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@article{Liu_Beyer_Aebersold_2016, | ||
title = {On the Dependency of Cellular Protein Levels on mRNA Abundance}, | ||
volume = {165}, | ||
ISSN = {0092-8674}, | ||
DOI = {10.1016/j.cell.2016.03.014}, | ||
abstractNote = {The question of how genomic information is expressed to determine phenotypes is of central importance for basic and translational life science research and has been studied by transcriptomic and proteomic profiling. Here, we review the relationship between protein and mRNA levels under various scenarios, such as steady state, long-term state changes, and short-term adaptation, demonstrating the complexity of gene expression regulation, especially during dynamic transitions. The spatial and temporal variations of mRNAs, as well as the local availability of resources for protein biosynthesis, strongly influence the relationship between protein levels and their coding transcripts. We further discuss the buffering of mRNA fluctuations at the level of protein concentrations. We conclude that transcript levels by themselves are not sufficient to predict protein levels in many scenarios and to thus explain genotype-phenotype relationships and that high-quality data quantifying different levels of gene expression are indispensable for the complete understanding of biological processes.}, | ||
number = {3}, | ||
journal = {Cell}, | ||
author = {Liu, Yansheng and Beyer, Andreas and Aebersold, Ruedi}, | ||
year = {2016}, | ||
month = {Apr}, | ||
pages = {535–550}, | ||
language = {en} | ||
} | ||
@article{Mimitou_Lareau_Chen_Zorzetto-Fernandes_Hao_Takeshima_Luo_Huang_Yeung_Papalexi_et al._2021, | ||
title = {Scalable, multimodal profiling of chromatin accessibility, gene expression and protein levels in single cells}, | ||
volume = {39}, | ||
ISSN = {1546-1696}, | ||
DOI = {10.1038/s41587-021-00927-2}, | ||
abstractNote = {Recent technological advances have enabled massively parallel chromatin profiling with scATAC-seq (single-cell assay for transposase accessible chromatin by sequencing). Here we present ATAC with select antigen profiling by sequencing (ASAP-seq), a tool to simultaneously profile accessible chromatin and protein levels. Our approach pairs sparse scATAC-seq data with robust detection of hundreds of cell surface and intracellular protein markers and optional capture of mitochondrial DNA for clonal tracking, capturing three distinct modalities in single cells. ASAP-seq uses a bridging approach that repurposes antibody:oligonucleotide conjugates designed for existing technologies that pair protein measurements with single-cell RNA sequencing. Together with DOGMA-seq, an adaptation of CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) for measuring gene activity across the central dogma of gene regulation, we demonstrate the utility of systematic multi-omic profiling by revealing coordinated and distinct changes in chromatin, RNA and surface proteins during native hematopoietic differentiation and peripheral blood mononuclear cell stimulation and as a combinatorial decoder and reporter of multiplexed perturbations in primary T cells.}, | ||
number = {1010}, | ||
journal = {Nature Biotechnology}, | ||
publisher = {Nature Publishing Group}, | ||
author = {Mimitou, Eleni P. and Lareau, Caleb A. and Chen, Kelvin Y. and Zorzetto-Fernandes, Andre L. and Hao, Yuhan and Takeshima, Yusuke and Luo, Wendy and Huang, Tse-Shun and Yeung, Bertrand Z. and Papalexi, Efthymia and Thakore, Pratiksha I. and Kibayashi, Tatsuya and Wing, James Badger and Hata, Mayu and Satija, Rahul and Nazor, Kristopher L. and Sakaguchi, Shimon and Ludwig, Leif S. and Sankaran, Vijay G. and Regev, Aviv and Smibert, Peter}, | ||
year = {2021}, | ||
month = {Oct}, | ||
pages = {1246–1258}, | ||
language = {en} | ||
} | ||
@article{Mulè_Martins_Tsang_2022, | ||
title = {Normalizing and denoising protein expression data from droplet-based single cell profiling}, | ||
volume = {13}, | ||
ISSN = {2041-1723}, | ||
DOI = {10.1038/s41467-022-29356-8}, | ||
abstractNote = {Multimodal single-cell profiling methods that measure protein expression with oligo-conjugated antibodies hold promise for comprehensive dissection of cellular heterogeneity, yet the resulting protein counts have substantial technical noise that can mask biological variations. Here we integrate experiments and computational analyses to reveal two major noise sources and develop a method called “dsb” (denoised and scaled by background) to normalize and denoise droplet-based protein expression data. We discover that protein-specific noise originates from unbound antibodies encapsulated during droplet generation; this noise can thus be accurately estimated and corrected by utilizing protein levels in empty droplets. We also find that isotype control antibodies and the background protein population average in each cell exhibit significant correlations across single cells, we thus use their shared variance to correct for cell-to-cell technical noise in each cell. We validate these findings by analyzing the performance of dsb in eight independent datasets spanning multiple technologies, including CITE-seq, ASAP-seq, and TEA-seq. Compared to existing normalization methods, our approach improves downstream analyses by better unmasking biologically meaningful cell populations. Our method is available as an open-source R package that interfaces easily with existing single cell software platforms such as Seurat, Bioconductor, and Scanpy and can be accessed at “dsb [https://cran.r-project.org/package=dsb]”.}, | ||
number = {11}, | ||
journal = {Nature Communications}, | ||
publisher = {Nature Publishing Group}, | ||
author = {Mulè, Matthew P. and Martins, Andrew J. and Tsang, John S.}, | ||
year = {2022}, | ||
month = {Apr}, | ||
pages = {2099}, | ||
language = {en} | ||
} | ||
@article{Peterson_Zhang_Kumar_Wong_Li_Wilson_Moore_McClanahan_Sadekova_Klappenbach_2017, | ||
title = {Multiplexed quantification of proteins and transcripts in single cells}, | ||
volume = {35}, | ||
ISSN = {1546-1696}, | ||
DOI = {10.1038/nbt.3973}, | ||
abstractNote = {High-throughput quantification of protein and gene expression in single cells is achieved using DNA-barcoded antibodies.}, | ||
number = {1010}, | ||
journal = {Nature Biotechnology}, | ||
publisher = {Nature Publishing Group}, | ||
author = {Peterson, Vanessa M. and Zhang, Kelvin Xi and Kumar, Namit and Wong, Jerelyn and Li, Lixia and Wilson, Douglas C. and Moore, Renee and McClanahan, Terrill K. and Sadekova, Svetlana and Klappenbach, Joel A.}, | ||
year = {2017}, | ||
month = {Oct}, | ||
pages = {936–939}, | ||
language = {en} | ||
} | ||
@article{Stoeckius_Hafemeister_Stephenson_Houck-Loomis_Chattopadhyay_Swerdlow_Satija_Smibert_2017, | ||
title = {Simultaneous epitope and transcriptome measurement in single cells}, | ||
volume = {14}, | ||
ISSN = {1548-7105}, | ||
DOI = {10.1038/nmeth.4380}, | ||
number = {99}, | ||
journal = {Nature Methods}, | ||
publisher = {Nature Publishing Group}, | ||
author = {Stoeckius, Marlon and Hafemeister, Christoph and Stephenson, William and Houck-Loomis, Brian and Chattopadhyay, Pratip K. and Swerdlow, Harold and Satija, Rahul and Smibert, Peter}, | ||
year = {2017}, | ||
month = {Sep}, | ||
pages = {865–868}, | ||
language = {en} | ||
} | ||
@article{Sun_Bugarin-Estrada_Overend_Walker_Tucci_Bashford-Rogers_2021, | ||
title = {Double-jeopardy: scRNA-seq doublet/multiplet detection using multi-omic profiling}, | ||
volume = {1}, | ||
ISSN = {2667-2375}, | ||
DOI = {10.1016/j.crmeth.2021.100008}, | ||
abstractNote = {The computational detection and exclusion of cellular doublets and/or multiplets is a cornerstone for the identification the true biological signals from single-cell RNA sequencing (scRNA-seq) data. Current methods do not sensitively identify both heterotypic and homotypic doublets and/or multiplets. Here, we describe a machine learning approach for doublet/multiplet detection utilizing VDJ-seq and/or CITE-seq data to predict their presence based on transcriptional features associated with identified hybrid droplets. This approach highlights the utility of leveraging multi-omic single-cell information for the generation of high-quality datasets. Our method has high sensitivity and specificity in inflammatory-cell-dominant scRNA-seq samples, thus presenting a powerful approach to ensuring high-quality scRNA-seq data.}, | ||
number = {1}, | ||
journal = {Cell Reports Methods}, | ||
author = {Sun, Bo and Bugarin-Estrada, Emmanuel and Overend, Lauren Elizabeth and Walker, Catherine Elizabeth and Tucci, Felicia Anna and Bashford-Rogers, Rachael Jennifer Mary}, | ||
year = {2021}, | ||
month = {May}, | ||
pages = {100008}, | ||
language = {en} | ||
} | ||
@article{Xie_Ding, | ||
title = {The Intriguing Landscape of Single-Cell Protein Analysis}, | ||
volume = {n/a}, | ||
ISSN = {2198-3844}, | ||
DOI = {10.1002/advs.202105932}, | ||
abstractNote = {Profiling protein expression at single-cell resolution is essential for fundamental biological research (such as cell differentiation and tumor microenvironmental examination) and clinical precision medicine where only a limited number of primary cells are permitted. With the recent advances in engineering, chemistry, and biology, single-cell protein analysis methods are developed rapidly, which enable high-throughput and multiplexed protein measurements in thousands of individual cells. In combination with single cell RNA sequencing and mass spectrometry, single-cell multi-omics analysis can simultaneously measure multiple modalities including mRNAs, proteins, and metabolites in single cells, and obtain a more comprehensive exploration of cellular signaling processes, such as DNA modifications, chromatin accessibility, protein abundance, and gene perturbation. Here, the recent progress and applications of single-cell protein analysis technologies in the last decade are summarized. Current limitations, challenges, and possible future directions in this field are also discussed.}, | ||
number = {n/a}, | ||
journal = {Advanced Science}, | ||
author = {Xie, Haiyang and Ding, Xianting}, | ||
pages = {2105932}, | ||
language = {en} | ||
} |
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