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 ## Abstract {.page_break_before}
 
-**Instructions**: Describe your collaborative project, highlighting key achievements of the project; limited to 250 words.
+**Instructions**: Describe your collaborative project, highlighting
+key achievements of the project; limited to 250 words.
+
+The HCA provides a reference atlas to human cell types, states, and
+the biological processes in which they engage. The utility of the
+reference therefore requires that one can easily compare references to
+each other, or a new sample to the compendium of reference
+samples. Low-dimensional representations, because they compress the
+space, provide the building blocks for search approaches that can be
+practically applied across very large datasets such as the HCA. 
+Our seed network proposes to compress HCA data
+into fewer dimensions that preserve the important attributes of the
+original high dimensional data and yield interpretable, searchable
+features.
+We hypothesize that building an ensemble of low
+dimensional representations across latent space methods will provide a
+reduced dimensional space that captures biological sources of
+variability and is robust to measurement noise.
+We will identify techniques that learn interpretable,
+biologically-aligned representations, improve techniques for fast and
+accurate quantification, and implement these base enabling
+technologies and methods for search, analysis, and latent space
+transformations as freely available, open source software tools.
+By using and extending our base enabling technologies, we will provide
+three principle tools and resources for the HCA: 
+1) software to enable fast and accurate search and annotation using
+low-dimensional representations of cellular features, 
+2) a versioned and annotated catalog of latent spaces corresponding to
+signatures of cell types, states, and biological attributes across the
+the HCA, and 
+3) short course and educational materials that will increase the use
+and impact of low-dimensional representations and the HCA in general.