A multi-modal data resource for investigating topographic heterogeneity in patient-derived xenograft tumors.

Patient-derived xenografts (PDXs) are an essential pre-clinical resource for investigating tumor biology. However, cellular heterogeneity within and across PDX tumors can strongly impact the interpretation of PDX studies. Here, we generated a multi-modal, large-scale dataset to investigate PDX heterogeneity in metastatic colorectal cancer (CRC) across tumor models, spatial scales and genomic, transcriptomic, proteomic and imaging assay modalities. To showcase this dataset, we present analysis to assess sources of PDX variation, including anatomical orientation within the implanted tumor, mouse contribution, and differences between replicate PDX tumors. A unique aspect of our dataset is deep characterization of intra-tumor heterogeneity via immunofluorescence imaging, which enables investigation of variation across multiple spatial scales, from subcellular to whole tumor levels. Our study provides a benchmark data resource to investigate PDX models of metastatic CRC and serves as a template for future, quantitative investigations of spatial heterogeneity within and across PDX tumor models

Multiscale, multimodal analysis of tumor heterogeneity in IDH1 mutant vs wild-type diffuse gliomas.

Glioma is recognized to be a highly heterogeneous CNS malignancy, whose diverse cellular composition and cellular interactions have not been well characterized. To gain new clinical- and biological-insights into the genetically-bifurcated IDH1 mutant (mt) vs wildtype (wt) forms of glioma, we integrated data from protein, genomic and MR imaging from 20 treatment-naïve glioma cases and 16 recurrent GBM cases. Multiplexed immunofluorescence (MxIF) was used to generate single cell data for 43 protein markers representing all cancer hallmarks, Genomic sequencing (exome and RNA (normal and tumor) and magnetic resonance imaging (MRI) quantitative features (protocols were T1-post, FLAIR and ADC) from whole tumor, peritumoral edema and enhancing core vs equivalent normal region were also collected from patients. Based on MxIF analysis, 85,767 cells (glioma cases) and 56,304 cells (GBM cases) were used to generate cell-level data for 24 biomarkers. K-means clustering was used to generate 7 distinct groups of cells with divergent biomarker profiles and deconvolution was used to assign RNA data into three classes. Spatial and molecular heterogeneity metrics were generated for the cell data. All features were compared between IDH mt and IDHwt patients and were finally combined to provide a holistic/integrated comparison. Protein expression by hallmark was generally lower in the IDHmt vs wt patients. Molecular and spatial heterogeneity scores for angiogenesis and cell invasion also differed between IDHmt and wt gliomas irrespective of prior treatment and tumor grade; these differences also persisted in the MR imaging features of peritumoral edema and contrast enhancement volumes. A coherent picture of enhanced angiogenesis in IDHwt tumors was derived from multiple platforms (genomic, proteomic and imaging) and scales from individual proteins to cell clusters and heterogeneity, as well as bulk tumor RNA and imaging features. Longer overall survival for IDH1mt glioma patients may reflect mutation-driven alterations in cellular, molecular, and spatial heterogeneity which manifest in discernable radiological manifestations

Proteomics: Clinical and research applications in respiratory diseases

The proteome is the study of the protein content of a definable component of an organism in biology. However, the tissue‐specific expression of proteins and the varied post‐translational modifications, splice variants and protein–protein complexes that may form, make the study of protein a challenging yet vital tool in answering many of the unanswered questions in medicine and biology to date. Indeed, the spatial, temporal and functional composition of proteins in the human body has proven difficult to elucidate for many years. Given the effect of microRNA and epigenetic regulation on silencing and enhancing gene transcription, the study of protein arguably provides more accurate information on homeostasis and perturbation in health and disease. There have been significant advances in the field of proteomics in recent years, with new technologies and platforms available to the research community. In this review, we briefly discuss some of these new technologies and developments in the context of respiratory disease. We also discuss the types of data science approaches to analyses and interpretation of the large volumes of data generated in proteomic studies. We discuss the application of these technologies with regard to respiratory disease and highlight the potential for proteomics in generating major advances in the understanding of respiratory pathophysiology into the future.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146450/1/resp13383_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146450/2/resp13383.pd

Gene expression profiling en association with prion-related lesions in the medulla oblongata of symptomatic natural scrapie animals.

The pathogenesis of natural scrapie and other prion diseases remains unclear. Examining transcriptome variations in infected versus control animals may highlight new genes potentially involved in some of the molecular mechanisms of prion-induced pathology. The aim of this work was to identify disease-associated alterations in the gene expression profiles of the caudal medulla oblongata (MO) in sheep presenting the symptomatic phase of natural scrapie. The gene expression patterns in the MO from 7 sheep that had been naturally infected with scrapie were compared with 6 controls using a Central Veterinary Institute (CVI) custom designed 4×44K microarray. The microarray consisted of a probe set on the previously sequenced ovine tissue library by CVI and was supplemented with all of the Ovis aries transcripts that are currently publicly available. Over 350 probe sets displayed greater than 2-fold changes in expression. We identified 148 genes from these probes, many of which encode proteins that are involved in the immune response, ion transport, cell adhesion, and transcription. Our results confirm previously published gene expression changes that were observed in murine models with induced scrapie. Moreover, we have identified new genes that exhibit differential expression in scrapie and could be involved in prion neuropathology. Finally, we have investigated the relationship between gene expression profiles and the appearance of the main scrapie-related lesions, including prion protein deposition, gliosis and spongiosis. In this context, the potential impacts of these gene expression changes in the MO on scrapie development are discussed

Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps

Mass spectrometry; Proteomics; WorkflowsEspectrometría de masas; Proteómica; Flujos de trabajoEspectrometria de masses; Proteòmica; Fluxos de treballThe qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography–mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.This research was funded by the Research Council of Norway INFRASTRUKTUR-program (project number: 295910)