scPerturb: harmonized single-cell perturbation data.

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.

High-confidence calling of normal epithelial cells allows identification of a novel stem-like cell state in the colorectal cancer microenvironment.

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 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 cells in single-cell data. We find that our method and others based on gene expression or allelic imbalances identify overlapping sets of colorectal cancer versus normal colon 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.

Key benefits of dexamethasone and antibody treatment in COVID-19 hamster models revealed by single-cell transcriptomics.

For coronavirus disease 2019 (COVID-19), effective and well-understood treatment options are still scarce. Since vaccine efficacy is challenged by novel variants, short-lasting immunity, and vaccine hesitancy, understanding and optimizing therapeutic options remains essential. We aimed at better understanding the effects of two standard-of-care drugs, dexamethasone and anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies, on infection and host responses. By using two COVID-19 hamster models, pulmonary immune responses were analyzed to characterize effects of single or combinatorial treatments. Pulmonary viral burden was reduced by anti-SARS-CoV-2 antibody treatment and unaltered or increased by dexamethasone alone. Dexamethasone exhibited strong anti-inflammatory effects and prevented fulminant disease in a severe disease model. Combination therapy showed additive benefits with both anti-viral and anti-inflammatory potency. Bulk and single-cell transcriptomic analyses confirmed dampened inflammatory cell recruitment into lungs upon dexamethasone treatment and identified a specifically responsive subpopulation of neutrophils, thereby indicating a potential mechanism of action. Our analyses confirm the anti-inflammatory properties of dexamethasone and suggest possible mechanisms, validate anti-viral effects of anti-SARS-CoV-2 antibody treatment, and reveal synergistic effects of a combination therapy, thus informing more effective COVID-19 therapies.

Single-cell-resolved interspecies comparison shows a shared inflammatory axis and a dominant neutrophil-endothelial program in severe COVID-19.

A key issue for research on COVID-19 pathogenesis is the lack of biopsies from patients and of samples at the onset of infection. To overcome these hurdles, hamsters were shown to be useful models for studying this disease. Here, we further leverage the model to molecularly survey the disease progression from time-resolved single-cell RNA sequencing data collected from healthy and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected Syrian and Roborovski hamster lungs. We compare our data to human COVID-19 studies, including bronchoalveolar lavage, nasal swab, and postmortem lung tissue, and identify a shared axis of inflammation dominated by macrophages, neutrophils, and endothelial cells, which we show to be transient in Syrian and terminal in Roborovski hamsters. Our data suggest that, following SARS-CoV-2 infection, commitment to a type 1- or type 3-biased immunity determines moderate versus severe COVID-19 outcomes, respectively.

Live-attenuated vaccine sCPD9 elicits superior mucosal and systemic immunity to SARS-CoV-2 variants in hamsters.

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.

Mitogen-activated protein kinase activity drives cell trajectories in colorectal cancer.

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.

Generalizing RNA velocity to transient cell states through dynamical modeling.

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.