An unsupervised method for physical cell interaction profiling of complex tissues.

Cellular identity in complex multicellular organisms is determined in part by the physical organization of cells. However, large-scale investigation of the cellular interactome remains technically challenging. Here we develop cell interaction by multiplet sequencing (CIM-seq), an unsupervised and high-throughput method to analyze direct physical cell-cell interactions between cell types present in a tissue. CIM-seq is based on RNA sequencing of incompletely dissociated cells, followed by computational deconvolution into constituent cell types. CIM-seq estimates parameters such as number of cells and cell types in each multiplet directly from sequencing data, making it compatible with high-throughput droplet-based methods. When applied to gut epithelium or whole dissociated lung and spleen, CIM-seq correctly identifies known interactions, including those between different cell lineages and immune cells. In the colon, CIM-seq identifies a previously unrecognized goblet cell subtype expressing the wound-healing marker Plet1, which is directly adjacent to colonic stem cells. Our results demonstrate that CIM-seq is broadly applicable to unsupervised profiling of cell-type interactions in different tissue types.

STAT3 is activated in multicellular spheroids of colon carcinoma cells and mediates expression of IRF9 and interferon stimulated genes.

Three-dimensional cell cultures, such as multicellular spheroids (MCS), reflect the in vivo architecture of solid tumours and multicellular drug resistance. We previously identified interferon regulatory factor 9 (IRF9) to be responsible for the up-regulation of a subset of interferon (IFN)-stimulated genes (ISGs) in MCS of colon carcinoma cells. This set of ISGs closely resembled a previously identified IFN-related DNA-damage resistance signature (IRDS) that was correlated to resistance to chemo- and radiotherapy. In this study we found that transcription factor STAT3 is activated upstream of IRF9 and binds to the IRF9 promoter in MCS of HCT116 colorectal carcinoma cells. Transferring conditioned media (CM) from high cell density conditions to non-confluent cells resulted in STAT3 activation and increased expression of IRF9 and a panel of IRDS genes, also observed in MCS, suggesting the involvement of a soluble factor. Furthermore, we identified gp130/JAK signalling to be responsible for STAT3 activation, IRF9, and IRDS gene expression in MCS and by CM. Our data suggests a novel mechanism where STAT3 is activated in high cell density conditions resulting in increased expression of IRF9 and, in turn, IRDS genes, underlining a mechanism by which drug resistance is regulated.

A drug screening assay on cancer cells chronically adapted to acidosis.

BACKGROUND: Drug screening for the identification of compounds with anticancer activity is commonly performed using cell lines cultured under normal oxygen pressure and physiological pH. However, solid tumors are characterized by a microenvironment with limited access to nutrients, reduced oxygen supply and acidosis. Tumor hypoxia and acidosis have been identified as important drivers of malignant progression and contribute to multicellular resistance to different forms of therapy. Tumor acidosis represents an important mechanism mediating drug resistance thus the identification of drugs active on acid-adapted cells may improve the efficacy of cancer therapy. METHODS: Here, we characterized human colon carcinoma cells (HCT116) chronically adapted to grow at pH 6.8 and used them to screen the Prestwick drug library for cytotoxic compounds. Analysis of gene expression profiles in parental and low pH-adapted cells showed several differences relating to cell cycle, metabolism and autophagy. RESULTS: The screen led to the identification of several compounds which were further selected for their preferential cytotoxicity towards acid-adapted cells. Amongst 11 confirmed hits, we primarily focused our investigation on the benzoporphyrin derivative Verteporfin (VP). VP significantly reduced viability in low pH-adapted HCT116 cells as compared to parental HCT116 cells and normal immortalized epithelial cells. The cytotoxic activity of VP was enhanced by light activation and acidic pH culture conditions, likely via increased acid-dependent drug uptake. VP displayed the unique property to cause light-dependent cross-linking of proteins and resulted in accumulation of polyubiquitinated proteins without inducing inhibition of the proteasome. CONCLUSIONS: Our study provides an example and a tool to identify anticancer drugs targeting acid-adapted cancer cells.

An antisense RNA capable of modulating the expression of the tumor suppressor microRNA-34a.

The microRNA-34a is a well-studied tumor suppressor microRNA (miRNA) and a direct downstream target of TP53 with roles in several pathways associated with oncogenesis, such as proliferation, cellular growth, and differentiation. Due to its broad tumor suppressive activity, it is not surprising that miR34a expression is altered in a wide variety of solid tumors and hematological malignancies. However, the mechanisms by which miR34a is regulated in these cancers is largely unknown. In this study, we find that a long noncoding RNA transcribed antisense to the miR34a host gene, is critical for miR34a expression and mediation of its cellular functions in multiple types of human cancer. We name this long noncoding RNA lncTAM34a, and characterize its ability to facilitate miR34a expression under different types of cellular stress in both TP53-deficient and wild-type settings.

ClusterSignificance: a bioconductor package facilitating statistical analysis of class cluster separations in dimensionality reduced data.

SUMMARY: Multi-dimensional data generated via high-throughput experiments is increasingly used in conjunction with dimensionality reduction methods to ascertain if resulting separations of the data correspond with known classes. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. Despite this, the evaluation of class separations is often subjective and performed via visualization. Here we present the ClusterSignificance package; a set of tools designed to assess the statistical significance of class separations downstream of dimensionality reduction algorithms. In addition, we demonstrate the design and utility of the ClusterSignificance package and utilize it to determine the importance of long non-coding RNA expression in the identity of multiple hematological malignancies. AVAILABILITY AND IMPLEMENTATION: ClusterSignificance is an R package available via Bioconductor (https://bioconductor.org/packages/ClusterSignificance) under GPL-3. CONTACT: dan.grander@ki.se. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An emerging role for long non-coding RNAs in cancer metastasis.

Metastasis is a multistep process beginning with the dissemination of tumor cells from a primary site and leading to secondary tumor development in an anatomically distant location. Although significant progress has been made in understanding the molecular characteristics of metastasis, many questions remain regarding the intracellular mechanisms governing transition through the various metastatic stages. Long non-coding RNAs (lncRNAs) are capable of modulating both transcriptional and post-transcriptional regulation, and thus, coordinating a wide array of diverse cellular processes. Current evidence indicates that lncRNAs may also play a crucial role in the metastatic process through regulation of metastatic signaling cascades as well as interaction with specific metastatic factors. Here we summarize a subset of lncRNAs with proposed roles in metastasis and, when applicable, highlight the mechanism by which they function.