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Cancer Immunology at the Crossroads

Revolutionizing Cancer Immunology: The Power of Next-Generation Sequencing Technologies

Meromit Singer and Ana C. Anderson
Meromit Singer
1Department of Microbiology and Immunobiology, Harvard Medical School, Boston, Massachusetts.
2Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts
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Ana C. Anderson
3Evergrande Center for Immunologic Diseases and Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, Massachusetts.
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  • For correspondence: acanderson@bwh.harvard.edu
DOI: 10.1158/2326-6066.CIR-18-0281 Published February 2019
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    Figure 1.

    Single-cell data analysis methods. Several analysis steps are taken to generate an initial characterization of single-cell data (following or in parallel to normalization of technical noise and artifacts). A, Various linear (e.g., PCA: principal component analysis) and nonlinear (e.g., tSNE; t-distributed stochastic neighborhood embedding and diffusion maps) dimensionality reduction methods can be used for identifying the main discriminants of the data of interest and for visualization. Clustering of cells by their transcriptomes can identify sets of cells that comprise units within the system (diffusion component illustration based on ref. 37). B, Gene sets that covary across the data identify gene modules of interest with respect to heterogeneity and potential functionality of the cell subpopulations within a sample (figure based on ref. 50). C, Integration of additional data types and sources can enable broader insights into the scRNA-seq data set. Shown are two examples. Left, scoring single cells for the extent to which they express predefined gene signatures to infer function and characteristics of populations identified. Right, integration of single-cell TCR information generated in parallel to the scRNA-seq data (figure based on ref. 12).

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  • Table 1.

    Comparison of commonly used RNA-seq protocolsa

    ProtocolSMART-Seq2Cel-Seq2, MARS-seq, STRT10X Chromium, Drop-seq, Indrop
    Capture methodPlate-basedPlate-basedDroplet-based
    TranscriptFull-length3′ or 5′3′ or 5′
    UMINoYesYes
    ThroughputMediumMediumHigh
    TCR/BCR annotationYesPossible with additional primer amplificationSpecific to method
    Pooling stepLateEarly/LateEarly
    • ↵aSome methods are not cited due to space constraints.

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Cancer Immunology Research: 7 (2)
February 2019
Volume 7, Issue 2
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Revolutionizing Cancer Immunology: The Power of Next-Generation Sequencing Technologies
Meromit Singer and Ana C. Anderson
Cancer Immunol Res February 1 2019 (7) (2) 168-173; DOI: 10.1158/2326-6066.CIR-18-0281

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Revolutionizing Cancer Immunology: The Power of Next-Generation Sequencing Technologies
Meromit Singer and Ana C. Anderson
Cancer Immunol Res February 1 2019 (7) (2) 168-173; DOI: 10.1158/2326-6066.CIR-18-0281
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