High risk clear cell renal cell carcinoma microenvironments contain protumour immunophenotypes lacking specific immune checkpoints.

Perioperative immune checkpoint inhibitor (ICI) trials for intermediate high-risk clear cell renal cell carcinoma (ccRCC) have failed to consistently demonstrate improved patient outcomes. These unsuccessful ICI trials suggest that the tumour infiltrating immunophenotypes, termed here as the immune cell types, states and their spatial location within the tumour microenvironment (TME), were unfavourable for ICI treatment. Defining the tumour infiltrating immune cells may assist with the identification of predictive immunophenotypes within the TME that are favourable for ICI treatment. To define the immunophenotypes within the ccRCC TME, fresh para-tumour (pTME, n = 2), low-grade (LG, n = 4, G1-G2) and high-grade (HG, n = 4, G3-G4) tissue samples from six patients with ccRCC presenting at a tertiary referral hospital underwent spatial transcriptomics sequencing (ST-seq). Within the generated ST-seq datasets, immune cell types and states, termed here as exhausted/pro-tumour state or non-exhausted/anti-tumour state, were identified using multiple publicly available single-cell RNA and T-cell receptor sequencing datasets as references. HG TMEs revealed abundant exhausted/pro-tumour immune cells with no consistent increase in expression of PD-1, PD-L1 and CTLA4 checkpoints and angiogenic genes. Additional HG TME immunophenotype characteristics included: pro-tumour tissue-resident monocytes with consistently increased expression of HAVCR2 and LAG3 checkpoints; an exhausted CD8+ T cells sub-population with stem-like progenitor gene expression; and pro-tumour tumour-associated macrophages and monocytes within the recurrent TME with the expression of TREM2. Whilst limited by a modest sample size, this study represents the largest ST-seq dataset on human ccRCC. Our study reveals that high-risk ccRCC TMEs are infiltrated by exhausted/pro-tumour immunophenotypes lacking specific checkpoint gene expression confirming that HG ccRCC TME are immunogenic but not ICI favourable.

Spatial Transcriptomics in Kidney Tissue.

Unlike bulk and single-cell/single-nuclei RNA sequencing methods, spatial transcriptome sequencing (ST-seq) resolves transcriptome expression within the spatial context of intact tissue. This is achieved by integrating histology with RNA sequencing. These methodologies are completed sequentially on the same tissue section placed on a glass slide with printed oligo-dT spots, termed ST-spots. Transcriptomes within the tissue section are captured by the underlying ST-spots and receive a spatial barcode in the process. The sequenced ST-spot transcriptomes are subsequently aligned with the hematoxylin and eosin (H&E) image, giving morphological context to the gene expression signatures within intact tissue. We have successfully employed ST-seq to characterize mouse and human kidney tissue. Here, we describe in detail the application of Visium Spatial Tissue Optimization (TO) and Visium Spatial Gene Expression (GEx) protocols for ST-seq in fresh frozen kidney tissue.

The Utility of Spatial Transcriptomics for Solid Organ Transplantation.

Spatial transcriptomics (ST) measures and maps transcripts within intact tissue sections, allowing the visualization of gene activity within the spatial organization of complex biological systems. This review outlines advances in genomic sequencing technologies focusing on in situ sequencing-based ST, including applications in transplant and relevant nontransplant settings. We describe the experimental and analytical pipelines that underpin the current generation of spatial technologies. This context is important for understanding the potential role ST may play in expanding our knowledge, including in organ transplantation, and the important caveats/limitations when interpreting the vast data output generated by such methodological platforms.

Spatially Resolved Transcriptomes of Mammalian Kidneys Illustrate the Molecular Complexity and Interactions of Functional Nephron Segments.

Available transcriptomes of the mammalian kidney provide limited information on the spatial interplay between different functional nephron structures due to the required dissociation of tissue with traditional transcriptome-based methodologies. A deeper understanding of the complexity of functional nephron structures requires a non-dissociative transcriptomics approach, such as spatial transcriptomics sequencing (ST-seq). We hypothesize that the application of ST-seq in normal mammalian kidneys will give transcriptomic insights within and across species of physiology at the functional structure level and cellular communication at the cell level. Here, we applied ST-seq in six mice and four human kidneys that were histologically absent of any overt pathology. We defined the location of specific nephron structures in the captured ST-seq datasets using three lines of evidence: pathologist's annotation, marker gene expression, and integration with public single-cell and/or single-nucleus RNA-sequencing datasets. We compared the mouse and human cortical kidney regions. In the human ST-seq datasets, we further investigated the cellular communication within glomeruli and regions of proximal tubules-peritubular capillaries by screening for co-expression of ligand-receptor gene pairs. Gene expression signatures of distinct nephron structures and microvascular regions were spatially resolved within the mouse and human ST-seq datasets. We identified 7,370 differentially expressed genes (p adj < 0.05) distinguishing species, suggesting changes in energy production and metabolism in mouse cortical regions relative to human kidneys. Hundreds of potential ligand-receptor interactions were identified within glomeruli and regions of proximal tubules-peritubular capillaries, including known and novel interactions relevant to kidney physiology. Our application of ST-seq to normal human and murine kidneys confirms current knowledge and localization of transcripts within the kidney. Furthermore, the generated ST-seq datasets provide a valuable resource for the kidney community that can be used to inform future research into this complex organ.

DNA methylation in schizophrenia in different patient-derived cell types.

DNA methylation of gene promoter regions represses transcription and is a mechanism via which environmental risk factors could affect cells during development in individuals at risk for schizophrenia. We investigated DNA methylation in patient-derived cells that might shed light on early development in schizophrenia. Induced pluripotent stem cells may reflect a "ground state" upon which developmental and environmental influences would be minimal. Olfactory neurosphere-derived cells are an adult-derived neuro-ectodermal stem cell modified by developmental and environmental influences. Fibroblasts provide a non-neural control for life-long developmental and environmental influences. Genome-wide profiling of DNA methylation and gene expression was done in these three cell types from the same individuals. All cell types had distinct, statistically significant schizophrenia-associated differences in DNA methylation and linked gene expression, with Gene Ontology analysis showing that the differentially affected genes clustered in networks associated with cell growth, proliferation, and movement, functions known to be affected in schizophrenia patient-derived cells. Only five gene loci were differentially methylated in all three cell types. Understanding the role of epigenetics in cell function in the brain in schizophrenia is likely to be complicated by similar cell type differences in intrinsic and environmentally induced epigenetic regulation.

Stemformatics: visualisation and sharing of stem cell gene expression.

Genome-scale technologies are increasingly adopted by the stem cell research community, because of the potential to uncover the molecular events most informative about a stem cell state. These technologies also present enormous challenges around the sharing and visualisation of data derived from different laboratories or under different experimental conditions. Stemformatics is an easy to use, publicly accessible portal that hosts a large collection of exemplar stem cell data. It provides fast visualisation of gene expression across a range of mouse and human datasets, with transparent links back to the original studies. One difficulty in the analysis of stem cell signatures is the paucity of public pathways/gene lists relevant to stem cell or developmental biology. Stemformatics provides a simple mechanism to create, share and analyse gene sets, providing a repository of community-annotated stem cell gene lists that are informative about pathways, lineage commitment, and common technical artefacts. Stemformatics can be accessed at stemformatics.org.

Variability in the generation of induced pluripotent stem cells: importance for disease modeling.

In the field of disease modeling, induced pluripotent stem cells (iPSCs) have become an appealing choice, especially for diseases that do not have an animal model. They can be generated from patients with known clinical features and compared with cells from healthy controls to identify the biological bases of disease. This study was undertaken to determine the variability in iPSC lines derived from different individuals, with the aim of determining criteria for selecting iPSC lines for disease models. We generated and characterized 18 iPSC lines from eight donors and considered variability at three levels: (a) variability in the criteria that define iPSC lines as pluripotent cells, (b) variability in cell lines from different donors, and (c) variability in cell lines from the same donor. We found that variability in transgene expression and pluripotency marker levels did not prevent iPSCs from fulfilling all other criteria for pluripotency, including teratoma formation. We found low interindividual and interclonal variability in iPSCs that fulfilled the most stringent criteria for pluripotency, with very high correlation in their gene expression profiles. Interestingly, some cell lines exhibited reprogramming instability, spontaneously regressing from a fully to a partially reprogrammed state. This was associated with a low percentage of cells expressing the pluripotency marker stage-specific embryonic antigen-4. Our study shows that it is possible to define a similar "ground state" for each cell line as the basis for making patient versus control comparisons, an essential step in order to identify disease-associated variability above individual and cell line variability.

attract: A method for identifying core pathways that define cellular phenotypes.

attract is a knowledge-driven analytical approach for identifying and annotating the gene-sets that best discriminate between cell phenotypes. attract finds distinguishing patterns within pathways, decomposes pathways into meta-genes representative of these patterns, and then generates synexpression groups of highly correlated genes from the entire transcriptome dataset. attract can be applied to a wide range of biological systems and is freely available as a Bioconductor package and has been incorporated into the MeV software system.

Variance of gene expression identifies altered network constraints in neurological disease.

Gene expression analysis has become a ubiquitous tool for studying a wide range of human diseases. In a typical analysis we compare distinct phenotypic groups and attempt to identify genes that are, on average, significantly different between them. Here we describe an innovative approach to the analysis of gene expression data, one that identifies differences in expression variance between groups as an informative metric of the group phenotype. We find that genes with different expression variance profiles are not randomly distributed across cell signaling networks. Genes with low-expression variance, or higher constraint, are significantly more connected to other network members and tend to function as core members of signal transduction pathways. Genes with higher expression variance have fewer network connections and also tend to sit on the periphery of the cell. Using neural stem cells derived from patients suffering from Schizophrenia (SZ), Parkinson's disease (PD), and a healthy control group, we find marked differences in expression variance in cell signaling pathways that shed new light on potential mechanisms associated with these diverse neurological disorders. In particular, we find that expression variance of core networks in the SZ patient group was considerably constrained, while in contrast the PD patient group demonstrated much greater variance than expected. One hypothesis is that diminished variance in SZ patients corresponds to an increased degree of constraint in these pathways and a corresponding reduction in robustness of the stem cell networks. These results underscore the role that variation plays in biological systems and suggest that analysis of expression variance is far more important in disease than previously recognized. Furthermore, modeling patterns of variability in gene expression could fundamentally alter the way in which we think about how cellular networks are affected by disease processes.

MicroRNAs and their isomiRs function cooperatively to target common biological pathways.

BACKGROUND: Variants of microRNAs (miRNAs), called isomiRs, are commonly reported in deep-sequencing studies; however, the functional significance of these variants remains controversial. Observational studies show that isomiR patterns are non-random, hinting that these molecules could be regulated and therefore functional, although no conclusive biological role has been demonstrated for these molecules. RESULTS: To assess the biological relevance of isomiRs, we have performed ultra-deep miRNA-seq on ten adult human tissues, and created an analysis pipeline called miRNA-MATE to align, annotate, and analyze miRNAs and their isomiRs. We find that isomiRs share sequence and expression characteristics with canonical miRNAs, and are generally strongly correlated with canonical miRNA expression. A large proportion of isomiRs potentially derive from AGO2 cleavage independent of Dicer. We isolated polyribosome-associated mRNA, captured the mRNA-bound miRNAs, and found that isomiRs and canonical miRNAs are equally associated with translational machinery. Finally, we transfected cells with biotinylated RNA duplexes encoding isomiRs or their canonical counterparts and directly assayed their mRNA targets. These studies allow us to experimentally determine genome-wide mRNA targets, and these experiments showed substantial overlap in functional mRNA networks suppressed by both canonical miRNAs and their isomiRs. CONCLUSIONS: Together, these results find isomiRs to be biologically relevant and functionally cooperative partners of canonical miRNAs that act coordinately to target pathways of functionally related genes. This work exposes the complexity of the miRNA-transcriptome, and helps explain a major miRNA paradox: how specific regulation of biological processes can occur when the specificity of miRNA targeting is mediated by only 6 to 11 nucleotides.

A cross-study transcriptional analysis of Parkinson's disease.

The study of Parkinson's disease (PD), like other complex neurodegenerative disorders, is limited by access to brain tissue from patients with a confirmed diagnosis. Alternatively the study of peripheral tissues may offer some insight into the molecular basis of disease susceptibility and progression, but this approach still relies on brain tissue to benchmark relevant molecular changes against. Several studies have reported whole-genome expression profiling in post-mortem brain but reported concordance between these analyses is lacking. Here we apply a standardised pathway analysis to seven independent case-control studies, and demonstrate increased concordance between data sets. Moreover data convergence increased when the analysis was limited to the five substantia nigra (SN) data sets; this highlighted the down regulation of dopamine receptor signaling and insulin-like growth factor 1 (IGF1) signaling pathways. We also show that case-control comparisons of affected post mortem brain tissue are more likely to reflect terminal cytoarchitectural differences rather than primary pathogenic mechanisms. The implementation of a correction factor for dopaminergic neuronal loss predictably resulted in the loss of significance of the dopamine signaling pathway while axon guidance pathways increased in significance. Interestingly the IGF1 signaling pathway was also over-represented when data from non-SN areas, unaffected or only terminally affected in PD, were considered. Our findings suggest that there is greater concordance in PD whole-genome expression profiling when standardised pathway membership rather than ranked gene list is used for comparison.

Fibroblast and lymphoblast gene expression profiles in schizophrenia: are non-neural cells informative?

Lymphoblastoid cell lines (LCLs) and fibroblasts provide conveniently derived non-neuronal samples in which to investigate the aetiology of schizophrenia (SZ) using gene expression profiling. This assumes that heritable mechanisms associated with risk of SZ have systemic effects and result in changes to gene expression in all tissues. The broad aim of this and other similar studies is that comparison of the transcriptomes of non-neuronal tissues from SZ patients and healthy controls may identify gene/pathway dysregulation underpinning the neurobiological defects associated with SZ. Using microarrays consisting of 18,664 probes we compared gene expression profiles of LCLs from SZ cases and healthy controls. To identify robust associations with SZ that were not patient or tissue specific, we also examined fibroblasts from an independent series of SZ cases and controls using the same microarrays. In both tissue types ANOVA analysis returned approximately the number of differentially expressed genes expected by chance. No genes were significantly differentially expressed in either tissue when corrected for multiple testing. Even using relaxed parameters (p < or = 0.05, without multiple testing correction) there were still no differentially expressed genes that also displayed > or = 2-fold change between the groups of SZ cases and controls common to both LCLs and fibroblasts. We conclude that despite encouraging data from previous microarray studies assessing non-neural tissues, the lack of a convergent set of differentially expressed genes associated with SZ using fibroblasts and LCLs indicates the utility of non-neuronal tissues for detection of gene expression differences and/or pathways associated with SZ remains to be demonstrated.

Gene expression analysis in absence epilepsy using a monozygotic twin design.

PURPOSE: To identify genes involved in idiopathic absence epilepsies by analyzing gene expression using a monozygotic (MZ) twin design. METHODS: Genome-wide gene expression in lymphoblastoid cell lines (LCLs) was determined using microarrays derived from five discordant and four concordant MZ twin pairs with idiopathic absence epilepsies and five unaffected MZ twin pairs. Gene expression was analyzed using three strategies: discordant MZ twins were compared as matched pairs, MZ twins concordant for epilepsy were compared to control MZ twins, and a singleton design of affected versus unaffected MZ twin individuals was used irrespective of twin pairing. An overlapping gene list was generated from these analyses. Dysregulation of genes recognized from the microarray experiment was validated using quantitative real time PCR (qRT-PCR) in the twin sample and in an independent sample of 18 sporadic absence cases and 24 healthy controls. RESULTS: Sixty-five probe sets were identified from the three combined microarray analysis strategies. Sixteen genes were chosen for validation and nine of these genes confirmed by qRT-PCR in the twin sample. Differential expression for EGR1 (an immediate early gene) and RCN2 (coding for the calcium-binding protein Reticulocalbin 2) were reconfirmed by qRT-PCR in the independent sample. DISCUSSION: Using a unique sample of discordant MZ twins, our study identified genes with altered expression, which suggests novel mechanisms in idiopathic absence epilepsy. Dysregulation of EGR1 and RCN2 is implicated in idiopathic absence epilepsy.

Replicated effects of sex and genotype on gene expression in human lymphoblastoid cell lines.

The expression level for 15,887 transcripts in lymphoblastoid cell lines from 19 monozygotic twin pairs (10 male, 9 female) were analysed for the effects of genotype and sex. On an average, the effect of twin pairs explained 31% of the variance in normalized gene expression levels, consistent with previous broad sense heritability estimates. The effect of sex on gene expression levels was most noticeable on the X chromosome, which contained 15 of the 20 significantly differentially expressed genes. A high concordance was observed between the sex difference test statistics and surveys of genes escaping X chromosome inactivation. Notably, several autosomal genes showed significant differences in gene expression between the sexes despite much of the cellular environment differences being effectively removed in the cell lines. A publicly available gene expression data set from the CEPH families was used to validate the results. The heritability of gene expression levels as estimated from the two data sets showed a highly significant positive correlation, particularly when both estimates were close to one and thus had the smallest standard error. There was a large concordance between the genes significantly differentially expressed between the sexes in the two data sets. Analysis of the variability of probe binding intensities within a probe set indicated that results are robust to the possible presence of polymorphisms in the target sequences.