Cell Type Atlas
Cell Type Atlas - Related Videos
03:38
Developing a Cerebral Blood Flow Atlas for Blood Flow Mapping
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2025
Source: Tan, Z., et al., Construction and Application of Cerebral Functional Region-Based Cerebral Blood Flow Atlas Using Magnetic Resonance Imaging-Arterial Spin Labeling. J. Vis. Exp. (2024).This video demonstrates the method to construct a cerebral blood flow atlas by integrating magnetic resonance imaging or MRI-arterial spin labeling data with segmented brain regions, visualizing blood flow patterns, and quantifying flow levels across functional areas.
09:08
Production of Tissue Microarrays, Immunohistochemistry Staining and Digitalization Within the Human Protein Atlas
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Cited by 202 •
2012
Tissue microarrays allows for an efficient method to gain concurrent information from a multitude of tissues. Representative parts of tissues are assembled into a single paraffin block. Sections from the block are used for immunohistochemistry and analysis of protein expression patterns. Digital scanning generates corresponding images for distribution of data.
09:29
Stereotactic Atlas-Guided Laser Capture Microdissection of Brain Regions Affected by Traumatic Injury
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Cited by 3 •
2017
We describe the use of laser capture microdissection to obtain samples of distinct cell populations from different brain regions for gene and microRNA analysis. This technique allows the study of differential effects of traumatic brain injury in specific regions of the rat brain.
09:48
Separation of Spermatogenic Cell Types Using STA-PUT Velocity Sedimentation
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Cited by 111 •
2013
The STA-PUT method allows for the separation of different populations of spermatogenic cells based on size and density.
01:24
T Cell Types and Functions
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2024
When T cells with CD4 markers are activated, they give rise to two types of effector cells: helper T cells and regulatory T cells. Meanwhile, T cells with CD8 markers differentiate into effector cytotoxic T cells. The differentiation of CD4 T cells into helper T cell subsets, such as Th1, Th2, and Th17 cells, is dependent on the antigen type, antigen-presenting cell, and regulatory cytokines. Th1 cells stimulate dendritic cells to express necessary co-stimulatory molecules on their surfaces for...
FAQs
Marker genes are genes whose expression patterns help separate one cell population from another. Researchers compare gene-expression profiles across cells, identify genes enriched in particular groups, and use those patterns to assign cellular identities. Computational clustering strengthens this process by grouping cells with similar molecular profiles, while marker genes provide interpretable evidence for the resulting classifications.
Gene expression reveals which genes are active, but other molecular features can add information about cellular state and identity. Combining these measurements helps researchers distinguish closely related populations and connect genome activity with functional differences. This broader molecular view is especially useful when cells share many expressed genes but occupy different biological states.
Unrecognized populations can emerge when computational analysis detects a consistent group of cells with a distinct molecular profile. Researchers can then examine the genes enriched in that group and relate the pattern to a biological state, tissue, organism, or developmental stage. This approach allows cellular diversity to be inferred from molecular differences rather than from established classifications alone.
Linking cellular profiles with genetic variation helps researchers examine how differences in the genome correspond to molecular states in specific cell populations. Instead of interpreting genomic data without cellular context, investigators can ask which cell types show particular patterns or changes. This connection supports more precise analysis of how genome activity contributes to tissue organization and biological variation.
A typical workflow begins by collecting cells from defined tissues, organisms, or developmental stages and measuring gene expression or other molecular features. Researchers then organize the resulting profiles computationally, cluster cells according to molecular similarity, and use marker genes to distinguish populations. The final reference links these cellular identities with biological states and relevant genetic information.
Disease research can use these atlases to compare cellular profiles and identify disease-associated changes in particular populations. Because the reference connects molecular features with cell identities, researchers can determine where altered genome activity occurs within a tissue. The resulting context can clarify affected cell states, support disease interpretation, and help identify potential therapeutic targets.
Including developmental stages allows researchers to track how cellular identities and genome activity change over time. Comparisons across stages can connect distinct cell populations with developmental programs and show when particular molecular states arise. This temporal perspective helps explain how tissues acquire their cellular diversity and provides context for changes that may later be associated with disease.
A Cell Type Atlas provides a cellular reference for genomic findings that might otherwise be difficult to localize or interpret. By associating genetic variation and genome activity with defined cell populations, researchers can assess which cellular contexts are relevant to a biological state or disease-associated change. This improves interpretation by connecting genome-level observations to tissue and cell-level organization.