PMID- 35416655
OWN - NLM
STAT- MEDLINE
DCOM- 20221111
LR  - 20221222
IS  - 1936-086X (Electronic)
IS  - 1936-0851 (Linking)
VI  - 16
IP  - 4
DP  - 2022 Apr 26
TI  - Tissue Imprinting on 2D Nanoflakes-Capped Silicon Nanowires for Lipidomic Mass 
      Spectrometry Imaging and Cancer Diagnosis.
PG  - 6916-6928
LID - 10.1021/acsnano.2c02616 [doi]
AB  - Spatially resolved tissue lipidomics is essential for accurate intraoperative and 
      postoperative cancer diagnosis by revealing molecular information in the tumor 
      microenvironment. Matrix-free laser desorption ionization mass spectrometry 
      imaging (LDI-MSI) is an emerging attractive technology for label-free 
      visualization of metabolites distributions in biological specimens. However, the 
      development of LDI-MSI technology that could conveniently and authentically 
      reveal molecular distribution on tissue samples is still a challenge. Herein, we 
      present a tissue imprinting technology by retaining tissue lipids on 2D 
      nanoflakes-capped silicon nanowires (SiNWs) for further mass spectrometry imaging 
      and cancer diagnosis. The 2D nanoflakes were prepared by liquid exfoliation of 
      molybdenum disulfide (MoS(2)) with nitrogen-doped graphene quantum dots (NGQDs), 
      which serve as both intercalation agent and dispersant. The obtained NGQD@MoS(2) 
      nanoflakes were then decorated on the tip of vertical SiNWs, forming a hybrid 
      NGQD@MoS(2)/SiNWs nanostructure, which display excellent lipid extraction 
      ability, enhanced LDI efficiency and molecule imaging capability. The peak number 
      and total ion intensity of different lipids species on animal lung tissues 
      obtained by tissue imprinting LDI-MSI on NGQD@MoS(2)/SiNWs were  approximately 4-5 times 
      greater than those on SiNWs substrate. As a proof-of-concept demonstration, the 
      NGQD@MoS(2)/SiNWs nanostructure was further applied to visualize phospholipids on 
      sliced non small cell lung cancer (NSCLC) tissue along with the adjacent normal 
      tissue. On the basis of selected feature lipids and machine learning algorithm, a 
      prediction model was constructed to discriminate NSCLC tissues from the adjacent 
      normal tissues with an accuracy of 100% for the discovery cohort and 91.7% for 
      the independent validation cohort.
FAU - Liu, Xingyue
AU  - Liu X
AD  - Lab of Nanomedicine and Omic-based Diagnostics, Institute of Analytical 
      Chemistry, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. 
      China.
FAU - Chen, Zhao
AU  - Chen Z
AD  - Department of Thoracic Surgery, Sir Run Run Shaw Hospital, School of Medicine, 
      Zhejiang University, Hangzhou 310016, P. R. China.
FAU - Wang, Tao
AU  - Wang T
AD  - Lab of Nanomedicine and Omic-based Diagnostics, Institute of Analytical 
      Chemistry, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. 
      China.
FAU - Jiang, Xinrong
AU  - Jiang X
AD  - Lab of Nanomedicine and Omic-based Diagnostics, Institute of Analytical 
      Chemistry, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. 
      China.
FAU - Qu, Xuetong
AU  - Qu X
AD  - Lab of Nanomedicine and Omic-based Diagnostics, Institute of Analytical 
      Chemistry, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. 
      China.
FAU - Duan, Wei
AU  - Duan W
AUID- ORCID: 0000-0002-6609-1043
AD  - Lab of Nanomedicine and Omic-based Diagnostics, Institute of Analytical 
      Chemistry, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. 
      China.
FAU - Xi, Fengna
AU  - Xi F
AUID- ORCID: 0000-0001-8564-8911
AD  - Department of Chemistry, Key Laboratory of Surface & Interface Science of 
      Polymer Materials of Zhejiang Province, Zhejiang Sci-Tech University, Hangzhou 
      310018, P. R. China.
FAU - He, Zhengfu
AU  - He Z
AUID- ORCID: 0000-0003-3167-9493
AD  - Department of Thoracic Surgery, Sir Run Run Shaw Hospital, School of Medicine, 
      Zhejiang University, Hangzhou 310016, P. R. China.
FAU - Wu, Jianmin
AU  - Wu J
AUID- ORCID: 0000-0002-0999-9194
AD  - Lab of Nanomedicine and Omic-based Diagnostics, Institute of Analytical 
      Chemistry, Department of Chemistry, Zhejiang University, Hangzhou 310058, P. R. 
      China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220413
PL  - United States
TA  - ACS Nano
JT  - ACS nano
JID - 101313589
RN  - Z4152N8IUI (Silicon)
RN  - 81AH48963U (Molybdenum)
RN  - 0 (Lipids)
SB  - IM
MH  - Animals
MH  - Lipidomics
MH  - Silicon/chemistry
MH  - *Nanowires/chemistry
MH  - Molybdenum
MH  - *Carcinoma, Non-Small-Cell Lung
MH  - Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization/methods
MH  - *Lung Neoplasms/diagnostic imaging
MH  - Molecular Imaging
MH  - Lipids/analysis
MH  - Tumor Microenvironment
OTO - NOTNLM
OT  - 2D nanoflakes
OT  - lipidomics
OT  - lung cancer
OT  - mass spectrometry imaging
OT  - silicon nanowires
EDAT- 2022/04/14 06:00
MHDA- 2022/11/15 06:00
CRDT- 2022/04/13 12:12
PHST- 2022/04/14 06:00 [pubmed]
PHST- 2022/11/15 06:00 [medline]
PHST- 2022/04/13 12:12 [entrez]
AID - 10.1021/acsnano.2c02616 [doi]
PST - ppublish
SO  - ACS Nano. 2022 Apr 26;16(4):6916-6928. doi: 10.1021/acsnano.2c02616. Epub 2022 
      Apr 13.