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BABEL enables cross-modality translation between multiomic profiles at single-cell resolution

Simultaneous profiling of multiomic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility—for example, generating paired measurements of single-cell transcriptome (single-cell RNA sequencing [scR...

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Autores principales: Wu, Kevin E., Yost, Kathryn E., Chang, Howard Y., Zou, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: National Academy of Sciences 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8054007/
https://www.ncbi.nlm.nih.gov/pubmed/33827925
http://dx.doi.org/10.1073/pnas.2023070118
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author Wu, Kevin E.
Yost, Kathryn E.
Chang, Howard Y.
Zou, James
author_facet Wu, Kevin E.
Yost, Kathryn E.
Chang, Howard Y.
Zou, James
author_sort Wu, Kevin E.
collection PubMed
description Simultaneous profiling of multiomic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility—for example, generating paired measurements of single-cell transcriptome (single-cell RNA sequencing [scRNA-seq]) and chromatin accessibility (single-cell assay for transposase-accessible chromatin using sequencing [scATAC-seq])—widespread application of joint profiling is challenging due to its experimental complexity, noise, and cost. Here, we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging an interoperable neural network model, BABEL can predict single-cell expression directly from a cell’s scATAC-seq and vice versa after training on relevant data. This makes it possible to computationally synthesize paired multiomic measurements when only one modality is experimentally available. Across several paired single-cell ATAC and gene expression datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to cell types within new biological contexts not seen during training. Starting from scATAC-seq of patient-derived basal cell carcinoma (BCC), BABEL generated single-cell expression that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of experimental BCC scRNA-seq data for diverse cell types related to BABEL’s training data. We further show that BABEL can incorporate additional single-cell data modalities, such as protein epitope profiling, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation.
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spelling pubmed-80540072021-05-04 BABEL enables cross-modality translation between multiomic profiles at single-cell resolution Wu, Kevin E. Yost, Kathryn E. Chang, Howard Y. Zou, James Proc Natl Acad Sci U S A Biological Sciences Simultaneous profiling of multiomic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility—for example, generating paired measurements of single-cell transcriptome (single-cell RNA sequencing [scRNA-seq]) and chromatin accessibility (single-cell assay for transposase-accessible chromatin using sequencing [scATAC-seq])—widespread application of joint profiling is challenging due to its experimental complexity, noise, and cost. Here, we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging an interoperable neural network model, BABEL can predict single-cell expression directly from a cell’s scATAC-seq and vice versa after training on relevant data. This makes it possible to computationally synthesize paired multiomic measurements when only one modality is experimentally available. Across several paired single-cell ATAC and gene expression datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to cell types within new biological contexts not seen during training. Starting from scATAC-seq of patient-derived basal cell carcinoma (BCC), BABEL generated single-cell expression that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of experimental BCC scRNA-seq data for diverse cell types related to BABEL’s training data. We further show that BABEL can incorporate additional single-cell data modalities, such as protein epitope profiling, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation. National Academy of Sciences 2021-04-13 2021-04-07 /pmc/articles/PMC8054007/ /pubmed/33827925 http://dx.doi.org/10.1073/pnas.2023070118 Text en Copyright © 2021 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by/4.0/This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Biological Sciences
Wu, Kevin E.
Yost, Kathryn E.
Chang, Howard Y.
Zou, James
BABEL enables cross-modality translation between multiomic profiles at single-cell resolution
title BABEL enables cross-modality translation between multiomic profiles at single-cell resolution
title_full BABEL enables cross-modality translation between multiomic profiles at single-cell resolution
title_fullStr BABEL enables cross-modality translation between multiomic profiles at single-cell resolution
title_full_unstemmed BABEL enables cross-modality translation between multiomic profiles at single-cell resolution
title_short BABEL enables cross-modality translation between multiomic profiles at single-cell resolution
title_sort babel enables cross-modality translation between multiomic profiles at single-cell resolution
topic Biological Sciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8054007/
https://www.ncbi.nlm.nih.gov/pubmed/33827925
http://dx.doi.org/10.1073/pnas.2023070118
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