IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-45659-4.html
   My bibliography  Save this article

Pick-up single-cell proteomic analysis for quantifying up to 3000 proteins in a Mammalian cell

Author

Listed:
  • Yu Wang

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Center
    Zhejiang University)

  • Zhi-Ying Guan

    (Zhejiang University)

  • Shao-Wen Shi

    (ZJU-Hangzhou Global Scientific and Technological Innovation Center)

  • Yi-Rong Jiang

    (Zhejiang University)

  • Jie Zhang

    (China Medical University)

  • Yi Yang

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Center)

  • Qiong Wu

    (Zhejiang University)

  • Jie Wu

    (Zhejiang University)

  • Jian-Bo Chen

    (Zhejiang University)

  • Wei-Xin Ying

    (Zhejiang University)

  • Qin-Qin Xu

    (Zhejiang University)

  • Qian-Xi Fan

    (Zhejiang University)

  • Hui-Feng Wang

    (ZJU-Hangzhou Global Scientific and Technological Innovation Center)

  • Li Zhou

    (Shanghai Omicsolution Co.)

  • Ling Wang

    (Shanghai Omicsolution Co.)

  • Jin Fang

    (China Medical University)

  • Jian-Zhang Pan

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Center)

  • Qun Fang

    (Zhejiang University
    ZJU-Hangzhou Global Scientific and Technological Innovation Center
    Zhejiang University)

Abstract

The shotgun proteomic analysis is currently the most promising single-cell protein sequencing technology, however its identification level of ~1000 proteins per cell is still insufficient for practical applications. Here, we develop a pick-up single-cell proteomic analysis (PiSPA) workflow to achieve a deep identification capable of quantifying up to 3000 protein groups in a mammalian cell using the label-free quantitative method. The PiSPA workflow is specially established for single-cell samples mainly based on a nanoliter-scale microfluidic liquid handling robot, capable of achieving single-cell capture, pretreatment and injection under the pick-up operation strategy. Using this customized workflow with remarkable improvement in protein identification, 2449–3500, 2278–3257 and 1621–2904 protein groups are quantified in single A549 cells (n = 37), HeLa cells (n = 44) and U2OS cells (n = 27) under the DIA (MBR) mode, respectively. Benefiting from the flexible cell picking-up ability, we study HeLa cell migration at the single cell proteome level, demonstrating the potential in practical biological research from single-cell insight.

Suggested Citation

  • Yu Wang & Zhi-Ying Guan & Shao-Wen Shi & Yi-Rong Jiang & Jie Zhang & Yi Yang & Qiong Wu & Jie Wu & Jian-Bo Chen & Wei-Xin Ying & Qin-Qin Xu & Qian-Xi Fan & Hui-Feng Wang & Li Zhou & Ling Wang & Jin Fa, 2024. "Pick-up single-cell proteomic analysis for quantifying up to 3000 proteins in a Mammalian cell," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45659-4
    DOI: 10.1038/s41467-024-45659-4
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-45659-4
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-45659-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Jongmin Woo & Sarah M. Williams & Lye Meng Markillie & Song Feng & Chia-Feng Tsai & Victor Aguilera-Vazquez & Ryan L. Sontag & Ronald J. Moore & Dehong Hu & Hardeep S. Mehta & Joshua Cantlon-Bruce & T, 2021. "High-throughput and high-efficiency sample preparation for single-cell proteomics using a nested nanowell chip," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    2. Ilias Angelidis & Lukas M. Simon & Isis E. Fernandez & Maximilian Strunz & Christoph H. Mayr & Flavia R. Greiffo & George Tsitsiridis & Meshal Ansari & Elisabeth Graf & Tim-Matthias Strom & Monica Nag, 2019. "An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics," Nature Communications, Nature, vol. 10(1), pages 1-17, December.
    3. Kevin Lebrigand & Virginie Magnone & Pascal Barbry & Rainer Waldmann, 2020. "High throughput error corrected Nanopore single cell transcriptome sequencing," Nature Communications, Nature, vol. 11(1), pages 1-8, December.
    4. Erwin M. Schoof & Benjamin Furtwängler & Nil Üresin & Nicolas Rapin & Simonas Savickas & Coline Gentil & Eric Lechman & Ulrich auf dem Keller & John E. Dick & Bo T. Porse, 2021. "Quantitative single-cell proteomics as a tool to characterize cellular hierarchies," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    5. Valdemaras Petrosius & Pedro Aragon-Fernandez & Nil Üresin & Gergo Kovacs & Teeradon Phlairaharn & Benjamin Furtwängler & Jeff Op De Beeck & Sarah L. Skovbakke & Steffen Goletz & Simon Francis Thomsen, 2023. "Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Valdemaras Petrosius & Pedro Aragon-Fernandez & Nil Üresin & Gergo Kovacs & Teeradon Phlairaharn & Benjamin Furtwängler & Jeff Op De Beeck & Sarah L. Skovbakke & Steffen Goletz & Simon Francis Thomsen, 2023. "Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Claudia Ctortecka & Natalie M. Clark & Brian W. Boyle & Anjali Seth & D. R. Mani & Namrata D. Udeshi & Steven A. Carr, 2024. "Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    3. Manuel Matzinger & Anna Schmücker & Ramesh Yelagandula & Karel Stejskal & Gabriela Krššáková & Frédéric Berger & Karl Mechtler & Rupert L. Mayer, 2024. "Micropillar arrays, wide window acquisition and AI-based data analysis improve comprehensiveness in multiple proteomic applications," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    4. Yudong Gao & Daichi Shonai & Matthew Trn & Jieqing Zhao & Erik J. Soderblom & S. Alexandra Garcia-Moreno & Charles A. Gersbach & William C. Wetsel & Geraldine Dawson & Dmitry Velmeshev & Yong-hui Jian, 2024. "Proximity analysis of native proteomes reveals phenotypic modifiers in a mouse model of autism and related neurodevelopmental conditions," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    5. Henry Webel & Lili Niu & Annelaura Bach Nielsen & Marie Locard-Paulet & Matthias Mann & Lars Juhl Jensen & Simon Rasmussen, 2024. "Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    6. Benjamin C. Orsburn & Yuting Yuan & Namandjé N. Bumpus, 2022. "Insights into protein post-translational modification landscapes of individual human cells by trapped ion mobility time-of-flight mass spectrometry," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    7. Sandra Curras-Alonso & Juliette Soulier & Thomas Defard & Christian Weber & Sophie Heinrich & Hugo Laporte & Sophie Leboucher & Sonia Lameiras & Marie Dutreix & Vincent Favaudon & Florian Massip & Tho, 2023. "An interactive murine single-cell atlas of the lung responses to radiation injury," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    8. Cheng-Kai Shiau & Lina Lu & Rachel Kieser & Kazutaka Fukumura & Timothy Pan & Hsiao-Yun Lin & Jie Yang & Eric L. Tong & GaHyun Lee & Yuanqing Yan & Jason T. Huse & Ruli Gao, 2023. "High throughput single cell long-read sequencing analyses of same-cell genotypes and phenotypes in human tumors," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    9. Moujtaba Y. Kasmani & Paytsar Topchyan & Ashley K. Brown & Ryan J. Brown & Xiaopeng Wu & Yao Chen & Achia Khatun & Donia Alson & Yue Wu & Robert Burns & Chien-Wei Lin & Matthew R. Kudek & Jie Sun & We, 2023. "A spatial sequencing atlas of age-induced changes in the lung during influenza infection," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    10. Livius Penter & Mehdi Borji & Adi Nagler & Haoxiang Lyu & Wesley S. Lu & Nicoletta Cieri & Katie Maurer & Giacomo Oliveira & Aziz M. Al’Khafaji & Kiran V. Garimella & Shuqiang Li & Donna S. Neuberg & , 2024. "Integrative genotyping of cancer and immune phenotypes by long-read sequencing," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    11. Ahmed A. Raslan & Tho X. Pham & Jisu Lee & Konstantinos Kontodimas & Andrew Tilston-Lunel & Jillian Schmottlach & Jeongmin Hong & Taha Dinc & Andreea M. Bujor & Nunzia Caporarello & Aude Thiriot & Ulr, 2024. "Lung injury-induced activated endothelial cell states persist in aging-associated progressive fibrosis," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    12. Seoyeon Lee & Mohammad Naimul Islam & Kaveh Boostanpour & Dvir Aran & Guangchun Jin & Stephanie Christenson & Michael A. Matthay & Walter L. Eckalbar & Daryle J. DePianto & Joseph R. Arron & Liam Mage, 2021. "Molecular programs of fibrotic change in aging human lung," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    13. Jianxin Shi & Kouya Shiraishi & Jiyeon Choi & Keitaro Matsuo & Tzu-Yu Chen & Juncheng Dai & Rayjean J. Hung & Kexin Chen & Xiao-Ou Shu & Young Tae Kim & Maria Teresa Landi & Dongxin Lin & Wei Zheng & , 2023. "Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    14. Kim Theilgaard-Mönch & Sachin Pundhir & Kristian Reckzeh & Jinyu Su & Marta Tapia & Benjamin Furtwängler & Johan Jendholm & Janus Schou Jakobsen & Marie Sigurd Hasemann & Kasper Jermiin Knudsen & Jack, 2022. "Transcription factor-driven coordination of cell cycle exit and lineage-specification in vivo during granulocytic differentiation," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    15. Anna Cioce & Beatriz Calle & Tatiana Rizou & Sarah C. Lowery & Victoria L. Bridgeman & Keira E. Mahoney & Andrea Marchesi & Ganka Bineva-Todd & Helen Flynn & Zhen Li & Omur Y. Tastan & Chloe Roustan &, 2022. "Cell-specific bioorthogonal tagging of glycoproteins," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    16. Christina Bligaard Pedersen & Søren Helweg Dam & Mike Bogetofte Barnkob & Michael D. Leipold & Noelia Purroy & Laura Z. Rassenti & Thomas J. Kipps & Jennifer Nguyen & James Arthur Lederer & Satyen Har, 2022. "cyCombine allows for robust integration of single-cell cytometry datasets within and across technologies," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    17. Zhuo-Xing Shi & Zhi-Chao Chen & Jia-Yong Zhong & Kun-Hua Hu & Ying-Feng Zheng & Ying Chen & Shang-Qian Xie & Xiao-Chen Bo & Feng Luo & Chong Tang & Chuan-Le Xiao & Yi-Zhi Liu, 2023. "High-throughput and high-accuracy single-cell RNA isoform analysis using PacBio circular consensus sequencing," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    18. Aleksandr Ianevski & Anil K. Giri & Tero Aittokallio, 2022. "Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    19. Zilu Ye & Pierre Sabatier & Javier Martin-Gonzalez & Akihiro Eguchi & Maico Lechner & Ole Østergaard & Jingsheng Xie & Yuan Guo & Lesley Schultz & Rafaela Truffer & Dorte B. Bekker-Jensen & Nicolai Ba, 2024. "One-Tip enables comprehensive proteome coverage in minimal cells and single zygotes," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    20. Zhoufeng Wang & Zhe Li & Kun Zhou & Chengdi Wang & Lili Jiang & Li Zhang & Ying Yang & Wenxin Luo & Wenliang Qiao & Gang Wang & Yinyun Ni & Shuiping Dai & Tingting Guo & Guiyi Ji & Minjie Xu & Yiying , 2021. "Deciphering cell lineage specification of human lung adenocarcinoma with single-cell RNA sequencing," Nature Communications, Nature, vol. 12(1), pages 1-15, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45659-4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.