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Multiple studies identified metabolic changes within the tumor and its microenvironment during carcinogenesis. Yet, the mechanisms by which tumors affect the host metabolism are unclear. We find that systemic inflammation induced by the cancer leads to liver infiltration of myeloid cells at early extrahepatic carcinogenesis. The infiltrating immune cells via IL-6-pSTAT3 immune-hepatocyte crosstalk cause the depletion of a master metabolic regulator, HNF4a, consequently leading to systemic metabolic changes that promote breast and pancreatic cancer proliferation and a worse outcome. Preserving HNF4 levels maintains liver metabolism and restricts carcinogenesis. Standard liver biochemical tests can identify early metabolic changes and predict patients' outcomes and weight loss. Thus, the tumor induces early metabolic changes in its macro-environment with diagnostic and potentially therapeutic implications for the host.

International audience; The coordinated differentiation of progenitor cells into specialized cell types and their spatial organization into distinct domains is central to embryogenesis. Here, we developed and applied an unbiased spatially resolved single-cell transcriptomics method to identify the genetic programs underlying the emergence of specialized cell types during mouse limb development and their spatial integration. We identify multiple transcription factors whose expression patterns are predominantly associated with cell type specification or spatial position, suggesting two parallel yet highly interconnected regulatory systems. We demonstrate that the embryonic limb undergoes a complex multiscale reorganization upon perturbation of one of its spatial organizing centers, including the loss of specific cell populations, alterations of preexisting cell states’ molecular identities, and changes in their relative spatial distribution. Our study shows how multidimensional single-cell, spatially resolved molecular atlases can allow the deconvolution of spatial identity and cell fate and reveal the interconnected genetic networks that regulate organogenesis and its reorganization upon genetic alterations.

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.

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.

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.

The mutational mechanisms underlying recurrent deletions in clonal hematopoiesis are not entirely clear. In the current study we inspect the genomic regions around recurrent deletions in myeloid malignancies, and identify microhomology-based signatures in CALR, ASXL1 and SRSF2 loci. We demonstrate that these deletions are the result of double stand break repair by a PARP1 dependent microhomology-mediated end joining (MMEJ) pathway. Importantly, we provide evidence that these recurrent deletions originate in pre-leukemic stem cells. While DNA polymerase theta (POLQ) is considered a key component in MMEJ repair, we provide evidence that pre-leukemic MMEJ (preL-MMEJ) deletions can be generated in POLQ knockout cells. In contrast, aphidicolin (an inhibitor of replicative polymerases and replication) treatment resulted in a significant reduction in preL-MMEJ. Altogether, our data indicate an association between POLQ independent MMEJ and clonal hematopoiesis and elucidate mutational mechanisms involved in the very first steps of leukemia evolution., The mutational mechanisms that produce insertions and deletions that lead to clonal hematopoiesis are poorly understood. Here the authors show evidence that frequent deletions that are relevant to myeloid malignancies could be produced by PARP1-dependent microhomology-mediated end joining.

Neuroblastoma is a pediatric tumor of the developing sympathetic nervous system. However, the cellular origin of neuroblastoma has yet to be defined. Here we studied the single-cell transcriptomes of neuroblastomas and normal human developing adrenal glands at various stages of embryonic and fetal development. We defined normal differentiation trajectories from Schwann cell precursors over intermediate states to neuroblasts or chromaffin cells and showed that neuroblastomas transcriptionally resemble normal fetal adrenal neuroblasts. Importantly, neuroblastomas with varying clinical phenotypes matched different temporal states along normal neuroblast differentiation trajectories, with the degree of differentiation corresponding to clinical prognosis. Our work highlights the roles of oncogenic MYCN and loss of TFAP2B in blocking differentiation and may provide the basis for designing therapeutic interventions to overcome differentiation blocks.

The coordinated differentiation of progenitor cells into specialized cell types and their spatial organization into distinct domains is central to embryogenesis. Here, we applied a new unbiased spatially resolved single-cell transcriptomics method to identify the genetic programs that underlie the emergence of specialized cell types during limb development and their integration in space. We uncovered combinations of transcription factors whose expression patterns are predominantly associated with cell type specification or spatial position, enabling the deconvolution of cell fate and position identity. We demonstrate that the embryonic limb undergoes a complex multi-scale re-organization upon perturbation of one of its spatial organizing centers, including the loss of specific cell populations, specific alterations in the molecular identities of other pre-existing cell states and changes in their relative spatial distribution. Altogether, our study shows how multi-dimensional single-cell and spatially resolved molecular atlases could reveal the interconnected genetic networks that regulate the intricacies of organogenesis and its reorganization upon genetic alterations.

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–Delbruck 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.

Comment in: DePICting T cell-APC crosstalk in cancer. Zhang T, Dong C. Nat Cancer. 2022 Mar;3(3):265-267. doi: 10.1038/s43018-022-00345-6. PMID: 35352059 No abstract available.; International audience; Despite their key regulatory role and therapeutic potency, the molecular signatures of interactions between T cells and antigen-presenting myeloid cells within the tumor microenvironment remain poorly characterized. Here, we systematically characterize these interactions using RNA sequencing of physically interacting cells (PIC-seq) and find that CD4+PD-1+CXCL13+ T cells are a major interacting hub with antigen-presenting cells in the tumor microenvironment of human non-small cell lung carcinoma. We define this clonally expanded, tumor-specific and conserved T-cell subset as T-helper tumor (Tht) cells. Reconstitution of Tht cells in vitro and in an ovalbumin-specific αβ TCR CD4+ T-cell mouse model, shows that the Tht program is primed in tumor-draining lymph nodes by dendritic cells presenting tumor antigens, and that their function is important for harnessing the antitumor response of anti-PD-1 treatment. Our molecular and functional findings support the modulation of Tht-dendritic cell interaction checkpoints as a major interventional strategy in immunotherapy.

Funder: Cancer Research UK, Funder: Wellcome Trust, 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.

The functional activity and differentiation potential of cells are determined by their interactions with surrounding cells. Approaches that allow unbiased characterization of cell states while at the same time providing spatial information are of major value to assess this environmental influence. However, most current techniques are hampered by a tradeoff between spatial resolution and cell profiling depth. Here, we develop a photocage-based technology that allows isolation and in-depth analysis of live cells from regions of interest in complex ex vivo systems, including primary human tissues. The use of a highly sensitive 4-nitrophenyl(benzofuran) cage coupled to a set of nanobodies allows high-resolution photo-uncaging of different cell types in areas of interest. Single-cell RNA-sequencing of spatially defined CD8+ T cells is used to exemplify the feasibility of identifying location-dependent cell states. The technology described here provides a valuable tool for the analysis of spatially defined cells in diverse biological systems, including clinical samples. The development of a photocage-nanobody based technology enabled in-depth analysis of live cells from tissues while retaining their spatial information.

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.

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.

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.

Genome-wide analysis of cellular transcriptomes using RNA-seq or expression arrays is a major mainstay of current biological and biomedical research. EXPANDER (EXPression ANalyzer and DisplayER) is a comprehensive software package for analysis of expression data, with built-in support for 18 different organisms. It is designed as a "one-stop shop" platform for transcriptomic analysis, allowing for execution of all analysis steps starting with gene expression data matrix. Analyses offered include low-level preprocessing and normalization, differential expression analysis, clustering, bi-clustering, supervised grouping, high-level functional and pathway enrichment tests, and networks and motif analyses. A variety of options is offered for each step, using established algorithms, including many developed and published by our laboratory. EXPANDER has been continuously developed since 2003, having to date over 18,000 downloads and 540 citations. One of the innovations in the recent version is support for combined analysis of gene expression and ChIP-seq data to enhance the inference of transcriptional networks and their functional interpretation. EXPANDER implements cutting-edge algorithms and makes them accessible to users through user-friendly interface and intuitive visualizations. It is freely available to users at http://acgt.cs.tau.ac.il/expander/.

Human tissues comprise trillions of cells that populate a complex space of molecular phenotypes and functions and that vary in abundance by 4-9 orders of magnitude. Relying solely on unbiased sampling to characterize cellular niches becomes infeasible, as the marginal utility of collecting more cells diminishes quickly. Furthermore, in many clinical samples, the relevant cell types are scarce and efficient processing is critical. We developed an integrated pipeline for index sorting and massively parallel single-cell RNA sequencing (MARS-seq2.0) that builds on our previously published MARS-seq approach. MARS-seq2.0 is based on >1 million cells sequenced with this pipeline and allows identification of unique cell types across different tissues and diseases, as well as unique model systems and organisms. Here, we present a detailed step-by-step procedure for applying the method. In the improved procedure, we combine sub-microliter reaction volumes, optimization of enzymatic mixtures and an enhanced analytical pipeline to substantially lower the cost, improve reproducibility and reduce well-to-well contamination. Data analysis combines multiple layers of quality assessment and error detection and correction, graphically presenting key statistics for library complexity, noise distribution and sequencing saturation. Importantly, our combined FACS and single-cell RNA sequencing (scRNA-seq) workflow enables intuitive approaches for depletion or enrichment of cell populations in a data-driven manner that is essential to efficient sampling of complex tissues. The experimental protocol, from cell sorting to a ready-to-sequence library, takes 2-3 d. Sequencing and processing the data through the analytical pipeline take another 1-2 d.

Significance We provide a dataset of millions of hormone tests from medical records that shows seasonality with a winter−spring peak in hormones for reproduction, growth, metabolism, and stress adaptation. Together with a long history of studies on a winter−spring peak in human function and growth, the hormone seasonality indicates that, like other animals, humans may have a physiological peak season for basic biological functions. We further use the specific seasonal phases of the hormones to suggest a model for a circannual clock in humans and animals that can keep track of the seasons, similar in spirit to the circadian clock that keeps track of time of day., Hormones control the major biological functions of stress response, growth, metabolism, and reproduction. In animals, these hormones show pronounced seasonality, with different set-points for different seasons. In humans, the seasonality of these hormones remains unclear, due to a lack of datasets large enough to discern common patterns and cover all hormones. Here, we analyze an Israeli health record on 46 million person-years, including millions of hormone blood tests. We find clear seasonal patterns: The effector hormones peak in winter−spring, whereas most of their upstream regulating pituitary hormones peak only months later, in summer. This delay of months is unexpected because known delays in the hormone circuits last hours. We explain the precise delays and amplitudes by proposing and testing a mechanism for the circannual clock: The gland masses grow with a timescale of months due to trophic effects of the hormones, generating a feedback circuit with a natural frequency of about a year that can entrain to the seasons. Thus, humans may show coordinated seasonal set-points with a winter−spring peak in the growth, stress, metabolism, and reproduction axes.

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.

Despite their key regulatory role and therapeutic potency, the molecular signatures of interactions between T cells and antigen-presenting myeloid cells within the tumor microenvironment remain poorly characterized. Here, we systematically characterize these interactions using RNA sequencing of physically interacting cells (PIC-seq) and find that CD4

Mice deficient for all Ten-eleven translocation (TET) genes exhibit early gastrulation lethality. Yet, separating cause and effect in such embryonic failure is challenging. To isolate cell-autonomous effects of TET loss, we used temporal single-cell atlases from embryos with partial or complete mutant contributions. Strikingly, when developing within a wild-type embryo, TET mutant cells retain near-complete differentiation potential, while embryos solely comprised of mutant cells are defective in epiblast to ectoderm transition with degenerated mesoderm potential. We map de-repressions of early epiblast factors and failure to activate signaling from nascent mesoderm as intrinsic drivers of TET phenotypes. We further suggest near-complete loss of enhancer demethylation as a possible mechanism for mis-regulation in TET deficient cells. Collectively, our work demonstrates an unbiased approach for defining intrinsic and extrinsic embryonic gene function based on temporal differentiation atlases, and disentangles the intracellular effects of the demethylation machinery from its broader tissue-level ramifications.

The functional activity and differentiation potential of cells is determined by their interaction with surrounding cells. Approaches that allow the unbiased characterization of cell states while at the same time providing spatial information are of major value to assess this environmental influence. However, most current techniques are hampered by a trade-off between spatial resolution and cell profiling depth. Here, we developed a photoswitch-based technology that allows the isolation and in-depth analysis of live cells from regions of interest in complex ex vivo systems, including human tissues. The use of a highly sensitive 4-nitrophenyl(benzofuran)-cage coupled to nanobodies allowed photoswitching of cells in areas of interest with low-intensity violet light and without detectable phototoxicity. Single cell RNA sequencing of spatially defined CD8+ T cells was used to exemplify the feasibility of identifying location-dependent cell states at the single cell level. Finally, we demonstrate the efficient labeling and photoswitching of cells in live primary human tumor tissue. The technology described here provides a valuable tool for the analysis of spatially defined cells in diverse biological systems, including clinical samples.