Sequence Type Lineages
Sequence Type Lineages - Related Videos
01:21
Lineage Commitment
0 Views •
2023
Commitment is the process whereby stem cells: lose their ability to form all cell types and irreversibly change into a specific type. The multipotent hematopoietic stem cells, (HSCs), differentiate into the multipotent hematopoietic progenitor cells, (HPCs). The HPCs express many lineage-specific cytokine receptors. Each of these receptors binds specific cytokines, activates distinct signaling pathways, and expresses a particular gene set. The HPCs further differentiate to form committed...
10:41
Wild-type Blocking PCR Combined with Direct Sequencing as a Highly Sensitive Method for Detection of Low-Frequency Somatic Mutations
0 Views •
Cited by 5 •
2017
Wild-type blocking PCR followed by direct sequencing offers a highly sensitive method of detection for low frequency somatic mutations in a variety of sample types.
14:46
Efficient Generation of hiPSC Neural Lineage Specific Knockin Reporters Using the CRISPR/Cas9 and Cas9 Double Nickase System
0 Views •
Cited by 5 •
2015
Genome editing tools such as the CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)/Cas (CRISPR-associated) system have greatly improved gene targeting efficiency in human induced pluripotent stem cells (hiPSCs). This manuscript describes a protocol for generating lineage specific hiPSC reporter using CRISPR/Cas system assisted homologous recombination.
06:30
Cell Lineage Analyses and Gene Function Studies Using Twin-spot MARCM
0 Views •
Cited by 2 •
2017
Here, we present a protocol for a mosaic labeling technique that permits the visualization of neurons derived from a common progenitor cell in two distinct colors. This facilitates neural lineage analysis with the capability of birth-dating individual neurons and studying gene function in the same neurons of different individuals.
07:35
Lineage Labeling of Zebrafish Cells with Laser Uncagable Fluorescein Dextran
0 Views •
Cited by 8 •
2011
This protocol delineates a way to label and trace the fate of small groups of cells zebrafish embryos using UV-uncaging of caged fluorescein, followed by whole mount immunolabeling to amplify the signal from the uncaged fluorescein.
FAQs
A sequence type represents one specific combination of numbered alleles across the selected housekeeping-gene fragments. A sequence type lineage groups sequence types that share sufficient allelic similarity, so the lineage describes a broader population relationship rather than one exact profile. This distinction lets researchers examine both fine-scale isolate differences and larger patterns of microbial population structure.
Statistical analysis of sequence type profiles can describe genetic diversity, compare microbial populations, identify clusters, and assess patterns of spread. These analyses help determine whether sampled isolates show limited or varied sequence-type representation and whether particular profiles or lineages are distributed differently across populations. The results provide a structured basis for interpreting population structure and transmission.
Allelic similarity supplies the basis for deciding which distinct sequence types belong to a broader lineage. Sequence types do not need to be identical to show a population relationship, because related profiles may share many alleles while differing at others. Grouping them in this way can reveal broader relatedness and make population-level patterns easier to evaluate statistically.
Researchers first obtain sequence information from the defined housekeeping-gene fragments, assign each distinct allele a numerical designation, and combine those designations into an allele profile. They then compare profiles using allelic similarity to identify related sequence types and broader lineages. Statistical evaluation of the resulting groups can address diversity, clustering, population comparisons, and spread.
During surveillance or an outbreak investigation, researchers can compare sequence types and lineages among microbial isolates to identify clusters and examine their distribution. Shared or closely related profiles can highlight groups that merit epidemiological attention, while differences among profiles help describe population structure. The same framework also supports monitoring patterns relevant to antimicrobial resistance across sampled populations.
Standardized allele numbers, profiles, and lineage groupings provide a common framework for comparing isolates generated in different laboratories or geographic regions. Statistical comparisons can then evaluate diversity, cluster distribution, and relatedness without relying only on local naming conventions. This consistency strengthens epidemiological surveillance and makes regional patterns of microbial spread easier to examine together.