Quantitative proteomic analysis of HeLa cells in response to biocompatible Fe2C@C nanoparticles: O-16/O-18-labelling & HPLC-ESI-orbit-trap profiling approach

The effective detection of molecular biomarkers, such as proteins, lipids, carbohydrates, and pathogens, in a living body is a huge challenge in the field of nanomedicine. Here, we have investigated the comparative quantitative proteomics analysis of the molecular response of HeLa cells to biocompatible Fe 2 C@C nanoparticles (NPs) using 16 O/ 18 O isotopic labelling of the cell culture. The relative binding efficiency of proteins to Fe 2 C@C NPs was calculated. HPLC-ESI-orbit-trap analysis found 51 differentially expressed proteins, out of which 23 were over-expressed and 28 down-regulated. This study showed that Fe 2 C@C NPs alter the expression of the proteins involved in endocytosis, cell-cycle regulation, and cell membrane protrusion. Further, the quantification and validation of the mass spectrometry (MS) results was successfully confirmed by western blot analysis of cytochrome C. The change in the expression of proteins can be useful for early stage disease diagnoses and the development of tailored therapeutic strategies. This study is the first large-scale characterization of low abundance proteins on Fe 2 C@C NPs, providing the biochemical basis for the assessment of the suitability of magnetic NPs as biomedical markers and emerging functional probes.

Publication Details

Journal
Figshare
Published
2024-10-30
Primary Topic
Nanoparticle-Based Drug Delivery
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Quantitative proteomic analysis of HeLa cells in response to biocompatible Fe2C@C nanoparticles: O-16/O-18-labelling & HPLC-ESI-orbit-trap profiling approach

Figshare
Nanoparticle-Based Drug Delivery
article

Quantitative proteomic analysis of HeLa cells in response to biocompatible Fe2C@C nanoparticles: O-16/O-18-labelling & HPLC-ESI-orbit-trap profiling approach

article en

Abstract

The effective detection of molecular biomarkers, such as proteins, lipids, carbohydrates, and pathogens, in a living body is a huge challenge in the field of nanomedicine. Here, we have investigated the comparative quantitative proteomics analysis of the molecular response of HeLa cells to biocompatible Fe 2 C@C nanoparticles (NPs) using 16 O/ 18 O isotopic labelling of the cell culture. The relative binding efficiency of proteins to Fe 2 C@C NPs was calculated. HPLC-ESI-orbit-trap analysis found 51 differentially expressed proteins, out of which 23 were over-expressed and 28 down-regulated. This study showed that Fe 2 C@C NPs alter the expression of the proteins involved in endocytosis, cell-cycle regulation, and cell membrane protrusion. Further, the quantification and validation of the mass spectrometry (MS) results was successfully confirmed by western blot analysis of cytochrome C. The change in the expression of proteins can be useful for early stage disease diagnoses and the development of tailored therapeutic strategies. This study is the first large-scale characterization of low abundance proteins on Fe 2 C@C NPs, providing the biochemical basis for the assessment of the suitability of magnetic NPs as biomedical markers and emerging functional probes.

Figshare
Openalex Percentile: Top 60%
Nanoparticle-Based Drug Delivery
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Quantitative proteomic analysis of HeLa cells in response to biocompatible Fe2C@C nanoparticles: O-16/O-18-labelling & HPLC-ESI-orbit-trap profiling approach · Figshare (2024) | TGRS Research Map | TGRS