Gamma secretase modulators and BACE inhibitors reduce Aß production without altering gene expression in Alzheimer's disease iPSC-derived neurons and mice.

In drug discovery, as well as in the study of disease biology, it is fundamental to develop models that recapitulate aspects of a disorder, in order to understand the pathology and test therapeutic approaches. Patient-derived induced pluripotent stem cells (iPSCs) offer the potential of obtaining tissue-specific cells with a given human genotype. Here we derived neural cultures from Alzheimer's disease patient iPSCs and characterized their response to three classes of compounds that reduce the production of Aß42, a major driving force of this pathology. We characterized their effect on the cells, looking at Tau proteostasis and gene expression changes by RNAseq. ß-secretase inhibitor and γ-secretase modulators left the transcriptional balance of the cells virtually unaffected, while γ-secretase inhibitors caused drastic gene expression changes due to Notch inhibition. We observed similar effects in vivo, treating mice with the same compound classes. Our results show that ß-secretase inhibitors and γ-secretase modulators are attractive candidates for modulating Aß production in Alzheimer's disease. Moreover, we demonstrate that the response to compounds obtained with iPSC-derived neurons is similar to the one observable in vivo.

Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics.

The consensus molecular subtypes (CMS) of colorectal cancer (CRC) is the most widely-used gene expression-based classification and has contributed to a better understanding of disease heterogeneity and prognosis. Nevertheless, CMS intratumoral heterogeneity restricts its clinical application, stressing the necessity of further characterizing the composition and architecture of CRC. Here, we used Spatial Transcriptomics (ST) in combination with single-cell RNA sequencing (scRNA-seq) to decipher the spatially resolved cellular and molecular composition of CRC. In addition to mapping the intratumoral heterogeneity of CMS and their microenvironment, we identified cell communication events in the tumor-stroma interface of CMS2 carcinomas. This includes tumor growth-inhibiting as well as -activating signals, such as the potential regulation of the ETV4 transcriptional activity by DCN or the PLAU-PLAUR ligand-receptor interaction. Our study illustrates the potential of ST to resolve CRC molecular heterogeneity and thereby help advance personalized therapy.

Group-level impacts of within- and between-subject hemodynamic variability in fMRI.

Inter-subject fMRI analyses have specific issues regarding the reliability of the results concerning both the detection of brain activation patterns and the estimation of the underlying dynamics. Among these issues lies the variability of the hemodynamic response function (HRF), that is usually accounted for using functional basis sets in the general linear model context. Here, we use the joint detection-estimation approach (JDE) (Makni et al., 2008; Vincent et al., 2010) which combines regional nonparametric HRF inference with spatially adaptive regularization of activation clusters to avoid global smoothing of fMRI images. We show that the JDE-based inference brings a significant improvement in statistical sensitivity for detecting evoked activity in parietal regions. In contrast, the canonical HRF associated with spatially adaptive regularization is more sensitive in other regions, such as motor cortex. This different regional behavior is shown to reflect a larger discrepancy of HRF with the canonical model. By varying parallel imaging acceleration factor, SNR-specific region-based hemodynamic parameters (activation delay and duration) were extracted from the JDE inference. Complementary analyses highlighted their significant departure from the canonical parameters and the strongest between-subject variability that occurs in the parietal region, irrespective of the SNR value. Finally, statistical evidence that the fluctuation of the HRF shape is responsible for the significant change in activation detection performance is demonstrated using paired t-tests between hemodynamic parameters inferred by GLM and JDE.

Combined cSLO-OCT imaging as a tool in preclinical ocular toxicity testing: A comparison to standard in-vivo and pathology methods.

INTRODUCTION: Confocal scanning laser ophthalmoscopy and optical coherence tomography (cSLO-OCT) became available for human and animal ophthalmic examinations in recent years. The purpose of this study was to evaluate lesion detection and localization with cSLO-OCT imaging in an experimental outer retinal toxicity model and to compare cSLO-OCT to standard examination methods (indirect ophthalmoscopy (IO), fundus photography (FP) and central section histopathology). METHODS: A test compound was orally administered to albino rats (n = 4) for four weeks (part A) and to albino (n = 2) and pigmented (n = 2) rats for eight weeks (part B). Control animals received vehicle only. Retinal changes were documented using cSLO-OCT, IO, FP, angiography and histopathology. Retinal thicknesses were compared between groups using a mixed effects model. RESULTS: All compound-treated animals developed progressive multifocal hyperreflective spot changes mostly confined to the retinal pigment epithelium. In study parts A and B, cSLO identified fundus lesions earlier than IO/FP in albino rats. In study part B, cSLO quantified fundus lesions more accurately than IO/FP in albino rats but no difference was seen in pigmented rats. Central section histopathology revealed no abnormalities in three out of four compound-treated animals in part B. Altogether, without cSLO-OCT, present fundus changes would have remained undetected in one of four compound-treated animals in both parts A and B. DISCUSSION: Integration of combined cSLO-OCT imaging into toxicology study design can improve toxicity study readouts and facilitate longitudinal examination of single animals at multiple time points, leading to a reduction of experimental animal numbers.

An Introduction to Machine Learning.

In the last few years, machine learning (ML) and artificial intelligence have seen a new wave of publicity fueled by the huge and ever-increasing amount of data and computational power as well as the discovery of improved learning algorithms. However, the idea of a computer learning some abstract concept from data and applying them to yet unseen situations is not new and has been around at least since the 1950s. Many of these basic principles are very familiar to the pharmacometrics and clinical pharmacology community. In this paper, we want to introduce the foundational ideas of ML to this community such that readers obtain the essential tools they need to understand publications on the topic. Although we will not go into the very details and theoretical background, we aim to point readers to relevant literature and put applications of ML in molecular biology as well as the fields of pharmacometrics and clinical pharmacology into perspective.

Genome sequencing for rightward hemispheric language dominance.

Most people have left-hemisphere dominance for various aspects of language processing, but only roughly 1% of the adult population has atypically reversed, rightward hemispheric language dominance (RHLD). The genetic-developmental program that underlies leftward language laterality is unknown, as are the causes of atypical variation. We performed an exploratory whole-genome-sequencing study, with the hypothesis that strongly penetrant, rare genetic mutations might sometimes be involved in RHLD. This was by analogy with situs inversus of the visceral organs (left-right mirror reversal of the heart, lungs and so on), which is sometimes due to monogenic mutations. The genomes of 33 subjects with RHLD were sequenced and analyzed with reference to large population-genetic data sets, as well as 34 subjects (14 left-handed) with typical language laterality. The sample was powered to detect rare, highly penetrant, monogenic effects if they would be present in at least 10 of the 33 RHLD cases and no controls, but no individual genes had mutations in more than five RHLD cases while being un-mutated in controls. A hypothesis derived from invertebrate mechanisms of left-right axis formation led to the detection of an increased mutation load, in RHLD subjects, within genes involved with the actin cytoskeleton. The latter finding offers a first, tentative insight into molecular genetic influences on hemispheric language dominance.

Flexible multivariate hemodynamics fMRI data analyses and simulations with PyHRF.

As part of fMRI data analysis, the pyhrf package provides a set of tools for addressing the two main issues involved in intra-subject fMRI data analysis: (1) the localization of cerebral regions that elicit evoked activity and (2) the estimation of activation dynamics also known as Hemodynamic Response Function (HRF) recovery. To tackle these two problems, pyhrf implements the Joint Detection-Estimation framework (JDE) which recovers parcel-level HRFs and embeds an adaptive spatio-temporal regularization scheme of activation maps. With respect to the sole detection issue (1), the classical voxelwise GLM procedure is also available through nipy, whereas Finite Impulse Response (FIR) and temporally regularized FIR models are concerned with HRF estimation (2) and are specifically implemented in pyhrf. Several parcellation tools are also integrated such as spatial and functional clustering. Parcellations may be used for spatial averaging prior to FIR/RFIR analysis or to specify the spatial support of the HRF estimates in the JDE approach. These analysis procedures can be applied either to volume-based data sets or to data projected onto the cortical surface. For validation purpose, this package is shipped with artificial and real fMRI data sets, which are used in this paper to compare the outcome of the different available approaches. The artificial fMRI data generator is also described to illustrate how to simulate different activation configurations, HRF shapes or nuisance components. To cope with the high computational needs for inference, pyhrf handles distributing computing by exploiting cluster units as well as multi-core machines. Finally, a dedicated viewer is presented, which handles n-dimensional images and provides suitable features to explore whole brain hemodynamics (time series, maps, ROI mask overlay).