 Abstract tissues are highly complex, containing spatial heterogeneity in gene expression. Single cell INA sequencing SC-INasec has enabled researchers to study individual cells, but it does not preserve spatial information. To address this issue, we developed SC-SPACE, a novel approach for identifying spatially variable cell subpopulations from SC-INasec data. This technique uses Visium, STARMAP, or Slide Sector Creator Pseudospace with spatial transcriptome references, then applies clustering algorithms to identify spatially variable cell subpopulations. We tested SC-SPACE on simulated and real datasets and found that it was able to accurately and robustly identify spatially variable cell subpopulations. Additionally, SC-SPACE was used to reconstruct the spatial architecture of several organs, including the brain cortex, small intestine villi, liver lobules, kidneys, embryonic hearts, and melanomas. Finally, SC-SPACE was applied to analyze the spatial distribution of cancerous cells in COVID-19 patients, demonstrating its potential use in discovering spatial therapeutic markers. This article was authored by Jinyang Qian, Jie Liao, Zike Liu, and others. We are article.tv, links in the description below.