Identification of a heterogeneous and dynamic ciliome during embryonic development and cell differentiation.

Primary cilia are nearly ubiquitous organelles that transduce molecular and mechanical signals. Although the basic structure of the cilium and the cadre of genes that contribute to ciliary formation and function (the ciliome) are believed to be evolutionarily conserved, the presentation of ciliopathies with narrow, tissue-specific phenotypes and distinct molecular readouts suggests that an unappreciated heterogeneity exists within this organelle. Here, we provide a searchable transcriptomic resource for a curated primary ciliome, detailing various subgroups of differentially expressed genes within the ciliome that display tissue and temporal specificity. Genes within the differentially expressed ciliome exhibited a lower level of functional constraint across species, suggesting organism and cell-specific function adaptation. The biological relevance of ciliary heterogeneity was functionally validated by using Cas9 gene-editing to disrupt ciliary genes that displayed dynamic gene expression profiles during osteogenic differentiation of multipotent neural crest cells. Collectively, this novel primary cilia-focused resource will allow researchers to explore longstanding questions related to how tissue and cell-type specific functions and ciliary heterogeneity may contribute to the range of phenotypes associated with ciliopathies.

Assessment of the Electrolyte Heterogeneity of Tissues in Mandibular Bone-Infiltrating Head and Neck Cancer Using Laser-Induced Breakdown Spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) was recently introduced as a rapid bone analysis technique in bone-infiltrating head and neck cancers. Research efforts on laser surgery systems with controlled tissue feedback are currently limited to animal specimens and the use of nontumorous tissues. Accordingly, this study aimed to characterize the electrolyte composition of tissues in human mandibular bone-infiltrating head and neck cancer. Mandible cross-sections from 12 patients with bone-invasive head and neck cancers were natively investigated with LIBS. Representative LIBS spectra (n = 3049) of the inferior alveolar nerve, fibrosis, tumor stroma, and cell-rich tumor areas were acquired and histologically validated. Tissue-specific differences in the LIBS spectra were determined by receiver operating characteristics analysis and visualized by principal component analysis. The electrolyte emission values of calcium (Ca) and potassium (K) significantly (p < 0.0001) differed in fibrosis, nerve tissue, tumor stroma, and cell-rich tumor areas. Based on the intracellular detection of Ca and K, LIBS ensures the discrimination between the inferior alveolar nerve and cell-rich tumor tissue with a sensitivity of ≥95.2% and a specificity of ≥87.2%. The heterogeneity of electrolyte emission values within tumorous and nontumorous tissue areas enables LIBS-based tissue recognition in mandibular bone-infiltrating head and neck cancer.

Advances in spatial transcriptomics and related data analysis strategies.

Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequencing (scRNA-seq) cannot provide spatial information, while spatial transcriptomics technologies allow gene expression information to be obtained from intact tissue sections in the original physiological context at a spatial resolution. Various biological insights can be generated into tissue architecture and further the elucidation of the interaction between cells and the microenvironment. Thus, we can gain a general understanding of histogenesis processes and disease pathogenesis, etc. Furthermore, in silico methods involving the widely distributed R and Python packages for data analysis play essential roles in deriving indispensable bioinformation and eliminating technological limitations. In this review, we summarize available technologies of spatial transcriptomics, probe into several applications, discuss the computational strategies and raise future perspectives, highlighting the developmental potential.

Vesalius: high-resolution in silico anatomization of spatial transcriptomic data using image analysis.

Characterization of tissue architecture promises to deliver insights into development, cell communication, and disease. In silico spatial domain retrieval methods have been developed for spatial transcriptomics (ST) data assuming transcriptional similarity of neighboring barcodes. However, domain retrieval approaches with this assumption cannot work in complex tissues composed of multiple cell types. This task becomes especially challenging in cellular resolution ST methods. We developed Vesalius to decipher tissue anatomy from ST data by applying image processing technology. Vesalius uniquely detected territories composed of multiple cell types and successfully recovered tissue structures in high-resolution ST data including in mouse brain, embryo, liver, and colon. Utilizing this tissue architecture, Vesalius identified tissue morphology-specific gene expression and regional specific gene expression changes for astrocytes, interneuron, oligodendrocytes, and entorhinal cells in the mouse brain.

Psoriatic Arthritis: Pathogenesis and Targeted Therapies.

Psoriatic arthritis (PsA), a heterogeneous chronic inflammatory immune-mediated disease characterized by musculoskeletal inflammation (arthritis, enthesitis, spondylitis, and dactylitis), generally occurs in patients with psoriasis. PsA is also associated with uveitis and inflammatory bowel disease (Crohn's disease and ulcerative colitis). To capture these manifestations as well as the associated comorbidities, and to recognize their underlining common pathogenesis, the name of psoriatic disease was coined. The pathogenesis of PsA is complex and multifaceted, with an interplay of genetic predisposition, triggering environmental factors, and activation of the innate and adaptive immune system, although autoinflammation has also been implicated. Research has identified several immune-inflammatory pathways defined by cytokines (IL-23/IL-17, TNF), leading to the development of efficacious therapeutic targets. However, heterogeneous responses to these drugs occur in different patients and in the different tissues involved, resulting in a challenge to the global management of the disease. Therefore, more translational research is necessary in order to identify new targets and improve current disease outcomes. Hopefully, this may become a reality through the integration of different omics technologies that allow better understanding of the relevant cellular and molecular players of the different tissues and manifestations of the disease. In this narrative review, we aim to provide an updated overview of the pathophysiology, including the latest findings from multiomics studies, and to describe current targeted therapies.

Sensei: how many samples to tell a change in cell type abundance?

Cellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github.io/sensei/table_beta.html .

Spatially Resolved Transcriptomes-Next Generation Tools for Tissue Exploration.

Recent advances in spatially resolved transcriptomics have greatly expanded the knowledge of complex multicellular biological systems. The field has quickly expanded in recent years, and several new technologies have been developed that all aim to combine gene expression data with spatial information. The vast array of methodologies displays fundamental differences in their approach to obtain this information, and thus, demonstrate method-specific advantages and shortcomings. While the field is moving forward at a rapid pace, there are still multiple challenges presented to be addressed, including sensitivity, labor extensiveness, tissue-type dependence, and limited capacity to obtain detailed single-cell information. No single method can currently address all these key parameters. In this review, available spatial transcriptomics methods are described and their applications as well as their strengths and weaknesses are discussed. Future developments are explored and where the field is heading to is deliberated upon.

Characterisation of 3D Bioprinted Human Breast Cancer Model for In Vitro Drug and Metabolic Targeting.

Monolayer cultures, the less standard three-dimensional (3D) culturing systems, and xenografts are the main tools used in current basic and drug development studies of cancer research. The aim of biofabrication is to design and construct a more representative in vivo 3D environment, replacing two-dimensional (2D) cell cultures. Here, we aim to provide a complex comparative analysis of 2D and 3D spheroid culturing, and 3D bioprinted and xenografted breast cancer models. We established a protocol to produce alginate-based hydrogel bioink for 3D bioprinting and the long-term culturing of tumour cells in vitro. Cell proliferation and tumourigenicity were assessed with various tests. Additionally, the results of rapamycin, doxycycline and doxorubicin monotreatments and combinations were also compared. The sensitivity and protein expression profile of 3D bioprinted tissue-mimetic scaffolds showed the highest similarity to the less drug-sensitive xenograft models. Several metabolic protein expressions were examined, and the in situ tissue heterogeneity representing the characteristics of human breast cancers was also verified in 3D bioprinted and cultured tissue-mimetic structures. Our results provide additional steps in the direction of representing in vivo 3D situations in in vitro studies. Future use of these models could help to reduce the number of animal experiments and increase the success rate of clinical phase trials.

CDSeqR: fast complete deconvolution for gene expression data from bulk tissues.

BACKGROUND: Biological tissues consist of heterogenous populations of cells. Because gene expression patterns from bulk tissue samples reflect the contributions from all cells in the tissue, understanding the contribution of individual cell types to the overall gene expression in the tissue is fundamentally important. We recently developed a computational method, CDSeq, that can simultaneously estimate both sample-specific cell-type proportions and cell-type-specific gene expression profiles using only bulk RNA-Seq counts from multiple samples. Here we present an R implementation of CDSeq (CDSeqR) with significant performance improvement over the original implementation in MATLAB and an added new function to aid cell type annotation. The R package would be of interest for the broader R community. RESULT: We developed a novel strategy to substantially improve computational efficiency in both speed and memory usage. In addition, we designed and implemented a new function for annotating the CDSeq estimated cell types using single-cell RNA sequencing (scRNA-seq) data. This function allows users to readily interpret and visualize the CDSeq estimated cell types. In addition, this new function further allows the users to annotate CDSeq-estimated cell types using marker genes. We carried out additional validations of the CDSeqR software using synthetic, real cell mixtures, and real bulk RNA-seq data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project. CONCLUSIONS: The existing bulk RNA-seq repositories, such as TCGA and GTEx, provide enormous resources for better understanding changes in transcriptomics and human diseases. They are also potentially useful for studying cell-cell interactions in the tissue microenvironment. Bulk level analyses neglect tissue heterogeneity, however, and hinder investigation of a cell-type-specific expression. The CDSeqR package may aid in silico dissection of bulk expression data, enabling researchers to recover cell-type-specific information.

Microstructural heterogeneity of the collagenous network in the loaded and unloaded periodontal ligament and its biomechanical implications.

The periodontal ligament (PDL) is a highly heterogeneous fibrous connective tissue and plays a critical role in distributing occlusal forces and regulating tissue remodeling. Its mechanical properties are largely determined by the extracellular matrix, comprising a collagenous fiber network interacting with the capillary system as well as interstitial fluid containing proteoglycans. While the phase-contrast micro-CT technique has portrayed the 3D microscopic heterogeneity of PDL, the topological parameters of its network, which is crucial to understanding the multiscale constitutive behavior of this tissue, has not been characterized quantitatively. This study aimed to provide new understanding of such microscopic heterogeneity of the PDL with quantifications at both tissue and collagen network levels in a spatial manner, by combining phase-contrast micro-CT imaging and a purpose-built image processing algorithm for fiber analysis. Both variations within a PDL and among the PDL with different shapes, i.e. round-shaped and kidney-shaped PDLs, are described in terms of tissue thickness, fiber distribution, local fiber densities, and fiber orientation (namely azimuthal and elevation angles). Furthermore, the tissue and collagen fiber network responses to mechanical loading were evaluated in a similar manner. A 3D helical alignment pattern was observed in the fiber network, which appears to regulate and adapt a screw-like tooth motion under occlusion. The microstructural heterogeneity quantified here allows development of sample-specific constitutive models to characterize the PDL's functional and pathological loading responses, thereby providing a new multiscale framework for advancing our knowledge of this complex limited mobility soft-hard tissue interface.

Cellular heterogeneity and microenvironmental control of skin cancer.

Healthy tissues harbour a surprisingly high number of cells that carry well-known cancer-causing mutations without impacting their physiological function. In recent years, strong evidence accumulated that the immediate environment of mutant cells profoundly impact their prospect of malignant progression. In this review, focusing on the skin, we investigate potential key mechanisms that ensure tissue homeostasis despite the presence of mutant cells, as well as critical factors that may nudge the balance from homeostasis to tumour formation. Functional in vivo studies and single-cell transcriptome analyses have revealed a tremendous cellular heterogeneity and plasticity within epidermal (stem) cells and their respective niches, revealing for example wild-type epithelial cells, fibroblasts or immune-cell subsets as critical in preventing cancer formation and malignant progression. It's the same cells, however, that can drive carcinogenesis. Therefore, understanding the abundance and molecular variation of cell types in health and disease, and how they interact and modulate the local signalling environment will thus be key for new therapeutic avenues in our battle against cancer.

Quantification of Human Epidermal Growth Factor Receptor 2 by Immunopeptide Enrichment and Targeted Mass Spectrometry in Formalin-Fixed Paraffin-Embedded and Frozen Breast Cancer Tissues.

BACKGROUND: Conventional HER2-targeting therapies improve outcomes for patients with HER2-positive breast cancer (BC), defined as tumors showing HER2 protein overexpression by immunohistochemistry and/or ERBB2 gene amplification determined by in situ hybridization (ISH). Emerging HER2-targeting compounds show benefit in some patients with neither HER2 protein overexpression nor ERBB2 gene amplification, creating a need for new assays to select HER2-low tumors for treatment with these compounds. We evaluated the analytical performance of a targeted mass spectrometry-based assay for quantifying HER2 protein in formalin-fixed paraffin-embedded (FFPE) and frozen BC biopsies. METHODS: We used immunoaffinity-enrichment coupled to multiple reaction monitoring-mass spectrometry (immuno-MRM-MS) to quantify HER2 protein (as peptide GLQSLPTHDPSPLQR) in 96 frozen and 119 FFPE BC biopsies. We characterized linearity, lower limit of quantification (LLOQ), and intra- and inter-day variation of the assay in frozen and FFPE tissue matrices. We determined concordance between HER2 immuno-MRM-MS and predicate immunohistochemistry and ISH assays and examined the benefit of multiplexing the assay to include proteins expressed in tumor subcompartments (e.g., stroma, adipose, lymphocytes, epithelium) to account for tissue heterogeneity. RESULTS: HER2 immuno-MRM-MS assay linearity was ≥103, assay coefficient of variation was 7.8% (FFPE) and 5.9% (frozen) for spiked-in analyte, and 7.7% (FFPE) and 7.9% (frozen) for endogenous measurements. Immuno-MRM-MS-based HER2 measurements strongly correlated with predicate assay HER2 determinations, and concordance was improved by normalizing to glyceraldehyde-3-phosphate dehydrogenase. HER2 was quantified above the LLOQ in all tumors. CONCLUSIONS: Immuno-MRM-MS can be used to quantify HER2 in FFPE and frozen BC biopsies, even at low HER2 expression levels.

Characterization of the inflammatory post-ischemic tissue by full volumetric analysis of a multimodal imaging dataset.

INTRODUCTION: In vivo positron emission tomography (PET) and magnetic resonance imaging (MRI) support non-invasive assessment of the spatiotemporal expression of proteins of interest and functional/structural changes. Our work promotes the use of a volumetric analysis on multimodal imaging datasets to assess the spatio-temporal dynamics and interaction of two imaging biomarkers, with a special focus on two neuroinflammation-related biomarkers, the translocator protein (TSPO) and matrix metalloproteinases (MMPs), in the acute and chronic post-ischemic phase. AIM: To improve our understating of the neuroinflammatory reaction and tissue heterogeneity during the post ischemic phase, we aimed (i) to assess the spatio-temporal distribution of two radiotracers, [18F]DPA-714 (TSPO) and [18F]BR-351 (MMPs), (ii) to investigate their spatial interaction, including exclusive and overlapping areas, and (iii) their relationship with the T2w-MRI ischemic lesion in a transient middle cerebral artery occlusion (tMCAo) mouse model using an atlas-based volumetric analysis. METHODS: As described by Zinnhardt et al. (2015), a total of N = 30 C57BL/6 mice underwent [18F]DPA-714 and [18F]BR-351 PET-CT and subsequent MR imaging 24-48 h (n = 8), 7 ± 1 days (n = 8), 14 ± 1 days (n = 7), and 21 ± 1 days (n = 7) after 30 min transient middle cerebral artery occlusion (tMCAo). To further investigate the spatio-temporal distribution of [18F]DPA-714 and [18F]BR-351, an atlas-based ipsilesional volume of interest (VOI) was applied to co-registered PET-CT images and thresholded by the mean uptake + 2.5*standard deviation of a contralateral striatal control VOI. Mean lesion-to-contralateral ratios (L/C), volume extension (V in voxel), percentages of overlap and exclusive tracer uptake areas were determined. Both tracer volumes were also compared to the lesion extent depicted by T2w-MR imaging. RESULTS: Both imaging biomarkers showed a constant small percentage of overlap across all time points (14.0 ± 14.2%). [18F]DPA-714 reached its maximum extent and uptake at day 14 post ischemia (V = 12,143 ± 6262 voxels, L/C = 2.32 ± 0.48). The majority of [18F]DPA-714 volume (82.4 ± 16.1%) was exclusive for [18F]DPA-714 and showed limited overlap with [18F]BR-351 and T2w-MRI lesion volumes. On the other hand, [18F]BR-351 reached its maximum extent already 24-48 h after tMCAo (V = 7279 ± 4518 voxels) and significantly decreased at day 14 (V = 1706 ± 1202 voxels). Focal spots of residual activity were still observed at day 21 post ischemia (L/C = 2.10 ± 0.37). The majority of [18F]BR-351 volume was exclusive for [18F]BR-351 (81.50 ± 25.07%) at 24-48 h and showed 64.84 ± 28.29% of overlap with [18F]DPA-714 from day 14 post ischemia while only 9.28 ± 13.45% of the [18F]BR-351 volume were overlapping the T2w-MRI lesion. The percentage of exclusive area of [18F]DPA-714 and [18F]BR-351 uptakes regarding T2w-MR lesion increased over time, suggesting that TSPO and MMPs are mostly localized in the peri­infarct region at latter time points. CONCLUSION: This study promotes the use of an unbiased volumetric analyses of multi-modal imaging data sets to improve the characterization of pathological tissue heterogeneity. This approach improves our understanding of (i) the dynamics of disease-related multi-modal imaging biomarkers, (ii) their spatiotemporal interactions and (iii) the post-ischemic tissue heterogeneity. Our results indicate acute MMPs activation after tMCAo preceding TSPO-dependent (micro-)gliosis. The spatial distribution of MMPs and gliosis is regionally independent with only minor (< 20%) overlapping areas in peri­infarct regions.

Microscopic susceptibility anisotropy imaging.

PURPOSE: The gradient-echo MR signal in brain white matter depends on the orientation of the fibers with respect to the external magnetic field. To map microstructure-specific magnetic susceptibility in orientationally heterogeneous material, it is thus imperative to regress out unwanted orientation effects. METHODS: This work introduces a novel framework, referred to as microscopic susceptibility anisotropy imaging, that disentangles the 2 principal effects conflated in gradient-echo measurements, (a) the susceptibility properties of tissue microenvironments, especially the myelin microstructure, and (b) the axon orientation distribution relative to the magnetic field. Specifically, we utilize information about the orientational tissue structure inferred from diffusion MRI data to factor out the B0 -direction dependence of the frequency difference signal. RESULTS: A human pilot study at 3 T demonstrates proxy maps of microscopic susceptibility anisotropy unconfounded by fiber crossings and orientation dispersion as well as magnetic field direction. The developed technique requires only a dual-echo gradient-echo scan acquired at 1 or 2 head orientations with respect to the magnetic field and a 2-shell diffusion protocol achievable on standard scanners within practical scan times. CONCLUSIONS: The quantitative recovery of microscopic susceptibility features in the presence of orientational heterogeneity potentially improves the assessment of microstructural tissue integrity.

Multiparametric statistical quantification of pH heterogeneity by 1 H MRS and MRSI of extracellular pH markers: Proof of principle.

Acid production and transport in numerous biological tissues and medical conditions are active areas of research. Heterogeneity of pH within a given homogeneous-appearing tissue volume has been reported, but none of the conventional methods currently available for measuring tissue pH provides quantitative parameters describing the frequency of occurrence of pH values within such a volume. We have previously presented a multiparametric noninvasive in vivo approach, providing at least 10 different statistical descriptors of pH heterogeneity based on a novel type of line shape analysis developed for pH-sensitive 31 P MRS resonances. However, this method suffers from lack of sensitivity, thus making rapid and spatially resolved measurements difficult. We present here the proof of principle of a new, more sensitive approach to statistical characterization of extracellular pH heterogeneity based on 1 H MRS, with the potential of being combined with spatial resolution. We experimentally study a range of test solutions of a reporter molecule that has previously been shown to possess a 1 H MRS resonance whose chemical shift varies with pH, including when injected intravenously into experimental animals (imidazole ethoxycarbonylpropionic acid, [IEPA]). Statistical pH heterogeneity descriptors are determined for phantoms mimicking tissue pH heterogeneity. To this end, the pH-sensitive 1 H MRS resonance is transformed into a pH curve. Subsequently, the digital points of this pH profile are used to build a histogram using dedicated algorithms. The following descriptors are computed from this histogram: weighted mean pH and median pH, pH standard deviation, pH range, pH mode(s), pH kurtosis, pH skewness and pH entropy. Our new method is also validated by analyzing previously published in vivo MRSI spectra. The proof of principle provided in this work should form the basis of further in vivo studies in physiology and medicine, eg in cancer research, but also in other fields such as kidney and muscle research.

A method for multiparametric statistical quantification of the heterogeneity of free Na+ concentration by 19 F MR spectroscopy: Proof of principle in silico and in vitro.

Sodium(I) (Na+ ) is one of the most important cations in mammalian tissues. Since Na+ plays a key role in basic cell function, noninvasive methods for measuring intracellular concentrations of free sodium ions in biological tissue have been developed on the basis of 19 F NMR spectroscopy. However, intracellular Na+ levels are often not uniform throughout a tissue volume (or voxel) being measured. In such cases, [Na+ ] heterogeneity is not reflected in results obtained by the classical technique, and may even result in biased average values. For this reason, we have designed an approach for quantifying [Na+ ] heterogeneity. First, the 19 F MRS resonance from FCrown-1 serving as a "Na+ probe" is transformed into a [Na+ ] curve. Then the digital points of the resulting [Na+ ] profile are used to construct a histogram with specially developed algorithms. From each [Na+ ] histogram, at least eight quantitative parameters describing the underlying statistical [Na+ ] distribution were computed: weighted median, weighted mean, standard deviation, range, mode(s), kurtosis, skewness, and entropy. In addition to our new paradigm, we present a first validation based on (i) computer simulations and (ii) experimentally obtained 19 F MR spectra of model solutions. This basic proof of principle warrants future in vivo experiments, in particular because of its ability to provide quantitative information complementary to that made available by commonly used 23 Na MRI: (i) multiparametric statistical characterization of [Na+ ] distributions; (ii) total [Na+ ] heterogeneity analysis not intrinsically limited by the size of any MRI voxels; and (iii) analysis of unequivocally intracellular [Na+ ], as opposed to measurement of a combination of intra- and extracellular [Na+ ].

A Bayesian approach to determine the composition of heterogeneous cancer tissue.

BACKGROUND: Cancer Tissue Heterogeneity is an important consideration in cancer research as it can give insights into the causes and progression of cancer. It is known to play a significant role in cancer cell survival, growth and metastasis. Determining the compositional breakup of a heterogeneous cancer tissue can also help address the therapeutic challenges posed by heterogeneity. This necessitates a low cost, scalable algorithm to address the challenge of accurate estimation of the composition of a heterogeneous cancer tissue. METHODS: In this paper, we propose an algorithm to tackle this problem by utilizing the data of accurate, but high cost, single cell line cell-by-cell observation methods in low cost aggregate observation method for heterogeneous cancer cell mixtures to obtain their composition in a Bayesian framework. RESULTS: The algorithm is analyzed and validated using synthetic data and experimental data. The experimental data is obtained from mixtures of three separate human cancer cell lines, HCT116 (Colorectal carcinoma), A2058 (Melanoma) and SW480 (Colorectal carcinoma). CONCLUSION: The algorithm provides a low cost framework to determine the composition of heterogeneous cancer tissue which is a crucial aspect in cancer research.

Cholesterol synthesis pathway genes in prostate cancer are transcriptionally downregulated when tissue confounding is minimized.

BACKGROUND: The relationship between cholesterol and prostate cancer has been extensively studied for decades, where high levels of cellular cholesterol are generally associated with cancer progression and less favorable outcomes. However, the role of in vivo cellular cholesterol synthesis in this process is unclear, and data on the transcriptional activity of cholesterol synthesis pathway genes in tissue from prostate cancer patients are inconsistent. METHODS: A common problem with cancer tissue data from patient cohorts is the presence of heterogeneous tissue which confounds molecular analysis of the samples. In this study we present a general method to minimize systematic confounding from stroma tissue in any prostate cancer cohort comparing prostate cancer and normal samples. In particular we use samples assessed by histopathology to identify genes enriched and depleted in prostate stroma. These genes are then used to assess stroma content in tissue samples from other prostate cancer cohorts where no histopathology is available. Differential expression analysis is performed by comparing cancer and normal samples where the average stroma content has been balanced between the sample groups. In total we analyzed seven patient cohorts with prostate cancer consisting of 1713 prostate cancer and 230 normal tissue samples. RESULTS: When stroma confounding was minimized, differential gene expression analysis over all cohorts showed robust and consistent downregulation of nearly all genes in the cholesterol synthesis pathway. Additional Gene Ontology analysis also identified cholesterol synthesis as the most significantly altered metabolic pathway in prostate cancer at the transcriptional level. CONCLUSION: The surprising observation that cholesterol synthesis genes are downregulated in prostate cancer is important for our understanding of how prostate cancer cells regulate cholesterol levels in vivo. Moreover, we show that tissue heterogeneity explains the lack of consistency in previous expression analysis of cholesterol synthesis genes in prostate cancer.

Physiological roles of macrophages.

Macrophages are present in mammals from midgestation, contributing to physiologic homeostasis throughout life. Macrophages arise from yolk sac and foetal liver progenitors during embryonic development in the mouse and persist in different organs as heterogeneous, self-renewing tissue-resident populations. Bone marrow-derived blood monocytes are recruited after birth to replenish tissue-resident populations and to meet further demands during inflammation, infection and metabolic perturbations. Macrophages of mixed origin and different locations vary in replication and turnover, but are all active in mRNA and protein synthesis, fulfilling organ-specific and systemic trophic functions, in addition to host defence. In this review, we emphasise selected properties and non-immune functions of tissue macrophages which contribute to physiologic homeostasis.

Challenges in endometriosis miRNA studies - From tissue heterogeneity to disease specific miRNAs.

In order to uncover miRNA changes in endometriosis pathogenesis, both endometriotic lesions and endometrial biopsies, as well as stromal and epithelial cells isolated from these tissues have been investigated and a large number of dysregulated miRNAs have been reported. However, the concordance between the result of different studies has remained small. One potential explanation for limited overlap between the proposed disease-related miRNAs could be the heterogeneity in tissue composition, as some studies have compared highly heterogeneous whole-lesion biopsies with endometrial tissue, some have compared the endometrium from patients and controls, and some have used pure cell fractions isolated from lesions and endometrium. This review focuses on the results of published miRNA studies in endometriosis to reveal the potential impact of tissue heterogeneity on the discovery of disease-specific miRNA alterations in endometriosis. Additionally, functional studies that explore the roles of endometriosis-involved miRNAs are discussed.