Alejandro Granados

Reconstructing the lineage histories of individual cells can reveal the dynamics of developmental and disease processes. In engineered recording systems, cells stochastically edit synthetic barcode sequences as they proliferate, creating distinct, heritable edit patterns that can be used to reconstruct the lineage trees relating individual cells in a manner analogous to phylogenetic reconstruction. However, recording depth is often limited by the kinetics of the editing process: the rate of editing declines exponentially over time for an array of independently editable targets, leading to most edits occurring in early generations. Here, we introduce the hypercascade, a regenerative molecular recording system that takes advantage of the predictability of A-to-G base editing to progressively create new target sites over time. The hypercascade packs 4 editable target sites in every 20 bp of sequence, enabling high density information storage. More importantly, the hypercascade’s regenerative logic leads to an approximately constant rate of mutation accumulation over time. This in turn facilitates reconstruction of deep lineage relationships. We demonstrate this by reconstructing trees spanning 23 days of editing and approximately 17 generations after a single polyclonal engineering step. Finally, simulations show that the hypercascade has the potential to record chromatin state transition dynamics across multiple genomic loci in parallel. The hypercascade thus provides a flexible and broadly useful tool for molecular recording.

Different glioblastoma (GBM) subtypes have been identified based on the tumor microenvironment (TME). The discovery of new therapies for these hard-to-treat tumors requires a thorough characterization of preclinical models, including their TME, to apply preclinical results to the most similar GBM subtype. Using single-nucleus RNA sequencing (snRNA-seq), we characterized the tumor and TME in an immunocompetent mouse model with intracranially implanted GBM stem cells at different stages and treatments. Visium spatial transcriptomics confirmed the location of annotated cells. This model exhibits GBM targets related to integration into neural circuits - Grik2, Nlgn3, Gap43 or Kcnn4-, immunoevasion - Nt5e, Cd274 or Irf8- and immunosuppression - Csf1r, Arg1, Mrc1 and Tgfb1. The landscape of cytokines, checkpoint ligands and receptors uncovered Mrc1, PD-L1, TIM-3 or B7-H3, among the immunotherapy targets that can be addressed in this model. The comparison with human GBMs unveiled crucial similarities with TME^Med GBM, the most frequent subtype.

During embryonic development, gene regulatory networks (GRNs) drive molecular differentiation of cell types. However, the temporal dynamics of these networks remain poorly understood. Here, we present Zebrahub-Multiome, a single-cell multiomic atlas that captures chromatin accessibility and gene expression from 94,562 cells across six stages of zebrafish embryogenesis (10-24 hours post-fertilization), capturing key developmental stages from the end of gastrulation to the onset of organogenesis. By measuring regulatory element activity alongside transcriptional output from the same cells, we identify 640,000 cis-regulatory elements organized into 402 hierarchically structured modules corresponding to specific developmental pathways. Early embryonic stages employ broadly shared regulatory programs that progressively fragment into lineage-specific modules. Timeresolved gene regulatory network inference reveals that transcription factors undergo functional transitions – from multilineage regulators to specialized, lineage-committed factors. These quantitative measurements reveal the regulatory network rewiring that drives cell fate specification. Our interactive web portal ( zebrahub.org/epigenomics ) enables exploration of gene dynamics, regulatory networks, and perturbation predictions, providing a quantitative framework for understanding vertebrate developmental regulation.

Single-cell RNA sequencing is revolutionizing our understanding of cell state dynamics, allowing researchers to observe the progression of individual cells’ transcriptomic profiles over time. Among the computational techniques used to predict future cellular states, RNA velocity has emerged as a predominant tool for modeling transcriptional dynamics. RNA velocity leverages the mRNA maturation process to generate velocity vectors that predict the likely future state of a cell, offering insights into cellular differentiation, aging, and disease progression. Although this technique has shown promise across biological fields, the performance accuracy varies depending on the RNA velocity method and dataset. We established a comparative pipeline and analyzed the performance of five RNA velocity methods on three datasets based on local consistency, method agreement, identification of driver genes, and robustness to sequencing depth. This benchmark provides a resource for scientists to understand the strengths and limitations of different RNA velocity methods.

Background: Infection by coronavirus SARS-CoV2 is a severe and often deadly disease that has implications for the respiratory system and multiple organs across the human body. While the effects in the lung have been extensively studied, less is known about the impact COVID-19 has across other organs.Methods: Here, we contribute a single-nuclei RNA-sequencing atlas comprising six human organs across 20 autopsies where we analyzed the transcriptional changes due to COVID-19 in multiple cell types. The integration of data from multiple organs enabled the identification of systemic transcriptional changes.Results: Computational cross-organ analysis for endothelial cells and macrophages identified systemic transcriptional changes in these cell types in COVID-19 samples. In addition, analysis of gene modules showed enrichment of specific signaling pathways across multiple organs in COVID-19 autopsies.Conclusions: Altogether, the COVID Tissue Atlas enables the investigation of both cell type-specific and cross-organ transcriptional responses to COVID-19, providing insights into the molecular networks affected by the disease and highlighting novel potential targets for therapies and drug development.Funding: The Chan-Zuckerberg Initiative, The Chan-Zuckerberg Biohub.

Assigning cell identity to clusters of single cells is an essential step towards extracting biological insights from many genomics datasets. Although annotation workflows for datasets built with a single modality are well established, limitations exist in annotating cell types in datasets with multiple modalities due to the need for a framework to exploit them jointly. While, in principle, different modalities could convey complementary information about cell identity, it is unclear to what extent they can be combined to improve the accuracy and resolution of cell type annotations. Here, we present a conceptual framework to examine and jointly interrogate distinct modalities to identify cell types. We integrated our framework into a series of vignettes, using immune cells as a well-studied example, and demonstrate cell type annotation workflows ranging from using single-cell RNA-seq datasets alone, to using multiple modalities such as single-cell Multiome (RNA and chromatin accessibility), CITE-seq (RNA and surface proteins). In some cases, one or other single modality is superior to the other for identification of specific cell types, in others combining the two modalities improves resolution and the ability to identify finer subpopulations. Finally, we use interactive software from CZ CELLxGENE community tools to visualize and integrate histological and spatial transcriptomic data.

Elucidating the developmental processes of organisms requires a comprehensive understanding of cellular lineages in the spatial, temporal, and molecular domains. In this study, we introduce Zebrahub, a dynamic atlas of zebrafish embryonic development that integrates single-cell sequencing time course data with lineage reconstructions facilitated by light-sheet microscopy. This atlas offers high-resolution and in-depth molecular insights into zebrafish development, achieved through the sequencing of individual embryos across ten developmental stages, complemented by trajectory reconstructions. Zebrahub also incorporates an interactive tool to navigate the complex cellular flows and lineages derived from light-sheet microscopy data, enabling in silico fate mapping experiments. To demonstrate the versatility of our multi-modal resource, we utilize Zebrahub to provide fresh insights into the pluripotency of Neuro-Mesodermal Progenitors (NMPs). Our publicly accessible web-based platform, Zebrahub, is a foundational resource for studying developmental processes at both transcriptional and spatiotemporal levels, providing researchers with an integrated approach to exploring and analyzing the complexities of cellular lineages during zebrafish embryogenesis.

IntMEMOIR traces cell lineages: Cell lineage plays a pivotal role in cell fate determination. Chow et al. demonstrate the use of an integrase-based synthetic barcode system called intMEMOIR, which uses the serine integrase Bxb1 to perform irreversible nucleotide edits. Inducible editing either deletes or inverts its target region, thus encoding information in three-state memory elements, or trits, and avoiding undesired recombination events. Using intMEMOIR combined with single-molecule fluorescence in situ hybridization, the authors were able to identify clonal structures as well as gene expression patterns in the fly brain, enabling both clonal analysis and expression profiling with intact spatial information. The ability to visualize cell lineage relationships directly within their native tissue context provides insights into development and disease. Science , this issue p. eabb3099