PMID- 37114830
OWN - NLM
STAT- MEDLINE
DCOM- 20230731
LR  - 20230731
IS  - 2198-3844 (Electronic)
IS  - 2198-3844 (Linking)
VI  - 10
IP  - 21
DP  - 2023 Jul
TI  - Efficient Generation of Paired Single-Cell Multiomics Profiles by Deep Learning.
PG  - e2301169
LID - 10.1002/advs.202301169 [doi]
LID - 2301169
AB  - Recent advances in single-cell sequencing technology have made it possible to 
      measure multiple paired omics simultaneously in a single cell such as cellular 
      indexing of transcriptomes and epitopes by sequencing (CITE-seq) and 
      single-nucleus chromatin accessibility and mRNA expression sequencing 
      (SNARE-seq). However, the widespread application of these single-cell multiomics 
      profiling technologies has been limited by their experimental complexity, noise 
      in nature, and high cost. In addition, single-omics sequencing technologies have 
      generated tremendous and high-quality single-cell datasets but have yet to be 
      fully utilized. Here, single-cell multiomics generation (scMOG), a deep 
      learning-based framework to generate single-cell assay for transposase-accessible 
      chromatin (ATAC) data in silico is developed from experimentally available 
      single-cell RNA-seq measurements and vice versa. The results demonstrate that 
      scMOG can accurately perform cross-omics generation between RNA and ATAC, and 
      generate paired multiomics data with biological meanings when one omics is 
      experimentally unavailable and out of training datasets. The generated ATAC, 
      either alone or in combination with measured RNA, exhibits equivalent or superior 
      performance to that of the experimentally measured counterparts throughout 
      multiple downstream analyses. scMOG is also applied to human lymphoma data, which 
      proves to be more effective in identifying tumor samples than the experimentally 
      measured ATAC data. Finally, the performance of scMOG is investigated in other 
      omics such as proteomics and it still shows robust performance on surface protein 
      generation.
CI  - (c) 2023 The Authors. Advanced Science published by Wiley-VCH GmbH.
FAU - Lan, Meng
AU  - Lan M
AD  - School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, 
      710071, China.
FAU - Zhang, Shixiong
AU  - Zhang S
AUID- ORCID: 0000-0002-0314-9199
AD  - School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, 
      710071, China.
FAU - Gao, Lin
AU  - Gao L
AD  - School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi, 
      710071, China.
LA  - eng
GR  - 62132015/National Natural Science Foundation of China/
GR  - 62102294/National Natural Science Foundation of China/
GR  - U22A2037/National Natural Science Foundation of China/
GR  - ZYTS23209/Fundamental Research Funds for the Central Universities/
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230428
PL  - Germany
TA  - Adv Sci (Weinh)
JT  - Advanced science (Weinheim, Baden-Wurttemberg, Germany)
JID - 101664569
RN  - 0 (Chromatin)
RN  - 0 (Membrane Proteins)
RN  - 63231-63-0 (RNA)
SB  - IM
MH  - Humans
MH  - *Deep Learning
MH  - Multiomics
MH  - Chromatin/genetics
MH  - Membrane Proteins
MH  - RNA
PMC - PMC10375161
OTO - NOTNLM
OT  - deep learning
OT  - multiomics
OT  - single cells
COIS- The authors declare no conflict of interest.
EDAT- 2023/04/28 12:42
MHDA- 2023/07/31 11:42
PMCR- 2023/04/28
CRDT- 2023/04/28 08:13
PHST- 2023/04/08 00:00 [revised]
PHST- 2023/02/20 00:00 [received]
PHST- 2023/07/31 11:42 [medline]
PHST- 2023/04/28 12:42 [pubmed]
PHST- 2023/04/28 08:13 [entrez]
PHST- 2023/04/28 00:00 [pmc-release]
AID - ADVS5639 [pii]
AID - 10.1002/advs.202301169 [doi]
PST - ppublish
SO  - Adv Sci (Weinh). 2023 Jul;10(21):e2301169. doi: 10.1002/advs.202301169. Epub 2023 
      Apr 28.