A simple method to quantify protein abundances from one thousand cells
Open accessPreprint — published as 10.1021/acsomega.0c01191 · Paper page
Abstract
The rise of single-cell transcriptomics has created an urgent need for similar approaches that use a minimal number of cells to quantify expression levels of proteins. We integrated and optimized multiple recent developments to establish a proteomics workflow to quantify proteins from as few as 1,000 mammalian stem cells. The method uses chemical peptide labeling, does not require specific equipment other than cell lysis tools, and quantifies >2,500 proteins with high reproducibility. We validated the method by comparing mouse embryonic stem cells and in vitro differentiated motor neurons. We identify differentially expressed proteins with small fold-changes, and a dynamic range in abundance similar to that of standard methods. Protein abundance measurements obtained with our protocol compare well to corresponding transcript abundance and to measurements using standard inputs. The protocol is also applicable to other systems, such as FACS-purified cells from the tunicate Ciona . Therefore, we offer a straightforward and accurate method to acquire proteomics data from minimal input samples.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566 · see citing works
Zhu, Y., Piehowski, P. D., Zhao, R., Chen, J., Shen, Y., Moore, R. J., Shukla, A. K., Petyuk, V. A., Campbell-Thompson, M., Mathews, C. E., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03367-w · Cited by 587 · see citing works
Flagged first, then by citations
- Authors
- Vitrinel, B., Iannitelli, D. E., Mazzoni, E. O., Christiaen, L., & Vogel, C.
- Affiliations
- New York University (US)
- NYU Langone Health (US)
- Corresponding author
- Vogel, Christine
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2019-09-04
- OpenAlex
- W2971748632
- Cited by
- 4
- References
- 2
- Citations by year
- 2021: 1 · 2020: 2 · 2019: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Funders
- American Heart Association (R01 HL108643, 15CVD01, 18PRE33990254)
- Zegar Family Foundation
- York University
- Fondation Leducq (15CVD01, 18PRE33990254, R01 HL108643, HL108643)
- National Institutes of Health (15CVD01, 18PRE33990254, NHLBI R01)
- National Heart, Lung, and Blood Institute (R01HL108643, HL108643, 15CVD01)
- National Institute of General Medical Sciences (R35GM127089)
- National Institute of Neurological Disorders and Stroke (1F31NS103447-01A1)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Ubiquitin and proteasome pathways
- Keywords
- single-cell proteomics, protein abundance quantification, mouse embryonic stem cells, motor neurons, differentially expressed proteins, low-input proteomics, Ciona
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review