Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity

Liu, Longqi; Liu, Chuanyu; Quintero, Andrés; Wu, Liang; Yuan, Yue; Wang, Mingyue; Cheng, Mengnan; Leng, Lizhi; Xu, Liqin; Dong, Guoyi; Li, Rui; Liu, Yang; Wei, Xiaoyü; Xu, Jiangshan; Chen, Xiaowei; Lu, Haorong; Chen, Dongsheng; Wang, Quanlei; Zhou, Qing; Lin, Xinxin; Li, Guibo; Liu, Shiping; Wang, Qi; Wang, Hongru; Fink, J. Lynn; Gao, Zhengliang; Liu, Xin; Hou, Yong; Zhu, Shida; Yang, Huanming; Ye, Yunming; Lin, Ge; Chen, Fang; Herrmann, Carl; Eils, Roland; Shang, Zhouchun; Xu, Xun

doi:10.1038/s41467-018-08205-7

Cited by 182 publications

(153 citation statements)

References 44 publications

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“…Mathematical and biological considerations may equally contribute for depicting phenotypic complexity. Simultaneously assaying chromatin accessibility and the transcriptome within the same single cell will provide more accurate information about its' functional stage within an evolutionary process and will enable deciphering communication-derived heterogeneity of complex heterologous cell populations (137).…”

Section: Holistic Communicative Contextmentioning

confidence: 99%

Anakoinosis: Correcting Aberrant Homeostasis of Cancer Tissue—Going Beyond Apoptosis Induction

et al. 2019

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The current approach to systemic therapy for metastatic cancer is aimed predominantly at inducing apoptosis of cancer cells by blocking tumor-promoting signaling pathways or by eradicating cell compartments within the tumor. In contrast, a systems view of therapy primarily considers the communication protocols that exist at multiple levels within the tumor complex, and the role of key regulators of such systems. Such regulators may have far-reaching influence on tumor response to therapy and therefore patient survival. This implies that neoplasia may be considered as a cell non-autonomous disease. The multi-scale activity ranges from intra-tumor cell compartments, to the tumor, to the tumor-harboring organ to the organism. In contrast to molecularly targeted therapies, a systems approach that identifies the complex communications networks driving tumor growth offers the prospect of disrupting or "normalizing" such aberrant communicative behaviors and therefore attenuating tumor growth. Communicative reprogramming, a treatment strategy referred to as anakoinosis, requires novel therapeutic instruments, so-called master modifiers to deliver concerted tumor growth-attenuating action. The diversity of biological outcomes following pro-anakoinotic tumor therapy, such as differentiation, trans-differentiation, control of tumor-associated inflammation, etc. demonstrates that long-term tumor control may occur in multiple forms, inducing even continuous complete remission. Accordingly, pro-anakoinotic therapies dramatically extend the repertoire for achieving tumor control and may activate apoptosis pathways for controlling resistant metastatic tumor disease and hematologic neoplasia.

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Section: Holistic Communicative Contextmentioning

confidence: 99%

Anakoinosis: Correcting Aberrant Homeostasis of Cancer Tissue—Going Beyond Apoptosis Induction

et al. 2019

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“…Many protocols have been developed to quantify transcriptome [1], such as CEL-seq2, Smart-seq2, Drop-seq, and 10X Chromium, and techniques that measure single-cell chromatin accessibility (scATAC-seq) and DNA methylation have also become available [2]. More recently, several single-cell multiomics technologies have emerged for measuring multiple types of molecules in the same individual cell, such as scM&T-seq [3], scNMT-seq [4], scTrio-seq [5], sci-CAR-seq [6], and scCAT-seq [7]. The resulting singlecell multiomics data has potential of providing new insights regarding the multiple regulatory layers that control cellular heterogeneity [8,9].…”

Section: Introductionmentioning

confidence: 99%

scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles

2020

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Simultaneous measurements of transcriptomic and epigenomic profiles in the same individual cells provide an unprecedented opportunity to understand cell fates. However, effective approaches for the integrative analysis of such data are lacking. Here, we present a single-cell aggregation and integration (scAI) method to deconvolute cellular heterogeneity from parallel transcriptomic and epigenomic profiles. Through iterative learning, scAI aggregates sparse epigenomic signals in similar cells learned in an unsupervised manner, allowing coherent fusion with transcriptomic measurements. Simulation studies and applications to three real datasets demonstrate its capability of dissecting cellular heterogeneity within both transcriptomic and epigenomic layers and understanding transcriptional regulatory mechanisms.

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“…As a future work, it would be interesting to extend this study using paired scRNA and single-cell open-chromatin data [ 40–42 ], particularly for the design of dynamic features. Using this type of data allows us to estimate the TF activity in accessible chromatin regions defined on the basis of individual single cells.…”

Section: Discussionmentioning

confidence: 99%

Prediction of single-cell gene expression for transcription factor analysis

et al. 2020

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Background Single-cell RNA sequencing is a powerful technology to discover new cell types and study biological processes in complex biological samples. A current challenge is to predict transcription factor (TF) regulation from single-cell RNA data. Results Here, we propose a novel approach for predicting gene expression at the single-cell level using cis-regulatory motifs, as well as epigenetic features. We designed a tree-guided multi-task learning framework that considers each cell as a task. Through this framework we were able to explain the single-cell gene expression values using either TF binding affinities or TF ChIP-seq data measured at specific genomic regions. TFs identified using these models could be validated by the literature. Conclusion Our proposed method allows us to identify distinct TFs that show cell type–specific regulation. This approach is not limited to TFs but can use any type of data that can potentially be used in explaining gene expression at the single-cell level to study factors that drive differentiation or show abnormal regulation in disease. The implementation of our workflow can be accessed under an MIT license via https://github.com/SchulzLab/Triangulate.

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Deconvolution of single-cell multi-omics layers reveals regulatory heterogeneity

Cited by 182 publications

References 44 publications

Anakoinosis: Correcting Aberrant Homeostasis of Cancer Tissue—Going Beyond Apoptosis Induction

Anakoinosis: Correcting Aberrant Homeostasis of Cancer Tissue—Going Beyond Apoptosis Induction

scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles

Prediction of single-cell gene expression for transcription factor analysis

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