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High-Throughput Secretomic Analysis of Single Cells to Assess Functional Cellular Heterogeneity

Open access
Lu, Yao, Chen, Jonathan J., Mu, Luye, Xue, Qiong, Wu, Yu, Wu, Pei-Hsun, Li, Jie, Vortmeyer, Alexander O., Miller-Jensen, Kathryn, Wirtz, Denis, Fan, Rong·2013·Analytical Chemistry

Abstract

Secreted proteins dictate a range of cellular functions in human health and disease. Because of the high degree of cellular heterogeneity and, more importantly, polyfunctionality of individual cells, there is an unmet need to simultaneously measure an array of proteins from single cells and to rapidly assay a large number of single cells (more than 1000) in parallel. We describe a simple bioanalytical assay platform consisting of a large array of subnanoliter microchambers integrated with high-density antibody barcode microarrays for highly multiplexed protein detection from over a thousand single cells in parallel. This platform has been tested for both cell lines and complex biological samples such as primary cells from patients. We observed distinct heterogeneity among the single cell secretomic signatures that, for the first time, can be directly correlated to the cells' physical behavior such as migration. Compared to the state-of-the-art protein secretion assay such as ELISpot and emerging microtechnology-enabled assays, our approach offers both high throughput and high multiplicity. It also has a number of clinician-friendly features such as ease of operation, low sample consumption, and standardized data analysis, representing a potentially transformative tool for informative monitoring of cellular function and immunity in patients.

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Citations

Cites

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Cited by

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Metadata

Authors
Lu, Y., Chen, J. J., Mu, L., Xue, Q., Wu, Y., Wu, P.-H., Li, J., Vortmeyer, A. O., Miller-Jensen, K., Wirtz, D., & Fan, R.
Venue
Analytical Chemistry
Published
2013-02-01
Volume
85
Issue
4
Pages
2548-2556
ISSN
  • 0003-2700 (ISSN-L)
  • 1520-6882
OpenAlex
W2168204150
Cited by
173
References
8
FWCI
6.46
Type
journal article
Language
en
Open access
green

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.