Count Matrix Research Articles

The Mclust Analysis of Tumor Budding Unveils the Role of the Collagen Family in Cervical Cancer Progression.

In RNA-seq data analysis, condensing the gene count matrix size is pivotal for downstream investigations, particularly pathway analysis. For this purpose, harnessing machine learning attracts increasing interest, while conventional methodologies depend on p-value comparisons. In this study, 20 tissue samples from real-world cervical cancers were subjected to sequencing, followed by the application of the Mclust algorithm to delineate an optimal cluster. By stratifying tumor budding into high and low groups and quantifying the epithelial-to-mesenchymal transition (EMT) score to scrutinize tumor budding, we discerned 24 EMT-related genes, with 5 showing strong associations with cervical cancer prognosis. Our observations elucidate a biological flow wherein EMT, Matrix Metallopep-tidase 2 (MMP2), and extracellular matrix (ECM) degradation are interconnected, ultimately leading to collagen type VI and exacerbating the prognosis of cervical cancer. The present study underscores an alternative method for selecting useful EMT-related genes by employing an appropriate clustering algorithm, thereby avoiding classical methods while unveiling novel insights into cervical cancer etiology and prognosis. Moreover, when comparing high and low tumor budding, collagen type VI emerges as a potential gene marker for the prognosis of cervical cancer.

Abstract We018: The Molecular Mechanism of Coronary Collateral Growth Induced by Repetitive Ischemia by Single Cell-RNA Sequencing

Coronary heart diseases (CHD) are the leading cause of mortality and morbidity in the United States. A well-developed coronary collateral circulation ameliorates the consequences of CHD, reducing the incidence of sudden death and infarct sizes following coronary occlusion. Stimulation of coronary collateral growth (CCG) is also an alternative therapeutic approach for patients with intractable angina pectoris. The importance of CCG is undisputable, but the process and mechanism underlying the CCG is unclear. We developed a mouse model of CCG by repetitive ischemia (RI) in adults, and the CCG was validated with contrast echo to measure the coronary blood flow 3D images of micro-CT to quantitate the CCG. In this study, we use single cell-RNA sequencing (scRNA-Seq) to analyze the molecular signaling of CCG at different time points during the RI process. We digested the hearts from RI days 5, 10, and 17 and control (naïve) mice, harvested non-cardiomyocytes, performed scRNA-Seq with 10x Genomics and sequenced with Next-Seq 500 (Illumina).The fastq files are converted to a gene count matrix using kallisto|bustools with refdata-gex-mm10-2020-A employed as the reference mouse genome. The Kallisto bustools output is then converted in R to files compatible for uploading to Cellenics, an open-source platform for single-cell RNA-Seq analysis. Dimensionality reduction is done with Principal Components Analysis (PCA) with 30 principal components. We will use UMAP to visualize cell clusters. Cellenics uses scType to annotate the cell populations automatically when building clusters of cell populations. Our preliminary data show dynamic kinetics of non-cardiomyocytes induced by RI over the timeline of CCG and different cell populations of non-cardiomyocytes and EC expression profiling varied in response to RI throughout CCG. We will validate the critical signaling in CCG and explore the potential targets to induce CCG.

Gene representation bias in spatial transcriptomics.

For sequencing-based spatial transcriptomics data, the gene-spot count matrix is highly sparse. This feature is similar to scRNA-seq. The goal of this paper is to identify whether there exist genes that are frequently under-detected in Visium compared to bulk RNA-seq, and the underlying potential mechanism of under-detection in Visium. We collected paired Visium and bulk RNA-seq data for 28 human samples and 19 mouse samples, which covered diverse tissue sources. We compared the two data types and observed that there indeed exists a collection of genes frequently under-detected in Visium compared to bulk RNA-seq. We performed a motif search to examine the last 350 bp of the frequently under-detected genes, and we observed that the poly (T) motif was significantly enriched in genes identified from both human and mouse data, which matches with our previous finding about frequently under-detected genes in scRNA-seq. We hypothesized that the poly (T) motif may be able to form a hairpin structure with the poly (A) tails of their mRNA transcripts, making it difficult for their mRNA transcripts to be captured during Visium library preparation.

P-172 Mitochondria are dysregulated in poor quality oocytes and can be targeted in vitro to alter oocyte quality

Abstract Study question Does mitochondrial gene expression differ between good and poor quality oocytes and can mitochondria be targeted in vitro to alter oocyte quality? Summary answer Mitochondrial-related genes are dysregulated in poor quality oocytes and mitochondrial inhibitors can be used during IVM to alter oocyte quality. What is known already Good oocyte quality is essential for successful pregnancy yet we still have a poor understanding of the optimal composition of a good egg. Mitochondrial number and activity are important for oocyte quality and during maturation the ultrastructure of the mitochondria changes to a hooded or ring form. We have observed differences in the timing of this morphological change between good and poor quality oocytes in a sheep model. Oligomycin treatment of oocytes decreases the proportion of hooded mitochondria and increases mitochondrial membrane potential (MMP) indicating that hooded mitochondria may form during maturation to reduce mitochondrial activity prior to fertilisation. Study design, size, duration Gene expression differences were compared between poorer quality peripubertal lamb oocytes and adult oocytes harvested during estrus from pools of ∼80 abattoir-derived ovaries on three separate dates for each age. Denuded oocytes (GV stage) were frozen in three pools of ∼20 per age and total RNA isolated. Sheep oocytes (∼30 per group) were treated with oligomycin, FCCP or vehicle control in final 2 hours of IVM on five separate dates, fertilised and cultured. Participants/materials, setting, methods RNA sequencing cleaned reads were mapped using HISAT2 on sheep genome version ARS-UI_Ramb_v2. Count matrix were generated using featureCount and differential gene expression was performed using DESeq2. After normalisation, Wald test was used to define genes differentially expressed. Genes were considered significantly altered with a FDR < 5% after Benjamini-Hochberg correction. Mean cleavage rate was compared to vehicle control in five replicate experiments using one-way ANOVA with Benjamini, Krieger and Yekutieli FDR < 5%. Main results and the role of chance Oligomycin (ATP synthase inhibitor) decreased cleavage rate compared to control (P < 0.001) while FCCP (electron transport chain uncoupler) had no effect. There were 163 genes up-regulated and 194 genes down-regulated in lamb GV oocytes compared to adult GV stage (FDR <5%). Metascape pathway analysis of the up-regulated genes in lamb oocytes showed the most enriched pathway was Thermogenesis which included these genes: ACOT9, AJUBA, ATP5ME, ATP5MF, CALML4, CREB3L3, CRPPA, DNAJC15, DPM3, FMC1, LDHA, NDUFA12, NDUFAF2, NDUFS5, NT5C, PHGDH, POLR2G, PPARG, PRPS1L1, SDHAF3, SLIRP, TIMM8B and UBB. Six of these up-regulated genes were in the top 20 most highly expressed genes. Thermogenesis is the process by which the mitochondrial electron transport chain becomes uncoupled to dissipate heat and may also control production of free radicals by mitochondria and their response to oxidants. These genes and other mitochondrial related genes that were up-regulated in lamb GV oocytes (CKS2, DCN, PCP4L1, SLC51B) are involved in formation of the electron transport chain complexes, oxidative phosphorylation, ATP synthesis, mitochondrial organization and regulation of membrane potential. Together these results indicate that mitochondrial function is dysregulated in poorer quality oocytes and that treatment with mitochondrial inhibitors during IVM can alter oocyte quality. Limitations, reasons for caution This study was carried out in sheep oocytes and results would need to be confirmed in human oocytes if possible. Further research is needed to determine if decreasing MMP can improve oocyte quality and embryo development. Wider implications of the findings This animal model study indicates that mitochondria may be targeted during IVM to alter oocyte quality and this knowledge could be applied to develop ways of increasing human IVM and ART success rates. Trial registration number not applicable

CountASAP: A Lightweight, Easy to Use Python Package for Processing ASAPseq Data.

Declining sequencing costs coupled with the increasing availability of easy-to-use kits for the isolation of DNA and RNA transcripts from single cells have driven a rapid proliferation of studies centered around genomic and transcriptomic data. Simultaneously, a wealth of new techniques have been developed that utilize single cell technologies to interrogate a broad range of cell-biological processes. One recently developed technique, transposase-accessible chromatin with sequencing (ATAC) with select antigen profiling by sequencing (ASAPseq), provides a combination of chromatin accessibility assessments with measurements of cell-surface marker expression levels. While software exists for the characterization of these datasets, there currently exists no tool explicitly designed to reformat ASAP surface marker FASTQ data into a count matrix which can then be used for these downstream analyses. To address this, we created CountASAP, an easy-to-use Python package purposefully designed to transform FASTQ files from ASAP experiments into count matrices compatible with commonly-used downstream bioinformatic analysis packages. CountASAP takes advantage of the independence of the relevant data structures to perform fully parallelized matches of each sequenced read to user-supplied input ASAP oligos and unique cell-identifier sequences.

SCTC: inference of developmental potential from single-cell transcriptional complexity.

Inferring the developmental potential of single cells from scRNA-Seq data and reconstructing the pseudo-temporal path of cell development are fundamental but challenging tasks in single-cell analysis. Although single-cell transcriptional diversity (SCTD) measured by the number of expressed genes per cell has been widely used as a hallmark of developmental potential, it may lead to incorrect estimation of differentiation states in some cases where gene expression does not decrease monotonously during the development process. In this study, we propose a novel metric called single-cell transcriptional complexity (SCTC), which draws on insights from the economic complexity theory and takes into account the sophisticated structure information of scRNA-Seq count matrix. We show that SCTC characterizes developmental potential more accurately than SCTD, especially in the early stages of development where cells typically have lower diversity but higher complexity than those in the later stages. Based on the SCTC, we provide an unsupervised method for accurate, robust, and transferable inference of single-cell pseudotime. Our findings suggest that the complexity emerging from the interplay between cells and genes determines the developmental potential, providing new insights into the understanding of biological development from the perspective of complexity theory.

Unsupervised Gene-Cell Collective Representation Learning with Optimal Transport

Cell type identification plays a vital role in single-cell RNA sequencing (scRNA-seq) data analysis. Although many deep embedded methods to cluster scRNA-seq data have been proposed, they still fail in elucidating the intrinsic properties of cells and genes. Here, we present a novel end-to-end deep graph clustering model for single-cell transcriptomics data based on unsupervised Gene-Cell Collective representation learning and Optimal Transport (scGCOT) which integrates both cell and gene correlations. Specifically, scGCOT learns the latent embedding of cells and genes simultaneously and reconstructs the cell graph, the gene graph, and the gene expression count matrix. A zero-inflated negative binomial (ZINB) model is estimated via the reconstructed count matrix to capture the essential properties of scRNA-seq data. By leveraging the optimal transport-based joint representation alignment, scGCOT learns the clustering process and the latent representations through a mutually supervised self optimization strategy. Extensive experiments with 14 competing methods on 15 real scRNA-seq datasets demonstrate the competitive edges of scGCOT.

In silico approach for the identification of tRNA-derived small non-coding RNAs in SARS-CoV infection.

tsRNAs (tRNA-derived small non-coding RNAs), including tRNA halves (tiRNAs) and tRNA fragments (tRFs), have been implicated in some viral infections, such as respiratory viral infections. However, their involvement in SARS-CoV infection is completely unknown. A comprehensive analysis was performed to determine tsRNA populations in a mouse model of SARS-CoV-infected samples containing the wild-type and attenuated viruses. Data from the Gene Expression Omnibus (GEO) dataset at NCBI (accession ID GSE90624 ) was used for this study. A count matrix was generated for the tRNAs. Differentially expressed tRNAs, followed by tsRNAs derived from each significant tRNAs at different conditions and time points between the two groups WT(SARS-CoV-MA15-WT) vs Mock and ΔE (SARS-CoV-MA15-ΔE) vs Mock were identified. Notably, significantly differentially expressed tRNAs at 2dpi but not at 4dpi. The tsRNAs originating from differentially expressed tRNAs across all the samples belonging to each condition (WT, ΔE, and Mock) were identified. Intriguingly, tRFs (tRNA-derived RNA fragments) exhibited higher levels compared to tiRNAs (tRNA-derived stress-induced RNAs) across all samples associated with WT SARS-CoV strain compared to ΔE and mock-infected samples. This discrepancy suggests a non-random formation of tsRNAs, hinting at a possible involvement of tsRNAs in SARS-CoV viral infection.

Imputation for Single-cell RNA-seq Data with Non-negative Matrix Factorization and Transfer Learning.

Single-cell RNA sequencing (scRNA-seq) has been proven to be an effective technology for investigating the heterogeneity and transcriptome dynamics due to the single-cell resolution. However, one of the major problems for data obtained by scRNA-seq is excessive zeros in the count matrix, which hinders the downstream analysis enormously. Here, we present a method that integrates non-negative matrix factorization and transfer learning (NMFTL) to impute the scRNA-seq data. It borrows gene expression information from the additional dataset and adds graph-regularized terms to the decomposed matrices. These strategies not only maintain the intrinsic geometrical structure of the data itself but also further improve the accuracy of estimating the expression values by adding the transfer term in the model. The real data analysis result demonstrates that the proposed method outperforms the existing matrix-factorization-based imputation methods in recovering dropout entries, preserving gene-to-gene and cell-to-cell relationships, and in the downstream analysis, such as cell clustering analysis, the proposed method also has a good performance. For convenience, we have implemented the "NMFTL" method with R scripts, which could be available at https://github.com/FocusPaka/NMFTL.

Spatially Resolved Transcriptomics Unveils Unique T-Cell Dysfunctional States and Prognostic Gene Expression Signatures in TP53-Mutated Acute Myeloid Leukemia

Introduction Somatic TP53 mutations and 17p deletions with genomic loss of TP53 occur in 8% to 10% of AML and correlate with primary induction failure, high risk of relapse, and dismal prognosis. We have previously shown that TP53 abnormalities are associated with a pro-inflammatory and immunosuppressive tumor microenvironment (TME), including high CD274 and FoxP3 expression, with dysfunctional natural killer (NK)/CD8 + T-cell states and with response to flotetuzumab, a CD123 × CD3 bispecific T-cell engager. In the current study, we explore the cellular neighborhoods of TP53-mutated ( TP53-m) AML using spatially resolved transcriptomics and we employ machine learning to identify prognostic RNA signatures. Methods We analyzed the expression of >1,800 immune and TME genes in bone marrow (BM) formalin-fixed paraffin-embedded (FFPE) sections (5-µm) from a discovery cohort of 11 patients with chemotherapy-naïve AML (N=5 TP53-m and N=6 TP53-wt cases) using the GeoMx™ Digital Spatial Profiling (DSP) platform (Cancer Transcriptome Atlas with next generation sequencing read-out; NanoString Technologies, Seattle, WA). Imaging was performed at 20× magnification and a total of 151 circular regions of interest (ROIs) with a 300-600 µm diameter were selected (Fig. 1A). We then generated a ROI-by-gene count matrix for deconvolution and downstream pathway analysis. For each ROI, enrichment scores reflecting immune senescence/dysfunction and leukemia stemness were calculated with gene set variation analysis (GSVA), using a manually curated compendium of published gene signatures as input. We also employed public bulk and single-cell RNA-sequencing (sc-RNA-seq) AML cohorts for in silico modeling and validation. Results We initially captured transcripts from ROIs of TP53-m and TP53-wt AML with varying degrees of BM infiltration with T cells (CD3 hotspots) and AML blasts, and we found that TP53-m is enriched in T cell-specific, type I/II interferon (IFN) and pro-inflammatory genes ( IL1B), as well as leukemia stem cell (LSC)-related gene programs (LSC17). After dichotomizing ROIs based on a median split of CD3D RNA expression, we observed that T-cell low ROIs express higher levels of monocyte-macrophage and myeloid derived suppressor cell (MDSC)-related genes, including S100A9 and S100A8. These findings point to previously unappreciated links between BM infiltration with immune-modulatory myeloid cell types and leukemia stemness / T-cell abundance. We then tested for differential cell density in neighborhoods (regions of transcriptionally similar cells) across TP53-m and TP53-wt AML using MiloR in a sc-RNA-seq AML dataset annotated by cell type (N=42 cases, of which N=3 with TP53-m; Lasry A, et al. Nat. Cancer 2023). This orthogonal approach highlighted a significantly higher frequency (FDR <0.05) of multipotent and granulocyte-macrophage progenitor neighborhoods in TP53-m AML, alongside suppression of adaptive immune system pathways (Fig. 1B). We next inferred active receptor-ligand pairs mediating the crosstalk between T cells and leukemic blasts in the TME of TP53-m AML, and we uncovered decreased MHC class I/II-mediated signaling, curbed CD8 +/CD4 + T cell-to-AML interactions and enhanced CD6/ CDCP1( CD318) signaling (Fig. 1C). We also found that CD8 + and CD4 + T cells from TP53-m AML are enriched in effector memory (T EM) / terminally differentiated effector memory (T EMRA) and regulatory T-cell gene programs, respectively, compared with their cellular counterparts from TP53-wt patients (Fig. 1D). In particular, T EM cells from TP53-m AML expressed genes known to regulate T-cell dysfunctionality, foremost PRDM1, and transcription factors ( ZEB2) expected to drive the differentiation of killer cell lectin-like receptor (KLR) + exhausted T-cell states with residual cytolytic potential. Finally, we applied the least absolute shrinkage and selection operator (LASSO) statistical method for feature selection to our discovery AML dataset, and we constructed a TP53-m-related ‘spatial metagene’ that predicted poor overall survival in TCGA-LAML (N=170) and Beat-AML2 cases (N=444; Fig. 1E). Conclusions Spatial gene programs of leukemia stemness, T-cell dysfunction and immune suppression are enriched in TP53-m AML. It remains to be determined whether TP53-m AML with a pre-existing but ineffective T-cell response may be amenable to T cell-targeting immunotherapies.

ScPipe: an extended preprocessing pipeline for comprehensive single-cell ATAC-Seq data integration in R/Bioconductor.

scPipe is a flexible R/Bioconductor package originally developed to analyse platform-independent single-cell RNA-Seq data. To expand its preprocessing capability to accommodate new single-cell technologies, we further developed scPipe to handle single-cell ATAC-Seq and multi-modal (RNA-Seq and ATAC-Seq) data. After executing multiple data cleaning steps to remove duplicated reads, low abundance features and cells of poor quality, a SingleCellExperiment object is created that contains a sparse count matrix with features of interest in the rows and cells in the columns. Quality control information (e.g. counts per cell, features per cell, total number of fragments, fraction of fragments per peak) and any relevant feature annotations are stored as metadata. We demonstrate that scPipe can efficiently identify 'true' cells and provides flexibility for the user to fine-tune the quality control thresholds using various feature and cell-based metrics collected during data preprocessing. Researchers can then take advantage of various downstream single-cell tools available in Bioconductor for further analysis of scATAC-Seq data such as dimensionality reduction, clustering, motif enrichment, differential accessibility and cis-regulatory network analysis. The scPipe package enables a complete beginning-to-end pipeline for single-cell ATAC-Seq and RNA-Seq data analysis in R.

Synthesizing differentially private location traces including co-locations

Privacy-preserving location synthesizers have been widely studied to perform private geo-data analysis. They have also been used for generating datasets for research or competitions. However, existing location synthesizers do not take into account the friendship information of users. Because friends tend to visit the same place at the same time in practice, a location synthesizer should consider such co-locations of friends to generate a more realistic dataset. In this paper, we propose a novel location synthesizer that generates location traces including co-locations of friends. Our location synthesizer models the information about the co-locations with two parameters: friendship probability and co-location count matrix. Our synthesizer generates a synthetic graph based on the friendship probability and then generates synthetic co-locations using the synthetic graph and the co-location count matrix. The two parameters in our synthesizer provide strong privacy guarantees—the friendship probability provides node differential privacy (DP) and the co-location count matrix provides user-level DP. We evaluate our synthesizer using two real datasets. Our experimental results show that our synthesizer preserves co-locations and other statistical features while providing DP with reasonable privacy budgets, e.g., 0.2-node DP and 2-user-level DP.

A benchmark of computational pipelines for single-cell histone modification data

BackgroundSingle-cell histone post translational modification (scHPTM) assays such as scCUT&Tag or scChIP-seq allow single-cell mapping of diverse epigenomic landscapes within complex tissues and are likely to unlock our understanding of various mechanisms involved in development or diseases. Running scHTPM experiments and analyzing the data produced remains challenging since few consensus guidelines currently exist regarding good practices for experimental design and data analysis pipelines.ResultsWe perform a computational benchmark to assess the impact of experimental parameters and data analysis pipelines on the ability of the cell representation to recapitulate known biological similarities. We run more than ten thousand experiments to systematically study the impact of coverage and number of cells, of the count matrix construction method, of feature selection and normalization, and of the dimension reduction algorithm used. This allows us to identify key experimental parameters and computational choices to obtain a good representation of single-cell HPTM data. We show in particular that the count matrix construction step has a strong influence on the quality of the representation and that using fixed-size bin counts outperforms annotation-based binning. Dimension reduction methods based on latent semantic indexing outperform others, and feature selection is detrimental, while keeping only high-quality cells has little influence on the final representation as long as enough cells are analyzed.ConclusionsThis benchmark provides a comprehensive study on how experimental parameters and computational choices affect the representation of single-cell HPTM data. We propose a series of recommendations regarding matrix construction, feature and cell selection, and dimensionality reduction algorithms.

Longitudinal single-cell (sc) immune profiling of cerebrospinal fluid (CSF) from melanoma patients (pts) with leptomeningeal disease (LMD) treated with intrathecal (IT) and intravenous (IV) nivolumab (N).

2015 Background: LMD is associated with an extremely poor prognosis in melanoma pts, who currently have very limited treatment options. Our group previously reported initial safety and efficacy results from the dose escalation portion of a first in human phase I/IB trial using IT and IV N (NCT03025256) in melanoma LMD pts. Here we present longitudinal sc profiling of immune cells in CSF from a subset of pts in this trial to better understand the immune composition of this unique microenvironment and features associated with treatment and outcomes. Methods: 39 CSF samples were collected longitudinally from 5 long survivors (LS; IT N number of doses received 10-92) and 5 rapid progressors (RP; IT N doses 2-9) and profiled by sc RNA sequencing. Raw FASTQ reads were aligned to the GRCh38 human reference genome using Cell Ranger v6.0.0 to generate the count matrix. All samples were aggregated by Seurat v4.3.0 and data integration was performed using Harmony v0.1.0. Cells were filtered by following criteria: (1) > 20% mitochondrial content, or (2) express < 100 or > 2,500-5,000 genes, according to sample specific distribution. Empty droplets and doublets were identified by EmptyDrops function from the R package DropletUtils v1.18.0 and DoubletFinder v2.0.3, respectively. We applied normalization, FindVariableFeatures (n = 2,000), scaling, PCA, UMAP and FindClusters to summarize the integrated samples and determine cell clusters. Cell types were annotated based on (1) canonical marker genes using FindAllMarkers, (2) published gene expression signatures, and (3) reference-based tool using Symphony v.0.1.0 on published data: GSE164378 and GSE158803. Statistical analysis was conducted using the Wilcoxon rank-sum test. Results: A total of 10 patients were included in this pilot analysis. Five pts were LS (2 males; IT dose 5 mg, 1 pt; 10 mg, 2 pts; 20 mg, 2 pts; 2 BRAF mutant; 5 negative CSF cytology at baseline) and 5 pts were RP (3 males; IT dose 5 mg, 1 pt; 20 mg, 2 pts; 50 mg, 2 pts; 4 BRAF mutant; 4 positive CSF cytology at baseline). At baseline [prior to cycle (C)1 of IT N], LS pts had a higher percentage of NK cells (relative to total immune cells; P = 0.036) and mucosal-associated invariant T cells (relative to total T cells, P = 0.021) than RP pts. Prior to C2, LS had a significantly higher percentage of T cells (relative to all cells; P = 0.036). The abundance of immune cells and tumor cells were not different between the two groups at C3, at which point pts have received two doses of IT N and one dose of IV N. Conclusions: Exploratory sc analysis of longitudinal CSF samples of LMD pts suggest that baseline immune features may predict outcomes with IT N. Analysis of additional pts is ongoing to provide further insights.

STellaris: a web server for accurate spatial mapping of single cells based on spatial transcriptomics data.

Single-cell RNA sequencing (scRNA-seq) provides insights into gene expression heterogeneities in diverse cell types underlying homeostasis, development and pathological states. However, the loss of spatial information hinders its applications in deciphering spatially related features, such as cell-cell interactions in a spatial context. Here, we present STellaris (https://spatial.rhesusbase.com), a web server aimed to rapidly assign spatial information to scRNA-seq data based on their transcriptomic similarity with public spatial transcriptomics (ST) data. STellaris is founded on 101 manually curated ST datasets comprising 823 sections across different organs, developmental stages and pathological states from humans and mice. STellaris accepts raw count matrix and cell type annotation of scRNA-seq data as the input, and maps single cells to spatial locations in the tissue architecture of properly matched ST section. Spatially resolved information for intercellular communications, such as spatial distance and ligand-receptor interactions (LRIs), are further characterized between annotated cell types. Moreover, we also expanded the application of STellaris in spatial annotation of multiple regulatory levels with single-cell multiomics data, using the transcriptome as a bridge. STellaris was applied to several case studies to showcase its utility of adding value to the ever-growing scRNA-seq data from a spatial perspective.

DEGGs: an R package with shiny app for the identification of differentially expressed gene-gene interactions in high-throughput sequencing data.

The discovery of differential gene-gene correlations across phenotypical groups can help identify the activation/deactivation of critical biological processes underlying specific conditions. The presented R package, provided with a count and design matrix, extract networks of group-specific interactions that can be interactively explored through a shiny user-friendly interface. For each gene-gene link, differential statistical significance is provided through robust linear regression with an interaction term. DEGGs is implemented in R and available on GitHub at https://github.com/elisabettasciacca/DEGGs. The package is also under submission on Bioconductor.

LBA01-01 UROPATHOGENS IN THE PROSTATE ARE ASSOCIATED WITH PROSTATE SIZE, INDEPENDENT OF AGE EFFECTS

You have accessJournal of UrologyCME1 Apr 2023LBA01-01 UROPATHOGENS IN THE PROSTATE ARE ASSOCIATED WITH PROSTATE SIZE, INDEPENDENT OF AGE EFFECTS Alec Sun, Prajit Khooblall, Juan Sebastian Rodriguez-Alvarez, Smita De, and Aaron Miller Alec SunAlec Sun More articles by this author , Prajit KhooblallPrajit Khooblall More articles by this author , Juan Sebastian Rodriguez-AlvarezJuan Sebastian Rodriguez-Alvarez More articles by this author , Smita DeSmita De More articles by this author , and Aaron MillerAaron Miller More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003360.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Benign prostatic hyperplasia (BPH) affects an estimated 70% of men between the ages of 60-69, yet the etiology is not well understood. Recently, the human urinary tract microbiome has been associated with a variety of urologic diseases, especially those mediated by an inflammatory pathway. Here, we sought to determine associations between age-independent prostate size and microbiome. METHODS: Men over 18 years old undergoing Holmium Laser Enucleation of the Prostate (HoLEP) for BPH with no history of prostate cancer, prostate surgery, or pelvic radiation were recruited. Patients were excluded if they had a positive preprocedural urine culture, recent UTI requiring antibiotics, bladder stones, or if they were catheter-dependent due to obstruction. From each patient, prostate tissue, midstream urine, and urethral and specimen container swabs were collected. All non-prostate samples were used as contamination controls. Patient data such as age, prostate-specific antigen (PSA) level, BPH symptoms, and prostate size were recorded. All samples underwent DNA extraction, 16S sequencing, and analysis in R statistical software. After quality control, reads associated with the controls were removed. High-quality, decontaminated data were assessed for diversity (alpha, beta, taxonomy). The correlation between amplicon sequence variants (ASVs) and patient metrics were quantified through Sparcc correlations, which was designed for count matrix correlations. RESULTS: 20 patients qualified, consented, and were analyzed in this study. Mean age was 68.6 years, mean PSA was 3.4 ng/mL, and mean prostate size was 107.9 g. After bioinformatic decontamination of samples with the negative controls, diversity analyses showed site-specific differences between the urine, urethral swab, and prostate microbiomes were greater than inter-individual variability, indicative of distinct microbiomes. Common uropathogens were positively associated with prostate size. These included five ASVs belonging to Enterobacter cloaceae (p=0.02-0.03), four Planococcaceae ASVs (p=0.008-0.03), and four Acinetobacter (p=0.014-0.026). Only one ASV in the Ralstonia genus exhibited a significant association with age (p=0.015). CONCLUSIONS: This study is the first to characterize the prostatic microbiome in BPH and to link prostate size to specific common bacterial uropathogens while controlling for age and contamination. Further research with a larger sample size and culturomics will provide insight into the mechanisms of how the prostate microbiome contributes to enlarged size. Source of Funding: Endourology Society Summer Student Scholarship © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e1176 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Alec Sun More articles by this author Prajit Khooblall More articles by this author Juan Sebastian Rodriguez-Alvarez More articles by this author Smita De More articles by this author Aaron Miller More articles by this author Expand All Advertisement PDF downloadLoading ...

Genomic relatedness and dissemination of bla NDM-5 among Acinetobacter baumannii isolated from hospital environments and clinical specimens in Thailand.

Acinetobacter baumannii (A. baumannii) is an important cause of nosocomial infection, especially in intensive care units (ICUs). It has the propensity to tolerate various environments and multiple classes of antibiotics. Our study aimed to characterize the comparative genomes of A. baumannii from hospital environments and clinical isolates. Clinical and environmental A. baumannii isolates were collected from a university hospital. Antibiotic susceptibility testing was performed, antibiotic resistance genes (ARGs) were characterized, and repetitive element palindromic-PCR (rep-PCR) typing was performed. Eight representative A. baumannii isolated from environmental and clinical samples from the same wards were selected for whole-genome sequencing (WGS) using the Illumina platform. A total of 106 A. baumannii isolates were obtained from 312 hospital environmental samples. A high percentage of samples with A. baumannii colonization were detected from AMBU bags (77.9%), followed by bedrails (66.7%) and suction tubes (66.7%). We found that 93.4% of the environmental isolates were multidrug-resistant A. baumannii (MDRAB), and 44.7% were extremely drug-resistant A. baumannii (XDRAB). bla OXA-23 bla NDM, and bla OXA-58 were present in 80.2%, 78.3%, and 0.9% of all isolates, respectively. Sixty-one A. baumannii isolates were collected from patient specimens in the same ward. Among all A. baumannii clinical isolates, MDRAB and XDRAB accounted for 82% and 55.7%, respectively. The most dominant ARGs identified was bla OXA-23 (80.3%), followed by bla NDM (55.7%). The genetic diversity of all isolates using rep-PCR could be divided into 33 genotypes. The genome size of eight A. baumannii ranged from 3.78-4.01 Mb. We found six of eight strains to be bla NDM-5-harboring A. baumannii. Mobile genetic elements (MGEs), such as integron1 (intl1), located upstream of bla NDM-5 were observed. The phylogenomic relationship of the core and pan genomes as well as the single nucleotide polymorphism (SNP) count matrix revealed the genetic similarity of A. baumannii environmental and clinical strains obtained from the same ward. This study confirmed that A. baumannii colonized in hospital environments were the main reservoir of nosocomial infection and provides critical information to guide the control of A. baumannii infection.

Racial disparities in tumor microbiome in young-onset and average-onset colorectal cancer.

13 Background: The occurrence of young-onset colorectal cancer (yoCRC) is rising alarmingly, with a disproportionate incidence in Black patients as compared to White. We and others have previously shown differences in tumor microbiomes between yoCRC and average-onset colorectal cancer (aoCRC), but it is unknown if these differences contribute to racial disparities. We therefore analyzed racial differences in the intra-tumoral microbiome of CRC in young adults (<50 years). Methods: We analyzed 277 samples of histologically confirmed cases of stage I-IV CRC patients identified as either non-Hispanic Black or non-Hispanic White, who underwent surgical resection at Cleveland Clinic from year 2000-2020, who consented to a prospective biorepository. Fresh frozen specimens from the primary tumor with paired adjacent nonmalignant tissue were analyzed. The 16s rRNA gene was amplified and sequenced using V4 region primers and Illumina’s MiSeq system. Using DADA2, the quality filtered 16S rRNA amplicon reads were clustered and annotated as amplicon sequence variants (ASVs). The abundance count matrix was analyzed for alpha and beta diversity and differential abundance analysis (DAA) using Phyloseq package. Statistical tests included analysis of variance (ANOVA), permutational multivariate analysis (PERMANOVA), linear regression and Wilcoxon test. DAA and correlation analysis were controlled for false discovery rate using Benjamini Hochberg correction. Results: Differential abundance analysis highlighted key differences in the microbiome composition of Black and White patients with yoCRC. Amongst Black patients, the most prevalent taxa were Limosilactobacillus, Bacillus, Staphylococcus, Listeria and Akkermansia, whereas amongst White patients the most prevalent taxa were Enterococcus and Escherichia-Shigella (Table). Microbial profiles were also significantly different between Black patients with yoCRC and aoCRC and between White patients with yoCRC and aoCRC. At ASV level, the microbiome of Black yoCRC is more similar (R2 =0.87) to Black aoCRC, in comparison to white yoCRC (R2=0.57). Conclusions: We found significant differences between the intra-tumoral microbiome of yoCRC in the United States by race. Future epidemiologic studies and public health interventions need to account for these differences to reduce risk of yoCRC across various US populations. [Table: see text]

Complex hierarchical structures in single-cell genomics data unveiled by deep hyperbolic manifold learning.

With the advances in single-cell sequencing techniques, numerous analytical methods have been developed for delineating cell development. However, most are based on Euclidean space, which would distort the complex hierarchical structure of cell differentiation. Recently, methods acting on hyperbolic space have been proposed to visualize hierarchical structures in single-cell RNA-seq (scRNA-seq) data and have been proven to be superior to methods acting on Euclidean space. However, these methods have fundamental limitations and are not optimized for the highly sparse single-cell count data. To address these limitations, we propose scDHMap, a model-based deep learning approach to visualize the complex hierarchical structures of scRNA-seq data in low-dimensional hyperbolic space. The evaluations on extensive simulation and real experiments show that scDHMap outperforms existing dimensionality-reduction methods in various common analytical tasks as needed for scRNA-seq data, including revealing trajectory branches, batch correction, and denoising the count matrix with high dropout rates. In addition, we extend scDHMap to visualize single-cell ATAC-seq data.