Ultrasensitive, Multiplexed Buoyant Sensor for Monitoring Cytokines in Biofluids.

cytokines digital immune biomarkers multiplexed biosensors ultrasensitive protein detection

Journal

Nano letters
ISSN: 1530-6992
Titre abrégé: Nano Lett
Pays: United States
ID NLM: 101088070

Informations de publication

Date de publication:
22 Nov 2023
Historique:
medline: 23 11 2023
pubmed: 3 11 2023
entrez: 3 11 2023
Statut: ppublish

Résumé

Multiplexed quantification of low-abundance protein biomarkers in complex biofluids is important for biomedical research and clinical diagnostics. However, in situ sampling without perturbing biological systems remains challenging. In this work, we report a buoyant biosensor that enables in situ monitoring of protein analytes at attomolar concentrations with a 15 min temporal resolution. The buoyant biosensor implemented with fluorescent nanolabels enabled the ultrasensitive and multiplexed detection and quantification of cytokines. Implementing the biosensor in a digital manner (i.e., counting the individual nanolabels) further improves the low detection limit. We demonstrate that the biosensor enables the detection and quantification of the time-varying concentrations of cytokines (e.g., IL-6 and TNF-α) in macrophage culture media without perturbing the live cells. The easy-to-apply biosensor with attomolar sensitivity and multiplexing capability can enable an in situ analysis of protein biomarkers in various biofluids and tissues to aid in understanding biological processes and diagnosing and treating diverse diseases.

Identifiants

pubmed: 37922456
doi: 10.1021/acs.nanolett.3c02516
doi:

Substances chimiques

Cytokines 0
Biomarkers 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

10171-10178

Auteurs

Heng Guo (H)

Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, United States.

Rohit Gupta (R)

Department of Mechanical Engineering and Materials Science, Institute of Materials Science and Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States.

Dhavan Sharma (D)

Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, United States.

Elizabeth Zhanov (E)

Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, United States.

Connor Malone (C)

Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, United States.

Ravi Jada (R)

Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, United States.

Ying Liu (Y)

Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, United States.

Mayank Garg (M)

Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, United States.

Srikanth Singamaneni (S)

Department of Mechanical Engineering and Materials Science, Institute of Materials Science and Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States.

Feng Zhao (F)

Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, United States.

Limei Tian (L)

Department of Biomedical Engineering, Texas A&M University, College Station, Texas 77843, United States.
Center for Remote Health Technologies and Systems, Texas A&M University, College Station, Texas 77843, United States.

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Classifications MeSH