Evidence map›Paper›PMID 28764719›Full record

ArticleMicrobial cell factories2017

A combination of HPLC and automated data analysis for monitoring the efficiency of high-pressure homogenization.

Britta Eggenreich, Vignesh Rajamanickam, David Johannes Wurm, Jens Fricke, Christoph Herwig, Oliver Spadiut

Abstract read
In one paragraph

Article in Microbial cell factories, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Pichia pastoris Cell Disruption by High-Pressure Homogenization (HPH).Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  2. Article
  3. Selective Release of Recombinant Periplasmic Protein FromFrontiers in bioengineering and biotechnology · 2020
    Article
  4. Article
  5. 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

6 authors.

Britta EggenreichResearch Division Biochemical Engineering, Institute of Chemical, Environmental and Biological Engineering, TU Wien, Gumpendorfer Strasse 1a, 1060, Vienna, Austria.
Vignesh RajamanickamResearch Division Biochemical Engineering, Institute of Chemical, Environmental and Biological Engineering, TU Wien, Gumpendorfer Strasse 1a, 1060, Vienna, Austria.
David Johannes WurmResearch Division Biochemical Engineering, Institute of Chemical, Environmental and Biological Engineering, TU Wien, Gumpendorfer Strasse 1a, 1060, Vienna, Austria.
Jens FrickeResearch Division Biochemical Engineering, Institute of Chemical, Environmental and Biological Engineering, TU Wien, Gumpendorfer Strasse 1a, 1060, Vienna, Austria.
Christoph HerwigResearch Division Biochemical Engineering, Institute of Chemical, Environmental and Biological Engineering, TU Wien, Gumpendorfer Strasse 1a, 1060, Vienna, Austria.
Oliver SpadiutResearch Division Biochemical Engineering, Institute of Chemical, Environmental and Biological Engineering, TU Wien, Gumpendorfer Strasse 1a, 1060, Vienna, Austria. oliver.spadiut@tuwien.ac.at.ORCID http://orcid.org/0000-0003-0916-0644

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCell disruption is a key unit operation to make valuable, intracellular target products accessible for further downstream unit operations. Independent of the applied cell disruption method, each cell disruption process must be evaluated with respect to disruption efficiency and potential product loss. Current state-of-the-art methods, like measuring the total amount of released protein and plating-out assays, are usually time-delayed and involve manual intervention making them error-prone. An automated method to monitor cell disruption efficiency at-line is not available to date.

resultsIn the current study we implemented a methodology, which we had originally developed to monitor E. coli cell integrity during bioreactor cultivations, to automatically monitor and evaluate cell disruption of a recombinant E. coli strain by high-pressure homogenization. We compared our tool with a library of state-of-the-art methods, analyzed the effect of freezing the biomass before high-pressure homogenization and finally investigated this unit operation in more detail by a multivariate approach.

conclusionA combination of HPLC and automated data analysis describes a valuable, novel tool to monitor and evaluate cell disruption processes. Our methodology, which can be used both in upstream (USP) and downstream processing (DSP), describes a valuable tool to evaluate cell disruption processes as it can be implemented at-line, gives results within minutes after sampling and does not need manual intervention.

Indexed as

Chromatography, High Pressure LiquidArea Under CurveAutomationBatch Cell Culture TechniquesBioreactorsEscherichia coliPressureRecombinant ProteinsROC CurveRecombinant ProteinsCell disruptionData analysisE.coliHigh-pressure homogenizationHPLC

Identifiers

PMID28764719
PMCPMC5540504

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LicenceCC BY
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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.