A global view of T cell metabolism in systemic lupus erythematosus

Impaired metabolism is recognized as an important contributor to pathogenicity of T cells in Systemic Lupus Erythematosus (SLE). Over the last two decades, we have acquired significant knowledge about the signaling and transcriptomic programs related to metabolic rewiring in healthy and SLE T cells. However, our understanding of metabolic network activity derives largely from studying metabolic pathways in isolation. Here, we argue that enzymatic activities are necessarily coupled through mass and energy balance constraints with in-built network-wide dependencies and compensation mechanisms. Therefore, metabolic rewiring of T cells in SLE must be understood in the context of the entire network, including changes in metabolic demands such as shifts in biomass composition and cytokine secretion rates as well as changes in uptake/excretion rates of multiple nutrients and waste products. As a way forward, we suggest cell physiology experiments and integration of orthogonal metabolic measurements through computational modeling towards a comprehensive understanding of T cell metabolism in lupus.

Generation and molecular characterization of human pluripotent stem cell-derived pharyngeal foregut endoderm

Approaches to study human pharyngeal foregut endoderm-a developmental intermediate that is linked to various human syndromes involving pharynx development and organogenesis of tissues such as thymus, parathyroid, and thyroid-have been hampered by scarcity of tissue access and cellular models. We present an efficient stepwise differentiation method to generate human pharyngeal foregut endoderm from pluripotent stem cells. We determine dose and temporal requirements of signaling pathway engagement for optimized differentiation and characterize the differentiation products on cellular and integrated molecular level. We present a computational classification tool, "CellMatch," and transcriptomic classification of differentiation products on an integrated mouse scRNA-seq developmental roadmap confirms cellular maturation. Integrated transcriptomic and chromatin analyses infer differentiation stage-specific gene regulatory networks. Our work provides the method and integrated multiomic resource for the investigation of disease-relevant loci and gene regulatory networks and their role in developmental defects affecting the pharyngeal endoderm and its derivatives.

Multimodal Mass Spectrometry Imaging of Rat Brain Using IR-MALDESI and NanoPOTS-LC-MS/MS

Multimodal mass spectrometry imaging (MSI) is a critical technique used for deeply investigating biological systems by combining multiple MSI platforms in order to gain the maximum molecular information about a sample that would otherwise be limited by a single analytical technique. The aim of this work was to create a multimodal MSI approach that measures metabolomic and proteomic data from a single biological organ by combining infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) for metabolomic MSI and nanodroplet processing in one pot for trace samples (nanoPOTS) LC-MS/MS for spatially resolved proteome profiling. Adjacent tissue sections of rat brain were analyzed by each platform, and each data set was individually analyzed using previously optimized workflows. IR-MALDESI data sets were annotated by accurate mass and spectral accuracy using HMDB, METLIN, and LipidMaps databases, while nanoPOTS-LC-MS/MS data sets were searched against the rat proteome using the Sequest HT algorithm and filtered with a 1% FDR. The combined data revealed complementary molecular profiles distinguishing the corpus callosum against other sampled regions of the brain. A multiomic pathway integration showed a strong correlation between the two data sets when comparing average abundances of metabolites and corresponding enzymes in each brain region. This work demonstrates the first steps in the creation of a multimodal MSI technique that combines two highly sensitive and complementary imaging platforms. Raw data files are available in METASPACE (https://metaspace2020.eu/project/pace-2021) and MassIVE (identifier: MSV000088211).