Single Cell Applications
Single Cell Applications - Related Videos
15:00
Applications of the Single-probe: Mass Spectrometry Imaging and Single Cell Analysis under Ambient Conditions
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Cited by 44 •
2016
Here, we present protocols to perform both ambient mass spectrometry imaging (MSI) of tissues and in-situ live single cell MS (SCMS) analysis using the single-probe, which is a miniaturized multifunctional device for MS analysis.
07:03
Isolation of Adipose Tissue Nuclei for Single-Cell Genomic Applications
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Cited by 12 •
2020
This publication describes a protocol for the isolation of nuclei from mature adipocytes, purification by fluorescence-activated sorting, and single-cell level transcriptomics.
08:30
Application of MultiColor FlpOut Technique to Study High Resolution Single Cell Morphologies and Cell Interactions of Glia in Drosophila
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Cited by 4 •
2017
Cells display different morphologies and establish a variety of interactions with their neighbors. This protocol describes how to reveal the morphology of single cells and to investigate cell-cell interaction by using the well-established Gal4/UAS expression system.
07:27
Transcriptome Analysis of Single Cells
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Cited by 72 •
2011
In this article we describe a simple method for the harvesting of single cells from rat primary neuronal cultures and subsequent transcriptome analysis using aRNA amplification. This approach is generalizable to any cell type.
05:39
Shock Wave Application to Cell Cultures
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Cited by 22 •
2014
Shock waves nowadays are well known for their regenerative effects. Therefore in vitro experiments are of increasing interest. We therefore developed a model for in vitro shock wave trials (IVSWT) that enables us to mimic in vivo conditions thereby avoiding distracting physical effects.
FAQs
The key analytical advantage comes from retaining each cell’s molecular profile rather than averaging signals across a sample. Bulk analysis can make a rare disease-associated population appear negligible or merge biologically different states into one pattern. Examining cells separately therefore helps distinguish cellular subgroups and connect particular molecular signatures with health or disease.
Single-cell sequencing first captures genetic or molecular information from individual cells. Computational analysis then compares the resulting profiles, groups cells with shared patterns, and separates populations that differ in state. This division of labor links laboratory measurement with interpretation: sequencing supplies cell-level data, while computation organizes it into biologically meaningful cellular categories.
Cells within the same broad tissue or disease context may not behave identically. A state-specific pattern can reveal a disease-associated population, a particular immune response, or a developmental stage that would be obscured in an averaged measurement. This resolution gives medical studies a more precise basis for interpreting how disease-related cellular changes are distributed.
A supported workflow begins by obtaining genetic or molecular measurements from individual cells, followed by computational comparison of the resulting profiles. Cells are then grouped according to shared patterns, and distinct populations or states are identified for biological interpretation. In medicine, this workflow can be applied to characterize tumors, immune responses, developmental processes, and disease-associated cells.
Medical studies can apply these approaches to tumors, immune responses, developmental processes, and disease-associated cell populations. In each setting, the goal is to determine which cellular groups or states are present and how their molecular profiles differ. These findings can support biomarker discovery and improve understanding of how health and disease are organized at the cellular level.
By resolving cellular heterogeneity, single-cell measurements can help identify biomarkers, support diagnosis, guide treatment selection, and monitor disease-related changes. They also contribute to precision medicine by showing which cellular populations may be relevant to an individual condition. This information can support development of therapies designed to target specific cell populations rather than treating all cells as equivalent.