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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.

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. Analyses of individual shoot meristem transcriptomes in wild-type and mutant tomato, at high temporal resolution, produce remarkably precise information about gene expression patterns during the transition from vegetative to floral growth that translates into genetic hierarchies.

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.

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. Here the authors show that DNMT3A and DNMT3B are differentially regulated during endoderm and mesoderm differentiation in mouse development and characterize the dynamic DNMT3A and DNMT3B sequence specificity during gastrulation.

Hematopoietic stem and progenitor cells (HSPCs) are intended to deliver life-long, consistent output. However, with age, we observe changes in blood counts and clonal disorders. Better understanding of inter-individual variation in HSPC behavior is needed to understand the transition from health to age-related hematological disorders. Here we study 360K single circulating HSPCs (CD34+) from 99 healthy individuals together with clinical information and clonal hematopoiesis (CH) profiles to characterize population variability in hematopoiesis. Individuals with CH were linked with reduced frequencies of lymphocyte progenitors and higher RDW. We describe a Lamin-A transcriptional signature across the HSPC spectrum and show it is reduced in CH individuals. We define and estimate HSPC composition bias and an age-related increased S-phase gene signature and show how they form a heterogeneous and multifactorial aging trend in the blood. The new comprehensive model of normal HSPC variation will allow the study of stem cell-related disorders. As a proof of concept, we present methodologies for analyzing myeloid malignancies in comparison to our reference atlas. Together, our data and methodologies shed light on age-related changes in blood counts, CH and can be used to study stem cell-related disorders in the future.Competing Interest StatementZS, DL, EM and GY are all employees and shareholders of Ultima Genomics. LS is a shareholder of Sequentify. The remaining authors declare no competing interests

The hourglass model describes the convergence of species within the same phylum to a similar body plan during development, yet 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 6.0-8.5, and compare the species using a new framework for time-resolved single-cell differentiation-flows analysis. We find convergence toward similar cell state compositions at E7.5, underlied by 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 cells programs, which in the rabbit do not activate mesoderm genes. Comparative analysis of temporal differentiation models provides a new basis for studying the evolution of gastrulation dynamics across mammals.Competing Interest StatementThe authors have declared no competing interest.Footnotes* minor text and figure updates.* https://tanaylab.weizmann.ac.il/rabemb_wex

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 consisting of annotated metacell gene expression distributions. We then infer 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 existing atlas states from novel or artifactual behaviors that are not observed in the atlas. It also identifies significant expression differences observed in query states that are mapped coherently onto the atlas. We showcase MCProj functionality by analyzing blood gene expression from multiple sources and technologies, suggesting it as a method of choice for scRNA-seq analysis following extensive cell atlas projects.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://github.com/tanaylab/metacells

Scaling scRNA-seq to profile millions of cells is increasingly feasible. Such data is crucial for the construction of high-resolution maps of transcriptional manifolds. But current analysis strategies, in particular dimensionality reduction and two-phase clustering, offers only limited scaling and sensitivity to define such manifolds. Here 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 denoted as metacells. We show the algorithm outperforms current solutions in time, memory and quality. Importantly, Metacell-2 also improves outlier cell detection and rare cell type identification, as we exemplify by analysis of human bone marrow cell atlas and mouse embryonic data. Metacell-2 is implemented over the scanpy framework for easy integration in any analysis pipeline.