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Shah RK, Cygan E, Kozlik T, Colina A, Zamora AE. Utilizing immunogenomic approaches to prioritize targetable neoantigens for personalized cancer immunotherapy. Front Immunol 2023; 14:1301100. [PMID: 38149253 PMCID: PMC10749952 DOI: 10.3389/fimmu.2023.1301100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 11/29/2023] [Indexed: 12/28/2023] Open
Abstract
Advancements in sequencing technologies and bioinformatics algorithms have expanded our ability to identify tumor-specific somatic mutation-derived antigens (neoantigens). While recent studies have shown neoantigens to be compelling targets for cancer immunotherapy due to their foreign nature and high immunogenicity, the need for increasingly accurate and cost-effective approaches to rapidly identify neoantigens remains a challenging task, but essential for successful cancer immunotherapy. Currently, gene expression analysis and algorithms for variant calling can be used to generate lists of mutational profiles across patients, but more care is needed to curate these lists and prioritize the candidate neoantigens most capable of inducing an immune response. A growing amount of evidence suggests that only a handful of somatic mutations predicted by mutational profiling approaches act as immunogenic neoantigens. Hence, unbiased screening of all candidate neoantigens predicted by Whole Genome Sequencing/Whole Exome Sequencing may be necessary to more comprehensively access the full spectrum of immunogenic neoepitopes. Once putative cancer neoantigens are identified, one of the largest bottlenecks in translating these neoantigens into actionable targets for cell-based therapies is identifying the cognate T cell receptors (TCRs) capable of recognizing these neoantigens. While many TCR-directed screening and validation assays have utilized bulk samples in the past, there has been a recent surge in the number of single-cell assays that provide a more granular understanding of the factors governing TCR-pMHC interactions. The goal of this review is to provide an overview of existing strategies to identify candidate neoantigens using genomics-based approaches and methods for assessing neoantigen immunogenicity. Additionally, applications, prospects, and limitations of some of the current single-cell technologies will be discussed. Finally, we will briefly summarize some of the recent models that have been used to predict TCR antigen specificity and analyze the TCR receptor repertoire.
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Affiliation(s)
- Ravi K. Shah
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Erin Cygan
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Tanya Kozlik
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Alfredo Colina
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Anthony E. Zamora
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, United States
- Department of Microbiology and Immunology, Medical College of Wisconsin, Milwaukee, WI, United States
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Hou Y, Wang H, Fu R, Wang X, Yu J, Zhang S, Huang Q, Sun Y, Fukuda T. A review on microrobots driven by optical and magnetic fields. LAB ON A CHIP 2023; 23:848-868. [PMID: 36629004 DOI: 10.1039/d2lc00573e] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Due to their small sizes, microrobots are advantageous for accessing hard-to-reach spaces for delivery and measurement. However, their small sizes also bring challenges in on-board powering, thus usually requiring actuation by external energy. Microrobots actuated by external energy have been applied to the fields of physics, biology, medical science, and engineering. Among these actuation sources, light and magnetic fields show advantages in high precision and high biocompatibility. This paper reviews the recent advances in the design, actuation, and applications of microrobots driven by light and magnetic fields. For light-driven microrobots, we summarized the uses of optical tweezers, optoelectronic tweezers, and heat-mediated optical manipulation techniques. For magnetically driven microrobots, we summarized the uses of torque-driven microrobots, force-driven microrobots, and shape-deformable microrobots. Then, we compared the two types of field-driven microrobots and reviewed their advantages and disadvantages. The paper concludes with an outlook for the joint use of optical and magnetic field actuation in microrobots.
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Affiliation(s)
- Yaozhen Hou
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China.
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, 100081, China
| | - Huaping Wang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China.
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, 100081, China
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 100081, China
| | - Rongxin Fu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - Xian Wang
- Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ONT, M5G 1X8, Canada
| | - Jiangfan Yu
- School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
- Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), Shenzhen 518129, China
| | - Shuailong Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China.
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, 100081, China
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 100081, China
| | - Qiang Huang
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing, 100081, China.
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, 100081, China
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 100081, China
| | - Yu Sun
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada
| | - Toshio Fukuda
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, 100081, China
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Singh B, Kumar A, Saini AK, Saini RV, Thakur R, Mohammed SA, Tuli HS, Gupta VK, Areeshi MY, Faidah H, Jalal NA, Haque S. Strengthening microbial cell factories for efficient production of bioactive molecules. Biotechnol Genet Eng Rev 2023:1-34. [PMID: 36809927 DOI: 10.1080/02648725.2023.2177039] [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: 08/11/2022] [Accepted: 01/21/2023] [Indexed: 02/24/2023]
Abstract
High demand of bioactive molecules (food additives, antibiotics, plant growth enhancers, cosmetics, pigments and other commercial products) is the prime need for the betterment of human life where the applicability of the synthetic chemical product is on the saturation due to associated toxicity and ornamentations. It has been noticed that the discovery and productivity of such molecules in natural scenarios are limited due to low cellular yields as well as less optimized conventional methods. In this respect, microbial cell factories timely fulfilling the requirement of synthesizing bioactive molecules by improving production yield and screening more promising structural homologues of the native molecule. Where the robustness of the microbial host can be potentially achieved by taking advantage of cell engineering approaches such as tuning functional and adjustable factors, metabolic balancing, adapting cellular transcription machinery, applying high throughput OMICs tools, stability of genotype/phenotype, organelle optimizations, genome editing (CRISPER/Cas mediated system) and also by developing accurate model systems via machine-learning tools. In this article, we provide an overview from traditional to recent trends and the application of newly developed technologies, for strengthening the systemic approaches and providing future directions for enhancing the robustness of microbial cell factories to speed up the production of biomolecules for commercial purposes.
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Affiliation(s)
- Bharat Singh
- Department of Biotechnology and Central Research Cell, MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, India
| | - Ankit Kumar
- TERI-Deakin Nanobiotechnology Centre, TERI Gram, The Energy and Resources Institute, Gurugram, India
| | - Adesh Kumar Saini
- Department of Biotechnology and Central Research Cell, MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, India
| | - Reena Vohra Saini
- Department of Biotechnology and Central Research Cell, MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, India
| | - Rahul Thakur
- Department of Biotechnology and Central Research Cell, MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, India
| | - Shakeel A Mohammed
- Department of Biotechnology and Central Research Cell, MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, India
| | - Hardeep Singh Tuli
- Department of Biotechnology and Central Research Cell, MMEC, Maharishi Markandeshwar (Deemed to be University), Mullana-Ambala, India
| | - Vijai Kumar Gupta
- Biorefining and Advanced Materials Research Centre, Scotland's Rural College (SRUC), Edinburgh, UK
| | - Mohammed Y Areeshi
- Medical Laboratory Technology Department, College of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
- Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan, Saudi Arabia
| | - Hani Faidah
- Department of Microbiology, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Naif A Jalal
- Department of Microbiology, Faculty of Medicine, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Shafiul Haque
- Research and Scientific Studies Unit, College of Nursing and Allied Health Sciences, Jazan University, Jazan, Saudi Arabia
- Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut, Lebanon
- Centre of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates
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Martin HG, Radivojevic T, Zucker J, Bouchard K, Sustarich J, Peisert S, Arnold D, Hillson N, Babnigg G, Marti JM, Mungall CJ, Beckham GT, Waldburger L, Carothers J, Sundaram S, Agarwal D, Simmons BA, Backman T, Banerjee D, Tanjore D, Ramakrishnan L, Singh A. Perspectives for self-driving labs in synthetic biology. Curr Opin Biotechnol 2023; 79:102881. [PMID: 36603501 DOI: 10.1016/j.copbio.2022.102881] [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] [Received: 10/06/2022] [Revised: 11/23/2022] [Accepted: 12/07/2022] [Indexed: 01/04/2023]
Abstract
Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. While there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single target for affecting the incredibly wide repertoire of biological cell behavior. However, the level of investment required for the creation of biological SDLs is only warranted if directed toward solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.
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Affiliation(s)
- Hector G Martin
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Joint BioEnergy Institute, Emeryville, CA, United States; BCAM, Basque Center for Applied Mathematics, Bilbao, Spain.
| | - Tijana Radivojevic
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Joint BioEnergy Institute, Emeryville, CA, United States
| | - Jeremy Zucker
- Earth and Biological Sciences Division, Pacific Northwest National Laboratories, Richland, WA, United States
| | - Kristofer Bouchard
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Lawrence Berkeley National Laboratory, Scientific Data Division, Berkeley, CA, United States; Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience, Berkeley, CA, United States
| | - Jess Sustarich
- Joint BioEnergy Institute, Emeryville, CA, United States; Biomaterials and Biomanufacturing Division, Sandia National Laboratories, Livermore, CA, United States
| | - Sean Peisert
- Lawrence Berkeley National Laboratory, Scientific Data Division, Berkeley, CA, United States; University of California, Davis, Department of Computer Science, Davis, CA, United States
| | - Dan Arnold
- Lawrence Berkeley National Laboratory, Energy Storage and Distributed Resources Division, Berkeley, CA, United States
| | - Nathan Hillson
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Joint BioEnergy Institute, Emeryville, CA, United States
| | - Gyorgy Babnigg
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Biosciences Division, Argonne National Laboratory, Argonne, IL, United States
| | - Jose M Marti
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Joint BioEnergy Institute, Emeryville, CA, United States; Global Security Computing Applications Division, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Christopher J Mungall
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States
| | - Gregg T Beckham
- Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Resources and Enabling Sciences Center, National Renewable Energy Laboratory, Golden, CO 80401, United States
| | - Lucas Waldburger
- Department of Bioengineering, University of California, Berkeley, CA, United States
| | - James Carothers
- Department of Chemical Engineering, Molecular Engineering & Sciences Institute and Center for Synthetic Biology, University of Washington, Seattle, WA, United States
| | - ShivShankar Sundaram
- Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States; Center for Bioengineering, Lawrence Livermore National Laboratory, Livermore, CA, United States
| | - Deb Agarwal
- Lawrence Berkeley National Laboratory, Scientific Data Division, Berkeley, CA, United States
| | - Blake A Simmons
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Joint BioEnergy Institute, Emeryville, CA, United States
| | - Tyler Backman
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Joint BioEnergy Institute, Emeryville, CA, United States
| | - Deepanwita Banerjee
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Joint BioEnergy Institute, Emeryville, CA, United States
| | - Deepti Tanjore
- Lawrence Berkeley National Laboratory, Biological Systems and Engineering Division, Berkeley, CA, United States; Department of Energy, Agile BioFoundry, Emeryville, CA, United States; Advanced Biofuels and Bioproducts Process Development Unit, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Lavanya Ramakrishnan
- Lawrence Berkeley National Laboratory, Scientific Data Division, Berkeley, CA, United States
| | - Anup Singh
- Joint BioEnergy Institute, Emeryville, CA, United States; Engineering Directorate, Lawrence Livermore National Laboratory, Livermore, CA, United States
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Zhang S, Xu B, Elsayed M, Nan F, Liang W, Valley JK, Liu L, Huang Q, Wu MC, Wheeler AR. Optoelectronic tweezers: a versatile toolbox for nano-/micro-manipulation. Chem Soc Rev 2022; 51:9203-9242. [DOI: 10.1039/d2cs00359g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
This review covers the fundamentals, recent progress and state-of-the-art applications of optoelectronic tweezers technology, and demonstrates that optoelectronic tweezers technology is a versatile and powerful toolbox for nano-/micro-manipulation.
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Affiliation(s)
- Shuailong Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, Room 711, Building No 6, Science and Technology Park, 5 Zhongguancun South St, Haidian District, Beijing, 100081, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, 100081, China
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 100081, China
| | - Bingrui Xu
- School of Mechatronical Engineering, Beijing Institute of Technology, Room 711, Building No 6, Science and Technology Park, 5 Zhongguancun South St, Haidian District, Beijing, 100081, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, 100081, China
| | - Mohamed Elsayed
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
| | - Fan Nan
- Institute of Nanophotonics, Jinan University, Guangzhou 511443, China
| | - Wenfeng Liang
- School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang, 110168, China
| | - Justin K. Valley
- Berkeley Lights, Inc, 5858 Horton Street #320, Emeryville, CA 94608, USA
| | - Lianqing Liu
- State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
- Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
| | - Qiang Huang
- School of Mechatronical Engineering, Beijing Institute of Technology, Room 711, Building No 6, Science and Technology Park, 5 Zhongguancun South St, Haidian District, Beijing, 100081, China
- Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing, 100081, China
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 100081, China
| | - Ming C. Wu
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, USA
| | - Aaron R. Wheeler
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, M5S 3G9, Canada
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, M5S 3E1, Canada
- Department of Chemistry, University of Toronto, Toronto, ON, M5S 3H6, Canada
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