We introduce quanTIseq, a method to quantify the fractions of ten immune cell types from bulk RNA-sequencing data. quanTIseq was extensively validated in blood and tumor samples using simulated, flow cytometry, and immunohistochemistry data. quanTIseq analysis of 8000 tumor samples revealed that cytotoxic T cell infiltration is more strongly associated with the activation of the CXCR3/CXCL9 axis than with mutational load and that deconvolution-based cell scores have prognostic value in several solid cancers. Finally, we used quanTIseq to show how kinase inhibitors modulate the immune contexture and to reveal immune-cell types that underlie differential patients’ responses to checkpoint blockers. Availability: quanTIseq is available at http://icbi.at/quantiseq . Electronic supplementary material The online version of this article (10.1186/s13073-019-0638-6) contains supplementary material, which is available to authorized users.

SummaryMesenchymal stem cells (MSC) are capable of differentiating into bone, fat, cartilage, tendon and other organ progenitor cells. Despite the abundance of MSC within the organism, little is known about their in vivo properties or about their corresponding in vivo niches. We therefore isolated MSC from spongy (cancellous) bone biopsies of healthy adults. When compared with the surrounding marrow, a fourfold higher number of colony-forming units was found within the tight meshwork of trabecular bone surface. At these sites, oxygen concentrations range from 1% to 7%. In MSC cultured at oxygen as low as 3%, rates for cell death and hypoxia-induced gene transcription remained unchanged, while in vitro proliferative lifespan was significantly increased, with about 10 additional population doublings before reaching terminal growth arrest. However, differentiation capacity into adipogenic progeny was diminished and no osteogenic differentiation was detectable at 3% oxygen. In turn, MSC that had previously been cultured at 3% oxygen could subsequently be stimulated to successfully differentiate at 20% oxygen. These data support our preliminary finding that primary MSC are enriched at the surface of spongy bone. Low oxygen levels in this location provide a milieu that extends cellular lifespan and furthermore is instructive for the stemness of MSC allowing proliferation upon stimulation while suppressing differentiation.

Aging is a multifactorial process where deterioration of body functions is driven by stochastic damage while counteracted by distinct genetically encoded repair systems. To better understand the genetic component of aging, many studies have addressed the gene and protein expression profiles of various aging model systems engaging different organisms from yeast to human. The recently identified small non-coding miRNAs are potent post-transcriptional regulators that can modify the expression of up to several hundred target genes per single miRNA, similar to transcription factors. Increasing evidence shows that miRNAs contribute to the regulation of most if not all important physiological processes, including aging. However, so far the contribution of miRNAs to age-related and senescence-related changes in gene expression remains elusive. To address this question, we have selected four replicative cell aging models including endothelial cells, replicated CD8+ T cells, renal proximal tubular epithelial cells, and skin fibroblasts. Further included were three organismal aging models including foreskin, mesenchymal stem cells, and CD8+ T cell populations from old and young donors. Using locked nucleic acid-based miRNA microarrays, we identified four commonly regulated miRNAs, miR-17 down-regulated in all seven; miR-19b and miR-20a, down-regulated in six models; and miR-106a down-regulated in five models. Decrease in these miRNAs correlated with increased transcript levels of some established target genes, especially the cdk inhibitor p21/CDKN1A. These results establish miRNAs as novel markers of cell aging in humans.

We introduce quanTIseq, a method to quantify the tumor immune contexture, determined by the type and density of tumor-infiltrating immune cells. quanTIseq is based on a novel deconvolution algorithm for RNA sequencing data that was validated with independent data sets. Complementing the deconvolution output with image data from tissue slides enables in silico multiplexed immunodetection and provides an efficient method for the immunophenotyping of a large number of tumor samples.Cancer immunotherapy with antibodies targeting immune checkpoints has shown durable benefit or even curative potential in various cancers 1,2 . As only a fraction of patients are responsive to immune checkpoint blockers, efforts are underway to identify predictive markers as well as mechanistic rationale for combination therapies with synergistic potential. Thus, comprehensive and quantitative immunological characterization of tumors in a large number of clinical samples is of utmost importance, but it is currently hampered by the lack of simple and efficient methods. Therefore, we developed quanTIseq, a computational pipeline for the quantification of the Tumor Immune contexture using RNA-seq data and images of haematoxylin and eosin (H&E)-stained tissue slides (Fig. 1a). As part of quanTIseq, we first developed a deconvolution algorithm based on constrained least squares regression 12 . We then designed a signature matrix from a compendium of 51 RNA-seq data sets (Supplementary (Fig. 1c).To validate quanTIseq we first used both simulated data and published data. We simulated 1,700RNA-seq data sets from human breast tumors by mixing various numbers of reads from tumor and immune-cell RNA-seq data, considering different immune compositions and sequencing depths.quanTIseq obtained a high correlation between the true and the estimated fractions and accurately quantified tumor content (measured by the fraction of "other" cells) (Supplementary Figure 1). We then validated quanTIseq using experimental data from a previous study 13 , in which peripheral blood mononuclear cell (PBMC) mixtures were subjected to both, RNA-seq and flow cytometry. A high accuracy of quanTIseq estimates was also observed with this data set ( Fig. 1d and Supplementary Figure 2). Additionally, we successfully validated quanTIseq using two previous published data sets (Supplementary Figures 3 and 4).As most of the validation data sets available in the literature are based on microarray data or consider a limited number of phenotypes, we generated RNA-seq and flow cytometry data from mixtures of peripheral-blood immune cells collected from nine healthy donors. Flow cytometry was used to quantify all the immune sub-populations considered by quanTIseq signature matrix except macrophages, which are not present in blood. Comparison between quanTIseq cell estimates and flow cytometry fractions showed a high correlation at a single and multiple cell-type level ( Fig. 1e and Supplementary Figure 5).We then validated quanTIseq using two independent data sets. The first data...