Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Immunofluorescence Microscopy01:12

Immunofluorescence Microscopy

13.7K
A fluorescence microscope uses fluorescent chromophores called fluorochromes, which can absorb energy from a light source and then emit this energy as visible light. Fluorochromes include naturally fluorescent substances (such as chlorophylls) and fluorescent stains that are added to the specimen to create contrast. Dyes such as Texas red and FITC are examples of fluorochromes. Other examples include the nucleic acid dyes 4’,6’-diamidino-2-phenylindole (DAPI), and acridine orange.
13.7K
Two-Dimensional Microscopy in Microbiology01:29

Two-Dimensional Microscopy in Microbiology

1.7K
Two-dimensional (2D) microscopy encompasses a range of optical techniques that capture images within a single focal plane, offering detailed representations of microscopic structures. These techniques are essential in biological and medical research, enabling the visualization of cellular and subcellular structures with different levels of contrast and specificity.There are several major types of 2D microscopy, each with strengths and applications.Bright-Field MicroscopyBright-field microscopy...
1.7K
Atomic Force Microscopy01:08

Atomic Force Microscopy

4.5K
Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...
4.5K
Overview of Microscopy Techniques01:22

Overview of Microscopy Techniques

17.0K
The early pioneers of microscopy opened a window into the invisible world of microorganisms. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes that leveraged nonvisible light, such as fluorescence microscopy that uses an ultraviolet light source and electron microscopy that uses short-wavelength electron beams. These advances significantly improved magnification, image resolution, and contrast. By comparison, the...
17.0K
Three-Dimensional Microscopy in Microbiology01:28

Three-Dimensional Microscopy in Microbiology

877
Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...
877
Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

21.2K
Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
21.2K

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

A comprehensive benchmark of sequence-based subcellular localization predictors for human proteins.

Nature methods·2026
Same author

Dissecting autonomous enzymatic variability in single cells.

Nature communications·2026
Same author

A genome-scale CRISPRi perturbation atlas of human induced pluripotent stem cells.

Nature biotechnology·2026
Same author

Cell shapes decode molecular phenotypes in image-based spatial proteomics.

Cell systems·2026
Same author

A framework for the exploration of subcellular compartmentalization of RNA-binding proteins.

Nature communications·2026
Same author

A high-resolution spatial map of cilia-associated proteins in the human fallopian tube.

Nature communications·2026

Video Experimental Relacionado

Updated: Feb 11, 2026

Combining Augmented Reality and 3D Printing to Display Patient Models on a Smartphone
09:26

Combining Augmented Reality and 3D Printing to Display Patient Models on a Smartphone

Published on: January 2, 2020

19.1K

Ver más: el futuro de la microscopía aumentada

Devin P Sullivan1, Emma Lundberg1

  • 1Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH - Royal Institute of Technology, Stockholm, 171 21, Sweden.

Cell
|April 21, 2018
PubMed
Resumen

Las imágenes de microscopía libre de etiquetas pueden predecir características celulares como el tipo y el estado. El aprendizaje profundo permite la multiplexación computacional desde imágenes baratas sin etiquetas, avanzando el análisis celular.

Más Videos Relacionados

Using Generative Art to Convey Past and Future Climate Transitions
06:10

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

1.5K
Pedicle Screw Placement Using an Augmented Reality Head-Mounted Display in a Porcine Model
06:18

Pedicle Screw Placement Using an Augmented Reality Head-Mounted Display in a Porcine Model

Published on: May 24, 2024

2.7K

Videos de Experimentos Relacionados

Last Updated: Feb 11, 2026

Combining Augmented Reality and 3D Printing to Display Patient Models on a Smartphone
09:26

Combining Augmented Reality and 3D Printing to Display Patient Models on a Smartphone

Published on: January 2, 2020

19.1K
Using Generative Art to Convey Past and Future Climate Transitions
06:10

Using Generative Art to Convey Past and Future Climate Transitions

Published on: March 31, 2023

1.5K
Pedicle Screw Placement Using an Augmented Reality Head-Mounted Display in a Porcine Model
06:18

Pedicle Screw Placement Using an Augmented Reality Head-Mounted Display in a Porcine Model

Published on: May 24, 2024

2.7K

Área de la Ciencia:

  • Biología celular
  • Imágenes computacionales
  • Aprendizaje automático

Sus antecedentes:

  • La microscopía es crucial para la investigación de la biología celular.
  • Los métodos tradicionales a menudo requieren etiquetas fluorescentes, que pueden ser costosas y perturbar la función celular.
  • Se necesitan métodos rentables y menos invasivos para el análisis detallado de las células.

Objetivo del estudio:

  • Investigar el potencial de las imágenes de microscopía sin etiqueta para predecir la información celular.
  • Desarrollar un marco de aprendizaje profundo capaz de extraer datos biológicos ricos de imágenes sin etiquetas.
  • Demostrar la viabilidad de ensayos multiplexados por computación utilizando microscopía sin etiqueta.

Principales métodos:

  • Utilizó un marco de aprendizaje profundo aplicado a imágenes de microscopía de células sin etiqueta.
  • Entrenó el modelo para predecir las etiquetas fluorescentes asociadas con el tipo de célula, el estado celular y la distribución de orgánulos.
  • Validado la precisión predictiva del modelo.

Principales resultados:

  • Las imágenes celulares libres de etiquetas se utilizaron con éxito para predecir las etiquetas fluorescentes.
  • El modelo de aprendizaje profundo infería con precisión el tipo de célula, el estado y la distribución de orgánulos.
  • Demostró la capacidad de predecir múltiples características biológicas a partir de una sola imagen sin etiqueta.

Conclusiones:

  • La microscopía sin etiquetas, combinada con el aprendizaje profundo, ofrece una poderosa alternativa al etiquetado fluorescente tradicional.
  • Este enfoque permite ensayos computacionalmente multiplexados, lo que reduce significativamente los costos y la complejidad.
  • Los hallazgos abren nuevas vías para el análisis celular de alto contenido y bajo costo en la investigación biológica.