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| 标题： | Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment. |
| 时间： | 2018-10-13 13:36:09 |
| DOI： | 10.1016/j.cell.2018.05.060 |
| PMID： | 29961579 |
| 作者： | E Azizi;AJ Carr;G Plitas;AE Cornish;C Konopacki |
| 摘要： | Knowledge of immune cell phenotypes in the tumor microenvironment is essential for understanding mechanisms of cancer progression and immunotherapy response. We profiled 45,000 immune cells from eight breast carcinomas, as well as matche... |
| 大小： | 17776 kb |
| 页数： | 54 PAGES |
| 目录： |
CELL10248_proof.pdf
Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment- Introduction
Results- Single-Cell RNA-Seq of Breast Carcinoma-Resident Immune Cells
- Integration of Data across Multiple Tumors
- Breast Tumor Immune Cell Atlas Reveals Substantial Diversity in Cell States
- Tissue Environment Affects the Diversity of Immune Phenotypic States
- Immune Cells Undergo Phenotypic Expansion in the Tumor Microenvironment
- Intratumoral T Cells Reside on Continuous Components of Variation
- Intratumoral T Cell Clusters Are Characterized by Diverse Patterns of Environmental Signatures
- Paired Single-Cell RNA and TCR Sequencing Reveals the Range of Activation States of Individual T Cell Clonotypes
- T Cell States Are Shaped by Distinct TCR Usage
- Activation and Differentiation Explain Variation in Intratumoral Myeloid Cells
- Discussion
- Supplemental Information
- Acknowledgments
- References
STAR★Methods- Key Resources Table
- Contact for Reagent and Resource Sharing
Method Details- Sample Collection
- Library Preparation for inDrop
- RNA-Seq library preparation for 10x Genomics single-cell 5′ and VDJ sequencing
- Construction of new barcode sets for inDrop
- Increasing the throughput
- Sequencing and fastq quality control
- CyTOF sample preparation & data collection
Quantification and Statistical Analyses
Data preprocessing: SEQC- Overview
- Fastq Demultiplexing
- Substitution Error Rate Estimation
- Pre-alignment filtering
- Cell barcode correction
- UMI validation
- Annotation Construction
- Alignment
- Multi-Alignment Correction
- Molecular Identifier Correction
- Raw Digital Expression Matrix Construction
Cell Selection and Filtering- Size Selection
- Coverage Selection
- Filtration of dead or dying cells
- Low-complexity cell filtration
- Information Storage & Run Time
- Data Quality Analysis of Breast Leukocytes
- Library Consistency and Quality Control
- Individual Sample Normalization and Clustering
- Cluster Cell Type Annotation
- Gene Signature Summarization Across Patients
- Gene Signatures for Cluster Annotation and Analysis
Biscuit Clustering and Normalization for Merging Samples- Summary of Biscuit model
- Biscuit Implementation
- Entropy Metric to Evaluate Batch Effect Correction
- Quantification of Cell Type Enrichment in Tissues
- Creating a Global Immune Atlas using Biscuit
- Cluster Robustness
- Mixing of Samples in Clusters
- Distances between Clusters
- Contribution of Covariance in Defining Clusters
- Defining Phenotypic Volume
- Diffusion Component Analysis
- Significance of Differences in Covariances in Raw Data
- Comparison of Treg clusters to previous studies
- Continuity of Cells along Components
- Differences across patients
- Preprocessing of paired 5′ scRNA-seq and TCR-seq data from 10x
- Analysis of 10x Genomics paired TCR and scRNA sequencing data
- Evaluation of the role of TCR diversity in driving a continuous spectrum of T cell activation
- Evaluation of the role of TCR repertoire in explaining phenotypic states in T cells
- CyTOF Data Processing and Analysis
- Data and Software Availability
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