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Yu Z, Coorens THH, Uddin MM, Ardlie KG, Lennon N, Natarajan P. Genetic variation across and within individuals. Nat Rev Genet 2024; 25:548-562. [PMID: 38548833 DOI: 10.1038/s41576-024-00709-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] [Accepted: 02/09/2024] [Indexed: 04/12/2024]
Abstract
Germline variation and somatic mutation are intricately connected and together shape human traits and disease risks. Germline variants are present from conception, but they vary between individuals and accumulate over generations. By contrast, somatic mutations accumulate throughout life in a mosaic manner within an individual due to intrinsic and extrinsic sources of mutations and selection pressures acting on cells. Recent advancements, such as improved detection methods and increased resources for association studies, have drastically expanded our ability to investigate germline and somatic genetic variation and compare underlying mutational processes. A better understanding of the similarities and differences in the types, rates and patterns of germline and somatic variants, as well as their interplay, will help elucidate the mechanisms underlying their distinct yet interlinked roles in human health and biology.
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Affiliation(s)
- Zhi Yu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Md Mesbah Uddin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Niall Lennon
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Pradeep Natarajan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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2
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Guo J, Sun D, Li K, Dai Q, Geng S, Yang Y, Mo M, Zhu Z, Shao C, Wang W, Song J, Yang C, Zhang H. Metabolic Labeling and Digital Microfluidic Single-Cell Sequencing for Single Bacterial Genotypic-Phenotypic Analysis. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024:e2402177. [PMID: 39077951 DOI: 10.1002/smll.202402177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 06/23/2024] [Indexed: 07/31/2024]
Abstract
Accurate assessment of phenotypic and genotypic characteristics of bacteria can facilitate comprehensive cataloguing of all the resistance factors for better understanding of antibiotic resistance. However, current methods primarily focus on individual phenotypic or genotypic profiles across different colonies. Here, a Digital microfluidic-based automated assay for whole-genome sequencing of single-antibiotic-resistant bacteria is reported, enabling Genotypic and Phenotypic Analysis of antibiotic-resistant strains (Digital-GPA). Digital-GPA can efficiently isolate and sequence antibiotic-resistant bacteria illuminated by fluorescent D-amino acid (FDAA)-labeling, producing high-quality single-cell amplified genomes (SAGs). This enables identifications of both minor and major mutations, pinpointing substrains with distinctive resistance mechanisms. Digital-GPA can directly process clinical samples to detect and sequence resistant pathogens without bacterial culture, subsequently provide genetic profiles of antibiotic susceptibility, promising to expedite the analysis of hard-to-culture or slow-growing bacteria. Overall, Digital-GPA opens a new avenue for antibiotic resistance analysis by providing accurate and comprehensive molecular profiles of antibiotic resistance at single-cell resolution.
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Affiliation(s)
- Junnan Guo
- Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China
| | - Di Sun
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Kunjie Li
- Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China
| | - Qi Dai
- Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China
| | - Shichen Geng
- Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China
| | - Yuanyuan Yang
- Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China
| | - Mengwu Mo
- Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China
| | - Zhi Zhu
- Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China
| | - Chen Shao
- Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China
| | - Wei Wang
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Jia Song
- Institute for Developmental and Regenerative Cardiovascular Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
| | - Chaoyong Yang
- Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China
| | - Huimin Zhang
- Department of Chemical Biology, College of Chemistry and Chemical Engineering, School of Life Sciences, Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, 361005, China
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3
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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Chang T, Gavelis GS, Brown JM, Stepanauskas R. Genomic representativeness and chimerism in large collections of SAGs and MAGs of marine prokaryoplankton. MICROBIOME 2024; 12:126. [PMID: 39010229 PMCID: PMC11247762 DOI: 10.1186/s40168-024-01848-3] [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: 02/09/2024] [Accepted: 05/28/2024] [Indexed: 07/17/2024]
Abstract
BACKGROUND Single amplified genomes (SAGs) and metagenome-assembled genomes (MAGs) are the predominant sources of information about the coding potential of uncultured microbial lineages, but their strengths and limitations remain poorly understood. Here, we performed a direct comparison of two previously published collections of thousands of SAGs and MAGs obtained from the same, global environment. RESULTS We found that SAGs were less prone to chimerism and more accurately reflected the relative abundance and the pangenome content of microbial lineages inhabiting the epipelagic of the tropical and subtropical ocean, as compared to MAGs. SAGs were also better suited to link genome information with taxa discovered through 16S rRNA amplicon analyses. Meanwhile, MAGs had the advantage of more readily recovering genomes of rare lineages. CONCLUSIONS Our analyses revealed the relative strengths and weaknesses of the two most commonly used genome recovery approaches in environmental microbiology. These considerations, as well as the need for better tools for genome quality assessment, should be taken into account when designing studies and interpreting data that involve SAGs or MAGs. Video Abstract.
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Affiliation(s)
- Tianyi Chang
- Bigelow Laboratory for Ocean Sciences, East Boothbay, Maine, 04544, USA
| | - Gregory S Gavelis
- Bigelow Laboratory for Ocean Sciences, East Boothbay, Maine, 04544, USA
| | - Julia M Brown
- Bigelow Laboratory for Ocean Sciences, East Boothbay, Maine, 04544, USA
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5
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Qiao Y, Cheng T, Miao Z, Cui Y, Tu J. Recent Innovations and Technical Advances in High-Throughput Parallel Single-Cell Whole-Genome Sequencing Methods. SMALL METHODS 2024:e2400789. [PMID: 38979872 DOI: 10.1002/smtd.202400789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2024] [Indexed: 07/10/2024]
Abstract
Single-cell whole-genome sequencing (scWGS) detects cell heterogeneity at the aspect of genomic variations, which are inheritable and play an important role in life processes such as aging and cancer progression. The recent explosive development of high-throughput single-cell sequencing methods has enabled high-performance heterogeneity detection through a vast number of novel strategies. Despite the limitation on total cost, technical advances in high-throughput single-cell whole-genome sequencing methods are made for higher genome coverage, parallel throughput, and level of integration. This review highlights the technical advancements in high-throughput scWGS in the aspects of strategies design, data efficiency, parallel handling platforms, and their applications on human genome. The experimental innovations, remaining challenges, and perspectives are summarized and discussed.
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Affiliation(s)
- Yi Qiao
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Tianguang Cheng
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Zikun Miao
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Yue Cui
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Jing Tu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
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6
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Rhaman MS, Ali M, Ye W, Li B. Opportunities and Challenges in Advancing Plant Research with Single-cell Omics. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae026. [PMID: 38996445 DOI: 10.1093/gpbjnl/qzae026] [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: 04/12/2023] [Revised: 01/12/2024] [Accepted: 01/15/2024] [Indexed: 07/14/2024]
Abstract
Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies in transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.
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Affiliation(s)
- Mohammad Saidur Rhaman
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Muhammad Ali
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Wenxiu Ye
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
| | - Bosheng Li
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Weifang 261325, China
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Rupp BT, Cook CD, Purcell EA, Pop M, Radomski AE, Mesyngier N, Bailey RC, Nagrath S. CellMag-CARWash: A High Throughput Droplet Microfluidic Device for Live Cell Isolation and Single Cell Applications. Adv Biol (Weinh) 2024; 8:e2400066. [PMID: 38741244 DOI: 10.1002/adbi.202400066] [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: 04/04/2024] [Indexed: 05/16/2024]
Abstract
The recent push toward understanding an individual cell's behavior and identifying cellular heterogeneity has created an unmet need for technologies that can probe live cells at the single-cell level. Cells within a population are known to exhibit heterogeneous responses to environmental cues. These differences can lead to varied cellular states, behavior, and responses to therapeutics. Techniques are needed that are not only capable of processing and analyzing cellular populations at the single cell level, but also have the ability to isolate specific cell populations from a complex sample at high throughputs. The new CellMag-Coalesce-Attract-Resegment Wash (CellMag-CARWash) system combines positive magnetic selection with droplet microfluidic devices to isolate cells of interest from a mixture with >93% purity and incorporate treatments within individual droplets to observe single cell biological responses. This workflow is shown to be capable of probing the single cell extracellular vesicle (EV) secretion of MCF7 GFP cells. This article reports the first measurement of β-Estradiol's effect on EV secretion from MCF7 cells at the single cell level. Single cell processing revealed that MCF7 GFP cells possess a heterogeneous response to β-Estradiol stimulation with a 1.8-fold increase relative to the control.
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Affiliation(s)
- Brittany T Rupp
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Claire D Cook
- Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Emma A Purcell
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Matei Pop
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Abigail E Radomski
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Nicolas Mesyngier
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ryan C Bailey
- Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Sunitha Nagrath
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, 48109, USA
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8
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Chang X, Zheng Y, Xu K. Single-Cell RNA Sequencing: Technological Progress and Biomedical Application in Cancer Research. Mol Biotechnol 2024; 66:1497-1519. [PMID: 37322261 PMCID: PMC11217094 DOI: 10.1007/s12033-023-00777-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/23/2023] [Indexed: 06/17/2023]
Abstract
Single-cell RNA-seq (scRNA-seq) is a revolutionary technology that allows for the genomic investigation of individual cells in a population, allowing for the discovery of unusual cells associated with cancer and metastasis. ScRNA-seq has been used to discover different types of cancers with poor prognosis and medication resistance such as lung cancer, breast cancer, ovarian cancer, and gastric cancer. Besides, scRNA-seq is a promising method that helps us comprehend the biological features and dynamics of cell development, as well as other disorders. This review gives a concise summary of current scRNA-seq technology. We also explain the main technological steps involved in implementing the technology. We highlight the present applications of scRNA-seq in cancer research, including tumor heterogeneity analysis in lung cancer, breast cancer, and ovarian cancer. In addition, this review elucidates potential applications of scRNA-seq in lineage tracing, personalized medicine, illness prediction, and disease diagnosis, which reveals that scRNA-seq facilitates these events by producing genetic variations on the single-cell level.
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Affiliation(s)
- Xu Chang
- Department of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Yunxi Zheng
- Department of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China
| | - Kai Xu
- Department of Otolaryngology, Head and Neck Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang University, Nanchang, 330006, Jiangxi, People's Republic of China.
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Sharkey C, White R, Finocchiaro M, Thomas J, Estevam J, Konry T. Advancing Point-of-Care Applications with Droplet Microfluidics: From Single-Cell to Multicellular Analysis. Annu Rev Biomed Eng 2024; 26:119-139. [PMID: 38316063 DOI: 10.1146/annurev-bioeng-110222-102142] [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: 02/07/2024]
Abstract
Recent advances in single-cell and multicellular microfluidics technology have provided powerful tools for studying cancer biology and immunology. The ability to create controlled microenvironments, perform high-throughput screenings, and monitor cellular interactions at the single-cell level has significantly advanced our understanding of tumor biology and immune responses. We discuss cutting-edge multicellular and single-cell microfluidic technologies and methodologies utilized to investigate cancer-immune cell interactions and assess the effectiveness of immunotherapies. We explore the advantages and limitations of the wide range of 3D spheroid and single-cell microfluidic models recently developed, highlighting the various approaches in device generation and applications in immunotherapy screening for potential opportunities for point-of-care approaches.
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Affiliation(s)
- Christina Sharkey
- Department of Pharmaceutical Sciences, Northeastern University, Boston, Massachusetts, USA;
- Department of Surgery, Division of Urology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Rachel White
- Department of Pharmaceutical Sciences, Northeastern University, Boston, Massachusetts, USA;
| | - Michael Finocchiaro
- Department of Pharmaceutical Sciences, Northeastern University, Boston, Massachusetts, USA;
| | - Judene Thomas
- Department of Pharmaceutical Sciences, Northeastern University, Boston, Massachusetts, USA;
- Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts, USA
| | - Jose Estevam
- Department of Pharmaceutical Sciences, Northeastern University, Boston, Massachusetts, USA;
| | - Tania Konry
- Department of Pharmaceutical Sciences, Northeastern University, Boston, Massachusetts, USA;
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10
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Popova I, Savelyeva E, Degtyarevskaya T, Babaskin D, Vokhmintsev A. Evaluation of proteome dynamics: Implications for statistical confidence in mass spectrometric determination. Proteomics 2024; 24:e2300351. [PMID: 38700052 DOI: 10.1002/pmic.202300351] [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: 09/13/2023] [Revised: 01/30/2024] [Accepted: 04/17/2024] [Indexed: 05/05/2024]
Abstract
Single-cell proteomics is currently far less productive than other approaches. Still, the proteomic community is having trouble adapting to the limitation of having to examine fewer cells than they would like. Studies on a small number of cells should be carefully planned to maximize the chances of success in this situation. This study aims to determine how sample size and measurement speed (slope)/variation affect the accuracy of a protein proteome mass spectrometric determination. The determination accuracy was shown to increase, and the false positive rate was shown to decrease as the sample size increased from 7 to 100 cells and the measurement slope/variation (S/V) ratio increased from 1 to 6. Furthermore, it was discovered that the number of cells in the sample increased the accuracy of this estimate. Thus, for 100 cells, the measurement S/V ratio was typically estimated to be very close to the real-world value, with a standard deviation of 0.35. For sample sizes from 7 to 100 cells, this accuracy was seen when calculating the measurement S/V ratio. The findings can help researchers plan experiments for mass spectroscopic protein proteome determination and other research purposes.
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Affiliation(s)
- Inga Popova
- Department of Pathological Physiology, I.M. Sechenov First Moscow State Medical University, Moscow, Russian Federation
| | - Ekaterina Savelyeva
- Department of Medical Genetics, I.M. Sechenov First Moscow State Medical University, Moscow, Russian Federation
| | - Tatyana Degtyarevskaya
- Department of Biology and General Genetic, I.M. Sechenov First Moscow State Medical University, Moscow, Russian Federation
| | - Dmitrii Babaskin
- Department of Pharmacy, I.M. Sechenov First Moscow State Medical University, Moscow, Russian Federation
| | - Andrei Vokhmintsev
- Department of Medical Informatics and Biophysics, Tyumen State Medical University of the Ministry of Healthcare of the Russian Federation, Tyumen, Russian Federation
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11
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Guo Z, Zhao Y, Jin Z, Chang Y, Wang X, Guo G, Zhao Y. Monolithic 3D nanoelectrospray emitters based on a continuous fluid-assisted etching strategy for glass droplet microfluidic chip-mass spectrometry. Chem Sci 2024; 15:7781-7788. [PMID: 38784731 PMCID: PMC11110156 DOI: 10.1039/d4sc01700e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Accepted: 04/23/2024] [Indexed: 05/25/2024] Open
Abstract
Glass microfluidic chips are suitable for coupling with mass spectrometry (MS) due to their flexible design, optical transparency and resistance to organic reagents. However, due to the high hardness and brittleness of glass, there is a lack of simple and feasible technology to manufacture a monolithic nanospray ionization (nESI) emitter on a glass microchip, which hinders its coupling with mass spectrometry. Here, a continuous fluid-assisted etching strategy is proposed to fabricate monolithic three-dimensional (3D) nESI emitters integrated into glass microchips. A continuous fluid of methanol is adopted to protect the inner wall of the channels and the bonding interface of the glass microfluidic chip from being wet-etched, forming sharp 3D nESI emitters. The fabricated 3D nESI emitter can form a stable electrospray plume, resulting in consistent nESI detection of acetylcholine with an RSD of 4.5% within 10 min. The fabricated 3D emitter is integrated on a glass microfluidic chip designed with a T-junction droplet generator, which can realize efficient analysis of acetylcholine in picoliter-volume droplets by nESI-MS. Stability testing of over 20 000 droplets detected by the established system resulted in an RSD of 9.1% over approximately 180 min. The detection of ten neurochemicals in rat cerebrospinal fluid droplets is achieved. The established glass droplet microfluidic chip-MS system exhibits potential for broad applications such as in vivo neurochemical monitoring and single-cell analysis in the future.
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Affiliation(s)
- Ziyang Guo
- Department of Chemistry, Beijing University of Technology Beijing 100124 China
| | - Yingqi Zhao
- Department of Chemistry, Beijing University of Technology Beijing 100124 China
| | - Zhao Jin
- Department of Chemistry, Beijing University of Technology Beijing 100124 China
| | - Yaran Chang
- Department of Chemistry, Beijing University of Technology Beijing 100124 China
| | - Xiayan Wang
- Department of Chemistry, Beijing University of Technology Beijing 100124 China
| | - Guangsheng Guo
- Department of Chemistry, Beijing University of Technology Beijing 100124 China
- Minzu University of China Beijing 100081 China
| | - Yaoyao Zhao
- Department of Chemistry, Beijing University of Technology Beijing 100124 China
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12
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Park J, Kadam PS, Atiyas Y, Chhay B, Tsourkas A, Eberwine JH, Issadore DA. High-Throughput Single-Cell, Single-Mitochondrial DNA Assay Using Hydrogel Droplet Microfluidics. Angew Chem Int Ed Engl 2024; 63:e202401544. [PMID: 38470412 DOI: 10.1002/anie.202401544] [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: 01/23/2024] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 03/13/2024]
Abstract
There is growing interest in understanding the biological implications of single cell heterogeneity and heteroplasmy of mitochondrial DNA (mtDNA), but current methodologies for single-cell mtDNA analysis limit the scale of analysis to small cell populations. Although droplet microfluidics have increased the throughput of single-cell genomic, RNA, and protein analysis, their application to sub-cellular organelle analysis has remained a largely unsolved challenge. Here, we introduce an agarose-based droplet microfluidic approach for single-cell, single-mtDNA analysis, which allows simultaneous processing of hundreds of individual mtDNA molecules within >10,000 individual cells. Our microfluidic chip encapsulates individual cells in agarose beads, designed to have a sufficiently dense hydrogel network to retain mtDNA after lysis and provide a robust scaffold for subsequent multi-step processing and analysis. To mitigate the impact of the high viscosity of agarose required for mtDNA retention on the throughput of microfluidics, we developed a parallelized device, successfully achieving ~95 % mtDNA retention from single cells within our microbeads at >700,000 drops/minute. To demonstrate utility, we analyzed specific regions of the single-mtDNA using a multiplexed rolling circle amplification (RCA) assay. We demonstrated compatibility with both microscopy, for digital counting of individual RCA products, and flow cytometry for higher throughput analysis.
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Affiliation(s)
- Juhwan Park
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Parnika S Kadam
- Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Yasemin Atiyas
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Bonirath Chhay
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Andrew Tsourkas
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - James H Eberwine
- Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - David A Issadore
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
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13
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Li X, Xu L, Demaree B, Noecker C, Bisanz JE, Weisgerber DW, Modavi C, Turnbaugh PJ, Abate AR. Microbiome single cell atlases generated with a commercial instrument. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.08.551713. [PMID: 37609281 PMCID: PMC10441329 DOI: 10.1101/2023.08.08.551713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Single cell sequencing is useful for resolving complex systems into their composite cell types and computationally mining them for unique features that are masked in pooled sequencing. However, while commercial instruments have made single cell analysis widespread for mammalian cells, analogous tools for microbes are limited. Here, we present EASi-seq (Easily Accessible Single microbe sequencing). By adapting the single cell workflow of the commercial Mission Bio Tapestri instrument, this method allows for efficient sequencing of individual microbes' genomes. EASi-seq allows thousands of microbes to be sequenced per run and, as we show, can generate detailed atlases of human and environmental microbiomes. The ability to capture large shotgun genome datasets from thousands of single microbes provides new opportunities in discovering and analyzing species subpopulations. To facilitate this, we develop a companion bioinformatic pipeline that clusters microbes by similarity, improving whole genome assembly, strain identification, taxonomic classification, and gene annotation. In addition, we demonstrate integration of metagenomic contigs with the EASi-seq datasets to reduce capture bias and increase coverage. Overall, EASi-seq enables high quality single cell genomic data for microbiome samples using an accessible workflow that can be run on a commercially available platform.
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14
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Lin S, Feng D, Han X, Li L, Lin Y, Gao H. Microfluidic platform for omics analysis on single cells with diverse morphology and size: A review. Anal Chim Acta 2024; 1294:342217. [PMID: 38336406 DOI: 10.1016/j.aca.2024.342217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/04/2024] [Accepted: 01/04/2024] [Indexed: 02/12/2024]
Abstract
BACKGROUND Microfluidic techniques have emerged as powerful tools in single-cell research, facilitating the exploration of omics information from individual cells. Cell morphology is crucial for gene expression and physiological processes. However, there is currently a lack of integrated analysis of morphology and single-cell omics information. A critical challenge remains: what platform technologies are the best option to decode omics data of cells that are complex in morphology and size? RESULTS This review highlights achievements in microfluidic-based single-cell omics and isolation of cells based on morphology, along with other cell sorting methods based on physical characteristics. Various microfluidic platforms for single-cell isolation are systematically presented, showcasing their diversity and adaptability. The discussion focuses on microfluidic devices tailored to the distinct single-cell isolation requirements in plants and animals, emphasizing the significance of considering cell morphology and cell size in optimizing single-cell omics strategies. Simultaneously, it explores the application of microfluidic single-cell sorting technologies to single-cell sequencing, aiming to effectively integrate information about cell shape and size. SIGNIFICANCE AND NOVELTY The novelty lies in presenting a comprehensive overview of recent accomplishments in microfluidic-based single-cell omics, emphasizing the integration of different microfluidic platforms and their implications for cell morphology-based isolation. By underscoring the pivotal role of the specialized morphology of different cells in single-cell research, this review provides robust support for delving deeper into the exploration of single-cell omics data.
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Affiliation(s)
- Shujin Lin
- Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China; Central Laboratory at the Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine, Fujian-Macao Science and Technology Cooperation Base of Traditional Chinese Medicine-Oriented Chronic Disease Prevention and Treatment, Innovation and Transformation Center, Fujian University of Traditional Chinese Medicine, China
| | - Dan Feng
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xiao Han
- College of Biological Science and Engineering, Fuzhou University, Fuzhou, 350108, China.
| | - Ling Li
- Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China; The First Clinical Medical College of Fujian Medical University, Fuzhou, 350004, China; Hepatopancreatobiliary Surgery Department, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350004, China.
| | - Yao Lin
- Central Laboratory at the Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine, Fujian-Macao Science and Technology Cooperation Base of Traditional Chinese Medicine-Oriented Chronic Disease Prevention and Treatment, Innovation and Transformation Center, Fujian University of Traditional Chinese Medicine, China; Collaborative Innovation Center for Rehabilitation Technology, Fujian University of Traditional Chinese Medicine, China.
| | - Haibing Gao
- Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, China.
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15
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Shlyakhtina Y, Bloechl B, Moran KL, Portal MM. Protocol to study the inheritance and propagation of non-genetically encoded states using barcode decay lineage tracing. STAR Protoc 2024; 5:102809. [PMID: 38180835 PMCID: PMC10801334 DOI: 10.1016/j.xpro.2023.102809] [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: 10/08/2023] [Revised: 11/21/2023] [Accepted: 12/15/2023] [Indexed: 01/07/2024] Open
Abstract
Here, we present a protocol to perform barcode decay lineage tracing followed by single-cell transcriptome analysis (BdLT-Seq). We describe steps for BdLT-Seq experimental design, building barcoded episome reporters, performing episome transfection, and barcode retrieval. We then describe procedures for sequencing library construction while providing options for sample multiplexing and data analysis. This BdLT-Seq technique enables the assessment of clonal evolution in a directional manner while preserving isogeneity, thus allowing the comparison of non-genetic molecular features between isogenic cell lineages. For complete details on the use and execution of this protocol, please refer to Shlyakhtina et al. (2023).1.
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Affiliation(s)
- Yelyzaveta Shlyakhtina
- Cell Plasticity & Epigenetics Lab, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK; Cell Plasticity & Epigenetics Lab, Cancer Research UK - Cancer Research UK Scotland Institute, The University of Glasgow, Glasgow G61 1BD, UK
| | - Bianca Bloechl
- Cell Plasticity & Epigenetics Lab, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK; Cell Plasticity & Epigenetics Lab, Cancer Research UK - Cancer Research UK Scotland Institute, The University of Glasgow, Glasgow G61 1BD, UK
| | - Katherine L Moran
- Cell Plasticity & Epigenetics Lab, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK
| | - Maximiliano M Portal
- Cell Plasticity & Epigenetics Lab, Cancer Research UK Manchester Institute, The University of Manchester, Manchester M20 4BX, UK; Cell Plasticity & Epigenetics Lab, Cancer Research UK - Cancer Research UK Scotland Institute, The University of Glasgow, Glasgow G61 1BD, UK.
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16
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Liu R, Li X, Liu Y, Du L, Zhu Y, Wu L, Hu B. A high-speed microscopy system based on deep learning to detect yeast-like fungi cells in blood. Bioanalysis 2024; 16:289-303. [PMID: 38334080 DOI: 10.4155/bio-2023-0193] [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] [Indexed: 02/10/2024] Open
Abstract
Background: Blood-invasive fungal infections can cause the death of patients, while diagnosis of fungal infections is challenging. Methods: A high-speed microscopy detection system was constructed that included a microfluidic system, a microscope connected to a high-speed camera and a deep learning analysis section. Results: For training data, the sensitivity and specificity of the convolutional neural network model were 93.5% (92.7-94.2%) and 99.5% (99.1-99.5%), respectively. For validating data, the sensitivity and specificity were 81.3% (80.0-82.5%) and 99.4% (99.2-99.6%), respectively. Cryptococcal cells were found in 22.07% of blood samples. Conclusion: This high-speed microscopy system can analyze fungal pathogens in blood samples rapidly with high sensitivity and specificity and can help dramatically accelerate the diagnosis of fungal infectious diseases.
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Affiliation(s)
- Ruiqi Liu
- Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, Nanning, Guangxi, P.R. China
| | - Xiaojie Li
- Department of Laboratory Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, P.R. China
| | - Yingyi Liu
- Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, Nanning, Guangxi, P.R. China
| | - Lijun Du
- Department of Clinical Laboratory, Huadu District People's Hospital of Guangzhou, Guangdong, China
| | - Yingzhu Zhu
- Guangzhou Waterrock Gene Technology, Guangdong, China
| | - Lichuan Wu
- Guangxi Key Laboratory of Special Biomedicine, School of Medicine, Guangxi University, Nanning, Guangxi, P.R. China
| | - Bo Hu
- Department of Laboratory Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, P.R. China
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17
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Enders A, Grünberger A, Bahnemann J. Towards Small Scale: Overview and Applications of Microfluidics in Biotechnology. Mol Biotechnol 2024; 66:365-377. [PMID: 36515858 PMCID: PMC10881759 DOI: 10.1007/s12033-022-00626-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 11/26/2022] [Indexed: 12/15/2022]
Abstract
Thanks to recent and continuing technological innovations, modern microfluidic systems are increasingly offering researchers working across all fields of biotechnology exciting new possibilities (especially with respect to facilitating high throughput analysis, portability, and parallelization). The advantages offered by microfluidic devices-namely, the substantially lowered chemical and sample consumption they require, the increased energy and mass transfer they offer, and their comparatively small size-can potentially be leveraged in every sub-field of biotechnology. However, to date, most of the reported devices have been deployed in furtherance of healthcare, pharmaceutical, and/or industrial applications. In this review, we consider examples of microfluidic and miniaturized systems across biotechnology sub-fields. In this context, we point out the advantages of microfluidics for various applications and highlight the common features of devices and the potential for transferability to other application areas. This will provide incentives for increased collaboration between researchers from different disciplines in the field of biotechnology.
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Affiliation(s)
- Anton Enders
- Institute of Technical Chemistry, Leibniz University Hannover, Callinstraße 5, 30167, Hannover, Germany
| | - Alexander Grünberger
- Institute of Process Engineering in Life Sciences: Microsystems in Bioprocess Engineering, Karlsruhe Institute of Technology, Fritz-Haber-Weg 2, 76131, Karlsruhe, Germany
| | - Janina Bahnemann
- Institute of Physics, University of Augsburg, Universitätsstraße 1, 86159, Augsburg, Germany.
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18
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Zhou J, Dong J, Hou H, Huang L, Li J. High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications. LAB ON A CHIP 2024; 24:1307-1326. [PMID: 38247405 DOI: 10.1039/d3lc01012k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.
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Affiliation(s)
- Jianhua Zhou
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Jianpei Dong
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Hongwei Hou
- Beijing Life Science Academy, Beijing 102209, China
| | - Lu Huang
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Jinghong Li
- Department of Chemistry, Center for BioAnalytical Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Tsinghua University, Beijing 100084, China.
- New Cornerstone Science Laboratory, Shenzhen 518054, China
- Beijing Life Science Academy, Beijing 102209, China
- Center for BioAnalytical Chemistry, Hefei National Laboratory of Physical Science at Microscale, University of Science and Technology of China, Hefei 230026, China
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19
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Nan L, Zhang H, Weitz DA, Shum HC. Development and future of droplet microfluidics. LAB ON A CHIP 2024; 24:1135-1153. [PMID: 38165829 DOI: 10.1039/d3lc00729d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2024]
Abstract
Over the past two decades, advances in droplet-based microfluidics have facilitated new approaches to process and analyze samples with unprecedented levels of precision and throughput. A wide variety of applications has been inspired across multiple disciplines ranging from materials science to biology. Understanding the dynamics of droplets enables optimization of microfluidic operations and design of new techniques tailored to emerging demands. In this review, we discuss the underlying physics behind high-throughput generation and manipulation of droplets. We also summarize the applications in droplet-derived materials and droplet-based lab-on-a-chip biotechnology. In addition, we offer perspectives on future directions to realize wider use of droplet microfluidics in industrial production and biomedical analyses.
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Affiliation(s)
- Lang Nan
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong, China
| | - Huidan Zhang
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
| | - David A Weitz
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong, China
| | - Ho Cheung Shum
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong, China
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20
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Liao Y. Emerging tools for uncovering genetic and transcriptomic heterogeneities in bacteria. Biophys Rev 2024; 16:109-124. [PMID: 38495445 PMCID: PMC10937887 DOI: 10.1007/s12551-023-01178-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 12/11/2023] [Indexed: 03/19/2024] Open
Abstract
Bacterial communities display an astonishing degree of heterogeneities among their constituent cells across both the genomic and transcriptomic levels, giving rise to diverse social interactions and stress-adaptation strategies indispensable for proliferating in the natural environment (Ackermann in Nat Rev Microbiol 13:497-508, 2015). Our knowledge about bacterial heterogeneities and their physiological ramifications critically depends on our ability to unambiguously resolve the genetic and phenotypic states of the individual cells that make up the population. In this short review, I highlight several recently developed methods for studying bacterial heterogeneities, primarily focusing on single-cell techniques based on advanced sequencing and microscopy technologies. I will discuss the working principle of each technique as well as the types of problems each technique is best positioned to address. With significant improvements in resolution and throughput, these emerging tools together offer unprecedented and complementary views of various types of heterogeneities found within bacterial populations, paving the way for mechanistic dissections and systematic interventions in laboratory and clinical settings.
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Affiliation(s)
- Yi Liao
- Division of Life Science, Hong Kong University of Science and Technology, Hong Kong SAR, China
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21
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Lan F, Saba J, Ross TD, Zhou Z, Krauska K, Anantharaman K, Landick R, Venturelli OS. Massively parallel single-cell sequencing of diverse microbial populations. Nat Methods 2024; 21:228-235. [PMID: 38233503 PMCID: PMC11089590 DOI: 10.1038/s41592-023-02157-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 12/17/2023] [Indexed: 01/19/2024]
Abstract
Single-cell genetic heterogeneity is ubiquitous in microbial populations and an important aspect of microbial biology; however, we lack a broadly applicable and accessible method to study this heterogeneity in microbial populations. Here, we show a simple, robust and generalizable method for high-throughput single-cell sequencing of target genetic loci in diverse microbes using simple droplet microfluidics devices (droplet targeted amplicon sequencing; DoTA-seq). DoTA-seq serves as a platform to perform diverse assays for single-cell genetic analysis of microbial populations. Using DoTA-seq, we demonstrate the ability to simultaneously track the prevalence and taxonomic associations of >10 antibiotic-resistance genes and plasmids within human and mouse gut microbial communities. This workflow is a powerful and accessible platform for high-throughput single-cell sequencing of diverse microbial populations.
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Affiliation(s)
- Freeman Lan
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA.
| | - Jason Saba
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
- Microbiology Doctoral Training Program, University of Wisconsin-Madison, Madison, WI, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | - Tyler D Ross
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Zhichao Zhou
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | - Katie Krauska
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
| | | | - Robert Landick
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
| | - Ophelia S Venturelli
- Department of Biochemistry, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.
- Department of Chemical & Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA.
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22
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Xie Y, Tong Z, Xia T, Worch JC, Rho JY, Dove AP, O'Reilly RK. 2D Hierarchical Microbarcodes with Expanded Storage Capacity for Optical Multiplex and Information Encryption. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2308154. [PMID: 38014933 DOI: 10.1002/adma.202308154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 11/16/2023] [Indexed: 11/29/2023]
Abstract
The design of nanosegregated fluorescent tags/barcodes by geometrical patterning with precise dimensions and hierarchies could integrate multilevel optical information within one carrier and enhance microsized barcoding techniques for ultrahigh-density optical data storage and encryption. However, precise control of the spatial distribution in micro/nanosized matrices intrinsically limits the accessible barcoding applications in terms of material design and construction. Here, crystallization forces are leveraged to enable a rapid, programmable molecular packing and rapid epitaxial growth of fluorescent units in 2D via crystallization-driven self-assembly. The fluorescence encoding density, scalability, information storage capacity, and decoding techniques of the robust 2D polymeric barcoding platform are explored systematically. These results provide both a theoretical and an experimental foundation for expanding the fluorescence storage capacity, which is a longstanding challenge in state-of-the-art microbarcoding techniques and establish a generalized and adaptable coding platform for high-throughput analysis and optical multiplexing.
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Affiliation(s)
- Yujie Xie
- School of Chemistry, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
- School of Medicine, Shanghai University, Shanghai, 200444, China
| | - Zaizai Tong
- School of Chemistry, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
- College of Materials Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, China
| | - Tianlai Xia
- School of Chemistry, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Joshua C Worch
- School of Chemistry, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Julia Y Rho
- School of Chemistry, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Andrew P Dove
- School of Chemistry, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Rachel K O'Reilly
- School of Chemistry, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
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23
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Park J, Kadam PS, Atiyas Y, Chhay B, Tsourkas A, Eberwine JH, Issadore DA. High-throughput single-cell, single-mitochondrial DNA assay using hydrogel droplet microfluidics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.29.577854. [PMID: 38352577 PMCID: PMC10862758 DOI: 10.1101/2024.01.29.577854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
There is growing interest in understanding the biological implications of single cell heterogeneity and intracellular heteroplasmy of mtDNA, but current methodologies for single-cell mtDNA analysis limit the scale of analysis to small cell populations. Although droplet microfluidics have increased the throughput of single-cell genomic, RNA, and protein analysis, their application to sub-cellular organelle analysis has remained a largely unsolved challenge. Here, we introduce an agarose-based droplet microfluidic approach for single-cell, single-mtDNA analysis, which allows simultaneous processing of hundreds of individual mtDNA molecules within >10,000 individual cells. Our microfluidic chip encapsulates individual cells in agarose beads, designed to have a sufficiently dense hydrogel network to retain mtDNA after lysis and provide a robust scaffold for subsequent multi-step processing and analysis. To mitigate the impact of the high viscosity of agarose required for mtDNA retention on the throughput of microfluidics, we developed a parallelized device, successfully achieving ~95% mtDNA retention from single cells within our microbeads at >700,000 drops/minute. To demonstrate utility, we analyzed specific regions of the single mtDNA using a multiplexed rolling circle amplification (RCA) assay. We demonstrated compatibility with both microscopy, for digital counting of individual RCA products, and flow cytometry for higher throughput analysis.
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Nishikawa Y, Wagatsuma R, Tsukada Y, Chia-ling L, Chijiiwa R, Hosokawa M, Takeyama H. Large-scale single-virus genomics uncovers hidden diversity of river water viruses and diversified gene profiles. THE ISME JOURNAL 2024; 18:wrae124. [PMID: 38976038 PMCID: PMC11283719 DOI: 10.1093/ismejo/wrae124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 04/18/2024] [Accepted: 07/03/2024] [Indexed: 07/09/2024]
Abstract
Environmental viruses (primarily bacteriophages) are widely recognized as playing an important role in ecosystem homeostasis through the infection of host cells. However, the majority of environmental viruses are still unknown as their mosaic structure and frequent mutations in their sequences hinder genome construction in current metagenomics. To enable the large-scale acquisition of environmental viral genomes, we developed a new single-viral genome sequencing platform with microfluidic-generated gel beads. Amplification of individual DNA viral genomes in mass-produced gel beads allows high-throughput genome sequencing compared to conventional single-virus genomics. The sequencing analysis of river water samples yielded 1431 diverse viral single-amplified genomes, whereas viral metagenomics recovered 100 viral metagenome-assembled genomes at the comparable sequence depth. The 99.5% of viral single-amplified genomes were determined novel at the species level, most of which could not be recovered by a metagenomic assembly. The large-scale acquisition of diverse viral genomes identified protein clusters commonly detected in different viral strains, allowing the gene transfer to be tracked. Moreover, comparative genomics within the same viral species revealed that the profiles of various methyltransferase subtypes were diverse, suggesting an enhanced escape from host bacterial internal defense mechanisms. Our use of gel bead-based single-virus genomics will contribute to exploring the nature of viruses by accelerating the accumulation of draft genomes of environmental DNA viruses.
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Affiliation(s)
- Yohei Nishikawa
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), AIST-Waseda University, 3-4-1 Okubo, Tokyo 169-0082, Japan
- Research Organization for Nano & Life Innovation, Waseda University, 513 Waseda Tsurumaki-cho, Tokyo 162–0041, Japan
| | - Ryota Wagatsuma
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), AIST-Waseda University, 3-4-1 Okubo, Tokyo 169-0082, Japan
- Graduate School of Advanced Science and Engineering, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
| | - Yuko Tsukada
- Graduate School of Advanced Science and Engineering, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
| | - Lin Chia-ling
- Graduate School of Advanced Science and Engineering, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
| | - Rieka Chijiiwa
- Research Organization for Nano & Life Innovation, Waseda University, 513 Waseda Tsurumaki-cho, Tokyo 162–0041, Japan
| | - Masahito Hosokawa
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), AIST-Waseda University, 3-4-1 Okubo, Tokyo 169-0082, Japan
- Research Organization for Nano & Life Innovation, Waseda University, 513 Waseda Tsurumaki-cho, Tokyo 162–0041, Japan
- Graduate School of Advanced Science and Engineering, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
- Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Haruko Takeyama
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), AIST-Waseda University, 3-4-1 Okubo, Tokyo 169-0082, Japan
- Research Organization for Nano & Life Innovation, Waseda University, 513 Waseda Tsurumaki-cho, Tokyo 162–0041, Japan
- Graduate School of Advanced Science and Engineering, Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan
- Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
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25
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Jin J, Yamamoto R, Shiroguchi K. High-throughput identification and quantification of bacterial cells in the microbiota based on 16S rRNA sequencing with single-base accuracy using BarBIQ. Nat Protoc 2024; 19:207-239. [PMID: 38012397 DOI: 10.1038/s41596-023-00906-8] [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: 12/01/2022] [Accepted: 08/24/2023] [Indexed: 11/29/2023]
Abstract
Bacteria often function as a community, called the microbiota, consisting of many different bacterial species. The accurate identification of bacterial types and the simultaneous quantification of the cells of each bacterial type will advance our understanding of microbiota; however, this cannot be performed by conventional 16S rRNA sequencing methods as they only identify and quantify genes, which do not always represent cells. Here, we present a protocol for our developed method, barcoding bacteria for identification and quantification (BarBIQ). In BarBIQ, the 16S rRNA genes of single bacterial cells are amplified and attached to a unique cellular barcode in a droplet. Sequencing the tandemly linked cellular barcodes and 16S rRNA genes from many droplets (representing many cells with unique cellular barcodes) and clustering the sequences using the barcodes determines both the bacterial type for each cell based on 16S rRNA gene and the number of cells for each bacterial type based on the quantity of barcode types sequenced. Single-base accuracy for 16S rRNA sequencing is achieved via the barcodes and by avoiding chimera formation from 16S rRNA genes of different bacteria using droplets. For data processing, an easy-to-use bioinformatic pipeline is available ( https://github.com/Shiroguchi-Lab/BarBIQ_Pipeline_V1_2_0 ). This protocol allows researchers with experience in molecular biology but without bioinformatics experience to perform the process in ~2 weeks. We show the application of BarBIQ in mouse gut microbiota analysis as an example; however, this method is also applicable to other microbiota samples, including those from the mouth and skin, marine environments, soil and plants, as well as those from other terrestrial environments.
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Affiliation(s)
- Jianshi Jin
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, P.R. China.
- Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), Osaka, Japan.
| | - Reiko Yamamoto
- Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), Osaka, Japan
| | - Katsuyuki Shiroguchi
- Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), Osaka, Japan.
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26
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Yan J, Wu T, Zhang J, Gao Y, Wu JM, Wang S. Revolutionizing the female reproductive system research using microfluidic chip platform. J Nanobiotechnology 2023; 21:490. [PMID: 38111049 PMCID: PMC10729361 DOI: 10.1186/s12951-023-02258-7] [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: 08/19/2023] [Accepted: 12/07/2023] [Indexed: 12/20/2023] Open
Abstract
Comprehensively understanding the female reproductive system is crucial for safeguarding fertility and preventing diseases concerning women's health. With the capacity to simulate the intricate physio- and patho-conditions, and provide diagnostic platforms, microfluidic chips have fundamentally transformed the knowledge and management of female reproductive health, which will ultimately promote the development of more effective assisted reproductive technologies, treatments, and drug screening approaches. This review elucidates diverse microfluidic systems in mimicking the ovary, fallopian tube, uterus, placenta and cervix, and we delve into the culture of follicles and oocytes, gametes' manipulation, cryopreservation, and permeability especially. We investigate the role of microfluidics in endometriosis and hysteromyoma, and explore their applications in ovarian cancer, endometrial cancer and cervical cancer. At last, the current status of assisted reproductive technology and integrated microfluidic devices are introduced briefly. Through delineating the multifarious advantages and challenges of the microfluidic technology, we chart a definitive course for future research in the woman health field. As the microfluidic technology continues to evolve and advance, it holds great promise for revolutionizing the diagnosis and treatment of female reproductive health issues, thus propelling us into a future where we can ultimately optimize the overall wellbeing and health of women everywhere.
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Affiliation(s)
- Jinfeng Yan
- National Clinical Research Center for Obstetrical and Gynecological Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Avenue, Wuhan, 430030, China
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
- Engineering Research Center of Ceramic Materials for Additive Manufacturing, Ministry of Education, Wuhan, 430074, China
| | - Tong Wu
- National Clinical Research Center for Obstetrical and Gynecological Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Avenue, Wuhan, 430030, China
| | - Jinjin Zhang
- National Clinical Research Center for Obstetrical and Gynecological Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Avenue, Wuhan, 430030, China
| | - Yueyue Gao
- National Clinical Research Center for Obstetrical and Gynecological Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Avenue, Wuhan, 430030, China
| | - Jia-Min Wu
- State Key Laboratory of Materials Processing and Die & Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
- Engineering Research Center of Ceramic Materials for Additive Manufacturing, Ministry of Education, Wuhan, 430074, China.
| | - Shixuan Wang
- National Clinical Research Center for Obstetrical and Gynecological Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Key Laboratory of Cancer Invasion and Metastasis, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1095, Jiefang Avenue, Wuhan, 430030, China.
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27
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Sashittal P, Zhang H, Iacobuzio-Donahue CA, Raphael BJ. ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model. Genome Biol 2023; 24:272. [PMID: 38037115 PMCID: PMC10688497 DOI: 10.1186/s13059-023-03106-5] [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: 12/27/2022] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
A tumor contains a diverse collection of somatic mutations that reflect its past evolutionary history and that range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). However, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs, complicating the inference of tumor phylogenies. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers but constrains losses of SNVs according to clusters of cells. We derive an algorithm, ConDoR, that infers phylogenies from targeted scDNA-seq data using this model. We demonstrate the advantages of ConDoR on simulated and real scDNA-seq data.
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Affiliation(s)
| | - Haochen Zhang
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, NY, USA
| | - Christine A Iacobuzio-Donahue
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, NY, USA
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28
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Harriot J, Yeh M, Pabba M, DeVoe DL. Programmable Control of Nanoliter Droplet Arrays using Membrane Displacement Traps. ADVANCED MATERIALS TECHNOLOGIES 2023; 8:2300963. [PMID: 38495529 PMCID: PMC10939115 DOI: 10.1002/admt.202300963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Indexed: 03/19/2024]
Abstract
A unique droplet microfluidic technology enabling programmable deterministic control over complex droplet operations is presented. The platform provides software control over user-defined combinations of droplet generation, capture, ejection, sorting, splitting, and merging sequences to enable the design of flexible assays employing nanoliter-scale fluid volumes. The system integrates a computer vision system with an array of membrane displacement traps capable of performing selected unit operations with automated feedback control. Sequences of individual droplet handling steps are defined through a robust Python-based scripting language. Bidirectional flow control within the microfluidic chips is provided using an H-bridge channel topology, allowing droplets to be transported to arbitrary trap locations within the array for increased operational flexibility. By enabling automated software control over all droplet operations, the system significantly expands the potential of droplet microfluidics for diverse biological and biochemical applications by combining the functionality of robotic liquid handling with the advantages of droplet-based fluid manipulation.
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Affiliation(s)
- Jason Harriot
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742
- Fishell Institute for Biomedical Devices, University of Maryland, College Park, MD 20742
| | - Michael Yeh
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742
- Fishell Institute for Biomedical Devices, University of Maryland, College Park, MD 20742
| | - Mani Pabba
- Department of Computer Science, University of Maryland, College Park, MD 20742
| | - Don L. DeVoe
- Department of Mechanical Engineering, University of Maryland, College Park, MD 20742
- Fishell Institute for Biomedical Devices, University of Maryland, College Park, MD 20742
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29
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Moar P, Premeaux TA, Atkins A, Ndhlovu LC. The latent HIV reservoir: current advances in genetic sequencing approaches. mBio 2023; 14:e0134423. [PMID: 37811964 PMCID: PMC10653892 DOI: 10.1128/mbio.01344-23] [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] [Indexed: 10/10/2023] Open
Abstract
Multiple cellular HIV reservoirs in diverse anatomical sites can undergo clonal expansion and persist for years despite suppressive antiretroviral therapy, posing a major barrier toward an HIV cure. Commonly adopted assays to assess HIV reservoir size mainly consist of PCR-based measures of cell-associated total proviral DNA, intact proviruses and transcriptionally competent provirus (viral RNA), flow cytometry and microscopy-based methods to measure translationally competent provirus (viral protein), and quantitative viral outgrowth assay, the gold standard to measure replication-competent provirus; yet no assay alone can provide a comprehensive view of the total HIV reservoir or its dynamics. Furthermore, the detection of extant provirus by these measures does not preclude defects affecting replication competence. An accurate measure of the latent reservoir is essential for evaluating the efficacy of HIV cure strategies. Recent approaches have been developed, which generate proviral sequence data to create a more detailed profile of the latent reservoir. These sequencing approaches are valuable tools to understand the complex multicellular processes in a diverse range of tissues and cell types and have provided insights into the mechanisms of HIV establishment and persistence. These advancements over previous sequencing methods have allowed multiplexing and new assays have emerged, which can document transcriptional activity, chromosome accessibility, and in-depth cellular phenotypes harboring latent HIV, enabling the characterization of rare infected cells across restrictive sites such as the brain. In this manuscript, we provide a review of HIV sequencing-based assays adopted to address challenges in quantifying and characterizing the latent HIV reservoir.
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Affiliation(s)
- Preeti Moar
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York City, New York, USA
| | - Thomas A. Premeaux
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York City, New York, USA
| | - Andrew Atkins
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York City, New York, USA
| | - Lishomwa C. Ndhlovu
- Department of Medicine, Division of Infectious Diseases, Weill Cornell Medicine, New York City, New York, USA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York City, New York, USA
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30
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Madhu B, Miller BM, Levy M. Single-cell analysis and spatial resolution of the gut microbiome. Front Cell Infect Microbiol 2023; 13:1271092. [PMID: 37860069 PMCID: PMC10582963 DOI: 10.3389/fcimb.2023.1271092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 09/11/2023] [Indexed: 10/21/2023] Open
Abstract
Over the past decade it has become clear that various aspects of host physiology, metabolism, and immunity are intimately associated with the microbiome and its interactions with the host. Specifically, the gut microbiome composition and function has been shown to play a critical role in the etiology of different intestinal and extra-intestinal diseases. While attempts to identify a common pattern of microbial dysbiosis linked with these diseases have failed, multiple studies show that bacterial communities in the gut are spatially organized and that disrupted spatial organization of the gut microbiome is often a common underlying feature of disease pathogenesis. As a result, focus over the last few years has shifted from analyzing the diversity of gut microbiome by sequencing of the entire microbial community, towards understanding the gut microbiome in spatial context. Defining the composition and spatial heterogeneity of the microbiome is critical to facilitate further understanding of the gut microbiome ecology. Development in single cell genomics approach has advanced our understanding of microbial community structure, however, limitations in approaches exist. Single cell genomics is a very powerful and rapidly growing field, primarily used to identify the genetic composition of microbes. A major challenge is to isolate single cells for genomic analyses. This review summarizes the different approaches to study microbial genomes at single-cell resolution. We will review new techniques for microbial single cell sequencing and summarize how these techniques can be applied broadly to answer many questions related to the microbiome composition and spatial heterogeneity. These methods can be used to fill the gaps in our understanding of microbial communities.
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Affiliation(s)
| | | | - Maayan Levy
- Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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31
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Shin Y, Kwak T, Whang K, Jo Y, Hwang JH, Hwang I, An HJ, Lim Y, Choi I, Kim D, Lee LP, Kang T. Bubble-free diatoms polymerase chain reaction. Biosens Bioelectron 2023; 237:115489. [PMID: 37402347 DOI: 10.1016/j.bios.2023.115489] [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: 05/11/2023] [Revised: 06/16/2023] [Accepted: 06/18/2023] [Indexed: 07/06/2023]
Abstract
Polymerase chain reaction (PCR) in small fluidic systems not only improves speed and sensitivity of deoxyribonucleic acid (DNA) amplification but also achieves high-throughput quantitative analyses. However, air bubble trapping and growth during PCR has been considered as a critical problem since it causes the failure of DNA amplification. Here we report bubble-free diatom PCR by exploiting a hierarchically porous silica structure of single-celled algae. We show that femtoliters of PCR solution can be spontaneously loaded into the diatom interior without air bubble trapping due to the surface hydrophilicity and pore structure of the diatom. We discover that a large pressure gradient between air bubbles and nanopores rapidly removes residual air bubbles through the periodically arrayed nanopores during thermal cycling. We demonstrate the DNA amplification by diatom PCR without air bubble trapping and growth. Finally, we successfully detect DNA fragments of SARS-CoV-2 with as low as 10 copies/μl by devising a microfluidic device integrated with diatoms assembly. We believe that our work can be applied to many PCR applications for innovative molecular diagnostics and provides new opportunities for naturally abundant diatoms to create innovative biomaterials in real-world applications.
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Affiliation(s)
- Yonghee Shin
- Department of Chemical and Biomolecular Engineering, Sogang University, Seoul, 121-742, South Korea; Institute of Integrated Biotechnology, Sogang University, Seoul, 121-742, South Korea; Renal Division and Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Taejin Kwak
- Department of Mechanical Engineering, Sogang University, Seoul, 04107, South Korea
| | - Keumrai Whang
- Department of Chemical and Biomolecular Engineering, Sogang University, Seoul, 121-742, South Korea
| | - Yuseung Jo
- Department of Chemical and Biomolecular Engineering, Sogang University, Seoul, 121-742, South Korea
| | - Jeong Ha Hwang
- Department of Chemical and Biomolecular Engineering, Sogang University, Seoul, 121-742, South Korea
| | - Inhyeok Hwang
- Department of Chemical and Biomolecular Engineering, Sogang University, Seoul, 121-742, South Korea
| | - Hyun Ji An
- Department of Life Science, University of Seoul, Seoul, 02504, South Korea
| | - Youngwook Lim
- Department of Mechanical Engineering, Sogang University, Seoul, 04107, South Korea
| | - Inhee Choi
- Department of Life Science, University of Seoul, Seoul, 02504, South Korea
| | - Dongchoul Kim
- Department of Mechanical Engineering, Sogang University, Seoul, 04107, South Korea.
| | - Luke P Lee
- Renal Division and Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA; Department of Bioengineering, Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, CA, USA; Institute of Quantum Biophysics, Department of Biophysics, Sungkyunkwan University, Suwon, 16419, South Korea.
| | - Taewook Kang
- Department of Chemical and Biomolecular Engineering, Sogang University, Seoul, 121-742, South Korea; Institute of Integrated Biotechnology, Sogang University, Seoul, 121-742, South Korea.
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32
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Khan R, Mallory X. Assessing the performance of methods for cell clustering from single-cell DNA sequencing data. PLoS Comput Biol 2023; 19:e1010480. [PMID: 37824596 PMCID: PMC10597505 DOI: 10.1371/journal.pcbi.1010480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 10/24/2023] [Accepted: 09/20/2023] [Indexed: 10/14/2023] Open
Abstract
BACKGROUND Many cancer genomes have been known to contain more than one subclone inside one tumor, the phenomenon of which is called intra-tumor heterogeneity (ITH). Characterizing ITH is essential in designing treatment plans, prognosis as well as the study of cancer progression. Single-cell DNA sequencing (scDNAseq) has been proven effective in deciphering ITH. Cells corresponding to each subclone are supposed to carry a unique set of mutations such as single nucleotide variations (SNV). While there have been many studies on the cancer evolutionary tree reconstruction, not many have been proposed that simply characterize the subclonality without tree reconstruction. While tree reconstruction is important in the study of cancer evolutionary history, typically they are computationally expensive in terms of running time and memory consumption due to the huge search space of the tree structure. On the other hand, subclonality characterization of single cells can be converted into a cell clustering problem, the dimension of which is much smaller, and the turnaround time is much shorter. Despite the existence of a few state-of-the-art cell clustering computational tools for scDNAseq, there lacks a comprehensive and objective comparison under different settings. RESULTS In this paper, we evaluated six state-of-the-art cell clustering tools-SCG, BnpC, SCClone, RobustClone, SCITE and SBMClone-on simulated data sets given a variety of parameter settings and a real data set. We designed a simulator specifically for cell clustering, and compared these methods' performances in terms of their clustering accuracy, specificity and sensitivity and running time. For SBMClone, we specifically designed an ultra-low coverage large data set to evaluate its performance in the face of an extremely high missing rate. CONCLUSION From the benchmark study, we conclude that BnpC and SCG's clustering accuracy are the highest and comparable to each other. However, BnpC is more advantageous in terms of running time when cell number is high (> 1500). It also has a higher clustering accuracy than SCG when cluster number is high (> 16). SCClone's accuracy in estimating the number of clusters is the highest. RobustClone and SCITE's clustering accuracy are the lowest for all experiments. SCITE tends to over-estimate the cluster number and has a low specificity, whereas RobustClone tends to under-estimate the cluster number and has a much lower sensitivity than other methods. SBMClone produced reasonably good clustering (V-measure > 0.9) when coverage is > = 0.03 and thus is highly recommended for ultra-low coverage large scDNAseq data sets.
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Affiliation(s)
- Rituparna Khan
- Department of Computer Science, Florida State University, Tallahassee, Florida, United States of America
| | - Xian Mallory
- Department of Computer Science, Florida State University, Tallahassee, Florida, United States of America
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Hayashi M, Ohnuki S, Tsai Y, Kondo N, Zhou Y, Zhang H, Ishii NT, Ding T, Herbig M, Isozaki A, Ohya Y, Goda K. Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae. LAB ON A CHIP 2023; 23:4232-4244. [PMID: 37650583 DOI: 10.1039/d3lc00556a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Artificial intelligence (AI) has become a focal point across a multitude of societal sectors, with science not being an exception. Particularly in the life sciences, imaging flow cytometry has increasingly integrated AI for automated management and categorization of extensive cell image data. However, the necessity of AI over traditional classification methods when extending imaging flow cytometry to include cell sorting remains uncertain, primarily due to the time constraints between image acquisition and sorting actuation. AI-enabled image-activated cell sorting (IACS) methods remain substantially limited, even as recent advancements in IACS have found success while largely relying on traditional feature gating strategies. Here we assess the necessity of AI for image classification in IACS by contrasting the performance of feature gating, classical machine learning (ML), and deep learning (DL) with convolutional neural networks (CNNs) in the differentiation of Saccharomyces cerevisiae mutant images. We show that classical ML could only yield a 2.8-fold enhancement in target enrichment capability, albeit at the cost of a 13.7-fold increase in processing time. Conversely, a CNN could offer an 11.0-fold improvement in enrichment capability at an 11.5-fold increase in processing time. We further executed IACS on mixed mutant populations and quantified target strain enrichment via downstream DNA sequencing to substantiate the applicability of DL for the proposed study. Our findings validate the feasibility and value of employing DL in IACS for morphology-based genetic screening of S. cerevisiae, encouraging its incorporation in future advancements of similar technologies.
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Affiliation(s)
- Mika Hayashi
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Shinsuke Ohnuki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Japan.
| | - Yating Tsai
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Japan.
| | - Naoko Kondo
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Japan.
| | - Yuqi Zhou
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Hongqian Zhang
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Natsumi Tiffany Ishii
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Tianben Ding
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Maik Herbig
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
| | - Akihiro Isozaki
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
- Department of Mechanical Engineering, College of Science and Engineering, Ritsumeikan University, Shiga 525-8577, Japan.
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562, Japan.
- Collaborative Research Institute for Innovative Microbiology, The University of Tokyo, Tokyo 113-8654, Japan
| | - Keisuke Goda
- Department of Chemistry, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan.
- Department of Bioengineering, University of California, Los Angeles, California 90095, USA
- Institute of Technological Sciences, Wuhan University, Wuhan 430072, China
- CYBO, Tokyo 135-0064, Japan
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34
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Zhu W, Wang J, Luo H, Luo B, Li X, Liu S, Li C. Electrical Characterization and Analysis of Single Cells and Related Applications. BIOSENSORS 2023; 13:907. [PMID: 37887100 PMCID: PMC10605054 DOI: 10.3390/bios13100907] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/26/2023] [Accepted: 09/01/2023] [Indexed: 10/28/2023]
Abstract
Biological parameters extracted from electrical signals from various body parts have been used for many years to analyze the human body and its behavior. In addition, electrical signals from cancer cell lines, normal cells, and viruses, among others, have been widely used for the detection of various diseases. Single-cell parameters such as cell and cytoplasmic conductivity, relaxation frequency, and membrane capacitance are important. There are many techniques available to characterize biomaterials, such as nanotechnology, microstrip cavity resonance measurement, etc. This article reviews single-cell isolation and sorting techniques, such as the micropipette separation method, separation and sorting system (dual electrophoretic array system), DEPArray sorting system (dielectrophoretic array system), cell selector sorting system, and microfluidic and valve devices, and discusses their respective advantages and disadvantages. Furthermore, it summarizes common single-cell electrical manipulations, such as single-cell amperometry (SCA), electrical impedance sensing (EIS), impedance flow cytometry (IFC), cell-based electrical impedance (CEI), microelectromechanical systems (MEMS), and integrated microelectrode array (IMA). The article also enumerates the application and significance of single-cell electrochemical analysis from the perspectives of CTC liquid biopsy, recombinant adenovirus, tumor cells like lung cancer DTCs (LC-DTCs), and single-cell metabolomics analysis. The paper concludes with a discussion of the current limitations faced by single-cell analysis techniques along with future directions and potential application scenarios.
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Affiliation(s)
- Weitao Zhu
- Clinical Medicine (Eight-Year Program), West China School of Medicine, Sichuan University, Chengdu 610044, China; (W.Z.); (J.W.)
| | - Jiaao Wang
- Clinical Medicine (Eight-Year Program), West China School of Medicine, Sichuan University, Chengdu 610044, China; (W.Z.); (J.W.)
| | - Hongzhi Luo
- Department of Laboratory Medicine, The Third Affiliated Hospital of Zunyi Medical University (The First People’s Hospital of Zunyi), Zunyi 563002, China;
| | - Binwen Luo
- School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054, China;
| | - Xue Li
- Sichuan Hanyuan County People’s Hospital, Hanyuan 625300, China;
| | - Shan Liu
- Sichuan Provincial Key Laboratory for Human Disease Gene Study, Department of Medical Genetics, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu 610072, China
| | - Chenzhong Li
- Biomedical Engineering, School of Medicine, The Chinese University of Hong Kong, Shenzhen 518172, China;
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35
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Kayirangwa Y, Mohibullah M, Easley CJ. Droplet-based μChopper device with a 3D-printed pneumatic valving layer and a simple photometer for absorbance based fructosamine quantification in human serum. Analyst 2023; 148:4810-4819. [PMID: 37605899 PMCID: PMC10530610 DOI: 10.1039/d3an01149f] [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: 08/23/2023]
Abstract
The development of microfluidic systems for biological assays presents challenges, particularly in adapting traditional optical absorbance assays to smaller volumes or to microfluidic formats. This often requires assay modification or translation to a fluorescence version, which can be impractical. To address this issue, our group has developed the μChopper device, which uses microfluidic droplet formation as a surrogate for an optical beam chopper, allowing for lock-in analysis and improved limits of detection with both absorbance and fluorescence optics without modifying the optical path length. Here, we have adapted the μChopper to low-cost optics using a light-emitting diode (LED) source and photodiode detector, and we have fabricated the pnuematically valved devices entirely by 3D printing instead of traditional photolithography. Using a hybrid device structure, fluidic channels were made in polydimethylsiloxane (PDMS) by moulding onto a 3D-printed master then bonding to a prefabricated thin layer, and the pneumatic layer was directly made of 3D-printed resin. This hybrid structure allowed an optical slit to be fabricated directly under fluidic channels, with the LED interfaced closely above the channel. Vacuum-operated, normally closed valves provided precise temporal control of droplet formation from 0.6 to 2.0 Hz. The system was validated against the standard plate reader format using a colorimetric fructosamine assay and by quantifying fructosamine in human serum from normal and diabetic patients, where strong correlation was shown. Showing a standard benefit of microfluidics in analysis, the device required 6.4-fold less serum volume for each assay. This μChopper device and lower cost optical system should be applicable to various absorbance based assays in low volumes, and the reliance on inexpensive 3D printers makes it more accessible to users without cleanroom facilities.
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Affiliation(s)
- Yvette Kayirangwa
- Department of Chemistry and Biochemistry, Auburn University, Auburn, AL, USA.
| | - Md Mohibullah
- Department of Chemistry and Biochemistry, Auburn University, Auburn, AL, USA.
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36
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Abate A, Li X, Xu L, Demaree B, Noecker C, Bisanz J, Weisgerber D, Modavi C, Turnbaugh P. Microbiome single cell atlases generated with a commercial instrument. RESEARCH SQUARE 2023:rs.3.rs-3253785. [PMID: 37790580 PMCID: PMC10543498 DOI: 10.21203/rs.3.rs-3253785/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Single cell sequencing is useful for resolving complex systems into their composite cell types and computationally mining them for unique features that are masked in pooled sequencing. However, while commercial instruments have made single cell analysis widespread for mammalian cells, analogous tools for microbes are limited. Here, we present EASi-seq (Easily Accessible Single microbe sequencing). By adapting the single cell workflow of the commercial Mission Bio Tapestri instrument, this method allows for efficient sequencing of individual microbes' genomes. EASi-seq allows thousands of microbes to be sequenced per run and, as we show, can generate detailed atlases of human and environmental microbiomes. The ability to capture large shotgun genome datasets from thousands of single microbes provides new opportunities in discovering and analyzing species subpopulations. To facilitate this, we develop a companion bioinformatic pipeline that clusters microbes by similarity, improving whole genome assembly, strain identification, taxonomic classification, and gene annotation. In addition, we demonstrate integration of metagenomic contigs with the EASi-seq datasets to reduce capture bias and increase coverage. Overall, EASi-seq enables high quality single cell genomic data for microbiome samples using an accessible workflow that can be run on a commercially available platform.
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37
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Kim J, Kim S, Yeom H, Song SW, Shin K, Bae S, Ryu HS, Kim JY, Choi A, Lee S, Ryu T, Choi Y, Kim H, Kim O, Jung Y, Kim N, Han W, Lee HB, Lee AC, Kwon S. Barcoded multiple displacement amplification for high coverage sequencing in spatial genomics. Nat Commun 2023; 14:5261. [PMID: 37644058 PMCID: PMC10465490 DOI: 10.1038/s41467-023-41019-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023] Open
Abstract
Determining mutational landscapes in a spatial context is essential for understanding genetically heterogeneous cell microniches. Current approaches, such as Multiple Displacement Amplification (MDA), offer high genome coverage but limited multiplexing, which hinders large-scale spatial genomic studies. Here, we introduce barcoded MDA (bMDA), a technique that achieves high-coverage genomic analysis of low-input DNA while enhancing the multiplexing capabilities. By incorporating cell barcodes during MDA, bMDA streamlines library preparation in one pot, thereby overcoming a key bottleneck in spatial genomics. We apply bMDA to the integrative spatial analysis of triple-negative breast cancer tissues by examining copy number alterations, single nucleotide variations, structural variations, and kataegis signatures for each spatial microniche. This enables the assessment of subclonal evolutionary relationships within a spatial context. Therefore, bMDA has emerged as a scalable technology with the potential to advance the field of spatial genomics significantly.
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Affiliation(s)
- Jinhyun Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sungsik Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Huiran Yeom
- Division of Data Science, College of Information and Communication Technology, The University of Suwon, Hwaseong, 18323, Republic of Korea
| | - Seo Woo Song
- Basic Science and Engineering Initiative, Children's Heart Center, Stanford University, Stanford, CA, USA
| | - Kyoungseob Shin
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sangwook Bae
- Renal Division and Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Han Suk Ryu
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea
- Department of Pathology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Ji Young Kim
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Ahyoun Choi
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sumin Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Meteor Biotech, Co. Ltd., Seoul, 08826, Republic of Korea
| | - Taehoon Ryu
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Yeongjae Choi
- School of Materials Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea
| | - Hamin Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Okju Kim
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Yushin Jung
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Namphil Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Wonshik Han
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea
- Department of Surgery, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Han-Byoel Lee
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea.
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
- Department of Surgery, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
| | - Amos C Lee
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Meteor Biotech, Co. Ltd., Seoul, 08826, Republic of Korea.
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Inter-University Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea.
- Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul, 08826, Republic of Korea.
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38
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Hård J, Mold JE, Eisfeldt J, Tellgren-Roth C, Häggqvist S, Bunikis I, Contreras-Lopez O, Chin CS, Nordlund J, Rubin CJ, Feuk L, Michaëlsson J, Ameur A. Long-read whole-genome analysis of human single cells. Nat Commun 2023; 14:5164. [PMID: 37620373 PMCID: PMC10449900 DOI: 10.1038/s41467-023-40898-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023] Open
Abstract
Long-read sequencing has dramatically increased our understanding of human genome variation. Here, we demonstrate that long-read technology can give new insights into the genomic architecture of individual cells. Clonally expanded CD8+ T-cells from a human donor were subjected to droplet-based multiple displacement amplification (dMDA) to generate long molecules with reduced bias. PacBio sequencing generated up to 40% genome coverage per single-cell, enabling detection of single nucleotide variants (SNVs), structural variants (SVs), and tandem repeats, also in regions inaccessible by short reads. 28 somatic SNVs were detected, including one case of mitochondrial heteroplasmy. 5473 high-confidence SVs/cell were discovered, a sixteen-fold increase compared to Illumina-based results from clonally related cells. Single-cell de novo assembly generated a genome size of up to 598 Mb and 1762 (12.8%) complete gene models. In summary, our work shows the promise of long-read sequencing toward characterization of the full spectrum of genetic variation in single cells.
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Affiliation(s)
- Joanna Hård
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
- ETH AI Center, ETH Zurich, Zurich, Switzerland.
| | - Jeff E Mold
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Jesper Eisfeldt
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Christian Tellgren-Roth
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Susana Häggqvist
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Ignas Bunikis
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | | | | | - Jessica Nordlund
- Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Carl-Johan Rubin
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Lars Feuk
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Jakob Michaëlsson
- Center for Infectious Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Adam Ameur
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
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39
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Zhang Q, Ma S, Liu Z, Zhu B, Zhou Z, Li G, Meana JJ, González-Maeso J, Lu C. Droplet-based bisulfite sequencing for high-throughput profiling of single-cell DNA methylomes. Nat Commun 2023; 14:4672. [PMID: 37537185 PMCID: PMC10400590 DOI: 10.1038/s41467-023-40411-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 07/26/2023] [Indexed: 08/05/2023] Open
Abstract
The genome-wide DNA methylation profile, or DNA methylome, is a critical component of the overall epigenomic landscape that modulates gene activities and cell fate. Single-cell DNA methylomic studies offer unprecedented resolution for detecting and profiling cell subsets based on methylomic features. However, existing single-cell methylomic technologies are based on use of tubes or well plates and these platforms are not easily scalable for handling a large number of single cells. Here we demonstrate a droplet-based microfluidic technology, Drop-BS, to construct single-cell bisulfite sequencing libraries for DNA methylome profiling. Drop-BS takes advantage of the ultrahigh throughput offered by droplet microfluidics to prepare bisulfite sequencing libraries of up to 10,000 single cells within 2 days. We apply the technology to profile mixed cell lines, mouse and human brain tissues to reveal cell type heterogeneity. Drop-BS offers a promising solution for single-cell methylomic studies requiring examination of a large cell population.
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Affiliation(s)
- Qiang Zhang
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Sai Ma
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, 24061, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Zhengzhi Liu
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Bohan Zhu
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Zirui Zhou
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - Gaoshan Li
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA, 24061, USA
| | - J Javier Meana
- Department of Pharmacology, University of the Basque Country UPV/EHU, CIBERSAM, Biocruces Health Research Institute, E-48940, Leioa, Bizkaia, Spain
| | - Javier González-Maeso
- Department of Physiology and Biophysics, Virginia Commonwealth University School of Medicine, Richmond, VA, 23298, USA
| | - Chang Lu
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA, 24061, USA.
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40
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Thakur S, Haider S, Natrajan R. Implications of tumour heterogeneity on cancer evolution and therapy resistance: lessons from breast cancer. J Pathol 2023; 260:621-636. [PMID: 37587096 DOI: 10.1002/path.6158] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 06/11/2023] [Accepted: 06/14/2023] [Indexed: 08/18/2023]
Abstract
Tumour heterogeneity is pervasive amongst many cancers and leads to disease progression, and therapy resistance. In this review, using breast cancer as an exemplar, we focus on the recent advances in understanding the interplay between tumour cells and their microenvironment using single cell sequencing and digital spatial profiling technologies. Further, we discuss the utility of lineage tracing methodologies in pre-clinical models of breast cancer, and how these are being used to unravel new therapeutic vulnerabilities and reveal biomarkers of breast cancer progression. © 2023 The Pathological Society of Great Britain and Ireland.
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Affiliation(s)
- Shefali Thakur
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Syed Haider
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Rachael Natrajan
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
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41
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Chen M, Jiang J, Hou J. Single-cell technologies in multiple myeloma: new insights into disease pathogenesis and translational implications. Biomark Res 2023; 11:55. [PMID: 37259170 DOI: 10.1186/s40364-023-00502-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 05/12/2023] [Indexed: 06/02/2023] Open
Abstract
Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Although therapeutic advances have been made to improve clinical outcomes and to prolong patients' survival in the past two decades, MM remains largely incurable. Single-cell sequencing (SCS) is a powerful method to dissect the cellular and molecular landscape at single-cell resolution, instead of providing averaged results. The application of single-cell technologies promises to address outstanding questions in myeloma biology and has revolutionized our understanding of the inter- and intra-tumor heterogeneity, tumor microenvironment, and mechanisms of therapeutic resistance in MM. In this review, we summarize the recently developed SCS methodologies and latest MM research progress achieved by single-cell profiling, including information regarding the cancer and immune cell landscapes, tumor heterogeneities, underlying mechanisms and biomarkers associated with therapeutic response and resistance. We also discuss future directions of applying transformative SCS approaches with contribution to clinical translation.
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Affiliation(s)
- Mengping Chen
- Department of Hematology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Jinxing Jiang
- Department of Hematology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Jian Hou
- Department of Hematology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China.
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42
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Zhang Q, Ma S, Liu Z, Zhu B, Zhou Z, Li G, Meana JJ, González-Maeso J, Lu C. Droplet-based bisulfite sequencing for high-throughput profiling of single-cell DNA methylomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.26.542421. [PMID: 37293095 PMCID: PMC10245959 DOI: 10.1101/2023.05.26.542421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Genome-wide DNA methylation profile, or DNA methylome, is a critical component of the overall epigenomic landscape that modulates gene activities and cell fate. Single-cell DNA methylomic studies offer unprecedented resolution for detecting and profiling cell subsets based on methylomic features. However, existing single-cell methylomic technologies are all based on use of tubes or well plates and these platforms are not easily scalable for handling a large number of single cells. Here we demonstrate a droplet-based microfluidic technology, Drop-BS, to construct single-cell bisulfite sequencing libraries for DNA methylome profiling. Drop-BS takes advantage of the ultrahigh throughput offered by droplet microfluidics to prepare bisulfite sequencing libraries of up to 10,000 single cells within 2 d. We applied the technology to profile mixed cell lines, mouse and human brain tissues to reveal cell type heterogeneity. Drop-BS will pave the way for single-cell methylomic studies requiring examination of a large cell population.
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Affiliation(s)
- Qiang Zhang
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA 24061, USA
| | - Sai Ma
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA 24061, USA
- Present address: Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Zhengzhi Liu
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA 24061, USA
| | - Bohan Zhu
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA 24061, USA
| | - Zirui Zhou
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA 24061, USA
| | - Gaoshan Li
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA 24061, USA
| | - J. Javier Meana
- Department of Pharmacology, University of the Basque Country UPV/EHU, CIBERSAM, Biocruces Health Research Institute, E-48940 Leioa, Bizkaia, Spain
| | - Javier González-Maeso
- Department of Physiology and Biophysics, Virginia Commonwealth University School of Medicine, Richmond, VA 23298, USA
| | - Chang Lu
- Department of Chemical Engineering, Virginia Tech, Blacksburg, VA 24061, USA
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43
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Wong CC, Yu J. Gut microbiota in colorectal cancer development and therapy. Nat Rev Clin Oncol 2023:10.1038/s41571-023-00766-x. [PMID: 37169888 DOI: 10.1038/s41571-023-00766-x] [Citation(s) in RCA: 99] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/13/2023] [Indexed: 05/13/2023]
Abstract
Colorectal cancer (CRC) is one of the commonest cancers globally. A unique aspect of CRC is its intimate association with the gut microbiota, which forms an essential part of the tumour microenvironment. Research over the past decade has established that dysbiosis of gut bacteria, fungi, viruses and Archaea accompanies colorectal tumorigenesis, and these changes might be causative. Data from mechanistic studies demonstrate the ability of the gut microbiota to interact with the colonic epithelia and immune cells of the host via the release of a diverse range of metabolites, proteins and macromolecules that regulate CRC development. Preclinical and some clinical evidence also underscores the role of the gut microbiota in modifying the therapeutic responses of patients with CRC to chemotherapy and immunotherapy. Herein, we summarize our current understanding of the role of gut microbiota in CRC and outline the potential translational and clinical implications for CRC diagnosis, prevention and treatment. Emphasis is placed on how the gut microbiota could now be better harnessed by developing targeted microbial therapeutics as chemopreventive agents against colorectal tumorigenesis, as adjuvants for chemotherapy and immunotherapy to boost drug efficacy and safety, and as non-invasive biomarkers for CRC screening and patient stratification. Finally, we highlight the hurdles and potential solutions to translating our knowledge of the gut microbiota into clinical practice.
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Affiliation(s)
- Chi Chun Wong
- Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jun Yu
- Institute of Digestive Disease and Department of Medicine and Therapeutics, State Key Laboratory of Digestive Disease, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
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44
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李 政, 彭 显. [Application of Droplet-Based Microfluidics in Microbial Research]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2023; 54:673-678. [PMID: 37248604 PMCID: PMC10475413 DOI: 10.12182/20230560303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Indexed: 05/31/2023]
Abstract
Droplet-based microfluidics is a technology that generates and manipulates highly uniform droplets, ranging from picoliter to nanoliter droplets, in microchannels under precise control. In biological research, each droplet can be used to encapsulate a small group of cells or even a single cell, and then serve as an individual container for biochemical reaction, which is well suited for high-throughput and high-resolution biochemical analysis. In the field of microbial research, from cultivation and identification of microbes to the investigation of the spatiotemporal dynamics of microbial communities, from precise quantitation of microbiota to systematic study of microbial interactions, and from the isolation of rare and unculturable microbes to the development of genetically engineered strains, droplet microfluidic technology has played an important promotional role in all these aspects. Droplet microfluidics shows potential for becoming a basic tool for exploring single-cell microbes in microbiological research. In this review, we gave a brief overview of the technical basis of droplet microfluidics. Then, we presented its latest applications in microbial research and had some discussions, aiming to provide a reference for relevant research on microorganisms.
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Affiliation(s)
- 政毅 李
- 口腔疾病研究国家重点实验室 四川大学华西口腔医院 (成都 610041)State Key Laboratory of Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
| | - 显 彭
- 口腔疾病研究国家重点实验室 四川大学华西口腔医院 (成都 610041)State Key Laboratory of Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
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45
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Ogbue O, Unlu S, Ibodeng GO, Singh A, Durmaz A, Visconte V, Molina JC. Single-Cell Next-Generation Sequencing to Monitor Hematopoietic Stem-Cell Transplantation: Current Applications and Future Perspectives. Cancers (Basel) 2023; 15:cancers15092477. [PMID: 37173944 PMCID: PMC10177286 DOI: 10.3390/cancers15092477] [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: 04/02/2023] [Revised: 04/22/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
Acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS) are genetically complex and diverse diseases. Such complexity makes challenging the monitoring of response to treatment. Measurable residual disease (MRD) assessment is a powerful tool for monitoring response and guiding therapeutic interventions. This is accomplished through targeted next-generation sequencing (NGS), as well as polymerase chain reaction and multiparameter flow cytometry, to detect genomic aberrations at a previously challenging leukemic cell concentration. A major shortcoming of NGS techniques is the inability to discriminate nonleukemic clonal hematopoiesis. In addition, risk assessment and prognostication become more complicated after hematopoietic stem-cell transplantation (HSCT) due to genotypic drift. To address this, newer sequencing techniques have been developed, leading to more prospective and randomized clinical trials aiming to demonstrate the prognostic utility of single-cell next-generation sequencing in predicting patient outcomes following HSCT. This review discusses the use of single-cell DNA genomics in MRD assessment for AML/MDS, with an emphasis on the HSCT time period, including the challenges with current technologies. We also touch on the potential benefits of single-cell RNA sequencing and analysis of accessible chromatin, which generate high-dimensional data at the cellular resolution for investigational purposes, but not currently used in the clinical setting.
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Affiliation(s)
- Olisaemeka Ogbue
- Internal Medicine, Cleveland Clinic Fairview Hospital, Cleveland, OH 44111, USA
| | - Serhan Unlu
- Internal Medicine, Cleveland Clinic Fairview Hospital, Cleveland, OH 44111, USA
| | - Gogo-Ogute Ibodeng
- Internal Medicine, Infirmary Health's Thomas Hospital, Fairhope, AL 36607, USA
| | - Abhay Singh
- Department of Hematology Medical Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH 44106, USA
| | - Arda Durmaz
- Department of Translational Hematology and Oncology Research, Cleveland Clinic Taussig Cancer Center, Cleveland, OH 44106, USA
| | - Valeria Visconte
- Department of Translational Hematology and Oncology Research, Cleveland Clinic Taussig Cancer Center, Cleveland, OH 44106, USA
| | - John C Molina
- Department of Hematology Medical Oncology, Taussig Cancer Center, Cleveland Clinic, Cleveland, OH 44106, USA
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Cao X, Buryska T, Yang T, Wang J, Fischer P, Streets A, Stavrakis S, deMello A. Towards an active droplet-based microfluidic platform for programmable fluid handling. LAB ON A CHIP 2023; 23:2029-2038. [PMID: 37000567 PMCID: PMC10091362 DOI: 10.1039/d3lc00015j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Droplet-based microfluidic systems have emerged as powerful alternatives to conventional high throughput screening platforms, due to their operational flexibility, high-throughput nature and ability to efficiently process small fluid volumes. However, the challenges associated with performing bespoke operations on user-defined droplets often limit their utility in screening applications that involve complex workflows. To this end, the marriage of droplet- and valve-based microfluidic technologies offers the prospect of balancing the controllability of droplet manipulations and analytical throughput. In this spirit, we present a microfluidic platform that combines the capabilities of integrated microvalve technology with droplet-based sample compartmentalization to realize a highly adaptable programmable fluid handling functionality. The microfluidic device consists of a programmable formulator linked to an automated droplet generation device and storage array. The formulator leverages multiple inputs coupled to a mixing ring to produce combinatorial solution mixtures, with a peristaltic pump enabling titration of reagents into the ring with picoliter resolution. The platform allows for the execution of user-defined reaction protocols within an array of storage chambers by consecutively merging programmable sequences of pL-volume droplets containing specified reagents. The precision in formulating solutions with small differences in concentration is perfectly suited for the accurate estimation of kinetic parameters. The utility of our platform is showcased through the performance of enzymatic kinetic measurements of beta-galactosidase and horseradish peroxidase with fluorogenic substrates. The presented platform provides for a range of automated manipulations and paves the way for a more diverse range of droplet-based biological experiments.
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Affiliation(s)
- Xiaobao Cao
- Guangzhou Laboratory, Guangzhou International Bio Island, Guangzhou, Guangdong Province, China
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland.
| | - Tomas Buryska
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland.
| | - Tianjin Yang
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland.
- Department of Biochemistry, University of Zurich, 8057 Zurich, Switzerland
| | - Jing Wang
- Institute of Environmental Engineering, ETH Zürich, 8093, Zürich, Switzerland
| | - Peter Fischer
- IFNH Food Process Engineering Group, ETH Zürich, 8092, Zürich, Switzerland
| | - Aaron Streets
- Department of Bioengineering, University of California, Berkeley, California, USA
| | - Stavros Stavrakis
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland.
| | - Andrew deMello
- Institute for Chemical and Bioengineering, ETH Zürich, 8093 Zürich, Switzerland.
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Zhang Y, Wang Y, Tang M, Zhou J, Zhang T. The microbial dark matter and "wanted list" in worldwide wastewater treatment plants. MICROBIOME 2023; 11:59. [PMID: 36973807 PMCID: PMC10045942 DOI: 10.1186/s40168-023-01503-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Wastewater treatment plants (WWTPs) are one of the largest biotechnology applications in the world and are of critical importance to modern urban societies. An accurate evaluation of the microbial dark matter (MDM, microorganisms whose genomes remain uncharacterized) proportions in WWTPs is of great value, while there is no such research yet. This study conducted a global meta-analysis of MDM in WWTPs with 317,542 prokaryotic genomes from the Genome Taxonomy Database and proposed a "wanted list" for priority targets in further investigations of activated sludge. RESULTS Compared with the Earth Microbiome Project data, WWTPs had relatively lower genome-sequenced proportions of prokaryotes than other ecosystems, such as the animal related environments. Analysis showed that the median proportions of the genome-sequenced cells and taxa (100% identity and 100% coverage in 16S rRNA gene region) in WWTPs reached 56.3% and 34.5% for activated sludge, 48.6% and 28.5% for aerobic biofilm, and 48.3% and 28.5% for anaerobic digestion sludge, respectively. This result meant MDM had high proportions in WWTPs. Besides, all of the samples were occupied by a few predominant taxa, and the majority of the sequenced genomes were from pure cultures. The global-scale "wanted list" for activated sludge contained four phyla that have few representatives and 71 operational taxonomic units with the majority of them having no genome or isolate yet. Finally, several genome mining methods were verified to successfully recover genomes from activated sludge such as hybrid assembly of the second- and third-generation sequencing. CONCLUSIONS This work elucidated the proportion of MDM in WWTPs, defined the "wanted list" of activated sludge for future investigations, and certified potential genome recovery methods. The proposed methodology of this study can be applied to other ecosystems and improve understanding of ecosystem structure across diverse habitats. Video Abstract.
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Affiliation(s)
- Yulin Zhang
- Environmental Microbiome Engineering and Biotechnology Lab, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Yulin Wang
- Environmental Microbiome Engineering and Biotechnology Lab, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Mingxi Tang
- Environmental Microbiome Engineering and Biotechnology Lab, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Jizhong Zhou
- Institute for Environmental Genomics, Department of Microbiology and Plant Biology, and School of Civil Engineering and Environmental Sciences, University of Oklahoma, Norman, OK, USA
| | - Tong Zhang
- Environmental Microbiome Engineering and Biotechnology Lab, Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
- Shenzhen Bay Laboratory, Shenzhen, China.
- Peking University Shenzhen Graduate School, Shenzhen, China.
- Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, China.
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48
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Nan L, Mao T, Shum HC. Self-synchronization of reinjected droplets for high-efficiency droplet pairing and merging. MICROSYSTEMS & NANOENGINEERING 2023; 9:24. [PMID: 36910256 PMCID: PMC9995457 DOI: 10.1038/s41378-023-00502-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/10/2023] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
Droplet merging serves as a powerful tool to add reagents to moving droplets for biological and chemical reactions. However, unsynchronized droplet pairing impedes high-efficiency merging. Here, we develop a microfluidic design for the self-synchronization of reinjected droplets. A periodic increase in the hydrodynamic resistance caused by droplet blocking a T-junction enables automatic pairing of droplets. After inducing spacing, the paired droplets merge downstream under an electric field. The blockage-based design can achieve a 100% synchronization efficiency even when the mismatch rate of droplet frequencies reaches 10%. Over 98% of the droplets can still be synchronized at nonuniform droplet sizes and fluctuating reinjection flow rates. Moreover, the droplet pairing ratio can be adjusted flexibly for on-demand sample addition. Using this system, we merge two groups of droplets encapsulating enzyme/substrate, demonstrating its capacity to conduct multi-step reactions. We also combine droplet sorting and merging to coencapsulate single cells and single beads, providing a basis for high-efficiency single-cell sequencing. We expect that this system can be integrated with other droplet manipulation systems for a broad range of chemical and biological applications.
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Affiliation(s)
- Lang Nan
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong China
| | - Tianjiao Mao
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Ho Cheung Shum
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong China
- Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
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49
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Shlyakhtina Y, Bloechl B, Portal MM. BdLT-Seq as a barcode decay-based method to unravel lineage-linked transcriptome plasticity. Nat Commun 2023; 14:1085. [PMID: 36841849 PMCID: PMC9968323 DOI: 10.1038/s41467-023-36744-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 02/14/2023] [Indexed: 02/26/2023] Open
Abstract
Cell plasticity is a core biological process underlying a myriad of molecular and cellular events taking place throughout organismal development and evolution. It has been postulated that cellular systems thrive to balance the organization of meta-stable states underlying this phenomenon, thereby maintaining a degree of populational homeostasis compatible with an ever-changing environment and, thus, life. Notably, albeit circumstantial evidence has been gathered in favour of the latter conceptual framework, a direct observation of meta-state dynamics and the biological consequences of such a process in generating non-genetic clonal diversity and divergent phenotypic output remains largely unexplored. To fill this void, here we develop a lineage-tracing technology termed Barcode decay Lineage Tracing-Seq. BdLT-Seq is based on episome-encoded molecular identifiers that, supported by the dynamic decay of the tracing information upon cell division, ascribe directionality to a cell lineage tree whilst directly coupling non-genetic molecular features to phenotypes in comparable genomic landscapes. We show that cell transcriptome states are both inherited, and dynamically reshaped following constrained rules encoded within the cell lineage in basal growth conditions, upon oncogene activation and throughout the process of reversible resistance to therapeutic cues thus adjusting phenotypic output leading to intra-clonal non-genetic diversity.
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Affiliation(s)
- Yelyzaveta Shlyakhtina
- Cell Plasticity & Epigenetics Lab, Cancer Research UK - Manchester Institute, The University of Manchester, SK10 4TG, Manchester, UK
| | - Bianca Bloechl
- Cell Plasticity & Epigenetics Lab, Cancer Research UK - Manchester Institute, The University of Manchester, SK10 4TG, Manchester, UK
| | - Maximiliano M Portal
- Cell Plasticity & Epigenetics Lab, Cancer Research UK - Manchester Institute, The University of Manchester, SK10 4TG, Manchester, UK.
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50
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Zhang H, Karnoub ER, Umeda S, Chaligné R, Masilionis I, McIntyre CA, Sashittal P, Hayashi A, Zucker A, Mullen K, Hong J, Makohon-Moore A, Iacobuzio-Donahue CA. Application of high-throughput single-nucleus DNA sequencing in pancreatic cancer. Nat Commun 2023; 14:749. [PMID: 36765116 PMCID: PMC9918733 DOI: 10.1038/s41467-023-36344-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 01/24/2023] [Indexed: 02/12/2023] Open
Abstract
Despite insights gained by bulk DNA sequencing of cancer it remains challenging to resolve the admixture of normal and tumor cells, and/or of distinct tumor subclones; high-throughput single-cell DNA sequencing circumvents these and brings cancer genomic studies to higher resolution. However, its application has been limited to liquid tumors or a small batch of solid tumors, mainly because of the lack of a scalable workflow to process solid tumor samples. Here we optimize a highly automated nuclei extraction workflow that achieves fast and reliable targeted single-nucleus DNA library preparation of 38 samples from 16 pancreatic ductal adenocarcinoma patients, with an average library yield per sample of 2867 single nuclei. We demonstrate that this workflow not only performs well using low cellularity or low tumor purity samples but reveals genomic evolution patterns of pancreatic ductal adenocarcinoma as well.
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Affiliation(s)
- Haochen Zhang
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Elias-Ramzey Karnoub
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Shigeaki Umeda
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ronan Chaligné
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ignas Masilionis
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Palash Sashittal
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Akimasa Hayashi
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology, Kyorin University School of Medicine, Mitaka City, Tokyo, Japan
| | - Amanda Zucker
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- School of Medicine, Oregon Health and Science University, Portland, OR, USA
| | - Katelyn Mullen
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jungeui Hong
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alvin Makohon-Moore
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Center for Discovery and Innovation, Hackensack Meridian Health, Nutley, NJ, USA
| | - Christine A Iacobuzio-Donahue
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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