Evolution and Impact of High Content Imaging

Abstract/outline: The field of high content imaging has steadily evolved and expanded substantially across many industry and academic research institutions since it was first described in the early 1990′s. High content imaging refers to the automated acquisition and analysis of microscopic images from a variety of biological sample types. Integration of high content imaging microscopes with multiwell plate handling robotics enables high content imaging to be performed at scale and support medium- to high-throughput screening of pharmacological, genetic and diverse environmental perturbations upon complex biological systems ranging from 2D cell cultures to 3D tissue organoids to small model organisms. In this perspective article the authors provide a collective view on the following key discussion points relevant to the evolution of high content imaging:• Evolution and impact of high content imaging: An academic perspective• Evolution and impact of high content imaging: An industry perspective• Evolution of high content image analysis• Evolution of high content data analysis pipelines towards multiparametric and phenotypic profiling applications• The role of data integration and multiomics• The role and evolution of image data repositories and sharing standards• Future perspective of high content imaging hardware and softwar

A Machine Learning Classifier Trained on Cancer Transcriptomes Detects NF1 Inactivation Signal in Glioblastoma

We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules. We developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM)

Elaboration and properties of plasticised chitosan-based exfoliated nano-biocomposites

A series of plasticised chitosan-based materials and nanocomposites were successfully prepared by thermomechanical kneading. During the processing, the montmorillonite (MMT) platelets were fully delaminated. The nanoclay type and content and the preparation method were seen to have an impact on the crystallinity, morphology, glass transition temperature, and mechanical properties of the samples. When higher content (5%) of MMT–Na+ or either content (2.5% or 5%) of chitosan-organomodified MMT (OMMT–Ch) was used, increases in crystallinity and glass transition temperature were observed. Compared to the neat chitosan, the plasticised chitosan-based nano-biocomposites showed drastically improved mechanical properties, which can be ascribed to the excellent dispersion and exfoliation of nanoclay and the strong affinity between the nanoclay and the chitosan matrix. The best mechanical properties obtained were Young's modulus of 164.3 MPa, tensile strength of 13.9 MPa, elongation at break of 62.1%, and energy at break of 0.671 MPa. While the degree of biodegradation was obviously increased by the presence of glycerol, a further increase might be observed especially by the addition of unmodified nanoclay. This could surprisingly contribute to full (100%) biodegradation after 160 days despite the well-known antimicrobial property of chitosan. The results in this study demonstrate the great potential of plasticised chitosan-based nano-biocomposites in applications such as e.g., biodegradable packaging materials

Genomic Profiling of Childhood Tumor Patient-Derived Xenograft Models to Enable Rational Clinical Trial Design.

Accelerating cures for children with cancer remains an immediate challenge as a result of extensive oncogenic heterogeneity between and within histologies, distinct molecular mechanisms evolving between diagnosis and relapsed disease, and limited therapeutic options. To systematically prioritize and rationally test novel agents in preclinical murine models, researchers within the Pediatric Preclinical Testing Consortium are continuously developing patient-derived xenografts (PDXs)-many of which are refractory to current standard-of-care treatments-from high-risk childhood cancers. Here, we genomically characterize 261 PDX models from 37 unique pediatric cancers; demonstrate faithful recapitulation of histologies and subtypes; and refine our understanding of relapsed disease. In addition, we use expression signatures to classify tumors for TP53 and NF1 pathway inactivation. We anticipate that these data will serve as a resource for pediatric oncology drug development and will guide rational clinical trial design for children with cancer

Epigenomic profiling of neuroblastoma cell lines.

Understanding the aberrant transcriptional landscape of neuroblastoma is necessary to provide insight to the underlying influences of the initiation, progression and persistence of this developmental cancer. Here, we present chromatin immunoprecipitation sequencing (ChIP-Seq) data for the oncogenic transcription factors, MYCN and MYC, as well as regulatory histone marks H3K4me1, H3K4me3, H3K27Ac, and H3K27me3 in ten commonly used human neuroblastoma-derived cell line models. In addition, for all of the profiled cell lines we provide ATAC-Seq as a measure of open chromatin. We validate specificity of global MYCN occupancy in MYCN amplified cell lines and functional redundancy of MYC occupancy in MYCN non-amplified cell lines. Finally, we show with H3K27Ac ChIP-Seq that these cell lines retain expression of key neuroblastoma super-enhancers (SE). We anticipate this dataset, coupled with available transcriptomic profiling on the same cell lines, will enable the discovery of novel gene regulatory mechanisms in neuroblastoma

Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas.

Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these "hidden responders" may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders

Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas.

DNA damage repair (DDR) pathways modulate cancer risk, progression, and therapeutic response. We systematically analyzed somatic alterations to provide a comprehensive view of DDR deficiency across 33 cancer types. Mutations with accompanying loss of heterozygosity were observed in over 1/3 of DDR genes, including TP53 and BRCA1/2. Other prevalent alterations included epigenetic silencing of the direct repair genes EXO5, MGMT, and ALKBH3 in ∼20% of samples. Homologous recombination deficiency (HRD) was present at varying frequency in many cancer types, most notably ovarian cancer. However, in contrast to ovarian cancer, HRD was associated with worse outcomes in several other cancers. Protein structure-based analyses allowed us to predict functional consequences of rare, recurrent DDR mutations. A new machine-learning-based classifier developed from gene expression data allowed us to identify alterations that phenocopy deleterious TP53 mutations. These frequent DDR gene alterations in many human cancers have functional consequences that may determine cancer progression and guide therapy