MCProj: metacell projection for interpretable and quantitative use of transcriptional atlases.

We describe MCProj-an algorithm for analyzing query scRNA-seq data by projections over reference single-cell atlases. We represent the reference as a manifold of annotated metacell gene expression distributions. We then interpret query metacells as mixtures of atlas distributions while correcting for technology-specific gene biases. This approach distinguishes and tags query cells that are consistent with atlas states from unobserved (novel or artifactual) behaviors. It also identifies expression differences observed in successfully mapped query states. We showcase MCProj functionality by projecting scRNA-seq data on a blood cell atlas, deriving precise, quantitative, and interpretable results across technologies and datasets.

Time-aligned hourglass gastrulation models in rabbit and mouse.

The hourglass model describes the convergence of species within the same phylum to a similar body plan during development; however, the molecular mechanisms underlying this phenomenon in mammals remain poorly described. Here, we compare rabbit and mouse time-resolved differentiation trajectories to revisit this model at single-cell resolution. We modeled gastrulation dynamics using hundreds of embryos sampled between gestation days 6.0 and 8.5 and compared the species using a framework for time-resolved single-cell differentiation-flows analysis. We find convergence toward similar cell-state compositions at E7.5, supported by the quantitatively conserved expression of 76 transcription factors, despite divergence in surrounding trophoblast and hypoblast signaling. However, we observed noticeable changes in specification timing of some lineages and divergence of primordial germ cell programs, which in the rabbit do not activate mesoderm genes. Comparative analysis of temporal differentiation models provides a basis for studying the evolution of gastrulation dynamics across mammals.

DNA methyltransferases 3A and 3B target specific sequences during mouse gastrulation.

In mammalian embryos, DNA methylation is initialized to maximum levels in the epiblast by the de novo DNA methyltransferases DNMT3A and DNMT3B before gastrulation diversifies it across regulatory regions. Here we show that DNMT3A and DNMT3B are differentially regulated during endoderm and mesoderm bifurcation and study the implications in vivo and in meso-endoderm embryoid bodies. Loss of both Dnmt3a and Dnmt3b impairs exit from the epiblast state. More subtly, independent loss of Dnmt3a or Dnmt3b leads to small biases in mesoderm-endoderm bifurcation and transcriptional deregulation. Epigenetically, DNMT3A and DNMT3B drive distinct methylation kinetics in the epiblast, as can be predicted from their strand-specific sequence preferences. The enzymes compensate for each other in the epiblast, but can later facilitate lineage-specific methylation kinetics as their expression diverges. Single-cell analysis shows that differential activity of DNMT3A and DNMT3B combines with replication-linked methylation turnover to increase epigenetic plasticity in gastrulation. Together, these findings outline a dynamic model for the use of DNMT3A and DNMT3B sequence specificity during gastrulation.

Metacell-2: a divide-and-conquer metacell algorithm for scalable scRNA-seq analysis.

Scaling scRNA-seq to profile millions of cells is crucial for constructing high-resolution maps of transcriptional manifolds. Current analysis strategies, in particular dimensionality reduction and two-phase clustering, offer only limited scaling and sensitivity to define such manifolds. We introduce Metacell-2, a recursive divide-and-conquer algorithm allowing efficient decomposition of scRNA-seq datasets of any size into small and cohesive groups of cells called metacells. Metacell-2 improves outlier cell detection and rare cell type identification, as shown with human bone marrow cell atlas and mouse embryonic data. Metacell-2 is implemented over the scanpy framework for easy integration in any analysis pipeline.

Dissection of floral transition by single-meristem transcriptomes at high temporal resolution.

The vegetative-to-floral transition is a dramatic developmental change of the shoot apical meristem, promoted by the systemic florigen signal. However, poor molecular temporal resolution of this dynamic process has precluded characterization of how meristems respond to florigen induction. Here, we develop a technology that allows sensitive transcriptional profiling of individual shoot apical meristems. Computational ordering of hundreds of tomato samples reconstructed the floral transition process at fine temporal resolution and uncovered novel short-lived gene expression programs that are activated before flowering. These programs are annulled only when both florigen and a parallel signalling pathway are eliminated. Functional screening identified genes acting at the onset of pre-flowering programs that are involved in the regulation of meristem morphogenetic changes but dispensable for the timing of floral transition. Induced expression of these short-lived transition-state genes allowed us to determine their genetic hierarchies and to bypass the need for the main flowering pathways. Our findings illuminate how systemic and autonomous pathways are integrated to control a critical developmental switch.