Cole Trapnell

Mammalian biology unfolds over time, within tissues and organs opaque to our eyes and instruments. We applied DNA Typewriter, a sequential molecular recorder, to record the cell lineage of a mouse over nearly two weeks of development. From one embryo, we reconstruct a time-calibrated, parsimony-supported, zygote-rooted phylogeny of 1.28 million transcriptionally profiled cells. A burst of editing unequivocally marks the daughters of the first cleavage, which serve as inline replicates. We quantify clonal dominance arising during gastrulation. Tree siblings share cell type far above chance; heterotypic siblings mark terminal differentiations. Temporal sweeps of clade co-occurrence recover a dated hierarchy of cell-type couplings; imputed labels for internal nodes recapitulate known state paths. A lineage-anchored ontogeny of mammalian development, long out of reach, is coming into view.

Pulmonary lymphocytosis is a common feature of chronic lung disease, but the mechanisms governing lymphocyte accumulation within the lung remain poorly understood. Here, we identify granulocyte-macrophage colony-stimulating factor (GM-CSF) as a central regulator of pulmonary lymphocyte homeostasis. Using genetic models, single-nucleus and spatial transcriptomics, cell trafficking analyses, and pulmonary macrophage replacement studies, we show that loss of GM-CSF signaling causes profound pulmonary lymphocytosis characterized by lymphoid follicle formation, local immunoglobulin production, and expansion of multiple lymphocyte lineages. Mechanistically, GM-CSF signaling deficiency blocks terminal alveolar macrophage differentiation and reprograms alveolar macrophages into inflammatory states marked by reduced PU.1 and PPARγ expression, enhanced NF-κB signaling, and expression of lymphocyte-recruiting chemokines. These changes promote lymphocyte recruitment, local proliferation, and lymphoid lineage commitment. Restoration of GM-CSF-responsive alveolar macrophages re-established macrophage identity, suppressed inflammatory chemokine programs, and reversed pulmonary lymphocytosis. Together, these findings identify alveolar macrophages as gatekeepers of adaptive immune homeostasis in the lung.

Mammalian biology unfolds over time, within tissues and organs opaque to our eyes and instruments. We applied DNA Typewriter, a sequential molecular recorder, to record the cell lineage of a mouse over nearly two weeks of development. From one embryo, we reconstruct a time-calibrated, parsimony-supported, zygote-rooted phylogeny of 1.28M transcriptionally-profiled cells. A burst of editing unequivocally marks the daughters of the first cleavage, which serve as inline replicates. We quantify clonal dominance arising during gastrulation. Tree siblings share cell type far above chance; heterotypic siblings mark terminal differentiations. Temporal sweeps of clade co-occurrence recover a dated hierarchy of cell-type couplings; imputed labels for internal nodes recapitulate known state paths. A lineage-anchored ontogeny of mammalian development, long out of reach, is coming into view.

Closely related neurons that share core differentiation programs can be difficult to distinguish by gene expression, even when they differ in connectivity and function. Cranial motor neurons (cMNs) exemplify this challenge, forming discrete nuclei that control behaviors as diverse as eye movement, feeding, facial expression, and regulation of visceral organ function. However, the developmental programs that drive cMN target choices and functional specialization have not been comprehensively studied in any vertebrate. Here, we present an integrated single-cell RNA-sequencing atlas of zebrafish cMN development from 18 to 144 hours post-fertilization, combining FACS-purified cMNs with cMNs computationally extracted from published whole-embryo datasets and perform extensive validation by HCR in situ hybridization. We find that each cranial motor nucleus expresses a distinct transcriptional signature, and in many cases, we identify transcriptional correlates to functional subtypes within individual nuclei as determined by retrograde labeling. We find that identity often precedes axon targeting, indicating that cranial motor neuron fate is genetically specified early in development. These distinct identities are shaped by the intersection of shared function, rhombomere origin, and developmental time. A cross-species comparison reveals a largely one-to-one correspondence between zebrafish and mouse cMN nuclei, indicating that these genetic programs are conserved. Together, this atlas provides a nucleus-resolved molecular framework for understanding cranial motor neuron diversification, and for interpreting human cranial dysinnervation disorders.

During embryogenesis, cell types arise in a predictable order because developmental regulators act sequentially. But how evolutionary changes in morphogenesis reshape the signaling environments that activate these regulators remains unknown. Across vertebrates, primitive myeloid cells emerge from bone morphogenetic protein (BMP)-patterned ventral mesoderm through a conserved regulatory program. Here we show that in the annual killifish, Nothobranchius furzeri, a vertebrate with highly derived embryogenesis, neutrophils emerge prior to gastrulation, before ventral mesoderm has formed. Vascular progenitors arise later from ventral mesoderm, whereas myeloid progenitors are largely absent from this tissue. BMP inhibition abolishes pre-gastrula neutrophil specification, while disruption of Nodal-dependent mesendoderm formation does not. These findings reveal that conserved cell type programs can be redeployed within an altered embryonic architecture.

Nucleoli, nuclear speckles and other compartments regulate transcription, RNA processing, and chromatin organization within the nucleus, yet the relationship of their morphology to developmental gene expression programs in vivo is poorly understood. Here, we develop a high-throughput Visual Cell Sorting (VCS) workflow for fixed cells and nuclei that combines antibody-based photoconversion; GPU-accelerated, real-time image analysis; and three-level single-cell combinatorial indexing RNA-seq (sci-RNA-seq3) to link nuclear compartment morphology to single-nucleus transcriptomes at embryo scale. We use VCS to analyze and sort over 1 million mouse embryo-derived nuclei by nucleolar, nuclear speckle, or nuclear size and construct a transcriptional atlas annotated with nuclear compartment phenotypes. Nuclear compartment size varies both between and within lineages and is shaped by proliferation and differentiation. In extracellular matrix protein-producing cell types such as fibroblasts, chondrocytes, and osteoblasts, nucleolar enlargement is uncoupled from cell cycle, and in erythroid cells exhibit a sharp nucleolar contraction preceding cell-cycle exit. We identify a 41-gene transcriptional signature whose expression tracks nucleolar size, enriched for ribosome biogenesis, mitochondrial metabolism, unfolded protein response, stress granule, and ubiquitin–proteasome pathway components. We used this nucleolar transcriptional signature to annotate mouse, zebrafish and human developmental atlases with nucleolar size information, revealing a conserved coupling between nucleolar activity and proteostasis programs. Our work establishes Visual Cell Sorting as a scalable platform for mapping image-based phenotypes to molecular programs; details the relationship between nuclear compartment phenotypes and development; and provides a transcriptional signature to estimate nucleolar size from existing single-cell datasets.

Understanding and modeling how a single human genome concurrently encodes gene regulatory programs for thousands of cell types remains a central challenge in genomics and machine learning. Most human cell types emerge during embryonic, fetal, and pediatric development which are inaccessible to comprehensive molecular profiling. To circumvent this, we hypothesized that the mismatch in evolutionary rates between cis- acting enhancers that modulate gene expression (fast) and the trans -acting regulatory factors that specify cell types (slow) creates an opportunity for ‘evolutionary transfer learning’. Specifically, models trained to predict cell type-specific enhancers in one species should generalize to the orthologous cell types and enhancers of related species. To test this, we generated a single-cell atlas of chromatin accessibility spanning mouse embryonic day 10 (E10) to birth (P0). Using combinatorial indexing ¹ , we profiled 3.9 million nuclei from 36 staged embryos, resolving genome-wide accessibility in 36 cell classes and 140 cell types. We then trained a series of multi-output deep learning models (CREsted ² ), each addressing limitations of the preceding approach, towards the goal of genome-wide prediction of distal enhancers across major developmental lineages. An ‘evolution-naive’ model achieved strong performance on heldout peaks, but exhibited two failure modes during genome-wide inference: overprediction at tandem repeats and conflation of promoter and distal enhancer grammars. An ‘evolution-aware’ model resolved these by regrouping accessible regions based on their retention and functional coherence across mammalian evolution, but failed to generalize across species. Finally, an ‘evolution-augmented’ model, STEAM (Synteny-aware Transfer learning for Enhancer Activity Modeling), incorporated enhancer orthologs from 241 mammalian genomes (Zoonomia ³ ) in a synteny-supervised manner. This increased the effective data scale by as much as 195-fold, markedly improving generalization across mammals despite greater label noise. We applied STEAM to the genome-wide inference of cell class-specific distal developmental enhancers in humans, mice ( HumMus ) and 239 additional mammals ³ ( BabaGanoush ), i.e. 32 × 241 = 7,712 genome-wide distal enhancer prediction tracks. Together, our results unify advances in single-cell profiling, deep learning, and comparative genomics into a framework for the evolutionary transfer learning of noncoding regulatory grammars. More broadly, our work supports the view that model organisms and evolutionarily diverse genomes are indispensable resources for accelerating and enhancing the AI-enabled exploration of human biology. Note: An interactive version of this preprint, together with count matrices, CREsted models, prediction tracks, code and reproducible figures, is available at this link (ref ⁴ ).

Brain aging is a complex process with profound health and societal consequences. However, the molecular and cellular pathways that govern its temporal progression–along with any cell type-, region-, and sex-specific heterogeneity in such progression–remain poorly defined. Here, we present a transcriptomic atlas of 5.3 million cells from 582 samples spanning 11 brain regions of 55 rhesus macaques (29 female, 26 male), aged 5 months (early life) to 21 years (late adulthood). We annotate 12 major cell classes and 225 subclusters, including region-specific subtypes of excitatory and inhibitory neurons, astrocytes, and ependymal cells. We identify a vulnerable excitatory neuron population in the superficial cortical lamina and a cortical interneuron population that are less abundant later in life, along with subtle, region-specific, age-associated compositional differences in subpopulations of microglia and oligodendrocytes, whose detection required single-cell resolution. Finally, we chart convergent and divergent age-associated molecular signatures across brain regions and cell classes–where some of these signatures are sex-specific and could underlie sex biases in neurological disorders. We find that age-associated transcriptional programs not only overlap substantially with those seen in Alzheimer’s disease (AD), but also unfold along distinct temporal trajectories across brain regions, suggesting that aging and AD may share molecular roots that emerge at different life stages and in region-specific, sex-specific windows of vulnerability. This work provides a temporal, regional, and sex-stratified atlas of the aging primate brain, offering insights into cell type-specific vulnerabilities and regional heterogeneity with translational human relevance.

The genomes of flowering plants consist largely of transposable elements (TEs), some of which modulate gene regulation and function. However, the repetitive nature of TEs and difficulty of mapping individual TEs by short-read sequencing have hindered our understanding of their regulatory potential. Here we show that long-read chromatin fibre sequencing (Fiber-seq) comprehensively identifies accessible chromatin regions (ACRs) and CpG methylation across the maize genome. We uncover stereotypical ACR patterns at young TEs that degenerate with evolutionary age, resulting in TE enhancers preferentially marked by a novel plant-specific epigenetic feature: simultaneous hyper-CpG methylation and chromatin accessibility. We show that TE ACRs are co-opted as gene promoters and that ACR-containing TEs can facilitate gene amplification. Lastly, we uncover a pervasive epigenetic signature—hypo-5mCpG methylation and diffuse chromatin accessibility—directing TEs to specific loci, including the loci that sparked McClintock’s discovery of TEs.

Summary: Improvements in single-cell sequencing have enabled phenotyping at organism-scale and molecular resolution, but interpreting such experiments poses computational challenges. Identifying the genes and cell types directly impacted by genetic, chemical, or environmental perturbations requires explicit modeling of lineage relationships amongst many cell types, over time, from datasets with millions of cells collected from thousands of specimens. We describe two software tools, “Hooke” and “Platt”, which exploit the rich statistical patterns within single-cell datasets to characterize the direct molecular and cellular consequences of experimental perturbations. We apply Hooke and Platt to a single-cell atlas of thousands of perturbed zebrafish embryos to synthesize a coherent map of lineage dependencies and leverage it to reveal previously unappreciated roles for fate-determining transcription factors. We show that cell type covariation in single-cell datasets is a powerful source of information for inferring how cells depend on genes and one another in the program of vertebrate development.