targeted-metabolomics-on-rare-primary-cells

Targeted Metabolomics on Rare Primary Cells

Cellular function critically depends on metabolism, and the function of the underlying metabolic networks can be studied by measuring small molecule ...

Max Planck Institute of Immunobiology and Epigenetics

Membrane proteins are organized in nanoscale compartments. Their reorganization plays a crucial role in receptor activation and cell signaling. To monitor the organization and reorganization of membrane proteins, we developed a new branched proximity hybridization assay (bPHA) allowing better quantification of the nanoscale protein-protein proximity. In this assay, oligo-coupled binding probes, such as aptamer, nanobody, and antibodies, are used to translate the proximity of target proteins to the proximity of oligos. The closely positioned oligos then serve as a template for a maximum of 400-fold branched DNA (bDNA) signal amplification. The amplified bPHA signal is recorded by flow cytometer, thus enabling proximity studies with high throughput, multiplexing, and single-cell resolution. To demonstrate the potential of the bPHA method, we measured the reorganization of the immunoglobulin M (IgM)- and immunoglobulin D (IgD)-class B cell antigen receptor (BCR) on the plasma membrane and the recruitment of spleen tyrosine kinase (Syk) to the BCR upon B lymphocyte activation.

A new branched proximity hybridization assay for the quantification of nanoscale protein-protein proximity.

CD8+ T cells detect and kill infected or cancerous cells. When activated from their naïve state, T cells undergo a complex transition, including major metabolic reprogramming. Detailed resolution of metabolic dynamics is needed to advance the field of immunometabolism. Here, we outline methodologies that when utilized in parallel achieve broad coverage of the metabolome. Specifically, we used a combination of 2 flow injection analysis (FIA) and 3 liquid chromatography (LC) methods in combination with positive and negative mode high-resolution mass spectrometry (MS) to study the transition from naïve to effector T cells with fine-grained time resolution. Depending on the method, between 54% and 98% of measured metabolic features change in a time-dependent manner, with the major changes in both polar metabolites and lipids occurring in the first 48 h. The statistical analysis highlighted the remodeling of the polyamine biosynthesis pathway, with marked differences in the dynamics of precursors, intermediates, and cofactors. Moreover, phosphatidylcholines, the major class of membrane lipids, underwent a drastic shift in acyl chain composition with polyunsaturated species decreasing from 60% to 25% of the total pool and specifically depleting species containing a 20:4 fatty acid. We hope that this data set with a total of over 11,000 features recorded with multiple MS methodologies for 9 time points will be a useful resource for future work.

Metabolic Dynamics of In Vitro CD8+ T Cell Activation.

Hematopoietic stem cells (HSCs) rely on complex regulatory networks to preserve stemness. Due to the scarcity of HSCs, technical challenges have limited our insights into the interplay between metabolites, transcription, and the epigenome. In this study, we generated low-input metabolomics, transcriptomics, chromatin accessibility, and chromatin immunoprecipitation data, revealing distinct metabolic hubs that are enriched in HSCs and their downstream multipotent progenitors. Mechanistically, we uncover a non-classical retinoic acid (RA) signaling axis that regulates HSC function. We show that HSCs rely on Cyp26b1, an enzyme conventionally considered to limit RA effects in the cell. In contrast to the traditional view, we demonstrate that Cyp26b1 is indispensable for production of the active metabolite 4-oxo-RA. Further, RA receptor beta (Rarb) is required for complete transmission of 4-oxo-RA-mediated signaling to maintain stem cells. Our findings emphasize that a single metabolite controls stem cell fate by instructing epigenetic and transcriptional attributes.

Multilayer omics analysis reveals a non-classical retinoic acid signaling axis that regulates hematopoietic stem cell identity.

Preprocessing of liquid chromatography-mass spectrometry (LC-MS) raw data facilitates downstream statistical and biological data analyses. In the case of targeted LC-MS data, consistent recognition of chromatographic peaks is a main challenge, in particular, for low abundant signals. Fully automatic preprocessing is faster than manual peak review and does not depend on the individual operator. Here, we present the R package automRm for fully automatic preprocessing of LC-MS data recorded in MRM mode. Using machine learning (ML) for detection of chromatographic peaks and quality control of reported results enables the automatic recognition of complex patterns in raw data. In addition, this approach renders automRm generally applicable to a wide range of analytical methods including hydrophilic interaction liquid chromatography (HILIC), which is known for sample-to-sample variations in peak shape and retention time. We demonstrate the impact of the choice of training data set, of the applied ML algorithm, and of individual peak characteristics on automRm's ability to correctly report chromatographic peaks. Next, we show that automRm can replicate results obtained by manual peak review on published data. Moreover, automRm outperforms alternative software solutions regarding the variation in peak integration among replicate measurements and the number of correctly reported peaks when applied to a HILIC-MS data set. The R package is freely available from gitlab (https://gitlab.gwdg.de/joerg.buescher/automrm).

automRm: An R Package for Fully Automatic LC-QQQ-MS Data Preprocessing Powered by Machine Learning.

Metabolism is important for the regulation of hematopoietic stem cells (HSCs) and drives cellular fate. Due to the scarcity of HSCs, it has been technically challenging to perform metabolome analyses gaining insight into HSC metabolic regulatory networks. Here, we present two targeted liquid chromatography-mass spectrometry approaches that enable the detection of metabolites after fluorescence-activated cell sorting when sample amounts are limited. One protocol covers signaling lipids and retinoids, while the second detects tricarboxylic acid cycle metabolites and amino acids. For complete details on the use and execution of this protocol, please refer to Schönberger et al. (2022).

Targeted LC-MS/MS-based metabolomics and lipidomics on limited hematopoietic stem cell numbers.

How lipidome changes support CD8 effector T (T) cell differentiation is not well understood. Here we show that, although naive T cells are rich in polyunsaturated phosphoinositides (PIP with 3-4 double bonds), T cells have unique PIP marked by saturated fatty acyl chains (0-2 double bonds). PIP are precursors for second messengers. Polyunsaturated phosphatidylinositol bisphosphate (PIP) exclusively supported signaling immediately upon T cell antigen receptor activation. In late T cells, activity of phospholipase C-γ1, the enzyme that cleaves PIP into downstream mediators, waned, and saturated PIP became essential for sustained signaling. Saturated PIP was more rapidly converted to PIP with subsequent recruitment of phospholipase C-γ1, and loss of saturated PIP impaired T cell fitness and function, even in cells with abundant polyunsaturated PIP. Glucose was the substrate for de novo PIP synthesis, and was rapidly utilized for saturated PIP generation. Thus, separate PIP pools with distinct acyl chain compositions and metabolic dependencies drive important signaling events to initiate and then sustain effector function during CD8+ T cell differentiation.

Phosphoinositide acyl chain saturation drives CD8 effector T cell signaling and function.

Metabolism plays a fundamental role in regulating cellular functions and fate decisions. Liquid chromatography-mass spectrometry (LC-MS)-based targeted metabolomic approaches provide high-resolution insights into the metabolic state of a cell. However, the typical sample size is in the order of 10-10 cells and thus not compatible with rare cell populations, especially in the case of a prior flow cytometry-based purification step. Here, we present a comprehensively optimized protocol for targeted metabolomics on rare cell types, such as hematopoietic stem cells and mast cells. Only 5000 cells per sample are required to detect up to 80 metabolites above background. The use of regular-flow liquid chromatography allows for robust data acquisition, and the omission of drying or chemical derivatization avoids potential sources of error. Cell-type-specific differences are preserved while the addition of internal standards, generation of relevant background control samples, and targeted metabolite with quantifiers and qualifiers ensure high data quality. This protocol could help numerous studies to gain thorough insights into cellular metabolic profiles and simultaneously reduce the number of laboratory animals and the time-consuming and costly experiments associated with rare cell-type purification.

Michael Mitterer

Max Planck Institute of Immunobiology and Epigenetics

Targeted Metabolomics on Rare Primary Cells