Methods in molecular biology最新文献

Metabolomics data analysis includes, next to the preprocessing, several additional repetitive tasks that can however be heavily dataset dependent or experiment setup specific due to the vast heterogeneity in instrumentation, protocols, or also compounds/samples that are being measured. To address this, various toolboxes and software packages in Python or R have been and are being developed providing researchers and analysts with bioinformatic/chemoinformatic tools to create their own workflows tailored toward their specific needs. This chapter presents tools and example workflows for common tasks focusing on the functionality provided by R packages developed as part of the RforMassSpectrometry initiative. These tasks include, among others, examples to work with chemical formulae, handle and process mass spectrometry data, or calculate similarities between fragment spectra.

Hox genes are highly conserved developmental regulators instrumental to the formation of a wide range of diverse body plans across metazoans. While significant progress in the field of Hox gene research has been made, persistent challenges in unraveling their mechanisms of action and full repertoire of functions remain. To date, investigations of Hox gene function have been primarily conducted in research models belonging to ecdysozoa and vertebrata. Herein we summarize recent findings on Hox genes' roles in the asexual reproduction of the regenerative flatworm planaria, a member of the understudied superphylum Spiralia. We detail our optimized methods for planarian culture, gene perturbation, and induction of asexual reproduction. We aim to provide an experimentally tractable means to dissect Hox gene adult tissue functions underlying planarian asexual reproduction with broader relevance to Hox genes' established and emerging roles in regulating cellular behaviors, developmental patterning, animal behavior, and tissue regeneration.

Mutations are acquired frequently, such t`hat each cell's genome inscribes its history of cell divisions. Loss of heterozygosity (LOH) accumulates throughout the genome, offering large encoding capacity for phylogenetic inference of cell lineage.In this chapter, we demonstrate a method, using single-cell RNA sequencing, for reconstructing cell lineages from inferred LOH events in a Bayesian manner, annotating the lineage with cell phenotypes, and marking developmental time points based on X-chromosome inactivation. This type of retrospective analysis could be incorporated into scRNA-seq pipelines and was initially developed to investigate Emx1+ cortical projection neuron and glia lineages from C57Bl/6J (B6) and CAST/EiJ (CA) interstrain F1 mice, describing progenitor cells giving rise to multiple cortical cell types through stereotyped expansion and distinct waves of neurogenesis.

Gene expression memory-based lineage inference (GEMLI) is a computational tool allowing to predict cell lineages solely from single-cell RNA-sequencing (scRNA-seq) datasets and is publicly available as an R package on GitHub. GEMLI is based on the occurrence of gene expression memory, i.e., the gene-specific maintenance of expression levels through cell divisions. This represents a shift away from experimental lineage tracing techniques based on genetic marks or physical cell lineage separation and greatly eases and expands lineage annotation. GEMLI allows to study cell lineages during differentiation in development, homeostasis, and regeneration, as well as disease onset and progression in various physiological and pathological contexts. This makes it possible to dissect cell type-specific gene expression memory, to discriminate symmetric and asymmetric cell fate decisions, and to reconstruct individual multicellular structures from pooled scRNA-seq datasets. GEMLI is particularly promising for its ability to identify small lineages in human samples, a context in which no other lineage tracing methods are applicable. In this chapter, we provide a detailed protocol of the GEMLI R package usage on gene expression matrices derived from standard scRNA-seq on various platforms. We cover the use of the main function to predict cell lineages and how to adjust its parameters to different tasks. We also show how lineage information is extracted, visualized, and fine-tuned. Finally, we describe the use of the package's functions for the detailed analysis of the predicted cell lineages. This includes the analysis of gene expression memory, cell type composition of individual large lineages, and identification of lineages at the transition point between two cell types.

Induced pluripotent stem cell (iPSC)-derived organoids provide models to study human organ development. Single-cell transcriptomics enables highly resolved descriptions of cell states within these systems; however, approaches are needed to directly determine the lineage relationship between cells. Here we provide a detailed protocol (Fig. 1) for the application of iTracer (He Z, Maynard A, Jain A, et al., Nat Methods 19:90-99, 2022), a recently published lineage recorder that combines reporter barcodes with inducible CRISPR-Cas9 scarring and is compatible with single-cell and spatial transcriptomics. iTracer is used to explore clonality and lineage dynamics during brain organoid development. More broadly, iTracer can be adapted to any iPSC-derived culture system to dissect lineage dynamics during normal or perturbed development.

Hox genes play a pivotal role during development. Their expression is tightly controlled in a spatiotemporal manner, ensuring that specific body structures develop at the correct locations and times during development. Various genomics approaches have been used to capture temporal and dynamic regulation of Hox gene expression at the nucleosome/chromatin level. This chapter focuses on the utilization of capture MNase-seq and Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq), two advanced techniques that enable the exploration of chromatin accessibility and nucleosome positioning within these critical genomic regions.

Somatic cells can be transformed into induced pluripotent stem cells (iPSCs) using a technique called reprogramming. This process involves introducing Yamanaka factors (Oct4, Sox2, Klf4, and c-Myc) to the cells through retroviral supernatants. This chapter outlines a protocol for reprogramming mouse embryonic fibroblasts (MEFs) using the hormone triiodo-L-thyronine (T3) to enhance the generation of iPSCs. It also describes how to analyze these iPSCs by colony staining for alkaline phosphatase activity, a standard marker for identifying pluripotent embryonic stem cells. To further study iPSCs, individual colonies must be selected and expanded, and pluripotency is examined by analyzing gene expression profiles using quantitative real-time PCR to measure the endogenous expression of pluripotency genes. Integrating T3 into reprogramming methods may significantly improve the production of functional iPSCs. This advancement could open new avenues for research in cell plasticity, disease modeling, and regenerative therapies.

The thyroid gland, located at the base of the neck, regulates metabolism and hormone balance through hormones like T4 and T3, which are essential for growth, neurological development, and energy production. Thyroid diseases affect 10% of the global population, making accurate and up-to-date information on surgical interventions and advancements crucial for improving clinical outcomes. Thyroid gland surgery is a dynamic field that has experienced remarkable advances in diagnosis, surgical techniques, and postoperative management. These include new advances in surgical techniques that improve precision, reduce surgical trauma, and speed up patient recovery, identification of biomarkers, and understanding of the molecular characteristics of tumors that allow for more targeted therapeutic strategies, and incorporation of advanced technologies that improve diagnostic accuracy and efficacy. This review aims to guide healthcare professionals and lay the groundwork for future research and innovative treatments in thyroid surgery.

The hypothalamus secretes the thyroid-releasing hormone (TRH) that induces the pituitary gland to release the thyroid-stimulating hormone (TSH) which stimulates thyroid follicular cells to release the thyroid hormones (THs), thyroxine (T4), and triiodothyronine (T3). The process of synthesizing T3 and T4 hormones involves various enzymatic steps, starting with the iodination of L-tyrosine residues present in the protein thyroglobulin. Thyroid hormones are released into the bloodstream, where they bind to thyroid hormone distributor proteins (THDPs) which transport them in the circulation. The conversion of T4 to T3 (the more biologically active hormone) in target tissues is facilitated by selenoprotein enzymes known as deiodinases. THs can bind to different molecules located on the plasma membrane, such as integrin αvβ3, through which they exercise regulatory non-genomic control. Nevertheless, most of thyroid hormone's actions are mediated intracellularly by binding to thyroid hormone receptors (TRs). Thyroid hormone receptors act as ligand-dependent transcription factors, Thyroid hormone receptors activate thyroid hormone response elements on gene promoters through canonical signaling. Thyroid hormones mediate several critical physiological processes including organ development, cell differentiation, metabolism, and cell growth and maintenance.

Mitochondria are considered one of the main sites of reactive oxygen species (ROS) production in the eukaryotic cells. For this reason, mitochondrial dysfunction associated with increased ROS production underlies various pathological conditions as well as promotes aging. Chronically increased rates of ROS production contribute to oxidative damage to macromolecules, i.e., DNA, proteins, and lipids. Accumulation of unrepaired oxidative damage may result in progressive cell dysfunction, which can finally trigger cell death. The main by-product of mitochondrial oxidative phosphorylation is superoxide, which is generated by the leak of electrons from the mitochondrial respiratory chain complexes leading to one-electron reduction of oxygen. Mitochondrial superoxide dismutase (MnSOD, SOD2) as well as cytosolic superoxide dismutase (Cu/ZnSOD, SOD1), whose smaller pool is localized in the mitochondrial intermembrane space, converts superoxide to H₂O₂, which can be then degraded by the catalase to harmless H₂O.In this chapter, we focus on the relationship between one of the bioenergetic parameters, which is mitochondrial membrane potential, and the rate of ROS formation. We present a set of various methods enabling the characterization of these parameters applicable to isolated mitochondria or intact cells. We also present examples of experimental data demonstrating that the magnitude and direction (increase or decrease) of a change in mitochondrial ROS production depend on the mitochondrial metabolic state.