Toward a Common Coordinate Framework for the Human Body

Abstract

Understanding the genetic and molecular drivers of phenotypic heterogeneity across individuals is central to biology. As new technologies enable fine-grained and spatially resolved molecular profiling, we need new computational approaches to integrate data from the same organ across different individuals into a consistent reference and to construct maps of molecular and cellular organization at histological and anatomical scales. Here, we review previous efforts and discuss challenges involved in establishing such a common coordinate framework, the underlying map of tissues and organs. We focus on strategies to handle anatomical variation across individuals and highlight the need for new technologies and analytical methods spanning multiple hierarchical scales of spatial resolution.

Figure 1.. Types of coordinate systems

Different tissue or organ structures may require different types of coordinate systems in order to adapt to differing levels of inter-individual specimen variability. Highly replicable structured tissues, such as embryos, may be able to employ anatomical plane coordinate systems in a CCF, whereas increasing specimen variability will require higher degrees of flexibility in the coordinate system used. In tissues with greater inter-specimen variability, landmark-based coordinate systems or nonlinear approaches, rather than anatomical plane coordinates, are more robust to the presence of non-conserved spatial structures in different individuals.

Figure 2.. Hierarchical organization of coordinate systems

A common coordinate framework that encompasses the entire human body will require a hierarchy of coordinate systems covering different scales. From the whole-organ (macro) scale, the CCF will allow a study of the relative differences in size and shape of different body organs between individuals. Zooming in further, additional common coordinate framework layers at progressively finer scales will allow similar analysis of inter-individual anatomical variation at the intra-organ regional (meso), histological (micro) and cllular (fine) scales.

Figure 3.. Methods for CCF assembly

(A) Methods for constructing a common coordinate framework often begin with the selection of a ‘reference’ template for future downstream alignment. This typically represents a single sample that is most similar to the population average, and will serve as the starting point for construction of the CCF. (B) Following template selection, all samples in the population can be mapped to the reference template, transforming each individually into the coordinate space of the template. Alternatively, an iterative approach can be used where samples are aligned pairwise to the template, and the template averaged and updated at each iteration until convergence. An iterative approach can be more computationally expensive than pairwise alignment, but helps reduce bias toward any single sample in the final CCF. (C) Another approach for the construction of a common coordinate framework is to reconstruct a tissue from its own features. In this case, the spatial relationships between cells are not known a priori, but are instead inferred from features measured in the cells.

Figure 4.. Mapping new datasets to a CCF

(A) Mapping within a modality (e.g. transcriptomic data) involves the registration of query datasets to the reference CCF. The transformation used to register the query with the reference can then be studied to learn sources of anatomical variation between individuals. (B) Cross-modality mapping relies on the identification of corresponding features between separate query datasets and the reference. These can be features that are independently identifiable across the modalities used (e.g. lung branchpoints identified using CT and PET data). They can also represent molecular features, i.e. the expression level of genes or proteins, that can be measured across different assay types and facilitate integration.