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Most genes in higher eukaryotes express isoforms with distinct 3’ untranslated regions (3’ UTRs), generated by alternative polyadenylation (APA). Since 3’ UTRs are predominant locations of post-transcriptional regulation, APA can render such programs conditional, and can also alter protein sequences via alternative last exon (ALE) isoforms. We previously used 3’-sequencing from diverse Drosophila samples to define multiple tissue-specific APA landscapes. Here, we exploit comprehensive single nucleus RNA-sequencing data (Fly Cell Atlas) to elucidate cell-type expression of 3’ UTRs across >250 adult Drosophila cell types. We reveal the cellular bases of multiple tissue-specific APA/ALE programs, such as 3’ UTR lengthening in differentiated neurons and 3’ UTR shortening in spermatocytes and spermatids. We trace dynamic 3’ UTR patterns across cell lineages, including in the male germline, and discover new APA patterns in the intestinal stem cell lineage. Finally, we correlate expression of RNA binding proteins (RBPs), miRNAs and global levels of cleavage and polyadenylation (CPA) factors in several cell types that exhibit characteristic APA landscapes, yielding candidate regulators of transcriptome complexity. These analyses provide a comprehensive foundation for future investigations of mechanisms and biological impacts of alternative 3’ isoforms across the major cell types of this widely-studied model organism. Single cell data provides cellular resolution on gene expression, but is rarely mined for isoforms. Analysis of 3' isoforms across ~250 Drosophila cell types reveals the cellular bases for numerous tissue-specific 3' programs, identifies new 3' programs, and nominates candidate trans-acting factors

Background The sequence content of the 3′ UTRs of many mRNA transcripts is regulated through alternative polyadenylation (APA). The study of this process using RNAseq data, though, has been historically challenging. Results To combat this problem, we developed LABRAT, an APA isoform quantification method. LABRAT takes advantage of newly developed transcriptome quantification techniques to accurately determine relative APA site usage and how it varies across conditions. Using LABRAT, we found consistent relationships between gene-distal APA and subcellular RNA localization in multiple cell types. We also observed connections between transcription speed and APA site choice as well as tumor-specific transcriptome-wide shifts in APA isoform abundance in hundreds of patient-derived tumor samples that were associated with patient prognosis. We investigated the effects of APA on transcript expression and found a weak overall relationship, although many individual genes showed strong correlations between relative APA isoform abundance and overall gene expression. We interrogated the roles of 191 RNA-binding proteins in the regulation of APA isoforms, finding that dozens promote broad, directional shifts in relative APA isoform abundance both in vitro and in patient-derived samples. Finally, we find that APA site shifts in the two classes of APA, tandem UTRs and alternative last exons, are strongly correlated across many contexts, suggesting that they are coregulated. Conclusions We conclude that LABRAT has the ability to accurately quantify APA isoform ratios from RNAseq data across a variety of sample types. Further, LABRAT is able to derive biologically meaningful insights that connect APA isoform regulation to cellular and molecular phenotypes.

Assignment of cell types from single-cell RNA sequencing (scRNA-seq) data remains a time-consuming and error-prone process. Current packages for identity assignment use limited types of reference data and often have rigid data structure requirements. We developed the clustifyr R package to leverage several external data types, including gene expression profiles to assign likely cell types using data from scRNA-seq, bulk RNA-seq, microarray expression data, or signature gene lists. We benchmark various parameters of a correlation-based approach and implement gene list enrichment methods. clustifyr is a lightweight and effective cell-type assignment tool developed for compatibility with various scRNA-seq analysis workflows. clustifyr is publicly available at https://github.com/rnabioco/clustifyr

Liver lymphatic vessels support liver function by draining interstitial fluid, cholesterol, fat, and immune cells for surveillance in the liver draining lymph node. Chronic liver disease is associated with increased inflammation and immune cell infiltrate. However, it is currently unknown if or how lymphatic vessels respond to increased inflammation and immune cell infiltrate in the liver during chronic disease. Here we demonstrate that lymphatic vessel abundance increases in patients with chronic liver disease and is associated with areas of fibrosis and immune cell infiltration. Using single-cell mRNA sequencing and multi-spectral immunofluorescence analysis we identified liver lymphatic endothelial cells and found that chronic liver disease results in lymphatic endothelial cells (LECs) that are in active cell cycle with increased expression of CCL21. Additionally, we found that LECs from patients with NASH adopt a transcriptional program associated with increased IL13 signaling. Moreover, we found that oxidized low density lipoprotein, associated with NASH pathogenesis, induced the transcription and protein production of IL13 in LECs both and in a mouse model. Finally, we show that oxidized low density lipoprotein reduced the transcription of and decreased lymphatic stability. Together these data indicate that LECs are active participants in the liver, expanding in an attempt to maintain tissue homeostasis. However, when inflammatory signals, such as oxidized low density lipoprotein are increased, as in NASH, lymphatic function declines and liver homeostasis is impeded.

Prevailing poly(dT)-primed 3′ single-cell RNA-seq protocols generate barcoded cDNA fragments containing the reverse transcriptase priming site or in principle the polyadenylation site. Direct sequencing across this site was historically difficult because of DNA sequencing errors induced by the homopolymeric primer at the 'barcode' end. Here, we evaluate the capability of 'avidity base chemistry' DNA sequencing from Element Biosciences to sequence through the primer and enable accurate paired-end read alignment and precise quantification of polyadenylation sites. We find that the Element Aviti instrument sequences through the thymine homopolymer into the subsequent cDNA sequence without detectable loss of accuracy. The additional sequence enables direct and independent assignment of reads to polyadenylation sites, which bypasses the complexities and limitations of conventional approaches but does not consistently improve read mapping rates compared to single-end alignment. We also characterize low-level artifacts and demonstrate necessary adjustments to adapter trimming and sequence alignment regardless of platform, particularly in the context of extended read lengths. Our analyses confirm that Element avidity sequencing is an effective alternative to Illumina sequencing for standard single-cell RNA-seq, particularly for polyadenylation site measurement but do not rule out the potential for similar performance from other emerging platforms.

TP53 is mutated in 50% of all cancers, and its function is often compromised in cancers where it is not mutated. Here we demonstrate that the pro-tumorigenic/metastatic Six1 homeoprotein decreases p53 levels through a mechanism that does not involve the negative regulator of p53, MDM2. Instead, Six1 regulates p53 via a dual mechanism involving upregulation of microRNA-27a and downregulation of ribosomal protein L26 (RPL26). Mutation analysis confirms that RPL26 inhibits miR-27a binding and prevents microRNA-mediated downregulation of p53. The clinical relevance of this interaction is underscored by the finding that Six1 expression strongly correlates with decreased RPL26 across numerous tumour types. Importantly, we find that Six1 expression leads to marked resistance to therapies targeting the p53–MDM2 interaction. Thus, we identify a competitive mechanism of p53 regulation, which may have consequences for drugs aimed at reinstating p53 function in tumours. p53 is a tumour suppressor that is mutated in a large number of cancers and its expression is controlled largely by the ubiquitin ligase MDM2. Here, the authors show that the homeoprotein, Six1, can regulate p53 in an MDM2- independent manner via regulation of miR-27a and the RNA binding protein, RPL26.

Acute myeloid leukemia (AML) is the most common acute leukemia in adults. Leukemia stem cells (LSCs) drive the initiation and perpetuation of AML, are quantifiably associated with worse clinical outcomes, and often persist after conventional chemotherapy resulting in relapse 1 – 5 . In this report, we show that treatment of older patients with AML with the B cell lymphoma 2 (BCL-2) inhibitor venetoclax in combination with azacitidine results in deep and durable remissions and is superior to conventional treatments. We hypothesized that these promising clinical results were due to targeting LSCs. Analysis of LSCs from patients undergoing treatment with venetoclax + azacitidine showed disruption of the tricarboxylic acid (TCA) cycle manifested by decreased α-ketoglutarate and increased succinate levels, suggesting inhibition of electron transport chain complex II. In vitro modeling confirmed inhibition of complex II via reduced glutathionylation of succinate dehydrogenase. These metabolic perturbations suppress oxidative phosphorylation (OXPHOS), which efficiently and selectively targets LSCs. Our findings show for the first time that a therapeutic intervention can eradicate LSCs in patients with AML by disrupting the metabolic machinery driving energy metabolism, resulting in promising clinical activity in a patient population with historically poor outcomes. Targeting of mitochondrial metabolism in combination with BCL-2 inhibition eradicates leukemia stem cells and induces long-lasting responses in patients with acute myeloid leukemia.

While robust tools exist for the analysis of single-cell datasets in both Python and R, interoperability is limited, and analysis tools generally only accept one object class. Considerable programming expertise is required to integrate tools across package ecosystems into a comprehensive analysis, due to their differing languages and internal data structures. This complicates validation of results and leads to inconsistent visualizations between analysis suites. Conversion between object formats is the most common solution, but this is difficult and error-prone due to the rapid pace of development of the analysis suites and their underlying data structures. To address this, we created SCUBA (Single-Cell Unified Backend API), an R package that implements a unified data access API for all common R and Python single-cell object formats. SCUBA extends the data access approach from the widely used Seurat package to SingleCellExperiment and anndata objects. SCUBA also implements new data-specific access functions for all supported object types. Performance scales well across all SCUBA-supported formats. In addition to performance, SCUBA offers several advantages over object conversion for the visualization and further analysis of pre-processed single-cell data. First, SCUBA extracts only data required for the operation at hand, leaving the original object unmodified. This process is simpler, less error prone, and less memory intensive than object conversion, which operates on the entire dataset. Second, code written with SCUBA can use any supported object class as input, with simple and consistent syntax across object formats. This allows a single analysis script or package (like our interactive single-cell browser, scExploreR) to work seamlessly with multiple object types, reducing the complexity of the code and improving both readability and reproducibility. Adoption of SCUBA will ultimately improve collaboration and reproducible research in single-cell analysis by lowering the barriers between package ecosystems.

Venetoclax with azacitidine (ven/aza) has emerged as a promising regimen for acute myeloid leukemia (AML), with a high percentage of clinical remissions in newly diagnosed patients. However, approximately 30% of newly diagnosed and the majority of relapsed patients do not achieve remission with ven/aza. We previously reported that ven/aza efficacy is based on eradication of AML stem cells through a mechanism involving inhibition of amino acid metabolism, a process which is required in primitive AML cells to drive oxidative phosphorylation. Herein we demonstrate that resistance to ven/aza occurs via up-regulation of fatty acid oxidation (FAO), which occurs due to RAS pathway mutations, or as a compensatory adaptation in relapsed disease. Utilization of FAO obviates the need for amino acid metabolism, thereby rendering ven/aza ineffective. Pharmacological inhibition of FAO restores sensitivity to ven/aza in drug resistant AML cells. We propose inhibition of FAO as a therapeutic strategy to address ven/aza resistance.

Analysis of the human hematopoietic progenitor compartment is being transformed by single-cell multimodal approaches. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) enables coupled surface protein and transcriptome profiling, thereby revealing genomic programs underlying progenitor states. To perform CITE-seq systematically on primary human bone marrow cells, we used titrations with 266 CITE-seq antibodies (antibody-derived tags) and machine learning to optimize a panel of 132 antibodies. Multimodal analysis resolved >80 stem, progenitor, immune, stromal and transitional cells defined by distinctive surface markers and transcriptomes. This dataset enables flow cytometry solutions for in silico-predicted cell states and identifies dozens of cell surface markers consistently detected across donors spanning race and sex. Finally, aligning annotations from this atlas, we nominate normal marrow equivalents for acute myeloid leukemia stem cell populations that differ in clinical response. This atlas serves as an advanced digital resource for hematopoietic progenitor analyses in human health and disease. In this Resource article, the authors integrate genomic, bioinformatic and flow cytometric data from human bone marrow to provide an atlas of hematopoietic progenitor cell states in health and disease.