Multiplex Imaging Capacity
Multiplex Imaging Capacity - Related Videos
08:43
Highly Multiplexed, Super-resolution Imaging of T Cells Using madSTORM
0 Views •
Cited by 5 •
2017
We demonstrate a method to image multiple molecules within heterogeneous nano-structures at single molecule accuracy using sequential binding and elution of fluorescently labeled antibodies.
01:47
Lung Capacity
0 Views •
2019
The air in the lungs is measured in volumes and capacities. Lung volume measures reflect the amount of air taken in, released, or left over after a lung function, like a single inhalation. Lung capacity measures are sums of two or more lung volume measures. There are four defined lung volume measures: tidal volume, inspiratory reserve volume, expiratory reserve volume, and residual volume. Tidal volume is the amount of air inhaled and exhaled in a normal breath. Inspiratory...
06:25
Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
0 Views •
2014
A method for overcoming the optical diffraction limit is presented. The method includes a two-step process: optical phase retrieval using iterative Gerchberg-Saxton algorithm, and imaging system shifting followed by repetition of the first step. A synthetically increased lens aperture is generated along the direction of movement, yielding higher imaging resolution.
02:36
Multiplexed Fluorescence Imaging of Viral Nucleic Acids and Protein in Infected Human Lymphocytes
0 Views •
2026
Source: Shah, R., et al. Single-Cell Multiplexed Fluorescence Imaging to Visualize Viral Nucleic Acids and Proteins and Monitor HIV, HTLV, HBV, HCV, Zika Virus, and Influenza Infection. J. Vis. Exp. (2020)This video demonstrates simultaneous visualization of HIV-1 DNA, RNA, and capsid protein in infected human lymphocytes using branched DNA hybridization and immunofluorescence, enabling detailed analysis of viral replication stages within individual cells.
07:42
Super-Resolution Imaging and Shared Management: A Protocol for Confocal Microscopy with Multiplex Detection
0 Views •
2026
This protocol details the advanced imaging applications, and shared-resource management of a laser-scanning confocal microscope system integrated with a multiplexed array detector for high-resolution cellular and subcellular imaging. The workflow enables multimodal imaging from confocal to super-resolution (~120 nm) within a single platform, making nanoscale visualization more accessible.
FAQs
Each molecular, cellular, or structural target receives an identifiable optical, chemical, or spatial signature. The imaging system then records signals that can be associated with those signatures, while image processing helps separate information that overlaps in the specimen. This organization allows multiple target types to remain distinguishable and supports analysis of biological features in their original context.
Image processing helps disentangle overlapping information collected from the same specimen. Rather than treating the image as a single undifferentiated signal, researchers can separate target-associated information and examine it quantitatively. This step is important when several molecular, cellular, or structural features occupy related regions and must be interpreted together to understand organization or change.
Capacity depends on how many targets can be assigned distinct signatures and subsequently distinguished in the acquired image data. The available signatures may be optical, chemical, spatial, or a combination of these approaches. Capture can occur simultaneously or sequentially, with processing used to organize the resulting signals for multicomponent analysis.
Because multiple target classes are examined within the same specimen, their distributions can be considered together rather than as isolated measurements. Researchers can relate molecular signals to cellular states and structural organization in place. This contextual view supports interpretation of how biological features are arranged and how disease-associated or engineered changes affect that organization.
A study first identifies the molecular, cellular, or structural targets of interest and assigns distinguishable signatures to them. The system then captures the relevant signals either simultaneously or sequentially. Image processing separates and organizes the information, after which researchers quantify target distributions, cell states, tissue architecture, or other features relevant to the investigation.
In bioengineering, researchers can use the approach to evaluate cell states, tissue architecture, and interactions between cells or tissues and biomaterials within one specimen. Comparing these features in context helps characterize how engineered systems are organized and how biological components respond to their surrounding material environment.
The method supports diagnostics research and systems-level studies of biological function by combining information from multiple target types. It can reveal coordinated patterns among molecular, cellular, and structural features, including changes associated with disease. These measurements provide a broader basis for examining biological organization than an analysis focused on a single signal.