# Secretome analysis
Secretome analysis is a cell-biology method that identifies and quantifies the set of proteins released by cells into their extracellular environment, most often by mass spectrometry of conditioned culture medium. The corresponding field, [secretomics](https://www.edgepedia.org/secretomics), is defined as a proteomics-based approach to identify and quantify all proteins secreted by a cell, and is inherently unbiased toward any particular secretion pathway.[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10793180/) A complementary gene-level resource, the Human Protein Atlas secretome, annotates each predicted secreted protein with its abundance, site of origin, final localization, and detectability in blood.[2](https://www.science.org/doi/10.1126/scisignal.aaz0274)
| Key fact | Value |
|---|---|
| Scale of the secreted subproteome | ~3,000 secreted proteins plus >2,000 membrane proteins, ~25% of the mammalian proteome[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10793180/) |
| Human secretome gene count (Human Protein Atlas criteria) | 2,641 genes, 13% of all human genes; 730 predicted to enter the blood[3](https://backend.orbit.dtu.dk/ws/portalfiles/portal/236401061/1_s2.0_S1871678420301321_main.pdf) |
| Serum background problem | Full medium with 10% bovine serum reaches 5–6 µg/µl protein, >95% from the top 10 blood proteins; unstimulated secretomes reach 0.05–0.1 µg/µl[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10793180/) |
| Purity target | A good secretome preparation should exceed 90% secreted-protein composition[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10793180/) |
| Typical input | \( 10^{7} \)–\( 5 \times 10^{7} \) cells yielding 15–20 ml conditioned medium[4](https://wp.unil.ch/paf/files/2023/07/Secretome-general-protocol-03112016.pdf) |
| Detection range | Mass spectrometry reaches ng/mL concentrations; antibody-based methods reach pg/mL[5](https://sage.cnpereading.com/doi/10.1177/2211068212454738) |
## How it works
The core measurement is shotgun liquid chromatography–tandem mass spectrometry (LC-MS/MS) of proteins accumulated in conditioned medium. The central difficulty is the boundary problem: what counts as secreted. Classical secretion delivers proteins carrying an N-terminal signal peptide through the ER–Golgi pathway; in UniProtKB (November 2018, 20,408 reviewed human proteins), 3,656 proteins carry this annotation, about 18% of the reviewed proteome, and UniProtKB requires at least two of four algorithms (Phobius, Predotar, SignalP, TargetP) to predict a signal peptide.[6](https://www.sciencedirect.com/science/article/abs/pii/S1570963919301098) A substantial share of the measured secretome, however, arrives by unconventional secretion or by proteolytic shedding: an unexpectedly high number of secreted proteins in vitro and in vivo are cleaved membrane-protein ectodomains, and unconventionally secreted proteins can constitute around 50% of a cell's secretome depending on the cell system.[6](https://www.sciencedirect.com/science/article/abs/pii/S1570963919301098)
The second boundary is contamination. Conditioned medium also contains intracellular proteins released by cell death and lysis, and a single conventional metabolic-labeling measurement cannot distinguish these sources, because tags such as SILAC (stable-isotope amino-acid labeling used to trace protein origins in mass spectrometry) or AHA label intracellular proteins before they are secreted; specialized dynamic-labeling designs such as SIDLS can, however, help separate them.[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10793180/)
## How it is done
A standard protocol grows cells in FBS-free, phenol-red-free DMEM or RPMI for 12–24 h, balancing three goals: reducing serum protein background, minimizing cell death and lysis, and maximizing secreted proteins in the conditioned medium.[4](https://wp.unil.ch/paf/files/2023/07/Secretome-general-protocol-03112016.pdf) Cell death is monitored by measuring LDH or other leakage markers in the medium.[4](https://wp.unil.ch/paf/files/2023/07/Secretome-general-protocol-03112016.pdf) The medium is then clarified by sequential centrifugation at 300 × g (cells), 2,000 × g (dead cells), and 10,000 × g (debris), concentrated roughly tenfold with buffer exchange through 3 kDa Amicon filters into 100 mM ammonium bicarbonate, digested, and analyzed by LC-MS/MS.[4](https://wp.unil.ch/paf/files/2023/07/Secretome-general-protocol-03112016.pdf) A published variant adds ultracentrifugation at 120,000 g for 90 min to pellet exosomes before the 3 kDa concentration step.[7](https://pmc.ncbi.nlm.nih.gov/articles/PMC9756135/)
Distinguishing bona fide secretion from lysis relies on comparison. A media-to-lysate (M/L) ratiometric TMT approach showed signal-peptide-containing secreted liver proteins at M/L ratios of roughly 12–80 versus 0.1–0.8 for cytoplasmic proteins (p = 0.0019); brefeldin A treatment, which blocks ER–Golgi trafficking, validated the approach and flagged candidates on BFA-resistant unconventional routes.[7](https://pmc.ncbi.nlm.nih.gov/articles/PMC9756135/) Comparative secretomics, which contrasts the secretome with the total proteome, yields 30–70% classically secreted proteins and 4–29% candidate unconventional-secretion proteins.[8](https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2022.878027/full) The SIDLS approach (stable isotope dynamic labeling of secretomes) distinguishes secretory proteins from proteins released by dying cells and revealed leaderless secreted proteins, including HDGF, PRDX2, AKR1B10, and [C1QBP](https://www.edgepedia.org/c1qbp), with secretion kinetics comparable to classical secretory proteins.[8](https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2022.878027/full)
## Origin
The term secretome was introduced in work on the bacterium [Bacillus subtilis](https://www.edgepedia.org/bacillus-subtilis) to describe its secreted proteins.[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10793180/) From that bacterial starting point the field moved to mammalian 2D-gel and MALDI-based surveys and then to quantitative shotgun workflows. Named methodological landmarks include the matrisome, defined in silico and characterized in vivo by Alexandra Naba and colleagues in 2011;[9](https://doi.org/10.1074/mcp.m111.014647) the pSILAC strategy of selective enrichment of newly synthesized proteins for quantitative secretome analysis, reported by Katrin Eichelbaum and colleagues in 2012;[10](https://doi.org/10.1038/nbt.2356) the dual SILAC strategy for quantifying constitutive and cell–cell induced secretion, reported by Michael Stiess and colleagues in 2015;[11](https://doi.org/10.1021/acs.jproteome.5b00199) SPECS, secretome protein enrichment with click sugars, reported by Alperen Serdaroglu and colleagues in 2016;[12](https://doi.org/10.1002/pmic.201600423) OutCyte, a tool for predicting unconventional protein secretion, reported by Linlin Zhao and colleagues in 2019;[13](https://doi.org/10.1038/s41598-019-55351-z) the comprehensive human secretome resource of [Mathias Uhlén](https://www.edgepedia.org/mathias-uhlen) and colleagues in 2019;[2](https://www.science.org/doi/10.1126/scisignal.aaz0274) the Secret3D workflow, reported by Vittoria Matafora and Angela Bachi in 2020;[14](https://doi.org/10.1016/j.xpro.2020.100162) and comparative secretomics for defining bona fide secreted proteins, reported by Gereon Poschmann and colleagues in 2020.[15](https://doi.org/10.1002/pmic.202000178)
## Variants
Several named variants carve the secretome into subcompartments. The matrisome covers the extracellular-matrix fraction, estimated at about 4% of the human proteome; proteomic analysis of extracellular matrix from pancreatic cells identified 214 matrisomal proteins, with N-glycosylation on 99 and phosphorylation on 18.[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10793180/) • [9](https://doi.org/10.1074/mcp.m111.014647) Extracellular-vesicle secretomics profiles vesicle cargo under the MISEV quality standards.[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10793180/) [In silico](https://www.edgepedia.org/in-silico) prediction tools annotate which proteins can be secreted: signal-peptide pipelines underlie the UniProtKB classical annotation,[6](https://www.sciencedirect.com/science/article/abs/pii/S1570963919301098) and OutCyte adds a two-module machine-learning predictor for unconventional secretion; a benchmark found SecretomeP performs much worse than originally thought.[8](https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2022.878027/full) • [13](https://doi.org/10.1038/s41598-019-55351-z)
Quantification is routine. Label-free approaches (ion intensity, spectral counting) and stable-isotope methods (SILAC, \( {}^{18}\mathrm{O} \) proteolytic labeling, ICAT, iTRAQ) were established early;[5](https://sage.cnpereading.com/doi/10.1177/2211068212454738) TMT and DIA now dominate condition-to-condition comparisons. The SPECS chemistry enriches cell-derived secreted proteins from serum-containing media using clickable azido-sugars (ManNAcAz, GalNAcAz) and alkyne-biotin pull-down,[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10793180/) • [12](https://doi.org/10.1002/pmic.201600423) and combining TMT labeling with click-chemistry enrichment of glycosylated proteins at a 10:1 boosting-to-sample ratio tripled the number of proteins identified in secretomes from serum-cultured cells.[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10793180/) [In vivo](https://www.edgepedia.org/in-vivo), the secretome mouse provides a genetic platform to delineate tissue-specific secretion,[16](https://doi.org/10.1073/pnas.2005134118) and cell type-selective secretome profiling in vivo was reported by Wei Wei and colleagues in 2020.[17](https://doi.org/10.1038/s41589-020-00698-y) On the instrument side, a dia-PASEF workflow in human iPSC-derived macrophages robustly identified over 900 protein groups in under 15 min of acquisition time with high reproducibility.[18](https://pureportal.strath.ac.uk/en/publications/dia-pasef-enables-rapid-profiling-of-the-human-secretome-for-deep/) For data-independent acquisition generally, benchmarking favors DIA-NN for robustness across spectral library types and Spectronaut directDIA for library-free analysis, and working at 75% data completeness is a good trade-off between detected proteins and missing-value imputation.[19](https://www.nature.com/articles/s41467-025-65174-4) Vesicle profiling has also sharpened: a density-gradient–protein correlation profiling workflow with DIA LC-MS/MS analyzed over 9,000 proteins in human cancer cell lines and biofluids, defining a core signature of 1,499 high-confidence small extracellular vesicle proteins, and found that 80% of proteins in conventionally sEV-enriched density-gradient fractions lacked genuine low-density profiles, indicating persistent non-vesicular contamination.[20](https://link.springer.com/article/10.1038/s41556-026-01878-z)
## Applications
Published applications center on signaling and disease mechanisms. Temporal profiling of LPS responses in macrophages over 24 h resolved secretion trajectories that distinguish acute from chronic inflammatory states, with early TNF and IL-6 secretion preceding delayed CXCL10 and CCL8 chemokine secretion, and identified a cholesterol efflux signature, marked by APOA1 and [PON1](https://www.edgepedia.org/pon1) secretion, in response to [Mycobacterium tuberculosis](https://www.edgepedia.org/mycobacterium-tuberculosis) infection.[18](https://pureportal.strath.ac.uk/en/publications/dia-pasef-enables-rapid-profiling-of-the-human-secretome-for-deep/) The Human Protein Atlas resource supports biomarker interpretation by annotating which secreted proteins are detectable in blood; notably, many proteins predicted to be secreted are retained intracellularly, and another large group are retained locally at the tissue of expression rather than released into blood.[2](https://www.science.org/doi/10.1126/scisignal.aaz0274)
## Limitations and alternatives
Serum is the dominant limitation: full medium with 10% bovine serum reaches 5–6 µg/µl protein of which >95% is the top 10 blood proteins, masking low-abundance secreted proteins, while serum-free conditioning remains the gold standard.[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10793180/) • [8](https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2022.878027/full) Serum starvation itself creates a stress condition that distorts secretome composition, although certain cell types tolerate serum-free culture up to 48 h with negligible stress symptoms; AHA labeling alters the proteome detectably after 6 h and more strongly at 24 h.[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10793180/) • [8](https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2022.878027/full) • [6](https://www.sciencedirect.com/science/article/abs/pii/S1570963919301098) Lysis contamination is the second failure mode, addressed by LDH monitoring, M/L or comparative designs, and the >90% purity target.[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10793180/) • [7](https://pmc.ncbi.nlm.nih.gov/articles/PMC9756135/) The M/L approach also misses dual-location proteins such as FGF2, galectin-3, and histone-3A, and cell-surface or insoluble ECM proteins.[7](https://pmc.ncbi.nlm.nih.gov/articles/PMC9756135/)
The nearest alternative family is proximity labeling, in which a promiscuous biotin ligase fused to a compartment-specific protein biotinylates nearby secreted and membrane proteins in living cells; TurboID, reported by Tess Branon and colleagues in 2018, enables efficient labeling in cells and organisms,[21](https://doi.org/10.1038/nbt.4201) building on APEX2, developed by directed evolution by Stephanie Lam and colleagues in 2014.[22](https://doi.org/10.1038/nmeth.3179) Sec61-targeted labeling in liver cells and mice characterizes secreted proteins in vitro and in vivo.[1](https://pmc.ncbi.nlm.nih.gov/articles/PMC10793180/) Affinity-based assays remain more sensitive (pg/mL) but far lower in multiplexing than MS.[5](https://sage.cnpereading.com/doi/10.1177/2211068212454738) Single-cell secretomic analysis, reported by Yao Lu and colleagues in 2013, assesses functional heterogeneity at the single-cell level.[23](https://doi.org/10.1021/ac400082e)
## References
1. [Secretome Analysis: Reading Cellular Sign Language to Understand Intercellular Communication](https://pmc.ncbi.nlm.nih.gov/articles/PMC10793180/)
2. [The human secretome](https://www.science.org/doi/10.1126/scisignal.aaz0274)
3. [High throughput generation of a resource of the human secretome in mammalian cells](https://backend.orbit.dtu.dk/ws/portalfiles/portal/236401061/1_s2.0_S1871678420301321_main.pdf)
4. [Secretome analysis: general recommendations and protocol (UNIL PAF facility, v3, 2016)](https://wp.unil.ch/paf/files/2023/07/Secretome-general-protocol-03112016.pdf)
5. [Automated Mass Spectrometry–Based Functional Assay for the Routine Analysis of the Secretome](https://sage.cnpereading.com/doi/10.1177/2211068212454738)
6. [Pitfalls and opportunities in the characterization of unconventionally secreted proteins by secretome analysis](https://www.sciencedirect.com/science/article/abs/pii/S1570963919301098)
7. [Identification of secreted proteins by comparison of protein abundance in conditioned media and cell lysates](https://pmc.ncbi.nlm.nih.gov/articles/PMC9756135/)
8. [Secretomics to Decipher the Unconventional Protein Secretion Landscape](https://www.frontiersin.org/journals/cell-and-developmental-biology/articles/10.3389/fcell.2022.878027/full)
9. [Alexandra Naba and colleagues (2011). The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices. Molecular & Cellular Proteomics.](https://doi.org/10.1074/mcp.m111.014647)
10. [Katrin Eichelbaum and colleagues (2012). Selective enrichment of newly synthesized proteins for quantitative secretome analysis. Nature Biotechnology.](https://doi.org/10.1038/nbt.2356)
11. [Michael Stiess and colleagues (2015). A Dual SILAC Proteomic Labeling Strategy for Quantifying Constitutive and Cell–Cell Induced Protein Secretion. Journal of Proteome Research.](https://doi.org/10.1021/acs.jproteome.5b00199)
12. [Alperen Serdaroglu and colleagues (2016). An optimised version of the secretome protein enrichment with click sugars (SPECS) method leads to enhanced coverage of the secretome. PROTEOMICS.](https://doi.org/10.1002/pmic.201600423)
13. [Linlin Zhao and colleagues (2019). OutCyte: a novel tool for predicting unconventional protein secretion. Scientific Reports.](https://doi.org/10.1038/s41598-019-55351-z)
14. [Vittoria Matafora, Angela Bachi (2020). Secret3D Workflow for Secretome Analysis. STAR Protocols.](https://doi.org/10.1016/j.xpro.2020.100162)
15. [Gereon Poschmann and colleagues (2020). Comparative Secretomics Gives Access to High Confident Secretome Data: Evaluation of Different Methods for the Determination of Bona Fide Secreted Proteins. PROTEOMICS.](https://doi.org/10.1002/pmic.202000178)
16. [Jie Liu and colleagues (2021). The secretome mouse provides a genetic platform to delineate tissue-specific in vivo secretion. Proceedings of the National Academy of Sciences.](https://doi.org/10.1073/pnas.2005134118)
17. [Wei Wei and colleagues (2020). Cell type-selective secretome profiling in vivo. Nature Chemical Biology.](https://doi.org/10.1038/s41589-020-00698-y)
18. [dia-PASEF enables rapid profiling of the human secretome for deeper insights into cellular dynamics and inflammatory mechanisms](https://pureportal.strath.ac.uk/en/publications/dia-pasef-enables-rapid-profiling-of-the-human-secretome-for-deep/)
19. [Benchmarking informatics workflows for data-independent acquisition single-cell proteomics](https://www.nature.com/articles/s41467-025-65174-4)
20. [Defining the reference proteomes for small extracellular vesicles and non-vesicular components](https://link.springer.com/article/10.1038/s41556-026-01878-z)
21. [Tess C Branon and colleagues (2018). Efficient proximity labeling in living cells and organisms with TurboID. Nature Biotechnology.](https://doi.org/10.1038/nbt.4201)
22. [Stephanie S Lam and colleagues (2014). Directed evolution of APEX2 for electron microscopy and proximity labeling. Nature Methods.](https://doi.org/10.1038/nmeth.3179)
23. [Yao Lu and colleagues (2013). High-Throughput Secretomic Analysis of Single Cells to Assess Functional Cellular Heterogeneity. Analytical Chemistry.](https://doi.org/10.1021/ac400082e)
---
*Topic: Encyclopedia › Life and health › Biological foundations › Cell biology › Extracellular matrix and cell-matrix interactions*
*Initially written Sep 29, 2026 · Edited: Oct 15, 2026*
*Copyright 2026 EdgeChat AI, a subsidiary of Biostate AI.*
License: Edgepedia Community License 1.0, https://www.edgepedia.org/edgepedia/license