Single Unit Tracking
Single Unit Tracking - Related Videos
08:39
Tracking Single Proteins in Lipid Bilayers Using Fluorescence Microscopy
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2025
This article provides a detailed description of how to create samples for single-protein tracking in solid-supported lipid bilayers. It also explains a straightforward fluorescence microscope with single-molecule sensitivity and a fast frame rate. Finally, we outline the procedure for extracting single-protein trajectories.
16:21
Visualizing Protein-DNA Interactions in Live Bacterial Cells Using Photoactivated Single-molecule Tracking
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Cited by 31 •
2014
Photoactivated localization microscopy (PALM) combined with single-molecule tracking allows direct observation and quantification of protein-DNA interactions in live Escherichia coli cells.
11:00
Single-unit In vivo Recordings from the Optic Chiasm of Rat
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Cited by 3 •
2010
Retinal ganglion cells transmit visual information from the eye to the brain with sequences of action potentials. Here, we demonstrate how to record the action potentials of single ganglion cells in vivo from anesthetized rats.
02:53
Tracking Bacterial Growth at Single-Cell Resolution in a Microfluidic System
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2025
Source: Cabeen, M. T., et al. Single-cell Microfluidic Analysis of Bacillus subtilis. J. Vis. Exp. (2018)The video demonstrates the use of a microfluidic device to observe bacterial growth at the single-cell level. It showcases how non-motile bacterial cells, confined within micro-scale trenches, are continuously nourished and imaged under a fluorescence microscope. The setup enables real-time visualization of cell division and lineage tracking across multiple generations.
12:32
Retrograde Fluorescent Labeling Allows for Targeted Extracellular Single-unit Recording from Identified Neurons In vivo
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Cited by 13 •
2013
Retrograde transport of fluorescent dye labels a sub-population of neurons based on anatomical projection. Labeled axons can be visually targeted in vivo, permitting extracellular recording from identified axons. This technique facilitates recording when neurons cannot be labeled through genetic manipulation or are difficult to isolate using 'blind' in vivo approaches.
FAQs
The recording approach detects action potentials and separates the resulting spike train from nearby neural activity. This separation allows researchers to attribute changes in firing to an individual cell rather than to an undifferentiated mixture of signals. Reliable isolation is important because the analysis depends on comparing one neuron’s activity with sensory, motor, learning, or behavioral events.
Firing rate indicates how frequently a neuron produces action potentials, while spike timing shows when those events occur relative to an experimental event. Examining both measures can reveal how information is encoded in neural activity. Comparing these patterns across stimulation, movement, or behavior helps investigators characterize the cell’s response and infer aspects of neural coding.
Following individual neurons preserves the activity patterns of single cells instead of combining them immediately into a population signal. This resolution helps researchers examine how particular neurons respond and how their firing changes across conditions. The resulting single-cell observations can also contribute to models of population activity, linking cellular responses with broader circuit-level function.
Researchers compare the timing and rate of a neuron’s activity with experimental events and with changing behavioral conditions. Consistent relationships between firing and those events can indicate how a cell participates in information processing. Across recordings, these patterns help investigate circuit function and connectivity, including how neural activity relates to perception, movement, learning, or memory.
A typical workflow uses a fine electrode or another high-resolution recording approach to monitor electrical activity, detects action potentials, and separates individual spike trains from surrounding signals. Researchers then follow firing changes while presenting sensory stimulation or observing movement, learning, or behavior. Finally, they compare activity timing and rate with the relevant experimental events.
This method is useful when the research question requires cellular resolution during perception, motor control, memory, learning, or behavior. It can show how the firing of individual neurons changes as an organism encounters specific events or performs actions. The same approach also supports investigations of neurological disorders by relating altered neuronal activity to experimental conditions.
Single-neuron firing patterns provide detailed activity measurements that can be related to movement, sensory information, or other experimental events. Those measurements support brain-computer interface research by supplying neural signals for investigation and interpretation. They also help build models of population activity, connecting the responses of individual cells with broader descriptions of how neural systems represent information.