Evidence map›Paper›PMID 41450608›Full record

ArticleBio-protocol2025

Optimized Secretome Sample Preparation From High Volume Cell Culture Media for LC-MS/MS Proteomic Analysis.

Basil Baby Mattamana, Raju Gajjela, Jyoti K C, Robert A Parish, Peter Allen Faull

Abstract read
In one paragraph

Article in Bio-protocol, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

5 authors.

Basil Baby MattamanaProteomics Core, Northwestern University, Chicago, IL, USA.
Raju GajjelaProteomics Core, Northwestern University, Chicago, IL, USA.
Jyoti K CProteomics Core, Northwestern University, Chicago, IL, USA.
Robert A ParishProteomics Core, Northwestern University, Chicago, IL, USA.
Peter Allen FaullProteomics Core, Northwestern University, Chicago, IL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The cellular secretome is a rich source of biomarkers and extracellular signaling molecules, but proteomic profiling remains challenging, especially when processing culture volumes greater than 5 mL. Low protein abundance, high serum contamination, and sample loss during preparation limit reproducibility and sensitivity in mass spectrometry-based workflows. Here, we present an optimized and scalable protocol that integrates (i) 50 kDa molecular weight cutoff ultrafiltration, (ii) spin column depletion of abundant serum proteins, and (iii) acetone/TCA precipitation for protein recovery. This workflow enables balanced recovery of both low- and high-molecular-weight proteins while reducing background from serum albumin, thereby improving sensitivity, reproducibility, and dynamic range for LC-MS/MS analysis. Validated in human mesenchymal stromal cell cultures, the protocol is broadly applicable across diverse cell types and experimental designs, making it well-suited for biomarker discovery and extracellular proteomics. Key features • Enables efficient concentration and cleanup of ≥5-500 mL of conditioned media, suitable for low-abundance secreted protein recovery. • Combines 50 kDa ultrafiltration, optional HSA/IgG depletion, and acetone/TCA precipitation for robust removal of serum contaminants and improved signal-to-noise. • Adaptable to various mammalian cell types and serum-free or serum-containing media; scalable for adherent and suspension cultures.

Indexed as

Albumin/IgG depletionAmicon 50 kDaBottom-up proteomicsConditioned mediumExtracellular proteomicsLC-MS/MSProtein identificationSecretomeUltrafiltration

Identifiers

PMID41450608
PMCPMC12738067

What OpenQuestion holds

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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.