Cell Type Annotation
Cell Type Annotation - Related Videos
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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics
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Cited by 8 •
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Combination of genomics, co-expression gene analysis and the identification of target compounds via metabolism give gene functional annotation.
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Genome Annotation and Assembly
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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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mirMachine: A One-Stop Shop for Plant miRNA Annotation
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Herein, we present a new and fully automated miRNA pipeline, mirMachine that 1) can identify known and novel miRNAs more accurately and 2) is fully automated and freely available. Users can now execute a short submission script to run the fully automated mirMachine pipeline.
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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
Distinct identities are inferred by comparing measured cellular features with known signatures rather than relying on a single observation. Gene-expression profiles, protein markers, morphology, and chromatin states can each contribute evidence. This comparison helps separate neighboring populations that share some characteristics while revealing the point at which progenitor cells begin to acquire specialized features.
Each provides a distinct type of measured evidence about a cell. Using them together gives researchers several comparison points for assigning labels and examining cellular diversity. This is particularly useful when related populations share some characteristics, because a gene-expression profile, protein marker pattern, morphological trait, or chromatin state may distinguish them in different ways.
Annotations can be compared across progenitor and specialized populations to identify when cellular characteristics change. A shift in the measured features used for labeling may indicate that progenitor cells are acquiring specialized characteristics. This makes annotation useful for following differentiation, distinguishing developmental states, and organizing cellular identities across the progression of tissue formation.
First, researchers examine the available measurements, such as gene-expression profiles, protein markers, morphology, or chromatin states. They then compare those features with known cell-type signatures and assign identities to individual cells or clusters. Repeating this comparison across developmental samples, tissues, or time points supports interpretation of changing populations and helps organize developmental patterns.
It is useful when investigators need to characterize tissue composition during embryonic development, examine stem-cell differentiation, or study how tissues form. The approach can separate progenitor populations from more specialized cells and show how cellular diversity changes during development. These applications connect measured cellular features with specific developmental processes rather than treating a tissue as uniform.
Mapping assigned identities across tissues and time can reveal lineage relationships and show how cellular populations change during development. The same strategy can also identify disease-associated changes in cellular identity. Together, these outcomes help researchers relate cell-level measurements to tissue organization, developmental history, and altered states, providing context for interpreting complex biological samples.