A heterogeneous pharmaco-transcriptomic landscape induced by targeting a single oncogenic kinase.

Over-activation of the epidermal growth factor receptor (EGFR) is a hallmark of glioblastoma. However, EGFR-targeted therapies have led to minimal clinical response. While delivery of EGFR inhibitors (EGFRis) to the brain constitutes a major challenge, how additional drug-specific features alter efficacy remains poorly understood. We apply highly multiplex single-cell chemical genomics to define the molecular response of glioblastoma to EGFRis. Using a deep generative framework, we identify shared and drug-specific transcriptional programs that group EGFRis into distinct molecular classes. We identify programs that differ by the chemical properties of EGFRis, including induction of adaptive transcription and modulation of immunogenic gene expression. Finally, we demonstrate that pro-immunogenic expression changes associated with a subset of tyrphostin family EGFRis increase the ability of T-cells to target glioblastoma cells.

PERCEPTION predicts patient response and resistance to treatment using single-cell transcriptomics of their tumors.

Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients' sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics.

Multimodal profiling reveals tissue-directed signatures of human immune cells altered with age.

The immune system comprises multiple cell lineages and heterogeneous subsets found in blood and tissues throughout the body. While human immune responses differ between sites and over age, the underlying sources of variation remain unclear as most studies are limited to peripheral blood. Here, we took a systems approach to comprehensively profile RNA and surface protein expression of over 1.25 million immune cells isolated from blood, lymphoid organs, and mucosal tissues of 24 organ donors aged 20-75 years. We applied a multimodal classifier to annotate the major immune cell lineages (T cells, B cells, innate lymphoid cells, and myeloid cells) and their corresponding subsets across the body, leveraging probabilistic modeling to define bases for immune variations across donors, tissue, and age. We identified dominant tissue-specific effects on immune cell composition and function across lineages for lymphoid sites, intestines, and blood-rich tissues. Age-associated effects were intrinsic to both lineage and site as manifested by macrophages in mucosal sites, B cells in lymphoid organs, and T and NK cells in blood-rich sites. Our results reveal tissue-specific signatures of immune homeostasis throughout the body and across different ages. This information provides a basis for defining the transcriptional underpinnings of immune variation and potential associations with disease-associated immune pathologies across the human lifespan.

Optimizing sequence design strategies for perturbation MPRAs: a computational evaluation framework.

The advent of perturbation-based massively parallel reporter assays (MPRAs) technique has facilitated the delineation of the roles of non-coding regulatory elements in orchestrating gene expression. However, computational efforts remain scant to evaluate and establish guidelines for sequence design strategies for perturbation MPRAs. In this study, we propose a framework for evaluating and comparing various perturbation strategies for MPRA experiments. Within this framework, we benchmark three different perturbation approaches from the perspectives of alteration in motif-based profiles, consistency of MPRA outputs, and robustness of models that predict the activities of putative regulatory motifs. While our analyses show very similar results across multiple benchmarking metrics, the predictive modeling for the approach involving random nucleotide shuffling shows significant robustness compared with the other two approaches. Thus, we recommend designing sequences by randomly shuffling the nucleotides of the perturbed site in perturbation-MPRA, followed by a coherence check to prevent the introduction of other variations of the target motifs. In summary, our evaluation framework and the benchmarking findings create a resource of computational pipelines and highlight the potential of perturbation-MPRA in predicting non-coding regulatory activities.

Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells.

RNA velocity has been rapidly adopted to guide interpretation of transcriptional dynamics in snapshot single-cell data; however, current approaches for estimating RNA velocity lack effective strategies for quantifying uncertainty and determining the overall applicability to the system of interest. Here, we present veloVI (velocity variational inference), a deep generative modeling framework for estimating RNA velocity. veloVI learns a gene-specific dynamical model of RNA metabolism and provides a transcriptome-wide quantification of velocity uncertainty. We show that veloVI compares favorably to previous approaches with respect to goodness of fit, consistency across transcriptionally similar cells and stability across preprocessing pipelines for quantifying RNA abundance. Further, we demonstrate that veloVI's posterior velocity uncertainty can be used to assess whether velocity analysis is appropriate for a given dataset. Finally, we highlight veloVI as a flexible framework for modeling transcriptional dynamics by adapting the underlying dynamical model to use time-dependent transcription rates.

Time-resolved single-cell transcriptomics defines immune trajectories in glioblastoma.

Deciphering the cell-state transitions underlying immune adaptation across time is fundamental for advancing biology. Empirical in vivo genomic technologies that capture cellular dynamics are currently lacking. We present Zman-seq, a single-cell technology recording transcriptomic dynamics across time by introducing time stamps into circulating immune cells, tracking them in tissues for days. Applying Zman-seq resolved cell-state and molecular trajectories of the dysfunctional immune microenvironment in glioblastoma. Within 24 hours of tumor infiltration, cytotoxic natural killer cells transitioned to a dysfunctional program regulated by TGFB1 signaling. Infiltrating monocytes differentiated into immunosuppressive macrophages, characterized by the upregulation of suppressive myeloid checkpoints Trem2, Il18bp, and Arg1, over 36 to 48 hours. Treatment with an antagonistic anti-TREM2 antibody reshaped the tumor microenvironment by redirecting the monocyte trajectory toward pro-inflammatory macrophages. Zman-seq is a broadly applicable technology, enabling empirical measurements of differentiation trajectories, which can enhance the development of more efficacious immunotherapies.

ConvexML: Scalable and accurate inference of single-cell chronograms from CRISPR/Cas9 lineage tracing data.

CRISPR/Cas9 gene editing technology has enabled lineage tracing for thousands of cells in vivo. However, most of the analysis of CRISPR/Cas9 lineage tracing data has so far been limited to the reconstruction of single-cell tree topologies, which depict lineage relationships between cells, but not the amount of time that has passed between ancestral cell states and the present. Time-resolved trees, known as chronograms, would allow one to study the evolutionary dynamics of cell populations at an unprecedented level of resolution. Indeed, time-resolved trees would reveal the timing of events on the tree, the relative fitness of subclones, and the dynamics underlying phenotypic changes in the cell population - among other important applications. In this work, we introduce the first scalable and accurate method to refine any given single-cell tree topology into a single-cell chronogram by estimating its branch lengths. To do this, we leverage a statistical model of CRISPR/Cas9 cutting with missing data, paired with a conservative version of maximum parsimony that reconstructs only the ancestral states that we are confident about. As part of our method, we propose a novel approach to represent and handle missing data - specifically, double-resection events - which greatly simplifies and speeds up branch length estimation without compromising quality. All this leads to a convex maximum likelihood estimation (MLE) problem that can be readily solved in seconds with off-the-shelf convex optimization solvers. To stabilize estimates in low-information regimes, we propose a simple penalized version of MLE using a minimum branch length and pseudocounts. We benchmark our method using simulations and show that it performs well on several tasks, outperforming more naive baselines. Our method, which we name 'ConvexML', is available through the cassiopeia open source Python package.

Hepatitis C virus infects and perturbs liver stem cells.

Hepatitis C virus (HCV) is the leading cause of death from liver disease. How HCV infection causes lasting liver damage and increases cancer risk remains unclear. Here, we identify bipotent liver stem cells as novel targets for HCV infection, and their erroneous differentiation as the potential cause of impaired liver regeneration and cancer development. We show 3D organoids generated from liver stem cells from actively HCV-infected individuals carry replicating virus and maintain low-grade infection over months. Organoids can be infected with a primary HCV isolate. Virus-inclusive single-cell RNA sequencing uncovered transcriptional reprogramming in HCV+ cells supporting hepatocytic differentiation, cancer stem cell development, and viral replication while stem cell proliferation and interferon signaling are disrupted. Our data add a new pathogenesis mechanism-infection of liver stem cells-to the biology of HCV infection that may explain progressive liver damage and enhanced cancer risk through an altered stem cell state.ImportanceThe hepatitis C virus (HCV) causes liver disease, affecting millions. Even though we have effective antivirals that cure HCV, they cannot stop terminal liver disease. We used an adult stem cell-derived liver organoid system to understand how HCV infection leads to the progression of terminal liver disease. Here, we show that HCV maintains low-grade infections in liver organoids for the first time. HCV infection in liver organoids leads to transcriptional reprogramming causing cancer cell development and altered immune response. Our finding shows how HCV infection in liver organoids mimics HCV infection and patient pathogenesis. These results reveal that HCV infection in liver organoids contributes to liver disease progression.

Best practices for perturbation MPRA-a computational evaluation framework of sequence design strategies.

The advent of the perturbation-based massively parallel reporter assays (MPRAs) technique has enabled delineating of the roles of non-coding regulatory elements in orchestrating gene expression. However, computational efforts remain scant to evaluate and establish guidelines for sequence design strategies for perturbation MPRAs. Here, we propose a framework for evaluating and comparing various perturbation strategies for MPRA experiments. Under this framework, we benchmark three different perturbation approaches from the perspectives of alteration in motif-based profiles, consistency of MPRA outputs, and robustness of models that predict the activities of putative regulatory motifs. Although our analyses show similar while significant results in multiple metrics, the method of randomly shuffling nucleotides outperform the other two methods. Thus, we still recommend designing sequences by randomly shuffling the nucleotides of the perturbed site in perturbation-MPRA. The evaluation framework, together with the benchmarking findings in our work, creates a resource of computational pipelines and illustrates the promise of perturbation-MPRA for predicting non-coding regulatory activities.

Single-cell multiomic analysis of thymocyte development reveals drivers of CD4+ T cell and CD8+ T cell lineage commitment.

The development of CD4+ T cells and CD8+ T cells in the thymus is critical to adaptive immunity and is widely studied as a model of lineage commitment. Recognition of self-peptide major histocompatibility complex (MHC) class I or II by the T cell antigen receptor (TCR) determines the CD8+ or CD4+ T cell lineage choice, respectively, but how distinct TCR signals drive transcriptional programs of lineage commitment remains largely unknown. Here we applied CITE-seq to measure RNA and surface proteins in thymocytes from wild-type and T cell lineage-restricted mice to generate a comprehensive timeline of cell states for each T cell lineage. These analyses identified a sequential process whereby all thymocytes initiate CD4+ T cell lineage differentiation during a first wave of TCR signaling, followed by a second TCR signaling wave that coincides with CD8+ T cell lineage specification. CITE-seq and pharmaceutical inhibition experiments implicated a TCR-calcineurin-NFAT-GATA3 axis in driving the CD4+ T cell fate. Our data provide a resource for understanding cell fate decisions and implicate a sequential selection process in guiding lineage choice.

MultiVI: deep generative model for the integration of multimodal data.

Jointly profiling the transcriptome, chromatin accessibility and other molecular properties of single cells offers a powerful way to study cellular diversity. Here we present MultiVI, a probabilistic model to analyze such multiomic data and leverage it to enhance single-modality datasets. MultiVI creates a joint representation that allows an analysis of all modalities included in the multiomic input data, even for cells for which one or more modalities are missing. It is available at scvi-tools.org .

An empirical Bayes method for differential expression analysis of single cells with deep generative models.

Detecting differentially expressed genes is important for characterizing subpopulations of cells. In scRNA-seq data, however, nuisance variation due to technical factors like sequencing depth and RNA capture efficiency obscures the underlying biological signal. Deep generative models have been extensively applied to scRNA-seq data, with a special focus on embedding cells into a low-dimensional latent space and correcting for batch effects. However, little attention has been paid to the problem of utilizing the uncertainty from the deep generative model for differential expression (DE). Furthermore, the existing approaches do not allow for controlling for effect size or the false discovery rate (FDR). Here, we present lvm-DE, a generic Bayesian approach for performing DE predictions from a fitted deep generative model, while controlling the FDR. We apply the lvm-DE framework to scVI and scSphere, two deep generative models. The resulting approaches outperform state-of-the-art methods at estimating the log fold change in gene expression levels as well as detecting differentially expressed genes between subpopulations of cells.

Theoretical guarantees for phylogeny inference from single-cell lineage tracing.

Lineage-tracing technologies based on Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated protein 9 (CRISPR-Cas9) genome editing have emerged as a powerful tool for investigating development in single-cell contexts, but exact reconstruction of the underlying clonal relationships in experiment is complicated by features of the data. These complications are functions of the experimental parameters in these systems, such as the Cas9 cutting rate, the diversity of indel outcomes, and the rate of missing data. In this paper, we develop two theoretically grounded algorithms for the reconstruction of the underlying single-cell phylogenetic tree as well as asymptotic bounds for the number of recording sites necessary for exact recapitulation of the ground truth phylogeny at high probability. In doing so, we explore the relationship between the problem difficulty and the experimental parameters, with implications for experimental design. Lastly, we provide simulations showing the empirical performance of these algorithms and showing that the trends in the asymptotic bounds hold empirically. Overall, this work provides a theoretical analysis of phylogenetic reconstruction in single-cell CRISPR-Cas9 lineage-tracing technologies.

Sodium perturbs mitochondrial respiration and induces dysfunctional Tregs.

FOXP3+ regulatory T cells (Tregs) are central for peripheral tolerance, and their deregulation is associated with autoimmunity. Dysfunctional autoimmune Tregs display pro-inflammatory features and altered mitochondrial metabolism, but contributing factors remain elusive. High salt (HS) has been identified to alter immune function and to promote autoimmunity. By investigating longitudinal transcriptional changes of human Tregs, we identified that HS induces metabolic reprogramming, recapitulating features of autoimmune Tregs. Mechanistically, extracellular HS raises intracellular Na+, perturbing mitochondrial respiration by interfering with the electron transport chain (ETC). Metabolic disturbance by a temporary HS encounter or complex III blockade rapidly induces a pro-inflammatory signature and FOXP3 downregulation, leading to long-term dysfunction in vitro and in vivo. The HS-induced effect could be reversed by inhibition of mitochondrial Na+/Ca2+ exchanger (NCLX). Our results indicate that salt could contribute to metabolic reprogramming and that short-term HS encounter perturb metabolic fitness and long-term function of human Tregs with important implications for autoimmunity.

Induction of a colitogenic phenotype in Th1-like cells depends on interleukin-23 receptor signaling.

Interleukin-23 receptor plays a critical role in inducing inflammation and autoimmunity. Here, we report that Th1-like cells differentiated in vitro with IL-12 + IL-21 showed similar IL-23R expression to that of pathogenic Th17 cells using eGFP reporter mice. Fate mapping established that these cells did not transition through a Th17 cell state prior to becoming Th1-like cells, and we observed their emergence in vivo in the T cell adoptive transfer colitis model. Using IL-23R-deficient Th1-like cells, we demonstrated that IL-23R was required for the development of a highly colitogenic phenotype. Single-cell RNA sequencing analysis of intestinal T cells identified IL-23R-dependent genes in Th1-like cells that differed from those expressed in Th17 cells. The perturbation of one of these regulators (CD160) in Th1-like cells inhibited the induction of colitis. We thus uncouple IL-23R as a purely Th17 cell-specific factor and implicate IL-23R signaling as a pathogenic driver in Th1-like cells inducing tissue inflammation.

Alveolar macrophages in early stage COPD show functional deviations with properties of impaired immune activation.

Despite its high prevalence, the cellular and molecular mechanisms of chronic obstructive pulmonary disease (COPD) are far from being understood. Here, we determine disease-related changes in cellular and molecular compositions within the alveolar space and peripheral blood of a cohort of COPD patients and controls. Myeloid cells were the largest cellular compartment in the alveolar space with invading monocytes and proliferating macrophages elevated in COPD. Modeling cell-to-cell communication, signaling pathway usage, and transcription factor binding predicts TGF-ß1 to be a major upstream regulator of transcriptional changes in alveolar macrophages of COPD patients. Functionally, macrophages in COPD showed reduced antigen presentation capacity, accumulation of cholesteryl ester, reduced cellular chemotaxis, and mitochondrial dysfunction, reminiscent of impaired immune activation.

NF-κB inhibitor alpha has a cross-variant role during SARS-CoV-2 infection in ACE2-overexpressing human airway organoids.

As SARS-CoV-2 continues to spread worldwide, tractable primary airway cell models that accurately recapitulate the cell-intrinsic response to arising viral variants are needed. Here we describe an adult stem cell-derived human airway organoid model overexpressing the ACE2 receptor that supports robust viral replication while maintaining 3D architecture and cellular diversity of the airway epithelium. ACE2-OE organoids were infected with SARS-CoV-2 variants and subjected to single-cell RNA-sequencing. NF-κB inhibitor alpha was consistently upregulated in infected epithelial cells, and its mRNA expression positively correlated with infection levels. Confocal microscopy showed more IκBα expression in infected than bystander cells, but found concurrent nuclear translocation of NF-κB that IκBα usually prevents. Overexpressing a nondegradable IκBα mutant reduced NF-κB translocation and increased viral infection. These data demonstrate the functionality of ACE2-OE organoids in SARS-CoV-2 research and identify an incomplete NF-κB feedback loop as a rheostat of viral infection that may promote inflammation and severe disease.

Ets21C sustains a pro-regenerative transcriptional program in blastema cells of Drosophila imaginal discs.

An important unanswered question in regenerative biology is to what extent regeneration is accomplished by the reactivation of gene regulatory networks used during development versus the activation of regeneration-specific transcriptional programs. Following damage, Drosophila imaginal discs, the larval precursors of adult structures, can regenerate missing portions by localized proliferation of damage-adjacent tissue. Using single-cell transcriptomics in regenerating wing discs, we have obtained a comprehensive view of the transcriptome of regenerating discs and identified two regeneration-specific cell populations within the blastema, Blastema1 and Blastema2. Collectively, these cells upregulate multiple genes encoding secreted proteins that promote regeneration including Pvf1, upd3, asperous, Mmp1, and the maturation delaying factor Ilp8. Expression of the transcription factor Ets21C is restricted to this regenerative secretory zone; it is not expressed in undamaged discs. Ets21C expression is activated by the JNK/AP-1 pathway, and it can function in a type 1 coherent feedforward loop with AP-1 to sustain expression of downstream genes. Without Ets21C function, the blastema cells fail to maintain the expression of a number of genes, which leads to premature differentiation and severely compromised regeneration. As Ets21C is dispensable for normal development, these observations indicate that Ets21C orchestrates a regeneration-specific gene regulatory network. We have also identified cells resembling both Blastema1 and Blastema2 in scribble tumorous discs. They express the Ets21C-dependent gene regulatory network, and eliminating Ets21C function reduces tumorous growth. Thus, mechanisms that function during regeneration can be co-opted by tumors to promote aberrant growth.

Identifying cell state-associated alternative splicing events and their coregulation.

Alternative splicing shapes the transcriptome and contributes to each cell's unique identity, but single-cell RNA sequencing (scRNA-seq) has struggled to capture the impact of alternative splicing. We previously showed that low recovery of mRNAs from single cells led to erroneous conclusions about the cell-to-cell variability of alternative splicing. Here, we present a method, Psix, to confidently identify splicing that changes across a landscape of single cells, using a probabilistic model that is robust against the data limitations of scRNA-seq. Its autocorrelation-inspired approach finds patterns of alternative splicing that correspond to patterns of cell identity, such as cell type or developmental stage, without the need for explicit cell clustering, labeling, or trajectory inference. Applying Psix to data that follow the trajectory of mouse brain development, we identify exons whose alternative splicing patterns cluster into modules of coregulation. We show that the exons in these modules are enriched for binding by distinct neuronal splicing factors and that their changes in splicing correspond to changes in expression of these splicing factors. Thus, Psix reveals cell type-dependent splicing patterns and the wiring of the splicing regulatory networks that control them. Our new method will enable scRNA-seq analysis to go beyond transcription to understand the roles of post-transcriptional regulation in determining cell identity.