Cell Type Identification
Cell Type Identification - Related Videos
09:34
Identification of Growth Inhibition Phenotypes Induced by Expression of Bacterial Type III Effectors in Yeast
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Cited by 34 •
2010
In this video, we describe a procedure for the expression of bacterial type III effectors in yeast and the identification of effector-induced growth inhibition phenotypes. Such phenotypes can be subsequently exploited to elucidate effector functions and targets.
07:04
Fiber Type Identification of Human Skeletal Muscle
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Cited by 4 •
2023
This protocol demonstrates single-fiber isolation from freeze-dried human skeletal muscle and fiber-type classification according to Myosin heavy chain (MHC) isoform using the dot blotting technique. Identified MHC I and II fiber samples can then be further analyzed for fiber type-specific differences in protein expression using western blotting.
11:09
Rodent Identification I
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2015
Source: Kay Stewart, RVT, RLATG, CMAR; Valerie A. Schroeder, RVT, RLATG. University of Notre Dame, IN A fundamental requirement of biomedical research is the proper identification of research animals. It is essential that the right animal is utilized for procedures and data collection. Laboratory mice and rats can be identified with the following permanent methods: ear tags, ear punch codes, microchip implantation, tail tattoos for adult mice, and toe tattoos for neonates. Temporary methods of...
14:12
Rodent Identification II
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2015
Source: Kay Stewart, RVT, RLATG, CMAR; Valerie A. Schroeder, RVT, RLATG. University of Notre Dame, IN Animal records must be accurately maintained to ensure that data collection is correct. Records range from maintaining information on cage cards to having a detailed database with all of the relevant information on each animal. The primary component of recordkeeping is the individual identification of research animals. There are a variety of methods suitable for identifying mice and rats. This...
07:51
Optogenetics Identification of a Neuronal Type with a Glass Optrode in Awake Mice
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Cited by 6 •
2018
This work introduces a method to perform an optogenetic single-unit recording reliably from an awake mouse using a custom-made glass optrode.
FAQs
Reliable classification usually comes from converging evidence rather than a single feature. Gene-expression markers and protein profiles indicate molecular identity, morphology describes cellular form, electrophysiology captures functional behavior, and connectivity places the cell within a circuit. Computational analysis can integrate these measurements, helping distinguish populations whose individual characteristics overlap.
Molecular markers provide measurable signals associated with cellular identity. Researchers can examine gene expression through in situ hybridization or single-cell RNA sequencing and assess protein profiles with immunohistochemistry. Comparing these signals across cells helps reveal distinct neuronal or glial populations and complements structural or physiological evidence when morphology alone cannot separate closely related cells.
Electrophysiological behavior adds functional information that molecular and structural measurements may not capture. Patch-clamp recording can characterize how individual cells behave electrically, while connectivity shows their relationships within neural circuits. Together, these features help link cellular identity to circuit position and function, strengthening classifications based on molecular markers or morphology.
A typical workflow begins by selecting features relevant to the research question, such as morphology, gene expression, protein profiles, electrophysiology, or connectivity. Researchers then collect complementary measurements using methods such as immunohistochemistry, in situ hybridization, single-cell RNA sequencing, or patch-clamp recording. Computational analysis can integrate the results into cell-type classifications.
These methods provide different molecular perspectives. Immunohistochemistry detects protein profiles, in situ hybridization identifies gene-expression markers within cells or tissue locations, and single-cell RNA sequencing measures gene-expression patterns at single-cell resolution. Using them together can connect molecular signatures with cellular position and improve the interpretation of distinct neuronal or glial populations.
The approach supports investigations that need to connect particular cells with larger biological processes. In neuroscience, it can clarify neural-circuit organization, track cellular changes during development, examine disease mechanisms, and help identify potential therapeutic targets. Combining molecular, structural, physiological, and connectivity data makes these applications more informative than relying on one cellular characteristic alone.