Cd45ra Cd90
Cd45ra Cd90 - Related Videos
18:48
In Vitro and In Vivo Assessment of T, B and Myeloid Cells Suppressive Activity and Humoral Responses from Transplant Recipients
0 Views •
Cited by 1 •
2017
Here, we present a protocol to induce tolerance in transplantation, and assess in vitro and in vivo the suppressive capacity of distinct cell subsets from the recipient and the immune status of the recipient toward donor or exogenous antigens.
05:21
Culturing and Harvesting of HSPC Clones: A Method to Obtain the Singly-Sorted HSPC Clones
0 Views •
2023
This video describes the protocol for isolating HSPCs from the bone marrow and sorting them using a fluorescent activated cell sorting technique, which is a method to obtain singly-sorted HSPCs. We culture these sorted cells to obtain HSPC clones.
08:28
Ex Vivo Imaging of Resident CD8 T Lymphocytes in Human Lung Tumor Slices Using Confocal Microscopy
0 Views •
Cited by 21 •
2017
This protocol describes a method to image resident tumor-infiltrating CD8 T cells labeled with fluorescently coupled antibodies within human lung tumor slices. This technique permits real-time analyses of CD8 T cell migration using confocal microscopy.
11:44
Generation of a Humanized Mouse Liver Using Human Hepatic Stem Cells
0 Views •
Cited by 3 •
2016
Here, we present a novel humanized mouse liver model generated in Alb-toxin receptor mediated cell knockout (TRECK)/SCID mice following the transplantation of immature and expandable human hepatic stem cells.
04:23
Real-Time Analysis of Resident T-cell Migration in Different Tumor Regions Using Confocal Microscopy
0 Views •
2025
This video describes the imaging of resident tumor-infiltrating CD8 T cells labeled with fluorescently coupled antibodies within human lung tumor slices. This technique permits real-time analyses of CD8 T cell migration using confocal microscopy.
FAQs
The informative feature is often the distribution of both markers across cells rather than either signal alone. CD45RA identifies a CD45 isoform, while CD90 expression varies among hematopoietic, stromal, and other populations. Comparing their expression patterns can separate phenotypically distinct subsets and support analysis of differentiation states within immune, stem-like, or tumor-associated compartments.
CD45RA reports expression of a specific CD45 isoform, giving researchers an additional phenotypic feature for examining leukocyte populations. When assessed alongside CD90 and other experimental markers, it can help resolve immune subsets and evaluate differences in cellular state. This is relevant when cancer studies examine how immune populations relate to tumor-associated compartments.
CD90 expression can help distinguish populations because it is not uniform across hematopoietic, stromal, and other cell types. Its presence or absence therefore provides context about the composition of a sample and may help identify stem-like or tumor-associated subsets. Researchers can use these differences to compare cellular phenotypes across cancer-related specimens or conditions.
Joint analysis creates a multidimensional phenotype that can distinguish subsets sharing one marker but differing in the other. This combined pattern may clarify whether cells belong to immune, stromal, stem-like, or tumor-associated compartments and can support assessment of differentiation states. Such resolution is useful for selecting populations for downstream functional investigation rather than relying on a single surface feature.
Researchers detect both surface markers with antibody-based flow cytometry, which enables phenotypic analysis of cells according to their marker expression. Cell sorting can extend this workflow by separating selected populations after detection. The resulting profiles provide a basis for comparing cellular subsets and determining which groups warrant further study in a cancer model.
After antibody-based detection and sorting, selected populations can be isolated for functional studies. This allows investigators to examine differences between phenotypically defined subsets rather than analyzing a mixed sample as one group. In cancer research, that strategy supports focused investigation of immune, stem-like, stromal, or tumor-associated cell populations and their potential roles in disease-related processes.
Marker profiling can support studies of tumor–immune interactions, cancer-associated cell populations, and disease progression. By comparing expression-defined subsets, researchers can examine how cellular composition or differentiation states relate to these processes. The approach is therefore useful for organizing complex tumor samples into populations that can be evaluated in a more targeted way.
Expression patterns can provide phenotypic information relevant to candidate biomarkers for diagnosis or treatment response. Researchers may compare defined cellular subsets and assess whether their profiles are associated with disease-related features or response-related observations. These markers support that characterization process, while interpretation depends on the cellular context and the broader cancer research design.