DIMet: an open-source tool for differential analysis of targeted isotope-labeled metabolomics data.

Motivation: Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics (SIRM) and downstream data analyses are widely used techniques for unraveling cells' metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of SIRM data, there is currently no available resource providing a comprehensive toolbox.

Lactate dehydrogenases promote glioblastoma growth and invasion via a metabolic symbiosis.

Lactate is a central metabolite in brain physiology but also contributes to tumor development. Glioblastoma (GB) is the most common and malignant primary brain tumor in adults, recognized by angiogenic and invasive growth, in addition to its altered metabolism. We show herein that lactate fuels GB anaplerosis by replenishing the tricarboxylic acid (TCA) cycle in absence of glucose.

Interrogating RNA and protein spatial subcellular distribution in smFISH data with DypFISH.

Advances in single-cell RNA sequencing have allowed for the identification of cellular subtypes on the basis of quantification of the number of transcripts in each cell. However, cells might also differ in the spatial distribution of molecules, including RNAs. Here, we present DypFISH, an approach to quantitatively investigate the subcellular localization of RNA and protein.

DeepSpot: A deep neural network for RNA spot enhancement in single-molecule fluorescence in-situ hybridization microscopy images.

Detection of RNA spots in single-molecule fluorescence in-situ hybridization microscopy images remains a difficult task, especially when applied to large volumes of data. The variable intensity of RNA spots combined with the high noise level of the images often requires manual adjustment of the spot detection thresholds for each image. In this work, we introduce DeepSpot, a Deep Learning-based tool specifically designed for RNA spot enhancement that enables spot detection without the need to resort to image per image parameter tuning.

The invasive proteome of glioblastoma revealed by laser-capture microdissection.

Background: Glioblastomas are heterogeneous tumors composed of a necrotic and tumor core and an invasive periphery.

Methods: Here, we performed a proteomics analysis of laser-capture micro-dissected glioblastoma core and invasive areas of patient-derived xenografts.

Results: Bioinformatics analysis identified enriched proteins in central and invasive tumor areas.