Neither the Hubble nor the Webb-Space telescopes have beamed such images to us. These images are not clusters of stars, planetary systems, gas nebulae, dust clouds, or dark matter. And they are not gazes into astronomical depths of space; they are glimpses into microscopic dimensions of our bodies, its tissues, and cells. Molecular biology techniques, coupled with bioinformatics, confront us with an incredible flood of data. The scale and complexity of this data dictate that we can only interpret them by reducing their complexity and presenting them visually.
The images show point clouds in which each point represents a cell. Each cell is represented by thousands of its messenger RNA (mRNA) sequences. Dimension-reducing, statistical clustering algorithms such as tSNE (t-distributed stochastic neighbor embedding) or UMAP (Uniform Manifold Approximation and Projection) use these sequences to analyze which cells read which genes and synthesize them as proteins. This determines the function of the cells and cells with similar gene expression patterns are represented as clouds of points with a specific color. The array of multiple point clouds shows the complexity of a tissue.
Shapes and colors become visible that carry us off into the infinity surrounding us, but actually reflect ourselves. The biological flood of data is transformed into a sensual field of association in which the microcosm merges with the macrocosm.