Spatially exploring RNA biology in archival formalin-fixed paraffin-embedded tissues.

The capability to spatially explore RNA biology in formalin-fixed paraffin-embedded (FFPE) tissues holds transformative potential for histopathology research. Here, we present pathology-compatible deterministic barcoding in tissue (Patho-DBiT) by combining in situ polyadenylation and computational innovation for spatial whole transcriptome sequencing, tailored to probe the diverse RNA species in clinically archived FFPE samples. It permits spatial co-profiling of gene expression and RNA processing, unveiling region-specific splicing isoforms, and high-sensitivity transcriptomic mapping of clinical tumor FFPE tissues stored for 5 years. Furthermore, genome-wide single-nucleotide RNA variants can be captured to distinguish malignant subclones from non-malignant cells in human lymphomas. Patho-DBiT also maps microRNA regulatory networks and RNA splicing dynamics, decoding their roles in spatial tumorigenesis. Single-cell level Patho-DBiT dissects the spatiotemporal cellular dynamics driving tumor clonal architecture and progression. Patho-DBiT stands poised as a valuable platform to unravel rich RNA biology in FFPE tissues to aid in clinical pathology evaluation.

An atlas of epithelial cell states and plasticity in lung adenocarcinoma.

Understanding the cellular processes that underlie early lung adenocarcinoma (LUAD) development is needed to devise intervention strategies1. Here we studied 246,102 single epithelial cells from 16 early-stage LUADs and 47 matched normal lung samples. Epithelial cells comprised diverse normal and cancer cell states, and diversity among cancer cells was strongly linked to LUAD-specific oncogenic drivers. KRAS mutant cancer cells showed distinct transcriptional features, reduced differentiation and low levels of aneuploidy. Non-malignant areas surrounding human LUAD samples were enriched with alveolar intermediate cells that displayed elevated KRT8 expression (termed KRT8+ alveolar intermediate cells (KACs) here), reduced differentiation, increased plasticity and driver KRAS mutations. Expression profiles of KACs were enriched in lung precancer cells and in LUAD cells and signified poor survival. In mice exposed to tobacco carcinogen, KACs emerged before lung tumours and persisted for months after cessation of carcinogen exposure. Moreover, they acquired Kras mutations and conveyed sensitivity to targeted KRAS inhibition in KAC-enriched organoids derived from alveolar type 2 (AT2) cells. Last, lineage-labelling of AT2 cells or KRT8+ cells following carcinogen exposure showed that KACs are possible intermediates in AT2-to-tumour cell transformation. This study provides new insights into epithelial cell states at the root of LUAD development, and such states could harbour potential targets for prevention or intervention.

ß-Hydroxybutyrate suppresses colorectal cancer.

Colorectal cancer (CRC) is among the most frequent forms of cancer, and new strategies for its prevention and therapy are urgently needed1. Here we identify a metabolite signalling pathway that provides actionable insights towards this goal. We perform a dietary screen in autochthonous animal models of CRC and find that ketogenic diets exhibit a strong tumour-inhibitory effect. These properties of ketogenic diets are recapitulated by the ketone body ß-hydroxybutyrate (BHB), which reduces the proliferation of colonic crypt cells and potently suppresses intestinal tumour growth. We find that BHB acts through the surface receptor Hcar2 and induces the transcriptional regulator Hopx, thereby altering gene expression and inhibiting cell proliferation. Cancer organoid assays and single-cell RNA sequencing of biopsies from patients with CRC provide evidence that elevated BHB levels and active HOPX are associated with reduced intestinal epithelial proliferation in humans. This study thus identifies a BHB-triggered pathway regulating intestinal tumorigenesis and indicates that oral or systemic interventions with a single metabolite may complement current prevention and treatment strategies for CRC.

Monocyte Single-Cell Multimodal Profiling in Cardiovascular Disease Risk States.

BACKGROUND: Monocytes are a critical innate immune system cell type that serves homeostatic and immunoregulatory functions. They have been identified historically by the cell surface expression of CD14 and CD16. However, recent single-cell studies have revealed that they are much more heterogeneous than previously realized. METHODS: We utilized cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-cell RNA sequencing to describe the comprehensive transcriptional and phenotypic landscape of 437 126 monocytes. RESULTS: This high-dimensional multimodal approach identified vast phenotypic diversity and functionally distinct subsets, including IFN-responsive, MHCIIhi (major histocompatibility complex class II), monocyte-platelet aggregates, as well as nonclassical, and several subpopulations of classical monocytes. Using flow cytometry, we validated the existence of MHCII+CD275+ MHCIIhi, CD42b+ monocyte-platelet aggregates, CD16+CD99- nonclassical monocytes, and CD99+ classical monocytes. Each subpopulation exhibited unique characteristics, developmental trajectories, transcriptional regulation, and tissue distribution. In addition, alterations associated with cardiovascular disease risk factors, including race, smoking, and hyperlipidemia were identified. Moreover, the effect of hyperlipidemia was recapitulated in mouse models of elevated cholesterol. CONCLUSIONS: This integrative and cross-species comparative analysis provides a new perspective on the comparison of alterations in monocytes in pathological conditions and offers insights into monocyte-driven mechanisms in cardiovascular disease and the potential for monocyte subpopulation targeted therapies.

High-Dimensional Single-Cell Multimodal Landscape of Human Carotid Atherosclerosis.

BACKGROUND: Atherosclerotic plaques are complex tissues composed of a heterogeneous mixture of cells. However, our understanding of the comprehensive transcriptional and phenotypic landscape of the cells within these lesions is limited. METHODS: To characterize the landscape of human carotid atherosclerosis in greater detail, we combined cellular indexing of transcriptomes and epitopes by sequencing and single-cell RNA sequencing to classify all cell types within lesions (n=21; 13 symptomatic) to achieve a comprehensive multimodal understanding of the cellular identities of atherosclerosis and their association with clinical pathophysiology. RESULTS: We identified 25 cell populations, each with a unique multiomic signature, including macrophages, T cells, NK (natural killer) cells, mast cells, B cells, plasma cells, neutrophils, dendritic cells, endothelial cells, fibroblasts, and smooth muscle cells (SMCs). Among the macrophages, we identified 2 proinflammatory subsets enriched in IL-1B (interleukin-1B) or C1Q expression, 2 TREM2-positive foam cells (1 expressing inflammatory genes), and subpopulations with a proliferative gene signature and SMC-specific gene signature with fibrotic pathways upregulated. Further characterization revealed various subsets of SMCs and fibroblasts, including SMC-derived foam cells. These foamy SMCs were localized in the deep intima of coronary atherosclerotic lesions. Utilizing cellular indexing of transcriptomes and epitopes by sequencing data, we developed a flow cytometry panel, using cell surface proteins CD29, CD142, and CD90, to isolate SMC-derived cells from lesions. Lastly, we observed reduced proportions of efferocytotic macrophages, classically activated endothelial cells, and contractile and modulated SMC-derived cells, while inflammatory SMCs were enriched in plaques of clinically symptomatic versus asymptomatic patients. CONCLUSIONS: Our multimodal atlas of cell populations within atherosclerosis provides novel insights into the diversity, phenotype, location, isolation, and clinical relevance of the unique cellular composition of human carotid atherosclerosis. These findings facilitate both the mapping of cardiovascular disease susceptibility loci to specific cell types and the identification of novel molecular and cellular therapeutic targets for the treatment of the disease.

Real-Time Direct Monitoring of Chirality Fixation and Recognition at the Single-Molecule Level.

Chirality, a fundamental attribute of nature, significantly influences a wide range of phenomena related to physical properties, chemical reactions, biological pharmacology, and so on. As a pivotal aspect of chirality research, chirality recognition contributes to the synthesis of complex chiral products from simple chiral compounds and exhibits intricate interplay between chiral materials. However, macroscopic detection technologies cannot unveil the dynamic process and intrinsic mechanisms of single-molecule chirality recognition. Herein, we present a single-molecule detection platform based on graphene-molecule-graphene single-molecule junctions to measure the chirality recognition involving interactions between amines and chiral alcohols. This approach leads to the realization of in situ and real-time direct observation of chirality recognition at the single-molecule level, demonstrating that chiral alcohols exhibit compelling potential to induce the formation of the corresponding chiral configuration of molecules. The amalgamation of theoretical analyses with experimental findings reveals a synergistic action between electrostatic interactions and steric hindrance effects in the chirality recognition process, thus substantiating the microscopic mechanism governing the chiral structure-activity relationship. These studies open up a pathway for exploring novel chiral phenomena from the fundamental limits of chemistry, such as chiral origin and chiral amplification, and offer important insights into the precise synthesis of chiral materials.

DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data.

Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi-batch data are analyzed using complex downstream models which are more likely to pick up on and implicitly incorporate batch-related information. Previously proposed image harmonization methods have sought to remove these batch effects; however, batch effects remain detectable in the data after applying these methods. We present DeepComBat, a deep learning harmonization method based on a conditional variational autoencoder and the ComBat method. DeepComBat combines the strengths of statistical and deep learning methods in order to account for the multivariate relationships between features while simultaneously relaxing strong assumptions made by previous deep learning harmonization methods. As a result, DeepComBat can perform multivariate harmonization while preserving data structure and avoiding the introduction of synthetic artifacts. We apply this method to cortical thickness measurements from a cognitive-aging cohort and show DeepComBat qualitatively and quantitatively outperforms existing methods in removing batch effects while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically motivated deep learning harmonization methods.

Network enrichment significance testing in brain-phenotype association studies.

Functional networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about spatial properties of imaging data, leading to inflated false positive rates. We seek to address this gap in existing methodology by borrowing insight from a method widely used in genetics research for testing enrichment of associations between a set of genes and a phenotype of interest. We propose network enrichment significance testing (NEST), a flexible framework for testing the specificity of brain-phenotype associations to functional networks or other sub-regions of the brain. We apply NEST to study enrichment of associations with structural and functional brain imaging data from a large-scale neurodevelopmental cohort study.

Applications of single-cell genomics and computational strategies to study common disease and population-level variation.

The advent and rapid development of single-cell technologies have made it possible to study cellular heterogeneity at an unprecedented resolution and scale. Cellular heterogeneity underlies phenotypic differences among individuals, and studying cellular heterogeneity is an important step toward our understanding of the disease molecular mechanism. Single-cell technologies offer opportunities to characterize cellular heterogeneity from different angles, but how to link cellular heterogeneity with disease phenotypes requires careful computational analysis. In this article, we will review the current applications of single-cell methods in human disease studies and describe what we have learned so far from existing studies about human genetic variation. As single-cell technologies are becoming widely applicable in human disease studies, population-level studies have become a reality. We will describe how we should go about pursuing and designing these studies, particularly how to select study subjects, how to determine the number of cells to sequence per subject, and the needed sequencing depth per cell. We also discuss computational strategies for the analysis of single-cell data and describe how single-cell data can be integrated with bulk tissue data and data generated from genome-wide association studies. Finally, we point out open problems and future research directions.

A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics.

Recent developments of single-cell RNA-seq (scRNA-seq) technologies have led to enormous biological discoveries. As the scale of scRNA-seq studies increases, a major challenge in analysis is batch effects, which are inevitable in studies involving human tissues. Most existing methods remove batch effects in a low-dimensional embedding space. Although useful for clustering, batch effects are still present in the gene expression space, leaving downstream gene-level analysis susceptible to batch effects. Recent studies have shown that batch effect correction in the gene expression space is much harder than in the embedding space. Methods such as Seurat 3.0 rely on the mutual nearest neighbor (MNN) approach to remove batch effects in gene expression, but MNN can only analyze two batches at a time, and it becomes computationally infeasible when the number of batches is large. Here, we present CarDEC, a joint deep learning model that simultaneously clusters and denoises scRNA-seq data while correcting batch effects both in the embedding and the gene expression space. Comprehensive evaluations spanning different species and tissues showed that CarDEC outperforms Scanorama, DCA + Combat, scVI, and MNN. With CarDEC denoising, non-highly variable genes offer as much signal for clustering as the highly variable genes (HVGs), suggesting that CarDEC substantially boosted information content in scRNA-seq. We also showed that trajectory analysis using CarDEC's denoised and batch-corrected expression as input revealed marker genes and transcription factors that are otherwise obscured in the presence of batch effects. CarDEC is computationally fast, making it a desirable tool for large-scale scRNA-seq studies.

SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network.

Recent advances in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive characterization of gene expression patterns in the context of tissue microenvironment. To elucidate spatial gene expression variation, we present SpaGCN, a graph convolutional network approach that integrates gene expression, spatial location and histology in SRT data analysis. Through graph convolution, SpaGCN aggregates gene expression of each spot from its neighboring spots, which enables the identification of spatial domains with coherent expression and histology. The subsequent domain guided differential expression (DE) analysis then detects genes with enriched expression patterns in the identified domains. Analyzing seven SRT datasets using SpaGCN, we show it can detect genes with much more enriched spatial expression patterns than competing methods. Furthermore, genes detected by SpaGCN are transferrable and can be utilized to study spatial variation of gene expression in other datasets. SpaGCN is computationally fast, platform independent, making it a desirable tool for diverse SRT studies.

MuSiC2: cell-type deconvolution for multi-condition bulk RNA-seq data.

Cell-type composition of intact bulk tissues can vary across samples. Deciphering cell-type composition and its changes during disease progression is an important step toward understanding disease pathogenesis. To infer cell-type composition, existing cell-type deconvolution methods for bulk RNA sequencing (RNA-seq) data often require matched single-cell RNA-seq (scRNA-seq) data, generated from samples with similar clinical conditions, as reference. However, due to the difficulty of obtaining scRNA-seq data in diseased samples, only limited scRNA-seq data in matched disease conditions are available. Using scRNA-seq reference to deconvolve bulk RNA-seq data from samples with different disease conditions may lead to a biased estimation of cell-type proportions. To overcome this limitation, we propose an iterative estimation procedure, MuSiC2, which is an extension of MuSiC, to perform deconvolution analysis of bulk RNA-seq data generated from samples with multiple clinical conditions where at least one condition is different from that of the scRNA-seq reference. Extensive benchmark evaluations indicated that MuSiC2 improved the accuracy of cell-type proportion estimates of bulk RNA-seq samples under different conditions as compared with the traditional MuSiC deconvolution. MuSiC2 was applied to two bulk RNA-seq datasets for deconvolution analysis, including one from human pancreatic islets and the other from human retina. We show that MuSiC2 improves current deconvolution methods and provides more accurate cell-type proportion estimates when the bulk and single-cell reference differ in clinical conditions. We believe the condition-specific cell-type composition estimates from MuSiC2 will facilitate the downstream analysis and help identify cellular targets of human diseases.

Human Macrophage Long Intergenic Noncoding RNA, SIMALR, Suppresses Inflammatory Macrophage Apoptosis via NTN1 (Netrin-1).

BACKGROUND: Long noncoding RNAs (lncRNAs) have emerged as novel regulators of macrophage biology and inflammatory cardiovascular diseases. However, studies focused on lncRNAs in human macrophage subtypes, particularly human lncRNAs that are not conserved in rodents, are limited. METHODS: Through RNA-sequencing of human monocyte-derived macrophages, we identified suppressor of inflammatory macrophage apoptosis lncRNA (SIMALR). Lipopolysaccharide/IFNγ (interferon γ) stimulated human macrophages were treated with SIMALR antisense oligonucleotides and subjected to RNA-sequencing to investigate the function of SIMALR. Western blots, luciferase assay, and RNA immunoprecipitation were performed to validate function and potential mechanism of SIMALR. RNAscope was performed to identify SIMALR expression in human carotid atherosclerotic plaques. RESULTS: RNA-sequencing of human monocyte-derived macrophages identified SIMALR, a human macrophage-specific long intergenic noncoding RNA that is highly induced in lipopolysaccharide/IFNγ-stimulated macrophages. SIMALR knockdown in lipopolysaccharide/IFNγ stimulated THP1 human macrophages induced apoptosis of inflammatory macrophages, as shown by increased protein expression of cleaved PARP (poly[ADP-ribose] polymerase), caspase 9, caspase 3, and Annexin V+. RNA-sequencing of control versus SIMALR knockdown in lipopolysaccharide/IFNγ-stimulated macrophages showed Netrin-1 (NTN1) to be significantly decreased upon SIMALR knockdown. We confirmed that NTN1 knockdown in lipopolysaccharide/IFNγ-stimulated macrophages induced apoptosis. The SIMALR knockdown-induced apoptotic phenotype was rescued by adding recombinant NTN1. NTN1 promoter-luciferase reporter activity was increased in HEK293T (human embryonic kidney 293) cells treated with lentiviral overexpression of SIMALR. NTN1 promoter activity is known to require HIF1α (hypoxia-inducible factor 1 subunit alpha), and our studies suggest that SIMALR may interact with HIF1α to regulate NTN1 transcription, thereby regulating macrophages apoptosis. SIMALR was found to be expressed in macrophages in human carotid atherosclerotic plaques of symptomatic patients. CONCLUSIONS: SIMALR is a nonconserved, human macrophage lncRNA expressed in atherosclerosis that suppresses macrophage apoptosis. SIMALR partners with HIF1α (hypoxia-inducible factor 1 subunit alpha) to regulate NTN1, which is a known macrophage survival factor. This work illustrates the importance of interrogating the functions of human lncRNAs and exploring their translational and therapeutic potential in human atherosclerosis.

Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data.

Allelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling covariate effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provides a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases.

Asian Cohort for Alzheimer's Disease (ACAD) pilot study on genetic and non-genetic risk factors for Alzheimer's disease among Asian Americans and Canadians.

INTRODUCTION: Clinical research in Alzheimer's disease (AD) lacks cohort diversity despite being a global health crisis. The Asian Cohort for Alzheimer's Disease (ACAD) was formed to address underrepresentation of Asians in research, and limited understanding of how genetics and non-genetic/lifestyle factors impact this multi-ethnic population. METHODS: The ACAD started fully recruiting in October 2021 with one central coordination site, eight recruitment sites, and two analysis sites. We developed a comprehensive study protocol for outreach and recruitment, an extensive data collection packet, and a centralized data management system, in English, Chinese, Korean, and Vietnamese. RESULTS: ACAD has recruited 606 participants with an additional 900 expressing interest in enrollment since program inception. DISCUSSION: ACAD's traction indicates the feasibility of recruiting Asians for clinical research to enhance understanding of AD risk factors. ACAD will recruit > 5000 participants to identify genetic and non-genetic/lifestyle AD risk factors, establish blood biomarker levels for AD diagnosis, and facilitate clinical trial readiness. HIGHLIGHTS: The Asian Cohort for Alzheimer's Disease (ACAD) promotes awareness of under-investment in clinical research for Asians. We are recruiting Asian Americans and Canadians for novel insights into Alzheimer's disease. We describe culturally appropriate recruitment strategies and data collection protocol. ACAD addresses challenges of recruitment from heterogeneous Asian subcommunities. We aim to implement a successful recruitment program that enrolls across three Asian subcommunities.

Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization.

Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.

Omnibus and robust deconvolution scheme for bulk RNA sequencing data integrating multiple single-cell reference sets and prior biological knowledge.

MOTIVATION: Cell-type deconvolution of bulk tissue RNA sequencing (RNA-seq) data is an important step toward understanding the variations in cell-type composition among disease conditions. Owing to recent advances in single-cell RNA sequencing (scRNA-seq) and the availability of large amounts of bulk RNA-seq data in disease-relevant tissues, various deconvolution methods have been developed. However, the performance of existing methods heavily relies on the quality of information provided by external data sources, such as the selection of scRNA-seq data as a reference and prior biological information. RESULTS: We present the Integrated and Robust Deconvolution (InteRD) algorithm to infer cell-type proportions from target bulk RNA-seq data. Owing to the innovative use of penalized regression with a new evaluation criterion for deconvolution, InteRD has three primary advantages. First, it is able to effectively integrate deconvolution results from multiple scRNA-seq datasets. Second, InteRD calibrates estimates from reference-based deconvolution by taking into account extra biological information as priors. Third, the proposed algorithm is robust to inaccurate external information imposed in the deconvolution system. Extensive numerical evaluations and real-data applications demonstrate that InteRD yields more accurate and robust cell-type proportion estimates that agree well with known biology. AVAILABILITY AND IMPLEMENTATION: The proposed InteRD framework is implemented in R and the package is available at https://cran.r-project.org/web/packages/InteRD/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Surgical Outcome of Proximal Isolated Subclavian Artery Aneurysms: A Single-Center Retrospective Observational Study.

OBJECTIVE: The objective of this study was to introduce our institutional experience of treatment strategies (cervical subclavian artery reconstruction, thoracotomy subclavian artery reconstruction and endovascular treatment) for proximal isolated subclavian artery aneurysms (PISAAs). METHODS: we retrospectively analyzed 15 consecutive patients with PISAAs treated by different treatment strategies (cervical reconstruction, thoracotomy reconstruction and endovascular treatment) in our institution from May 2016 to May 2022. Baseline data, surgery-related data, postoperative information and long-term follow-up were assessed. RESULTS: A total of 17 PISAAs in 15 consecutive patients were treated in our institution. The success rates of subclavian artery reconstruction in the cervical reconstruction, the thoracotomy reconstruction and the endovascular treatment were 100%, 100 and 83.33%, respectively. About the involved vertebral artery, the reconstruction rates in the cervical reconstruction, the thoracotomy reconstruction, and the endovascular treatment were 80%, 75%, and 0, respectively. The intraoperative blood loss in the thoracotomy reconstruction was significantly higher than that in the cervical reconstruction and the endovascular treatment (p<0.05). The total operation time of the thoracotomy reconstruction was significantly longer than that of the cervical reconstruction and the endovascular treatment (p<0.05). In terms of postoperative ventilator use time, total postoperative drainage fluid, total postoperative drainage time, and ICU duration, both the thoracotomy reconstruction and the cervical reconstruction were significantly more than the endovascular treatment (p<0.05). During the follow-up, one patient in the endovascular treatment underwent re-intervention 22 months after surgery due to in-stent occlusion. CONCLUSIONS: For patients with PISAAs, different treatment strategies are recommended depending on the size of the aneurysms and whether the involved vertebral arteries require reconstruction. CLINICAL IMPACT: This article is the largest study on the treatment strategies of PISAAs. By comparing the prognosis and complications of endovascular treatment with those of open surgery, it provides a certain reference basis for the choice of treatment for patients with PISAAs. For patients with aneurysms' diameter of >50 mm, the thoracotomy subclavian artery reconstruction is recommended; for patients with aneurysms' diameter of <30 mm requiring reconstruction of the involved vertebral arteries, the cervical subclavian artery reconstruction is recommended; for patients with aneurysms' diameter of <30 mm not requiring reconstruction of the involved vertebral arteries, the endovascular treatment is recommended.

Is Revascularization of V1 Segment of Vertebral Artery Combined with Ipsilateral Carotid Endarterectomy Safe?

BACKGROUND: The recommendation of the European Society for Vascular Surgery (ESVS) is that vertebral revascularization combined with ipsilateral CEA (carotid endarterectomy) should not be performed in the same operation. ESVS believes that vertebral revascularization combined with ipsilateral CEA increases perioperative death/stroke rates. In our opinion, revascularization of the first segment of vertebral artery (V1) combined with ipsilateral CEA is safe compared to vertebral V1 revascularization in the perioperative period. The purpose of this study is to prove that revascularization of V1 segment of vertebral artery combined with ipsilateral CEA is secure in the perioperative period. METHODS: We describe our experience with homochronous revascularization of V1 segment of vertebral artery with ipsilateral CEA (group B) and simple revascularization of V1 segment of vertebral artery (group A) in 48 consecutive patients during a 5-year period. O.Y. (Ouyang) incisions were used in both groups. We compare the results of the 2 procedures with aspects of mortality, stroke, morbidity, incident rates of complications, and so on. RESULTS: There was no significant difference between patients in group A and group B in terms of red blood cell reduction, postoperative ventilator using time, postoperative drainage volume, postoperative drainage days, postoperative hospitalize duration, and incident rates of postoperative complications. The postoperative complications include death, stroke, Horner syndrome, vocal paralysis, hypoglossal nerve paralysis, wound hematomas, and lymphatic leakage. CONCLUSIONS: Revascularization of vertebral artery combined with ipsilateral CEA should be divided into revascularization of V1 segment of vertebral artery combined with ipsilateral CEA and revascularization of V3 segment of vertebral artery with ipsilateral CEA. Revascularization of V1 segment of vertebral artery combined with ipsilateral CEA is safe; it can be performed for suitable patients who are fit for indications. O.Y. incisions can fully expose the target blood vessels and simplify the procedures without transecting the sternocleidomastoid muscles in operations.