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RNA velocity has opened up new ways of studying cellular differentiation in single-cell RNA-sequencing data. It describes the rate of gene expression change for an individual gene at a given time point based on the ratio of its spliced and unspliced messenger RNA (mRNA). However, errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. Here we present scVelo, a method that overcomes these limitations by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to systems with transient cell states, which are common in development and in response to perturbations. We apply scVelo to disentangling subpopulation kinetics in neurogenesis and pancreatic endocrinogenesis. We infer gene-specific rates of transcription, splicing and degradation, recover each cell’s position in the underlying differentiation processes and detect putative driver genes. scVelo will facilitate the study of lineage decisions and gene regulation. scVelo reconstructs transient cell states and differentiation pathways from single-cell RNA-sequencing data.

In colorectal cancer, oncogenic mutations transform a hierarchically organized and homeostatic epithelium into invasive cancer tissue lacking visible organization. We sought to define transcriptional states of colorectal cancer cells and signals controlling their development by performing single‐cell transcriptome analysis of tumors and matched non‐cancerous tissues of twelve colorectal cancer patients. We defined patient‐overarching colorectal cancer cell clusters characterized by differential activities of oncogenic signaling pathways such as mitogen‐activated protein kinase and oncogenic traits such as replication stress. RNA metabolic labeling and assessment of RNA velocity in patient‐derived organoids revealed developmental trajectories of colorectal cancer cells organized along a mitogen‐activated protein kinase activity gradient. This was in contrast to normal colon organoid cells developing along graded Wnt activity. Experimental targeting of EGFR‐BRAF‐MEK in cancer organoids affected signaling and gene expression contingent on predictive KRAS/BRAF mutations and induced cell plasticity overriding default developmental trajectories. Our results highlight directional cancer cell development as a driver of non‐genetic cancer cell heterogeneity and re‐routing of trajectories as a response to targeted therapy. SYNOPSIS Colorectal cancer (CRC) cells can adopt a range of transcriptomic states. This study uses single cell RNA sequencing of primary CRC tissue and organoids to identify patient‐overarching CRC cell transcriptome clusters. RNA metabolic labelling indicates preferred CRC cell developmental trajectories. CRC cells of multiple patients clustered into six groups – termed TC1‐4, Goblet‐like, and stem‐like – characterized by differential transcriptional footprints of oncogenic signaling pathways. CRC organoid cells develop along a decreasing MAPK gradient. Experimental targeting of EGFR‐MAPK in CRC organoids re‐routes developmental trajectories. Clinically relevant inhibition of EGFR‐MAPK can result in preferential CRC cell development towards endpoints expressing high levels of stem cell markers. Graphical Abstract Colorectal cancer (CRC) cells can adopt a range of transcriptomic states. This study uses single cell RNA sequencing of primary CRC tissue and organoids to identify patient‐overarching CRC cell transcriptome clusters. RNA metabolic labelling indicates preferred CRC cell developmental trajectories.

Background Synthetic lethal interactions are attractive therapeutic candidates as they enable selective targeting of cancer cells in which somatic alterations have disrupted one member of a synthetic lethal gene pair while leaving normal tissues untouched, thus minimising off-target toxicity. Despite this potential, the number of well-established and validated synthetic lethal gene pairs is modest. Results We generate a dual-guide CRISPR/Cas9 Library and analyse 472 predicted synthetic lethal pairs in 27 cancer cell Lines from melanoma, pancreatic and lung cancer Lineages. We report a robust collection of 117 genetic interactions within and across cancer types and explore their candidacy as therapeutic targets. We show that SLC25A28 is an attractive target since its synthetic lethal paralog partner SLC25A37 is homozygously deleted pan-cancer. We generate knockout mice for Slc25a28 revealing that, except for cataracts in some mice, these animals are normal; suggesting inhibition of SLC25A28 is unlikely to be associated with profound toxicity. Conclusions We provide and validate an extensive collection of synthetic lethal interactions across cancer types.

A recent study proposes a strategy for the prediction of genetic perturbation outcomes by breaking it down into three subtasks: identifying differentially expressed genes, determining expression change directions, and estimating gene expression magnitudes.

Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation–response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation–response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth. scPerturb is an information resource for single-cell perturbation data analysis and comparison.

Vaccines play a critical role in combating the COVID-19 pandemic. Future control of the pandemic requires improved vaccines with high efficacy against newly emerging SARS-CoV-2 variants and the ability to reduce virus transmission. Here we compare immune responses and preclinical efficacy of the mRNA vaccine BNT162b2, the adenovirus-vectored spike vaccine Ad2-spike and the live-attenuated virus vaccine candidate sCPD9 in Syrian hamsters, using both homogeneous and heterologous vaccination regimens. Comparative vaccine efficacy was assessed by employing readouts from virus titrations to single-cell RNA sequencing. Our results show that sCPD9 vaccination elicited the most robust immunity, including rapid viral clearance, reduced tissue damage, fast differentiation of pre-plasmablasts, strong systemic and mucosal humoral responses, and rapid recall of memory T cells from lung tissue after challenge with heterologous SARS-CoV-2. Overall, our results demonstrate that live-attenuated vaccines offer advantages over currently available COVID-19 vaccines. Comparison of mucosal and systemic immunity after vaccination with the live-attenuated vaccine sCPD9, mRNA vaccine BNT162b2 or an adenovirus-vectored vaccine following SARS-CoV-2 challenge in hamsters.

The introduction of RNA velocity in single cells has opened up new ways of studying cellular differentiation. The originally proposed framework obtains velocities as the deviation of the observed ratio of spliced and unspliced mRNA from an inferred steady state. Errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. With scVelo (https://scvelo.org), we address these restrictions by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to a wide variety of systems comprising transient cell states, which are common in development and in response to perturbations. We infer gene-specific rates of transcription, splicing and degradation, and recover the latent time of the underlying cellular processes. This latent time represents the cell's internal clock and is based only on its transcriptional dynamics. Moreover, scVelo allows us to identify regimes of regulatory changes such as stages of cell fate commitment and, therein, systematically detects putative driver genes. We demonstrate that scVelo enables disentangling heterogeneous subpopulation kinetics with unprecedented resolution in hippocampal dentate gyrus neurogenesis and pancreatic endocrinogenesis. We anticipate that scVelo will greatly facilitate the study of lineage decisions, gene regulation, and pathway activity identification. Footnotes * https://scvelo.org

The molecular programs that drive proliferation and differentiation of intestinal stem cells (ISCs) are essential for organismal fitness. Notch signalling regulates the binary fate decision of ISCs, favouring enterocyte commitment when Notch activity is high and enteroendocrine cell (EE) fate when activity is low. However, the gene regulatory mechanisms that underlie this process on an organ scale remain poorly understood. Here, we find that the expression of the C2H2-type zinc-finger transcription factor Chronophage (Cph), homologous to mammalian BCL11, increases specifically along the ISC-to-EE lineage when Notch is inactivated. We show that the expression of Cph is regulated by the Achaete-Scute Complex (AS-C) gene, scute, which directly binds to multiple sites within the Cph locus to promote its expression. Our genetic and single-cell RNA sequencing experiments demonstrate that Cph maintains the ISC and EE populations and is necessary to remodel the transcriptome of progenitor cells with low Notch activity. By identifying and functionally validating Cph target genes, we uncover a novel role for sugar free frosting (sff) in directing proliferative and lineage commitment steps of ISCs. Our results shed light on the mechanisms by which Cph sustains intestinal epithelial homeostasis and could represent a conserved strategy for balancing proliferation and differentiation in different tissues and species.

Single-cell analyses can be confounded by assigning unrelated groups of cells to common developmental trajectories. For instance, cancer cells and admixed normal epithelial cells could potentially adopt similar cell states thus complicating analyses of their developmental potential. Here, we develop and benchmark CCISM (for Cancer Cell Identification using Somatic Mutations) to exploit genomic single nucleotide variants for the disambiguation of cancer cells from genomically normal non-cancer epithelial cells in single-cell data. In colorectal cancer datasets, we find that our method and others based on gene expression or allelic imbalances identify overlapping sets of cancer versus normal epithelial cells, depending on molecular characteristics of individual cancers. Further, we define consensus cell identities of normal and cancer epithelial cells with higher transcriptome cluster homogeneity than those derived using existing tools. Using the consensus identities, we identify significant shifts of cell state distributions in genomically normal epithelial cells developing in the cancer microenvironment, with immature states increased at the expense of terminal differentiation throughout the colon, and a novel stem-like cell state arising in the left colon. Trajectory analyses show that the new cell state extends the pseudo-time range of normal colon stem-like cells in a cancer context. We identify cancer-associated fibroblasts as sources of WNT and BMP ligands potentially contributing to increased plasticity of stem cells in the cancer microenvironment. Our analyses advocate careful interpretation of cell heterogeneity and plasticity in the cancer context and the consideration of genomic information in addition to gene expression data when possible.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://zenodo.org/records/10692019* https://github.com/bihealth/CCISM

In single-cell data workflows and modeling, distance metrics are commonly used in loss functions, model evaluation, and subpopulation analysis. However, these metrics behave differently depending on the source of variation, conditions and subpopulations in single-cell expression profiles due to data sparsity and high dimensionality. Thus, the metrics used for downstream tasks in this domain should be carefully selected. We establish a set of benchmarks with three evaluation measures, capturing desirable facets of absolute and relative distance behavior. Based on seven datasets using perturbation as ground truth, we evaluated 16 distance metrics applied to scRNA-seq data and demonstrated their application to three use cases. We find that linear metrics such as mean squared error (MSE) performed best across our three evaluation criteria. Therefore, we recommend the use of MSE for comparing single-cell RNA-seq populations and evaluating gene expression prediction models.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://github.com/theislab/perturbation-metrics