Guide RNA Detection and Visualization: Methods and Protocols (Methods in Molecular Biology, Book 714)

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For cases in which temporal information is available, supervised learning-based approaches can be more accurate. Single-cell clustering using bifurcation analysis SCUBA 89 , for example, implements bifurcation analysis and has been used to recover lineages during early development in mouse embryos from gene expression profiles at multiple time-point measurements.

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One adaptation of this technique, Div-Seq, bypasses the need for tissue dissociation by directly sequencing isolated nuclei. As enzymatic dissociation is known to disrupt RNA composition and compromise integrity, studying cells from complex tissues e. Initial approaches for trajectory inference were based on linear paths; however, recent work has integrated the concept of branching 92 , which may be crucial for understanding dynamic cell systems.

Lander and colleagues 93 have recently proposed a more flexible probabilistic framework and utilized this approach to reconstruct known and unknown cell fate maps during the reprogramming of fibroblasts to induced pluripotent stem cells. We expect that additional biological insights gleaned from cell lineage determination or from experiments involving the perturbation of regulators at branching points will be valuable for enhancing our understanding of complex cellular systems. Even though the primary focus of this article is RNA-seq-based methods, we also note that cellular hierarchy can also be reconstructed from proteomic 94 , 95 or epigenomic measures One can imagine numerous exciting medical applications that can utilize this technology.

Tumor heterogeneity is a common phenomenon that can occur both within and between tumors, and we expect that scRNA-seq can be applied to illuminate unknown tumor features that cannot be discerned from conventional bulk transcriptomic studies. For example, this technique could be used to assess transcriptional heterogeneity during the development of drug tolerance in cancer cells 97 and to analyze the expression profiles of specific pathways Fig.

In this way, scRNA-seq may help generate models of cancer evolution. Additionally, this technique could also be applied to reconstruct clonal and phylogenetic relationships between cells by modeling transcriptional kinetics We further anticipate that RNA can be assessed as a part of routine clinical evaluation, and parallel measurements of both genomic and transcriptomic information in the same cell could elucidate the phenotypic consequences of DNA and RNA variants.

Lineage tracing is a long-standing fundamental question in biology aimed at understanding how a single-celled embryo gives rise to various cells types that are organized into complex tissue and organs Fig. As a proof-of-concept, researchers at Caltech have recently developed a method using the sequential readout of mRNA levels in a single cell to reconstruct lineage phylogeny over many generations Another interesting potential application of scRNA-seq includes identifying genes involved in stem cell regulatory networks. We are just now starting to understand how stem cells are triggered to become functional cells, which is information that is essential for understanding the basic biological processes underlying human health and diseases.

As sequencing costs decrease, it will be possible to routinely analyze more than a million cells within the next 5 years The Human Cell Atlas , which aims to map 35 trillion cells from the human body, has already started a few pilot studies. The initial plan is to sequence all RNA transcripts in 30 million to million cells and then use gene expression profiles to classify and identify new cell types.

It is anticipated, for example, that scRNA-seq of highly diverse immune system cells will deepen our understanding of their inherent heterogeneity, particularly regarding lymphocyte behavior. A study from the Broad Institute has further highlighted the utility of scRNA-seq by uncovering a subset of 18 seemingly identical immune cells that show stark differences in gene expression patterns from cell to cell Several emerging scRNA-seq studies have focused on deepening our understanding of cells in the brain , It is likely that the information gleaned from these analyses can be utilized to identify novel pathways involved in neuro-related diseases, providing new therapeutic targets for biomarker discovery.

We envision that future applications of scRNA-seq in biology and biomedical research will also provide novel insights into physiological structure—function relationships in various tissue and organs. Ultimately, with improvements in the availability of standardized bioinformatics pipelines, this work will reveal novel insights into biological systems and create new opportunities for therapeutic development.

Li, L. Coexistence of quiescent and active adult stem cells in mammals. Science , — Huang, S. Non-genetic heterogeneity of cells in development: more than just noise. Development , — Shalek, A. Single cell RNA Seq reveals dynamic paracrine control of cellular variation.

Single-cell RNA sequencing technologies and bioinformatics pipelines

Nature , — Eldar, A. Functional roles for noise in genetic circuits. Maamar, H. Noise in gene expression determines cell fate in Bacillus subtilis. Eberwine, J. Analysis of gene expression in single live neurons. USA 89 , — Brady, G. Representative in vitro cDNA amplification from individual hemopoietic cells and colonies. Methods Mol. Cell Biol. Klein, C. Combined transcriptome and genome analysis of single micrometastatic cells.

Kurimoto, K. An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis. Nucleic Acids Res.


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Xie, D. Rewirable gene regulatory networks in the preimplantation embryonic development of three mammalian species. Genome Res. Tietjen, I. Single-cell transcriptional analysis of neuronal progenitors. Neuron 38 , — Tang, F.

RNA Detection and Visualization: Methods and Protocols by Jeffrey E. Gerst - fezaperwhooco.ga

Methods 6 , — Shaffer, S. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Petropoulos, S. Cell , Trapnell, C. Pseudo-temporal ordering of individual cells reveals dynamics and regulators of cell fate decisions. Stubbington, M. T cell fate and clonality inference from single cell transcriptomes. Methods 13 , — Brehm-Stecher, B. Single-cell microbiology: tools, technologies, and applications.

Guo, F.

Electrophoretic mobility shift assay

Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res. Julius, M. Demonstration that antigen-binding cells are precursors of antibody-producing cells after purification with a fluorescence-activated cell sorter.

USA 69 , — Nichterwitz, S. Laser capture microscopy coupled with Smart-seq2 for precise spatial transcriptomic profiling. Whitesides, G.

Introduction

The origins and the future of microfluidics. Jacobson, S. High-efficiency, two-dimensional separations of protein digests on microfluidic devices. Khandurina, J. Lagally, E. Single-molecule DNA amplification and analysis in an integrated microfluidic device. Thorsen, T. Microfluidic large-scale integration. Chiu, D. Using three-dimensional microfluidic networks for solving computationally hard problems. USA 98 , — Long-term monitoring of bacteria undergoing programmed population control in a microchemostat.

Marcus, J. Microfluidic single-cell mRNA isolation and analysis.


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Dynamic pattern formation in a vesicle-generating microfluidic device. Utada, A. Monodisperse double emulsions generated from a microcapillary device. Islam, S. Quantitative single-cell RNA-seq with unique molecular identifiers. Methods 11 , — Arezi, B. Novel mutations in Moloney murine leukemia virus reverse transcriptase increase thermostability through tighter binding to template-primer. Gerard, G. The role of template-primer in protection of reverse transcriptase from thermal inactivation.