Why does single-cell counting matter for target validation in atherosclerosis?

Single-cell counting enables precise quantification of phenotypic shifts in lesion-resident populations, such as smooth muscle cell-derived macrophages, following target inhibition. This provides mechanistic insight into whether a therapeutic agent modulates pathogenic cellular transitions. Quantitative changes in colocalized markers (e.g., YFP+/galectin-3+) support target engagement and pathway-specific effects.

How does isolating the brachiocephalic artery improve discovery pipeline consistency?

Isolating the brachiocephalic artery ensures standardized sampling of a consistent, atheroprone vascular bed across animals. This reduces variability in lesion burden and composition, improving comparability between control and treatment groups. Precise dissection minimizes contamination from surrounding tissues, enhancing signal specificity in downstream immunostaining and imaging.

What quantitative measurements enable mechanistic de-risking of atherosclerotic lesions?

Quantitative outputs include the number of phenotype-specific cells (e.g., YFP+/α-SMA+ smooth muscle cells) and their proportion relative to total DAPI+ nuclei. These measurements reveal shifts in cellular composition, such as decreased contractile smooth muscle cells or increased inflammatory phenotypes, upon intervention. Such data help de-risk targets by linking modulation to defined cellular outcomes in lesion subregions like the fibrous cap.

Why do replication requirements matter for cross-functional collaboration in lesion analysis?

Replication ensures that observed cellular changes are robust and not due to technical variability in dissection, staining, or imaging. Consistent results across replicates build confidence in target-mediated effects, supporting alignment between discovery, preclinical, and translational teams. Standardized protocols reduce inter-lab variability, enabling reliable data sharing and decision-making in multi-site projects.

What statistical analysis capabilities are required before implementing this protocol in discovery workflows?

Implementation requires the ability to quantify single-cell events across z-stacks and calculate percentages or densities of marker-positive cells. Statistical comparison (e.g., t-tests, ANOVA) of these metrics between groups is essential to determine significant changes in cellular composition. Access to image analysis tools like ImageJ and compatibility with statistical software (e.g., GraphPad, R) are necessary for rigorous data interpretation.

Analiza ilościowa składu komórkowego w zaawansowanych zmianach miażdżycowych myszy śledzących linię komórek mięśni gładkich

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Cited by 1

09:06 min

February 20th, 2019

10.3791/59139-v

February 20th, 2019

7.8K views

Sidney Mahan¹ , Mingjun Liu¹ , Richard A. Baylis²^,³ , Delphine Gomez¹^,⁴

¹Heart, Lung, Blood and Vascular Medicine Institute, University of Pittsburgh, ²Robert M. Berne Cardiovascular Research Center, University of Virginia, ³Department of Biochemistry and Molecular Genetics, University of Virginia, ⁴Division of Cardiology, University of Pittsburgh School of Medicine

Proponujemy ustandaryzowany protokół do scharakteryzowania składu komórkowego późnych zmian miażdżycowych u myszy, w tym systematyczne metody preparacji zwierząt, osadzania tkanek, cięcia, barwienia i analizy tętnic ramienno-głowowych od myszy śledzących linię komórek mięśni gładkich w atheroprone.