Single-cell Map of Diverse Immune Phenotypes Driven by the Tumor Microenvironment

Elham Azizi, Ambrose J. Carr, George Plitas, Andrew E. Cornish, Catherine Konopacki, Sandhya Prabhakaran, Juozas Nainys, Kenmin Wu, Vaidotas Kiseliovas, Manu Setty, Kristy Choi, Rachel M. Fromme, Phuong Dao, Peter T. McKenney, Ruby C. Wasti, Krishna Kadaveru, Linas Mazutis, Alexander Y. Rudensky, Dana Pe'er

Preprint posted on April 02, 2018

Enormous details and variance of the immune cells present in breast tumors are normalized across patients

Selected by Tim Fessenden


Immunologists have long struggled against, or defended, their reliance on cell surface markers to study subsets of immune cells. Hiding behind cell surface markers, we fear, is great biological complexity that goes unseen. Similarly, gene expression studies on bulk populations often mask biologically relevant variation among individual cells. Thus a growing camp of immunologists have turned to single cell RNA sequencing (scRNA seq) to refine canonical immune cell subsets and measure gene expression changes particular to them (Tirosh et al). Yet with the ability to query individual cells comes the new challenge of clustering them into biologically meaningful populations, despite the huge variation within or between them. With a new kind of measurement must come new methods to normalize and compare the results they provide. This is especially the case for integrating data obtained from patients into clinically meaningful categories such as molecular subtypes or metastatic site from cancer patients.

Schematic detailing the workflow used by Azizi et al.


The authors obtained tissue from eight breast cancer patients representing different subtypes. For three of these patients the authors also obtained adjacent normal tissue, and from one patient they obtained a lymph node metastasis. They enriched for immune cells and sequenced via the InDrop platform, sequencing over 47,000 immune cells in aggregate. Following sequencing, their first pass analysis showed that immune cells exhibited significant patient-specific batch effects. A major advance put forth by this preprint addresses this challenge using BISCUIT, described in a prior paper, to remove such effects and normalize scRNA seq data across all eight patients, enabling comparisons among them (Prabhakaran et al).

Their most detailed and provocative observations concern tumor-resident T cells, which were not clustered into pools of distinct phenotypes such as naïve or activated. Individual cells rather were smeared along a continuum in which gene modules for these states were coexpressed and vary widely. This continuum was best captured along axes of activation, terminal differentiation and signatures of hypoxia. To capture the total variation among cells the authors described the “phenotypic volume,” i.e. the summed diversity of cell states, which was vastly expanded within tumors vs normal tissue. This observation of continuous cell states agrees with previous work published by the Pe’er group, showing continuous gene expression changes in response to competing cytokine inputs (Antebi and Reich-Zeliger, et al).

After posting these results, the authors later added data from T cell receptor (TCR) sequencing by using both InDrop and 10X platorms, to query TCR clones in three additional patients. Somewhat unsurprisingly, clonotypes clustered with restricted suites of cell states, rather than exhibiting the full panoply of T cell phenotypes. They argue, convincingly, that T cell clones likely respond uniquely to microenvironments of their antigen, yielding spatially distributed T cell phenotypes according to antigen distribution and microenvironmental cues.

Turning to cells of the myeloid lineage, the authors find a similar phenomenon in macrophages. Contrary to models espousing mutually exclusive M1 and M2 subsets, macrophages express M1 and M2-type gene modules simultaneously. Indeed, M1 and M2 genes were positively correlated in macrophages.


In light of their observations, the authors argue for refined models of T cell activation and differentiation in malignant tissues that account for their apparent plasticity as they encounter aberrant environments. The notion that the local environment strongly determines T cell phenotypes predicts trouble for T cell therapies that use genetic modifications to hardwired cell activation, as these may not surmount microenvironmental effects within a target tissue. How accurately the signature for hypoxia actually reports on hypoxic environments is not assessed by this study. Yet the broader points – of continuous variation in T cell phenotype, trending with signatures for hypoxia – already presents a set of provocative and testable predictions.

With the new level of detail offered by scRNA seq, biologists promise (and are promised) to advance our understanding of immunology and, in this case, immunotherapy for cancer. However, these more detailed measurements threaten to outpace our ability to meaningfully analyze cell populations, and to apply these analyses across patients. Thus we might temper our enthusiasm for this vast increase in granularity.


Does the correlation between hypoxia and differentiation gene expression signatures reflect a direct causative relationship?

Would normalizing hypoxic microenvironments necessarily change T cell functions?

How can we make comparisons between patients, or conclusions on patient cohorts, when our yardstick is high-dimensional datasets that encompass enormous variation?


Prabhakaran, S., Azizi, E., Carr, A., and Pe’er, D. (2016). Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data. In Proceedings of The 33rd International Conference on Machine Learning, B. Maria Florina, and Q.W. Kilian, eds. (Proceedings of Machine Learning Research: PMLR), pp.1070–1079.

Tirosh, I., Izar, B., Prakadan, S.M., Wadsworth, M.H., 2nd, Treacy, D., Trombetta, J.J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016). Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189-196.

Yaron E. Antebi, Shlomit Reich-Zeliger, Yuval Hart , Avi Mayo , Inbal Eizenberg , Jacob Rimer , Prabhakar Putheti, Dana Pe’er, Nir Friedman. (2013). Mapping Differentiation under Mixed Culture Conditions Reveals a Tunable Continuum of T Cell Fates. PLoS Biol. 11(7): e1001616.


Read preprint (1 votes)

  • Have your say

    Your email address will not be published. Required fields are marked *

    Sign up to customise the site to your preferences and to receive alerts

    Register here

    Also in the cancer biology category:

    Mitotic chromosome alignment is required for proper nuclear envelope reassembly

    Cindy L Fonseca, Heidi LH Malaby, Leslie A Sepaniac, et al.

    Selected by Maiko Kitaoka

    SWI/SNF remains localized to chromatin in the presence of SCHLAP1

    Jesse R Raab, Keriayn N Smith, Camarie C Spear, et al.

    Selected by Carmen Adriaens


    Cancer modeling by Transgene Electroporation in Adult Zebrafish (TEAZ)

    Scott J Callahan, Stephanie Tepan, Yan M Zhang, et al.

    Selected by Hannah Brunsdon


    PDX Finder: A Portal for Patient-Derived tumor Xenograft Model Discovery

    Nathalie Conte, Jeremy Mason, Csaba Halmagyi, et al.

    Selected by Carmen Adriaens

    HIF1-alpha expressing cells induce a hypoxic-like response in neighbouring cancer cells

    Hannah Harrison, Henry J Pegg, Jamie Thompson, et al.

    Selected by Anh Hoang Le

    Long-term live imaging of the Drosophila adult midgut reveals real-time dynamics of cell division, differentiation, and loss

    Judy Martin, Erin Nicole Sanders, Paola Moreno-Roman, et al.

    Selected by Natalie Dye

    A role for RNA and DNA:RNA hybrids in the modulation of DNA repair by homologous recombination

    Giuseppina D'Alessandro, Marek Adamowicz, Donna Whelan, et al.

    Selected by Carmen Adriaens

    Nuclear envelope assembly defects link mitotic errors to chromothripsis

    Shiwei Liu, Mijung Kwon, Mark Mannino, et al.

    Selected by Gautam Dey

    Zebrafish as a model to investigate the effects of exercise in cancer

    Alexandra Yin, Nathaniel R. Campbell, Lee W. Jones, et al.

    Selected by Jacky G. Goetz

    Stopping Transformed Growth with Cytoskeletal Proteins: Turning a Devil into an Angel

    Bo Yang, Haguy Wolfenson, Naotaka Nakazawa, et al.

    Selected by Jon Humphries

    Cancer exosomes induce tumor neo-neurogenesis potentiating tumor growth

    Marianna Madeo, Paul L. Colbert, Daniel W. Vermeer, et al.

    Selected by Jacky G. Goetz


    Also in the genomics category:

    The genomic basis of colour pattern polymorphism in the harlequin ladybird

    Mathieu Gautier, Junichi Yamaguchi, Julien Foucaud, et al.

    Selected by Fillip Port

    Widespread inter-individual gene expression variability in Arabidopsis thaliana

    Sandra Cortijo, Zeynep Aydin, Sebastian Ahnert, et al.

    Selected by Martin Balcerowicz

    Single-cell Map of Diverse Immune Phenotypes Driven by the Tumor Microenvironment

    Elham Azizi, Ambrose J. Carr, George Plitas, et al.

    Selected by Tim Fessenden

    Cell type-specific interchromosomal interactions as a mechanism for transcriptional diversity

    Adan Horta, Kevin Monahan, Lisa Bashkirova, et al.

    Selected by Boyan Bonev

    PDX Finder: A Portal for Patient-Derived tumor Xenograft Model Discovery

    Nathalie Conte, Jeremy Mason, Csaba Halmagyi, et al.

    Selected by Carmen Adriaens

    An atlas of silencer elements for the human and mouse genomes

    Naresh Doni Jayavelu, Ajay Jajodia, Arpit Mishra, et al.

    Selected by Rafael Galupa


    Capturing the onset of PRC2-mediated repressive domain formation

    Ozgur Oksuz, Varun Narendra, Chul-Hwan Lee, et al.

    Selected by Boyan Bonev

    Heterochromatin drives organization of conventional and inverted nuclei

    Martin Falk, Yana Feodorova, Natasha Naumova, et al.

    Selected by Boyan Bonev

    The ancestral animal genetic toolkit revealed by diverse choanoflagellate transcriptomes

    Daniel Richter, Parinaz Fozouni, Michael Eisen, et al.

    Selected by Rafael Galupa

    Genome-wide selection scans integrated with association mapping reveal mechanisms of physiological adaptation across a salinity gradient in killifish

    Reid S. Brennan, Timothy M. Healy, Heather J. Bryant, et al.

    Selected by Andy Turko

    Precise temporal regulation of alternative splicing during neural development

    Sebastien M Weyn-Vanhentenryck, Huijuan Feng, Dmytro Ustianenko, et al.

    Selected by James Gagnon

    We want to make our website, and the services we provide, useful and reliable. This sometimes involves placing small amounts of information called cookies on the device you used to access the internet. If you continue to use this website we will assume you are happy to accept our cookies.