Sandrine Dudoit

The olfactory epithelium of adult mammals contains two populations of stem cells that support its remarkable ability to regenerate neuronal and non-neuronal cell types throughout life. How olfactory epithelial cell types are established during development, however, is not well understood. Here, we use genetic lineage tracing and single-cell RNA sequencing of the perinatal mouse olfactory epithelium to construct a developmental trajectory consisting of multiple lineages. We identify transitional states and lineage relationships between individual cells and establish Ascl1 ⁺ cells as the primary multipotent progenitors in the perinatal olfactory epithelium. Further, Ascl1 ⁺ cells become progressively restricted in their cell fate capacity over developmental time and appear to be transcriptionally primed toward specific lineages. We also predict signaling pathways that may contribute to lineage plasticity and niche permissiveness. Together, these results contribute to our understanding of how cell-intrinsic and -extrinsic signals contribute to the establishment of a stem cell niche.

Spatial transcriptomics allows the unprecedented examination of gene expression levels at the resolution of spatially-situated single cells in a high-throughput manner. As the technology is adopted more broadly, studies frequently collect data from multiple tissue samples, which leads to unique challenges that traditional spatial statistical methods are not equipped to handle. In particular, factors that differ across samples, such as different coordinate systems, different numbers and types of cells, different underlying tissue architectures, among others, preclude the application of traditional methods to our new setting. In this work, we propose a novel method, TESSERA, based on a spatial generalized linear model, for analyzing multi-sample spatial transcriptomics count data. Importantly, we provide a mathematical and computational framework for efficient and scalable model fitting and statistical inference to accompany the specification of our model. Our method for fitting the model enables the estimation of a common set of fixed effects across samples. This allows us to address a variety of differential expression questions, such as identification of which genes are differentially expressed between conditions (e.g., diseases, treatments), while accounting for spatial correlation between cells within a sample. We benchmark our proposed method on simulated data and apply it to a spatial transcriptomics dataset of human kidney samples. We find that our method provides a hitherto nonexistent extension to the multi-sample setting while remaining competitive with or outperforming existing algorithms in the single-sample setting.

Supplementary Table S1: Cancer types and morphology codes

Supplementary Table S4: Variance components, ICCs and CVs for logged peak areas

Supplementary Methods S1: Detailed nLC-HRMSMS methods

Supplementary Figure S1: Ensemble of classification models for selection of adduct features associated with NHL incidence

Supplementary Figure S2: Plots of ln(case/matched-control fold change) versus time to diagnosis (ttd) in days from recruitment for adduct features selected for MALES and FEMALES

Supplementary Table S2: Cys34 and Lys525 adducts detected

Supplementary Table S3: Fold changes and p-values for adducts

Supplementary Table S5: Features selected for association with NHL