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General General

Assessing single-cell transcriptomic variability through density-preserving data visualization.

In Nature biotechnology ; h5-index 151.0

Nonlinear data visualization methods, such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP), summarize the complex transcriptomic landscape of single cells in two dimensions or three dimensions, but they neglect the local density of data points in the original space, often resulting in misleading visualizations where densely populated subsets of cells are given more visual space than warranted by their transcriptional diversity in the dataset. Here we present den-SNE and densMAP, which are density-preserving visualization tools based on t-SNE and UMAP, respectively, and demonstrate their ability to accurately incorporate information about transcriptomic variability into the visual interpretation of single-cell RNA sequencing data. Applied to recently published datasets, our methods reveal significant changes in transcriptomic variability in a range of biological processes, including heterogeneity in transcriptomic variability of immune cells in blood and tumor, human immune cell specialization and the developmental trajectory of Caenorhabditis elegans. Our methods are readily applicable to visualizing high-dimensional data in other scientific domains.

Narayan Ashwin, Berger Bonnie, Cho Hyunghoon


oncology Oncology

Genome-wide landscape of RNA-binding protein target site dysregulation reveals a major impact on psychiatric disorder risk.

In Nature genetics ; h5-index 174.0

Despite the strong genetic basis of psychiatric disorders, the underlying molecular mechanisms are largely unmapped. RNA-binding proteins (RBPs) are responsible for most post-transcriptional regulation, from splicing to translation to localization. RBPs thus act as key gatekeepers of cellular homeostasis, especially in the brain. However, quantifying the pathogenic contribution of noncoding variants impacting RBP target sites is challenging. Here, we leverage a deep learning approach that can accurately predict the RBP target site dysregulation effects of mutations and discover that RBP dysregulation is a principal contributor to psychiatric disorder risk. RBP dysregulation explains a substantial amount of heritability not captured by large-scale molecular quantitative trait loci studies and has a stronger impact than common coding region variants. We share the genome-wide profiles of RBP dysregulation, which we use to identify DDHD2 as a candidate schizophrenia risk gene. This resource provides a new analytical framework to connect the full range of RNA regulation to complex disease.

Park Christopher Y, Zhou Jian, Wong Aaron K, Chen Kathleen M, Theesfeld Chandra L, Darnell Robert B, Troyanskaya Olga G


General General

Distinct metabolic programs established in the thymus control effector functions of γδ T cell subsets in tumor microenvironments.

In Nature immunology ; h5-index 124.0

Metabolic programming controls immune cell lineages and functions, but little is known about γδ T cell metabolism. Here, we found that γδ T cell subsets making either interferon-γ (IFN-γ) or interleukin (IL)-17 have intrinsically distinct metabolic requirements. Whereas IFN-γ+ γδ T cells were almost exclusively dependent on glycolysis, IL-17+ γδ T cells strongly engaged oxidative metabolism, with increased mitochondrial mass and activity. These distinct metabolic signatures were surprisingly imprinted early during thymic development and were stably maintained in the periphery and within tumors. Moreover, pro-tumoral IL-17+ γδ T cells selectively showed high lipid uptake and intracellular lipid storage and were expanded in obesity and in tumors of obese mice. Conversely, glucose supplementation enhanced the antitumor functions of IFN-γ+ γδ T cells and reduced tumor growth upon adoptive transfer. These findings have important implications for the differentiation of effector γδ T cells and their manipulation in cancer immunotherapy.

Lopes Noella, McIntyre Claire, Martin Stefania, Raverdeau Mathilde, Sumaria Nital, Kohlgruber Ayano C, Fiala Gina J, Agudelo Leandro Z, Dyck Lydia, Kane Harry, Douglas Aaron, Cunningham Stephen, Prendeville Hannah, Loftus Roisin, Carmody Colleen, Pierre Philippe, Kellis Manolis, Brenner Michael, Arg├╝ello Rafael J, Silva-Santos Bruno, Pennington Daniel J, Lynch Lydia


General General

Bayesian strategy selection identifies optimal solutions to complex problems using an example from GP prescribing.

In NPJ digital medicine

Complex health problems require multi-strategy, multi-target interventions. We present a method that uses machine learning techniques to choose optimal interventions from a set of possible interventions within a case study aiming to increase General Practitioner (GP) discussions of physical activity (PA) with their patients. Interventions were developed based on a causal loop diagram with 26 GPs across 13 clinics in Geelong, Australia. GPs prioritised eight from more than 80 potential interventions to increase GP discussion of PA with patients. Following a 2-week baseline, a multi-arm bandit algorithm was used to assign optimal strategies to GP clinics with the target outcome being GP PA discussion rates. The algorithm was updated weekly and the process iterated until the more promising strategies emerged (a duration of seven weeks). The top three performing strategies were continued for 3 weeks to improve the power of the hypothesis test of effectiveness for each strategy compared to baseline. GPs recorded a total of 11,176 conversations about PA. GPs identified 15 factors affecting GP PA discussion rates with patients including GP skills and awareness, fragmentation of care and fear of adverse outcomes. The two most effective strategies were correctly identified within seven weeks of the algorithm-based assignment of strategies. These were clinic reception staff providing PA information to patients at check in and PA screening questionnaires completed in the waiting room. This study demonstrates an efficient way to test and identify optimal strategies from multiple possible solutions.

Allender S, Hayward J, Gupta S, Sanigorski A, Rana S, Seward H, Jacobs S, Venkatesh S


General General

Deep learning-Based 3D inpainting of brain MR images.

In Scientific reports ; h5-index 158.0

The detailed anatomical information of the brain provided by 3D magnetic resonance imaging (MRI) enables various neuroscience research. However, due to the long scan time for 3D MR images, 2D images are mainly obtained in clinical environments. The purpose of this study is to generate 3D images from a sparsely sampled 2D images using an inpainting deep neural network that has a U-net-like structure and DenseNet sub-blocks. To train the network, not only fidelity loss but also perceptual loss based on the VGG network were considered. Various methods were used to assess the overall similarity between the inpainted and original 3D data. In addition, morphological analyzes were performed to investigate whether the inpainted data produced local features similar to the original 3D data. The diagnostic ability using the inpainted data was also evaluated by investigating the pattern of morphological changes in disease groups. Brain anatomy details were efficiently recovered by the proposed neural network. In voxel-based analysis to assess gray matter volume and cortical thickness, differences between the inpainted data and the original 3D data were observed only in small clusters. The proposed method will be useful for utilizing advanced neuroimaging techniques with 2D MRI data.

Kang Seung Kwan, Shin Seong A, Seo Seongho, Byun Min Soo, Lee Dong Young, Kim Yu Kyeong, Lee Dong Soo, Lee Jae Sung


Ophthalmology Ophthalmology

An objective structural and functional reference standard in glaucoma.

In Scientific reports ; h5-index 158.0

The current lack of consensus for diagnosing glaucoma makes it difficult to develop diagnostic tests derived from deep learning (DL) algorithms. In the present study, we propose an objective definition of glaucomatous optic neuropathy (GON) using clearly defined parameters from optical coherence tomography and standard automated perimetry. We then use the proposed objective definition as reference standard to develop a DL algorithm to detect GON on fundus photos. A DL algorithm was trained to detect GON on fundus photos, using the proposed objective definition as reference standard. The performance was evaluated on an independent test sample with sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and likelihood ratios (LR). The test sample had 2118 fundus photos from 585 eyes of 405 individuals. The AUC to discriminate between GON and normal was 0.92 with sensitivity of 77% at 95% specificity. LRs indicated that the DL algorithm provided large changes in the post-test probability of disease for the majority of eyes. A DL algorithm to evaluate fundus photos had high performance to discriminate GON from normal. The newly proposed objective definition of GON used as reference standard may increase the comparability of diagnostic studies of glaucoma across devices and populations.

Mariottoni Eduardo B, Jammal Alessandro A, Berchuck Samuel I, Shigueoka Leonardo S, Tavares Ivan M, Medeiros Felipe A