Curated Papers > High-impact authoritative literature on "single-cell sequencing"

Determining cell type abundance and expression from bulk tissues with digital cytometry.

Literature Information

DOI	10.1038/s41587-019-0114-2
PMID	31061481
Journal	Nature biotechnology
Impact Factor	41.7
JCR Quartile	Q1
Publication Year	2019
Times Cited	2402
Keywords	Digital Cytometry, Cell Type Abundance, Gene Expression Profiles
Literature Type	Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S.
ISSN	1087-0156
Pages	773-782
Issue	37(7)
Authors	Aaron M Newman, Chloé B Steen, Chih Long Liu, Andrew J Gentles, Aadel A Chaudhuri, Florian Scherer, Michael S Khodadoust, Mohammad S Esfahani, Bogdan A Luca, David Steiner, Maximilian Diehn, Ash A Alizadeh

TL;DR

CIBERSORTx is a novel machine learning method that enhances the CIBERSORT framework to infer cell-type-specific gene expression profiles from bulk tissue transcriptomes, making it applicable to large clinical cohorts and fixed specimens. This approach facilitates the characterization of tumor microenvironments, revealing distinct cell-type phenotypic states associated with specific mutations and responses to treatments, thus offering a cost-effective alternative to traditional single-cell RNA-sequencing.

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Digital Cytometry · Cell Type Abundance · Gene Expression Profiles

Abstract

Single-cell RNA-sequencing has emerged as a powerful technique for characterizing cellular heterogeneity, but it is currently impractical on large sample cohorts and cannot be applied to fixed specimens collected as part of routine clinical care. We previously developed an approach for digital cytometry, called CIBERSORT, that enables estimation of cell type abundances from bulk tissue transcriptomes. We now introduce CIBERSORTx, a machine learning method that extends this framework to infer cell-type-specific gene expression profiles without physical cell isolation. By minimizing platform-specific variation, CIBERSORTx also allows the use of single-cell RNA-sequencing data for large-scale tissue dissection. We evaluated the utility of CIBERSORTx in multiple tumor types, including melanoma, where single-cell reference profiles were used to dissect bulk clinical specimens, revealing cell-type-specific phenotypic states linked to distinct driver mutations and response to immune checkpoint blockade. We anticipate that digital cytometry will augment single-cell profiling efforts, enabling cost-effective, high-throughput tissue characterization without the need for antibodies, disaggregation or viable cells.

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Primary Questions Addressed

1. How does CIBERSORTx improve the accuracy of cell type abundance estimation compared to traditional methods?
2. What specific challenges does digital cytometry face when applied to different tumor types beyond melanoma?
3. In what ways can CIBERSORTx be integrated into routine clinical workflows for tissue analysis?
4. How does the use of single-cell RNA-sequencing data enhance the capabilities of CIBERSORTx in tissue dissection?
5. What are the implications of cell-type-specific phenotypic states on treatment strategies for tumors identified using CIBERSORTx?

Key Findings

Research Background and Purpose

The study addresses the challenges in understanding cellular heterogeneity within complex tissues, particularly in the context of cancer. Traditional methods like flow cytometry and immunohistochemistry are limited in their ability to analyze diverse cell types simultaneously. Single-cell RNA sequencing (scRNA-seq) offers a powerful alternative but is impractical for large cohorts and fixed samples. The authors aim to develop a computational framework, CIBERSORTx, to infer cell-type abundance and specific gene expression profiles from bulk tissue transcriptomes without physical cell isolation.

Main Methods/Materials/Experimental Design

CIBERSORTx builds on the previous CIBERSORT framework, enhancing it with machine learning techniques to estimate cell type-specific gene expression profiles. The method utilizes a signature matrix derived from single-cell or sorted cell populations to analyze bulk RNA profiles.

Technical Workflow

1. Signature Matrix Creation: A signature matrix is developed using cell type-specific gene expression data from scRNA-seq or FACS-purified populations.
2. Bulk Tissue RNA Profiling: RNA profiles from bulk tissues are analyzed using the signature matrix.
3. Deconvolution: The method estimates the proportions of different cell types in the bulk samples.
4. Expression Purification: CIBERSORTx purifies gene expression profiles for individual cell types without requiring physical separation.

Key Results and Findings

• CIBERSORTx demonstrated improved accuracy in estimating cell type proportions and gene expression profiles compared to traditional methods, particularly after applying batch correction techniques.
• The method was validated across various tumor types, including melanoma and non-small cell lung cancer (NSCLC), where it revealed cell type-specific phenotypic states associated with distinct genetic mutations.
• CIBERSORTx successfully inferred cell type-specific gene expression profiles from fixed tissue samples, which are typically challenging to analyze using single-cell methods.

Main Conclusions/Significance/Innovation

CIBERSORTx represents a significant advancement in digital cytometry, enabling detailed analysis of tissue composition and cellular states from bulk RNA-seq data. This approach provides a cost-effective, high-throughput method for characterizing complex tissues, which can enhance our understanding of tumor microenvironments and improve diagnostic and therapeutic strategies in cancer treatment.

Research Limitations and Future Directions

While CIBERSORTx effectively analyzes larger sample cohorts, it currently requires multiple bulk tissue samples for accurate expression purification. Future work should focus on refining the algorithm to accommodate smaller sample sizes and exploring its applicability to other species and genomic data types. The authors also suggest that integrating CIBERSORTx with other analytical techniques could further enhance the understanding of cellular interactions within tissues.

Literatures Citing This Work

1. Computational approaches for characterizing the tumor immune microenvironment. - Candace C Liu;Chloé B Steen;Aaron M Newman - Immunology (2019)
2. An elastic-net logistic regression approach to generate classifiers and gene signatures for types of immune cells and T helper cell subsets. - Arezo Torang;Paraag Gupta;David J Klinke - BMC bioinformatics (2019)
3. The TMEM106B FTLD-protective variant, rs1990621, is also associated with increased neuronal proportion. - Zeran Li;Fabiana H G Farias;Umber Dube;Jorge L Del-Aguila;Kathie A Mihindukulasuriya;Maria Victoria Fernandez;Laura Ibanez;John P Budde;Fengxian Wang;Allison M Lake;Yuetiva Deming;James Perez;Chengran Yang;Jorge A Bahena;Wei Qin;Joseph L Bradley;Richard Davenport;Kristy Bergmann;John C Morris;Richard J Perrin;Bruno A Benitez;Joseph D Dougherty;Oscar Harari;Carlos Cruchaga - Acta neuropathologica (2020)
4. Next-generation computational tools for interrogating cancer immunity. - Francesca Finotello;Dietmar Rieder;Hubert Hackl;Zlatko Trajanoski - Nature reviews. Genetics (2019)
5. Identification of non-cancer cells from cancer transcriptomic data. - Michele Bortolomeazzi;Mohamed Reda Keddar;Francesca D Ciccarelli;Lorena Benedetti - Biochimica et biophysica acta. Gene regulatory mechanisms (2020)
6. ADAPTS: Automated deconvolution augmentation of profiles for tissue specific cells. - Samuel A Danziger;David L Gibbs;Ilya Shmulevich;Mark McConnell;Matthew W B Trotter;Frank Schmitz;David J Reiss;Alexander V Ratushny - PloS one (2019)
7. RNA-seq from archival FFPE breast cancer samples: molecular pathway fidelity and novel discovery. - Nathan D Pennock;Sonali Jindal;Wesley Horton;Duanchen Sun;Jayasri Narasimhan;Lucia Carbone;Suzanne S Fei;Robert Searles;Christina A Harrington;Julja Burchard;Sheila Weinmann;Pepper Schedin;Zheng Xia - BMC medical genomics (2019)
8. SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references. - Meichen Dong;Aatish Thennavan;Eugene Urrutia;Yun Li;Charles M Perou;Fei Zou;Yuchao Jiang - Briefings in bioinformatics (2021)
9. Profiling Cell Type Abundance and Expression in Bulk Tissues with CIBERSORTx. - Chloé B Steen;Chih Long Liu;Ash A Alizadeh;Aaron M Newman - Methods in molecular biology (Clifton, N.J.) (2020)
10. Human Plasmacytoid Dendritic Cells and Cutaneous Melanoma. - Matilde Monti;Francesca Consoli;Raffaella Vescovi;Mattia Bugatti;William Vermi - Cells (2020)

... (2392 more literatures)

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