Mining alternative splicing patterns in scRNA-seq data using scASfind.

Single-cell RNA-seq (scRNA-seq) is widely used for transcriptome profiling, but most analyses focus on gene-level events, with less attention devoted to alternative splicing. Here, we present scASfind, a novel computational method to allow for quantitative analysis of cell type-specific splicing events using full-length scRNA-seq data. ScASfind utilizes an efficient data structure to store the percent spliced-in value for each splicing event. This makes it possible to exhaustively search for patterns among all differential splicing events, allowing us to identify marker events, mutually exclusive events, and events involving large blocks of exons that are specific to one or more cell types.

A metabolic switch orchestrated by IL-18 and the cyclic dinucleotide cGAMP programs intestinal tolerance.

Tissues are exposed to diverse inflammatory challenges that shape future inflammatory responses. While cellular metabolism regulates immune function, how metabolism programs and stabilizes immune states within tissues and tunes susceptibility to inflammation is poorly understood. Here, we describe an innate immune metabolic switch that programs long-term intestinal tolerance. Intestinal interleukin-18 (IL-18) stimulation elicited tolerogenic macrophages by preventing their proinflammatory glycolytic polarization via metabolic reprogramming to fatty acid oxidation (FAO). FAO reprogramming was triggered by IL-18 activation of SLC12A3 (NCC), leading to sodium influx, release of mitochondrial DNA, and activation of stimulator of interferon genes (STING). FAO was maintained in macrophages by a bistable switch that encoded memory of IL-18 stimulation and by intercellular positive feedback that sustained the production of macrophage-derived 2'3'-cyclic GMP-AMP (cGAMP) and epithelial-derived IL-18. Thus, a tissue-reinforced metabolic switch encodes durable immune tolerance in the gut and may enable reconstructing compromised immune tolerance in chronic inflammation.

Single-cell analysis reveals cellular and molecular factors counteracting HPV-positive oropharyngeal cancer immunotherapy outcomes.

BACKGROUND: Oropharyngeal squamous cell carcinoma (OPSCC) induced by human papillomavirus (HPV-positive) is associated with better clinical outcomes than HPV-negative OPSCC. However, the clinical benefits of immunotherapy in patients with HPV-positive OPSCC remain unclear. METHODS: To identify the cellular and molecular factors that limited the benefits associated with HPV in OPSCC immunotherapy, we performed single-cell RNA (n=20) and T-cell receptor sequencing (n=10) analyses of tonsil or base of tongue tumor biopsies prior to immunotherapy. Primary findings from our single-cell analysis were confirmed through immunofluorescence experiments, and secondary validation analysis were performed via publicly available transcriptomics data sets. RESULTS: We found significantly higher transcriptional diversity of malignant cells among non-responders to immunotherapy, regardless of HPV infection status. We also observed a significantly larger proportion of CD4+ follicular helper T cells (Tfh) in HPV-positive tumors, potentially due to enhanced Tfh differentiation. Most importantly, CD8+ resident memory T cells (Trm) with elevated KLRB1 (encoding CD161) expression showed an association with dampened antitumor activity in patients with HPV-positive OPSCC, which may explain their heterogeneous clinical outcomes. Notably, all HPV-positive patients, whose Trm presented elevated KLRB1 levels, showed low expression of CLEC2D (encoding the CD161 ligand) in B cells, which may reduce tertiary lymphoid structure activity. Immunofluorescence of HPV-positive tumors treated with immune checkpoint blockade showed an inverse correlation between the density of CD161+ Trm and changes in tumor size. CONCLUSIONS: We found that CD161+ Trm counteracts clinical benefits associated with HPV in OPSCC immunotherapy. This suggests that targeted inhibition of CD161 in Trm could enhance the efficacy of immunotherapy in HPV-positive oropharyngeal cancers. TRIAL REGISTRATION NUMBER: NCT03737968.

ApoE ε4-dependent alteration of CXCR3 + CD127 + CD4 + T cells is associated with elevated plasma neurofilament light chain in Alzheimer's disease.

Recent findings indicate a correlation between the peripheral adaptive immune system and neuroinflammation in Alzheimer's disease (AD). To characterize the composition of adaptive immune cells in the peripheral blood of AD patients, we utilized single-cell mass cytometry (CyTOF) to profile peripheral blood mononuclear cells (PBMCs). Concurrently, we assessed the concentration of proteins associated with AD and neuroinflammation in the plasma of the same subjects. We found that the abundance of proinflammatory CXCR3 + CD127 + Type 1 T helper (Th1) cells in AD patients was negatively correlated with the abundance of neurofilament light chain (NfL) protein. This correlation is apolipoprotein E (ApoE) ε4-dependent. Analyzing public single-cell RNA-sequencing (scRNA-seq) data, we found that, contrary to the scenario in the peripheral blood, the cell frequency of CXCR3 + CD127 + Th1 cells in the cerebrospinal fluid (CSF) of AD patients was increased compared to healthy controls (HCs). Moreover, the proinflammatory capacity of CXCR3 + CD127 + Th1 cells in the CSF of AD patients was further increased compared to HCs. These results reveal an association of a peripheral T-cell change with neuroinflammation in AD and suggest that dysregulation of peripheral adaptive immune responses, particularly involving CXCR3 + CD127 + Th1 cells, may potentially be mediated by factors such as ApoE ε4 genotype. One sentence summary: An apolipoprotein E (ApoE) ε4-dependent alteration of CD4 T cell subpopulation in peripheral blood is associated with neuroinflammation in patients with Alzheimer's disease.

Demuxafy: improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods.

Recent innovations in single-cell RNA-sequencing (scRNA-seq) provide the technology to investigate biological questions at cellular resolution. Pooling cells from multiple individuals has become a common strategy, and droplets can subsequently be assigned to a specific individual by leveraging their inherent genetic differences. An implicit challenge with scRNA-seq is the occurrence of doublets-droplets containing two or more cells. We develop Demuxafy, a framework to enhance donor assignment and doublet removal through the consensus intersection of multiple demultiplexing and doublet detecting methods. Demuxafy significantly improves droplet assignment by separating singlets from doublets and classifying the correct individual.

Integrative analysis of transcriptomic and epigenomic data reveals distinct patterns for developmental and housekeeping gene regulation.

BACKGROUND: Regulation of transcription is central to the emergence of new cell types during development, and it often involves activation of genes via proximal and distal regulatory regions. The activity of regulatory elements is determined by transcription factors (TFs) and epigenetic marks, but despite extensive mapping of such patterns, the extraction of regulatory principles remains challenging. RESULTS: Here we study differentially and similarly expressed genes along with their associated epigenomic profiles, chromatin accessibility and DNA methylation, during lineage specification at gastrulation in mice. Comparison of the three lineages allows us to identify genomic and epigenomic features that distinguish the two classes of genes. We show that differentially expressed genes are primarily regulated by distal elements, while similarly expressed genes are controlled by proximal housekeeping regulatory programs. Differentially expressed genes are relatively isolated within topologically associated domains, while similarly expressed genes tend to be located in gene clusters. Transcription of differentially expressed genes is associated with differentially open chromatin at distal elements including enhancers, while that of similarly expressed genes is associated with ubiquitously accessible chromatin at promoters. CONCLUSION: Based on these associations of (linearly) distal genes' transcription start sites (TSSs) and putative enhancers for developmental genes, our findings allow us to link putative enhancers to their target promoters and to infer lineage-specific repertoires of putative driver transcription factors, within which we define subgroups of pioneers and co-operators.

Joint analysis of mutational and transcriptional landscapes in human cancer reveals key perturbations during cancer evolution.

BACKGROUND: Tumors are able to acquire new capabilities, including traits such as drug resistance and metastasis that are associated with unfavorable clinical outcomes. Single-cell technologies have made it possible to study both mutational and transcriptomic profiles, but as most studies have been conducted on model systems, little is known about cancer evolution in human patients. Hence, a better understanding of cancer evolution could have important implications for treatment strategies. RESULTS: Here, we analyze cancer evolution and clonal selection by jointly considering mutational and transcriptomic profiles of single cells acquired from tumor biopsies from 49 lung cancer samples and 51 samples with chronic myeloid leukemia. Comparing the two profiles, we find that each clone is associated with a preferred transcriptional state. For metastasis and drug resistance, we find that the number of mutations affecting related genes increases as the clone evolves, while changes in gene expression profiles are limited. Surprisingly, we find that mutations affecting ligand-receptor interactions with the tumor microenvironment frequently emerge as clones acquire drug resistance. CONCLUSIONS: Our results show that lung cancer and chronic myeloid leukemia maintain a high clonal and transcriptional diversity, and we find little evidence in favor of clonal sweeps. This suggests that for these cancers selection based solely on growth rate is unlikely to be the dominating driving force during cancer evolution.

MEA-NAP compares microscale functional connectivity, topology, and network dynamics in organoid or monolayer neuronal cultures.

Microelectrode array (MEA) recordings are commonly used to compare firing and burst rates in neuronal cultures. MEA recordings can also reveal microscale functional connectivity, topology, and network dynamics-patterns seen in brain networks across spatial scales. Network topology is frequently characterized in neuroimaging with graph theoretical metrics. However, few computational tools exist for analyzing microscale functional brain networks from MEA recordings. Here, we present a MATLAB MEA network analysis pipeline (MEA-NAP) for raw voltage time-series acquired from single- or multi-well MEAs. Applications to 3D human cerebral organoids or 2D human-derived or murine cultures reveal differences in network development, including topology, node cartography, and dimensionality. MEA-NAP incorporates multi-unit template-based spike detection, probabilistic thresholding for determining significant functional connections, and normalization techniques for comparing networks. MEA-NAP can identify network-level effects of pharmacologic perturbation and/or disease-causing mutations and, thus, can provide a translational platform for revealing mechanistic insights and screening new therapeutic approaches.

Single-cell and spatial transcriptomics: Bridging current technologies with long-read sequencing.

Single-cell technologies have transformed biomedical research over the last decade, opening up new possibilities for understanding cellular heterogeneity, both at the genomic and transcriptomic level. In addition, more recent developments of spatial transcriptomics technologies have made it possible to profile cells in their tissue context. In parallel, there have been substantial advances in sequencing technologies, and the third generation of methods are able to produce reads that are tens of kilobases long, with error rates matching the second generation short reads. Long reads technologies make it possible to better map large genome rearrangements and quantify isoform specific abundances. This further improves our ability to characterize functionally relevant heterogeneity. Here, we show how researchers have begun to combine single-cell, spatial transcriptomics, and long-read technologies, and how this is resulting in powerful new approaches to profiling both the genome and the transcriptome. We discuss the achievements so far, and we highlight remaining challenges and opportunities.

Immune-restoring CAR-T cells display antitumor activity and reverse immunosuppressive TME in a humanized ccRCC mouse model.

One of the major barriers that have restricted successful use of chimeric antigen receptor (CAR) T cells in the treatment of solid tumors is an unfavorable tumor microenvironment (TME). We engineered CAR-T cells targeting carbonic anhydrase IX (CAIX) to secrete anti-PD-L1 monoclonal antibody (mAb), termed immune-restoring (IR) CAR G36-PDL1. We tested CAR-T cells in a humanized clear cell renal cell carcinoma (ccRCC) orthotopic mouse model with reconstituted human leukocyte antigen (HLA) partially matched human leukocytes derived from fetal CD34+ hematopoietic stem cells (HSCs) and bearing human ccRCC skrc-59 cells under the kidney capsule. G36-PDL1 CAR-T cells, haploidentical to the tumor cells, had a potent antitumor effect compared to those without immune-restoring effect. Analysis of the TME revealed that G36-PDL1 CAR-T cells restored active antitumor immunity by promoting tumor-killing cytotoxicity, reducing immunosuppressive cell components such as M2 macrophages and exhausted CD8+ T cells, and enhancing T follicular helper (Tfh)-B cell crosstalk.

Integrative single-cell characterization of a frugivorous and an insectivorous bat kidney and pancreas.

Frugivory evolved multiple times in mammals, including bats. However, the cellular and molecular components driving it remain largely unknown. Here, we use integrative single-cell sequencing (scRNA-seq and scATAC-seq) on insectivorous (Eptesicus fuscus; big brown bat) and frugivorous (Artibeus jamaicensis; Jamaican fruit bat) bat kidneys and pancreases and identify key cell population, gene expression and regulatory differences associated with the Jamaican fruit bat that also relate to human disease, particularly diabetes. We find a decrease in loop of Henle and an increase in collecting duct cells, and differentially active genes and regulatory elements involved in fluid and electrolyte balance in the Jamaican fruit bat kidney. The Jamaican fruit bat pancreas shows an increase in endocrine and a decrease in exocrine cells, and differences in genes and regulatory elements involved in insulin regulation. We also find that these frugivorous bats share several molecular characteristics with human diabetes. Combined, our work provides insights from a frugivorous mammal that could be leveraged for therapeutic purposes.

Decoupling NAD+ metabolic dependency in chondrosarcoma by targeting the SIRT1-HIF-2α axis.

Chondrosarcomas represent the second most common primary bone malignancy. Despite the vulnerability of chondrosarcoma cells to nicotinamide adenine dinucleotide (NAD+) depletion, targeting the NAD+ synthesis pathway remains challenging due to broad implications in biological processes. Here, we establish SIRT1 as a central mediator reinforcing the dependency of chondrosarcoma cells on NAD+ metabolism via HIF-2α-mediated transcriptional reprogramming. SIRT1 knockdown abolishes aggressive phenotypes of chondrosarcomas in orthotopically transplanted tumors in mice. Chondrosarcoma cells thrive under glucose starvation by accumulating NAD+ and subsequently activating the SIRT1-HIF-2α axis. Decoupling this link via SIRT1 inhibition unleashes apoptosis and suppresses tumor progression in conjunction with chemotherapy. Unsupervised clustering analysis identifies a high-risk chondrosarcoma patient subgroup characterized by the upregulation of NAD+ biosynthesis genes. Finally, SIRT1 inhibition abolishes HIF-2α transcriptional activity and sensitizes chondrosarcoma cells to doxorubicin-induced cytotoxicity, irrespective of underlying pathways to accumulate intracellular NAD+. We provide system-level guidelines to develop therapeutic strategies for chondrosarcomas.

Predicting the impact of sequence motifs on gene regulation using single-cell data.

The binding of transcription factors at proximal promoters and distal enhancers is central to gene regulation. Identifying regulatory motifs and quantifying their impact on expression remains challenging. Using a convolutional neural network trained on single-cell data, we infer putative regulatory motifs and cell type-specific importance. Our model, scover, explains 29% of the variance in gene expression in multiple mouse tissues. Applying scover to distal enhancers identified using scATAC-seq from the developing human brain, we identify cell type-specific motif activities in distal enhancers. Scover can identify regulatory motifs and their importance from single-cell data where all parameters and outputs are easily interpretable.

Therapy-associated remodeling of pancreatic cancer revealed by single-cell spatial transcriptomics and optimal transport analysis.

In combination with cell intrinsic properties, interactions in the tumor microenvironment modulate therapeutic response. We leveraged high-plex single-cell spatial transcriptomics to dissect the remodeling of multicellular neighborhoods and cell-cell interactions in human pancreatic cancer associated with specific malignant subtypes and neoadjuvant chemotherapy/radiotherapy. We developed Spatially Constrained Optimal Transport Interaction Analysis (SCOTIA), an optimal transport model with a cost function that includes both spatial distance and ligand-receptor gene expression. Our results uncovered a marked change in ligand-receptor interactions between cancer-associated fibroblasts and malignant cells in response to treatment, which was supported by orthogonal datasets, including an ex vivo tumoroid co-culture system. Overall, this study demonstrates that characterization of the tumor microenvironment using high-plex single-cell spatial transcriptomics allows for identification of molecular interactions that may play a role in the emergence of chemoresistance and establishes a translational spatial biology paradigm that can be broadly applied to other malignancies, diseases, and treatments.

Quasi-prime peptides: identification of the shortest peptide sequences unique to a species.

Determining the organisms present in a biosample has many important applications in agriculture, wildlife conservation, and healthcare. Here, we develop a universal fingerprint based on the identification of short peptides that are unique to a specific organism. We define quasi-prime peptides as sequences that are found in only one species, and we analyzed proteomes from 21 875 species, from viruses to humans, and annotated the smallest peptide kmer sequences that are unique to a species and absent from all other proteomes. We also perform simulations across all reference proteomes and observe a lower than expected number of peptide kmers across species and taxonomies, indicating an enrichment for nullpeptides, sequences absent from a proteome. For humans, we find that quasi-primes are found in genes enriched for specific gene ontology terms, including proteasome and ATP and GTP catalysis. We also provide a set of quasi-prime peptides for a number of human pathogens and model organisms and further showcase its utility via two case studies for Mycobacterium tuberculosis and Vibrio cholerae, where we identify quasi-prime peptides in two transmembrane and extracellular proteins with relevance for pathogen detection. Our catalog of quasi-prime peptides provides the smallest unit of information that is specific to a single organism at the protein level, providing a versatile tool for species identification.

Integrative single-cell characterization of frugivory adaptations in the bat kidney and pancreas.

Frugivory evolved multiple times in mammals, including bats. However, the cellular and molecular components driving it remain largely unknown. Here, we used integrative single-cell sequencing on insectivorous and frugivorous bat kidneys and pancreases and identified key cell population, gene expression and regulatory element differences associated with frugivorous adaptation that also relate to human disease, particularly diabetes. We found an increase in collecting duct cells and differentially active genes and regulatory elements involved in fluid and electrolyte balance in the frugivore kidney. In the frugivorous pancreas, we observed an increase in endocrine and a decrease in exocrine cells and differences in genes and regulatory elements involved in insulin regulation. Combined, our work provides novel insights into frugivorous adaptation that also could be leveraged for therapeutic purposes.

scHumanNet: a single-cell network analysis platform for the study of cell-type specificity of disease genes.

A major challenge in single-cell biology is identifying cell-type-specific gene functions, which may substantially improve precision medicine. Differential expression analysis of genes is a popular, yet insufficient approach, and complementary methods that associate function with cell type are required. Here, we describe scHumanNet (https://github.com/netbiolab/scHumanNet), a single-cell network analysis platform for resolving cellular heterogeneity across gene functions in humans. Based on cell-type-specific gene networks (CGNs) constructed under the guidance of the HumanNet reference interactome, scHumanNet displayed higher functional relevance to the cellular context than CGNs built by other methods on single-cell transcriptome data. Cellular deconvolution of gene signatures based on network compactness across cell types revealed breast cancer prognostic markers associated with T cells. scHumanNet could also prioritize genes associated with particular cell types using CGN centrality and identified the differential hubness of CGNs between disease and healthy conditions. We demonstrated the usefulness of scHumanNet by uncovering T-cell-specific functional effects of GITR, a prognostic gene for breast cancer, and functional defects in autism spectrum disorder genes specific for inhibitory neurons. These results suggest that scHumanNet will advance our understanding of cell-type specificity across human disease genes.

SC3s: efficient scaling of single cell consensus clustering to millions of cells.

BACKGROUND: Today it is possible to profile the transcriptome of individual cells, and a key step in the analysis of these datasets is unsupervised clustering. For very large datasets, efficient algorithms are required to ensure that analyses can be conducted with reasonable time and memory requirements. RESULTS: Here, we present a highly efficient k-means based approach, and we demonstrate that it scales favorably with the number of cells with regards to time and memory. CONCLUSIONS: We have demonstrated that our streaming k-means clustering algorithm gives state-of-the-art performance while resource requirements scale favorably for up to 2 million cells.

Production of human entorhinal stellate cell-like cells by forward programming shows an important role of Foxp1 in reprogramming.

Stellate cells are principal neurons in the entorhinal cortex that contribute to spatial processing. They also play a role in the context of Alzheimer's disease as they accumulate Amyloid beta early in the disease. Producing human stellate cells from pluripotent stem cells would allow researchers to study early mechanisms of Alzheimer's disease, however, no protocols currently exist for producing such cells. In order to develop novel stem cell protocols, we characterize at high resolution the development of the porcine medial entorhinal cortex by tracing neuronal and glial subtypes from mid-gestation to the adult brain to identify the transcriptomic profile of progenitor and adult stellate cells. Importantly, we could confirm the robustness of our data by extracting developmental factors from the identified intermediate stellate cell cluster and implemented these factors to generate putative intermediate stellate cells from human induced pluripotent stem cells. Six transcription factors identified from the stellate cell cluster including RUNX1T1, SOX5, FOXP1, MEF2C, TCF4, EYA2 were overexpressed using a forward programming approach to produce neurons expressing a unique combination of RELN, SATB2, LEF1 and BCL11B observed in stellate cells. Further analyses of the individual transcription factors led to the discovery that FOXP1 is critical in the reprogramming process and omission of RUNX1T1 and EYA2 enhances neuron conversion. Our findings contribute not only to the profiling of cell types within the developing and adult brain's medial entorhinal cortex but also provides proof-of-concept for using scRNAseq data to produce entorhinal intermediate stellate cells from human pluripotent stem cells in-vitro.