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Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
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Hydronium and hydroxide ions are present both in pure water and in all aqueous solutions, and their concentrations are inversely proportional as determined by the ion product of water (Kw). The concentrations of these ions in a solution are often critical determinants of the solution’s properties and the chemical behaviors of its other solutes. Two different solutions can differ in their hydronium or hydroxide ion concentrations by a million, billion, or even trillion times. A common means of...
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Alternative RNA splicing is the regulated splicing of exons and introns to produce different mature mRNAs from a single pre-mRNA. Unlike in constitutive splicing where a single gene produces a single type of mRNA, alternative splicing allows an organism to produce multiple proteins from a single gene and plays an important role in protein diversity.
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Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets
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Single-Cell RNA Sequencing: A New Window into Cell Scale Dynamics.

Sabyasachi Dasgupta1, Gary D Bader2, Sidhartha Goyal3

  • 1Department of Physics, University of Toronto, Toronto, Ontario, Canada.

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Single-cell genomics, using high-throughput single-cell RNA sequencing, offers unprecedented resolution for studying complex tissues. Computational methods analyze this data to define cell types, states, and developmental trajectories.

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Single-cell genomics provides high-resolution insights into multicellular systems.
  • High-throughput single-cell RNA sequencing quantifies gene expression in thousands of individual cells.
  • This enables deeper understanding of complex tissues than previously possible.

Purpose of the Study:

  • To outline the emerging field of single-cell genomics.
  • To describe the analysis pipeline from input data to inferring cellular interactions.
  • To highlight applications in defining cell types, states, and developmental trajectories.

Main Methods:

  • Quantifying transcription levels in single cells using high-throughput sequencing.
  • Developing novel computational methods for high-dimensional data analysis.
  • Building models to infer relations and transitions between cells.

Main Results:

  • Identification of distinct cell types and states within complex tissues.
  • Characterization of gene expression patterns defining cellular identities.
  • Inference of developmental trajectories and cell-to-cell interactions.

Conclusions:

  • Single-cell genomics is revolutionizing our understanding of cellular heterogeneity.
  • Computational analysis is key to unlocking the potential of single-cell data.
  • This field addresses fundamental questions in developmental biology and cell state dynamics.