Evidence map›Paper›PMID 40480342›Full record

ArticleMolecular & cellular proteomics : MCP2025

From Discovery to Delivery: A Rapid and Targeted Proteomics Workflow for Monitoring Chinese Hamster Ovary Biomanufacturing.

Charles Eldrid, Ellie Hawke, Kathleen M Cain, Kate Meeson, Joanne Watson, Reynard Spiess, Luke Johnston, William Smith, Matthew Russell, Robyn Hoare and 7 more

Abstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

17 authors.

Charles EldridManchester Institute of Biotechnology, University of Manchester, Manchester, UK. Electronic address: charles.eldrid@manchester.ac.uk.
Ellie HawkeManchester Institute of Biotechnology, University of Manchester, Manchester, UK.
Kathleen M CainManchester Institute of Biotechnology, University of Manchester, Manchester, UK.
Kate MeesonManchester Institute of Biotechnology, University of Manchester, Manchester, UK.
Joanne WatsonManchester Institute of Biotechnology, University of Manchester, Manchester, UK.
Reynard SpiessManchester Institute of Biotechnology, University of Manchester, Manchester, UK.
Luke JohnstonInstitute of Quantitative Biology, Biochemistry & Biotechnology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK.
William SmithManchester Institute of Biotechnology, University of Manchester, Manchester, UK.
Matthew RussellManchester Institute of Biotechnology, University of Manchester, Manchester, UK.
Robyn HoareFUJIFILM Diosynth Biotechnologies, Billingham, UK.
John RavenFUJIFILM Diosynth Biotechnologies, Billingham, UK.
Jean-Marc SchwartzFaculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
Magnus RattrayFaculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
Leon PybusFUJIFILM Diosynth Biotechnologies, Billingham, UK.
Alan DicksonManchester Institute of Biotechnology, University of Manchester, Manchester, UK.
Andrew PittManchester Institute of Biotechnology, University of Manchester, Manchester, UK.
Perdita BarranManchester Institute of Biotechnology, University of Manchester, Manchester, UK. Electronic address: perdita.barran@manchester.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chinese hamster ovary (CHO) cells are the industrial workhorse for manufacturing biopharmaceuticals, including monoclonal antibodies. CHO cell line development requires a more data-driven approach for the accelerated identification of hyperproductive cell lines. Traditional methods, which rely on time-consuming hierarchical screening, often fail to elucidate the underlying cellular mechanisms driving optimal bioreactor performance. Big data analytics, coupled with advancements in "omics" technologies, are revolutionizing the study of industrial cell lines. Translating this knowledge into practical methods widely utilized in industrial biomanufacturing remains a significant challenge. This study leverages discovery proteomics to characterize dynamic changes within the CHO cell proteome during a 14-day fed-batch bioreactor cultivation. Utilizing a global untargeted proteomics workflow on both a ZenoTOF 7600 and a Cyclic IMS QToF, we identify 3358 proteins and present a comprehensive data set that describes the molecular changes that occur within a well-characterized host chassis. By mapping relative abundances to key cellular processes, eight protein targets were selected as potential biomarkers. The abundance of these proteins through the production run is quantified using a 15-min targeted triple quadrupole (MRM) assay, which provides a molecular-level QC for cell viability. This discovery to target workflow has the potential to assist engineering of new chassis and provide simple readouts of successful bioreactor batches.

Indexed as

ProteomeProteomicsAnimalsBatch Cell Culture TechniquesBioreactorsCHO CellsCricetinaeCricetulusWorkflowProteomebiotechnologyCHO cell biologymultiple reaction monitoringproteomicsquantitative proteomics

Identifiers

PMID40480342
PMCPMC12274832

What Socratic 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 Socratic graph.