Single Cell Resolution
Single Cell Resolution - Related Videos
07:30
Imaging Cleared Embryonic and Postnatal Hearts at Single-cell Resolution
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Cited by 8 •
2016
We describe a protocol to volumetrically image fluorescent protein labeled cells deep inside intact embryonic and postnatal hearts. Utilizing tissue-clearing methods in combination with whole mount staining, single fluorescent protein-labeled cells inside an embryonic or postnatal heart can be imaged clearly and accurately.
08:48
Optical Recording of Suprathreshold Neural Activity with Single-cell and Single-spike Resolution
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Cited by 1 •
2012
Understanding the function of the vertebrate central nervous system requires recordings from many neurons because cortical function arises on the level of populations of neurons. Here we describe an optical method to record suprathreshold neural activity with single-cell and single-spike resolution, dithered random-access scanning. This method records somatic fluorescence calcium signals from up to 100 neurons with high temporal resolution. A maximum-likelihood algorithm deconvolves the...
07:38
Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper
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Cited by 9 •
2017
This manuscript uses the Fiji-based open-source software package VirusMapper to apply single-particle analysis to super-resolution microscopy images in order to generate precise models of nanoscale structure.
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.
08:43
Highly Multiplexed, Super-resolution Imaging of T Cells Using madSTORM
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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.
FAQs
The approach preserves differences among individual cells instead of combining their signals into a population average. That distinction can expose rare cell types, disease-associated populations, or cells in different states that would be diluted or obscured in bulk measurements. Linking each cell’s morphology, molecules, or gene expression to its identity makes those variations biologically interpretable.
Separating cells, or distinguishing them within their spatial context, allows measurements to remain associated with the correct cell. Without that association, signals from neighboring or mixed populations can be combined and lose their cellular meaning. This step therefore supports accurate connections between a cell’s observed features and its identity, state, or response to a perturbation.
These tools provide complementary ways to connect cellular features with individual cells. Microscopy can support analysis of morphology, flow cytometry can measure cellular molecules, and single-cell sequencing can examine gene expression. The choice of tool shapes which features are linked to cell identity and state, allowing researchers to study cellular variation from different biological perspectives.
Measurements collected at the individual-cell level can generate datasets that require careful computational analysis before biological conclusions are drawn. Analysis helps organize and interpret patterns across cell identities and states, while validation checks whether those patterns are reliable. Together, these steps strengthen conclusions about heterogeneity, developmental trajectories, disease-associated populations, and cellular responses.
A typical workflow begins by separating cells or distinguishing them spatially, followed by measurement with microscopy, flow cytometry, or single-cell sequencing. Researchers then associate morphology, molecular features, or gene expression with each cell’s identity and state. Computational analysis and validation follow, supporting interpretation of cellular populations, trajectories, interactions, or responses.
It is particularly useful when a population may contain rare cell types, cells at different developmental stages, or disease-associated subpopulations. Resolving those differences can help researchers investigate developmental trajectories and distinguish how cellular states vary within a tissue. The same strategy supports studies in development, immunity, cancer, and therapeutic response.
Measurements assigned to individual cells can show how distinct cellular populations respond to a perturbation rather than reporting only a combined tissue signal. They can also help examine relationships among cells within a tissue when spatial distinctions are retained. This supports analysis of tissue responses, cell-to-cell interactions, and variation in therapeutic response across cellular states.