English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Machine-learning-driven surface-enhanced raman scattering optophysiology reveals multiplexed metabolite gradients near cells

Lussier, F., Missirlis, D., Spatz, J. P., & Masson, J.-F. (2019). Machine-learning-driven surface-enhanced raman scattering optophysiology reveals multiplexed metabolite gradients near cells. ACS Nano, 13(2), 1403-1411. doi:10.1021/acsnano.8b07024.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0002-FCC4-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-FEDE-4
Genre: Journal Article

Files

show Files
hide Files
:
ACSNano_13_2019_1403.pdf (Any fulltext), 5MB
 
File Permalink:
-
Name:
ACSNano_13_2019_1403.pdf
Description:
-
Visibility:
Restricted (Max Planck Institute for Medical Research, MHMF; )
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-
:
ACSNano_13_2019_1403_Suppl.pdf (Supplementary material), 415KB
 
File Permalink:
-
Name:
ACSNano_13_2019_1403_Suppl.pdf
Description:
-
Visibility:
Restricted (Max Planck Institute for Medical Research, MHMF; )
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show
hide
Description:
-
Description:
-
Locator:
https://doi.org/10.1021/acsnano.8b07024 (Any fulltext)
Description:
-

Creators

show
hide
 Creators:
Lussier , Félix, Author
Missirlis, Dimitris1, Author              
Spatz, Joachim P.1, 2, Author              
Masson, Jean-François, Author
Affiliations:
1Cellular Biophysics, Max Planck Institute for Medical Research, Max Planck Society, ou_2364731              
2Biophysical Chemistry, Institute of Physical Chemistry, University of Heidelberg, 69120 Heidelberg, Germany, ou_persistent22              

Content

show
hide
Free keywords: ATP; dynamic surface-enhanced Raman scattering; HeLa; HUVEC; machine learning; nanobiosensor; plasmonics; SERS optophysiology; TensorFlow
 Abstract: The extracellular environment is a complex medium in which cells secrete and consume metabolites. Molecular gradients are thereby created near cells, triggering various biological and physiological responses. However, investigating these molecular gradients remains challenging because the current tools are ill-suited and provide poor temporal and special resolution while also being destructive. Herein, we report the development and application of a machine learning approach in combination with a surface-enhanced Raman spectroscopy (SERS) nanoprobe to measure simultaneously the gradients of at least eight metabolites in vitro near different cell lines. We found significant increase in the secretion or consumption of lactate, glucose, ATP, glutamine, and urea within 20 μm from the cells surface compared to the bulk. We also observed that cancerous cells (HeLa) compared to fibroblasts (REF52) have a greater glycolytic rate, as is expected for this phenotype. Endothelial (HUVEC) and HeLa cells exhibited significant increase in extracellular ATP compared to the control, shining light on the implication of extracellular ATP within the cancer local environment. Machine-learning-driven SERS optophysiology is generally applicable to metabolites involved in cellular processes, providing a general platform on which to study cell biology.

Details

show
hide
Language(s): eng - English
 Dates: 2018-09-132019-02-062019-02-062019-02-06
 Publication Status: Published in print
 Pages: 9
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1021/acsnano.8b07024
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: ACS Nano
  Other : ACS Nano
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Washington, DC : American Chemical Society
Pages: - Volume / Issue: 13 (2) Sequence Number: - Start / End Page: 1403 - 1411 Identifier: ISSN: 1936-0851
CoNE: https://pure.mpg.de/cone/journals/resource/1936-0851