zhong W, Chen J, He X, Ye L, Zeng Y, Gao Z, Li Y, Qu X. Cellular nucleus image-based smarter microscope system for single cell analysis. Biosens Bioelectron 2024; 250:116052. [PMID: 38266616 DOI: 10.1016/j.bios.2024.116052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 12/31/2023] [Accepted: 01/18/2024] [Indexed: 01/26/2024]
Abstract
Cell imaging technology is undoubtedly a powerful tool for studying single-cell heterogeneity due to its non-invasive and visual advantages. It covers microscope hardware, software, and image analysis techniques, which are hindered by low throughput owing to abundant hands-on time and expertise. Herein, a cellular nucleus image-based smarter microscope system for single-cell analysis is reported to achieve high-throughput analysis and high-content detection of cells. By combining the hardware of an automatic fluorescence microscope and multi-object recognition/acquisition software, we have achieved more advanced process automation with the assistance of Robotic Process Automation (RPA), which realizes a high-throughput collection of single-cell images. Automated acquisition of single-cell images has benefits beyond ease and throughout and can lead to uniform standard and higher quality images. We further constructed a single-cell image database-based convolutional neural network (Efficient Convolutional Neural Network, E-CNN) exceeding 20618 single-cell nucleus images. Computational analysis of large and complex data sets enhances the content and efficiency of single-cell analysis with the assistance of Artificial Intelligence (AI), which breaks through the super-resolution microscope's hardware limitation, such as specialized light sources with specific wavelengths, advanced optical components, and high-performance graphics cards. Our system can identify single-cell nucleus images that cannot be artificially distinguished with an accuracy of 95.3%. Overall, we build an ordinary microscope into a high-throughput analysis and high-content smarter microscope system, making it a candidate tool for Imaging cytology.
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Affiliation(s)
- Wentao Wang
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China
| | - Lin Yang
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China
| | - Hang Sun
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China
| | - Xiaohong Peng
- YueYang Central Hospital, YueYang, Hunan Province, 414000, China
| | - Junjie Yuan
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China
| | - Wenhao Zhong
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China
| | - Jinqi Chen
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China
| | - Xin He
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China
| | - Lingzhi Ye
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China
| | - Yi Zeng
- College of Chemistry and Chemical Engineering, Huanggang Normal University, Huanggang, 438000, China
| | - Zhifan Gao
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China.
| | - Yunhui Li
- Department of Laboratory Medical Center, General Hospital of Northern Theater Command, No.83, Wenhua Road, Shenhe District, Shenyang, Liaoning Province, 110016, China.
| | - Xiangmeng Qu
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong Province, 518017, China.
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12
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Cai L, Lin L, Lin S, Wang X, Chen Y, Zhu H, Zhu Z, Yang L, Xu X, Yang C. Highly Multiplexing, Throughput and Efficient Single-Cell Protein Analysis with Digital Microfluidics. SMALL METHODS 2024:e2400375. [PMID: 38607945 DOI: 10.1002/smtd.202400375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Indexed: 04/14/2024]
Abstract
Proteins as crucial components of cells are responsible for the majority of cellular processes. Sensitive and efficient protein detection enables a more accurate and comprehensive investigation of cellular phenotypes and life activities. Here, a protein sequencing method with high multiplexing, high throughput, high cell utilization, and integration based on digital microfluidics (DMF-Protein-seq) is proposed, which transforms protein information into DNA sequencing readout via DNA-tagged antibodies and labels single cells with unique cell barcodes. In a 184-electrode DMF-Protein-seq system, ≈1800 cells are simultaneously detected per experimental run. The digital microfluidics device harnessing low-adsorbed hydrophobic surface and contaminants-isolated reaction space supports high cell utilization (>90%) and high mapping reads (>90%) with the input cells ranging from 140 to 2000. This system leverages split&pool strategy on the DMF chip for the first time to overcome DMF platform restriction in cell analysis throughput and replace the traditionally tedious bench-top combinatorial barcoding. With the benefits of high efficiency and sensitivity in protein analysis, the system offers great potential for cell classification and drug monitoring based on protein expression at the single-cell level.
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Affiliation(s)
- Linfeng Cai
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Li Lin
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Shiyan Lin
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Xuanqun Wang
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Yingwen Chen
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Huanghuang Zhu
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Zhi Zhu
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Liu Yang
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Xing Xu
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Chaoyong Yang
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, Department of Chemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
- Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
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13
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Billerbeck S, Walker RSK, Pretorius IS. Killer yeasts: expanding frontiers in the age of synthetic biology. Trends Biotechnol 2024:S0167-7799(24)00067-2. [PMID: 38575438 DOI: 10.1016/j.tibtech.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/07/2024] [Accepted: 03/07/2024] [Indexed: 04/06/2024]
Abstract
Killer yeasts secrete protein toxins that are selectively lethal to other yeast and filamentous fungi. These exhibit exceptional genetic and functional diversity, and have several biotechnological applications. However, despite decades of research, several limitations hinder their widespread adoption. In this perspective we contend that technical advances in synthetic biology present an unprecedented opportunity to unlock the full potential of yeast killer systems across a spectrum of applications. By leveraging these new technologies, engineered killer toxins may emerge as a pivotal new tool to address antifungal resistance and food security. Finally, we speculate on the biotechnological potential of re-engineering host double-stranded (ds) RNA mycoviruses, from which many toxins derive, as a safe and noninfectious system to produce designer RNA.
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Affiliation(s)
- Sonja Billerbeck
- Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology institute, University of Groningen, Groningen 9747, AG, The Netherlands
| | - Roy S K Walker
- Department of Molecular Sciences, Macquarie University, Sydney, New South Wales 2109, Australia; ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Isak S Pretorius
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales 2109, Australia.
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14
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Bhardwaj S, Bulluss M, D'Aubeterre A, Derakhshani A, Penner R, Mahajan M, Mahajan VB, Dufour A. Integrating the analysis of human biopsies using post-translational modifications proteomics. Protein Sci 2024; 33:e4979. [PMID: 38533548 DOI: 10.1002/pro.4979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/07/2024] [Accepted: 03/16/2024] [Indexed: 03/28/2024]
Abstract
Proteome diversities and their biological functions are significantly amplified by post-translational modifications (PTMs) of proteins. Shotgun proteomics, which does not typically survey PTMs, provides an incomplete picture of the complexity of human biopsies in health and disease. Recent advances in mass spectrometry-based proteomic techniques that enrich and study PTMs are helping to uncover molecular detail from the cellular level to system-wide functions, including how the microbiome impacts human diseases. Protein heterogeneity and disease complexity are challenging factors that make it difficult to characterize and treat disease. The search for clinical biomarkers to characterize disease mechanisms and complexity related to patient diagnoses and treatment has proven challenging. Knowledge of PTMs is fundamentally lacking. Characterization of complex human samples that clarify the role of PTMs and the microbiome in human diseases will result in new discoveries. This review highlights the key role of proteomic techniques used to characterize unknown biological functions of PTMs derived from complex human biopsies. Through the integration of diverse methods used to profile PTMs, this review explores the genetic regulation of proteoforms, cells of origin expressing specific proteins, and several bioactive PTMs and their subsequent analyses by liquid chromatography and tandem mass spectrometry.
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Affiliation(s)
- Sonali Bhardwaj
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Mitchell Bulluss
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Ana D'Aubeterre
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Afshin Derakhshani
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Regan Penner
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - MaryAnn Mahajan
- Molecular Surgery Laboratory, Stanford University, Palo Alto, California, USA
| | - Vinit B Mahajan
- Molecular Surgery Laboratory, Stanford University, Palo Alto, California, USA
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California, USA
| | - Antoine Dufour
- Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- McCaig Institute for Bone and Joint Health, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
- Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
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15
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Tian T, Lin S, Yang C. Beyond single cells: microfluidics empowering multiomics analysis. Anal Bioanal Chem 2024; 416:2203-2220. [PMID: 38008783 DOI: 10.1007/s00216-023-05028-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 10/26/2023] [Accepted: 10/30/2023] [Indexed: 11/28/2023]
Abstract
Single-cell multiomics technologies empower simultaneous measurement of multiple types of molecules within individual cells, providing a more profound comprehension compared with the analysis of discrete molecular layers from different cells. Microfluidic technology, on the other hand, has emerged as a pivotal facilitator for high-throughput single-cell analysis, offering precise control and manipulation of individual cells. The primary focus of this review encompasses an appraisal of cutting-edge microfluidic platforms employed in the realm of single-cell multiomics analysis. Furthermore, it discusses technological advancements in various single-cell omics such as genomics, transcriptomics, epigenomics, and proteomics, with their perspective applications. Finally, it provides future prospects of these integrated single-cell multiomics methodologies, shedding light on the possibilities for future biological research.
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Affiliation(s)
- Tian Tian
- Chemistry and Biomedicine Innovation Center (ChemBIC), School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Shichao Lin
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province, Xiamen, 361005, China
| | - Chaoyong Yang
- Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province, Xiamen, 361005, China.
- The MOE Key Laboratory of Spectrochemical Analysis and Instrumentation, State Key Laboratory of Physical Chemistry of Solid Surfaces, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China.
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16
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Chen J, Chu Z, Zhang Q, Wang C, Luo P, Zhang Y, Xia F, Gu L, Wong YK, Shi Q, Xu C, Tang H, Wang J. STEP: profiling cellular-specific targets and pathways of bioactive small molecules in tissues via integrating single-cell transcriptomics and chemoproteomics. Chem Sci 2024; 15:4313-4321. [PMID: 38516082 PMCID: PMC10952072 DOI: 10.1039/d3sc04826h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 02/06/2024] [Indexed: 03/23/2024] Open
Abstract
Identifying the cellular targets of bioactive small molecules within tissues has been a major concern in drug discovery and chemical biology research. Compared to cell line models, tissues consist of multiple cell types and complicated microenvironments. Therefore, elucidating the distribution and heterogeneity of targets across various cells in tissues would enhance the mechanistic understanding of drug or toxin action in real-life scenarios. Here, we present a novel multi-omics integration pipeline called Single-cell TargEt Profiling (STEP) that enables the global profiling of protein targets in mammalian tissues with single-cell resolution. This pipeline integrates single-cell transcriptome datasets with tissue-level protein target profiling using chemoproteomics. Taking well-established classic drugs such as aspirin, aristolochic acid, and cisplatin as examples, we confirmed the specificity and precision of cellular drug-target profiles and their associated molecular pathways in tissues using the STEP analysis. Our findings provide more informative insights into the action modes of bioactive molecules compared to in vitro models. Collectively, STEP represents a novel strategy for profiling cellular-specific targets and functional processes with unprecedented resolution.
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Affiliation(s)
- Jiayun Chen
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Zheng Chu
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Qian Zhang
- School of Traditional Chinese Medicine and School of Pharmaceutical Sciences, Southern Medical University Guangzhou 510515 China
| | - Chen Wang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Piao Luo
- School of Traditional Chinese Medicine and School of Pharmaceutical Sciences, Southern Medical University Guangzhou 510515 China
| | - Ying Zhang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Fei Xia
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Liwei Gu
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Yin Kwan Wong
- Department of Nephrology, Shenzhen Key Laboratory of Kidney Diseases, Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital, The First Affiliated Hospital, Southern University of Science and Technology Shenzhen 518020 China
| | - Qiaoli Shi
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Chengchao Xu
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Huan Tang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
| | - Jigang Wang
- State Key Laboratory for Quality Ensurance and Sustainable Use of Dao-di Herbs, Artemisinin Research Center and Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences Beijing 100700 China
- School of Traditional Chinese Medicine and School of Pharmaceutical Sciences, Southern Medical University Guangzhou 510515 China
- Department of Nephrology, Shenzhen Key Laboratory of Kidney Diseases, Shenzhen Clinical Research Centre for Geriatrics, Shenzhen People's Hospital, The First Affiliated Hospital, Southern University of Science and Technology Shenzhen 518020 China
- State Key Laboratory of Antiviral Drugs, School of Pharmacy, Henan University Kaifeng 475004 China
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17
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Ghaffari S, Saleh M, Akbari B, Ramezani F, Mirzaei HR. Applications of single-cell omics for chimeric antigen receptor T cell therapy. Immunology 2024; 171:339-364. [PMID: 38009707 DOI: 10.1111/imm.13720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 11/13/2023] [Indexed: 11/29/2023] Open
Abstract
Chimeric antigen receptor (CAR) T cell therapy is a promising cancer treatment modality. The breakthroughs in CAR T cell therapy were, in part, possible with the help of cell analysis methods, such as single-cell analysis. Bulk analyses have provided invaluable information regarding the complex molecular dynamics of CAR T cells, but their results are an average of thousands of signals in CAR T or tumour cells. Since cancer is a heterogeneous disease where each minute detail of a subclone could change the outcome of the treatment, single-cell analysis could prove to be a powerful instrument in deciphering the secrets of tumour microenvironment for cancer immunotherapy. With the recent studies in all aspects of adoptive cell therapy making use of single-cell analysis, a comprehensive review of the recent preclinical and clinical findings in CAR T cell therapy was needed. Here, we categorized and summarized the key points of the studies in which single-cell analysis provided insights into the genomics, epigenomics, transcriptomics and proteomics as well as their respective multi-omics of CAR T cell therapy.
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Affiliation(s)
- Sasan Ghaffari
- Department of Immunology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida, USA
| | - Mahshid Saleh
- Wisconsin National Primate Research Center, University of Wisconsin Graduate School, Madison, Wisconsin, USA
| | - Behnia Akbari
- Department of Medical Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Faezeh Ramezani
- Department of Medical Biotechnology, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
- Department of Medical Laboratory Sciences, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Hamid Reza Mirzaei
- Department of Medical Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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18
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Qiao Y, Zhang Q, He Y, Cheng T, Tu J. A simple joint detection platform for high-throughput single-cell heterogeneity screening. Talanta 2024; 269:125460. [PMID: 38039667 DOI: 10.1016/j.talanta.2023.125460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 10/13/2023] [Accepted: 11/20/2023] [Indexed: 12/03/2023]
Abstract
Single cell heterogeneity plays an important role in many biological phenomena and distinguishing cells that exhibit certain mutation in sample could benefit clinical diagnose and drug screening. Typical single cell detection methods such as flow cytometry, in-situ hybridization, real-time amplification or sequencing test either protein or nucleic acid as target and usually require specialized instruments. Joint measurement of the both types of targets could be done by combining the above strategies precisely but also unwieldly. Methods for rapidly and parallelly screening single cells with target genotype and antigen is needed. In this study, we describe a gel plate platform to distinguish cell types based on their phenotypes on target gene and antigen with low equipment requirement. Integrated cell lysis and immobilization were done in the gel solidification step, after which antibody hybridization and real-time amplification were sequentially carried out without losing the original loci information of individual single cells so the three types of information of individual single cells could be combined to distinguished cells with expected genotype and phenotype. The easy-to-use gel platform has potential in point-of-care circumstances and single-cell stimulation response that have high requirements on efficiency and simplicity.
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Affiliation(s)
- Yi Qiao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Qiongdan Zhang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Yukun He
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Tianguang Cheng
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Jing Tu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China.
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19
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Liu Y, Wang Z, Zhuo Y, Wu H, Peng Y, Wang T, Peng T, Qiu L, Tan W. Aptamer-Based Multiparameter Analysis for Molecular Profiling of Hematological Malignancies. Anal Chem 2024; 96:3429-3435. [PMID: 38351845 DOI: 10.1021/acs.analchem.3c04717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
The subtypes of hematological malignancies (HM) with minimal molecular profile differences display an extremely heterogeneous clinical course and a discrepant response to certain treatment regimens. Profiling the surface protein markers offers a potent solution for precision diagnosis of HM by differentiating among the subtypes of cancer cells. Herein, we report the use of Cell-SELEX technology to generate a panel of high-affinity aptamer probes that are able to discriminate subtle differences among surface protein profiles between different HM cells. Experimental results show that these aptamers with apparent dissociation constants (Kd) below 10 nM display a unique recognition pattern on different HM subtypes. By combining a machine learning model on the basis of partial least-squares discriminant analysis, 100% accuracy was achieved for the classification of different HM cells. Furthermore, we preliminarily validated the effectiveness of the aptamer-based multiparameter analysis strategy from a clinical perspective by accurately classifying complex clinical samples, thus providing a promising molecular tool for precise HM phenotyping.
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Affiliation(s)
- Yue Liu
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Biology, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, Hunan 410082, China
| | - Zhimin Wang
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Biology, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, Hunan 410082, China
| | - Yuting Zhuo
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Biology, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, Hunan 410082, China
| | - Hui Wu
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Biology, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, Hunan 410082, China
| | - Ying Peng
- NHC Key Laboratory of Birth Defect for Research and Prevention (Hunan Provincial Maternal and Child Health Care Hospital), Changsha, Hunan 410008, China
| | - Tong Wang
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Biology, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, Hunan 410082, China
| | - Tianhuan Peng
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Biology, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, Hunan 410082, China
| | - Liping Qiu
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Biology, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, Hunan 410082, China
- The Key Laboratory of Zhejiang Province for Aptamers and Theranostics, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022 China
| | - Weihong Tan
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, College of Biology, Aptamer Engineering Center of Hunan Province, Hunan University, Changsha, Hunan 410082, China
- The Key Laboratory of Zhejiang Province for Aptamers and Theranostics, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022 China
- Institute of Molecular Medicine (IMM), Renji Hospital, Shanghai Jiao Tong University School of Medicine, and College of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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20
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Wang F, Liu C, Li J, Yang F, Song J, Zang T, Yao J, Wang G. SPDB: a comprehensive resource and knowledgebase for proteomic data at the single-cell resolution. Nucleic Acids Res 2024; 52:D562-D571. [PMID: 37953313 PMCID: PMC10767837 DOI: 10.1093/nar/gkad1018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/28/2023] [Accepted: 10/23/2023] [Indexed: 11/14/2023] Open
Abstract
The single-cell proteomics enables the direct quantification of protein abundance at the single-cell resolution, providing valuable insights into cellular phenotypes beyond what can be inferred from transcriptome analysis alone. However, insufficient large-scale integrated databases hinder researchers from accessing and exploring single-cell proteomics, impeding the advancement of this field. To fill this deficiency, we present a comprehensive database, namely Single-cell Proteomic DataBase (SPDB, https://scproteomicsdb.com/), for general single-cell proteomic data, including antibody-based or mass spectrometry-based single-cell proteomics. Equipped with standardized data process and a user-friendly web interface, SPDB provides unified data formats for convenient interaction with downstream analysis, and offers not only dataset-level but also protein-level data search and exploration capabilities. To enable detailed exhibition of single-cell proteomic data, SPDB also provides a module for visualizing data from the perspectives of cell metadata or protein features. The current version of SPDB encompasses 133 antibody-based single-cell proteomic datasets involving more than 300 million cells and over 800 marker/surface proteins, and 10 mass spectrometry-based single-cell proteomic datasets involving more than 4000 cells and over 7000 proteins. Overall, SPDB is envisioned to be explored as a useful resource that will facilitate the wider research communities by providing detailed insights into proteomics from the single-cell perspective.
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Affiliation(s)
- Fang Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
- AI Lab, Tencent, Shenzhen 518000, China
| | - Chunpu Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Jiawei Li
- College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
| | - Fan Yang
- AI Lab, Tencent, Shenzhen 518000, China
| | - Jiangning Song
- Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, VIC 3800, Australia
| | - Tianyi Zang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | | | - Guohua Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
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21
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Lian X, Zhang Y, Zhou Y, Sun X, Huang S, Dai H, Han L, Zhu F. SingPro: a knowledge base providing single-cell proteomic data. Nucleic Acids Res 2024; 52:D552-D561. [PMID: 37819028 PMCID: PMC10767818 DOI: 10.1093/nar/gkad830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 09/03/2023] [Accepted: 09/25/2023] [Indexed: 10/13/2023] Open
Abstract
Single-cell proteomics (SCP) has emerged as a powerful tool for detecting cellular heterogeneity, offering unprecedented insights into biological mechanisms that are masked in bulk cell populations. With the rapid advancements in AI-based time trajectory analysis and cell subpopulation identification, there exists a pressing need for a database that not only provides SCP raw data but also explicitly describes experimental details and protein expression profiles. However, no such database has been available yet. In this study, a database, entitled 'SingPro', specializing in single-cell proteomics was thus developed. It was unique in (a) systematically providing the SCP raw data for both mass spectrometry-based and flow cytometry-based studies and (b) explicitly describing experimental detail for SCP study and expression profile of any studied protein. Anticipating a robust interest from the research community, this database is poised to become an invaluable repository for OMICs-based biomedical studies. Access to SingPro is unrestricted and does not mandate a login at: http://idrblab.org/singpro/.
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Affiliation(s)
- Xichen Lian
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, Shanghai 315211, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yintao Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Ying Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shijie Huang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Haibin Dai
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Lianyi Han
- Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, Shanghai 315211, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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22
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Wang Y, Zhao J, Jiang Z, Ma Y, Zhang R. Single-Cell Proteomics by Barcoded Phage-Displayed Screening via an Integrated Microfluidic Chip. Methods Mol Biol 2024; 2793:101-112. [PMID: 38526726 DOI: 10.1007/978-1-0716-3798-2_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Recent advancements in the profiling of proteomes at the single-cell level necessitate the development of quantitative and versatile platforms, particularly for analyzing rare cells like circulating tumor cells (CTCs). In this chapter, we present an integrated microfluidic chip that utilizes magnetic nanoparticles to capture single tumor cells with exceptional efficiency. This chip enables on-chip incubation and facilitates in situ analysis of cell-surface protein expression. By combining phage-based barcoding with next-generation sequencing technology, we successfully monitored changes in the expression of multiple surface markers induced by CTC adherence. This innovative platform holds significant potential for comprehensive screening of multiple surface antigens simultaneously in rare cells, offering single-cell resolution. Consequently, it will contribute valuable insights into biological heterogeneity and human disease.
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Affiliation(s)
- Yujiao Wang
- State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing, China
| | - Jing Zhao
- State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing, China
- Department of Clinical Laboratory, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China
| | - Zhenwei Jiang
- State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing, China
| | - Yuan Ma
- State Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing, China.
| | - Rui Zhang
- Department of Clinical Laboratory, Beijing Chao-yang Hospital, Capital Medical University, Beijing, China.
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23
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Logsdon DM, Ezashi T, Yuan Y. Enzymatic Digestion and Single Cell Isolation of Peri-implantation Stage Human Trophoblast Cells. Methods Mol Biol 2024; 2728:25-34. [PMID: 38019389 DOI: 10.1007/978-1-0716-3495-0_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Recent developments in the in vitro culture of peri-implantation stage human embryos have expanded opportunities to investigate implantation stage human development and trophoblast differentiation in the absence of maternal tissues. Emerging single cell omics analyses have offered researchers new tools to explore unanswered biological questions to new depths. In order to investigate the dynamics of human trophoblast cell differentiation during implantation at the single-cell resolution, efficient cell dissociation approaches of trophoblasts from embryos are necessary. Here, we describe the protocol for extended culture of peri-implantation stage human embryos with enzymatic digestion and manual collection of individual cells for downstream assays.
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Affiliation(s)
| | - Toshihko Ezashi
- Colorado Centre for Reproductive Medicine, Lone Tree, CO, USA
| | - Ye Yuan
- Colorado Centre for Reproductive Medicine, Lone Tree, CO, USA.
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24
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Zhu L, Wong CCL. Nanoliter-Scale Sample Preparation for Single-Cell Proteomic Analysis Using Glass-Oil-Air-Droplet Chip. Methods Mol Biol 2024; 2817:45-56. [PMID: 38907146 DOI: 10.1007/978-1-0716-3934-4_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/23/2024]
Abstract
Single-cell proteomic analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems' heterogeneous populations. Mass spectrometry (MS)-based proteomics is a promising alternative for quantitative single-cell proteomics. Various techniques are continually evolving to address the challenges of limited sample material, detection sensitivity, and throughput constraints. In this chapter, we describe a nanoliter-scale glass-oil-air-droplet (gOAD) chip engineered for heat tolerance, which combines droplet-based microfluidics and shotgun proteomic analysis techniques to enable multistep sample pretreatment.
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Affiliation(s)
- Liu Zhu
- Department of Biochemistry and Biophysics, School of Basic Medical Sciences, Peking University, Beijing, China
| | - Catherine C L Wong
- The State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China
- Tsinghua University-Peking University Joint Center for Life Sciences, Tsinghua University, Beijing, China
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25
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Sun C, Shao Y, Iqbal J. Insect Insights at the Single-Cell Level: Technologies and Applications. Cells 2023; 13:91. [PMID: 38201295 PMCID: PMC10777908 DOI: 10.3390/cells13010091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 12/23/2023] [Accepted: 12/28/2023] [Indexed: 01/12/2024] Open
Abstract
Single-cell techniques are a promising way to unravel the complexity and heterogeneity of transcripts at the cellular level and to reveal the composition of different cell types and functions in a tissue or organ. In recent years, advances in single-cell RNA sequencing (scRNA-seq) have further changed our view of biological systems. The application of scRNA-seq in insects enables the comprehensive characterization of both common and rare cell types and cell states, the discovery of new cell types, and revealing how cell types relate to each other. The recent application of scRNA-seq techniques to insect tissues has led to a number of exciting discoveries. Here we provide an overview of scRNA-seq and its application in insect research, focusing on biological applications, current challenges, and future opportunities to make new discoveries with scRNA-seq in insects.
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Affiliation(s)
- Chao Sun
- Analysis Center of Agrobiology and Environmental Sciences, Zhejiang University, Hangzhou 310058, China;
| | - Yongqi Shao
- Institute of Sericulture and Apiculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
| | - Junaid Iqbal
- Institute of Sericulture and Apiculture, College of Animal Sciences, Zhejiang University, Hangzhou 310058, China
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26
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Demangel C, Surace L. Host-pathogen interactions from a metabolic perspective: methods of investigation. Microbes Infect 2023:105267. [PMID: 38007087 DOI: 10.1016/j.micinf.2023.105267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 10/21/2023] [Accepted: 11/21/2023] [Indexed: 11/27/2023]
Abstract
Metabolism shapes immune homeostasis in health and disease. This review presents the range of methods that are currently available to investigate the dialog between metabolism and immunity at the systemic, tissue and cellular levels, particularly during infection.
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Affiliation(s)
- Caroline Demangel
- Institut Pasteur, Université Paris Cité, Inserm U1224, Immunobiology and Therapy Unit, Paris, France
| | - Laura Surace
- Institute of Clinical Chemistry and Clinical Pharmacology, University Hospital Bonn, University of Bonn, Bonn, Germany
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27
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Tang L, Huang ZP, Mei H, Hu Y. Insights gained from single-cell analysis of chimeric antigen receptor T-cell immunotherapy in cancer. Mil Med Res 2023; 10:52. [PMID: 37941075 PMCID: PMC10631149 DOI: 10.1186/s40779-023-00486-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 10/10/2023] [Indexed: 11/10/2023] Open
Abstract
Advances in chimeric antigen receptor (CAR)-T cell therapy have significantly improved clinical outcomes of patients with relapsed or refractory hematologic malignancies. However, progress is still hindered as clinical benefit is only available for a fraction of patients. A lack of understanding of CAR-T cell behaviors in vivo at the single-cell level impedes their more extensive application in clinical practice. Mounting evidence suggests that single-cell sequencing techniques can help perfect the receptor design, guide gene-based T cell modification, and optimize the CAR-T manufacturing conditions, and all of them are essential for long-term immunosurveillance and more favorable clinical outcomes. The information generated by employing these methods also potentially informs our understanding of the numerous complex factors that dictate therapeutic efficacy and toxicities. In this review, we discuss the reasons why CAR-T immunotherapy fails in clinical practice and what this field has learned since the milestone of single-cell sequencing technologies. We further outline recent advances in the application of single-cell analyses in CAR-T immunotherapy. Specifically, we provide an overview of single-cell studies focusing on target antigens, CAR-transgene integration, and preclinical research and clinical applications, and then discuss how it will affect the future of CAR-T cell therapy.
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Affiliation(s)
- Lu Tang
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Medical Center of Cell Therapy for Neoplastic Disease, Wuhan, 430022, China
- Key Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, 430022, China
| | - Zhong-Pei Huang
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Medical Center of Cell Therapy for Neoplastic Disease, Wuhan, 430022, China
- Key Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, 430022, China
| | - Heng Mei
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Medical Center of Cell Therapy for Neoplastic Disease, Wuhan, 430022, China.
- Key Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, 430022, China.
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Yu Hu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Medical Center of Cell Therapy for Neoplastic Disease, Wuhan, 430022, China.
- Key Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, 430022, China.
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
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28
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Zhang S, Ghalandari B, Wang A, Li S, Chen Y, Wang Q, Jiang L, Ding X. Superparamagnetic Composite Nanobeads Anchored with Molecular Glues for Ultrasensitive Label-free Proteomics. Angew Chem Int Ed Engl 2023; 62:e202309806. [PMID: 37653561 DOI: 10.1002/anie.202309806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/31/2023] [Accepted: 08/31/2023] [Indexed: 09/02/2023]
Abstract
Mass spectrometry has emerged as a mainstream technique for label-free proteomics. However, proteomic coverage for trace samples is constrained by adsorption loss during repeated elution at sample pretreatment. Here, we demonstrated superparamagnetic composite nanoparticles functionalized with molecular glues (MGs) to enrich proteins in trace human biofluid. We showed high protein binding (>95 %) and recovery (≈90 %) rates by anchor-nanoparticles. We further proposed a Streamlined Workflow based on Anchor-nanoparticles for Proteomics (SWAP) method that enabled unbiased protein capture, protein digestion and pure peptides elution in one single tube. We demonstrated SWAP to quantify over 2500 protein groups with 100 HEK 293T cells. We adopted SWAP to profile proteomics with trace aqueous humor samples from cataract (n=15) and wet age-related macular degeneration (n=8) patients, and quantified ≈1400 proteins from 5 μL aqueous humor. SWAP simplifies sample preparation steps, minimizes adsorption loss and improves protein coverage for label-free proteomics with previous trace samples.
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Affiliation(s)
- Shuang Zhang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Behafarid Ghalandari
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Aiting Wang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Sijie Li
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Youming Chen
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Qingwen Wang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Lai Jiang
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Xianting Ding
- Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- State Key Laboratory of Oncogenes and Related Genes, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
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29
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Chen K, Wang Z. A Micropillar Array Based Microfluidic Device for Rare Cell Detection and Single-Cell Proteomics. Methods Protoc 2023; 6:80. [PMID: 37736963 PMCID: PMC10514859 DOI: 10.3390/mps6050080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 08/25/2023] [Accepted: 08/31/2023] [Indexed: 09/23/2023] Open
Abstract
Advancements in single-cell-related technologies have opened new possibilities for analyzing rare cells, such as circulating tumor cells (CTCs) and rare immune cells. Among these techniques, single-cell proteomics, particularly single-cell mass spectrometric analysis (scMS), has gained significant attention due to its ability to directly measure transcripts without the need for specific reagents. However, the success of single-cell proteomics relies heavily on efficient sample preparation, as protein loss in low-concentration samples can profoundly impact the analysis. To address this challenge, an effective handling system for rare cells is essential for single-cell proteomic analysis. Herein, we propose a microfluidics-based method that offers highly efficient isolation, detection, and collection of rare cells (e.g., CTCs). The detailed fabrication process of the micropillar array-based microfluidic device is presented, along with its application for CTC isolation, identification, and collection for subsequent proteomic analysis.
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Affiliation(s)
- Kangfu Chen
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL 60208, USA;
- Chan Zuckerberg Biohub Chicago, Chicago, IL 60607, USA
| | - Zongjie Wang
- Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL 60208, USA;
- Chan Zuckerberg Biohub Chicago, Chicago, IL 60607, USA
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30
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Zhang H, Liu Y, Fields L, Shi X, Huang P, Lu H, Schneider AJ, Tang X, Puglielli L, Welham NV, Li L. Single-cell lipidomics enabled by dual-polarity ionization and ion mobility-mass spectrometry imaging. Nat Commun 2023; 14:5185. [PMID: 37626051 PMCID: PMC10457347 DOI: 10.1038/s41467-023-40512-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 07/27/2023] [Indexed: 08/27/2023] Open
Abstract
Single-cell (SC) analysis provides unique insight into individual cell dynamics and cell-to-cell heterogeneity. Here, we utilize trapped ion mobility separation coupled with dual-polarity ionization mass spectrometry imaging (MSI) to enable high-throughput in situ profiling of the SC lipidome. Multimodal SC imaging, in which dual-polarity-mode MSI is used to perform serial data acquisition runs on individual cells, significantly enhanced SC lipidome coverage. High-spatial resolution SC-MSI identifies both inter- and intracellular lipid heterogeneity; this heterogeneity is further explicated by Uniform Manifold Approximation and Projection and machine learning-driven classifications. We characterize SC lipidome alteration in response to stearoyl-CoA desaturase 1 inhibition and, additionally, identify cell-layer specific lipid distribution patterns in mouse cerebellar cortex. This integrated multimodal SC-MSI technology enables high-resolution spatial mapping of intercellular and cell-to-cell lipidome heterogeneity, SC lipidome remodeling induced by pharmacological intervention, and region-specific lipid diversity within tissue.
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Affiliation(s)
- Hua Zhang
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Yuan Liu
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Lauren Fields
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Xudong Shi
- Division of Otolaryngology, Department of Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, 53792, USA
| | - Penghsuan Huang
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Haiyan Lu
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA
| | - Andrew J Schneider
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Xindi Tang
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA
| | - Luigi Puglielli
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Nathan V Welham
- Division of Otolaryngology, Department of Surgery, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, 53792, USA
| | - Lingjun Li
- School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, 53705, USA.
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, 53706, USA.
- Lachman Institute for Pharmaceutical Development, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Wisconsin Center for NanoBioSystems, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA.
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31
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Hlaváček A, Uhrová K, Weisová J, Křivánková J. Artificial Intelligence-Aided Massively Parallel Spectroscopy of Freely Diffusing Nanoscale Entities. Anal Chem 2023; 95:12256-12263. [PMID: 37552526 PMCID: PMC10448498 DOI: 10.1021/acs.analchem.3c01043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 07/25/2023] [Indexed: 08/09/2023]
Abstract
Massively parallel spectroscopy (MPS) of many single nanoparticles in an aqueous dispersion is reported. As a model system, bioconjugated photon-upconversion nanoparticles (UCNPs) with a near-infrared excitation are prepared. The UCNPs are doped either with Tm3+ (emission 450 and 802 nm) or Er3+ (emission 554 and 660 nm). These UCNPs are conjugated to biotinylated bovine serum albumin (Tm3+-doped) or streptavidin (Er3+-doped). MPS is correlated with an ensemble spectra measurement, and the limit of detection (1.6 fmol L-1) and the linearity range (4.8 fmol L-1 to 40 pmol L-1) for bioconjugated UCNPs are estimated. MPS is used for observing the bioaffinity clustering of bioconjugated UCNPs. This observation is correlated with a native electrophoresis and bioaffinity assay on a microtiter plate. A competitive MPS bioaffinity assay for biotin is developed and characterized with a limit of detection of 6.6 nmol L-1. MPS from complex biological matrices (cell cultivation medium) is performed without increasing background. The compatibility with polydimethylsiloxane microfluidics is proven by recording MPS from a 30 μm deep microfluidic channel.
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Affiliation(s)
- Antonín Hlaváček
- Institute of Analytical
Chemistry of the Czech Academy of Sciences, Veveří 97, 602 00 Brno, Czech
Republic
| | - Kateřina Uhrová
- Institute of Analytical
Chemistry of the Czech Academy of Sciences, Veveří 97, 602 00 Brno, Czech
Republic
| | - Julie Weisová
- Institute of Analytical
Chemistry of the Czech Academy of Sciences, Veveří 97, 602 00 Brno, Czech
Republic
| | - Jana Křivánková
- Institute of Analytical
Chemistry of the Czech Academy of Sciences, Veveří 97, 602 00 Brno, Czech
Republic
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32
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Keele GR, Zhang JG, Szpyt J, Korstanje R, Gygi SP, Churchill GA, Schweppe DK. Global and tissue-specific aging effects on murine proteomes. Cell Rep 2023; 42:112715. [PMID: 37405913 PMCID: PMC10588767 DOI: 10.1016/j.celrep.2023.112715] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 03/06/2023] [Accepted: 06/13/2023] [Indexed: 07/07/2023] Open
Abstract
Maintenance of protein homeostasis degrades with age, contributing to aging-related decline and disease. Previous studies have primarily surveyed transcriptional aging changes. To define the effects of age directly at the protein level, we perform discovery-based proteomics in 10 tissues from 20 C57BL/6J mice, representing both sexes at adult and late midlife ages (8 and 18 months). Consistent with previous studies, age-related changes in protein abundance often have no corresponding transcriptional change. Aging results in increases in immune proteins across all tissues, consistent with a global pattern of immune infiltration with age. Our protein-centric data reveal tissue-specific aging changes with functional consequences, including altered endoplasmic reticulum and protein trafficking in the spleen. We further observe changes in the stoichiometry of protein complexes with important roles in protein homeostasis, including the CCT/TriC complex and large ribosomal subunit. These data provide a foundation for understanding how proteins contribute to systemic aging across tissues.
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Affiliation(s)
| | | | - John Szpyt
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Steven P Gygi
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Devin K Schweppe
- Department of Genome Sciences, University of Washington, Seattle, WA 98105, USA.
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33
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Chen M, Jiang J, Hou J. Single-cell technologies in multiple myeloma: new insights into disease pathogenesis and translational implications. Biomark Res 2023; 11:55. [PMID: 37259170 DOI: 10.1186/s40364-023-00502-8] [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: 03/19/2023] [Accepted: 05/12/2023] [Indexed: 06/02/2023] Open
Abstract
Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Although therapeutic advances have been made to improve clinical outcomes and to prolong patients' survival in the past two decades, MM remains largely incurable. Single-cell sequencing (SCS) is a powerful method to dissect the cellular and molecular landscape at single-cell resolution, instead of providing averaged results. The application of single-cell technologies promises to address outstanding questions in myeloma biology and has revolutionized our understanding of the inter- and intra-tumor heterogeneity, tumor microenvironment, and mechanisms of therapeutic resistance in MM. In this review, we summarize the recently developed SCS methodologies and latest MM research progress achieved by single-cell profiling, including information regarding the cancer and immune cell landscapes, tumor heterogeneities, underlying mechanisms and biomarkers associated with therapeutic response and resistance. We also discuss future directions of applying transformative SCS approaches with contribution to clinical translation.
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Affiliation(s)
- Mengping Chen
- Department of Hematology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Jinxing Jiang
- Department of Hematology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Jian Hou
- Department of Hematology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
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34
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Zhang Y, Sun H, Lian X, Tang J, Zhu F. ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2207061. [PMID: 36950745 DOI: 10.1002/advs.202207061] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/13/2023] [Indexed: 05/27/2023]
Abstract
ANPELA is widely used for quantifying traditional bulk proteomic data. Recently, there is a clear shift from bulk proteomics to the single-cell ones (SCP), for which powerful cytometry techniques demonstrate the fantastic capacity of capturing cellular heterogeneity that is completely overlooked by traditional bulk profiling. However, the in-depth and high-quality quantification of SCP data is still challenging and severely affected by the large numbers of quantification workflows and extreme performance dependence on the studied datasets. In other words, the proper selection of well-performing workflow(s) for any studied dataset is elusory, and it is urgently needed to have a significantly enhanced and accelerated tool to address this issue. However, no such tool is developed yet. Herein, ANPELA is therefore updated to its 2.0 version (https://idrblab.org/anpela/), which is unique in providing the most comprehensive set of quantification alternatives (>1000 workflows) among all existing tools, enabling systematic performance evaluation from multiple perspectives based on machine learning, and identifying the optimal workflow(s) using overall performance ranking together with the parallel computation. Extensive validation on different benchmark datasets and representative application scenarios suggest the great application potential of ANPELA in current SCP research for gaining more accurate and reliable biological insights.
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Affiliation(s)
- Ying Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Xichen Lian
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Jing Tang
- Department of Bioinformatics, Chongqing Medical University, Chongqing, 400016, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
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35
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Gebreyesus ST, Muneer G, Huang CC, Siyal AA, Anand M, Chen YJ, Tu HL. Recent advances in microfluidics for single-cell functional proteomics. LAB ON A CHIP 2023; 23:1726-1751. [PMID: 36811978 DOI: 10.1039/d2lc01096h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic view of biological details underlying cellular processes, disease onset and progression, as well as facilitates unique biomarker identification from individual cells. Microfluidic-based strategies have become methods of choice for single-cell analysis because they allow facile assay integrations, such as cell sorting, manipulation, and content analysis. Notably, they have been serving as an enabling technology to improve the sensitivity, robustness, and reproducibility of recently developed SCP methods. Critical roles of microfluidics technologies are expected to further expand rapidly in advancing the next phase of SCP analysis to reveal more biological and clinical insights. In this review, we will capture the excitement of the recent achievements of microfluidics methods for both targeted and global SCP, including efforts to enhance the proteomic coverage, minimize sample loss, and increase multiplexity and throughput. Furthermore, we will discuss the advantages, challenges, applications, and future prospects of SCP.
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Affiliation(s)
- Sofani Tafesse Gebreyesus
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Nano Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Gul Muneer
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Institute of Biochemical Sciences, National Taiwan University, Taipei 10617, Taiwan
| | | | - Asad Ali Siyal
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
| | - Mihir Anand
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Institute of Biochemical Sciences, National Taiwan University, Taipei 10617, Taiwan
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei 10617, Taiwan
| | - Hsiung-Lin Tu
- Institute of Chemistry, Academia Sinica, Taipei 11529, Taiwan.
- Nano Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Chemical Biology and Molecular Biophysics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Genome and Systems Biology Degree Program, Academia Sinica and National Taiwan University, Taipei 10617, Taiwan
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36
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Maniyadath B, Zhang Q, Gupta RK, Mandrup S. Adipose tissue at single-cell resolution. Cell Metab 2023; 35:386-413. [PMID: 36889280 PMCID: PMC10027403 DOI: 10.1016/j.cmet.2023.02.002] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Revised: 01/22/2023] [Accepted: 02/03/2023] [Indexed: 03/09/2023]
Abstract
Adipose tissue exhibits remarkable plasticity with capacity to change in size and cellular composition under physiological and pathophysiological conditions. The emergence of single-cell transcriptomics has rapidly transformed our understanding of the diverse array of cell types and cell states residing in adipose tissues and has provided insight into how transcriptional changes in individual cell types contribute to tissue plasticity. Here, we present a comprehensive overview of the cellular atlas of adipose tissues focusing on the biological insight gained from single-cell and single-nuclei transcriptomics of murine and human adipose tissues. We also offer our perspective on the exciting opportunities for mapping cellular transitions and crosstalk, which have been made possible by single-cell technologies.
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Affiliation(s)
- Babukrishna Maniyadath
- Center for Functional Genomics and Tissue Plasticity, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M, Denmark
| | - Qianbin Zhang
- Department of Internal Medicine, Touchstone Diabetes Center, UT Southwestern Medical Center, Dallas, TX, USA
| | - Rana K Gupta
- Department of Internal Medicine, Touchstone Diabetes Center, UT Southwestern Medical Center, Dallas, TX, USA.
| | - Susanne Mandrup
- Center for Functional Genomics and Tissue Plasticity, Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense M, Denmark.
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37
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Zhang Y, Liu X, Li N, Xu Y, Ma Y, Huang Z, Luo H, Hou C, Huo D. Typing of cancer cells by microswimmer based on Co-Fe-MOF for one-step simultaneously detect multiple biomarkers. Biosens Bioelectron 2023; 230:115263. [PMID: 37003060 DOI: 10.1016/j.bios.2023.115263] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 03/19/2023] [Accepted: 03/23/2023] [Indexed: 03/29/2023]
Abstract
Capturing, identifying, and counting CTCs cancer cells that have escaped from the tumor and wandered into the bloodstream is a major challenge. We proposed a noval microswimmer dual-mode aptamer (electrochemical and fluorescent)-(Mapt-EF) homogeneous sensor with active capture/controlled release double signaling molecule/separation and release cell based on the Co-Fe-MOF nanomaterial for simultaneous one-step detection of multiple biomarkers protein tyrosine kinase-7 (PTK7), Epithelial cell adhesion molecule (EpCAM), and mucin-1 (MUC1) for diagnosis of multiple cancer cell types. The Co-Fe-MOF is a nano-enzyme capable of catalyzing the decomposition of hydrogen peroxide to release bubbles of oxygen, producing a driving force to conduct hydrogen peroxide through the liquid, and has the capacity to self-decompose during the catalytic process. Phosphoric acid is present in the aptamer chains of PTK7, EpCAM, and MUC1, and the aptamer chains are adsorbed to the surface of the Mapt-EF homogeneous sensor in the form of a gated switch to inhibit the catalytic decomposition activity of hydrogen peroxide. The Mapt-EF homogeneous sensor has the capability to actively target biomarkers that can be entrained by oxygen bubbles without being degraded. The detection time of the sensor was 20 min, the detection limits were 9.6 fg/mL, 8.4 fg/mL and 7.7 fg/mL with the linear range was 0-20 pg/mL, respectively. The Mapt-EF homogeneous sensor has high detection sensitivity, and its detection limit can reach the level of single cell at the lowest. The Mapt-EF homogeneous sensor has great application potential in clinical detection and analysis of tumor cells.
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38
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Wenzel TJ, Le J, He J, Alcorn J, Mousseau DD. Fundamental Neurochemistry Review: Incorporating a greater diversity of cell types, including microglia, in brain organoid cultures improves clinical translation. J Neurochem 2023; 164:560-582. [PMID: 36517959 DOI: 10.1111/jnc.15741] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 12/03/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022]
Abstract
Brain organoids have the potential to improve clinical translation, with the added benefit of reducing any extraneous use of experimental animals. As brain organoids are three-dimensional in vitro constructs that emulate the human brain, they bridge in vitro and in vivo studies more appropriately than monocultures. Although many factors contribute to the failure of extrapolating monoculture-based information to animal-based experiments and clinical trials, for the purpose of this review, we will focus on glia (non-neuronal brain cells), whose functions and transcriptome are particularly abnormal in monocultures. As discussed herein, glia require signals from-and contact with-other cell types to exist in their homeostatic state, which likely contributes to some of the differences between data derived from monocultures and data derived from brain organoids and even two-dimensional co-cultures. Furthermore, we highlight transcriptomic differences between humans and mice in regard to aging and Alzheimer's disease, emphasizing need for a model using the human genome-again, a benefit of brain organoids-to complement data derived from animals. We also identify an urgency for guidelines to improve the reporting and transparency of research using organoids. The lack of reporting standards creates challenges for the comparison and discussion of data from different articles. Importantly, brain organoids mark the first human model enabling the study of brain cytoarchitecture and development.
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Affiliation(s)
- Tyler J Wenzel
- Cell Signalling Laboratory, Department of Psychiatry, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada.,College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Jennifer Le
- Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Jim He
- Toxicology Centre, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Jane Alcorn
- College of Pharmacy and Nutrition, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
| | - Darrell D Mousseau
- Cell Signalling Laboratory, Department of Psychiatry, College of Medicine, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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39
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Single-cell proteomics enabled by next-generation sequencing or mass spectrometry. Nat Methods 2023; 20:363-374. [PMID: 36864196 DOI: 10.1038/s41592-023-01791-5] [Citation(s) in RCA: 66] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 01/24/2023] [Indexed: 03/04/2023]
Abstract
In the last decade, single-cell RNA sequencing routinely performed on large numbers of single cells has greatly advanced our understanding of the underlying heterogeneity of complex biological systems. Technological advances have also enabled protein measurements, further contributing to the elucidation of cell types and states present in complex tissues. Recently, there have been independent advances in mass spectrometric techniques bringing us one step closer to characterizing single-cell proteomes. Here we discuss the challenges of detecting proteins in single cells by both mass spectrometry and sequencing-based methods. We review the state of the art for these techniques and propose that there is a space for technological advancements and complementary approaches that maximize the advantages of both classes of technologies.
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40
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Zhao W, Johnston KG, Ren H, Xu X, Nie Q. Inferring neuron-neuron communications from single-cell transcriptomics through NeuronChat. Nat Commun 2023; 14:1128. [PMID: 36854676 PMCID: PMC9974942 DOI: 10.1038/s41467-023-36800-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 02/15/2023] [Indexed: 03/02/2023] Open
Abstract
Neural communication networks form the fundamental basis for brain function. These communication networks are enabled by emitted ligands such as neurotransmitters, which activate receptor complexes to facilitate communication. Thus, neural communication is fundamentally dependent on the transcriptome. Here we develop NeuronChat, a method and package for the inference, visualization and analysis of neural-specific communication networks among pre-defined cell groups using single-cell expression data. We incorporate a manually curated molecular interaction database of neural signaling for both human and mouse, and benchmark NeuronChat on several published datasets to validate its ability in predicting neural connectivity. Then, we apply NeuronChat to three different neural tissue datasets to illustrate its functionalities in identifying interneural communication networks, revealing conserved or context-specific interactions across different biological contexts, and predicting communication pattern changes in diseased brains with autism spectrum disorder. Finally, we demonstrate NeuronChat can utilize spatial transcriptomics data to infer and visualize neural-specific cell-cell communication.
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Affiliation(s)
- Wei Zhao
- Department of Mathematics and the NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, 92697, USA
| | - Kevin G Johnston
- Department of Mathematics and the NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, 92697, USA
| | - Honglei Ren
- Department of Mathematics and the NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, 92697, USA
| | - Xiangmin Xu
- Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA, 92697, USA.,Department of Biomedical Engineering, University of California, Irvine, CA, 92697, USA.,Department of Computer Science, University of California, Irvine, CA, 92697, USA.,The Center for Neural Circuit Mapping, University of California, Irvine, CA, 92697, USA
| | - Qing Nie
- Department of Mathematics and the NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA, 92697, USA. .,Department of Biomedical Engineering, University of California, Irvine, CA, 92697, USA. .,The Center for Neural Circuit Mapping, University of California, Irvine, CA, 92697, USA. .,Department of Developmental and Cell Biology, University of California, Irvine, CA, 92697, USA.
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41
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High-Resolution Bacterial Cytological Profiling Reveals Intrapopulation Morphological Variations upon Antibiotic Exposure. Antimicrob Agents Chemother 2023; 67:e0130722. [PMID: 36625642 PMCID: PMC9933734 DOI: 10.1128/aac.01307-22] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
Phenotypic heterogeneity is crucial to bacterial survival and could provide insights into the mechanism of action (MOA) of antibiotics, especially those with polypharmacological actions. Although phenotypic changes among individual cells could be detected by existing profiling methods, due to the data complexity, only population average data were commonly used, thereby overlooking the heterogeneity. In this study, we developed a high-resolution bacterial cytological profiling method that can capture morphological variations of bacteria upon antibiotic treatment. With an unprecedented single-cell resolution, this method classifies morphological changes of individual cells into known MOAs with an overall accuracy above 90%. We next showed that combinations of two antibiotics induce altered cell morphologies that are either unique or similar to that of an antibiotic in the combinations. With these combinatorial profiles, this method successfully revealed multiple cytological changes caused by a natural product-derived compound that, by itself, is inactive against Acinetobacter baumannii but synergistically exerts its multiple antibacterial activities in the presence of colistin. The findings have paved the way for future single-cell profiling in bacteria and have highlighted previously underappreciated intrapopulation variations caused by antibiotic perturbation.
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42
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Duran-Frigola M, Cigler M, Winter GE. Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence. J Am Chem Soc 2023; 145:2711-2732. [PMID: 36706315 PMCID: PMC9912273 DOI: 10.1021/jacs.2c11098] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Only around 20% of the human proteome is considered to be druggable with small-molecule antagonists. This leaves some of the most compelling therapeutic targets outside the reach of ligand discovery. The concept of targeted protein degradation (TPD) promises to overcome some of these limitations. In brief, TPD is dependent on small molecules that induce the proximity between a protein of interest (POI) and an E3 ubiquitin ligase, causing ubiquitination and degradation of the POI. In this perspective, we want to reflect on current challenges in the field, and discuss how advances in multiomics profiling, artificial intelligence, and machine learning (AI/ML) will be vital in overcoming them. The presented roadmap is discussed in the context of small-molecule degraders but is equally applicable for other emerging proximity-inducing modalities.
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Affiliation(s)
- Miquel Duran-Frigola
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria,Ersilia
Open Source Initiative, 28 Belgrave Road, CB1 3DE, Cambridge, United Kingdom,
| | - Marko Cigler
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria
| | - Georg E. Winter
- CeMM
Research Center for Molecular Medicine of the Austrian Academy of
Sciences, 1090 Vienna, Austria,
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43
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Zhao W, Johnston KG, Ren H, Xu X, Nie Q. Inferring neuron-neuron communications from single-cell transcriptomics through NeuronChat. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.12.523826. [PMID: 36712056 PMCID: PMC9882151 DOI: 10.1101/2023.01.12.523826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Neural communication networks form the fundamental basis for brain function. These communication networks are enabled by emitted ligands such as neurotransmitters, which activate receptor complexes to facilitate communication. Thus, neural communication is fundamentally dependent on the transcriptome. Here we develop NeuronChat, a method and package for the inference, visualization and analysis of neural-specific communication networks among pre-defined cell groups using single-cell expression data. We incorporate a manually curated molecular interaction database of neural signaling for both human and mouse, and benchmark NeuronChat on several published datasets to validate its ability in predicting neural connectivity. Then, we apply NeuronChat to three different neural tissue datasets to illustrate its functionalities in identifying interneural communication networks, revealing conserved or context-specific interactions across different biological contexts, and predicting communication pattern changes in diseased brains with autism spectrum disorder. Finally, we demonstrate NeuronChat can utilize spatial transcriptomics data to infer and visualize neural-specific cell-cell communication.
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Affiliation(s)
- Wei Zhao
- Department of Mathematics and the NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697
| | - Kevin G Johnston
- Department of Mathematics and the NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697
| | - Honglei Ren
- Department of Mathematics and the NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697
| | - Xiangmin Xu
- Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, CA 92697.,Department of Biomedical Engineering, University of California, Irvine, CA 92697.,Department of Computer Science, University of California, Irvine, CA 92697.,The Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA 92697.,The Center for Neural Circuit Mapping, University of California, Irvine, CA 92697
| | - Qing Nie
- Department of Mathematics and the NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697.,Department of Developmental and Cell Biology, University of California, Irvine, CA 92697.,Department of Biomedical Engineering, University of California, Irvine, CA 92697.,The Center for Neural Circuit Mapping, University of California, Irvine, CA 92697
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44
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Park J, Yu F, Fulcher JM, Williams SM, Engbrecht K, Moore RJ, Clair GC, Petyuk V, Nesvizhskii AI, Zhu Y. Evaluating Linear Ion Trap for MS3-Based Multiplexed Single-Cell Proteomics. Anal Chem 2023; 95:1888-1898. [PMID: 36637389 DOI: 10.1021/acs.analchem.2c03739] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
There is a growing demand to develop high-throughput and high-sensitivity mass spectrometry methods for single-cell proteomics. The commonly used isobaric labeling-based multiplexed single-cell proteomics approach suffers from distorted protein quantification due to co-isolated interfering ions during MS/MS fragmentation, also known as ratio compression. We reasoned that the use of MS3-based quantification could mitigate ratio compression and provide better quantification. However, previous studies indicated reduced proteome coverages in the MS3 method, likely due to long duty cycle time and ion losses during multilevel ion selection and fragmentation. Herein, we described an improved MS acquisition method for MS3-based single-cell proteomics by employing a linear ion trap to measure reporter ions. We demonstrated that linear ion trap can increase the proteome coverages for single-cell-level peptides with even higher gain obtained via the MS3 method. The optimized real-time search MS3 method was further applied to study the immune activation of single macrophages. Among a total of 126 single cells studied, over 1200 and 1000 proteins were quantifiable when at least 50 and 75% nonmissing data were required, respectively. Our evaluation also revealed several limitations of the low-resolution ion trap detector for multiplexed single-cell proteomics and suggested experimental solutions to minimize their impacts on single-cell analysis.
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Affiliation(s)
- Junho Park
- Department of Pharmacology, School of Medicine, CHA University, Seongnam-si, Gyeonggi-do, Seongnam 13488, Republic of Korea
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109-1382, United States
| | - James M Fulcher
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Sarah M Williams
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Kristin Engbrecht
- Nuclear, Chemistry, and Biology Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Geremy C Clair
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Vladislav Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109-1382, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109-1382, United States
| | - Ying Zhu
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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45
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Zhang T, Chen X, Chen D, Wang J, Chen J. Development of constrictional microchannels and the recurrent neural network in single-cell protein analysis. Front Bioeng Biotechnol 2023; 11:1195940. [PMID: 37207125 PMCID: PMC10190128 DOI: 10.3389/fbioe.2023.1195940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 04/12/2023] [Indexed: 05/21/2023] Open
Abstract
Introduction: As the golden approach of single-cell analysis, fluorescent flow cytometry can estimate single-cell proteins with high throughputs, which, however, cannot translate fluorescent intensities into protein numbers. Methods: This study reported a fluorescent flow cytometry based on constrictional microchannels for quantitative measurements of single-cell fluorescent levels and the recurrent neural network for data analysis of fluorescent profiles for high-accuracy cell-type classification. Results: As a demonstration, fluorescent profiles (e.g., FITC labeled β-actin antibody, PE labeled EpCAM antibody and PerCP labeled β-tubulin antibody) of individual A549 and CAL 27 cells were firstly measured and translated into protein numbers of 0.56 ± 0.43 × 104, 1.78 ± 1.06 × 106 and 8.11 ± 4.89 × 104 of A549 cells (ncell = 10232), and 3.47 ± 2.45 × 104, 2.65 ± 1.19 × 106 and 8.61 ± 5.25 × 104 of CAL 27 cells (ncell = 16376) based on the equivalent model of the constrictional microchannel. Then, the feedforward neural network was used to process these single-cell protein expressions, producing a classification accuracy of 92.0% for A549 vs. CAL 27 cells. In order to further increase the classification accuracies, as a key subtype of the recurrent neural network, the long short-term memory (LSTM) neural network was adopted to process fluorescent pulses sampled in constrictional microchannels directly, producing a classification accuracy of 95.5% for A549 vs. CAL 27 cells after optimization. Discussion: This fluorescent flow cytometry based on constrictional microchannels and recurrent neural network can function as an enabling tool of single-cell analysis and contribute to the development of quantitative cell biology.
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Affiliation(s)
- Ting Zhang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Xiao Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Deyong Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
| | - Junbo Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
- *Correspondence: Junbo Wang, ; Jian Chen,
| | - Jian Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
- *Correspondence: Junbo Wang, ; Jian Chen,
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46
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Yang Q, Huang X, Gao B, Gao L, Yu F, Wang F. Advances in electrochemiluminescence for single-cell analysis. Analyst 2022; 148:9-25. [PMID: 36475529 DOI: 10.1039/d2an01159j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Recent years have witnessed the emergence of innovative analytical methods with high sensitivity and spatiotemporal resolution that allowed qualitative and quantitative analysis to be carried out at single-cell and subcellular levels. Electrochemiluminescence (ECL) is a unique chemiluminescence of high-energy electron transfer triggered by electrical excitation. The ingenious combination of electrochemistry and chemiluminescence results in the distinct advantages of high sensitivity, a wide dynamic range and good reproducibility. Specifically, single-cell ECL (SCECL) analysis with excellent spatiotemporal resolution has emerged as a promising toolbox in bioanalysis for revealing individual cells' heterogeneity and stochastic processes. This review focuses on advances in SCECL analysis and bioimaging. The history and recent advances in ECL probes and strategies for system design are briefly reviewed. Subsequently, the latest advances in representative SCECL analysis techniques for bioassays, bioimaging and therapeutics are also highlighted. Then, the current challenges and future perspectives are discussed.
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Affiliation(s)
- Qian Yang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. .,Key Laboratory for Green Processing of Chemical Engineering of Xinjiang Bingtuan, School of Chemistry and Chemical Engineering, Shihezi University, Shihezi 832003, China.
| | - Xiaoyu Huang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Beibei Gao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Lu Gao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Feng Yu
- Key Laboratory for Green Processing of Chemical Engineering of Xinjiang Bingtuan, School of Chemistry and Chemical Engineering, Shihezi University, Shihezi 832003, China.
| | - Fu Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
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47
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Xu X, Zhang Q, Li M, Lin S, Liang S, Cai L, Zhu H, Su R, Yang C. Microfluidic single‐cell multiomics analysis. VIEW 2022. [DOI: 10.1002/viw.20220034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Affiliation(s)
- Xing Xu
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Qiannan Zhang
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Mingyin Li
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Shiyan Lin
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Shanshan Liang
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Linfeng Cai
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Huanghuang Zhu
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Rui Su
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Chaoyong Yang
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
- Institute of Molecular Medicine Renji Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
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48
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Chen S, Zhao J, Xu C, Zhang L, Shi B, Tian J, Zhao S. Intracellular Multicomponent Synchronous DNA-Walking Strategy for the Simultaneous Quantification of Tumor-Associated Proteins in a Single Cell. Anal Chem 2022; 94:15847-15855. [DOI: 10.1021/acs.analchem.2c03771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Shengyu Chen
- State Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin 541004, China
- Key Laboratory of Regional Ecological Environment Analysis and Pollution Control of West Guangxi, College Chemistry & Environment Engineering, Baise University, Baise 533000, China
| | - Jingjin Zhao
- State Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin 541004, China
| | - Chunhuan Xu
- State Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin 541004, China
| | - Liangliang Zhang
- State Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin 541004, China
| | - Bingfang Shi
- Key Laboratory of Regional Ecological Environment Analysis and Pollution Control of West Guangxi, College Chemistry & Environment Engineering, Baise University, Baise 533000, China
| | - Jianniao Tian
- State Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin 541004, China
| | - Shulin Zhao
- State Key Laboratory for the Chemistry and Molecular Engineering of Medicinal Resources, School of Chemistry and Pharmaceutical Sciences, Guangxi Normal University, Guilin 541004, China
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49
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Tajik M, Baharfar M, Donald WA. Single-cell mass spectrometry. Trends Biotechnol 2022; 40:1374-1392. [PMID: 35562238 DOI: 10.1016/j.tibtech.2022.04.004] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 04/04/2022] [Accepted: 04/09/2022] [Indexed: 01/21/2023]
Abstract
Owing to recent advances in mass spectrometry (MS), tens to hundreds of proteins, lipids, and small molecules can be measured in single cells. The ability to characterize the molecular heterogeneity of individual cells is necessary to define the full assortment of cell subtypes and identify their function. We review single-cell MS including high-throughput, targeted, mass cytometry-based approaches and antibody-free methods for broad profiling of the proteome and metabolome of single cells. The advantages and disadvantages of different methods are discussed, as well as the challenges and opportunities for further improvements in single-cell MS. These methods is being used in biomedicine in several applications including revealing tumor heterogeneity and high-content drug screening.
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Affiliation(s)
- Mohammad Tajik
- School of Chemistry, University of New South Wales, Sydney, New South Wales, 2052, Australia
| | - Mahroo Baharfar
- School of Chemical Engineering, University of New South Wales, Sydney, New South Wales, 2052, Australia
| | - William A Donald
- School of Chemistry, University of New South Wales, Sydney, New South Wales, 2052, Australia.
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50
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Petrosius V, Schoof EM. Recent advances in the field of single-cell proteomics. Transl Oncol 2022; 27:101556. [PMID: 36270102 PMCID: PMC9587008 DOI: 10.1016/j.tranon.2022.101556] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/16/2022] [Accepted: 09/21/2022] [Indexed: 11/06/2022] Open
Abstract
The field of single-cell omics is rapidly progressing. Although DNA and RNA sequencing-based methods have dominated the field to date, global proteome profiling has also entered the main stage. Single-cell proteomics was facilitated by advancements in different aspects of mass spectrometry (MS)-based proteomics, such as instrument design, sample preparation, chromatography and ion mobility. Single-cell proteomics by mass spectrometry (scp-MS) has moved beyond being a mere technical development, and is now able to deliver actual biological application and has been successfully applied to characterize different cell states. Here, we review some key developments of scp-MS, provide a background to the field, discuss the various available methods and foresee possible future directions.
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