Methods in molecular biology最新文献

Spatial transcriptomic tools are an upcoming and powerful way to investigate targeted gene expression patterns within tissues. These tools offer the unique advantage of visualizing and understanding gene expression while preserving tissue integrity, thereby maintaining the spatial context of genes. Curio is a robust spatial transcriptomic tool that facilitates high throughput comprehensive spatial gene expression analysis across the entir e transcriptome with high efficiency. Here, we present a bioinformatics protocol for performing whole transcriptome gene expression analysis of mouse brain tissue using Curio. Specifically, we demonstrate using computational techniques to visualize expression patterns of various HOX genes in the mouse brain.

Somatic mosaic variants, and especially somatic single nucleotide variants (sSNVs), occur in progenitor cells in the developing human brain frequently enough to provide permanent, unique, and cumulative markers of cell divisions and clones. Here, we describe an experimental workflow to perform lineage studies in the human brain using somatic variants. The workflow consists in two major steps: (1) sSNV calling through whole-genome sequencing (WGS) of bulk (non-single-cell) DNA extracted from human fresh-frozen tissue biopsies, and (2) sSNV validation and cell phylogeny deciphering through single nuclei whole-genome amplification (WGA) followed by targeted sequencing of sSNV loci.

Lineage tracing methods enable the identification of all progeny generated by a single cell. High-throughput lineage tracing in the mammalian brain involves parallel labeling of thousands of progenitor cells with genetic barcodes in vivo followed by single-cell RNA-seq of lineage relations and cell types. Here we describe the generation of barcoded lentivirus, microinjections into the embryonic day 9.5 mouse forebrain, dissociation of 2-week-old mouse brain tissue, single-cell RNA-seq library preparation, and data analysis using a custom software. Compared to traditional methods based on sparse fluorophore labeling of progenitor cells, lineage tracing with genetic barcodes and single-cell RNA-seq has a >100-fold higher throughput while using >10 times fewer mice.

The recent development of genetic lineage recorders, designed to register the genealogical history of cells using induced somatic mutations, has opened the possibility of reconstructing complete animal cell lineages. To reconstruct a cell lineage tree from a molecular recorder, it is crucial to use an appropriate reconstruction algorithm. Current approaches include algorithms specifically designed for cell lineage reconstruction and the repurposing of phylogenetic algorithms. These methods have, however, the same objective: to uncover the hierarchical relationships between cells and the sequence of cell divisions that have occurred during development. In this chapter, I will use the phylogenetic software FastTree to reconstruct a lineage tree, in a step-by-step manner, using data from a simulated CRISPR-Cas9 recorder. To ensure reproducibility, the code is presented as a Jupyter Notebook, available (together with the necessary input files) at https://github.com/irepansalvador/lineage_reconstruction_chapter .

Multicolor MAGIC Markers strategies are useful lineage tracing tools to study brain development at a multicellular scale. In this chapter, we describe an in utero electroporation method to simultaneously label multiple neighboring progenitors and their respective progeny using these multicolor reporters. In utero electroporation enables the introduction of any gene of interest into embryonic neural progenitors lining the brain ventricles through a simple pipeline consisting of a micro-injection followed by the application of electrical pulses. Successful in utero electroporation requires a concise yet complete understanding of each step of the surgical protocol, spanning from the preoperative preparation to the postoperative care, as well as the MAGIC Markers tool outlined in this study. Besides a detailed protocol, we present non-integrative and integrative approaches to demonstrate the range of cell and lineage tracking possibilities of multicolored progenitors and their descent over time.

StarTrack is a powerful multicolor genetic tool designed to unravel cellular lineages arising from neural progenitor cells (NPCs). This innovative technique, based on retrospective clonal analysis and built upon the PiggyBac system, creates a unique and inheritable "color code" within NPCs. Through the stochastic integration of 12 distinct plasmids encoding six fluorescent proteins, StarTrack enables precise and comprehensive tracking of cellular fates and progenitor potentials. The versatility of this tool is further enhanced by the potential of combining multiple promoters. Whether through the use of fluorescent integrable constructs or driving the expression of the PiggyBac transposase, StarTrack broadens the horizons for lineage tracing from progenitors of multiple origins.StarTrack revolutionized our understanding of cellular origins and lineages, offering an invaluable resource for researchers in the field of neural development and lineage tracing. This protocol provides a comprehensive overview of the technique's capabilities and applications, shedding light on its significance within the scientific community.

SNARE proteins play a pivotal role in membrane fusion and various cellular processes. Accurate identification of SNARE proteins is crucial for elucidating their functions in both health and disease contexts. This chapter presents a novel approach employing multiscan convolutional neural networks (CNNs) combined with position-specific scoring matrix (PSSM) profiles to accurately recognize SNARE proteins. By leveraging deep learning techniques, our method significantly enhances the accuracy and efficacy of SNARE protein classification. We detail the step-by-step methodology, including dataset preparation, feature extraction using PSI-BLAST, and the design of the multiscan CNN architecture. Our results demonstrate that this approach outperforms existing methods, providing a robust and reliable tool for bioinformatics research.

The bimolecular fluorescence complementation (BiFC) technique is a powerful tool for visualizing protein-protein interactions in vivo. It involves genetically fused nonfluorescent fragments of green fluorescent protein (GFP) or its variants to the target proteins of interest. When these proteins interact, the GFP fragments come together, resulting in the reconstitution of a functional fluorescent protein complex that can be observed using fluorescence microscopy. In this chapter, we provide a detailed overview of the BiFC method and its application in studying protein-protein interactions in mouse hippocampal neurons. We discuss experimental procedures, including virus construct design, neuronal transduction, and imaging optimization. Additionally, we explore complementary assays for result validation and address potential challenges associated with BiFC experiments in the neuronal system. Overall, the BiFC offers researchers a valuable approach for investigating the spatial and temporal dynamics of protein interactions in living neuronal cells.