Spatiotemporal lineage tracing reveals the dynamic spatial architecture of tumor growth and metastasis.

Tumor progression is driven by dynamic interactions between cancer cells and their surrounding microenvironment. Investigating the spatiotemporal evolution of tumors can provide crucial insights into how intrinsic changes within cancer cells and extrinsic alterations in the microenvironment cooperate to drive different stages of tumor progression. Here, we integrate high-resolution spatial transcriptomics and evolving lineage tracing technologies to elucidate how tumor expansion, plasticity, and metastasis co-evolve with microenvironmental remodeling in a -driven mouse model of lung adenocarcinoma.

Slide-tags enables single-nucleus barcoding for multimodal spatial genomics.

Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed. However, missing from these measurements is the ability to routinely and easily spatially localize these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are tagged with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions.

Benchmarking long-read RNA-sequencing analysis tools using in silico mixtures.

The lack of benchmark data sets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform detection and differential expression analysis workflows. Here, we present a benchmark experiment using two human lung adenocarcinoma cell lines that were each profiled in triplicate together with synthetic, spliced, spike-in RNAs (sequins). Samples were deeply sequenced on both Illumina short-read and Oxford Nanopore Technologies long-read platforms.

Spatiotemporal transcriptomic maps of whole mouse embryos at the onset of organogenesis.

Spatiotemporal orchestration of gene expression is required for proper embryonic development. The use of single-cell technologies has begun to provide improved resolution of early regulatory dynamics, including detailed molecular definitions of most cell states during mouse embryogenesis. Here we used Slide-seq to build spatial transcriptomic maps of complete embryonic day (E) 8.

A posterior probability based Bayesian method for single-cell RNA-seq data imputation.

Single-cell RNA-sequencing (scRNA-seq) data suffer from a lot of zeros. Such dropout events impede the downstream data analyses. We propose BayesImpute to infer and impute dropouts from the scRNA-seq data.

Slide-tags: scalable, single-nucleus barcoding for multi-modal spatial genomics.

Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed. Missing from these measurements, however, is the ability to routinely and easily spatially localise these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are 'tagged' with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions.

The expanding vistas of spatial transcriptomics.

The formation and maintenance of tissue integrity requires complex, coordinated activities by thousands of genes and their encoded products. Until recently, transcript levels could only be quantified for a few genes in tissues, but advances in DNA sequencing, oligonucleotide synthesis and fluorescence microscopy have enabled the invention of a suite of spatial transcriptomics technologies capable of measuring the expression of many, or all, genes in situ. These technologies have evolved rapidly in sensitivity, multiplexing and throughput.

Single-cell multiomics reveal the scale of multilayered adaptations enabling CLL relapse during venetoclax therapy.

Venetoclax (VEN) inhibits the prosurvival protein BCL2 to induce apoptosis and is a standard therapy for chronic lymphocytic leukemia (CLL), delivering high complete remission rates and prolonged progression-free survival in relapsed CLL but with eventual loss of efficacy. A spectrum of subclonal genetic changes associated with VEN resistance has now been described. To fully understand clinical resistance to VEN, we combined single-cell short- and long-read RNA-sequencing to reveal the previously unappreciated scale of genetic and epigenetic changes underpinning acquired VEN resistance.

Benchmarking UMI-based single-cell RNA-seq preprocessing workflows.

Background: Single-cell RNA-sequencing (scRNA-seq) technologies and associated analysis methods have rapidly developed in recent years. This includes preprocessing methods, which assign sequencing reads to genes to create count matrices for downstream analysis. While several packaged preprocessing workflows have been developed to provide users with convenient tools for handling this process, how they compare to one another and how they influence downstream analysis have not been well studied.

Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing.

A modified Chromium 10x droplet-based protocol that subsamples cells for both short-read and long-read (nanopore) sequencing together with a new computational pipeline (FLAMES) is developed to enable isoform discovery, splicing analysis, and mutation detection in single cells. We identify thousands of unannotated isoforms and find conserved functional modules that are enriched for alternative transcript usage in different cell types and species, including ribosome biogenesis and mRNA splicing. Analysis at the transcript level allows data integration with scATAC-seq on individual promoters, improved correlation with protein expression data, and linked mutations known to confer drug resistance to transcriptome heterogeneity.

Modulation of Vagal Sensory Neurons High Mobility Group Box-1 and Receptor for Advanced Glycation End Products: Implications for Respiratory Viral Infections.

Vagal sensory neurons contribute to the symptoms and pathogenesis of inflammatory pulmonary diseases through processes that involve changes to their morphological and functional characteristics. The alarmin high mobility group box-1 (HMGB1) is an early mediator of pulmonary inflammation and can have actions on neurons in a range of inflammatory settings. We hypothesized that HMGB1 can regulate the growth and function of vagal sensory neurons and we set out to investigate this and the mechanisms involved.

Single-cell immune profiling reveals distinct immune response in asymptomatic COVID-19 patients.

While some individuals infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) present mild-to-severe disease, many SARS-CoV-2-infected individuals are asymptomatic. We sought to identify the distinction of immune response between asymptomatic and moderate patients. We performed single-cell transcriptome and T-cell/B-cell receptor (TCR/BCR) sequencing in 37 longitudinal collected peripheral blood mononuclear cell samples from asymptomatic, moderate, and severe patients with healthy controls.

The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools.

Application of Oxford Nanopore Technologies' long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information.

Clonal multi-omics reveals Bcor as a negative regulator of emergency dendritic cell development.

Despite advances in single-cell multi-omics, a single stem or progenitor cell can only be tested once. We developed clonal multi-omics, in which daughters of a clone act as surrogates of the founder, thereby allowing multiple independent assays per clone. With SIS-seq, clonal siblings in parallel "sister" assays are examined either for gene expression by RNA sequencing (RNA-seq) or for fate in culture.

Single-cell analyses reveal the clonal and molecular aetiology of Flt3L-induced emergency dendritic cell development.

Regulation of haematopoietic stem and progenitor cell (HSPC) fate is crucial during homeostasis and under stress conditions. Here we examine the aetiology of the Flt3 ligand (Flt3L)-mediated increase of type 1 conventional dendritic cells (cDC1s). Using cellular barcoding we demonstrate this occurs through selective clonal expansion of HSPCs that are primed to produce cDC1s and not through activation of cDC1 fate by other HSPCs.

A new lymphoid-primed progenitor marked by Dach1 downregulation identified with single cell multi-omics.

A classical view of blood cell development is that multipotent hematopoietic stem and progenitor cells (HSPCs) become lineage-restricted at defined stages. Linc-KitSca-1Flt3 cells, termed lymphoid-primed multipotent progenitors (LMPPs), have lost megakaryocyte and erythroid potential but are heterogeneous in their fate. Here, through single-cell RNA sequencing, we identify the expression of Dach1 and associated genes in this fraction as being coexpressed with myeloid/stem genes but inversely correlated with lymphoid genes.

CellBench: R/Bioconductor software for comparing single-cell RNA-seq analysis methods.

Motivation: Bioinformatic analysis of single-cell gene expression data is a rapidly evolving field. Hundreds of bespoke methods have been developed in the past few years to deal with various aspects of single-cell analysis and consensus on the most appropriate methods to use under different settings is still emerging. Benchmarking the many methods is therefore of critical importance and since analysis of single-cell data usually involves multi-step pipelines, effective evaluation of pipelines involving different combinations of methods is required.

Transcriptional Profiling of Individual Airway Projecting Vagal Sensory Neurons.

Bronchopulmonary sensory neurons are derived from the vagal sensory ganglia and are essential for monitoring the physical and chemical environment of the airways and lungs. Subtypes are heterogenous in their responsiveness to stimuli, phenotype, and developmental origin, but they collectively serve to regulate normal respiratory and pulmonary processes and elicit a diverse range of defensive physiological responses that protect against noxious stimuli. In this study, we aimed to investigate the transcriptional features of vagal bronchopulmonary sensory neurons using single-cell RNA sequencing (scRNA-seq) to provide a deeper insight into their molecular profiles.

Interconversion between Tumorigenic and Differentiated States in Acute Myeloid Leukemia.

Tumors are composed of phenotypically heterogeneous cancer cells that often resemble various differentiation states of their lineage of origin. Within this hierarchy, it is thought that an immature subpopulation of tumor-propagating cancer stem cells (CSCs) differentiates into non-tumorigenic progeny, providing a rationale for therapeutic strategies that specifically eradicate CSCs or induce their differentiation. The clinical success of these approaches depends on CSC differentiation being unidirectional rather than reversible, yet this question remains unresolved even in prototypically hierarchical malignancies, such as acute myeloid leukemia (AML).

Targeting enhancer switching overcomes non-genetic drug resistance in acute myeloid leukaemia.

Non-genetic drug resistance is increasingly recognised in various cancers. Molecular insights into this process are lacking and it is unknown whether stable non-genetic resistance can be overcome. Using single cell RNA-sequencing of paired drug naïve and resistant AML patient samples and cellular barcoding in a unique mouse model of non-genetic resistance, here we demonstrate that transcriptional plasticity drives stable epigenetic resistance.

Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments.

Single cell RNA-sequencing (scRNA-seq) technology has undergone rapid development in recent years, leading to an explosion in the number of tailored data analysis methods. However, the current lack of gold-standard benchmark datasets makes it difficult for researchers to systematically compare the performance of the many methods available. Here, we generated a realistic benchmark experiment that included single cells and admixtures of cells or RNA to create 'pseudo cells' from up to five distinct cancer cell lines.

Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data.

 The commercially available 10x Genomics protocol to generate droplet-based single cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method to use.