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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.

Propagation of clonal regulatory programs contributes to cancer development. It is poorly understood how epigenetic mechanisms interact with genetic drivers to shape this process. Here, we combine single-cell analysis of transcription and DNA methylation with a Luria–Delbrück experimental design to demonstrate the existence of clonally stable epigenetic memory in multiple types of cancer cells. Longitudinal transcriptional and genetic analysis of clonal colon cancer cell populations reveals a slowly drifting spectrum of epithelial-to-mesenchymal transcriptional identities that is seemingly independent of genetic variation. DNA methylation landscapes correlate with these identities but also reflect an independent clock-like methylation loss process. Methylation variation can be explained as an effect of global trans -acting factors in most cases. However, for a specific class of promoters—in particular, cancer–testis antigens—de-repression is correlated with and probably driven by loss of methylation in cis . This study indicates how genetic sub-clonal structure in cancer cells can be diversified by epigenetic memory. Longitudinal single-cell analysis of transcription and DNA methylation dynamics in cancer cell lines suggests a clonally stable epigenetic memory. Colon cancer cells show a spectrum of epithelial-to-mesenchymal identities that seems independent of genetic variation.

scRNA-seq profiles each represent a highly partial sample of mRNA molecules from a unique cell that can never be resampled, and robust analysis must separate the sampling effect from biological variance. We describe a methodology for partitioning scRNA-seq datasets into metacells : disjoint and homogenous groups of profiles that could have been resampled from the same cell. Unlike clustering analysis, our algorithm specializes at obtaining granular as opposed to maximal groups. We show how to use metacells as building blocks for complex quantitative transcriptional maps while avoiding data smoothing. Our algorithms are implemented in the MetaCell R/C++ software package.

DNA methylation is aberrant in cancer, but the dynamics, regulatory role and clinical implications of such epigenetic changes are still poorly understood. Here, reduced representation bisulfite sequencing (RRBS) profiles of 1538 breast tumors and 244 normal breast tissues from the METABRIC cohort are reported, facilitating detailed analysis of DNA methylation within a rich context of genomic, transcriptional, and clinical data. Tumor methylation from immune and stromal signatures are deconvoluted leading to the discovery of a tumor replication-linked clock with genome-wide methylation loss in non-CpG island sites. Unexpectedly, methylation in most tumor CpG islands follows two replication-independent processes of gain (MG) or loss (ML) that we term epigenomic instability. Epigenomic instability is correlated with tumor grade and stage, TP53 mutations and poorer prognosis. After controlling for these global trans-acting trends, as well as for X-linked dosage compensation effects, cis -specific methylation and expression correlations are uncovered at hundreds of promoters and over a thousand distal elements. Some of these targeted known tumor suppressors and oncogenes. In conclusion, this study demonstrates that global epigenetic instability can erode cancer methylomes and expose them to localized methylation aberrations in-cis resulting in transcriptional changes seen in tumors. Understanding the molecular mechanisms underlying DNA methylation in cancer and its clinical relevance remains crucial. Here, the authors study RRBS-based profiles of 1538 breast tumours and 244 normal breast tissues from the METABRIC cohort and report epigenomic instability and cis-regulatory effects.

Standardized lab tests are central for patient evaluation, differential diagnosis and treatment. Interpretation of these data is nevertheless lacking quantitative and personalized metrics. Here we report on the modeling of 2.1 billion lab measurements of 92 different lab tests from 2.8 million adults over a span of 18 years. Following unsupervised filtering of 131 chronic conditions and 5,223 drug–test pairs we performed a virtual survey of lab tests distributions in healthy individuals. Age and sex alone explain less than 10% of the within-normal test variance in 89 out of 92 tests. Personalized models based on patients’ history explain 60% of the variance for 17 tests and over 36% for half of the tests. This allows for systematic stratification of the risk for future abnormal test levels and subsequent emerging disease. Multivariate modeling of within-normal lab tests can be readily implemented as a basis for quantitative patient evaluation. A new approach based on machine-learning integration of 2.1 billion lab measurements of 92 different lab tests from 2.8 million adults, over a span of 18 years, produces models that can stratify one’s risk of having a future abnormal lab test level and subsequent emerging disease.

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.

Single-cell mapping of chromosomal accessibility patterns has recently led to improved predictive modelling of epigenomic activity from sequence. However, quantitative models explaining the epigenome using directly interpretable components are still lacking. Here we develop IceQream (IQ), a modelling strategy and inference algorithm for regressing accessibility from sequences using physical models of transcription factor (TF) binding. IQ uses spatial integration of sequences over a range of TF-DNA affinities and localization relative to the target locus. It infers TF effective concentrations as latent variables that activate or repress regulatory elements in a non-linear fashion. These are supplemented with synergistic and antagonistic pairwise interactions between TFs. Analysis of both human and mouse data shows that IQ derives similar, and in some cases, better performance compared to state-of-the-art deep neural network models. IQ provides an essential mechanistic and explicable baseline for further developments toward understanding gene and genome regulation from sequence. Cis regulatory elements endow genomes with sequence-encoded logic to drive cellular differentiation. Here, the authors introduce a biophysically principled sequence model that characterises complex TF-DNA interactions with accuracy that rivals state-of-the-art blackbox sequence foundation models.

Embryonic development involves massive proliferation and differentiation of cell lineages. This must be supported by chromosome replication and epigenetic reprogramming, but how proliferation and cell fate acquisition are balanced in this process is not well understood. Here we use single cell Hi-C to map chromosomal conformations in post-gastrulation mouse embryo cells and study their distributions and correlations with matching embryonic transcriptional atlases. We find that embryonic chromosomes show a remarkably strong cell cycle signature. Despite that, replication timing, chromosome compartment structure, topological associated domains (TADs) and promoter-enhancer contacts are shown to be variable between distinct epigenetic states. About 10% of the nuclei are identified as primitive erythrocytes, showing exceptionally compact and organized compartment structure. The remaining cells are broadly associated with ectoderm and mesoderm identities, showing only mild differentiation of TADs and compartment structures, but more specific localized contacts in hundreds of ectoderm and mesoderm promoter-enhancer pairs. The data suggest that while fully committed embryonic lineages can rapidly acquire specific chromosomal conformations, most embryonic cells are showing plastic signatures driven by complex and intermixed enhancer landscapes. Here the authors use single-cell Hi-C to investigate chromosome conformation in post-gastrulation mouse embryos. They find a distinct genome organization in primitive erythrocytes and conformations matching the mesodermal and ectodermal lineages.

The development of niches for tissue-specific stem cells is an important aspect of stem cell biology. Determination of niche size and niche numbers during organogenesis involves precise control of gene expression. How this is achieved in the context of a complex chromatin landscape is largely unknown. Here we show that the nuclear protein Combgap (Cg) supports correct ovarian niche formation in Drosophila by controlling ecdysone-Receptor (EcR)- mediated transcription and long-range chromatin contacts in the broad locus (BR-C). Both cg and BR-C promote ovarian growth and the development of niches for germ line stem cells. BR-C levels were lower when Combgap was either reduced or over-expressed, indicating an intricate regulation of the BR-C locus by Combgap. Polytene chromosome stains showed that Cg co-localizes with EcR, the major regulator of BR-C, at the BR-C locus and that EcR binding to chromatin was sensitive to changes in Cg levels. Proximity ligation assay indicated that the two proteins could reside in the same complex. Finally, chromatin conformation analysis revealed that EcR-bound regions within BR-C, which span ~30 KBs, contacted each other. Significantly, these contacts were stabilized in an ecdysone- and Combgap-dependent manner. Together, these results highlight Combgap as a novel regulator of chromatin structure that promotes transcription of ecdysone target genes and ovarian niche formation.