Redefining Mucosal Inflammation with Spatial Genomics.

The human oral mucosa contains one of the most complex cellular systems that are essential for normal physiology and defense against a wide variety of local pathogens. Evolving techniques and experimental systems have helped refine our understanding of this complex cellular network. Current single-cell RNA sequencing methods can resolve subtle differences between cell types and states, thus providing a great tool for studying the molecular and cellular repertoire of the oral mucosa in health and disease. However, it requires the dissociation of tissue samples, which means that the interrelationships between cells are lost. Spatial transcriptomic methods bypass tissue dissociation and retain this spatial information, thereby allowing gene expression to be assessed across thousands of cells within the context of tissue structural organization. Here, we discuss the contribution of spatial technologies in shaping our understanding of this complex system. We consider the impact on identifying disease cellular neighborhoods and how space defines cell state. We also discuss the limitations and future directions of spatial sequencing technologies with recent advances in machine learning. Finally, we offer a perspective on open questions about mucosal homeostasis that these technologies are well placed to address.

A Roadmap for the Human Oral and Craniofacial Cell Atlas.

Oral and craniofacial tissues are uniquely adapted for continuous and intricate functioning, including breathing, feeding, and communication. To achieve these vital processes, this complex is supported by incredible tissue diversity, variously composed of epithelia, vessels, cartilage, bone, teeth, ligaments, and muscles, as well as mesenchymal, adipose, and peripheral nervous tissue. Recent single cell and spatial multiomics assays-specifically, genomics, epigenomics, transcriptomics, proteomics, and metabolomics-have annotated known and new cell types and cell states in human tissues and animal models, but these concepts remain limitedly explored in the human postnatal oral and craniofacial complex. Here, we highlight the collaborative and coordinated efforts of the newly established Oral and Craniofacial Bionetwork as part of the Human Cell Atlas, which aims to leverage single cell and spatial multiomics approaches to first understand the cellular and molecular makeup of human oral and craniofacial tissues in health and to then address common and rare diseases. These powerful assays have already revealed the cell types that support oral tissues, and they will unravel cell types and molecular networks utilized across development, maintenance, and aging as well as those affected in diseases of the craniofacial complex. This level of integration and cell annotation with partner laboratories across the globe will be critical for understanding how multiple variables, such as age, sex, race, and ancestry, influence these oral and craniofacial niches. Here, we 1) highlight these recent collaborative efforts to employ new single cell and spatial approaches to resolve our collective biology at a higher resolution in health and disease, 2) discuss the vision behind the Oral and Craniofacial Bionetwork, 3) outline the stakeholders who contribute to and will benefit from this network, and 4) outline directions for creating the first Human Oral and Craniofacial Cell Atlas.

Estimating parametric fertility models with open birth interval data.

PIP: This paper demonstrates the approach of extending "date of last birth" (DLB) techniques to parametric models. Estimators for Coale-Trussell M and m parameters are constructed from open interval lengths. A new procedure was applied to Brazilian census data, producing maps and spatial statistics for "births last year" (BLY) and DLB m estimates in 723 municipalities in Minas Gerais. DLB estimators are less sensitive to sampling error than BLY estimators. This increased precision leads to clearer spatial patterns of fertility control, and to improved regression. This study produced two main points. The first is that parametric fertility models may be estimated from open-interval birth data in straightforward fashion. The second point is that use of DLB data can make a critical difference to the quality of statistical results. DLB data are often available to researchers, but they are seldom used to their full potential. When fertility data are collected in last-birth or open-interval form, the methods expounded in this paper can make significant improvements in the demographic analysis of small samples.^ieng