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Identifying cell receptors for the nanoparticle protein corona using genome screens


Nanotechnology provides platforms to deliver medical agents to specific cells. However, the nanoparticle’s surface becomes covered with serum proteins in the blood after administration despite engineering efforts to protect it with targeting or blocking molecules. Here, we developed a strategy to identify the main interactions between nanoparticle-adsorbed proteins and a cell by integrating mass spectrometry with pooled genome screens and Search Tool for the Retrieval of Interacting Genes analysis. We found that the low-density lipoprotein (LDL) receptor was responsible for approximately 75% of serum-coated gold nanoparticle uptake in U-87 MG cells. Apolipoprotein B and complement C8 proteins on the nanoparticle mediated uptake through the LDL receptor. In vivo, nanoparticle accumulation correlated with LDL receptor expression in the organs of mice. A detailed understanding of how adsorbed serum proteins bind to cell receptors will lay the groundwork for controlling the delivery of nanoparticles at the molecular level to diseased tissues for therapeutic and diagnostic applications.

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Fig. 1: Proposed integrated approach to identify receptor-adsorbed protein interactions that mediate nanoparticle uptake.
Fig. 2: Identifying proteins absorbed to the nanoparticle surface.
Fig. 3: Pooled genome-wide CRISPR screens identified genes involved in HS-GNP uptake.
Fig. 4: Identifying and validating interactions between proteins in the nanoparticle corona and cell receptors.
Fig. 5: LDL receptor expression correlates with nanoparticle organ accumulation in mice.

Data availability

The data that support the findings of this study are available within the paper and its Supplementary Information files. The sequencing data are uploaded on Figshare ( STRING (, IMEx ( and BioGRID ( databases are available online. Any other data generated and analyzed during this study are available from the corresponding author upon reasonable request.


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W.C.W.C. acknowledges Collaborative Health Research Program grant no. CPG-146468; Canadian Institute of Health Research grant nos. FDN159932 and MOP-1301431; Canadian Research Chairs Program grant no. 950-223824; and Nanomedicines Innovation Network, 2019-T3-01. J.M. acknowledges CIHR grant nos. CBT-438323 and GMX-463531 and is a Canada Research Chair in Functional Genetics. We thank NSERC (W.N., J.L.Y.W. and A.M.S.), Ontario Graduate Scholarships (J.Z.), the Cecil Yip Award (W.N., J.L.Y.W. and A.G.F.), Wildcat Foundation (W.N.), the Jennifer Dorrington Award (J.L.Y.W.), Faculty of Applied Science & Engineering Graduate Student Endowment Fund (B.B.) and the Barbara and Frank Milligan family (J.L.Y.W.) for student fellowships and scholarships. We thank S. Zhao and O. Subedar at the SickKids-UHN Flow Cytometry Facility for assistance with cell sorting; A. Androschuk from Sefton Laboratory for help with the RT–qPCR; the Nanoscale Biomedical Imaging Facility, The Hospital for Sick Children, Toronto, Canada, for assistance with TEM, M. Ganguly, Gregory Ossetchkine and V. Bradaschia at the The Centre for Phenogenomics, Toronto, Canada for assistance with histology studies.

Author information

Authors and Affiliations



W.N., J.L.Y.W., A.M.S. and W.C.W.C. conceptualized the project. W.N., J.L.Y.W. and W.C.W.C. designed the research. W.N. and J.L.Y.W. performed the CRISPR screen and validation experiments. Z.P.L., J.L.Y.W. and W.N. performed flow cytometry experiments. B.B. and W.N. performed competition experiments, Y.Z. and W.N. performed mass spectrometry experiments. W.N., J.L.Y.W. and Z.P.L performed the animal experiments. A.H. prepped sequencing libraries. K.C. and J.M. advised on CRISPR screen experiments. W.N. and A.G.F. performed the double knockout experiments. W.N., J.L.Y.W., A.M.S. and W.C.W.C. analyzed the data. W.N., J.L.Y.W. and W.C.W.C. wrote the manuscript. All authors contributed to editing and revising the manuscript.

Corresponding author

Correspondence to Warren C. W. Chan.

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Competing interests

W.C.W.C. is a cofounder of Luna Nanotech. J.M. is a shareholder and consultant for Century Therapeutics.

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Nature Chemical Biology thanks Chung Hang Jonathan Choi, Emily Day and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Flow cytometry and fluorescent activated cell sorting (FACS) gating strategies.

a, Representative gating strategy used to quantify nanoparticle uptake and LDL receptor expression. Negative control in blue and samples in orange. b, Representative fraction of cells collected by FACS for the genome-wide screens. We isolated the bottom 13% of the histogram.

Extended Data Fig. 2 Nanoparticle uptake in knockout cells quantified by flow cytometry.

a, Knockouts of genes (as indicated) depleted in the bottom population of nanoparticle uptake from the genome-wide screen. The data is normalized to the wild type cells. Mean ± std are reported from 3 independent replicates. Statistics are calculated using one-way ANOVA tests with Dunnett multiple comparisons, where “ns” indicates not significant. b, Knockouts of genes (as indicated) from literature. These genes were not identified in our screen. The data is normalized to the negative control cells. Mean ± std are reported from 3 independent replicates. Statistics were calculated using one-way ANOVA tests with Dunnett multiple comparisons, where “ns” indicates not significant.

Extended Data Fig. 3 Quantification of nanoparticle uptake in U-87 MG cells treated with proprotein convertase subtilisin/kexin type 9 (PCSK9).

Data is normalized to the uptake condition without PCSK9. Mean ± std are reported from 3 independent replicates.

Extended Data Fig. 4

Schematic of hits from the genome-wide knockout screen grouped according to the step in the uptake pathway they are potentially involved.

Extended Data Fig. 5 Apolipoprotein E (ApoE) binds the LDL receptor to mediate nanoparticle uptake in the absence of apolipoprotein B (ApoB).

a, Uptake of ApoE coated gold nanoparticles in unedited, LACZ knockout and LDL receptor knockout cells. Mean ± std are reported from 3 independent replicates. b, Quantification of ApoE coated gold nanoparticle uptake with ApoE at 100-fold molar excess of ligands to nanoparticles. Data is normalized to uptake without competitor ligands. Mean ± std are reported from 3 independent replicates. P-values are indicated and calculated using an unpaired two-tailed t-test. c, Abundance of ApoB and ApoE in the C57BL/6 J mouse protein corona compared to human protein corona determined by mass spectrometry.

Extended Data Fig. 6 Depleting apolipoprotein B-100 (ApoB) from human serum.

a, Quantifying the concentration of ApoB in human serum before and after depletion with an enzyme-linked immunosorbent assay. Mean ± std are reported from 3 independent replicates. b, Amount of ApoB on the nanoparticles after coating with normal and ApoB-depleted human serum using an enzyme-linked immunosorbent assay. Mean ± std are reported from 3 independent replicates.

Extended Data Fig. 7 Uptake of serum-coated gold nanoparticles (HS-GNP) in different cell types.

a, HS-GNP uptake in wild-type (WT) and LDL receptor knockout (KO) HAP1 cells. Uptake was quantified by flow cytometry. Mean ± std are reported from 3 independent replicates. Statistics were calculated using a two-sided t-test. b, LDL receptor expression of A-431 and HEK-293T cells was quantified using immunocytochemistry staining and flow cytometry. Expression is displayed as the geometric mean of the fluorescence. Mean ± std are reported from 3 independent replicates. c, The amount of HS-GNP uptake in A-431 and HEK-293T cells. HS-GNP uptake was quantified using inductively coupled plasma-mass spectrometry. Mean ± std are reported from 3 independent replicates.

Extended Data Fig. 8 Quantification of LDL receptor protein expression using immunohistochemistry staining.

a, Representative images of LDL receptor staining in the heart, kidney, spleen, and liver across three mice. Scale bar = 50 µm. b, Quantification of the mean fluorescence intensity (MFI) from LDL receptor staining for the heart, kidney, spleen, and liver in each mouse. Mean ± std are reported from 3 regions of interest within the same organ tissue.

Extended Data Fig. 9 Measuring amount of overlap between gold nanoparticle (GNP) signal and LDL receptor (LDLR) signal on immunohistochemistry images of heart, kidney, spleen, and liver.

a, The portion of pixels positive with GNP that were also positive of LDL receptor signal Mean ± SEM are reported from 3 mice. b, Organ images from each mouse for computing the degree of overlap between GNP and LDLR. Scale bar = 50 µm.

Extended Data Fig. 10 Immunohistochemistry images of LDL receptor staining controls.

LDL receptors were stained using fluorescently-labeled antibodies (red) and cell nuclei were stained using DAPI (blue). a, Representative mouse liver section stained with a rabbit IgG isotype control of one independent experiment. Scale bar = 20 µm. b, Representative mouse pancreas section and a mouse liver section stained with anti-LDL-receptor antibody of 2 independent experiments. Scale bar = 20 µm.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7 and Tables 1–7.

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Supplementary Data 1

Contains LC–MS/MS, drugz and mageck data.

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Ngo, W., Wu, J.L.Y., Lin, Z.P. et al. Identifying cell receptors for the nanoparticle protein corona using genome screens. Nat Chem Biol 18, 1023–1031 (2022).

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  • Unbiased matchmaking

    • Cecilia Ka Wing Chan
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    • Chung Hang Jonathan Choi

    Nature Chemical Biology (2022)


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