Evidence map›Paper›PMID 36937585›Full record

ArticleChemical science2023

All-in-One digital microfluidics pipeline for proteomic sample preparation and analysis.

Jiaxi Peng, Calvin Chan, Shuailong Zhang, Alexandros A Sklavounos, Maxwell E Olson, Erica Y Scott, Yechen Hu, Vigneshwar Rajesh, Bingyu B Li, M Dean Chamberlain and 3 more

Full text read
In one paragraph

Article in Chemical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Jiaxi PengDepartment of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada aaron.wheeler@utoronto.ca +1-416-946-3865 +1-416-946-3866.
Calvin ChanDepartment of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada aaron.wheeler@utoronto.ca +1-416-946-3865 +1-416-946-3866.ORCID https://orcid.org/0000-0003-0640-0921
Shuailong ZhangDepartment of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada aaron.wheeler@utoronto.ca +1-416-946-3865 +1-416-946-3866.ORCID https://orcid.org/0000-0003-1004-021X
Alexandros A SklavounosDepartment of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada aaron.wheeler@utoronto.ca +1-416-946-3865 +1-416-946-3866.ORCID https://orcid.org/0000-0003-1495-7156
Maxwell E OlsonDepartment of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada aaron.wheeler@utoronto.ca +1-416-946-3865 +1-416-946-3866.
Erica Y ScottDepartment of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada aaron.wheeler@utoronto.ca +1-416-946-3865 +1-416-946-3866.
Yechen HuDepartment of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada aaron.wheeler@utoronto.ca +1-416-946-3865 +1-416-946-3866.
Vigneshwar RajeshDepartment of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada aaron.wheeler@utoronto.ca +1-416-946-3865 +1-416-946-3866.ORCID https://orcid.org/0000-0001-7436-6943
Bingyu B LiDonnelly Centre for Cellular and Biomolecular Research, University of Toronto 160 College Street Toronto ON M5S 3E1 Canada.
M Dean ChamberlainDepartment of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada aaron.wheeler@utoronto.ca +1-416-946-3865 +1-416-946-3866.
Shen ZhangLunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital 600 University Avenue Toronto ON M5G 1X5 Canada.
Hui PengDepartment of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada aaron.wheeler@utoronto.ca +1-416-946-3865 +1-416-946-3866.ORCID https://orcid.org/0000-0002-2000-1647
Aaron R WheelerDepartment of Chemistry, University of Toronto 80 St. George Street Toronto ON M5S 3H6 Canada aaron.wheeler@utoronto.ca +1-416-946-3865 +1-416-946-3866.ORCID https://orcid.org/0000-0001-5230-7475

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Highly sensitive and reproducible analysis of samples containing low amounts of protein is restricted by sample loss and the introduction of contaminants during processing. Here, we report an All-in-One digital microfluidic (DMF) pipeline for proteomic sample reduction, alkylation, digestion, isotopic labeling and analysis. The system features end-to-end automation, with integrated thermal control for digestion, optimized droplet additives for sample manipulation and analysis, and an automated interface to liquid chromatography with tandem mass spectrometry (HPLC-MS/MS). Dimethyl labeling was integrated into the pipeline to allow for relative quantification of the trace samples at the nanogram level, and the new pipeline was applied to evaluating cancer cell lines and cancer tissue samples. Several known proteins (including HSP90AB1, HSPB1, LDHA, ENO1, PGK1, KRT18, and AKR1C2) and pathways were observed between model breast cancer cell lines related to hormone response, cell metabolism, and cell morphology. Furthermore, differentially quantified proteins (such as PGS2, UGDH, ASPN, LUM, COEA1, and PRELP) were found in comparisons of healthy and cancer breast tissues, suggesting potential utility of the All-in-One pipeline for the emerging application of proteomic cancer sub-typing. In sum, the All-in-One pipeline represents a powerful new tool for automated proteome processing and analysis, with the potential to be useful for evaluating mass-limited samples for a wide range of applications.

Identifiers

PMID36937585
PMCPMC10016607

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.