MMAD: microarray microdissection with analysis of differences is a computational tool for deconvoluting cell type-specific contributions from tissue samples

High-throughput genomic profiling of tumor-infiltrating leukocytes

Tumors are complex ecosystems comprised of diverse cell types including malignant cells, mesenchymal cells, and tumor-infiltrating leukocytes (TILs). While TILs are well known to play important roles in many aspects of cancer biology, recent developments in immuno-oncology have spurred considerable interest in TILs, particularly in relation to their optimal engagement by emerging immunotherapies. Traditionally, the enumeration of TIL phenotypic diversity and composition in solid tumors has relied on resolving single cells by flow cytometry and immunohistochemical methods. However, advances in genome-wide technologies and computational methods are now allowing TILs to be profiled with increasingly high resolution and accuracy directly from RNA mixtures of bulk tumor samples. In this review, we highlight recent progress in the development of in silico tumor dissection methods, and illustrate examples of how these strategies can be applied to characterize TILs in human tumors to facilitate personalized cancer therapy.

A Novel Prioritization Method in Identifying Recurrent Venous Thromboembolism-Related Genes

Identifying the genes involved in venous thromboembolism (VTE) recurrence is important not only for understanding the pathogenesis but also for discovering the therapeutic targets. We proposed a novel prioritization method called Function-Interaction-Pearson (FIP) by creating gene-disease similarity scores to prioritize candidate genes underling VTE. The scores were calculated by integrating and optimizing three types of resources including gene expression, gene ontology and protein-protein interaction. As a result, 124 out of top 200 prioritized candidate genes had been confirmed in literature, among which there were 34 antithrombotic drug targets. Compared with two well-known gene prioritization tools Endeavour and ToppNet, FIP was shown to have better performance. The approach provides a valuable alternative for drug targets discovery and disease therapy.

Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons

Documenting the extent of cellular diversity is a critical step in defining the functional organization of tissues and organs. To infer cell-type diversity from partial or incomplete transcription factor expression data, we devised a sparse Bayesian framework that is able to handle estimation uncertainty and can incorporate diverse cellular characteristics to optimize experimental design. Focusing on spinal V1 inhibitory interneurons, for which the spatial expression of 19 transcription factors has been mapped, we infer the existence of ~50 candidate V1 neuronal types, many of which localize in compact spatial domains in the ventral spinal cord. We have validated the existence of inferred cell types by direct experimental measurement, establishing this Bayesian framework as an effective platform for cell-type characterization in the nervous system and elsewhere.

MixChIP: a probabilistic method for cell type specific protein-DNA binding analysis

Background

Transcription factors (TFs) are proteins that bind to DNA and regulate gene expression. To understand details of gene regulation, characterizing TF binding sites in different cell types, diseases and among individuals is essential. However, sometimes TF binding can only be measured from biological samples that contain multiple cell or tissue types. Sample heterogeneity can have a considerable effect on TF binding site detection. While manual separation techniques can be used to isolate a cell type of interest from heterogeneous samples, such techniques are challenging and can change intra-cellular interactions, including protein-DNA binding. Computational deconvolution methods have emerged as an alternative strategy to study heterogeneous samples and numerous methods have been proposed to analyze gene expression. However, no computational method exists to deconvolve cell type specific TF binding from heterogeneous samples.

Results

We present a probabilistic method, MixChIP, to identify cell type specific TF binding sites from heterogeneous chromatin immunoprecipitation sequencing (ChIP-seq) data. Our method simultaneously estimates the binding strength in different cell types as well as the proportions of different cell types in each sample when only partial prior information about cell type composition is available. We demonstrate the utility of MixChIP by analyzing ChIP-seq data from two cell lines which we artificially mix to generate (simulated) heterogeneous samples and by analyzing ChIP-seq data from breast cancer patients measuring oestrogen receptor (ER) binding in primary breast cancer tissues. We show that MixChIP is more accurate in detecting TF binding sites from multiple heterogeneous ChIP-seq samples than the standard methods which do not account for sample heterogeneity.

Conclusions

Our results show that MixChIP can estimate cell-type proportions and identify cell type specific TF binding sites from heterogeneous ChIP-seq samples. Thus, MixChIP can be an invaluable tool in analyzing heterogeneous ChIP-seq samples, such as those originating from cancer studies. R implementation is available at http://research.ics.aalto.fi/csb/software/mixchip/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0834-3) contains supplementary material, which is available to authorized users.

Perturbation-expression analysis identifies RUNX1 as a regulator of human mammary stem cell differentiation

The search for genes that regulate stem cell self-renewal and differentiation has been hindered by a paucity of markers that uniquely label stem cells and early progenitors. To circumvent this difficulty we have developed a method that identifies cell-state regulators without requiring any markers of differentiation, termed Perturbation-Expression Analysis of Cell States (PEACS). We have applied this marker-free approach to screen for transcription factors that regulate mammary stem cell differentiation in a 3D model of tissue morphogenesis and identified RUNX1 as a stem cell regulator. Inhibition of RUNX1 expanded bipotent stem cells and blocked their differentiation into ductal and lobular tissue rudiments. Reactivation of RUNX1 allowed exit from the bipotent state and subsequent differentiation and mammary morphogenesis. Collectively, our findings show that RUNX1 is required for mammary stem cells to exit a bipotent state, and provide a new method for discovering cell-state regulators when markers are not available.

The discovery of stem cell regulators is a major goal of biological research, but progress is often limited by a lack of definitive markers capable of distinguishing stem cells from early progenitors. Even in cases where markers have been identified, they often only enrich for certain cell states and do not uniquely identify states. While useful in some contexts, such enriching markers are ineffective tools for discovering genes that regulate the transition of cells between states. We present a method for identifying these cell state regulatory genes without the need for pre-determined markers, termed Perturbation-Expression Analysis of Cell States (PEACS). PEACS uses a novel computational approach to analyze gene expression data from perturbed cellular populations, and can be applied broadly to identify regulators of stem and progenitor cell self-renewal or differentiation. Application of PEACS to mammary stem cells resulted in the identification of RUNX1 as a key regulator of exit from the bipotent state.

Robust enumeration of cell subsets from tissue expression profiles

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).