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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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Cartografía de la expresión génica

Mor Nitzan1,2,3, Nikos Karaiskos4, Nir Friedman5,6

  • 1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.

Nature
|November 22, 2019
PubMed
Resumen
Este resumen es generado por máquina.

Los investigadores desarrollaron un nuevo método para reconstruir la expresión génica espacial en los tejidos sin conocimiento previo. Este enfoque ayuda a comprender la comunicación celular y la organización en varios tejidos animales.

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Área de la Ciencia:

  • Genómica de una sola célula
  • Transcriptomía espacial
  • Biología computacional

Sus antecedentes:

  • La secuenciación de ARN multiplexado de células individuales está revolucionando las ciencias de la vida.
  • La disociación del tejido para el análisis de una sola célula conduce a la pérdida de información espacial.
  • Los métodos existentes para la reconstrucción espacial a menudo se basan en genes marcadores inexistentes.

Objetivo del estudio:

  • Desarrollar un nuevo marco computacional para reconstruir las posiciones espaciales de las células y la expresión génica a partir de datos de secuenciación de ARN de una sola célula.
  • Superar las limitaciones de los métodos existentes que requieren un conocimiento previo de los genes marcadores.
  • Identificar los principios organizativos de la expresión génica en los tejidos animales.

Principales métodos:

  • Formuló la reconstrucción espacial como un problema de transporte óptimo generalizado para la incrustación probabilística.
  • Desarrolló un algoritmo iterativo eficiente para resolver el problema de transporte óptimo.
  • Se aplicó el marco ("novoSpaRc") a diversas muestras biológicas, incluidos tejidos de mamíferos, embriones y organismos enteros.

Principales resultados:

  • Reconstruido con éxito patrones de expresión génica espacial en varios tejidos animales con un mínimo de conocimiento previo.
  • Identificó genes que son espacialmente informativos dentro de los tejidos reconstruidos.
  • Demostró que los perfiles de transcripción de las células cercanas son generalmente más similares que las células distantes, revelando un principio organizativo.

Conclusiones:

  • El marco "novoSpaRc" permite una reconstrucción espacial precisa de las células y la expresión génica sin depender de genes marcadores específicos.
  • El principio organizativo identificado de la expresión génica se puede aprovechar para inferir posiciones celulares.
  • El enfoque es versátil, compatible con cualquier tecnología de una sola célula y puede incorporar información espacial previa.