Opposing Functions of Interferon Coordinate Adaptive and Innate Immune Responses to Cancer Immune Checkpoint Blockade.

Interferon-gamma (IFNG) augments immune function yet promotes T cell exhaustion through PDL1. How these opposing effects are integrated to impact immune checkpoint blockade (ICB) is unclear. We show that while inhibiting tumor IFNG signaling decreases interferon-stimulated genes (ISGs) in cancer cells, it increases ISGs in immune cells by enhancing IFNG produced by exhausted T cells (TEX). In tumors with favorable antigenicity, these TEX mediate rejection. In tumors with neoantigen or MHC-I loss, TEX instead utilize IFNG to drive maturation of innate immune cells, including a PD1+TRAIL+ ILC1 population. By disabling an inhibitory circuit impacting PD1 and TRAIL, blocking tumor IFNG signaling promotes innate immune killing. Thus, interferon signaling in cancer cells and immune cells oppose each other to establish a regulatory relationship that limits both adaptive and innate immune killing. In melanoma and lung cancer patients, perturbation of this relationship is associated with ICB response independent of tumor mutational burden.

The interferon-stimulated gene RIPK1 regulates cancer cell intrinsic and extrinsic resistance to immune checkpoint blockade.

Interferon-gamma (IFN-γ) has pleiotropic effects on cancer immune checkpoint blockade (ICB), including roles in ICB resistance. We analyzed gene expression in ICB-sensitive versus ICB-resistant tumor cells and identified a strong association between interferon-mediated resistance and expression of Ripk1, a regulator of tumor necrosis factor (TNF) superfamily receptors. Genetic interaction screening revealed that in cancer cells, RIPK1 diverted TNF signaling through NF-κB and away from its role in cell death. This promoted an immunosuppressive chemokine program by cancer cells, enhanced cancer cell survival, and decreased infiltration of T and NK cells expressing TNF superfamily ligands. Deletion of RIPK1 in cancer cells compromised chemokine secretion, decreased ARG1+ suppressive myeloid cells linked to ICB failure in mice and humans, and improved ICB response driven by CASP8-killing and dependent on T and NK cells. RIPK1-mediated resistance required its ubiquitin scaffolding but not kinase function. Thus, cancer cells co-opt RIPK1 to promote cell-intrinsic and cell-extrinsic resistance to immunotherapy.

A nuclear pyruvate dehydrogenase complex is important for the generation of acetyl-CoA and histone acetylation.

DNA transcription, replication, and repair are regulated by histone acetylation, a process that requires the generation of acetyl-coenzyme A (CoA). Here, we show that all the subunits of the mitochondrial pyruvate dehydrogenase complex (PDC) are also present and functional in the nucleus of mammalian cells. We found that knockdown of nuclear PDC in isolated functional nuclei decreased the de novo synthesis of acetyl-CoA and acetylation of core histones. Nuclear PDC levels increased in a cell-cycle-dependent manner and in response to serum, epidermal growth factor, or mitochondrial stress; this was accompanied by a corresponding decrease in mitochondrial PDC levels, suggesting a translocation from the mitochondria to the nucleus. Inhibition of nuclear PDC decreased acetylation of specific lysine residues on histones important for G1-S phase progression and expression of S phase markers. Dynamic translocation of mitochondrial PDC to the nucleus provides a pathway for nuclear acetyl-CoA synthesis required for histone acetylation and epigenetic regulation.

Organization of the human intestine at single-cell resolution.

The intestine is a complex organ that promotes digestion, extracts nutrients, participates in immune surveillance, maintains critical symbiotic relationships with microbiota and affects overall health1. The intesting has a length of over nine metres, along which there are differences in structure and function2. The localization of individual cell types, cell type development trajectories and detailed cell transcriptional programs probably drive these differences in function. Here, to better understand these differences, we evaluated the organization of single cells using multiplexed imaging and single-nucleus RNA and open chromatin assays across eight different intestinal sites from nine donors. Through systematic analyses, we find cell compositions that differ substantially across regions of the intestine and demonstrate the complexity of epithelial subtypes, and find that the same cell types are organized into distinct neighbourhoods and communities, highlighting distinct immunological niches that are present in the intestine. We also map gene regulatory differences in these cells that are suggestive of a regulatory differentiation cascade, and associate intestinal disease heritability with specific cell types. These results describe the complexity of the cell composition, regulation and organization for this organ, and serve as an important reference map for understanding human biology and disease.

Quantifying common and distinct information in single-cell multimodal data with Tilted Canonical Correlation Analysis.

Multimodal single-cell technologies profile multiple modalities for each cell simultaneously, enabling a more thorough characterization of cell populations. Existing dimension-reduction methods for multimodal data capture the "union of information," producing a lower-dimensional embedding that combines the information across modalities. While these tools are useful, we focus on a fundamentally different task of separating and quantifying the information among cells that is shared between the two modalities as well as unique to only one modality. Hence, we develop Tilted Canonical Correlation Analysis (Tilted-CCA), a method that decomposes a paired multimodal dataset into three lower-dimensional embeddings-one embedding captures the "intersection of information," representing the geometric relations among the cells that is common to both modalities, while the remaining two embeddings capture the "distinct information for a modality," representing the modality-specific geometric relations. We analyze single-cell multimodal datasets sequencing RNA along surface antibodies (i.e., CITE-seq) as well as RNA alongside chromatin accessibility (i.e., 10x) for blood cells and developing neurons via Tilted-CCA. These analyses show that Tilted-CCA enables meaningful visualization and quantification of the cross-modal information. Finally, Tilted-CCA's framework allows us to perform two specific downstream analyses. First, for single-cell datasets that simultaneously profile transcriptome and surface antibody markers, we show that Tilted-CCA helps design the target antibody panel to complement the transcriptome best. Second, for developmental single-cell datasets that simultaneously profile transcriptome and chromatin accessibility, we show that Tilted-CCA helps identify development-informative genes and distinguish between transient versus terminal cell types.

ß-Aminopropionitrile Induces Distinct Pathologies in the Ascending and Descending Thoracic Aortic Regions of Mice.

BACKGROUND: ß-aminopropionitrile (BAPN) is a pharmacological inhibitor of LOX (lysyl oxidase) and LOXLs (LOX-like proteins). Administration of BAPN promotes aortopathies, although there is a paucity of data on experimental conditions to generate pathology. The objective of this study was to define experimental parameters and determine whether equivalent or variable aortopathies were generated throughout the aortic tree during BAPN administration in mice. METHODS: BAPN was administered in drinking water for a period ranging from 1 to 12 weeks. The impacts of BAPN were first assessed with regard to BAPN dose, and mouse strain, age, and sex. BAPN-induced aortic pathological characterization was conducted using histology and immunostaining. To investigate the mechanistic basis of regional heterogeneity, the ascending and descending thoracic aortas were harvested after 1 week of BAPN administration before the appearance of overt pathology. RESULTS: BAPN-induced aortic rupture predominantly occurred or originated in the descending thoracic aorta in young C57BL/6J or N mice. No apparent differences were found between male and female mice. For mice surviving 12 weeks of BAPN administration, profound dilatation was consistently observed in the ascending region, while there were more heterogeneous changes in the descending thoracic region. Pathological features were distinct between the ascending and descending thoracic regions. Aortic pathology in the ascending region was characterized by luminal dilatation and elastic fiber disruption throughout the media. The descending thoracic region frequently had dissections with false lumen formation, collagen deposition, and remodeling of the wall surrounding the false lumen. Cells surrounding the false lumen were predominantly positive for α-SMA (α-smooth muscle actin). One week of BAPN administration compromised contractile properties in both regions equivalently, and RNA sequencing did not show obvious differences between the 2 aortic regions in smooth muscle cell markers, cell proliferation markers, and extracellular components. CONCLUSIONS: BAPN-induced pathologies show distinct, heterogeneous features within and between ascending and descending aortic regions in mice.

Causal inference for heritable phenotypic risk factors using heterogeneous genetic instruments.

Over a decade of genome-wide association studies (GWAS) have led to the finding of extreme polygenicity of complex traits. The phenomenon that "all genes affect every complex trait" complicates Mendelian Randomization (MR) studies, where natural genetic variations are used as instruments to infer the causal effect of heritable risk factors. We reexamine the assumptions of existing MR methods and show how they need to be clarified to allow for pervasive horizontal pleiotropy and heterogeneous effect sizes. We propose a comprehensive framework GRAPPLE to analyze the causal effect of target risk factors with heterogeneous genetic instruments and identify possible pleiotropic patterns from data. By using GWAS summary statistics, GRAPPLE can efficiently use both strong and weak genetic instruments, detect the existence of multiple pleiotropic pathways, determine the causal direction and perform multivariable MR to adjust for confounding risk factors. With GRAPPLE, we analyze the effect of blood lipids, body mass index, and systolic blood pressure on 25 disease outcomes, gaining new information on their causal relationships and potential pleiotropic pathways involved.

Exploring intensive care nurses' perception of simulation-based learning: A systematic review and meta-synthesis.

AIM(S): To explore intensive care nurses' (ICN) perceptions of simulation-based learning (SBL). DESIGN: A systematic review and meta-synthesis. METHODS: The review followed the PRISMA guidelines for reporting a systematic review. A systematic search strategy was developed using a modified PICo framework. A comprehensive search was conducted in July 2023 in CINAHL, OVID Embase, Medline complete, Web of Science, ERIC and Scopus databases for articles published in English between 2013 and 2023. Data were extracted using the Joanna Briggs Institute QARI Data Extraction, with data synthesis guided by Braun and Clark's thematic analysis approach. Quality appraisal was assessed using the CASP tool. RESULTS: Eleven studies providing qualitative data were included for analysis. Analysis and meta-synthesis led to the construction of two themes: The learning experience and professional growth through collaboration. CONCLUSION: The review highlights the balance needed in finding the appropriate simulation approach, with the right level of fidelity, conducted at appropriately regular intervals, incorporating the correct makeup of professional team members, conducted in the right environment and facilitated by a skilled facilitator, to ensure best outcomes and return on investment for ICN's education. IMPLICATIONS FOR PRACTICE: These findings are a valuable resource for educators and organisations considering simulation-based learning initiatives in the intensive care setting. NO PATIENT OR PUBLIC CONTRIBUTION: This review involved analysis of existing literature and as such no unique patient or public involvement occurred. REPORTING METHOD: The systematic review followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) reporting guidelines.

Data denoising with transfer learning in single-cell transcriptomics.

Single-cell RNA sequencing (scRNA-seq) data are noisy and sparse. Here, we show that transfer learning across datasets remarkably improves data quality. By coupling a deep autoencoder with a Bayesian model, SAVER-X extracts transferable gene-gene relationships across data from different labs, varying conditions and divergent species, to denoise new target datasets.

Doctors' and Nurses' Attitudes of Acupuncture and Acupressure use in Perioperative Care: An Australian National Survey.

BACKGROUND: Acupuncture and acupressure are not being systematically used in the management of postoperative nausea and vomiting and pain, despite being included in the guidelines. AIM: To examine the beliefs, attitudes, and knowledge of Australian nurses/midwives and doctors toward the perioperative use of AA for the management of postoperative nausea and vomiting and pain; to explore the barriers and enablers influencing acupuncture and acupressure integration into hospital setting. METHODS: A mixed-mode approach was undertaken for data collection. An online approach was used to recruit respondents from Australian College of Perioperative Nurses. Three hospitals from three different Australian states were selected via convenience sampling. RESULTS: A total of 421 usable surveys were included in data analysis. The respondents comprised 14.3% doctors and 72.9% nurses/midwives. Overall, 69.4% were female, 85% were trained in Australia with 35% and 51.4% having knowledge or personal exposure to AA in general respectively. Over 60% of the respondents agreed AA should be routinely integrated into perioperative care, and over 80% would recommend AA to their patients if it was provided at their hospital, and, 75% would be willing to receive further education. The three main reported barriers included: perceived lack of scientific evidence (80.9%), unavailability of credentialed provider (77.2%) and lack of reimbursement (60.4%). CONCLUSIONS: Positive attitudes are reported by Australian doctors and nurses toward AA. This is despite of low levels of knowledge or personal exposure to AA. Further studies are required to explore the implementation of barriers and address respondent calls for further education.

SAVER: gene expression recovery for single-cell RNA sequencing.

In single-cell RNA sequencing (scRNA-seq) studies, only a small fraction of the transcripts present in each cell are sequenced. This leads to unreliable quantification of genes with low or moderate expression, which hinders downstream analysis. To address this challenge, we developed SAVER (single-cell analysis via expression recovery), an expression recovery method for unique molecule index (UMI)-based scRNA-seq data that borrows information across genes and cells to provide accurate expression estimates for all genes.

Gene expression distribution deconvolution in single-cell RNA sequencing.

Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene's expression distribution across cells, thus allowing the assessment of the dispersion, nonzero fraction, and other aspects of its distribution beyond the mean. These statistical characterizations of the gene expression distribution are critical for understanding expression variation and for selecting marker genes for population heterogeneity. However, scRNA-seq data are noisy, with each cell typically sequenced at low coverage, thus making it difficult to infer properties of the gene expression distribution from raw counts. Based on a reexamination of nine public datasets, we propose a simple technical noise model for scRNA-seq data with unique molecular identifiers (UMI). We develop deconvolution of single-cell expression distribution (DESCEND), a method that deconvolves the true cross-cell gene expression distribution from observed scRNA-seq counts, leading to improved estimates of properties of the distribution such as dispersion and nonzero fraction. DESCEND can adjust for cell-level covariates such as cell size, cell cycle, and batch effects. DESCEND's noise model and estimation accuracy are further evaluated through comparisons to RNA FISH data, through data splitting and simulations and through its effectiveness in removing known batch effects. We demonstrate how DESCEND can clarify and improve downstream analyses such as finding differentially expressed genes, identifying cell types, and selecting differentiation markers.

Healthcare Professionals' Attitudes to Integration of Acupuncture in Western Medicine: A Mixed-Method Systematic Review.

BACKGROUND: Acupuncture is practiced in many Western countries with increasing evidence supporting its use across the healthcare system. However, this nonpharmacological intervention is yet to be widely integrated into hospitals. Fundamental to the integration of any innovation into healthcare systems is the attitudes of healthcare professionals. AIMS: To explore healthcare professionals' attitudes towards acupuncture and identify the enablers and barriers to integration in the hospital setting. DESIGN: This review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines; we conducted a mixed-method systematic review following Joanna Briggs Institute Reviewer's Manual 2017. DATA SOURCES: PubMed, CINAHL, AMED, EMBASE, Cochrane Library, and SCOPUS were searched from their inception to December 2019 using different combination of keywords. METHODS: Attitude and perception data were extracted from the articles and constructs were categorized as barriers and enablers. A convergent integrated approach was adopted whereby qualitative data themes were integrated with quantitative data constructs to generate descriptive codes around enablers and barriers at intrapersonal, interpersonal, and environmental levels according to the framework proposed by Manias et al. (2014). RESULTS: Twenty-six studies comprising 19 survey and seven interview studies were identified, with 24 articles (92%) reporting positive attitudes, supporting the integration of acupuncture into Western medicine. Enablers identified were prior positive experience, belief in the treatment's efficacy, and patient demand. Barriers were lack of evidentiary knowledge or experience; lack of resources such as time, providers, and funding; and paradigm differences. Twenty studies (77%) focused on medical doctors or medical student perceptions. A conceptual framework for implementing and integrating acupuncture into Western medicine is proposed. CONCLUSION: Healthcare professionals' attitudes to acupuncture are positive. Barriers to integrating acupuncture into the hospital system exist at intrapersonal, interpersonal, and external levels, with lack of resources being the key barrier. Nurses' role in facilitating integration into Western medical pain management practice provides an avenue for future research. The proposed conceptual framework provides guidance for nursing researchers interested in the role of acupuncture and integrated medicine in patient-centric, value-based healthcare.

DNA copy number profiling using single-cell sequencing.

Currently, there is a lack of software for detecting copy number variations and constructing copy number profile for the whole genome from single-cell DNA sequencing data, which are often of low coverage and high technical noises. Here we introduce a new toolkit, SCNV, which features an efficient bin-free segmentation approach and provides the highest resolution possible for breakpoint detection and the subsequent copy number calling. SCNV can auto-tune parameters based on a set of normal cells from the same batch to adjust for the technical noise level of the data, facilitating its application to data gathered from different platforms and different studies.

Identification of large rearrangements in cancer genomes with barcode linked reads.

Large genomic rearrangements involve inversions, deletions and other structural changes that span Megabase segments of the human genome. This category of genetic aberration is the cause of many hereditary genetic disorders and contributes to pathogenesis of diseases like cancer. We developed a new algorithm called ZoomX for analysing barcode-linked sequence reads-these sequences can be traced to individual high molecular weight DNA molecules (>50 kb). To generate barcode linked sequence reads, we employ a library preparation technology (10X Genomics) that uses droplets to partition and barcode DNA molecules. Using linked read data from whole genome sequencing, we identify large genomic rearrangements, typically greater than 200kb, even when they are only present in low allelic fractions. Our algorithm uses a Poisson scan statistic to identify genomic rearrangement junctions, determine counts of junction-spanning molecules and calculate a Fisher's exact test for determining statistical significance for somatic aberrations. Utilizing a well-characterized human genome, we benchmarked this approach to accurately identify large rearrangement. Subsequently, we demonstrated that our algorithm identifies somatic rearrangements when present in lower allelic fractions as occurs in tumors. We characterized a set of complex cancer rearrangements with multiple classes of structural aberrations and with possible roles in oncogenesis.

Hybrid Plasmonic Fiber-Optic Sensors.

With the increasing demand of achieving comprehensive perception in every aspect of life, optical fibers have shown great potential in various applications due to their highly-sensitive, highly-integrated, flexible and real-time sensing capabilities. Among various sensing mechanisms, plasmonics based fiber-optic sensors provide remarkable sensitivity benefiting from their outstanding plasmon-matter interaction. Therefore, surface plasmon resonance (SPR) and localized SPR (LSPR)-based hybrid fiber-optic sensors have captured intensive research attention. Conventionally, SPR- or LSPR-based hybrid fiber-optic sensors rely on the resonant electron oscillations of thin metallic films or metallic nanoparticles functionalized on fiber surfaces. Coupled with the new advances in functional nanomaterials as well as fiber structure design and fabrication in recent years, new solutions continue to emerge to further improve the fiber-optic plasmonic sensors' performances in terms of sensitivity, specificity and biocompatibility. For instance, 2D materials like graphene can enhance the surface plasmon intensity at the metallic film surface due to the plasmon-matter interaction. Two-dimensional (2D) morphology of transition metal oxides can be doped with abundant free electrons to facilitate intrinsic plasmonics in visible or near-infrared frequencies, realizing exceptional field confinement and high sensitivity detection of analyte molecules. Gold nanoparticles capped with macrocyclic supramolecules show excellent selectivity to target biomolecules and ultralow limits of detection. Moreover, specially designed microstructured optical fibers are able to achieve high birefringence that can suppress the output inaccuracy induced by polarization crosstalk and meanwhile deliver promising sensitivity. This review aims to reveal and explore the frontiers of such hybrid plasmonic fiber-optic platforms in various sensing applications.

Integrative pipeline for profiling DNA copy number and inferring tumor phylogeny.

Summary: Copy number variation is an important and abundant source of variation in the human genome, which has been associated with a number of diseases, especially cancer. Massively parallel next-generation sequencing allows copy number profiling with fine resolution. Such efforts, however, have met with mixed successes, with setbacks arising partly from the lack of reliable analytical methods to meet the diverse and unique challenges arising from the myriad experimental designs and study goals in genetic studies. In cancer genomics, detection of somatic copy number changes and profiling of allele-specific copy number (ASCN) are complicated by experimental biases and artifacts as well as normal cell contamination and cancer subclone admixture. Furthermore, careful statistical modeling is warranted to reconstruct tumor phylogeny by both somatic ASCN changes and single nucleotide variants. Here we describe a flexible computational pipeline, MARATHON, which integrates multiple related statistical software for copy number profiling and downstream analyses in disease genetic studies. Availability and implementation: MARATHON is publicly available at https://github.com/yuchaojiang/MARATHON. Supplementary information: Supplementary data are available at Bioinformatics online.

Integrative DNA copy number detection and genotyping from sequencing and array-based platforms.

Motivation: Copy number variations (CNVs) are gains and losses of DNA segments and have been associated with disease. Many large-scale genetic association studies are performing CNV analysis using whole exome sequencing (WES) and whole genome sequencing (WGS). In many of these studies, previous single-nucleotide polymorphism (SNP)-array data are available. An integrated cross-platform analysis is expected to improve resolution and accuracy, yet there is no tool for effectively combining data from sequencing and array platforms. The detection of CNVs using sequencing data alone can also be further improved by the utilization of allele-specific reads. Results: We propose a statistical framework, integrated CNV (iCNV) detection algorithm, which can be applied to multiple study designs: WES only, WGS only, SNP array only, or any combination of SNP and sequencing data. iCNV applies platform-specific normalization, utilizes allele specific reads from sequencing and integrates matched NGS and SNP-array data by a hidden Markov model. We compare integrated two-platform CNV detection using iCNV to naïve intersection or union of platforms and show that iCNV increases sensitivity and robustness. We also assess the accuracy of iCNV on WGS data only and show that the utilization of allele-specific reads improve CNV detection accuracy compared to existing methods. Availability and implementation: https://github.com/zhouzilu/iCNV. Supplementary information: Supplementary data are available at Bioinformatics online.

Accounting for technical noise in differential expression analysis of single-cell RNA sequencing data.

Recent technological breakthroughs have made it possible to measure RNA expression at the single-cell level, thus paving the way for exploring expression heterogeneity among individual cells. Current single-cell RNA sequencing (scRNA-seq) protocols are complex and introduce technical biases that vary across cells, which can bias downstream analysis without proper adjustment. To account for cell-to-cell technical differences, we propose a statistical framework, TASC (Toolkit for Analysis of Single Cell RNA-seq), an empirical Bayes approach to reliably model the cell-specific dropout rates and amplification bias by use of external RNA spike-ins. TASC incorporates the technical parameters, which reflect cell-to-cell batch effects, into a hierarchical mixture model to estimate the biological variance of a gene and detect differentially expressed genes. More importantly, TASC is able to adjust for covariates to further eliminate confounding that may originate from cell size and cell cycle differences. In simulation and real scRNA-seq data, TASC achieves accurate Type I error control and displays competitive sensitivity and improved robustness to batch effects in differential expression analysis, compared to existing methods. TASC is programmed to be computationally efficient, taking advantage of multi-threaded parallelization. We believe that TASC will provide a robust platform for researchers to leverage the power of scRNA-seq.

Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing.

Cancer is a disease driven by evolutionary selection on somatic genetic and epigenetic alterations. Here, we propose Canopy, a method for inferring the evolutionary phylogeny of a tumor using both somatic copy number alterations and single-nucleotide alterations from one or more samples derived from a single patient. Canopy is applied to bulk sequencing datasets of both longitudinal and spatial experimental designs and to a transplantable metastasis model derived from human cancer cell line MDA-MB-231. Canopy successfully identifies cell populations and infers phylogenies that are in concordance with existing knowledge and ground truth. Through simulations, we explore the effects of key parameters on deconvolution accuracy and compare against existing methods. Canopy is an open-source R package available at https://cran.r-project.org/web/packages/Canopy/.