Evidence map›Paper›PMID 35213656›Full record

ArticlePloS one2022

Modular automated bottom-up proteomic sample preparation for high-throughput applications.

Yan Chen, Nurgul Kaplan Lease, Jennifer W Gin, Tadeusz L Ogorzalek, Paul D Adams, Nathan J Hillson, Christopher J Petzold

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.1field-weighted citation impact, top 27% of its field
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

1 citing paper in PubMed, 11 citations in OpenAlex.

  1. Article
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

7 authors at 1 institution in 1 country.

Yan ChenJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.
Nurgul Kaplan LeaseJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.
Jennifer W GinJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.
Tadeusz L OgorzalekJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.
Paul D AdamsJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.
Nathan J HillsonJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.ORCID 0000-0002-9169-3978
Christopher J PetzoldJoint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.ORCID 0000-0002-8270-5228
Lawrence Berkeley National Laboratory · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Manual proteomic sample preparation methods limit sample throughput and often lead to poor data quality when thousands of samples must be analyzed. Automated liquid handler systems are increasingly used to overcome these issues for many of the sample preparation steps. Here, we detail a step-by-step protocol to prepare samples for bottom-up proteomic analysis for Gram-negative bacterial and fungal cells. The full modular protocol consists of three optimized protocols to: (A) lyse Gram-negative bacteria and fungal cells; (B) quantify the amount of protein extracted; and (C) normalize the amount of protein and set up tryptic digestion. These protocols have been developed to facilitate rapid, low variance sample preparation of hundreds of samples, be easily implemented on widely-available Beckman-Coulter Biomek automated liquid handlers, and allow flexibility for future protocol development. By using this workflow 50 micrograms of protein from 96 samples can be prepared for tryptic digestion in under an hour. We validate these protocols by analyzing 47 Pseudomonas putida and Rhodosporidium toruloides samples and show that this modular workflow provides robust, reproducible proteomic samples for high-throughput applications. The expected results from these protocols are 94 peptide samples from Gram-negative bacterial and fungal cells prepared for bottom-up quantitative proteomic analysis without the need for desalting column cleanup and with protein relative quantity variance (CV%) below 15%.

Indexed as

AutomationChromatography, High Pressure LiquidMass SpectrometryProteomeProteomicsPseudomonas putidaReproducibility of ResultsRhodotorulaSpecimen HandlingProteome

Identifiers

PMID35213656
PMCPMC8880914
OpenAlexW4214585667

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.