1
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Buglakova E, Ekelöf M, Schwaiger-Haber M, Schlicker L, Molenaar MR, Shahraz M, Stuart L, Eisenbarth A, Hilsenstein V, Patti GJ, Schulze A, Snaebjornsson MT, Alexandrov T. Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer. Nat Metab 2024; 6:1695-1711. [PMID: 39251875 PMCID: PMC11422168 DOI: 10.1038/s42255-024-01118-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 07/26/2024] [Indexed: 09/11/2024]
Abstract
While heterogeneity is a key feature of cancer, understanding metabolic heterogeneity at the single-cell level remains a challenge. Here we present 13C-SpaceM, a method for spatial single-cell isotope tracing that extends the previously published SpaceM method with detection of 13C6-glucose-derived carbons in esterified fatty acids. We validated 13C-SpaceM on spatially heterogeneous models using liver cancer cells subjected to either normoxia-hypoxia or ATP citrate lyase depletion. This revealed substantial single-cell heterogeneity in labelling of the lipogenic acetyl-CoA pool and in relative fatty acid uptake versus synthesis hidden in bulk analyses. Analysing tumour-bearing brain tissue from mice fed a 13C6-glucose-containing diet, we found higher glucose-dependent synthesis of saturated fatty acids and increased elongation of essential fatty acids in tumours compared with healthy brains. Furthermore, our analysis uncovered spatial heterogeneity in lipogenic acetyl-CoA pool labelling in tumours. Our method enhances spatial probing of metabolic activities in single cells and tissues, providing insights into fatty acid metabolism in homoeostasis and disease.
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Affiliation(s)
- Elena Buglakova
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- Cell Biology and Biophysics Unit, EMBL, Heidelberg, Germany
| | - Måns Ekelöf
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Michaela Schwaiger-Haber
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Lisa Schlicker
- Division of Tumor Metabolism and Microenvironment, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Martijn R Molenaar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Mohammed Shahraz
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Lachlan Stuart
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Andreas Eisenbarth
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Volker Hilsenstein
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Gary J Patti
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Almut Schulze
- Division of Tumor Metabolism and Microenvironment, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, Heidelberg, Germany.
| | - Marteinn T Snaebjornsson
- Division of Tumor Metabolism and Microenvironment, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, Heidelberg, Germany.
| | - Theodore Alexandrov
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
- Metabolomics Core Facility, EMBL, Heidelberg, Germany.
- Molecular Medicine Partnership Unit, EMBL and Heidelberg University, Heidelberg, Germany.
- BioStudio, BioInnovation Institute, Copenhagen, Denmark.
- Department of Pharmacology, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
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2
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Filippi J, Casti P, Antonelli G, Murdocca M, Mencattini A, Corsi F, D'Orazio M, Pecora A, De Luca M, Curci G, Ghibelli L, Sangiuolo F, Neale SL, Martinelli E. Cell Electrokinetic Fingerprint: A Novel Approach Based on Optically Induced Dielectrophoresis (ODEP) for In-Flow Identification of Single Cells. SMALL METHODS 2024; 8:e2300923. [PMID: 38693090 DOI: 10.1002/smtd.202300923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 04/04/2024] [Indexed: 05/03/2024]
Abstract
A novel optically induced dielectrophoresis (ODEP) system that can operate under flow conditions is designed for automatic trapping of cells and subsequent induction of 2D multi-frequency cell trajectories. Like in a "ping-pong" match, two virtual electrode barriers operate in an alternate mode with varying frequencies of the input voltage. The so-derived cell motions are characterized via time-lapse microscopy, cell tracking, and state-of-the-art machine learning algorithms, like the wavelet scattering transform (WST). As a cell-electrokinetic fingerprint, the dynamic of variation of the cell displacements happening, over time, is quantified in response to different frequency values of the induced electric field. When tested on two biological scenarios in the cancer domain, the proposed approach discriminates cellular dielectric phenotypes obtained, respectively, at different early phases of drug-induced apoptosis in prostate cancer (PC3) cells and for differential expression of the lectine-like oxidized low-density lipoprotein receptor-1 (LOX-1) transcript levels in human colorectal adenocarcinoma (DLD-1) cells. The results demonstrate increased discrimination of the proposed system and pose an additional basis for making ODEP-based assays addressing cancer heterogeneity for precision medicine and pharmacological research.
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Affiliation(s)
- Joanna Filippi
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Paola Casti
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Gianni Antonelli
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Michela Murdocca
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, Rome, 00133, Italy
| | - Arianna Mencattini
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Francesca Corsi
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, Rome, 00133, Italy
- Department of Chemical Science and Technologies, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, Rome, 00133, Italy
| | - Michele D'Orazio
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Alessandro Pecora
- Italian Nation Research Council (CNR), Via del Fosso del Cavaliere 100, Rome, 00133, Italy
| | - Massimiliano De Luca
- Italian Nation Research Council (CNR), Via del Fosso del Cavaliere 100, Rome, 00133, Italy
| | - Giorgia Curci
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
| | - Lina Ghibelli
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, Rome, 00133, Italy
| | - Federica Sangiuolo
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, Rome, 00133, Italy
| | - Steven L Neale
- James Watt School of Engineering, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Eugenio Martinelli
- Department of Electronic Engineering, University of Rome Tor Vergata, Via del Politecnico 1, Rome, 00133, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), Via del Politecnico 1, Rome, 00133, Italy
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3
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Yang X, Mann KK, Wu H, Ding J. scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration. Genome Biol 2024; 25:198. [PMID: 39075536 PMCID: PMC11285326 DOI: 10.1186/s13059-024-03338-z] [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: 12/15/2023] [Accepted: 07/16/2024] [Indexed: 07/31/2024] Open
Abstract
Single-cell multi-omics data reveal complex cellular states, providing significant insights into cellular dynamics and disease. Yet, integration of multi-omics data presents challenges. Some modalities have not reached the robustness or clarity of established transcriptomics. Coupled with data scarcity for less established modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross, a tool leveraging variational autoencoders, generative adversarial networks, and the mutual nearest neighbors (MNN) technique for modality alignment. By enabling single-cell cross-modal data generation, multi-omics data simulation, and in silico cellular perturbations, scCross enhances the utility of single-cell multi-omics studies.
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Affiliation(s)
- Xiuhui Yang
- School of Software, Shandong University, 1500 Shunhua, Jinan, 250101, Shandong, China
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, H4A 3J1, QC, Canada
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, H3G 1Y6, Canada
| | - Koren K Mann
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, H3G 1Y6, Canada
| | - Hao Wu
- School of Software, Shandong University, 1500 Shunhua, Jinan, 250101, Shandong, China.
| | - Jun Ding
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, H4A 3J1, QC, Canada.
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, H3G 1Y6, Canada.
- Mila-Quebec AI Institute, Montreal, QC, H2S 3H1, Canada.
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4
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Bi X, Wang J, Xue B, He C, Liu F, Chen H, Lin LL, Dong B, Li B, Jin C, Pan J, Xue W, Ye J. SERSomes for metabolic phenotyping and prostate cancer diagnosis. Cell Rep Med 2024; 5:101579. [PMID: 38776910 PMCID: PMC11228451 DOI: 10.1016/j.xcrm.2024.101579] [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: 09/27/2023] [Revised: 03/08/2024] [Accepted: 04/29/2024] [Indexed: 05/25/2024]
Abstract
Molecular phenotypic variations in metabolites offer the promise of rapid profiling of physiological and pathological states for diagnosis, monitoring, and prognosis. Since present methods are expensive, time-consuming, and still not sensitive enough, there is an urgent need for approaches that can interrogate complex biological fluids at a system-wide level. Here, we introduce hyperspectral surface-enhanced Raman spectroscopy (SERS) to profile microliters of biofluidic metabolite extraction in 15 min with a spectral set, SERSome, that can be used to describe the structures and functions of various molecules produced in the biofluid at a specific time via SERS characteristics. The metabolite differences of various biofluids, including cell culture medium and human serum, are successfully profiled, showing a diagnosis accuracy of 80.8% on the internal test set and 73% on the external validation set for prostate cancer, discovering potential biomarkers, and predicting the tissue-level pathological aggressiveness. SERSomes offer a promising methodology for metabolic phenotyping.
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Affiliation(s)
- Xinyuan Bi
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Jiayi Wang
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Bingsen Xue
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China; Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Chang He
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Fugang Liu
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Haoran Chen
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Linley Li Lin
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Baijun Dong
- Department of Urology, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Science, Shanghai, P.R. China
| | - Butang Li
- Department of Urology, Ningbo Hangzhou Bay Hospital, Ningbo, Zhejiang, P.R. China
| | - Cheng Jin
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China; Shanghai Artificial Intelligence Laboratory, Shanghai, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, P.R. China.
| | - Jiahua Pan
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China.
| | - Wei Xue
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China.
| | - Jian Ye
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, P.R. China; Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, P.R. China.
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5
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Zhang W, Maeser D, Lee A, Huang Y, Gruener RF, Abdelbar IG, Jena S, Patel AG, Huang RS. Integration of Pan-Cancer Cell Line and Single-Cell Transcriptomic Profiles Enables Inference of Therapeutic Vulnerabilities in Heterogeneous Tumors. Cancer Res 2024; 84:2021-2033. [PMID: 38581448 PMCID: PMC11178452 DOI: 10.1158/0008-5472.can-23-3005] [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: 09/29/2023] [Revised: 10/18/2023] [Accepted: 04/01/2024] [Indexed: 04/08/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) greatly advanced the understanding of intratumoral heterogeneity by identifying distinct cancer cell subpopulations. However, translating biological differences into treatment strategies is challenging due to a lack of tools to facilitate efficient drug discovery that tackles heterogeneous tumors. Developing such approaches requires accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we developed a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening data sets. This method achieved high accuracy in separating cells into their correct cellular drug response statuses. In three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), the predicted results using scIDUC were accurate and mirrored biological expectations. In the first two tests, the framework identified drugs for cell subpopulations that were resistant to standard-of-care (SOC) therapies due to intrinsic resistance or tumor microenvironmental effects, and the results showed high consistency with experimental findings from the original studies. In the third test using newly generated SOC therapy-resistant cell lines, scIDUC identified efficacious drugs for the resistant line, and the predictions were validated with in vitro experiments. Together, this study demonstrates the potential of scIDUC to quickly translate scRNA-seq data into drug responses for individual cells, displaying the potential as a tool to improve the treatment of heterogenous tumors. SIGNIFICANCE A versatile method that infers cell-level drug response in scRNA-seq data facilitates the development of therapeutic strategies to target heterogeneous subpopulations within a tumor and address issues such as treatment failure and resistance.
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Affiliation(s)
- Weijie Zhang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Danielle Maeser
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Adam Lee
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Robert F. Gruener
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Israa G. Abdelbar
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
- Clinical Pharmacy Practice Department, The British University in Egypt, El Sherouk, 11837, Egypt
| | - Sampreeti Jena
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
| | - Anand G. Patel
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN 38105
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, TN 38105
| | - R. Stephanie Huang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455
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6
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Guo J, Chen PK, Chang S. Molecular-Scale Electronics: From Individual Molecule Detection to the Application of Recognition Sensing. Anal Chem 2024; 96:9303-9316. [PMID: 38809941 DOI: 10.1021/acs.analchem.3c04656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
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7
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Sun Y, Liu Y, Sun D, Liu K, Li Y, Liu Y, Zhang S. A facile single-cell patterning strategy based on harbor-like microwell microfluidics. Biomed Mater 2024; 19:045018. [PMID: 38772387 DOI: 10.1088/1748-605x/ad4e83] [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: 02/22/2024] [Accepted: 05/21/2024] [Indexed: 05/23/2024]
Abstract
Single-cell analysis is an effective method for conducting comprehensive heterogeneity studies ranging from cell phenotype to gene expression. The ability to arrange different cells in a predetermined pattern at single-cell resolution has a wide range of applications in cell-based analysis and plays an important role in facilitating interdisciplinary research by researchers in various fields. Most existing microfluidic microwell chips is a simple and straightforward method, which typically use small-sized microwells to accommodate single cells. However, this method imposes certain limitations on cells of various sizes, and the single-cell capture efficiency is relatively low without the assistance of external forces. Moreover, the microwells limit the spatiotemporal resolution of reagent replacement, as well as cell-to-cell communication. In this study, we propose a new strategy to prepare a single-cell array on a planar microchannel based on microfluidic flip microwells chip platform with large apertures (50 μm), shallow channels (50 μm), and deep microwells (50 μm). The combination of three configuration characteristics contributes to multi-cell trapping and a single-cell array within microwells, while the subsequent chip flipping accomplishes the transfer of the single-cell array to the opposite planar microchannel for cells adherence and growth. Further assisted by protein coating of bovine serum albumin and fibronectin on different layers, the single-cell capture efficiency in microwells is achieved at 92.1% ± 1%, while ultimately 85% ± 3.4% on planar microchannel. To verify the microfluidic flip microwells chip platform, the real-time and heterogeneous study of calcium release and apoptosis behaviours of single cells is carried out. To our knowledge, this is the first time that high-efficiency single-cell acquisition has been accomplished using a circular-well chip design that combines shallow channel, large aperture and deep microwell together. The chip is effective in avoiding the shearing force of high flow rates on cells, and the large apertures better allows cells to sedimentation. Therefore, this strategy owns the advantages of easy preparation and user-friendliness, which is especially valuable for researchers from different fields.
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Affiliation(s)
- Yingnan Sun
- Shandong Province Key Laboratory of Detection Technology for Tumor Makers, School of Medicine, Linyi University, Linyi 276005, People's Republic of China
| | - Yongshu Liu
- Shandong Province Key Laboratory of Detection Technology for Tumor Makers, School of Medicine, Linyi University, Linyi 276005, People's Republic of China
| | - Dezhi Sun
- Xinjiang Key Laboratory of Signal Detection and Processing, School of Computer Science and Technology, Xinjiang University, Urumqi 830046, People's Republic of China
| | - Kexin Liu
- Shandong Province Key Laboratory of Detection Technology for Tumor Makers, School of Medicine, Linyi University, Linyi 276005, People's Republic of China
| | - Yuyan Li
- Shandong Province Key Laboratory of Detection Technology for Tumor Makers, School of Medicine, Linyi University, Linyi 276005, People's Republic of China
| | - Yumin Liu
- Shandong Province Key Laboratory of Detection Technology for Tumor Makers, School of Medicine, Linyi University, Linyi 276005, People's Republic of China
| | - Shusheng Zhang
- Shandong Province Key Laboratory of Detection Technology for Tumor Makers, School of Medicine, Linyi University, Linyi 276005, People's Republic of China
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8
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Champagne A, Chebra I, Jain P, Ringuette Goulet C, Lauzier A, Guyon A, Neveu B, Pouliot F. An Extracellular Matrix Overlay Model for Bioluminescence Microscopy to Measure Single-Cell Heterogeneous Responses to Antiandrogens in Prostate Cancer Cells. BIOSENSORS 2024; 14:175. [PMID: 38667168 PMCID: PMC11048191 DOI: 10.3390/bios14040175] [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: 02/12/2024] [Revised: 03/23/2024] [Accepted: 03/28/2024] [Indexed: 04/28/2024]
Abstract
Prostate cancer (PCa) displays diverse intra-tumoral traits, impacting its progression and treatment outcomes. This study aimed to refine PCa cell culture conditions for dynamic monitoring of androgen receptor (AR) activity at the single-cell level. We introduced an extracellular matrix-Matrigel (ECM-M) culture model, enhancing cellular tracking during bioluminescence single-cell imaging while improving cell viability. ECM-M notably tripled the traceability of poorly adherent PCa cells, facilitating robust single-cell tracking, without impeding substrate permeability or AR response. This model effectively monitored AR modulation by antiandrogens across various PCa cell lines. Single-cell imaging unveiled heterogeneous antiandrogen responses within populations, correlating non-responsive cell proportions with drug IC50 values. Integrating ECM-M culture with the PSEBC-TSTA biosensor enabled precise characterization of ARi responsiveness within diverse cell populations. Our ECM-M model stands as a promising tool to assess heterogeneous single-cell treatment responses in cancer, offering insights to link drug responses to intracellular signaling dynamics. This approach enhances our comprehension of the nuanced and dynamic nature of PCa treatment responses.
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Affiliation(s)
- Audrey Champagne
- Centre de Recherche du CHU de Québec, Université Laval, Quebec, QC G1V 4G2, Canada (I.C.); (P.J.); (C.R.G.); (A.L.); (A.G.)
- Department of Surgery (Urology), Faculty of Medicine, Laval University, Quebec, QC G1R 2J6, Canada
| | - Imene Chebra
- Centre de Recherche du CHU de Québec, Université Laval, Quebec, QC G1V 4G2, Canada (I.C.); (P.J.); (C.R.G.); (A.L.); (A.G.)
- Department of Surgery (Urology), Faculty of Medicine, Laval University, Quebec, QC G1R 2J6, Canada
| | - Pallavi Jain
- Centre de Recherche du CHU de Québec, Université Laval, Quebec, QC G1V 4G2, Canada (I.C.); (P.J.); (C.R.G.); (A.L.); (A.G.)
- Department of Surgery (Urology), Faculty of Medicine, Laval University, Quebec, QC G1R 2J6, Canada
| | - Cassandra Ringuette Goulet
- Centre de Recherche du CHU de Québec, Université Laval, Quebec, QC G1V 4G2, Canada (I.C.); (P.J.); (C.R.G.); (A.L.); (A.G.)
- Department of Surgery (Urology), Faculty of Medicine, Laval University, Quebec, QC G1R 2J6, Canada
| | - Annie Lauzier
- Centre de Recherche du CHU de Québec, Université Laval, Quebec, QC G1V 4G2, Canada (I.C.); (P.J.); (C.R.G.); (A.L.); (A.G.)
- Department of Surgery (Urology), Faculty of Medicine, Laval University, Quebec, QC G1R 2J6, Canada
| | - Antoine Guyon
- Centre de Recherche du CHU de Québec, Université Laval, Quebec, QC G1V 4G2, Canada (I.C.); (P.J.); (C.R.G.); (A.L.); (A.G.)
- Department of Surgery (Urology), Faculty of Medicine, Laval University, Quebec, QC G1R 2J6, Canada
| | - Bertrand Neveu
- Centre de Recherche du CHU de Québec, Université Laval, Quebec, QC G1V 4G2, Canada (I.C.); (P.J.); (C.R.G.); (A.L.); (A.G.)
- Department of Surgery (Urology), Faculty of Medicine, Laval University, Quebec, QC G1R 2J6, Canada
| | - Frédéric Pouliot
- Centre de Recherche du CHU de Québec, Université Laval, Quebec, QC G1V 4G2, Canada (I.C.); (P.J.); (C.R.G.); (A.L.); (A.G.)
- Department of Surgery (Urology), Faculty of Medicine, Laval University, Quebec, QC G1R 2J6, Canada
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9
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Liu C, Yuan Z, Liu Q, Song K, Kong B, Su X. Siamese deep learning video flow cytometry for automatic and label-free clinical cervical cancer cell analysis. BIOMEDICAL OPTICS EXPRESS 2024; 15:2063-2077. [PMID: 38633087 PMCID: PMC11019674 DOI: 10.1364/boe.510022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/25/2023] [Accepted: 01/16/2024] [Indexed: 04/19/2024]
Abstract
Automatic and label-free screening methods may help to reduce cervical cancer mortality rates, especially in developing regions. The latest advances of deep learning in the biomedical optics field provide a more automatic approach to solving clinical dilemmas. However, existing deep learning methods face challenges, such as the requirement of manually annotated training sets for clinical sample analysis. Here, we develop Siamese deep learning video flow cytometry for the analysis of clinical cervical cancer cell samples in a smear-free manner. High-content light scattering images of label-free single cells are obtained via the video flow cytometer. Siamese deep learning, a self-supervised method, is built to introduce cell lineage cells into an analysis of clinical cells, which utilizes generated similarity metrics as label annotations for clinical cells. Compared with other deep learning methods, Siamese deep learning achieves a higher accuracy of up to 87.11%, with about 5.62% improvement for label-free clinical cervical cancer cell classification. The Siamese deep learning video flow cytometry demonstrated here is promising for automatic, label-free analysis of many types of cells from clinical samples without cell smears.
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Affiliation(s)
- Chao Liu
- School of Integrated Circuits, Shandong University, Jinan 250101, China
- Institute of Biomedical Engineering, School of Control Science & Engineering, Shandong University, Jinan 250061, China
| | - Zeng Yuan
- Department of Obstetrics and Gynecology, Qilu Hospital, Shandong University, Jinan 250012, China
| | - Qiao Liu
- Department of Molecular Medicine and Genetics, School of Basic Medical Sciences, Shandong University, Jinan 250012, China
| | - Kun Song
- Department of Obstetrics and Gynecology, Qilu Hospital, Shandong University, Jinan 250012, China
| | - Beihua Kong
- Department of Obstetrics and Gynecology, Qilu Hospital, Shandong University, Jinan 250012, China
| | - Xuantao Su
- School of Integrated Circuits, Shandong University, Jinan 250101, China
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10
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Ashby G, Keng KE, Hayden CC, Stachowiak JC. A live cell imaging-based assay for tracking particle uptake by clathrin-mediated endocytosis. Methods Enzymol 2024; 700:413-454. [PMID: 38971609 DOI: 10.1016/bs.mie.2024.02.010] [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: 07/08/2024]
Abstract
A popular strategy for therapeutic delivery to cells and tissues is to encapsulate therapeutics inside particles that cells internalize via endocytosis. The efficacy of particle uptake by endocytosis is often studied in bulk using flow cytometry and Western blot analysis and confirmed using confocal microscopy. However, these techniques do not reveal the detailed dynamics of particle internalization and how the inherent heterogeneity of many types of particles may impact their endocytic uptake. Toward addressing these gaps, here we present a live-cell imaging-based method that utilizes total internal reflection fluorescence microscopy to track the uptake of a large ensemble of individual particles in parallel, as they interact with the cellular endocytic machinery. To analyze the resulting data, we employ an open-source tracking algorithm in combination with custom data filters. This analysis reveals the dynamic interactions between particles and endocytic structures, which determine the probability of particle uptake. In particular, our approach can be used to examine how variations in the physical properties of particles (size, targeting, rigidity), as well as heterogeneity within the particle population, impact endocytic uptake. These data impact the design of particles toward more selective and efficient delivery of therapeutics to cells.
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Affiliation(s)
- Grant Ashby
- Department of Biomedical Engineering, The University of Texas at Austin
| | - Kayla E Keng
- Department of Biomedical Engineering, The University of Texas at Austin
| | - Carl C Hayden
- Department of Biomedical Engineering, The University of Texas at Austin
| | - Jeanne C Stachowiak
- Department of Biomedical Engineering, The University of Texas at Austin; Department of Chemical Engineering, The University of Texas at Austin.
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11
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Liu X, Shi L, Zhao Z, Shu J, Min W. VIBRANT: spectral profiling for single-cell drug responses. Nat Methods 2024; 21:501-511. [PMID: 38374266 PMCID: PMC11214684 DOI: 10.1038/s41592-024-02185-x] [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: 06/05/2023] [Accepted: 01/16/2024] [Indexed: 02/21/2024]
Abstract
High-content cell profiling has proven invaluable for single-cell phenotyping in response to chemical perturbations. However, methods with improved throughput, information content and affordability are still needed. We present a new high-content spectral profiling method named vibrational painting (VIBRANT), integrating mid-infrared vibrational imaging, multiplexed vibrational probes and an optimized data analysis pipeline for measuring single-cell drug responses. Three infrared-active vibrational probes were designed to measure distinct essential metabolic activities in human cancer cells. More than 20,000 single-cell drug responses were collected, corresponding to 23 drug treatments. The resulting spectral profile is highly sensitive to phenotypic changes under drug perturbation. Using this property, we built a machine learning classifier to accurately predict drug mechanism of action at single-cell level with minimal batch effects. We further designed an algorithm to discover drug candidates with new mechanisms of action and evaluate drug combinations. Overall, VIBRANT has demonstrated great potential across multiple areas of phenotypic screening.
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Affiliation(s)
- Xinwen Liu
- Department of Chemistry, Columbia University, New York, NY, USA
| | - Lixue Shi
- Department of Chemistry, Columbia University, New York, NY, USA
- Shanghai Xuhui Central Hospital, Zhongshan-Xuhui Hospital, and Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Shanghai Medical College, Fudan University, Shanghai, China
| | - Zhilun Zhao
- Department of Chemistry, Columbia University, New York, NY, USA
| | - Jian Shu
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Wei Min
- Department of Chemistry, Columbia University, New York, NY, USA.
- Department of Biomedical Engineering, Columbia University, New York, NY, USA.
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12
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Blankespoor M, Manzaneque T, Ghatkesar MK. Discrete Femtolitre Pipetting with 3D Printed Axisymmetrical Phaseguides. SMALL METHODS 2024; 8:e2300942. [PMID: 37840387 DOI: 10.1002/smtd.202300942] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Indexed: 10/17/2023]
Abstract
The capacity to precisely pipette femtoliter volumes of liquid enables many applications, for example, to functionalize a nanoscale surface and manipulate fluids inside a single-cell. A pressure-controlled pipetting method is the most preferred, since it enables the widest range of working liquids. However, precisely controlling femtoliter volumes by pressure is challenging. In this work, a new concept is proposed that makes use of axisymmetrical phaseguides inside a microfluidic channel to pipette liquid in discrete steps of known volume. An analytical model for the design of the femtopipettes is developed and verified experimentally. Femtopipettes are fabricated using a multi-scale 3D printing strategy integrating a digital light processing printed part and a two-photon-polymerization printed part. Three different variants are designed and fabricated with pipetting resolutions of 10 picoliters, 180 femtoliters and 50 femtoliters. As a demonstration, controlled amounts of a water-glycerol mixture were first aspirated and then dispensed into a mineral oil droplet.
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Affiliation(s)
- Maarten Blankespoor
- Department of Precision and Microsystems Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628CD, The Netherlands
| | - Tomás Manzaneque
- Department of Microelectronics, Delft University of Technology, Mekelweg 4, Delft, 2628CD, The Netherlands
| | - Murali Krishna Ghatkesar
- Department of Precision and Microsystems Engineering, Delft University of Technology, Mekelweg 2, Delft, 2628CD, The Netherlands
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13
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Buglakova E, Ekelöf M, Schwaiger-Haber M, Schlicker L, Molenaar MR, Mohammed S, Stuart L, Eisenbarth A, Hilsenstein V, Patti GJ, Schulze A, Snaebjornsson MT, Alexandrov T. 13C-SpaceM: Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.18.553810. [PMID: 38464218 PMCID: PMC10925155 DOI: 10.1101/2023.08.18.553810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Metabolism has emerged as a key factor in homeostasis and disease including cancer. Yet, little is known about the heterogeneity of metabolic activity of cancer cells due to the lack of tools to directly probe it. Here, we present a novel method, 13C-SpaceM for spatial single-cell isotope tracing of glucose-dependent de novo lipogenesis. The method combines imaging mass spectrometry for spatially-resolved detection of 13C6-glucose-derived 13C label incorporated into esterified fatty acids with microscopy and computational methods for data integration and analysis. We validated 13C-SpaceM on a spatially-heterogeneous normoxia-hypoxia model of liver cancer cells. Investigating cultured cells, we revealed single-cell heterogeneity of lipogenic acetyl-CoA pool labelling degree upon ACLY knockdown that is hidden in the bulk analysis and its effect on synthesis of individual fatty acids. Next, we adapted 13C-SpaceM to analyze tissue sections of mice harboring isocitrate dehydrogenase (IDH)-mutant gliomas. We found a strong induction of de novo fatty acid synthesis in the tumor tissue compared to the surrounding brain. Comparison of fatty acid isotopologue patterns revealed elevated uptake of mono-unsaturated and essential fatty acids in the tumor. Furthermore, our analysis uncovered substantial spatial heterogeneity in the labelling of the lipogenic acetyl-CoA pool indicative of metabolic reprogramming during microenvironmental adaptation. Overall, 13C-SpaceM enables novel ways for spatial probing of metabolic activity at the single cell level. Additionally, this methodology provides unprecedented insight into fatty acid uptake, synthesis and modification in normal and cancerous tissues.
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Affiliation(s)
- Elena Buglakova
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Måns Ekelöf
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Michaela Schwaiger-Haber
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Lisa Schlicker
- Division of Tumor Metabolism and Microenvironment, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Martijn R. Molenaar
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Shahraz Mohammed
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Lachlan Stuart
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Andreas Eisenbarth
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Volker Hilsenstein
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Gary J. Patti
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Almut Schulze
- Division of Tumor Metabolism and Microenvironment, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Marteinn T. Snaebjornsson
- Division of Tumor Metabolism and Microenvironment, German Cancer Research Center (DKFZ) and DKFZ-ZMBH Alliance, Heidelberg, Germany
| | - Theodore Alexandrov
- Structural and Computational Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- Metabolomics Core Facility, EMBL, Heidelberg, Germany
- Molecular Medicine Partnership Unit, EMBL and Heidelberg University, Heidelberg, Germany
- BioStudio, BioInnovation Institute, Copenhagen, Denmark
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14
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Zaman MA, Wu M, Ren W, Jensen MA, Davis RW, Hesselink L. Spectral tweezers: Single sample spectroscopy using optoelectronic tweezers. APPLIED PHYSICS LETTERS 2024; 124:071104. [PMID: 38356894 PMCID: PMC10864034 DOI: 10.1063/5.0191871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 01/23/2024] [Indexed: 02/16/2024]
Abstract
A scheme that combines optoelectronic tweezers (OET) with spectroscopic analysis is presented. Referred to as spectral tweezers, the approach uses a single focused light beam that acts both as the trapping beam for OET and the probe beam for spectroscopy. Having simultaneous manipulation and spectral characterization ability, the method is used to isolate single micro-samples from clusters and perform spectral measurements. Experimental results show that a characteristic spectral signature can be obtained for a given sample. The proposed approach can be easily integrated into the optical setups used for conventional OETs with only a few additional optical components, making it a convenient tool for bio-analytical applications.
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Affiliation(s)
- Mohammad Asif Zaman
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA
| | - Mo Wu
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA
| | - Wei Ren
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA
| | - Michael A. Jensen
- Department of Biochemistry, Stanford University, Stanford, California 94305, USA
| | - Ronald W. Davis
- Department of Biochemistry, Stanford University, Stanford, California 94305, USA
| | - Lambertus Hesselink
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA
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15
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Liu L, Zhang L, Zhang X, Dong X, Jiang X, Huang X, Li W, Xie X, Qiu X. Analysis of cellular response to drugs with a microfluidic single-cell platform based on hyperspectral imaging. Anal Chim Acta 2024; 1288:342158. [PMID: 38220290 DOI: 10.1016/j.aca.2023.342158] [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: 10/07/2023] [Revised: 12/07/2023] [Accepted: 12/16/2023] [Indexed: 01/16/2024]
Abstract
BACKGROUND Cellular response to pharmacological action of drugs is significant for drug development. Traditional detection method for cellular response to drugs normally rely on cell proliferation assay and metabolomics examination. In principle, these analytical methods often required cell labeling, invasion analysis, and hours of co-culture with drugs, which are relatively complex and time-consuming. Moreover, these methods can only indicate the drug effectiveness on cell colony rather than single cells. Thus, to meet the requirements of personal precision medicine, the development of drug response analysis on the high resolution of single cell is demanded. RESULTS To provide precise result for drug response on single-cell level, a microfluidic platform coupled with the label-free hyperspectral imaging was developed. With the help of horizontal single-cell trapping sieves, hundreds of single cells were trapped independently in microfluidic channels for the purposes of real-time drug delivery and single-cell hyperspectral image recording. To significantly identify the cellular hyperspectral change after drug stimulation, the differenced single-cell spectrum was proposed. Compared with the deep learning classification method based on hyperspectral images, an optimal performance can be achieved by the classification strategy based on differenced spectra. And the cellular response to different reagents, for example, K+, Epidermal Growth Factor (EGF), and Gefitinib at different concentrations can be accurately characterized by the differenced single-cell spectra analysis. SIGNIFICANCE AND NOVELTY The high-throughput, rapid analysis of cellular response to drugs at the single-cell level can be accurately performed by our platform. After systematically analyzing the materials and the structures of the single-cell microfluidic chip, the optimal single-cell trapping method was proposed to contribute to the further application of hyperspectral imaging on microfluidic single-cell analysis. And the hyperspectral characterization of single-cell with cancer drug stimulation proved the application potential of our method in personal cancer medication.
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Affiliation(s)
- Luyao Liu
- Institute of Microfluidic Chip Development in Biomedical Engineering, School of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Lulu Zhang
- Institute of Microfluidic Chip Development in Biomedical Engineering, School of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Xueyu Zhang
- School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China
| | - Xiaobin Dong
- Institute of Microfluidic Chip Development in Biomedical Engineering, School of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Xiaodan Jiang
- Institute of Microfluidic Chip Development in Biomedical Engineering, School of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Xiaoqi Huang
- School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China
| | - Wei Li
- School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China
| | - Xiaoming Xie
- School of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China
| | - Xianbo Qiu
- Institute of Microfluidic Chip Development in Biomedical Engineering, School of Information Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
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16
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Dunn KJ, Matlock A, Funkenbusch G, Yaqoob Z, So PTC, Berger AJ. Optical diffraction tomography for assessing single cell models in angular light scattering. BIOMEDICAL OPTICS EXPRESS 2024; 15:973-990. [PMID: 38404316 PMCID: PMC10890861 DOI: 10.1364/boe.512149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 01/09/2024] [Accepted: 01/09/2024] [Indexed: 02/27/2024]
Abstract
Angularly resolved light scattering (ALS) has become a useful tool for assessing the size and refractive index of biological scatterers at cellular and organelle length scales. Sizing organelle populations with ALS relies on Mie scattering theory models, which require significant assumptions about the object, including spherical scatterers and a homogeneous medium. These assumptions may incur greater error at the single cell level, where there are fewer scatterers to be averaged over. We investigate the validity of these assumptions using 3D refractive index (RI) tomograms measured via optical diffraction tomography (ODT). We compute the angular scattering on digitally manipulated tomograms with increasingly strong model assumptions, including RI-matched immersion media, homogeneous cytosol, and spherical organelles. We also compare the tomogram-computed angular scattering to experimental measurements of angular scattering from the same cells to ensure that the ODT-based approach accurately models angular scattering. We show that enforced RI-matching with the immersion medium and a homogeneous cytosol significantly affects the angular scattering intensity shape, suggesting that these assumptions can reduce the accuracy of size distribution estimates.
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Affiliation(s)
- Kaitlin J. Dunn
- The Institute of Optics, University of Rochester, Rochester, NY, USA
| | - Alex Matlock
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Zahid Yaqoob
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Peter T. C. So
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Andrew J. Berger
- The Institute of Optics, University of Rochester, Rochester, NY, USA
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17
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Zhou Z, Pang Y, Ji J, He J, Liu T, Ouyang L, Zhang W, Zhang XL, Zhang ZG, Zhang K, Sun W. Harnessing 3D in vitro systems to model immune responses to solid tumours: a step towards improving and creating personalized immunotherapies. Nat Rev Immunol 2024; 24:18-32. [PMID: 37402992 DOI: 10.1038/s41577-023-00896-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/17/2023] [Indexed: 07/06/2023]
Abstract
In vitro 3D models are advanced biological tools that have been established to overcome the shortcomings of oversimplified 2D cultures and mouse models. Various in vitro 3D immuno-oncology models have been developed to mimic and recapitulate the cancer-immunity cycle, evaluate immunotherapy regimens, and explore options for optimizing current immunotherapies, including for individual patient tumours. Here, we review recent developments in this field. We focus, first, on the limitations of existing immunotherapies for solid tumours, secondly, on how in vitro 3D immuno-oncology models are established using various technologies - including scaffolds, organoids, microfluidics and 3D bioprinting - and thirdly, on the applications of these 3D models for comprehending the cancer-immunity cycle as well as for assessing and improving immunotherapies for solid tumours.
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Affiliation(s)
- Zhenzhen Zhou
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Haidian District, Beijing, China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, China
- Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, Beijing, China
| | - Yuan Pang
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Haidian District, Beijing, China.
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, China.
- Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, Beijing, China.
| | - Jingyuan Ji
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Haidian District, Beijing, China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, China
- Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, Beijing, China
| | - Jianyu He
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Haidian District, Beijing, China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, China
- Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, Beijing, China
| | - Tiankun Liu
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Haidian District, Beijing, China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, China
- Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, Beijing, China
| | - Liliang Ouyang
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Haidian District, Beijing, China
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, China
- Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, Beijing, China
| | - Wen Zhang
- Department of Immunology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing, China
| | - Xue-Li Zhang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Zhi-Gang Zhang
- State Key Laboratory of Oncogenes and Related Genes, Shanghai Cancer Institute, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Kaitai Zhang
- State Key Laboratory of Molecular Oncology, Department of Aetiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing, China
| | - Wei Sun
- Biomanufacturing Center, Department of Mechanical Engineering, Tsinghua University, Haidian District, Beijing, China.
- Biomanufacturing and Rapid Forming Technology Key Laboratory of Beijing, Beijing, China.
- Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, Beijing, China.
- Department of Mechanical Engineering, Drexel University, Philadelphia, PA, USA.
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18
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Liu X, Wu S, Wu H, Zhang T, Qin H, Lin Y, Li B, Jiang X, Zheng X. Fully Active Delivery of Nanodrugs In Vivo via Remote Optical Manipulation. SMALL METHODS 2024; 8:e2301112. [PMID: 37880897 DOI: 10.1002/smtd.202301112] [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: 08/21/2023] [Revised: 09/29/2023] [Indexed: 10/27/2023]
Abstract
The active delivery of nanodrugs has been a bottleneck problem in nanomedicine. While modification of nanodrugs with targeting agents can enhance their retention at the lesion location, the transportation of nanodrugs in the circulation system is still a passive process. The navigation of nanodrugs with external forces such as magnetic field has been shown to be effective for active delivery, but the existing techniques are limited to specific materials like magnetic nanoparticles. In this study, an alternative actuation method is proposed based on optical manipulation for remote navigation of nanodrugs in vivo, which is compatible with most of the common drug carriers and exhibits significantly higher manipulation precision. By the programmable scanning of the laser beam, the motion trajectory and velocity of the nanodrugs can be precisely controlled in real time, making it possible for intelligent drug delivery, such as inverse-flow transportation, selective entry into specific vascular branch, and dynamic circumvention across obstacles. In addition, the controlled mass delivery of nanodrugs can be realized through indirect actuation by the microflow field. The developed optical manipulation method provides a new solution for the active delivery of nanodrugs, with promising potential for the treatment of blood diseases such as leukemia and thrombosis.
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Affiliation(s)
- Xiaoshuai Liu
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, Jinan University, Guangzhou, 511443, China
| | - Shuai Wu
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, Jinan University, Guangzhou, 511443, China
| | - Huaying Wu
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, Jinan University, Guangzhou, 511443, China
| | - Tiange Zhang
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, Jinan University, Guangzhou, 511443, China
| | - Haifeng Qin
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, Jinan University, Guangzhou, 511443, China
| | - Yufeng Lin
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, Jinan University, Guangzhou, 511443, China
| | - Baojun Li
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, Jinan University, Guangzhou, 511443, China
| | - Xiqun Jiang
- College of Chemistry and Chemical Engineering, Nanjing University, Nanjing, 210023, China
| | - Xianchuang Zheng
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, Jinan University, Guangzhou, 511443, China
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19
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Ino K, Utagawa Y, Shiku H. Microarray-Based Electrochemical Biosensing. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2024; 187:317-338. [PMID: 37306698 DOI: 10.1007/10_2023_229] [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: 06/13/2023]
Abstract
Microarrays are widely utilized in bioanalysis. Electrochemical biosensing techniques are often applied in microarray-based assays because of their simplicity, low cost, and high sensitivity. In such systems, the electrodes and sensing elements are arranged in arrays, and the target analytes are detected electrochemically. These sensors can be utilized for high-throughput bioanalysis and the electrochemical imaging of biosamples, including proteins, oligonucleotides, and cells. In this chapter, we summarize recent progress on these topics. We categorize electrochemical biosensing techniques for array detection into four groups: scanning electrochemical microscopy, electrode arrays, electrochemiluminescence, and bipolar electrodes. For each technique, we summarize the key principles and discuss the advantages, disadvantages, and bioanalysis applications. Finally, we present conclusions and perspectives about future directions in this field.
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Affiliation(s)
- Kosuke Ino
- Graduate School of Engineering, Tohoku University, Sendai, Miyagi, Japan.
| | - Yoshinobu Utagawa
- Graduate School of Environmental Studies, Tohoku University, Sendai, Miyagi, Japan
| | - Hitoshi Shiku
- Graduate School of Engineering, Tohoku University, Sendai, Miyagi, Japan.
- Graduate School of Environmental Studies, Tohoku University, Sendai, Miyagi, Japan.
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20
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Zhou X, Tan F, Zhang S, Zhang T. Deciphering the Underlying Mechanisms of Sanleng-Ezhu for the Treatment of Idiopathic Pulmonary Fibrosis Based on Network Pharmacology and Single-cell RNA Sequencing Data. Curr Comput Aided Drug Des 2024; 20:888-910. [PMID: 37559532 DOI: 10.2174/1573409920666230808120504] [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: 04/19/2023] [Revised: 06/13/2023] [Accepted: 06/27/2023] [Indexed: 08/11/2023]
Abstract
AIMS To decipher the underlying mechanisms of Sanleng-Ezhu for the treatment of idiopathic pulmonary fibrosis based on network pharmacology and single-cell RNA sequencing data. BACKGROUND Idiopathic Pulmonary Fibrosis (IPF) is the most common type of interstitial lung disease. Although the combination of herbs Sanleng (SL) and Ezhu (EZ) has shown reliable efficacy in the management of IPF, its underlying mechanisms remain unknown. METHODS Based on LC-MS/MS analysis and the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, we identified the bioactive components of SL-EZ. After obtaining the IPF-related dataset GSE53845 from the Gene Expression Omnibus (GEO) database, we performed the differential expression analysis and the weighted gene co-expression network analysis (WGCNA), respectively. We obtained lowly and highly expressed IPF subtype gene sets by comparing Differentially Expressed Genes (DEGs) with the most significantly negatively and positively related IPF modules in WGCNA. Subsequently, we performed Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on IPF subtype gene sets. The low- and highexpression MCODE subgroup feature genes were identified by the MCODE plug-in and were adopted for Disease Ontology (DO), GO, and KEGG enrichment analyses. Next, we performed the immune cell infiltration analysis of the MCODE subgroup feature genes. Single-cell RNA sequencing analysis demonstrated the cell types which expressed different MCODE subgroup feature genes. Molecular docking and animal experiments validated the effectiveness of SL-EZ in delaying the progression of pulmonary fibrosis. RESULTS We obtained 5 bioactive components of SL-EZ as well as their corresponding 66 candidate targets. After normalizing the samples of the GSE53845 dataset from the GEO database source, we obtained 1907 DEGs of IPF. Next, we performed a WGCNA analysis on the dataset and got 11 modules. Notably, we obtained 2 IPF subgroups by contrasting the most significantly up- and down-regulated modular genes in IPF with DEGs, respectively. The different IPF subgroups were compared with drugcandidate targets to obtain direct targets of action. After constructing the protein interaction networks between IPF subgroup genes and drug candidate targets, we applied the MCODE plug-in to filter the highest-scoring MCODE components. DO, GO, and KEGG enrichment analyses were applied to drug targets, IPF subgroup genes, and MCODE component signature genes. In addition, we downloaded the single-cell dataset GSE157376 from the GEO database. By performing quality control and dimensionality reduction, we clustered the scattered primary sample cells into 11 clusters and annotated them into 2 cell subtypes. Drug sensitivity analysis suggested that SL-EZ acts on different cell subtypes in IPF subgroups. Molecular docking revealed the mode of interaction between targets and their corresponding components. Animal experiments confirmed the efficacy of SL-EZ. CONCLUSION We found SL-EZ acted on epithelial cells mainly through the calcium signaling pathway in the lowly-expressed IPF subtype, while in the highly-expressed IPF subtype, SL-EZ acted on smooth muscle cells mainly through the viral infection, apoptosis, and p53 signaling pathway.
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Affiliation(s)
- Xianqiang Zhou
- Department of Traditional Chinese Medicine, Jing'an District Central Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Fang Tan
- Department of Neurology, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, 230031, Anhui Province, China
| | - Suxian Zhang
- Department of Traditional Chinese Medicine, Jing'an District Central Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Tiansong Zhang
- Department of Traditional Chinese Medicine, Jing'an District Central Hospital Affiliated to Fudan University, Shanghai, 200040, China
- Jing'an District Hospital of Traditional Chinese Medicine, Shanghai, 200072, China
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21
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Bordukova M, Makarov N, Rodriguez-Esteban R, Schmich F, Menden MP. Generative artificial intelligence empowers digital twins in drug discovery and clinical trials. Expert Opin Drug Discov 2024; 19:33-42. [PMID: 37887266 DOI: 10.1080/17460441.2023.2273839] [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: 07/29/2023] [Accepted: 10/18/2023] [Indexed: 10/28/2023]
Abstract
INTRODUCTION The concept of Digital Twins (DTs) translated to drug development and clinical trials describes virtual representations of systems of various complexities, ranging from individual cells to entire humans, and enables in silico simulations and experiments. DTs increase the efficiency of drug discovery and development by digitalizing processes associated with high economic, ethical, or social burden. The impact is multifaceted: DT models sharpen disease understanding, support biomarker discovery and accelerate drug development, thus advancing precision medicine. One way to realize DTs is by generative artificial intelligence (AI), a cutting-edge technology that enables the creation of novel, realistic and complex data with desired properties. AREAS COVERED The authors provide a brief introduction to generative AI and describe how it facilitates the modeling of DTs. In addition, they compare existing implementations of generative AI for DTs in drug discovery and clinical trials. Finally, they discuss technical and regulatory challenges that should be addressed before DTs can transform drug discovery and clinical trials. EXPERT OPINION The current state of DTs in drug discovery and clinical trials does not exploit the entire power of generative AI yet and is limited to simulation of a small number of characteristics. Nonetheless, generative AI has the potential to transform the field by leveraging recent developments in deep learning and customizing models for the needs of scientists, physicians and patients.
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Affiliation(s)
- Maria Bordukova
- Data & Analytics, Pharmaceutical Research and Early Development, Roche Innovation Center Munich (RICM), Penzberg, Germany
- Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Munich, Germany
- Department of Biology, Ludwig-Maximilians University Munich, Munich, Germany
| | - Nikita Makarov
- Data & Analytics, Pharmaceutical Research and Early Development, Roche Innovation Center Munich (RICM), Penzberg, Germany
- Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Munich, Germany
- Department of Biology, Ludwig-Maximilians University Munich, Munich, Germany
| | - Raul Rodriguez-Esteban
- Data & Analytics, Pharmaceutical Research and Early Development, Roche Innovation Center Basel (RICB), Basel, Switzerland
| | - Fabian Schmich
- Data & Analytics, Pharmaceutical Research and Early Development, Roche Innovation Center Munich (RICM), Penzberg, Germany
| | - Michael P Menden
- Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Munich, Germany
- Department of Biology, Ludwig-Maximilians University Munich, Munich, Germany
- Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, Australia
- German Center for Diabetes Research (DZD e.V.), Munich, Germany
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22
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Zhang D, Qiao L. Microfluidics Coupled Mass Spectrometry for Single Cell Multi-Omics. SMALL METHODS 2024; 8:e2301179. [PMID: 37840412 DOI: 10.1002/smtd.202301179] [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: 09/01/2023] [Revised: 10/02/2023] [Indexed: 10/17/2023]
Abstract
Population-level analysis masks significant heterogeneity between individual cells, making it difficult to accurately reflect the true intricacies of life activities. Microfluidics is a technique that can manipulate individual cells effectively and is commonly coupled with a variety of analytical methods for single-cell analysis. Single-cell omics provides abundant molecular information at the single-cell level, fundamentally revealing differences in cell types and biological states among cell individuals, leading to a deeper understanding of cellular phenotypes and life activities. Herein, this work summarizes the microfluidic chips designed for single-cell isolation, manipulation, trapping, screening, and sorting, including droplet microfluidic chips, microwell arrays, hydrodynamic microfluidic chips, and microchips with microvalves. This work further reviews the studies on single-cell proteomics, metabolomics, lipidomics, and multi-omics based on microfluidics and mass spectrometry. Finally, the challenges and future application of single-cell multi-omics are discussed.
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Affiliation(s)
- Dongxue Zhang
- Department of Chemistry, Institutes of Biomedical Sciences, and Minhang Hospital, Fudan University, Shanghai, 20000, China
| | - Liang Qiao
- Department of Chemistry, Institutes of Biomedical Sciences, and Minhang Hospital, Fudan University, Shanghai, 20000, China
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23
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Lu Y, Shan L, Cheng X, Zhu XL. Exploring the mechanism underlying the therapeutic effects of butein in colorectal cancer using network pharmacology and single-cell RNA sequencing data. J Gene Med 2024; 26:e3628. [PMID: 37963584 DOI: 10.1002/jgm.3628] [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: 07/21/2023] [Revised: 10/03/2023] [Accepted: 10/19/2023] [Indexed: 11/16/2023] Open
Abstract
BACKGROUND Butein has shown substantial potential as a cancer treatment, but its precise mechanism of action in colorectal cancer (CRC) remains unclear. This study aimed to uncover the underlying mechanisms through which butein operates in CRC and to identify potential biomarkers through a comprehensive investigation. METHODS Target genes associated with butein were sourced from SwissTargetPrediction, CTD, BindingDB and TargetNet. Gene expression data from the GSE38026 dataset and the single-cell dataset (GSE222300) were retrieved from the Gene Expression Omnibus database. The activation of disease-related pathways was assessed using Kyoto Encyclopedia of Genes and Genomes, Gene Ontology and differential gene analysis. Disease-associated genes were identified through differential analysis and weighted gene co-expression network analysis (WGCNA). The protein-protein interaction network was utilized to pinpoint potential drug targets. Molecular complex detection (MCODE) analysis was employed to uncover relevant genes influenced by butein within key subgroup networks. Machine learning techniques were applied for the screening of potential biomarkers, with receiver operating characteristic curves used to evaluate their clinical significance. Single-cell analysis was conducted to assess the pharmacological targets of butein in CRC, with validation performed using the external dataset GSE40967. RESULTS A total of 232 target genes for butein were identified. Functional enrichment analysis revealed significant enrichment of signaling pathways, including mitogen-activated protein kinase, JAK-STAT and NF-κB, among these genes. Differential analysis, in conjunction with WGCNA, yielded 520 disease-related genes. Subsequently, a disease-drug-gene network consisting of 727 targets was established, and a subnetwork containing 56 crucial genes was extracted. Important pathways such as the FoxO signaling pathway exhibited significant enrichment within these key genes. Machine learning applied to the 56 important genes led to the identification of a potential biomarker, UBE2C. Receiver operating characteristic analysis demonstrated the excellent clinical predictive utility of UBE2C. Single-cell analysis suggested that butein's therapeutic effects might be linked to its influence on epithelial and T cells, with UBE2C expression associated with these cell types. Validation using the external dataset GSE40967 further confirmed the exceptional clinical predictive capability of UBE2C. CONCLUSION This study combines network pharmacology with single-cell analysis to unravel the mechanisms underlying butein's effects in CRC. Notably, UBE2C emerged as a promising biomarker with superior clinical efficacy. These research findings contribute significantly to our understanding of specific molecular mechanisms, potentially shaping future clinical practices.
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Affiliation(s)
- Ye Lu
- Department of Hematology and Oncology, Soochow University Affiliated Taicang Hospital (The First People's Hospital of Taicang), Taicang, Jiangsu, China
- Suzhou Medical College of Soochow University/Soochow University Affiliated Taicang Hospital, Suzhou, Jiangsu, China
| | - Li Shan
- Department of Hematology and Oncology, Soochow University Affiliated Taicang Hospital (The First People's Hospital of Taicang), Taicang, Jiangsu, China
| | - Xu Cheng
- Department of Hematology and Oncology, Soochow University Affiliated Taicang Hospital (The First People's Hospital of Taicang), Taicang, Jiangsu, China
| | - Xiao-Li Zhu
- Department of Hematology and Oncology, Soochow University Affiliated Taicang Hospital (The First People's Hospital of Taicang), Taicang, Jiangsu, China
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24
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Song Y, Tian C, Lee Y, Yoon M, Yoon SE, Cho SY. Nanosensor Chemical Cytometry: Advances and Opportunities in Cellular Therapy and Precision Medicine. ACS MEASUREMENT SCIENCE AU 2023; 3:393-403. [PMID: 38145025 PMCID: PMC10740128 DOI: 10.1021/acsmeasuresciau.3c00038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/13/2023] [Accepted: 10/16/2023] [Indexed: 12/26/2023]
Abstract
With the definition of therapeutics now encompassing transplanted or engineered cells and their molecular products, there is a growing scientific necessity for analytics to understand this new category of drugs. This Perspective highlights the recent development of new measurement science on label-free single cell analysis, nanosensor chemical cytometry (NCC), and their potential for cellular therapeutics and precision medicine. NCC is based on microfluidics integrated with fluorescent nanosensor arrays utilizing the optical lensing effect of a single cell to real-time extract molecular properties and correlate them with physical attributes of single cells. This new class of cytometry can quantify the heterogeneity of the multivariate physicochemical attributes of the cell populations in a completely label-free and nondestructive way and, thus, suggest the vein-to-vein conditions for the safe therapeutic applications. After the introduction of the NCC technology, we suggest the technological development roadmap for the maturation of the new field: from the sensor/chip design perspective to the system/software development level based on hardware automation and deep learning data analytics. The advancement of this new single cell sensing technology is anticipated to aid rich and multivariate single cell data setting and support safe and reliable cellular therapeutics. This new measurement science can lead to data-driven personalized precision medicine.
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Affiliation(s)
- Youngho Song
- School
of Chemical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Changyu Tian
- School
of Chemical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Yullim Lee
- School
of Chemical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Minyeong Yoon
- School
of Chemical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Sang Eun Yoon
- Division
of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic of Korea
| | - Soo-Yeon Cho
- School
of Chemical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
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25
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Sidiropoulos DN, Ho WJ, Jaffee EM, Kagohara LT, Fertig EJ. Systems immunology spanning tumors, lymph nodes, and periphery. CELL REPORTS METHODS 2023; 3:100670. [PMID: 38086385 PMCID: PMC10753389 DOI: 10.1016/j.crmeth.2023.100670] [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: 05/06/2023] [Revised: 10/20/2023] [Accepted: 11/17/2023] [Indexed: 12/21/2023]
Abstract
The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.
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Affiliation(s)
- Dimitrios N Sidiropoulos
- Johns Hopkins University School of Medicine, Baltimore, MD, USA; Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Won Jin Ho
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Elizabeth M Jaffee
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Luciane T Kagohara
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA.
| | - Elana J Fertig
- Johns Hopkins Convergence Institute, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA; Johns Hopkins Bloomberg Kimmel Institute for Immunotherapy, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins Medicine, Baltimore, MD, USA; Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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26
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Alibekova Long M, Benman WKJ, Petrikas N, Bugaj LJ, Hughes AJ. Enhancing Single-Cell Western Blotting Sensitivity Using Diffusive Analyte Blotting and Antibody Conjugate Amplification. Anal Chem 2023; 95:17894-17902. [PMID: 37974303 DOI: 10.1021/acs.analchem.3c04130] [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] [Indexed: 11/19/2023]
Abstract
While there are many techniques to achieve highly sensitive, multiplex detection of RNA and DNA from single cells, detecting protein content often suffers from low limits of detection and throughput. Miniaturized, high-sensitivity Western blots on single cells (scWesterns) are attractive because they do not require advanced instrumentation. By physically separating analytes, scWesterns also uniquely mitigate limitations to target protein multiplexing posed by the affinity reagent performance. However, a fundamental limitation of scWesterns is their limited sensitivity for detecting low-abundance proteins, which arises from transport barriers posed by the separation gel against detection species. Here we address the sensitivity by decoupling the electrophoretic separation medium from the detection medium. We transfer scWestern separations to a nitrocellulose blotting medium with distinct mass transfer advantages over traditional in-gel probing, yielding a 5.9-fold improvement in the limit of detection. We next amplify probing of blotted proteins with enzyme-antibody conjugates, which are incompatible with traditional in-gel probing to achieve further improvement in the limit of detection to 1000 molecules, a 120-fold improvement. This enables us to detect 100% of cells in an EGFP-expressing population using fluorescently tagged and enzyme-conjugated antibodies compared to 84.5% of cells using in-gel detection. These results suggest the compatibility of nitrocellulose-immobilized scWesterns with a variety of affinity reagents─not previously accessible for in-gel use─for further signal amplification and detection of low-abundance targets.
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Affiliation(s)
- Mariia Alibekova Long
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Bioengineering Graduate Group, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, United States
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - William K J Benman
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Bioengineering Graduate Group, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, United States
| | - Nathan Petrikas
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Lukasz J Bugaj
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Bioengineering Graduate Group, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, United States
- Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Alex J Hughes
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Bioengineering Graduate Group, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, United States
- Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
- Center for Soft and Living Matter, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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27
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Pore AA, Kamyabi N, Bithi SS, Ahmmed SM, Vanapalli SA. Single-Cell Proliferation Microfluidic Device for High Throughput Investigation of Replicative Potential and Drug Resistance of Cancer Cells. Cell Mol Bioeng 2023; 16:443-457. [PMID: 38099214 PMCID: PMC10716102 DOI: 10.1007/s12195-023-00773-z] [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: 10/18/2022] [Accepted: 07/10/2023] [Indexed: 12/17/2023] Open
Abstract
Introduction Cell proliferation represents a major hallmark of cancer biology, and manifests itself in the assessment of tumor growth, drug resistance and metastasis. Tracking cell proliferation or cell fate at the single-cell level can reveal phenotypic heterogeneity. However, characterization of cell proliferation is typically done in bulk assays which does not inform on cells that can proliferate under given environmental perturbations. Thus, there is a need for single-cell approaches that allow longitudinal tracking of the fate of a large number of individual cells to reveal diverse phenotypes. Methods We fabricated a new microfluidic architecture for high efficiency capture of single tumor cells, with the capacity to monitor cell divisions across multiple daughter cells. This single-cell proliferation (SCP) device enabled the quantification of the fate of more than 1000 individual cancer cells longitudinally, allowing comprehensive profiling of the phenotypic heterogeneity that would be otherwise masked in standard cell proliferation assays. We characterized the efficiency of single cell capture and demonstrated the utility of the SCP device by exposing MCF-7 breast tumor cells to different doses of the chemotherapeutic agent doxorubicin. Results The single cell trapping efficiency of the SCP device was found to be ~ 85%. At the low doses of doxorubicin (0.01 µM, 0.001 µM, 0.0001 µM), we observed that 50-80% of the drug-treated cells had undergone proliferation, and less than 10% of the cells do not proliferate. Additionally, we demonstrated the potential of the SCP device in circulating tumor cell applications where minimizing target cell loss is critical. We showed selective capture of breast tumor cells from a binary mixture of cells (tumor cells and white blood cells) that was isolated from blood processing. We successfully characterized the proliferation statistics of these captured cells despite their extremely low counts in the original binary suspension. Conclusions The SCP device has significant potential for cancer research with the ability to quantify proliferation statistics of individual tumor cells, opening new avenues of investigation ranging from evaluating drug resistance of anti-cancer compounds to monitoring the replicative potential of patient-derived cells. Supplementary Information The online version contains supplementary material available at 10.1007/s12195-023-00773-z.
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Affiliation(s)
- Adity A. Pore
- Department of Chemical Engineering, Texas Tech University, Lubbock, TX USA
| | - Nabiollah Kamyabi
- Department of Chemical Engineering, Texas Tech University, Lubbock, TX USA
- Present Address: 10x Genomics, Pleasanton, CA USA
| | - Swastika S. Bithi
- Department of Chemical Engineering, Texas Tech University, Lubbock, TX USA
- Present Address: College of Engineering, West Texas A&M University, Canyon, TX USA
| | - Shamim M. Ahmmed
- Department of Chemical Engineering, Texas Tech University, Lubbock, TX USA
- Present Address: Manufacturing Integration Engineer, Intel Corporation, Hillsboro, OR USA
| | - Siva A. Vanapalli
- Department of Chemical Engineering, Texas Tech University, Lubbock, TX USA
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28
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Zhang W, Maeser D, Lee A, Huang Y, Gruener RF, Abdelbar IG, Jena S, Patel AG, Huang RS. Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.29.564598. [PMID: 37961545 PMCID: PMC10634928 DOI: 10.1101/2023.10.29.564598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Single-cell RNA sequencing greatly advanced our understanding of intratumoral heterogeneity through identifying tumor subpopulations with distinct biologies. However, translating biological differences into treatment strategies is challenging, as we still lack tools to facilitate efficient drug discovery that tackles heterogeneous tumors. One key component of such approaches tackles accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we present a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. Our method achieves high accuracy, with predicted sensitivities easily able to separate cells into their true cellular drug resistance status as measured by effect size (Cohen's d > 1.0). More importantly, we examine our method's utility with three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), and in each our predicted results are accurate and mirrored biological expectations. In the first two, we identified drugs for cell subpopulations that are resistant to standard-of-care (SOC) therapies due to intrinsic resistance or effects of tumor microenvironments. Our results showed high consistency with experimental findings from the original studies. In the third test, we generated SOC therapy resistant cell lines, used scIDUC to identify efficacious drugs for the resistant line, and validated the predictions with in-vitro experiments. Together, scIDUC quickly translates scRNA-seq data into drug response for individual cells, displaying the potential as a first-line tool for nuanced and heterogeneity-aware drug discovery.
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Affiliation(s)
- Weijie Zhang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Danielle Maeser
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Adam Lee
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Yingbo Huang
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Robert F Gruener
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Israa G Abdelbar
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
- Clinical Pharmacy Practice Department, The British University in Egypt, El Sherouk, 11837, Egypt
| | - Sampreeti Jena
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
| | - Anand G Patel
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN 38105
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN 38105
| | - R Stephanie Huang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455
- Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455
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29
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Clark IC, Fontanez KM, Meltzer RH, Xue Y, Hayford C, May-Zhang A, D'Amato C, Osman A, Zhang JQ, Hettige P, Ishibashi JSA, Delley CL, Weisgerber DW, Replogle JM, Jost M, Phong KT, Kennedy VE, Peretz CAC, Kim EA, Song S, Karlon W, Weissman JS, Smith CC, Gartner ZJ, Abate AR. Microfluidics-free single-cell genomics with templated emulsification. Nat Biotechnol 2023; 41:1557-1566. [PMID: 36879006 PMCID: PMC10635830 DOI: 10.1038/s41587-023-01685-z] [Citation(s) in RCA: 54] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 01/20/2023] [Indexed: 03/08/2023]
Abstract
Current single-cell RNA-sequencing approaches have limitations that stem from the microfluidic devices or fluid handling steps required for sample processing. We develop a method that does not require specialized microfluidic devices, expertise or hardware. Our approach is based on particle-templated emulsification, which allows single-cell encapsulation and barcoding of cDNA in uniform droplet emulsions with only a vortexer. Particle-templated instant partition sequencing (PIP-seq) accommodates a wide range of emulsification formats, including microwell plates and large-volume conical tubes, enabling thousands of samples or millions of cells to be processed in minutes. We demonstrate that PIP-seq produces high-purity transcriptomes in mouse-human mixing studies, is compatible with multiomics measurements and can accurately characterize cell types in human breast tissue compared to a commercial microfluidic platform. Single-cell transcriptional profiling of mixed phenotype acute leukemia using PIP-seq reveals the emergence of heterogeneity within chemotherapy-resistant cell subsets that were hidden by standard immunophenotyping. PIP-seq is a simple, flexible and scalable next-generation workflow that extends single-cell sequencing to new applications.
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Affiliation(s)
- Iain C Clark
- Department of Bioengineering, University of California, Berkeley, California Institute for Quantitative Biosciences, Berkeley, CA, USA
| | | | | | - Yi Xue
- Fluent Biosciences, Watertown, MA, USA
| | | | | | | | | | | | | | | | - Cyrille L Delley
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Daniel W Weisgerber
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Joseph M Replogle
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Marco Jost
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Microbiology, Harvard Medical School, Boston, MA, USA
| | - Kiet T Phong
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
| | - Vanessa E Kennedy
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Cheryl A C Peretz
- Department of Pediatrics, University of California San Francisco, San Francisco, CA, USA
| | - Esther A Kim
- Division of Plastic and Reconstructive Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Siyou Song
- Division of Plastic and Reconstructive Surgery, University of California San Francisco, San Francisco, CA, USA
| | - William Karlon
- Departments of Pathology and Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Jonathan S Weissman
- Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Catherine C Smith
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Zev J Gartner
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Adam R Abate
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.
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30
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Cheng H, Tang Y, Li Z, Guo Z, Heath JR, Xue M, Wei W. Non-Mass Spectrometric Targeted Single-Cell Metabolomics. Trends Analyt Chem 2023; 168:117300. [PMID: 37840599 PMCID: PMC10569257 DOI: 10.1016/j.trac.2023.117300] [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] [Indexed: 10/17/2023]
Abstract
Metabolic assays serve as pivotal tools in biomedical research, offering keen insights into cellular physiological and pathological states. While mass spectrometry (MS)-based metabolomics remains the gold standard for comprehensive, multiplexed analyses of cellular metabolites, innovative technologies are now emerging for the targeted, quantitative scrutiny of metabolites and metabolic pathways at the single-cell level. In this review, we elucidate an array of these advanced methodologies, spanning synthetic and surface chemistry techniques, imaging-based methods, and electrochemical approaches. We summarize the rationale, design principles, and practical applications for each method, and underscore the synergistic benefits of integrating single-cell metabolomics (scMet) with other single-cell omics technologies. Concluding, we identify prevailing challenges in the targeted scMet arena and offer a forward-looking commentary on future avenues and opportunities in this rapidly evolving field.
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Affiliation(s)
- Hanjun Cheng
- Institute for Systems Biology, Seattle, WA, 98109, United States
| | - Yin Tang
- Institute for Systems Biology, Seattle, WA, 98109, United States
| | - Zhonghan Li
- Department of Chemistry, University of California, Riverside, CA, 92521, United States
| | - Zhili Guo
- Department of Chemistry, University of California, Riverside, CA, 92521, United States
| | - James R. Heath
- Institute for Systems Biology, Seattle, WA, 98109, United States
| | - Min Xue
- Department of Chemistry, University of California, Riverside, CA, 92521, United States
| | - Wei Wei
- Institute for Systems Biology, Seattle, WA, 98109, United States
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31
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Ashby G, Keng KE, Hayden CC, Gollapudi S, Houser JR, Jamal S, Stachowiak JC. Selective Endocytic Uptake of Targeted Liposomes Occurs within a Narrow Range of Liposome Diameters. ACS APPLIED MATERIALS & INTERFACES 2023; 15:49988-50001. [PMID: 37862704 PMCID: PMC11165932 DOI: 10.1021/acsami.3c09399] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2023]
Abstract
Cell surface receptors facilitate signaling and nutrient uptake. These processes are dynamic, requiring receptors to be actively recycled by endocytosis. Due to their differential expression in disease states, receptors are often the target of drug-carrier particles, which are adorned with ligands that bind specifically to receptors. These targeted particles are taken into the cell by multiple routes of internalization, where the best-characterized pathway is clathrin-mediated endocytosis. Most studies of particle uptake have utilized bulk assays rather than observing individual endocytic events. As a result, the detailed mechanisms of particle uptake remain obscure. To address this gap, we employed a live-cell imaging approach to study the uptake of individual liposomes as they interact with clathrin-coated structures. By tracking individual internalization events, we find that the size of liposomes rather than the density of the ligands on their surfaces primarily determines their probability of uptake. Interestingly, targeting has the greatest impact on endocytosis of liposomes of intermediate diameters, with the smallest and largest liposomes being internalized or excluded, respectively, regardless of whether they are targeted. These findings, which highlight a previously unexplored limitation of targeted delivery, can be used to design more effective drug carriers.
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Affiliation(s)
- Grant Ashby
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, 78712, United States of America
| | - Kayla E. Keng
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, 78712, United States of America
| | - Carl C. Hayden
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, 78712, United States of America
| | - Sadhana Gollapudi
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, 78712, United States of America
| | - Justin R. Houser
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, 78712, United States of America
| | - Sabah Jamal
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, 78712, United States of America
| | - Jeanne C. Stachowiak
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, 78712, United States of America
- Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas, 78712, United States of America
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32
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Liu J, Wu R, Yuan S, Kelleher R, Chen S, Chen R, Zhang T, Obaidi I, Sheridan H. Pharmacogenomic Analysis of Combined Therapies against Glioblastoma Based on Cell Markers from Single-Cell Sequencing. Pharmaceuticals (Basel) 2023; 16:1533. [PMID: 38004399 PMCID: PMC10675611 DOI: 10.3390/ph16111533] [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: 08/27/2023] [Revised: 10/01/2023] [Accepted: 10/19/2023] [Indexed: 11/26/2023] Open
Abstract
Glioblastoma is the most common and aggressive form of primary brain cancer and the lack of viable treatment options has created an urgency to develop novel treatments. Personalized or predictive medicine is still in its infancy stage at present. This research aimed to discover biomarkers to inform disease progression and to develop personalized prophylactic and therapeutic strategies by combining state-of-the-art technologies such as single-cell RNA sequencing, systems pharmacology, and a polypharmacological approach. As predicted in the pyroptosis-related gene (PRG) transcription factor (TF) microRNA (miRNA) regulatory network, TP53 was the hub gene in the pyroptosis process in glioblastoma (GBM). A LASSO Cox regression model of pyroptosis-related genes was built to accurately and conveniently predict the one-, two-, and three-year overall survival rates of GBM patients. The top-scoring five natural compounds were parthenolide, rutin, baeomycesic acid, luteolin, and kaempferol, which have NFKB inhibition, antioxidant, lipoxygenase inhibition, glucosidase inhibition, and estrogen receptor agonism properties, respectively. In contrast, the analysis of the cell-type-specific differential expression-related targets of natural compounds showed that the top five subtype cells targeted by natural compounds were endothelial cells, microglia/macrophages, oligodendrocytes, dendritic cells, and neutrophil cells. The current approach-using the pharmacogenomic analysis of combined therapies-serves as a model for novel personalized therapeutic strategies for GBM treatment.
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Affiliation(s)
- Junying Liu
- NatPro Center, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, D02 PN40 Dublin, Ireland; (T.Z.); (I.O.); (H.S.)
| | - Ruixin Wu
- Preclinical Department, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, No. 274, Zhijiang Road, Jing’an District, Shanghai 200071, China;
| | - Shouli Yuan
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China;
| | - Robbie Kelleher
- School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland;
| | - Siying Chen
- The Second Affiliated Hospital, Nanchang University, Nanchang 330031, China;
| | - Rongfeng Chen
- National Center for Occupational Safety and Health, NHC, Beijing 102308, China;
| | - Tao Zhang
- NatPro Center, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, D02 PN40 Dublin, Ireland; (T.Z.); (I.O.); (H.S.)
- School of Food Science & Environmental Health, Technological University Dublin, D07 EWV4 Dublin, Ireland
| | - Ismael Obaidi
- NatPro Center, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, D02 PN40 Dublin, Ireland; (T.Z.); (I.O.); (H.S.)
| | - Helen Sheridan
- NatPro Center, School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, D02 PN40 Dublin, Ireland; (T.Z.); (I.O.); (H.S.)
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33
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Ng GYL, Tan SC, Ong CS. On the use of QDE-SVM for gene feature selection and cell type classification from scRNA-seq data. PLoS One 2023; 18:e0292961. [PMID: 37856458 PMCID: PMC10586655 DOI: 10.1371/journal.pone.0292961] [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: 04/10/2023] [Accepted: 10/03/2023] [Indexed: 10/21/2023] Open
Abstract
Cell type identification is one of the fundamental tasks in single-cell RNA sequencing (scRNA-seq) studies. It is a key step to facilitate downstream interpretations such as differential expression, trajectory inference, etc. scRNA-seq data contains technical variations that could affect the interpretation of the cell types. Therefore, gene selection, also known as feature selection in data science, plays an important role in selecting informative genes for scRNA-seq cell type identification. Generally speaking, feature selection methods are categorized into filter-, wrapper-, and embedded-based approaches. From the existing literature, methods from filter- and embedded-based approaches are widely applied in scRNA-seq gene selection tasks. The wrapper-based method that gives promising results in other fields has yet been extensively utilized for selecting gene features from scRNA-seq data; in addition, most of the existing wrapper methods used in this field are clustering instead of classification-based. With a large number of annotated data available today, this study applied a classification-based approach as an alternative to the clustering-based wrapper method. In our work, a quantum-inspired differential evolution (QDE) wrapped with a classification method was introduced to select a subset of genes from twelve well-known scRNA-seq transcriptomic datasets to identify cell types. In particular, the QDE was combined with different machine-learning (ML) classifiers namely logistic regression, decision tree, support vector machine (SVM) with linear and radial basis function kernels, as well as extreme learning machine. The linear SVM wrapped with QDE, namely QDE-SVM, was chosen by referring to the feature selection results from the experiment. QDE-SVM showed a superior cell type classification performance among QDE wrapping with other ML classifiers as well as the recent wrapper methods (i.e., FSCAM, SSD-LAHC, MA-HS, and BSF). QDE-SVM achieved an average accuracy of 0.9559, while the other wrapper methods achieved average accuracies in the range of 0.8292 to 0.8872.
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Affiliation(s)
- Grace Yee Lin Ng
- Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, Malaysia
| | - Shing Chiang Tan
- Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, Malaysia
| | - Chia Sui Ong
- Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, Malaysia
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Kikkeri K, Naba FM, Voldman J. Rapid, low-cost fabrication of electronic microfluidics via inkjet-printing and xurography (MINX). Biosens Bioelectron 2023; 237:115499. [PMID: 37473550 DOI: 10.1016/j.bios.2023.115499] [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: 12/07/2022] [Revised: 06/23/2023] [Accepted: 06/24/2023] [Indexed: 07/22/2023]
Abstract
Microfluidics has shown great promise for point-of-care assays due to unique chemical and physical advantages that occur at the micron scale. Furthermore, integration of electrodes into microfluidic systems provides additional capabilities for assay operation and electronic readout. However, while these systems are abundant in biological and biomedical research settings, translation of microfluidic devices with embedded electrodes are limited. In part, this is due to the reliance on expensive, inaccessible, and laborious microfabrication techniques. Although innovative prior work has simplified microfluidic fabrication or inexpensively patterned electrodes, low-cost, accessible, and robust methods to incorporate all these elements are lacking. Here, we present MINX, a low-cost <1 USD and rapid (∼minutes) fabrication technique to manufacture microfluidic device with embedded electrodes. We characterize the structures created using MINX, and then demonstrate the utility of the approach by using MINX to implement an electrochemical bead-based biomarker detection assay. We show that the MINX technique enables the scalable, inexpensive fabrication of microfluidic devices with electronic sensors using widely accessible desktop machines and low-cost materials.
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Affiliation(s)
- Kruthika Kikkeri
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Feven Moges Naba
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Joel Voldman
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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35
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Zhong J, Liang M, Ai Y. DUPLETS: Deformability-Assisted Dual-Particle Encapsulation Via Electrically Activated Sorting. SMALL METHODS 2023; 7:e2300089. [PMID: 37246250 DOI: 10.1002/smtd.202300089] [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: 01/24/2023] [Revised: 04/12/2023] [Indexed: 05/30/2023]
Abstract
Co-encapsulation of bead carriers and biological cells in microfluidics has become a powerful technique for various biological assays in single-cell genomics and drug screening because of its distinct capability of single-cell confinement. However, current co-encapsulation approaches exist a trade-off between cell/bead pairing rate and probability of multiple cells in individual droplets, significantly limiting the effective throughput of single-paired cell-bead droplets production. Deformability-assisted dUal-Particle encapsuLation via Electrically acTivated Sorting (DUPLETS) system is reported to overcome this problem. The DUPLETS can differentiate the encapsulated content in individual droplets and sort out targeted droplets via a combined screening of mechanical and electrical characteristics of single droplets in label-free manners and with the highest effective throughput in comparison to current commercial platforms. The DUPLETS has been demonstrated to enrich single-paired cell-bead droplets to over 80% (above eightfold higher than current co-encapsulation techniques). It eliminates multicell droplets to 0.1% whereas up to ≈24% in 10× Chromium. It is believed that merging DUPLETS into the current co-encapsulation platforms can meaningfully elevate sample quality in terms of high purity of single-paired cell-bead droplets, low fraction of multicell droplets, and high cell viability, which can benefit a multitude of biological assay applications.
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Affiliation(s)
- Jianwei Zhong
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore, 487372, Singapore
| | - Minhui Liang
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore, 487372, Singapore
| | - Ye Ai
- Pillar of Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore, 487372, Singapore
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36
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Meah A, Vedarethinam V, Bronstein R, Gujarati N, Jain T, Mallipattu SK, Li Y, Wang J. Single-Cell Spatial MIST for Versatile, Scalable Detection of Protein Markers. BIOSENSORS 2023; 13:852. [PMID: 37754086 PMCID: PMC10526469 DOI: 10.3390/bios13090852] [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: 07/25/2023] [Revised: 08/21/2023] [Accepted: 08/25/2023] [Indexed: 09/28/2023]
Abstract
High-multiplex detection of protein biomarkers across tissue regions has been an attractive spatial biology approach due to significant advantages over traditional immunohistochemistry (IHC) methods. Different from most methods, spatial multiplex in situ tagging (MIST) transfers the spatial protein expression information to an ultrahigh-density, large-scale MIST array. This technique has been optimized to reach single-cell resolution by adoption of smaller array units and 30% 8-arm PEG polymer as transfer medium. Tissue cell nuclei stained with lamin B have been clearly visualized on the MIST arrays and are colocalized with detection of nine mouse brain markers. Pseudocells defined at 10 μm in size have been used to fully profile tissue regions including cells and the intercellular space. We showcased the versatility of our technology by successfully detecting 20 marker proteins in kidney samples with the addition of five minutes atop the duration of standard immunohistochemistry protocols. Spatial MIST is amenable to iterative staining and detection on the same tissue samples. When 25 proteins were co-detected on 1 mouse brain section for each round and 5 rounds were executed, an ultrahigh multiplexity of 125 proteins was obtained for each pseudocell. With its unique abilities, this single-cell spatial MIST technology has the potential to become an important method in advanced diagnosis of complex diseases.
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Affiliation(s)
- Arafat Meah
- Multiplex Biotechnology Laboratory, Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
| | - Vadanasundari Vedarethinam
- Multiplex Biotechnology Laboratory, Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
| | - Robert Bronstein
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook School of Medicine, Stony Brook, NY 11794, USA
| | - Nehaben Gujarati
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook School of Medicine, Stony Brook, NY 11794, USA
| | - Tanya Jain
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Programs of Neurosciences, Weill Graduate School of Medical Sciences of Cornell University, New York, NY 10065, USA
| | - Sandeep K. Mallipattu
- Division of Nephrology and Hypertension, Department of Medicine, Stony Brook School of Medicine, Stony Brook, NY 11794, USA
- Renal Section, Northport VA Medical Center, Northport, NY 11768, USA
| | - Yueming Li
- Chemical Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Programs of Neurosciences, Weill Graduate School of Medical Sciences of Cornell University, New York, NY 10065, USA
- Programs of Pharmacology, Weill Graduate School of Medical Sciences of Cornell University, New York, NY 10021, USA
| | - Jun Wang
- Multiplex Biotechnology Laboratory, Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY 11794, USA
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37
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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: 15] [Impact Index Per Article: 15.0] [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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38
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Johnston SM, Webber KGI, Xie X, Truong T, Nydegger A, Lin HJL, Nwosu A, Zhu Y, Kelly RT. Rapid, One-Step Sample Processing for Label-Free Single-Cell Proteomics. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:1701-1707. [PMID: 37410391 PMCID: PMC11017373 DOI: 10.1021/jasms.3c00159] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/07/2023]
Abstract
Sample preparation for single-cell proteomics is generally performed in a one-pot workflow with multiple dispensing and incubation steps. These hours-long processes can be labor intensive and lead to long sample-to-answer times. Here we report a sample preparation method that achieves cell lysis, protein denaturation, and digestion in 1 h using commercially available high-temperature-stabilized proteases with a single reagent dispensing step. Four different one-step reagent compositions were evaluated, and the mixture providing the highest proteome coverage was compared to the previously employed multistep workflow. The one-step preparation increases proteome coverage relative to the previous multistep workflow while minimizing labor input and the possibility of human error. We also compared sample recovery between previously used microfabricated glass nanowell chips and injection-molded polypropylene chips and found the polypropylene provided improved proteome coverage. Combined, the one-step sample preparation and the polypropylene substrates enabled the identification of an average of nearly 2400 proteins per cell using a standard data-dependent workflow with Orbitrap mass spectrometers. These advances greatly simplify sample preparation for single-cell proteomics and broaden accessibility with no compromise in terms of proteome coverage.
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Affiliation(s)
- S Madisyn Johnston
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Kei G I Webber
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Xiaofeng Xie
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Thy Truong
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Alissia Nydegger
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Hsien-Jung L Lin
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Andikan Nwosu
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
| | - Ying Zhu
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Ryan T Kelly
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States
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39
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Dunn KJ, Berger AJ. Three-dimensional angular scattering simulations inform analysis of scattering from single cells. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:086501. [PMID: 37564163 PMCID: PMC10411915 DOI: 10.1117/1.jbo.28.8.086501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 06/16/2023] [Accepted: 07/21/2023] [Indexed: 08/12/2023]
Abstract
Significance Organelle sizes, which are indicative of cellular status, have implications for drug development and immunology research. At the single cell level, such information could be used to study the heterogeneity of cell response to drugs or pathogens. Aim Angularly resolved elastic light scattering is known to be sensitive to changes in organelle size distribution. We developed a Mie theory-based simulation of angular scattering from single cells to quantify the effects of noise on scattering and size estimates. Approach We simulated randomly sampled organelle sizes (drawn from a log normal distribution), interference between different organelles' scattering, and detector noise. We quantified each noise source's effect upon the estimated mean and standard deviation of organelle size distributions. Results The results demonstrate that signal-to-noise ratio in the angular scattering increased with the number of scatterers, cell area, and exposure time and decreased with the size distribution width. The error in estimating the mean of the size distributions remained below 5% for nearly all experimental parameters tested, but the widest size distribution tested (standard deviation of 600 nm) reached 20%. Conclusions The simulator revealed that sparse sampling of a broad size distribution can dominate the mismatch between actual and predicted size parameters. Alternative estimation strategies could reduce the discrepancy.
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Affiliation(s)
- Kaitlin J. Dunn
- University of Rochester, Institute of Optics, Rochester, New York, United States
| | - Andrew J. Berger
- University of Rochester, Institute of Optics, Rochester, New York, United States
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40
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Ashby G, Keng KE, Hayden CC, Gollapudi S, Houser JR, Jamal S, Stachowiak JC. Selective endocytic uptake of targeted liposomes occurs within a narrow range of liposome diameter. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.06.548000. [PMID: 37461728 PMCID: PMC10350051 DOI: 10.1101/2023.07.06.548000] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2023]
Abstract
Cell surface receptors facilitate signaling and nutrient uptake. These processes are dynamic, requiring receptors to be actively recycled by endocytosis. Due to their differential expression in disease states, receptors are often the target of drug-carrier particles, which are adorned with ligands that bind specifically to receptors. These targeted particles are taken into the cell by multiple routes of internalization, where the best-characterized pathway is clathrin-mediated endocytosis. Most studies of particle uptake have utilized bulk assays, rather than observing individual endocytic events. As a result, the detailed mechanisms of particle uptake remain obscure. To address this gap, we have employed a live-cell imaging approach to study the uptake of individual liposomes as they interact with clathrin-coated structures. By tracking individual internalization events, we find that the size of liposomes, rather than the density of the ligands on their surfaces, primarily determines their probability of uptake. Interestingly, targeting has the greatest impact on endocytosis of liposomes of intermediate diameters, with the smallest and largest liposomes being internalized or excluded, respectively, regardless of whether they are targeted. These findings, which highlight a previously unexplored limitation of targeted delivery, can be used to design more effective drug carriers.
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Affiliation(s)
- Grant Ashby
- Department of Biomedical Engineering, The University of Texas at Austin
| | - Kayla E Keng
- Department of Biomedical Engineering, The University of Texas at Austin
| | - Carl C Hayden
- Department of Biomedical Engineering, The University of Texas at Austin
| | - Sadhana Gollapudi
- Department of Biomedical Engineering, The University of Texas at Austin
| | - Justin R Houser
- Department of Biomedical Engineering, The University of Texas at Austin
| | - Sabah Jamal
- Department of Biomedical Engineering, The University of Texas at Austin
| | - Jeanne C Stachowiak
- Department of Biomedical Engineering, The University of Texas at Austin
- Department of Chemical Engineering, The University of Texas at Austin
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41
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Long MA, Benman W, Petrikas N, Bugaj LJ, Hughes AJ. Enhancing single-cell western blotting sensitivity using diffusive analyte blotting and antibody conjugate amplification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.13.544857. [PMID: 37398364 PMCID: PMC10312704 DOI: 10.1101/2023.06.13.544857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
While there are many techniques to achieve highly sensitive, multiplex detection of RNA and DNA from single cells, detecting protein contents often suffers from low limits of detection and throughput. Miniaturized, high-sensitivity western blots on single cells (scWesterns) are attractive since they do not require advanced instrumentation. By physically separating analytes, scWesterns also uniquely mitigate limitations to target protein multiplexing posed by affinity reagent performance. However, a fundamental limitation of scWesterns is their limited sensitivity for detecting low-abundance proteins, which arises from transport barriers posed by the separation gel against detection species. Here we address sensitivity by decoupling the electrophoretic separation medium from the detection medium. We transfer scWestern separations to a nitrocellulose blotting medium with distinct mass transfer advantages over traditional in-gel probing, yielding a 5.9-fold improvement in limit of detection. We next amplify probing of blotted proteins with enzyme-antibody conjugates which are incompatible with traditional in-gel probing to achieve further improvement in the limit of detection to 103 molecules, a 520-fold improvement. This enables us to detect 85% and 100% of cells in an EGFP-expressing population using fluorescently tagged and enzyme-conjugated antibodies respectively, compared to 47% of cells using in-gel detection. These results suggest compatibility of nitrocellulose-immobilized scWesterns with a variety of affinity reagents - not previously accessible for in-gel use - for further signal amplification and detection of low abundance targets.
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Affiliation(s)
- Mariia Alibekova Long
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, PA, USA
- Bioengineering Graduate Group, University of Pennsylvania, Philadelphia, PA, 19104, PA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, PA, USA
| | - William Benman
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, PA, USA
- Bioengineering Graduate Group, University of Pennsylvania, Philadelphia, PA, 19104, PA, USA
| | - Nathan Petrikas
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, PA, USA
- Currently at Tempus Labs Inc., Chicago, IL, USA
| | - Lukasz J. Bugaj
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, PA, USA
- Bioengineering Graduate Group, University of Pennsylvania, Philadelphia, PA, 19104, PA, USA
- Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Alex J. Hughes
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, PA, USA
- Bioengineering Graduate Group, University of Pennsylvania, Philadelphia, PA, 19104, PA, USA
- Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Cell and Developmental Biology, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Soft and Living Matter, University of Pennsylvania, Philadelphia, PA, 19104, USA
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42
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Nguyen DD, Lee S, Kim I. Recent Advances in Metaphotonic Biosensors. BIOSENSORS 2023; 13:631. [PMID: 37366996 PMCID: PMC10296124 DOI: 10.3390/bios13060631] [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: 04/29/2023] [Revised: 06/04/2023] [Accepted: 06/05/2023] [Indexed: 06/28/2023]
Abstract
Metaphotonic devices, which enable light manipulation at a subwavelength scale and enhance light-matter interactions, have been emerging as a critical pillar in biosensing. Researchers have been attracted to metaphotonic biosensors, as they solve the limitations of the existing bioanalytical techniques, including the sensitivity, selectivity, and detection limit. Here, we briefly introduce types of metasurfaces utilized in various metaphotonic biomolecular sensing domains such as refractometry, surface-enhanced fluorescence, vibrational spectroscopy, and chiral sensing. Further, we list the prevalent working mechanisms of those metaphotonic bio-detection schemes. Furthermore, we summarize the recent progress in chip integration for metaphotonic biosensing to enable innovative point-of-care devices in healthcare. Finally, we discuss the impediments in metaphotonic biosensing, such as its cost effectiveness and treatment for intricate biospecimens, and present a prospect for potential directions for materializing these device strategies, significantly influencing clinical diagnostics in health and safety.
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Affiliation(s)
- Dang Du Nguyen
- Department of Biophysics, Institute of Quantum Biophysics, Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Seho Lee
- Department of Biophysics, Institute of Quantum Biophysics, Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon 16419, Republic of Korea
| | - Inki Kim
- Department of Biophysics, Institute of Quantum Biophysics, Sungkyunkwan University, Suwon 16419, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon 16419, Republic of Korea
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43
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Yang L, Dutta P, Davuluri RV, Wang J. Rapid, High-Throughput Single-Cell Multiplex In Situ Tagging (MIST) Analysis of Immunological Disease with Machine Learning. Anal Chem 2023; 95:7779-7787. [PMID: 37141575 PMCID: PMC10365012 DOI: 10.1021/acs.analchem.3c01157] [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] [Indexed: 05/06/2023]
Abstract
The cascade of immune responses involves activation of diverse immune cells and release of a large amount of cytokines, which leads to either normal, balanced inflammation or hyperinflammatory responses and even organ damage by sepsis. Conventional diagnosis of immunological disorders based on multiple cytokines in the blood serum has varied accuracy, and it is difficult to distinguish normal inflammation from sepsis. Herein, we present an approach to detect immunological disorders through rapid, ultrahigh-multiplex analysis of T cells using single-cell multiplex in situ tagging (scMIST) technology. scMIST permits simultaneous detection of 46 markers and cytokines from single cells without the assistance of special instruments. A cecal ligation and puncture sepsis model was built to supply T cells from two groups of mice that survived the surgery or died after 1 day. The scMIST assays have captured the T cell features and the dynamics over the course of recovery. Compared with cytokines in the peripheral blood, T cell markers show different dynamics and cytokine levels. We have applied a random forest machine learning model to single T cells from two groups of mice. Through training, the model has been able to predict the group of mice through T cell classification and majority rule with 94% accuracy. Our approach pioneers the direction of single-cell omics and could be widely applicable to human diseases.
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Affiliation(s)
- Liwei Yang
- Multiplex Biotechnology Laboratory, Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY 11794
| | - Pratik Dutta
- Department of Biomedical Informatics, State University of New York at Stony Brook, Stony Brook, NY 11794
| | - Ramana V. Davuluri
- Department of Biomedical Informatics, State University of New York at Stony Brook, Stony Brook, NY 11794
| | - Jun Wang
- Multiplex Biotechnology Laboratory, Department of Biomedical Engineering, State University of New York at Stony Brook, Stony Brook, NY 11794
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44
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Yan D, Sun Z, Fang J, Cao S, Wang W, Chang X, Badirli S, Fu H, Liu Y. scRAA: the development of a robust and automatic annotation procedure for single-cell RNA sequencing data. J Biopharm Stat 2023:1-14. [PMID: 37162278 DOI: 10.1080/10543406.2023.2208671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
A critical task in single-cell RNA sequencing (scRNA-Seq) data analysis is to identify cell types from heterogeneous tissues. While the majority of classification methods demonstrated high performance in scRNA-Seq annotation problems, a robust and accurate solution is desired to generate reliable outcomes for downstream analyses, for instance, marker genes identification, differentially expressed genes, and pathway analysis. It is hard to establish a universally good metric. Thus, a universally good classification method for all kinds of scenarios does not exist. In addition, reference and query data in cell classification are usually from different experimental batches, and failure to consider batch effects may result in misleading conclusions. To overcome this bottleneck, we propose a robust ensemble approach to classify cells and utilize a batch correction method between reference and query data. We simulated four scenarios that comprise simple to complex batch effect and account for varying cell-type proportions. We further tested our approach on both lung and pancreas data. We found improved prediction accuracy and robust performance across simulation scenarios and real data. The incorporation of batch effect correction between reference and query, and the ensemble approach improve cell-type prediction accuracy while maintaining robustness. We demonstrated these through simulated and real scRNA-Seq data.
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Affiliation(s)
- Dongyan Yan
- Global Statistical Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Zhe Sun
- Global Statistical Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Jiyuan Fang
- Global Statistical Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Shanshan Cao
- Global Statistical Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Wenjie Wang
- Advance Analytics and Data Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Xinyue Chang
- Advance Analytics and Data Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Sarkhan Badirli
- Advance Analytics and Data Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Haoda Fu
- Advance Analytics and Data Science, Eli Lilly & Co, Indianapolis, Indiana, USA
| | - Yushi Liu
- Global Statistical Science, Eli Lilly & Co, Indianapolis, Indiana, USA
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45
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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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46
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Kato R, Yano TA, Tanaka T. Single-cell infrared vibrational analysis by optical trapping mid-infrared photothermal microscopy. Analyst 2023; 148:1285-1290. [PMID: 36811918 DOI: 10.1039/d2an01917e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
Single-cell analysis by means of vibrational spectroscopy combined with optical trapping is a reliable platform for unveiling cell-to-cell heterogeneities in vast populations. Although infrared (IR) vibrational spectroscopy provides rich molecular fingerprint information on biological samples in a label-free manner, its application with optical trapping has never been achieved due to weak gradient forces generated by the diffraction-limited focused IR beam and strong background of water absorption. Herein, we present single-cell IR vibrational analysis that incorporates mid-infrared photothermal (MIP) microscopy with optical trapping. Optically trapped single polymer particles and red blood cells (RBCs) in blood could be chemically identified owing to their IR vibrational fingerprints. This single-cell IR vibrational analysis further allowed us to probe the chemical heterogeneities of RBCs originating from the variation in the intracellular characteristics. Our demonstration paves the way for the IR vibrational analysis of single cells and chemical characterization in various fields.
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Affiliation(s)
- Ryo Kato
- Institute of Post-LED Photonics, Tokushima University, 2-1 Minamijosanjima-cho, Tokushima, Tokushima 770-0856, Japan. .,Innovative Photon Manipulation Research Team, RIKEN Center for Advanced Photonics, Wako, Saitama 351-0198, Japan. .,Metamaterials Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama 351-0198, Japan
| | - Taka-Aki Yano
- Institute of Post-LED Photonics, Tokushima University, 2-1 Minamijosanjima-cho, Tokushima, Tokushima 770-0856, Japan. .,Innovative Photon Manipulation Research Team, RIKEN Center for Advanced Photonics, Wako, Saitama 351-0198, Japan. .,Metamaterials Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama 351-0198, Japan
| | - Takuo Tanaka
- Institute of Post-LED Photonics, Tokushima University, 2-1 Minamijosanjima-cho, Tokushima, Tokushima 770-0856, Japan. .,Innovative Photon Manipulation Research Team, RIKEN Center for Advanced Photonics, Wako, Saitama 351-0198, Japan. .,Metamaterials Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama 351-0198, Japan
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47
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Layton TB, Williams L, Nanchahal J. Dupuytren's disease: a localised and accessible human fibrotic disorder. Trends Mol Med 2023; 29:218-227. [PMID: 36566101 DOI: 10.1016/j.molmed.2022.12.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/24/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022]
Abstract
We review the biology of Dupuytren's disease (DD), a common localised fibrotic disorder of the hand. The disease develops through a complex interplay of genetic and environmental factors, and epigenetic signalling. The early-stage disease nodules comprise a complex milieu of stromal and immune cells which interact to promote disease development. Recently, inhibition of tumour necrosis factor (TNF) locally resulted in softening and a decrease in nodule size, potentially controlling disease progression. Unlike fibrotic disorders of the visceral organs, the easy access to tissue in DD patients enables dissection of the cellular landscape and molecular signalling pathways. In addition, the study of DD may have wider benefits in enhancing our understanding of less-accessible fibrotic tissues.
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Affiliation(s)
- Thomas B Layton
- Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Roosevelt Drive, Oxford OX3 8FE, UK
| | - Lynn Williams
- Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Roosevelt Drive, Oxford OX3 8FE, UK
| | - Jagdeep Nanchahal
- Kennedy Institute of Rheumatology, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Roosevelt Drive, Oxford OX3 8FE, UK.
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48
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Yan X, Zheng R, Wu F, Li M. CLAIRE: contrastive learning-based batch correction framework for better balance between batch mixing and preservation of cellular heterogeneity. Bioinformatics 2023; 39:7055295. [PMID: 36821425 PMCID: PMC9985174 DOI: 10.1093/bioinformatics/btad099] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 12/27/2022] [Accepted: 02/22/2023] [Indexed: 02/24/2023] Open
Abstract
MOTIVATION Integration of growing single-cell RNA sequencing datasets helps better understand cellular identity and function. The major challenge for integration is removing batch effects while preserving biological heterogeneities. Advances in contrastive learning have inspired several contrastive learning-based batch correction methods. However, existing contrastive-learning-based methods exhibit noticeable ad hoc trade-off between batch mixing and preservation of cellular heterogeneities (mix-heterogeneity trade-off). Therefore, a deliberate mix-heterogeneity trade-off is expected to yield considerable improvements in scRNA-seq dataset integration. RESULTS We develop a novel contrastive learning-based batch correction framework, CIAIRE, which achieves superior mix-heterogeneity trade-off. The key contributions of CLAIRE are proposal of two complementary strategies: construction strategy and refinement strategy, to improve the appropriateness of positive pairs. Construction strategy dynamically generates positive pairs by augmenting inter-batch mutual nearest neighbors (MNN) with intra-batch k-nearest neighbors (KNN), which improves the coverage of positive pairs for the whole distribution of shared cell types between batches. Refinement strategy aims to automatically reduce the potential false positive pairs from the construction strategy, which resorts to the memory effect of deep neural networks. We demonstrate that CLAIRE possesses superior mix-heterogeneity trade-off over existing contrastive learning-based methods. Benchmark results on six real datasets also show that CLAIRE achieves the best integration performance against eight state-of-the-art methods. Finally, comprehensive experiments are conducted to validate the effectiveness of CLAIRE. AVAILABILITY AND IMPLEMENTATION The source code and data used in this study can be found in https://github.com/CSUBioGroup/CLAIRE-release. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xuhua Yan
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Ruiqing Zheng
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Fangxiang Wu
- Division of Biomedical Engineering, Department of Computer Science, Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
| | - Min Li
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China
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49
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He B, Xiao Y, Liang H, Huang Q, Du Y, Li Y, Garmire D, Sun D, Garmire LX. ASGARD is A Single-cell Guided Pipeline to Aid Repurposing of Drugs. Nat Commun 2023; 14:993. [PMID: 36813801 PMCID: PMC9945835 DOI: 10.1038/s41467-023-36637-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 02/10/2023] [Indexed: 02/24/2023] Open
Abstract
Single-cell RNA sequencing technology has enabled in-depth analysis of intercellular heterogeneity in various diseases. However, its full potential for precision medicine has yet to be reached. Towards this, we propose A Single-cell Guided Pipeline to Aid Repurposing of Drugs (ASGARD) that defines a drug score to recommend drugs by considering all cell clusters to address the intercellular heterogeneity within each patient. ASGARD shows significantly better average accuracy on single-drug therapy compared to two bulk-cell-based drug repurposing methods. We also demonstrated that it performs considerably better than other cell cluster-level predicting methods. In addition, we validate ASGARD using the drug response prediction method TRANSACT with Triple-Negative-Breast-Cancer patient samples. We find that many top-ranked drugs are either approved by the Food and Drug Administration or in clinical trials treating corresponding diseases. In conclusion, ASGARD is a promising drug repurposing recommendation tool guided by single-cell RNA-seq for personalized medicine. ASGARD is free for educational use at https://github.com/lanagarmire/ASGARD .
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Affiliation(s)
- Bing He
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA
| | - Yao Xiao
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA
| | - Haodong Liang
- Department of Statistics, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, USA
| | - Qianhui Huang
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA
| | - Yuheng Du
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA
| | - Yijun Li
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA
| | - David Garmire
- Department of Electrical Engineering and Computer Science, College of Engineering, University of Michigan, Ann Arbor, MI, USA
| | - Duxin Sun
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI, USA
| | - Lana X Garmire
- Department of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA.
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50
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Samernate T, Htoo HH, Sugie J, Chavasiri W, Pogliano J, Chaikeeratisak V, Nonejuie P. 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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 12/05/2022] [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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Affiliation(s)
- Thanadon Samernate
- Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, Thailand
| | - Htut Htut Htoo
- Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, Thailand
| | - Joseph Sugie
- Division of Biological Sciences, University of California, San Diego, La Jolla, California, USA
| | - Warinthorn Chavasiri
- Center of Excellence in Natural Products Chemistry, Department of Chemistry, Chulalongkorn University, Bangkok, Thailand
| | - Joe Pogliano
- Division of Biological Sciences, University of California, San Diego, La Jolla, California, USA
| | | | - Poochit Nonejuie
- Institute of Molecular Biosciences, Mahidol University, Nakhon Pathom, Thailand
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