Cerebral organoids derived from patients with Alzheimer's disease with PSEN1/2 mutations have defective tissue patterning and altered development.

During the past two decades, induced pluripotent stem cells (iPSCs) have been widely used to study human neural development and disease. Especially in the field of Alzheimer's disease (AD), remarkable effort has been put into investigating molecular mechanisms behind this disease. Then, with the advent of 3D neuronal cultures and cerebral organoids (COs), several studies have demonstrated that this model can adequately mimic familial and sporadic AD. Therefore, we created an AD-CO model using iPSCs derived from patients with familial AD forms and explored early events and the progression of AD pathogenesis. Our study demonstrated that COs derived from three AD-iPSC lines with PSEN1(A246E) or PSEN2(N141I) mutations developed the AD-specific markers in vitro, yet they also uncover tissue patterning defects and altered development. These findings are complemented by single-cell sequencing data confirming this observation and uncovering that neurons in AD-COs likely differentiate prematurely.

Distinct p53 phosphorylation patterns in chronic lymphocytic leukemia patients are reflected in the activation of circumjacent pathways upon DNA damage.

TP53 gene abnormalities represent the most important biomarker in chronic lymphocytic leukemia (CLL). Altered protein modifications could also influence p53 function, even in the wild-type protein. We assessed the impact of p53 protein phosphorylations on p53 functions as an alternative inactivation mechanism. We studied p53 phospho-profiles induced by DNA-damaging agents (fludarabine, doxorubicin) in 71 TP53-intact primary CLL samples. Doxorubicin induced two distinct phospho-profiles: profile I (heavily phosphorylated) and profile II (hypophosphorylated). Profile II samples were less capable of activating p53 target genes upon doxorubicin exposure, resembling TP53-mutant samples at the transcriptomic level, whereas standard p53 signaling was triggered in profile I. ATM locus defects were more common in profile II. The samples also differed in the basal activity of the hypoxia pathway: the highest level was detected in TP53-mutant samples, followed by profile II and profile I. Our study suggests that wild-type TP53 CLL cells with less phosphorylated p53 show TP53-mutant-like behavior after DNA damage. p53 hypophosphorylation and the related lower ability to respond to DNA damage are linked to ATM locus defects and the higher basal activity of the hypoxia pathway.

Single-cell RNA sequencing analysis of T helper cell differentiation and heterogeneity.

Single-cell transcriptomics has emerged as a powerful tool to investigate cells' biological landscape and focus on the expression profile of individual cells. Major advantage of this approach is an analysis of highly complex and heterogeneous cell populations, such as a specific subpopulation of T helper cells that are known to differentiate into distinct subpopulations. The need for distinguishing the specific expression profile is even more important considering the T cell plasticity. However, importantly, the universal pipelines for single-cell analysis are usually not sufficient for every cell type. Here, the aims are to analyze the diversity of T cell phenotypes employing classical in vitro cytokine-mediated differentiation of human T cells isolated from human peripheral blood by single-cell transcriptomic approach with support of labelled antibodies and a comprehensive bioinformatics analysis using combination of Seurat, Nebulosa, GGplot and others. The results showed high expression similarities between Th1 and Th17 phenotype and very distinct Th2 expression profile. In a case of Th2 highly specific marker genes SPINT2, TRIB3 and CST7 were expressed. Overall, our results demonstrate how donor difference, Th plasticity and cell cycle influence the expression profiles of distinct T cell populations. The results could help to better understand the importance of each step of the analysis when working with T cell single-cell data and observe the results in a more practical way by using our analyzed datasets.

decoupleR: ensemble of computational methods to infer biological activities from omics data.

Summary: Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor and Python package containing computational methods to extract these activities within a unified framework. decoupleR allows us to flexibly run any method with a given resource, including methods that leverage mode of regulation and weights of interactions, which are not present in other frameworks. Moreover, it leverages OmniPath, a meta-resource comprising over 100 databases of prior knowledge. Using decoupleR, we evaluated the performance of methods on transcriptomic and phospho-proteomic perturbation experiments. Our findings suggest that simple linear models and the consensus score across top methods perform better than other methods at predicting perturbed regulators. Availability and implementation: decoupleR's open-source code is available in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/decoupleR.html) for R and in GitHub (https://github.com/saezlab/decoupler-py) for Python. The code to reproduce the results is in GitHub (https://github.com/saezlab/decoupleR_manuscript) and the data in Zenodo (https://zenodo.org/record/5645208). Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Identification of Clinically Relevant Subgroups of Chronic Lymphocytic Leukemia Through Discovery of Abnormal Molecular Pathways.

Chronic lymphocytic leukemia (CLL) is the most common form of adult leukemia in the Western world with a highly variable clinical course. Its striking genetic heterogeneity is not yet fully understood. Although the CLL genetic landscape has been well-described, patient stratification based on mutation profiles remains elusive mainly due to the heterogeneity of data. Here we attempted to decrease the heterogeneity of somatic mutation data by mapping mutated genes in the respective biological processes. From the sequencing data gathered by the International Cancer Genome Consortium for 506 CLL patients, we generated pathway mutation scores, applied ensemble clustering on them, and extracted abnormal molecular pathways with a machine learning approach. We identified four clusters differing in pathway mutational profiles and time to first treatment. Interestingly, common CLL drivers such as ATM or TP53 were associated with particular subtypes, while others like NOTCH1 or SF3B1 were not. This study provides an important step in understanding mutational patterns in CLL.