Single-cell genomics studies have decoded the immune cell composition of several human prenatal organs but were limited in describing the developing immune system as a distributed network across tissues. We profiled nine prenatal tissues combining single-cell RNA sequencing, antigen-receptor sequencing, and spatial transcriptomics to reconstruct the developing human immune system. This revealed the late acquisition of immune-effector functions by myeloid and lymphoid cell subsets and the maturation of monocytes and T cells before peripheral tissue seeding. Moreover, we uncovered system-wide blood and immune cell development beyond primary hematopoietic organs, characterized human prenatal B1 cells, and shed light on the origin of unconventional T cells. Our atlas provides both valuable data resources and biological insights that will facilitate cell engineering, regenerative medicine, and disease understanding.

RNA velocity exploits the temporal information contained in spliced and unspliced RNA counts to infer transcriptional dynamics. Existing velocity models often rely on coarse biophysical simplifications or numerical approximations to solve the underlying ordinary differential equations (ODEs), which can compromise accuracy in challenging settings, such as complex or weak transcription rate changes across cellular trajectories. Here we present cell2fate, a formulation of RNA velocity based on a linearization of the velocity ODE, which allows solving a biophysically more accurate model in a fully Bayesian fashion. As a result, cell2fate decomposes the RNA velocity solutions into modules, providing a biophysical connection between RNA velocity and statistical dimensionality reduction. We comprehensively benchmark cell2fate in real-world settings, demonstrating enhanced interpretability and power to reconstruct complex dynamics and weak dynamical signals in rare and mature cell types. Finally, we apply cell2fate to the developing human brain, where we spatially map RNA velocity modules onto the tissue architecture, connecting the spatial organization of tissues with temporal dynamics of transcription.

Summary: Multiple distinct cell types of the human lung and airways have been defined by single cell RNA sequencing (scRNAseq). Here we present a multi-omics spatial lung atlas to define novel cell types which we map back into the macro- and micro-anatomical tissue context to define functional tissue microenvironments. Firstly, we have generated single cell and nuclei RNA sequencing, VDJ-sequencing and Visium Spatial Transcriptomics data sets from 5 different locations of the human lung and airways. Secondly, we define additional cell types/states, as well as spatially map novel and known human airway cell types, such as adult lung chondrocytes, submucosal gland (SMG) duct cells, distinct pericyte and smooth muscle subtypes, immune-recruiting fibroblasts, peribronchial and perichondrial fibroblasts, peripheral nerve associated fibroblasts and Schwann cells. Finally, we define a survival niche for IgA-secreting plasma cells at the SMG, comprising the newly defined epithelial SMG-Duct cells, and B and T lineage immune cells. Using our transcriptomic data for cell-cell interaction analysis, we propose a signalling circuit that establishes and supports this niche. Overall, we provide a transcriptional and spatial lung atlas with multiple novel cell types that allows for the study of specific tissue microenvironments such as the newly defined gland-associated lymphoid niche (GALN).