Methods in molecular biology最新文献

Hypothyroidism, characterized by inadequate production of thyroid hormones, and malaria, a mosquito-borne infectious disease caused by Plasmodium parasites, are significant health concerns worldwide. Understanding the interplay between these two conditions could offer insights into their complex relationship and potential therapeutic strategies. To induce hypothyroidism, pharmacological inhibition of thyroid hormone synthesis was employed. Subsequently, mice were infected with Plasmodium berghei ANKA to simulate cerebral malaria infection. It needs to monitor the progression of the disease in male mice before it can identify infiltrating immune system populations of interest in the brain by multiparametric techniques such as flow cytometry.

Electrophoretic Mobility Shift Assay (EMSA) is a powerful technique for studying nucleic acid and protein interactions. This technique is based on the principle that nucleic acid-protein complex and nucleic acid migrate at different rates due to differences in size and charge. Nucleic acid and protein interactions are fundamental to various biological processes, such as gene regulation, replication, transcription, and recombination. Transcription factors and DNA interaction regulate gene expression. Homeobox (Hox) genes encode a family of transcription factors and are essential during embryonic development. Understanding the specific interactions between Hox proteins and their DNA targets is critical for elucidating the mechanisms underlying their regulatory functions.This chapter explains the principles and methodologies of EMSA in the context of Hox genes. This chapter includes detailed experimental design, including the formulation of reagents, labeling DNA probes, preparation of nuclear extracts/recombinant proteins, and binding conditions. The step-by-step protocol has been provided as an initial reference point to help a researcher conduct EMSA.

A key goal of biology is to understand the origin of the many cell types that can be observed during diverse processes such as development, regeneration, and disease. Single-cell RNA-sequencing (scRNA-seq) is commonly used to identify cell types in a tissue or organ. However, organizing the resulting taxonomy of cell types into lineage trees to understand the origins of cell states and relationships between cells remains challenging. Here we present LINNAEUS (Spanjaard et al, Nat Biotechnol 36:469-473. https://doi.org/10.1038/nbt.4124 , 2018; Hu et al, Nat Genet 54:1227-1237. https://doi.org/10.1038/s41588-022-01129-5 , 2022) (LINeage tracing by Nuclease-Activated Editing of Ubiquitous Sequences)-a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes, generated by genome editing of transgenic reporter genes, LINNAEUS can be used to reconstruct organism-wide single-cell lineage trees. LINNAEUS provides a systematic approach for tracing the origin of novel cell types, or known cell types under different conditions.

Measurements of cell phylogeny based on natural or induced mutations, known as lineage barcodes, in conjunction with molecular phenotype have become increasingly feasible for a large number of single cells. In this chapter, we delve into Quantitative Fate Mapping (QFM) and its computational pipeline, which enables the interrogation of the dynamics of progenitor cells and their fate restriction during development. The methods described here include inferring cell phylogeny with the Phylotime model, and reconstructing progenitor state hierarchy, commitment time, population size, and commitment bias with the ICE-FASE algorithm. Evaluation of adequate sampling based on progenitor state coverage statistics is emphasized for interpreting the QFM results. Overall, this chapter describes a general framework for characterizing the dynamics of cell fate changes using lineage barcoding data.

The human genome is composed of distinct genomic regions that are susceptible to various types of somatic mutations. Among these, Short Tandem Repeats (STRs) stand out as the most mutable genetic elements. STRs are short repetitive polymorphic sequences, predominantly situated within noncoding sectors of the genome. The intrinsic repetition characterizing these sequences makes them highly mutable in vivo. Consequently, this characteristic provides the chance to unravel the natural developmental history of human viable cells retrospectively. However, STRs also introduce stutter noise in vitro amplification, which makes their analysis challenging. Here we describe our integrated biochemical-computational platform for single-cell lineage analysis. It consists of a pipeline whose inputs are single cells and whose output is a lineage tree of input cells.

Long non-coding RNAs (lncRNAs) are a type of non-coding RNA molecules exceeding 200 nucleotides in length and that do not encode proteins. The dysregulated expression of lncRNAs has been identified in various diseases, holding therapeutic significance. Over the past decade, numerous computational resources have been published in the field of lncRNA. In this chapter, we have provided a comprehensive review of the databases as well as predictive tools, that is, lncRNA databases, machine learning based algorithms, and tools predicting lncRNAs utilizing different techniques. The chapter will focus on the importance of lncRNA resources developed for different organisms specifically for humans, mouse, plants, and other model organisms. We have enlisted important databases, primarily focusing on comprehensive information related to lncRNA registries, associations with diseases, differential expression, lncRNA transcriptome, target regulations, and all-in-one resources. Further, we have also included the updated version of lncRNA resources. Additionally, computational identification of lncRNAs using algorithms like Deep learning, Support Vector Machine (SVM), and Random Forest (RF) was also discussed. In conclusion, this comprehensive overview concludes by summarizing vital in silico resources, empowering biologists to choose the most suitable tools for their lncRNA research endeavors. This chapter serves as a valuable guide, emphasizing the significance of computational approaches in understanding lncRNAs and their implications in various biological contexts.

In this chapter, we first survey strategies for the mathematical modeling of gene regulatory networks for capturing physiologically important dynamics in cells such as oscillations. We focus on models based on ordinary differential equations with various forms of nonlinear functions that describe gene regulations. We next use a small system of a microRNA and its mRNA target to illustrate a recently discovered oscillator driven by noncoding RNAs. This oscillator has unique features that distinguish it from conventional biological oscillators, including the absence of an imposed negative feedback loop and the divergence of the periods. The latter property may serve crucial biological functions for restoring heterogeneity of cell populations on the timescale of days. We describe general requirements for obtaining the limit cycle oscillations in terms of underlying biochemical reactions and kinetic rate constants. We discuss future directions stemming from this minimal, noncoding RNA-based model for gene expression oscillation.

Among non-coding RNAs, microRNAs are pivotal post-transcriptional regulators of gene expression in higher eukaryotes. Through a titration-based mechanism of interaction with their target RNAs, microRNAs can mediate a weak but pervasive form of RNA cross-regulation, as different endogenous RNAs can be effectively coupled by competing for microRNA binding (a phenomenon now known as "crosstalk"). Mathematical modeling has been proven of great help in unraveling many features of these competing endogenous RNA (ceRNA) interactions. However, although many studies have been devoted to the steady-state properties of this indirect regulatory layer, little is known about how the information encoded in frequency, amplitude, duration, and other features of regulatory signals can affect the resulting ceRNA crosstalk picture and hence the overall patterns of gene expression. Here, we focus on such dynamical aspects, with a special emphasis on the encoding and decoding of time-dependent signals.

Single-cell (sc) RNA, ATAC, and Multiome sequencing became powerful tools for uncovering biological and disease mechanisms. Unfortunately, manual analysis of sc data presents multiple challenges due to large data volumes and complexity of configuration parameters. This complexity, as well as not being able to reproduce a computational environment, affects the reproducibility of analysis results. The Scientific Data Analysis Platform ( https://SciDAP.com ) allows biologists without computational expertise to analyze sequencing-based data using portable and reproducible pipelines written in Common Workflow Language (CWL). Our suite of computational pipelines addresses the most common needs in scRNA-Seq, scATAC-Seq and scMultiome data analysis. When executed on SciDAP, it offers a user-friendly alternative to manual data processing, eliminating the need for coding expertise. In this protocol, we describe the use of SciDAP to analyze scMultiome data. Similar approaches can be used for analysis of scRNA-Seq, scATAC-Seq and scVDJ-Seq datasets.

It is widely discussed that eukaryotic mRNAs can encode several functional polypeptides. Recent progress in NGS and proteomics techniques has resulted in a huge volume of information on potential alternative translation initiation sites and open reading frames (altORFs). However, these data are still incomprehensive, and the vast majority of eukaryotic mRNAs annotated in conventional databases (e.g., GenBank) contain a single ORF (CDS) encoding a protein larger than some arbitrary threshold (commonly 100 amino acid residues). Indeed, some gene functions may relate to the polypeptides encoded by unannotated altORFs, and insufficient information in nucleotide sequence databanks may limit the interpretation of genomics and transcriptomics data. However, despite the need for special experiments to predict altORFs accurately, there are some simple methods for their preliminary mapping.