Cell-Type-Specific Alternative Splicing Governs Cell Fate in the Developing Cerebral Cortex.

Alternative splicing is prevalent in the mammalian brain. To interrogate the functional role of alternative splicing in neural development, we analyzed purified neural progenitor cells (NPCs) and neurons from developing cerebral cortices, revealing hundreds of differentially spliced exons that preferentially alter key protein domains-especially in cytoskeletal proteins-and can harbor disease-causing mutations. We show that Ptbp1 and Rbfox proteins antagonistically govern the NPC-to-neuron transition by regulating neuron-specific exons. Whereas Ptbp1 maintains apical progenitors partly through suppressing a poison exon of Flna in NPCs, Rbfox proteins promote neuronal differentiation by switching Ninein from a centrosomal splice form in NPCs to a non-centrosomal isoform in neurons. We further uncover an intronic human mutation within a PTBP1-binding site that disrupts normal skipping of the FLNA poison exon in NPCs and causes a brain-specific malformation. Our study indicates that dynamic control of alternative splicing governs cell fate in cerebral cortical development.

SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis.

MOTIVATION: Spatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells. RESULTS: To enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce computational resource requirements while maintaining high performance, including as compared to other down-sampling approaches. We further integrate SEraster with existing analysis tools to characterize cell-type spatial co-enrichment across length scales. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as spatially co-enriched cell-types that recapitulate expected organ structures. AVAILABILITY: SEraster is implemented as an R package on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

First-In-Human Phase I Study of Tinengotinib (TT-00420), a Multiple Kinase Inhibitor, as a Single Agent in Patients With Advanced Solid Tumors.

PURPOSE: This first-in-human phase I dose-escalation study evaluated the safety, pharmacokinetics, and efficacy of tinengotinib (TT-00420), a multi-kinase inhibitor targeting fibroblast growth factor receptors 1-3 (FGFRs 1-3), Janus kinase 1/2, vascular endothelial growth factor receptors, and Aurora A/B, in patients with advanced solid tumors. PATIENTS AND METHODS: Patients received tinengotinib orally daily in 28-day cycles. Dose escalation was guided by Bayesian modeling using escalation with overdose control. The primary objective was to assess dose-limiting toxicities (DLTs), maximum tolerated dose (MTD), and dose recommended for dose expansion (DRDE). Secondary objectives included pharmacokinetics and efficacy. RESULTS: Forty-eight patients were enrolled (dose escalation, n = 40; dose expansion, n = 8). MTD was not reached; DRDE was 12 mg daily. DLTs were palmar-plantar erythrodysesthesia syndrome (8 mg, n = 1) and hypertension (15 mg, n = 2). The most common treatment-related adverse event was hypertension (50.0%). In 43 response-evaluable patients, 13 (30.2%) achieved partial response (PR; n = 7) or stable disease (SD) ≥ 24 weeks (n = 6), including 4/11 (36.4%) with FGFR2 mutations/fusions and cholangiocarcinoma (PR n = 3; SD ≥ 24 weeks n = 1), 3/3 (100.0%) with hormone receptor (HR)-positive/HER2-negative breast cancer (PR n = 2; SD ≥ 24 weeks n = 1), 2/5 (40.0%) with triple-negative breast cancer (TNBC; PR n = 1; SD ≥ 24 weeks n = 1), and 1/1 (100.0%) with castrate-resistant prostate cancer (CRPC; PR). Four of 12 patients (33.3%; HR-positive/HER2-negative breast cancer, TNBC, prostate cancer, and cholangiocarcinoma) treated at DRDE had PRs. Tinengotinib's half-life was 28-34 hours. CONCLUSIONS: Tinengotinib was well tolerated with favorable pharmacokinetic characteristics. Preliminary findings indicated potential clinical benefit in FGFR inhibitor-refractory cholangiocarcinoma, HER2-negative breast cancer (including TNBC), and CRPC. Continued evaluation of tinengotinib is warranted in phase II trials.

Characterizing spatial gene expression heterogeneity in spatially resolved single-cell transcriptomic data with nonuniform cellular densities.

Recent technological advances have enabled spatially resolved measurements of expression profiles for hundreds to thousands of genes in fixed tissues at single-cell resolution. However, scalable computational analysis methods able to take into consideration the inherent 3D spatial organization of cell types and nonuniform cellular densities within tissues are still lacking. To address this, we developed MERINGUE, a computational framework based on spatial autocorrelation and cross-correlation analysis to identify genes with spatially heterogeneous expression patterns, infer putative cell-cell communication, and perform spatially informed cell clustering in 2D and 3D in a density-agnostic manner using spatially resolved transcriptomic data. We applied MERINGUE to a variety of spatially resolved transcriptomic data sets including multiplexed error-robust fluorescence in situ hybridization (MERFISH), spatial transcriptomics, Slide-seq, and aligned in situ hybridization (ISH) data. We anticipate that such statistical analysis of spatially resolved transcriptomic data will facilitate our understanding of the interplay between cell state and spatial organization in tissue development and disease.

Soluble PD-L1 reprograms blood monocytes to prevent cerebral edema and facilitate recovery after ischemic stroke.

Acute cerebral ischemia triggers a profound inflammatory response. While macrophages polarized to an M2-like phenotype clear debris and facilitate tissue repair, aberrant or prolonged macrophage activation is counterproductive to recovery. The inhibitory immune checkpoint Programmed Cell Death Protein 1 (PD-1) is upregulated on macrophage precursors (monocytes) in the blood after acute cerebrovascular injury. To investigate the therapeutic potential of PD-1 activation, we immunophenotyped circulating monocytes from patients and found that PD-1 expression was upregulated in the acute period after stroke. Murine studies using a temporary middle cerebral artery (MCA) occlusion (MCAO) model showed that intraperitoneal administration of soluble Programmed Death Ligand-1 (sPD-L1) significantly decreased brain edema and improved overall survival. Mice receiving sPD-L1 also had higher performance scores short-term, and more closely resembled sham animals on assessments of long-term functional recovery. These clinical and radiographic benefits were abrogated in global and myeloid-specific PD-1 knockout animals, confirming PD-1+ monocytes as the therapeutic target of sPD-L1. Single-cell RNA sequencing revealed that treatment skewed monocyte maturation to a non-classical Ly6Clo, CD43hi, PD-L1+ phenotype. These data support peripheral activation of PD-1 on inflammatory monocytes as a therapeutic strategy to treat neuroinflammation after acute ischemic stroke.

RNA velocity of single cells.

RNA abundance is a powerful indicator of the state of individual cells. Single-cell RNA sequencing can reveal RNA abundance with high quantitative accuracy, sensitivity and throughput1. However, this approach captures only a static snapshot at a point in time, posing a challenge for the analysis of time-resolved phenomena such as embryogenesis or tissue regeneration. Here we show that RNA velocity-the time derivative of the gene expression state-can be directly estimated by distinguishing between unspliced and spliced mRNAs in common single-cell RNA sequencing protocols. RNA velocity is a high-dimensional vector that predicts the future state of individual cells on a timescale of hours. We validate its accuracy in the neural crest lineage, demonstrate its use on multiple published datasets and technical platforms, reveal the branching lineage tree of the developing mouse hippocampus, and examine the kinetics of transcription in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

VeloViz: RNA velocity-informed embeddings for visualizing cellular trajectories.

MOTIVATION: Single-cell transcriptomics profiling technologies enable genome-wide gene expression measurements in individual cells but can currently only provide a static snapshot of cellular transcriptional states. RNA velocity analysis can help infer cell state changes using such single-cell transcriptomics data. To interpret these cell state changes inferred from RNA velocity analysis as part of underlying cellular trajectories, current approaches rely on visualization with principal components, t-distributed stochastic neighbor embedding and other 2D embeddings derived from the observed single-cell transcriptional states. However, these 2D embeddings can yield different representations of the underlying cellular trajectories, hindering the interpretation of cell state changes. RESULTS: We developed VeloViz to create RNA velocity-informed 2D and 3D embeddings from single-cell transcriptomics data. Using both real and simulated data, we demonstrate that VeloViz embeddings are able to capture underlying cellular trajectories across diverse trajectory topologies, even when intermediate cell states may be missing. By considering the predicted future transcriptional states from RNA velocity analysis, VeloViz can help visualize a more reliable representation of underlying cellular trajectories. AVAILABILITY AND IMPLEMENTATION: Source code is available on GitHub (https://github.com/JEFworks-Lab/veloviz) and Bioconductor (https://bioconductor.org/packages/veloviz) with additional tutorials at https://JEF.works/veloviz/. Datasets used can be found on Zenodo (https://doi.org/10.5281/zenodo.4632471). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fast, sensitive and accurate integration of single-cell data with Harmony.

The emerging diversity of single-cell RNA-seq datasets allows for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. However, it is challenging to analyze them together, particularly when datasets are assayed with different technologies, because biological and technical differences are interspersed. We present Harmony (https://github.com/immunogenomics/harmony), an algorithm that projects cells into a shared embedding in which cells group by cell type rather than dataset-specific conditions. Harmony simultaneously accounts for multiple experimental and biological factors. In six analyses, we demonstrate the superior performance of Harmony to previously published algorithms while requiring fewer computational resources. Harmony enables the integration of ~106 cells on a personal computer. We apply Harmony to peripheral blood mononuclear cells from datasets with large experimental differences, five studies of pancreatic islet cells, mouse embryogenesis datasets and the integration of scRNA-seq with spatial transcriptomics data.

Spatial transcriptome profiling by MERFISH reveals subcellular RNA compartmentalization and cell cycle-dependent gene expression.

The expression profiles and spatial distributions of RNAs regulate many cellular functions. Image-based transcriptomic approaches provide powerful means to measure both expression and spatial information of RNAs in individual cells within their native environment. Among these approaches, multiplexed error-robust fluorescence in situ hybridization (MERFISH) has achieved spatially resolved RNA quantification at transcriptome scale by massively multiplexing single-molecule FISH measurements. Here, we increased the gene throughput of MERFISH and demonstrated simultaneous measurements of RNA transcripts from ∼10,000 genes in individual cells with ∼80% detection efficiency and ∼4% misidentification rate. We combined MERFISH with cellular structure imaging to determine subcellular compartmentalization of RNAs. We validated this approach by showing enrichment of secretome transcripts at the endoplasmic reticulum, and further revealed enrichment of long noncoding RNAs, RNAs with retained introns, and a subgroup of protein-coding mRNAs in the cell nucleus. Leveraging spatially resolved RNA profiling, we developed an approach to determine RNA velocity in situ using the balance of nuclear versus cytoplasmic RNA counts. We applied this approach to infer pseudotime ordering of cells and identified cells at different cell-cycle states, revealing ∼1,600 genes with putative cell cycle-dependent expression and a gradual transcription profile change as cells progress through cell-cycle stages. Our analysis further revealed cell cycle-dependent and cell cycle-independent spatial heterogeneity of transcriptionally distinct cells. We envision that the ability to perform spatially resolved, genome-wide RNA profiling with high detection efficiency and accuracy by MERFISH could help address a wide array of questions ranging from the regulation of gene expression in cells to the development of cell fate and organization in tissues.

Linking transcriptional and genetic tumor heterogeneity through allele analysis of single-cell RNA-seq data.

Characterization of intratumoral heterogeneity is critical to cancer therapy, as the presence of phenotypically diverse cell populations commonly fuels relapse and resistance to treatment. Although genetic variation is a well-studied source of intratumoral heterogeneity, the functional impact of most genetic alterations remains unclear. Even less understood is the relative importance of other factors influencing heterogeneity, such as epigenetic state or tumor microenvironment. To investigate the relationship between genetic and transcriptional heterogeneity in a context of cancer progression, we devised a computational approach called HoneyBADGER to identify copy number variation and loss of heterozygosity in individual cells from single-cell RNA-sequencing data. By integrating allele and normalized expression information, HoneyBADGER is able to identify and infer the presence of subclone-specific alterations in individual cells and reconstruct the underlying subclonal architecture. By examining several tumor types, we show that HoneyBADGER is effective at identifying deletions, amplifications, and copy-neutral loss-of-heterozygosity events and is capable of robustly identifying subclonal focal alterations as small as 10 megabases. We further apply HoneyBADGER to analyze single cells from a progressive multiple myeloma patient to identify major genetic subclones that exhibit distinct transcriptional signatures relevant to cancer progression. Other prominent transcriptional subpopulations within these tumors did not line up with the genetic subclonal structure and were likely driven by alternative, nonclonal mechanisms. These results highlight the need for integrative analysis to understand the molecular and phenotypic heterogeneity in cancer.

Integrated single-cell genetic and transcriptional analysis suggests novel drivers of chronic lymphocytic leukemia.

Intra-tumoral genetic heterogeneity has been characterized across cancers by genome sequencing of bulk tumors, including chronic lymphocytic leukemia (CLL). In order to more accurately identify subclones, define phylogenetic relationships, and probe genotype-phenotype relationships, we developed methods for targeted mutation detection in DNA and RNA isolated from thousands of single cells from five CLL samples. By clearly resolving phylogenic relationships, we uncovered mutated LCP1 and WNK1 as novel CLL drivers, supported by functional evidence demonstrating their impact on CLL pathways. Integrative analysis of somatic mutations with transcriptional states prompts the idea that convergent evolution generates phenotypically similar cells in distinct genetic branches, thus creating a cohesive expression profile in each CLL sample despite the presence of genetic heterogeneity. Our study highlights the potential for single-cell RNA-based targeted analysis to sensitively determine transcriptional and mutational profiles of individual cancer cells, leading to increased understanding of driving events in malignancy.

Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis.

The transcriptional state of a cell reflects a variety of biological factors, from cell-type-specific features to transient processes such as the cell cycle, all of which may be of interest. However, identifying such aspects from noisy single-cell RNA-seq data remains challenging. We developed pathway and gene set overdispersion analysis (PAGODA) to resolve multiple, potentially overlapping aspects of transcriptional heterogeneity by testing gene sets for coordinated variability among measured cells.

Phase I dose-escalation trial of afatinib, an irreversible ErbB family blocker, in combination with gemcitabine or docetaxel in patients with relapsed or refractory solid tumors.

Background Afatinib, an irreversible ErbB family blocker, has shown synergistic antitumor activity and manageable tolerability in combination with chemotherapy. This phase I study assessed oral afatinib plus intravenous gemcitabine or docetaxel in patients with relapsed/refractory solid tumors. Methods Patients received afatinib (30, 40, or 50 mg) plus gemcitabine (1000 or 1250 mg/m2) or docetaxel (60 or 75 mg/m2). Dose escalation proceeded via a 3 + 3 design until the maximum tolerated dose (MTD) was reached. Adverse events (AEs), pharmacokinetics and antitumor activity were also assessed. Results Dose-limiting toxicities during Cycle 1 were reported in 6/39 patients receiving afatinib/gemcitabine (most commonly diarrhea, thrombocytopenia and vomiting) and 16/54 patients receiving afatinib/docetaxel (most commonly febrile neutropenia and stomatitis). The MTDs were established as afatinib 40 mg/gemcitabine 1000 mg/m2 and afatinib 30 mg/docetaxel 60 mg/m2. The most common drug-related AEs were diarrhea, asthenia and rash with afatinib/gemcitabine, and diarrhea, asthenia and stomatitis with afatinib/docetaxel. No relevant pharmacokinetic interactions were observed for either combination. Both combinations demonstrated clinical activity and durable disease control at the MTDs. Compared with the MTD, higher response rates were achieved with afatinib 30 mg/docetaxel 75 mg/m2 (28% vs 6%); however, this regimen was associated with problematic febrile neutropenia, an expected AE with docetaxel, that is often managed with growth factor support. Conclusions Afatinib/gemcitabine and afatinib/docetaxel demonstrated manageable safety profiles, with evidence of clinical efficacy at the MTDs. For afatinib/docetaxel, a dose level of afatinib 30 mg/docetaxel 75 mg/m2 produced higher response rates. Trial registration: NCT01251653 ( ClinicalTrials.gov ).

Matching phenotypes to whole genomes: Lessons learned from four iterations of the personal genome project community challenges.

The advent of next-generation sequencing has dramatically decreased the cost for whole-genome sequencing and increased the viability for its application in research and clinical care. The Personal Genome Project (PGP) provides unrestricted access to genomes of individuals and their associated phenotypes. This resource enabled the Critical Assessment of Genome Interpretation (CAGI) to create a community challenge to assess the bioinformatics community's ability to predict traits from whole genomes. In the CAGI PGP challenge, researchers were asked to predict whether an individual had a particular trait or profile based on their whole genome. Several approaches were used to assess submissions, including ROC AUC (area under receiver operating characteristic curve), probability rankings, the number of correct predictions, and statistical significance simulations. Overall, we found that prediction of individual traits is difficult, relying on a strong knowledge of trait frequency within the general population, whereas matching genomes to trait profiles relies heavily upon a small number of common traits including ancestry, blood type, and eye color. When a rare genetic disorder is present, profiles can be matched when one or more pathogenic variants are identified. Prediction accuracy has improved substantially over the last 6 years due to improved methodology and a better understanding of features.

Evaluation of the effects of formulation and food on the pharmacokinetics of lenvatinib (E7080) in healthy volunteers.

OBJECTIVE: To evaluate the effect of formulation and a high-fat meal on the pharmacokinetics of orally administered lenvatinib (E7080). MATERIALS: Lenvatinib 10-mg capsule and tablet. METHODS: Pharmacokinetics and safety of a single 10-mg lenvatinib dose were evaluated in healthy subjects in two randomized, two-period, crossover, phase 1, bioavailability trials. The first compared a new capsule formulation with an older tablet formulation (n = 20 subjects); the second evaluated the influence of a standard high-fat meal on the relative bioavailability of the capsule formulation (n = 16 subjects). Geometric least squares mean ratios of AUC0-∞, maximum observed concentration (Cmax), and AUC0-t were determined. tmax, tlag (food effect only), and t1/2,z were also calculated, and descriptive statistics were provided. RESULTS: A total of 36 healthy volunteers were enrolled in the two studies (mean ages 29 and 33 years). In the formulation study, AUC0-∞ and AUC0-t of the capsule formulation were ~ 10% less than the tablet formulation, and Cmax for the capsule formulation was ~ 14% lower. 90% Confidence intervals (CIs) for both AUCs were within the 80 - 125% CI, which is generally considered to denote bioequivalence, while the lower bound of the interval for Cmax was 79.8%. tmax and t1/2,z were comparable. For the capsule formulation, the mean (%CV) t1/2,z was 27.6 hours (27.3) and the median (range) tmax was 2.0 hours (2 - 4). In the food effect study, lenvatinib's AUC0-∞ and AUC0-t increased ~ 6% and 4% with the high-fat meal. Cmax following a high-fat meal was 5% lower than following administration in the fasted state. Administration with food delayed lenvatinib's tmax (2 vs. 4 hours). 90% CIs for AUCs were within the 80 - 125% CI, while the CI for Cmax was 72.1 - 126.4%. The single 10-mg dose demonstrated an acceptable tolerability profile; treatment-emergent adverse events occurred in 9 subjects (25%) overall and were typically mild in severity. CONCLUSIONS: These studies show that a new capsule formulation produces slightly lower exposure (~10 - 14%) to lenvatinib compared with the original tablet formulation, and that oral administration with a high-fat meal does not significantly affect exposure, although absorption is delayed. Thus, lenvatinib can be administered without regard to the timing of meals.

Gene count normalization in single-cell imaging-based spatially resolved transcriptomics.

Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals. Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue regions or cell types, we demonstrate how normalization methods based on detected gene counts per cell differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream analyses including differential gene expression, gene fold change, and spatially variable gene analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissue's component cell types. These effects are not observed with normalization approaches that do not use detected gene counts for gene expression magnitude adjustment, such as with cell volume or cell area normalization. We recommend using non-gene count-based normalization approaches when feasible and evaluating gene panel representativeness before using gene count-based normalization methods if necessary. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data.

Gene count normalization in single-cell imaging-based spatially resolved transcriptomics.

BACKGROUND: Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals. RESULTS: Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue regions or cell types, we demonstrate how normalization methods based on detected gene counts per cell differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream analyses including differential gene expression, gene fold change, and spatially variable gene analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissue's component cell types. These effects are not observed with normalization approaches that do not use detected gene counts for gene expression magnitude adjustment, such as with cell volume or cell area normalization. CONCLUSIONS: We recommend using non-gene count-based normalization approaches when feasible and evaluating gene panel representativeness before using gene count-based normalization methods if necessary. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data.

Single-cell morphology encodes functional subtypes of senescence in aging human dermal fibroblasts.

Cellular senescence is an established driver of aging, exhibiting context-dependent phenotypes across multiple biological length-scales. Despite its mechanistic importance, profiling senescence within cell populations is challenging. This is in part due to the limitations of current biomarkers to robustly identify senescent cells across biological settings, and the heterogeneous, non-binary phenotypes exhibited by senescent cells. Using a panel of primary dermal fibroblasts, we combined live single-cell imaging, machine learning, multiple senescence induction conditions, and multiple protein-based senescence biomarkers to show the emergence of functional subtypes of senescence. Leveraging single-cell morphologies, we defined eleven distinct morphology clusters, with the abundance of cells in each cluster being dependent on the mode of senescence induction, the time post-induction, and the age of the donor. Of these eleven clusters, we identified three bona-fide senescence subtypes (C7, C10, C11), with C10 showing the strongest age-dependence across a cohort of fifty aging individuals. To determine the functional significance of these senescence subtypes, we profiled their responses to senotherapies, specifically focusing on Dasatinib + Quercetin (D+Q). Results indicated subtype-dependent responses, with senescent cells in C7 being most responsive to D+Q. Altogether, we provide a robust single-cell framework to identify and classify functional senescence subtypes with applications for next-generation senotherapy screens, and the potential to explain heterogeneous senescence phenotypes across biological settings based on the presence and abundance of distinct senescence subtypes.