Spatial omics technologies at multimodal and single cell/subcellular level.

This study explores how different tissues react over time when exposed to certain treatments or changes in temperature, which is important because it helps us understand why some parts of our body might respond differently than others during illness. The researchers used special tools that can see and measure tiny differences at the cellular level within various types of human tissue samples from healthy individuals as well as those affected by pneumonia or different stages of COVID-19 infection. What they found is quite interesting - some methods, like a technique called absolute quantification (which measures how much each type of molecule present inside the cells), are better when we already have an idea about what to look for and want precise measurements. On the other hand, if researchers just started exploring without specific expectations or wanted to compare different conditions broadly across various samples, they might use a more discovery-based approach that doesn't require prior knowledge of molecule amounts but can still provide valuable insights into cellular differences and changes during diseases.

Title: Quantitative Spatial Imaging for Cellular Subpopulation Analysis in Pneumonia and Different Stages of COVID-19 Using GeoMx and Hyperion Systems Abstract: This study aimed to evaluate the concordance between spatial imaging technologies, specifically the GeoMx and Hyperion systems, when analyzing cell populations affected by pneumonia or different stages of COVID-19. The methodology involved absolute quantification of transcripts using these two platforms across multiple tissue samples subjected to enzymatic treatments and temperature variations over extended durations (rounds). Key Findings: Our results demonstrated a high correlation between the GeoMx and Hyperion systems in identifying differences in cell types, as well as expression levels of healthy versus pneumonia-affected cells. Furthermore, we observed significant concordance when comparing different stages of COVID-19 (r = 0.630). However, a lower correlation was found between the two platforms for assessing gene expression metrics in COVID-19 patients compared to those with healthy status (r = 0.