Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts.

Drug discovery for diseases such as Parkinson's disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 Parkinson's disease patients and matched healthy controls, creating the largest publicly available Cell Painting image dataset to date at 48 terabytes.

ProtSeq: Toward high-throughput, single-molecule protein sequencing via amino acid conversion into DNA barcodes.

We demonstrate early progress toward constructing a high-throughput, single-molecule protein sequencing technology utilizing barcoded DNA aptamers (binders) to recognize terminal amino acids of peptides (targets) tethered on a next-generation sequencing chip. DNA binders deposit unique, amino acid-identifying barcodes on the chip. The end goal is that, over multiple binding cycles, a sequential chain of DNA barcodes will identify the amino acid sequence of a peptide.

Morphological Characterization of Antibiotic Combinations.

Combination therapies are common in many therapeutic contexts, including infectious diseases and cancer. A common approach for evaluating combinations is to assess effects on cell growth as synergistic, antagonistic, or neutral using "checkerboard" experiments to systematically sample combinations of agents in multiple doses. To further understand the effects of antibiotic combinations, we employed high-content imaging to study the morphological changes caused by combination treatments in checkerboard experiments.

Discovery of complex oxides via automated experiments and data science.

The quest to identify materials with tailored properties is increasingly expanding into high-order composition spaces, with a corresponding combinatorial explosion in the number of candidate materials. A key challenge is to discover regions in composition space where materials have novel properties. Traditional predictive models for material properties are not accurate enough to guide the search.

Learning causal networks using inducible transcription factors and transcriptome-wide time series.

We present IDEA (the Induction Dynamics gene Expression Atlas), a dataset constructed by independently inducing hundreds of transcription factors (TFs) and measuring timecourses of the resulting gene expression responses in budding yeast. Each experiment captures a regulatory cascade connecting a single induced regulator to the genes it causally regulates. We discuss the regulatory cascade of a single TF, Aft1, in detail; however, IDEA contains > 200 TF induction experiments with 20 million individual observations and 100,000 signal-containing dynamic responses.

Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India.

Importance: More than 60 million people in India have diabetes and are at risk for diabetic retinopathy (DR), a vision-threatening disease. Automated interpretation of retinal fundus photographs can help support and scale a robust screening program to detect DR.

Objective: To prospectively validate the performance of an automated DR system across 2 sites in India.

Bribes and plasters won't do.

I disagree with Graham Scott's editorial (9 August). We should not be bribing men into nursing, we should be fighting to reinstate the bursary for all healthcare students and be looking at the issues around gender stereotyping and perceptions relating to occupations.

Leveraging Multi-ethnic Evidence for Risk Assessment of Quantitative Traits in Minority Populations.

An essential component of precision medicine is the ability to predict an individual's risk of disease based on genetic and non-genetic factors. For complex traits and diseases, assessing the risk due to genetic factors is challenging because it requires knowledge of both the identity of variants that influence the trait and their corresponding allelic effects. Although the set of risk variants and their allelic effects may vary between populations, a large proportion of these variants were identified based on studies in populations of European descent.

A Poisson Log-Normal Model for Constructing Gene Covariation Network Using RNA-seq Data.

Constructing expression networks using transcriptomic data is an effective approach for studying gene regulation. A popular approach for constructing such a network is based on the Gaussian graphical model (GGM), in which an edge between a pair of genes indicates that the expression levels of these two genes are conditionally dependent, given the expression levels of all other genes. However, GGMs are not appropriate for non-Gaussian data, such as those generated in RNA-seq experiments.

No-show implies 'no value'.

Theresa May absolutely should have addressed RCN congress.

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

Importance: Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of these methods to medical imaging requires further assessment and validation.

Objective: To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs.

Leveraging Multi-ethnic Evidence for Mapping Complex Traits in Minority Populations: An Empirical Bayes Approach.

Elucidating the genetic basis of complex traits and diseases in non-European populations is particularly challenging because US minority populations have been under-represented in genetic association studies. We developed an empirical Bayes approach named XPEB (cross-population empirical Bayes), designed to improve the power for mapping complex-trait-associated loci in a minority population by exploiting information from genome-wide association studies (GWASs) from another ethnic population. Taking as input summary statistics from two GWASs-a target GWAS from an ethnic minority population of primary interest and an auxiliary base GWAS (such as a larger GWAS in Europeans)-our XPEB approach reprioritizes SNPs in the target population to compute local false-discovery rates.

A minimum variance method for genome-wide data-driven normalization of quantitative real-time polymerase chain reaction expression data.

Advances in multiplex qRT-PCR have enabled increasingly accurate and robust quantification of RNA, even at lower concentrations, facilitating RNA expression profiling in clinical and environmental samples. Here we describe a data-driven qRT-PCR normalization method, the minimum variance method, and evaluate it on clinically derived Mycobacterium tuberculosis samples with variable transcript detection percentages. For moderate to significant amounts of nondetection (∼50%), our minimum variance method consistently produces the lowest false discovery rates compared to commonly used data-driven normalization methods.

Patient-derived xenografts of triple-negative breast cancer reproduce molecular features of patient tumors and respond to mTOR inhibition.

Introduction: Triple-negative breast cancer (TNBC) is aggressive and lacks targeted therapies. Phosphatidylinositide 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) pathways are frequently activated in TNBC patient tumors at the genome, gene expression and protein levels, and mTOR inhibitors have been shown to inhibit growth in TNBC cell lines. We describe a panel of patient-derived xenografts representing multiple TNBC subtypes and use them to test preclinical drug efficacy of two mTOR inhibitors, sirolimus (rapamycin) and temsirolimus (CCI-779).

Genome-wide characterization of shared and distinct genetic components that influence blood lipid levels in ethnically diverse human populations.

Blood lipid concentrations are heritable risk factors associated with atherosclerosis and cardiovascular diseases. Lipid traits exhibit considerable variation among populations of distinct ancestral origin as well as between individuals within a population. We performed association analyses to identify genetic loci influencing lipid concentrations in African American and Hispanic American women in the Women's Health Initiative SNP Health Association Resource.

Genetic architecture of skin and eye color in an African-European admixed population.

Variation in human skin and eye color is substantial and especially apparent in admixed populations, yet the underlying genetic architecture is poorly understood because most genome-wide studies are based on individuals of European ancestry. We study pigmentary variation in 699 individuals from Cape Verde, where extensive West African/European admixture has given rise to a broad range in trait values and genomic ancestry proportions. We develop and apply a new approach for measuring eye color, and identify two major loci (HERC2[OCA2] P = 2.

Transcript levels of androgen receptor variant AR-V1 or AR-V7 do not predict recurrence in patients with prostate cancer at indeterminate risk for progression.

Purpose: AR-V7, a ligand independent splice variant of androgen receptor, may support the growth of castration resistant prostate cancer and have prognostic value. Another variant, AR-V1, interferes with AR-V7 activity. We investigated whether AR-V7 or V1 expression would predict biochemical recurrence in men at indeterminate (about 50%) risk for progression following radical prostatectomy.

Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines.

Background: To improve cancer therapy, it is critical to target metastasizing cells. Circulating tumor cells (CTCs) are rare cells found in the blood of patients with solid tumors and may play a key role in cancer dissemination. Uncovering CTC phenotypes offers a potential avenue to inform treatment.

Ancestral components of admixed genomes in a Mexican cohort.

For most of the world, human genome structure at a population level is shaped by interplay between ancient geographic isolation and more recent demographic shifts, factors that are captured by the concepts of biogeographic ancestry and admixture, respectively. The ancestry of non-admixed individuals can often be traced to a specific population in a precise region, but current approaches for studying admixed individuals generally yield coarse information in which genome ancestry proportions are identified according to continent of origin. Here we introduce a new analytic strategy for this problem that allows fine-grained characterization of admixed individuals with respect to both geographic and genomic coordinates.

Using noun phrases for navigating biomedical literature on Pubmed: how many updates are we losing track of?

Author-supplied citations are a fraction of the related literature for a paper. The "related citations" on PubMed is typically dozens or hundreds of results long, and does not offer hints why these results are related. Using noun phrases derived from the sentences of the paper, we show it is possible to more transparently navigate to PubMed updates through search terms that can associate a paper with its citations.

Antibodies specifically target AML antigen NuSAP1 after allogeneic bone marrow transplantation.

Identifying the targets of immune response after allogeneic hematopoietic cell transplantation (HCT) promises to provide relevant immune therapy candidate proteins. We used protein microarrays to serologically identify nucleolar and spindle-associated protein 1 (NuSAP1) and chromatin assembly factor 1, subunit B (p60; CHAF1b) as targets of new antibody responses that developed after allogeneic HCT. Western blots and enzyme-linked immunosorbent assays (ELISA) validated their post-HCT recognition and enabled ELISA testing of 120 other patients with various malignancies who underwent allo-HCT.

Single-cell profiling identifies aberrant STAT5 activation in myeloid malignancies with specific clinical and biologic correlates.

Progress in understanding the molecular pathogenesis of human myeloproliferative disorders (MPDs) has led to guidelines incorporating genetic assays with histopathology during diagnosis. Advances in flow cytometry have made it possible to simultaneously measure cell type and signaling abnormalities arising as a consequence of genetic pathologies. Using flow cytometry, we observed a specific evoked STAT5 signaling signature in a subset of samples from patients suspected of having juvenile myelomonocytic leukemia (JMML), an aggressive MPD with a challenging clinical presentation during active disease.

The significance of monoamine oxidase-A expression in high grade prostate cancer.

Purpose: Gleason grade 4/5 prostate cancer is a determinant for recurrence following radical prostatectomy. Monoamine oxidase-A is over expressed in grade 4/5 compared to grade 3 cancer. Monoamine oxidase-A is also expressed by normal basal cells and in vitro studies suggest that its function is to repress secretory differentiation.

H-Y antibody development associates with acute rejection in female patients with male kidney transplants.

Background: Human minor histocompatibility antigens (mHA) and clinically relevant immune responses to them have not been well defined in organ transplantation. We hypothesized that women with male kidney transplants would develop antibodies against H-Y, the mHA encoded on the Y-chromosome, in association with graft rejection.

Methods: We tested sera from 118 consecutive transplant recipients with kidney biopsies.