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Photosensitizer proximity labeling captures the lipid and protein interactomes

Nature Chemical Biology (2026)Cite this article

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Abstract

The physical properties of cellular membranes are influenced by protein and lipid interactions. In situ proximity labeling interactomic methods are well suited to characterize these dynamic and often fleeting interactions. Yet, available methods require distinct chemistries for proteins and lipids. Here we establish a singlet oxygen-based photocatalytic proximity labeling platform (POCA) that reports intracellular interactomes for both proteins and lipids using cell-penetrant photosensitizer reagents. Cholesterol-directed POCA captured known and unprecedented cholesterol-binding proteins, including protein complexes sensitive to intracellular cholesterol levels and proteins uniquely captured by physiologically relevant lipoprotein uptake. Protein-directed POCA accurately mapped intracellular membrane complexes, defined sterol-dependent changes to the interactome of the cholesterol transport protein Aster-B and revealed singlet oxygen-mediated domain-specific Aster crosslinking. More broadly, we find that POCA is a versatile interactomics platform that is straightforward to implement, using the readily available HaloTag system, fulfilling unmet needs in intracellular singlet oxygen-based proximity labeling proteomics.

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Fig. 1: Establishing the Halo-POCA system.
Fig. 2: Investigation of the cholesterol transport protein Aster-B using Halo-POCA.
Fig. 3: Using POCA with sterol 1 identifies known cholesterol-interacting proteins.
Fig. 4: Proteomic profiling of cholesterol-associated proteins in hepatocytes using HDL–1.
Fig. 5: Competition of 1 by cholesterol identifies the EMC as a cholesterol-sensitive complex.

Data availability

All supporting data for this study can be found within the article and Supplementary Information. The MS data were deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) with identifiers PXD068104 and PXD068106 through the MassIVE database with identifiers MSV000099044 and MSV000099045. Publicly available databases used were the UniProtKB Consortium (https://www.uniprot.org/), the 2025 release of the GO Resource (https://geneontology.org/) molecular function, cellular component and biological process classes and the Alliance of Genome Resources protein orthology data (https://www.alliancegenome.org/downloads#orthology; version 5.4, retrieved May 2, 2023), which was curated by the Jackson laboratory into a M. musculus and H. sapiens homology dataset (http://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt; retrieved May 2, 2023). Source data are provided with this paper.

Code availability

All code used for this work is available from GitHub (https://github.com/BackusLab) and Zenodo (https://doi.org/10.5281/zenodo.17575690)77.

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Acknowledgements

We thank L. Lavis and the Lavis Lab (Janelia Research Campus, HHMI) for providing JF570–HTL and JF585–HTL. We thank D. Wüstner for generating and providing the HepG2-SR-B1–GFP cells. We thank K. Martin for providing LSM880 microscope access and S. Neumann for her assistance with microscopy and helpful discussions. We thank T. Pleiner for helpful discussions related to the EMC. We thank L. Dassama and S. Alfonso for their assistance with the SLiPP model. This study was supported by DP2 GM146246-02 (K.M.B.), Packard Fellowship (K.M.B.), 1P01HL146358 (P.T. and K.M.B.), Leducq Transatlantic Network of Excellence 19CVD04 (P.T.) and the California NanoSystems Institute 2021 Noble Family Innovation Fund Seed Project Award (K.M.B. and P.T.). J.P.K. was supported by an American Heart Association postdoctoral fellowship (903306). A.R.J., E.B. and M.V. were supported by National Institute of General Medical Sciences (NIGMS) UCLA Chemistry Biology Interface T32 GM136614. M.V. and R.T.N. were supported by the UCLA Molecular Biology Institute Whitcome Fellowship and by the Audree Fowler Protein Science Fellowship from the UCLA Molecular Biology Institute. R.T.N. was also supported by NIGMS T32 GM008042. S.-G.H was supported as a Jim Easton CDF Investigator and by an American Heart Association Postdoctoral Fellowship (25POST1375863). We thank the S10 program of the National Institutes of Health Office of Research Infrastructure Programs grant S10OD028644 for funding of nuclear magnetic resonance facilities. We thank all members of the K.M.B. and P.T. labs for their helpful suggestions.

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Authors and Affiliations

  1. Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Andrew P. Becker, Elijah Biletch, John Paul Kennelly, Ashley R. Julio, Miranda Villanueva, Rohith T. Nagari, Nikolas R. Burton, Tomoyuki Fukuta, Liujuan Cui, Xu Xiao, Peter Tontonoz & Keriann M. Backus

  2. Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA, USA

    Andrew P. Becker, Elijah Biletch, Ashley R. Julio, Daniel W. Turner, Nikolas R. Burton, Zaid Vellani & Keriann M. Backus

  3. Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    John Paul Kennelly, Rohith T. Nagari, Liujuan Cui, Xu Xiao & Peter Tontonoz

  4. Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA, USA

    Soon-Gook Hong, Miranda Villanueva, Rohith T. Nagari, Julia J. Mack & Keriann M. Backus

  5. Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Soon-Gook Hong & Julia J. Mack

  6. Graduate School of Pharmaceutical Sciences, University of Tokyo, Tokyo, Japan

    Tomoyuki Fukuta

  7. Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Zaid Vellani & Alexander Nguyen

  8. Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA, USA

    Keriann M. Backus

  9. Jonsson Cancer Center, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Keriann M. Backus

  10. UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles, Los Angeles, CA, USA

    Keriann M. Backus

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  1. Andrew P. Becker
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  2. Elijah Biletch
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Contributions

A.B. and K.M.B. conceptualized the study. A.B., JP.K., J.J.M., X.X., A.N., P.T. and K.M.B. contributed to the design and implementation of experiments. A.B., E.B., S.-G.H., JP.K., A.R.J., Z.V. and M.V. generated and analyzed the data. A.B., E.B., N.B., T.F. and D.T. performed the chemical synthesis. A.B., JP.K., R.T.N. and L.C. generated the reagents. A.B., E.B. and S.-G.H. performed confocal light microscopy. A.B. and K.M.B. wrote the manuscript with assistance from all authors.

Corresponding author

Correspondence to Keriann M. Backus.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Enrichment of POCA-modified peptides reveals a strong preference for histidine.

(A) Distribution of calculated median MS1 heavy/light ratios for proteins, n = 3 biological replicates per condition. (B) Most likely localization of the propargylamine-biotin modification masses based on open search and PTMProphet analysis. Modification masses for light (395.1742 Da), oxidized light (411.1696 Da), heavy (401.2118 Da), and oxidized heavy (417.2064) were extracted from the “global_profile” and the PSM counts for the most likely residues were plotted. (C,D) Annotated MS2 spectra show the assigned localization site using and closed search. For ‘A’–‘D’, HEK293T lysates were labeled with labeled with DBF-NHS (1 h at 22 °C) followed by POCA labeling with 1 mM propargylamine and then clicked to either either biotin-azide (“light”) or biotin-azide-d6 (“heavy”) followed by digest, enrichment and LC-MS/MS analysis. MS data can be found in Supplementary Data 1.

Extended Data Fig. 2 Relative protein labeling by Halo-POCA is dependent on the concentration of the amine capture reagent and duration of irradiation.

Following the workflow shown in Supplemental Fig. 1A, HEK239T cells transiently expressing either (A–C) Myc-HaloTag-NUP153 or (D,E) HaloTag-eGFP-mito were treated with JF570-HTL followed by media washouts, addition of PA (0–20 mM) and irradiation with yellow light. In parts ‘B’ and ‘D’ cells were irradiated for 5.0 min. In parts ‘C’ and ‘E’ cells were subjected to Halo-POCA with 10 mM PA with varying durations of irradiation (0–10 min). Other conditions are as follows; JF570-HTL treatment: 100 nM, 10 min; irradiation: 15 W yellow LED, 170,000 Lux max intensity, 5 min, on ice; lysis: 0.5% polyethylene glycol nonylphenyl ether/0.5% CHAPS in PBS with protease inhibitors. Protein labeling was visualized by CuAAC to biotin-azide followed by SDS-PAGE and streptavidin blot.

Source data

Extended Data Fig. 3 Halo-POCA affords spatio-restricted labeling of proteins proximal to a mitochondrial-targeted HaloTag fusion protein.

(A) Workflow for analysis of Halo-POCA labeled proteins by microscopy. (B) Images of individual channels or the merged channel without propargylamine present during Halo-POCA, n = 1. (C) Images of individual channels or the merged channel when propargylamine (10 mM) is present during irradiation, n = 1. HEK-293T cells transiently expressing HaloTag-eGFP-mito subjected to the Halo-POCA procedure without (part ‘B’) or with (part ‘C’) propargylamine capture. Cells were then washed, fixed, permeabilized, and had Cy5-azide appended via CuAAC. After washing, staining with DAPI, and mounting, cells were imaged on a Zeiss LSM880 confocal microscope. Experiment omitting propargylamine (part ‘B’) shows that the JF570 emission does not significantly overlap with Cy5 channel. Scale bars = 10 µm.

Extended Data Fig. 4 Strategy for investigating the cholesterol-dependent interactome of Aster fusion proteins using Halo-POCA and the enrichment of ER or PM proteins with the different fusions.

Investigating domain-dependent crosslinking and the cholesterol-dependent interactome of Aster fusion proteins. (A) Schematic cartoon indicating the different domains in each of the Aster fusion proteins used. (B) Schematic diagram showing the dependence on the ER domain (ERD) for Aster oligomerization. (C) Coexpression of ERD-containing Halotag-Aster fusion proteins results in crosslinking and gel mobility shifts. (D) Schematic workflow for investigating the cholesterol-dependent interactome of Aster-B using Halo-POCA. (E,F) Volcano plots showing the differential protein labeling when HEK293T cells expressing either full-length HaloTag-mAster-B (part ‘E’) or HaloTag-mAster-B-ΔERD (part ‘F’) are starved of cholesterol overnight and then left untreated (left side) or loaded with cholesterol (right side) before Halo-POCA labeling, pulldown, and proteomic identification of labeled proteins. In part ‘E,’ ER-annotated proteins are represented by black data points. In part ‘F,’ PM-annotated proteins are represented by black data points. Overnight cholesterol starvation refers to incubation with 1% LPDS medium supplemented with simvastatin (5 µM) and mevalonate (50 µM). In parts ‘E’ and ‘F,’ variances were calculated for each sample-condition pairing and a corresponding two-tailed t-test was performed to generate p-values. Enrichment criteria were set as p-value < 0.05 and log2(FC) > 1 or < −1. MS experiments for each condition were conducted in 3 biological replicates in HEK293T cells and data can be found in Supplementary Data 2. Cell lysis conditions are in Supplementary Table 6.

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Extended Data Fig. 5 POCA labeling with small molecule probes is dependent on the presence of probe, amine capture reagent, and irradiation.

(A) Efficient POCA labeling with mꞵCD-1 requires all three components (photosensitizer-containing probe, light, amine capture reagent), n = 1. (B) Concentration dependence of POCA labeling with mꞵCD-1, n = 1. The extent of protein labeling, inferred by fluorescence intensity, appears to saturate around 10 µM. (C) Irradiation time dependence of POCA labeling with mꞵCD-1; n = 1. (D) Efficient POCA labeling with mꞵCD-1 requires all three components (photosensitizer-containing probe, light, amine capture reagent); n = 1. (E) Comparison of relative protein labeling as a function of probe concentration, n = 1. HEK293T cells were incubated with the indicated concentration of the mβCD-complexed sterol probe (1 or NBII-165, 60 min) before they were subjected to the standard appropriate workflow as described in the methods. Cells were treated with 10 µM (except where indicated otherwise) mꞵCD-complexed 1 or 2 for 60 min, followed by 5 min yellow LED irradiation (170,00 Lux) in the presence of 10 mM propargylamine. Protein labeling was assessed by CuAAC with rhodamine-azide, SDS–PAGE separation, then in-gel fluorescence analysis. Cell lysis conditions are in Supplementary Table 6.

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Extended Data Fig. 6 Canonical cholesterol handling proteins are enriched by POCA using HDL-1 in HepG2 cells overexpressing SR-B1.

(A) Immunoblot showing the differences in SR-B1 expression. Data from the same experiment are shown in Fig. 4c of the main text. (B) Quantification of SR-B1 abundance by immunoblot and MS. (C–E) More proteins are enriched by HDL-POCA using chol-JF570 probe 1 in HepG2-SR-B1-GFP cells (pink) versus parental HepG2 cells (blue). HepG2 or HepG2-SR-B1-GFP cells were starved overnight of cholesterol (1% LDPS media supplemented with 5 μM simvastatin and 50 μM mevalonate) then taken through HDL-POCA with HDL-1 (100 ug/mL, 60 min). In ‘B’, the mean values (n = 3) and standard deviations are shown as the heights and error bars, respectively—statistical significance was calculated with unpaired two-tailed t-tests. For measures of statistical significance in parts ‘C’–’E’, variances were calculated for each sample-condition pairing and a corresponding two-tailed t-test was performed to generate p-values. Enrichment criteria were set as p-value < 0.05 and log2(FC) > 3. MS experiments for each condition were conducted in 3 biological replicates in HepG2 or HepG2-SR-B1-GFP cells. MS data were searched with FragPipe 22.0 and can be found in Supplementary Data 4. Cell lysis conditions are in Supplementary Table 6.

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Extended Data Fig. 7 ER membrane complex (EMC) subunits are readily labeled by different sterol probes.

ER membrane complex (EMC) subunits are readily labeled by different sterol probes, and the addition of cholesterol reduces the amount of soluble EMC7. (A) Schematic diagram of the EMC (pdb_00008s9s) with histidine residues available for POCA labeling colored red. Rendered in PyMol. (B) Light-dependent log2(fold change) values for the EMC subunits with the different sterol probes used in this study, n = 3 biological replicates per condition. (C) There is less soluble EMC7 in samples treated with mꞵCD-cholesterol, suggesting cholesterol in the ER membrane confers resistance to detergent-based extraction of the EMC, n = 1. (D) Quantification of relative protein abundance for EMC7 and OSBP for data shown in panel ‘C’. Measurements of proteins soluble in 0.2% PEGNPE are shown by circles and connected by lines. Measurements of proteins soluble in 0.4% PEGNPE are shown with triangles. Cell lysis conditions are in Supplementary Table 6.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–40, Tables 1–7, Note and Source data for supplementary figures.

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Supplementary Data 1

Data for Fig. 1 and related supplementary figures.

Supplementary Data 2

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Supplementary Data 3

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Supplementary Data 4

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Supplementary Data 5

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Supplementary Video 1

Video of probe 1 uptake by live-cell microscopy.

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Becker, A.P., Biletch, E., Kennelly, J.P. et al. Photosensitizer proximity labeling captures the lipid and protein interactomes. Nat Chem Biol (2026). https://doi.org/10.1038/s41589-026-02140-1

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