. 2015 May;12(5):453-7.

doi: 10.1038/nmeth.3337. Epub 2015 Mar 30.

Robust enumeration of cell subsets from tissue expression profiles

Aaron M Newman¹, Chih Long Liu¹, Michael R Green², Andrew J Gentles³, Weiguo Feng⁴, Yue Xu⁵, Chuong D Hoang⁵, Maximilian Diehn⁶, Ash A Alizadeh⁷

Affiliations

¹ 1] Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA. [2] Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, California, USA.
² 1] Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, California, USA. [2] Center for Cancer Systems Biology, Stanford University, Stanford, California, USA.
³ 1] Center for Cancer Systems Biology, Stanford University, Stanford, California, USA. [2] Department of Radiology, Stanford University, Stanford, California, USA.
⁴ Department of Radiation Oncology, Stanford University, Stanford, California, USA.
⁵ Department of Cardiothoracic Surgery, Division of Thoracic Surgery, Stanford University, Stanford, California, USA.
⁶ 1] Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA. [2] Department of Radiation Oncology, Stanford University, Stanford, California, USA. [3] Stanford Cancer Institute, Stanford University, Stanford, California, USA.
⁷ 1] Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA. [2] Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, California, USA. [3] Center for Cancer Systems Biology, Stanford University, Stanford, California, USA. [4] Stanford Cancer Institute, Stanford University, Stanford, California, USA. [5] Department of Medicine, Division of Hematology, Stanford Cancer Institute, Stanford University, Stanford, California, USA.

PMID: 25822800
PMCID: PMC4739640
DOI: 10.1038/nmeth.3337

Robust enumeration of cell subsets from tissue expression profiles

Aaron M Newman et al. Nat Methods. 2015 May.

. 2015 May;12(5):453-7.

doi: 10.1038/nmeth.3337. Epub 2015 Mar 30.

Authors

Aaron M Newman¹, Chih Long Liu¹, Michael R Green², Andrew J Gentles³, Weiguo Feng⁴, Yue Xu⁵, Chuong D Hoang⁵, Maximilian Diehn⁶, Ash A Alizadeh⁷

Affiliations

¹ 1] Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA. [2] Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, California, USA.
² 1] Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, California, USA. [2] Center for Cancer Systems Biology, Stanford University, Stanford, California, USA.
³ 1] Center for Cancer Systems Biology, Stanford University, Stanford, California, USA. [2] Department of Radiology, Stanford University, Stanford, California, USA.
⁴ Department of Radiation Oncology, Stanford University, Stanford, California, USA.
⁵ Department of Cardiothoracic Surgery, Division of Thoracic Surgery, Stanford University, Stanford, California, USA.
⁶ 1] Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA. [2] Department of Radiation Oncology, Stanford University, Stanford, California, USA. [3] Stanford Cancer Institute, Stanford University, Stanford, California, USA.
⁷ 1] Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA. [2] Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, California, USA. [3] Center for Cancer Systems Biology, Stanford University, Stanford, California, USA. [4] Stanford Cancer Institute, Stanford University, Stanford, California, USA. [5] Department of Medicine, Division of Hematology, Stanford Cancer Institute, Stanford University, Stanford, California, USA.

PMID: 25822800
PMCID: PMC4739640
DOI: 10.1038/nmeth.3337

Abstract

We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu/).

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Conflict of interest statement

Competing Financial Interests

The authors declare no competing financial interests.

Figures

**Figure 1**
Overview of CIBERSORT and application to leukocyte deconvolution. (a) Schematic of the approach. (b,c) Application of a leukocyte signature matrix (LM22) to deconvolution of (b) 208 arrays of distinct purified or enriched leukocyte subsets (Supplementary Table 2), and (c) 3,061 diverse human transcriptomes. Sensitivity (Sn) and specificity (Sp) in c are defined in relation to positive and negative groups (Online Methods). AUC, area under the curve. (d) CIBERSORT analysis of 24 whole blood samples for lymphocytes, monocytes, and neutrophils compared to measurements by Coulter counter. Concordance was measured by Pearson correlation (R) and linear regression (dashed line). ‘CIBERSORT fraction’ in b denotes the relative fraction assigned to each leukocyte subset by CIBERSORT. Resting and activated subsets in b are indicated by + and −, respectively.

**Figure 2**
Performance assessment on RNA mixtures from complex tissues. (a–c) CIBERSORT accuracy for leukocyte subset resolution in simulated tissues, showing (a) performance across added tumor content (x-axis) and noise (y-axis), (b) deviation of mixtures in a from original values, and (c) detection limits of a given cell type as a function of increasing tumor content (n = 5 random mixtures for each data point). (d) Comparison of six GEP deconvolution methods with CIBERSORT for the analyses shown in a–c (Supplementary Figs. 5,6). (e) Analysis of whole blood spiked into breast tissue. Left: Reported blood proportions versus immune-related gene expression (LM22 normalized immune index; Online Methods). Right: Stability of leukocyte deconvolution across methods. (f) CIBERSORT consistency across independent studies within and across cancer types (for leukocyte abbreviations, see Supplementary Table 1). (g–i) CIBERSORT performance compared between (g) 18 paired frozen and FFPE DLBCL samples, and compared to flow cytometry analysis of (h) 11 normal lung tissues and (i) 14 follicular lymphoma tumors. Asterisks in i indicate potential outliers from the same patient. Surface markers used for quantitation in h and i are indicated in parentheses. Results in e–i were obtained using LM22 and then collapsed into 11 major leukocyte types before analysis (Supplementary Table 1). Concordance was determined by Pearson correlation (R) and linear regression (dashed lines) in e and g–i. Values in c and h are presented as medians ± 95% confidence intervals.

**Figure 3**
Deep deconvolution and enumeration of individual cell subsets in 41 human subjects. (a–c) Direct comparison between CIBERSORT and flow cytometry for: (a) the indicated eight immune cell subsets in PBMCs from 20 subjects, (b) FOXP3+ Tregs in PBMCs from seven subjects, and (c) the indicated three immune cell subsets in lymph node biopsies from 14 subjects with follicular lymphoma (FL). Concordance was determined by Pearson correlation (R) and linear regression (solid lines). (d) Comparison of five expression-based deconvolution methods on the datasets analyzed in a–c. The shaded gray area denotes deconvolved cell types that significantly correlated with flow cytometry (P < 0.05, Pearson correlation). Scatterplots and RMSE values for all methods are provided in the supplement (Supplementary Figs. 12, 13 and Supplementary Table 4, respectively). In three instances, correlation coefficients could not be determined; these were assigned a value of zero for inclusion in this panel (Supplementary Table 4, Supplementary Fig. 12). Data are presented as means ± standard deviations.

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References

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Robust enumeration of cell subsets from tissue expression profiles

Affiliations

Robust enumeration of cell subsets from tissue expression profiles

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

Substances

Associated data

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources

Medical

Molecular Biology Databases