Modeling T cell temporal response to cancer immunotherapy rationalizes development of combinatorial treatment protocols.

Successful immunotherapy relies on triggering complex responses involving T cell dynamics in tumors and the periphery. Characterizing these responses remains challenging using static human single-cell atlases or mouse models. To address this, we developed a framework for in vivo tracking of tumor-specific CD8+ T cells over time and at single-cell resolution. Our tools facilitate the modeling of gene program dynamics in the tumor microenvironment (TME) and the tumor-draining lymph node (tdLN). Using this approach, we characterize two modes of anti-programmed cell death protein 1 (PD-1) activity, decoupling induced differentiation of tumor-specific activated precursor cells from conventional type 1 dendritic cell (cDC1)-dependent proliferation and recruitment to the TME. We demonstrate that combining anti-PD-1 therapy with anti-4-1BB agonist enhances the recruitment and proliferation of activated precursors, resulting in tumor control. These data suggest that effective response to anti-PD-1 therapy is dependent on sufficient influx of activated precursor CD8+ cells to the TME and highlight the importance of understanding system-level dynamics in optimizing immunotherapies.

Bispecific dendritic-T cell engager potentiates anti-tumor immunity.

Immune checkpoint inhibition treatment using aPD-1 monoclonal antibodies is a promising cancer immunotherapy approach. However, its effect on tumor immunity is narrow, as most patients do not respond to the treatment or suffer from recurrence. We show that the crosstalk between conventional type I dendritic cells (cDC1) and T cells is essential for an effective aPD-1-mediated anti-tumor response. Accordingly, we developed a bispecific DC-T cell engager (BiCE), a reagent that facilitates physical interactions between PD-1+ T cells and cDC1. BiCE treatment promotes the formation of active dendritic/T cell crosstalk in the tumor and tumor-draining lymph nodes. In vivo, single-cell and physical interacting cell analysis demonstrates the distinct and superior immune reprogramming of the tumors and tumor-draining lymph nodes treated with BiCE as compared to conventional aPD-1 treatment. By bridging immune cells, BiCE potentiates cell circuits and communication pathways needed for effective anti-tumor immunity.

Longitudinal machine learning uncouples healthy aging factors from chronic disease risks.

To understand human longevity, inherent aging processes must be distinguished from known etiologies leading to age-related chronic diseases. Such deconvolution is difficult to achieve because it requires tracking patients throughout their entire lives. Here, we used machine learning to infer health trajectories over the entire adulthood age range using extrapolation from electronic medical records with partial longitudinal coverage. Using this approach, our model tracked the state of patients who were healthy and free from known chronic disease risk and distinguished individuals with higher or lower longevity potential using a multivariate score. We showed that the model and the markers it uses performed consistently on data from Israeli, British and US populations. For example, mildly low neutrophil counts and alkaline phosphatase levels serve as early indicators of healthy aging that are independent of risk for major chronic diseases. We characterize the heritability and genetic associations of our longevity score and demonstrate at least 1 year of extended lifespan for parents of high-scoring patients compared to matched controls. Longitudinal modeling of healthy individuals is thereby established as a tool for understanding healthy aging and longevity.

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.

Single-cell multi-omics defines the cell-type-specific impact of splicing aberrations in human hematopoietic clonal outgrowths.

RNA splicing factors are recurrently mutated in clonal blood disorders, but the impact of dysregulated splicing in hematopoiesis remains unclear. To overcome technical limitations, we integrated genotyping of transcriptomes (GoT) with long-read single-cell transcriptomics and proteogenomics for single-cell profiling of transcriptomes, surface proteins, somatic mutations, and RNA splicing (GoT-Splice). We applied GoT-Splice to hematopoietic progenitors from myelodysplastic syndrome (MDS) patients with mutations in the core splicing factor SF3B1. SF3B1mut cells were enriched in the megakaryocytic-erythroid lineage, with expansion of SF3B1mut erythroid progenitor cells. We uncovered distinct cryptic 3' splice site usage in different progenitor populations and stage-specific aberrant splicing during erythroid differentiation. Profiling SF3B1-mutated clonal hematopoiesis samples revealed that erythroid bias and cell-type-specific cryptic 3' splice site usage in SF3B1mut cells precede overt MDS. Collectively, GoT-Splice defines the cell-type-specific impact of somatic mutations on RNA splicing, from early clonal outgrowths to overt neoplasia, directly in human samples.

Single cell Hi-C identifies plastic chromosome conformations underlying the gastrulation enhancer landscape.

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.

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.

Early Infiltration of Innate Immune Cells to the Liver Depletes HNF4α and Promotes Extrahepatic Carcinogenesis.

Multiple studies have 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 cancer leads to liver infiltration of myeloid cells at early extrahepatic carcinogenesis. The infiltrating immune cells via IL6-pSTAT3 immune-hepatocyte cross-talk cause the depletion of a master metabolic regulator, HNF4α, consequently leading to systemic metabolic changes that promote breast and pancreatic cancer proliferation and a worse outcome. Preserving HNF4α 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 macroenvironment with diagnostic and potentially therapeutic implications for the host. SIGNIFICANCE: Cancer growth requires a permanent nutrient supply starting from early disease stages. We find that the tumor extends its effect to the host's liver to obtain nutrients and rewires the systemic and tissue-specific metabolism early during carcinogenesis. Preserving liver metabolism restricts tumor growth and improves cancer outcomes. This article is highlighted in the In This Issue feature, p. 1501.

TATTOO-seq delineates spatial and cell type-specific regulatory programs in the developing limb.

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.

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.

The intrinsic and extrinsic effects of TET proteins during gastrulation.

Mice deficient for all ten-eleven translocation (TET) genes exhibit early gastrulation lethality. However, 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, whereas embryos solely comprising mutant cells are defective in epiblast to ectoderm transition with degenerated mesoderm potential. We map de-repressions of early epiblast factors (e.g., Dppa4 and Gdf3) and failure to activate multiple signaling from nascent mesoderm (Lefty, FGF, and Notch) as likely cell-intrinsic drivers of TET loss phenotypes. We further suggest loss of enhancer demethylation as the underlying mechanism. 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.

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.

The interaction of CD4+ helper T cells with dendritic cells shapes the tumor microenvironment and immune checkpoint blockade response.

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 αß 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.

Single-cell analysis of regions of interest (SCARI) using a photosensitive tag.

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.

DNA methylation landscapes of 1538 breast cancers reveal a replication-linked clock, epigenomic instability and cis-regulation.

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.

Personalized lab test models to quantify disease potentials in healthy individuals.

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.

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.

Evolutionary cell type mapping with single-cell genomics.

A fundamental characteristic of animal multicellularity is the spatial coexistence of functionally specialized cell types that are all encoded by a single genome sequence. Cell type transcriptional programs are deployed and maintained by regulatory mechanisms that control the asymmetric, differential access to genomic information in each cell. This genome regulation ultimately results in specific cellular phenotypes. However, the emergence, diversity, and evolutionary dynamics of animal cell types remain almost completely unexplored beyond a few species. Single-cell genomics is emerging as a powerful tool to build comprehensive catalogs of cell types and their associated gene regulatory programs in non-traditional model species. We review the current state of sampling efforts across the animal tree of life and challenges ahead for the comparative study of cell type programs. We also discuss how the phylogenetic integration of cell atlases can lead to the development of models of cell type evolution and a phylogenetic taxonomy of cells.

A stony coral cell atlas illuminates the molecular and cellular basis of coral symbiosis, calcification, and immunity.

Stony corals are colonial cnidarians that sustain the most biodiverse marine ecosystems on Earth: coral reefs. Despite their ecological importance, little is known about the cell types and molecular pathways that underpin the biology of reef-building corals. Using single-cell RNA sequencing, we define over 40 cell types across the life cycle of Stylophora pistillata. We discover specialized immune cells, and we uncover the developmental gene expression dynamics of calcium-carbonate skeleton formation. By simultaneously measuring the transcriptomes of coral cells and the algae within them, we characterize the metabolic programs involved in symbiosis in both partners. We also trace the evolution of these coral cell specializations by phylogenetic integration of multiple cnidarian cell type atlases. Overall, this study reveals the molecular and cellular basis of stony coral biology.

A single-embryo, single-cell time-resolved model for mouse gastrulation.

Mouse embryonic development is a canonical model system for studying mammalian cell fate acquisition. Recently, single-cell atlases comprehensively charted embryonic transcriptional landscapes, yet inference of the coordinated dynamics of cells over such atlases remains challenging. Here, we introduce a temporal model for mouse gastrulation, consisting of data from 153 individually sampled embryos spanning 36 h of molecular diversification. Using algorithms and precise timing, we infer differentiation flows and lineage specification dynamics over the embryonic transcriptional manifold. Rapid transcriptional bifurcations characterize the commitment of early specialized node and blood cells. However, for most lineages, we observe combinatorial multi-furcation dynamics rather than hierarchical transcriptional transitions. In the mesoderm, dozens of transcription factors combinatorially regulate multifurcations, as we exemplify using time-matched chimeric embryos of Foxc1/Foxc2 mutants. Our study rejects the notion of differentiation being governed by a series of binary choices, providing an alternative quantitative model for cell fate acquisition.