Deciphering Cell–Cell Interactions with Integrative Single‐Cell Secretion Profiling Article Swipe
Linmei Li
,
Haoran Su
,
Yahui Ji
,
Fengjiao Zhu
,
Jiu Deng
,
Xue Bai
,
Huibing Li
,
Xianming Liu
,
Yong Luo
,
Bingcheng Lin
,
Tingjiao Liu
,
Yao Lu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/advs.202301018
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1002/advs.202301018
Cell–cell interactions are the fundamental behaviors to regulate cellular activities. A comprehensive evaluation of intercellular interactions requires direct profiling of various signaling behaviors simultaneously at the single‐cell level, which remains lacking. Herein, an integrative single‐cell secretion analysis platform is presented to profile different secreted factors (four proteins, three extracellular vesicles (EV) phenotypes), spatial distances, and migration information (distances and direction) simultaneously from high‐throughput paired single cells using an antibody‐barcode microchip. Applying the platform to analyze the tumor–stromal and tumor–immune interactions with the human oral squamous cell carcinoma (OSCC) cell lines and primary OSCC cells reveals that the initial distances between cells would determine their migratory distances and direction to approach stable organization. The cell–cell in close proximity enhances protein secretions while attenuating EV secretions. Migration has a more profound correlation with protein secretions than EV secretions, in which absolute migration distance affects protein secretions significantly but not the direction. These findings highlight the significance of spatial organization in regulating cell signaling behaviors and demonstrate that the integrative single‐cell secretion profiling platform is well‐suited for a comprehensive dissection of intercellular communication and interactions, providing new avenues for understanding cell–cell interaction biology and how different signaling behaviors coordinate within the tumor microenvironment.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/advs.202301018
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202301018
- OA Status
- gold
- Cited By
- 21
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4367051120
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4367051120Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/advs.202301018Digital Object Identifier
- Title
-
Deciphering Cell–Cell Interactions with Integrative Single‐Cell Secretion ProfilingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-26Full publication date if available
- Authors
-
Linmei Li, Haoran Su, Yahui Ji, Fengjiao Zhu, Jiu Deng, Xue Bai, Huibing Li, Xianming Liu, Yong Luo, Bingcheng Lin, Tingjiao Liu, Yao LuList of authors in order
- Landing page
-
https://doi.org/10.1002/advs.202301018Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202301018Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202301018Direct OA link when available
- Concepts
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Cell, Profiling (computer programming), Secretion, Single-cell analysis, Cell biology, Computational biology, Chemistry, Biology, Computer science, Genetics, Biochemistry, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
21Total citation count in OpenAlex
- Citations by year (recent)
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2025: 11, 2024: 9, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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50Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.landing_page_url | https://doi.org/10.1002/advs.202301018 |
| publication_date | 2023-04-26 |
| publication_year | 2023 |
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