Mengbo Li

Motivation: Understanding complex cellular neighborhoods has provided new insights into tissue biology. Accurate neighborhood identification is crucial, yet existing methods often focus on hard assignment and do not generate cell-specific neighborhood profiles at single-cell level. Results: We developed hoodscanR, a Bioconductor package identifying and analyzing cellular neighborhoods in spatial data. The central output of hoodscanR is a cell-level neighborhood probability profile, which represents partial membership of each cell across multiple annotation-defined neighborhoods. This probabilistic representation supports downstream analyses including neighborhood visualization, uncertainty assessment, neighborhood-based clustering and neighborhood-aware differential expression. Applying hoodscanR to breast and lung cancer datasets, we showcase its ability to characterize mixed tissue environments and identify transcriptional changes in tumor cells from distinct spatial neighborhoods. Availability: The hoodscanR package is publicly available in Bioconductor at https://bioconductor.org/packages/release/bioc/html/hoodscanR.html.

BACKGROUND: Myocardial infarction (MI) is a leading cause of death worldwide and can eliminate up to a third of the cardiomyocytes within the human heart. Although cardiomyocytes undergo mitosis during early development, most cardiomyocytes cease cell cycling soon after birth. In contrast, rodent MI models have shown that cardiomyocytes increase mitosis in response to ischemia; however, this has not been shown in humans. METHODS: Using a unique premortem post-MI human heart, immunostaining, bulk RNA sequencing, proteomics, metabolomics, single-nucleus RNA sequencing and a novel post-MI human biopsy method, we investigated human cardiomyocyte mitosis post-MI. RESULTS: We show that adult human cardiomyocytes exhibit increased mitosis and cytokinesis in response to ischemia. CONCLUSIONS: Future development of therapeutics to enhance this intrinsic mitotic potential could lead to new treatments that reverse heart failure via cardiac regeneration.

Background: Approximately half of all high-grade serous ovarian carcinomas (HGSCs) have a therapeutically targetable defect in homologous recombination (HR) DNA repair. While there are genomic and transcriptomic methods, developed for other cancers, to identify HR deficient (HRD) samples, there are no gene expression-based tools to predict HR status in HGSC specifically. We have built a HGSC-specific model to predict HR status using gene expression. Methods: We separated The Cancer Genome Atlas (TCGA) cohort of HGSCs into training ( n = 288) and testing ( n = 73) sets and labelled each case as HRD or HR proficient (HRP) based on the clinical standard for classification. Using the training set, we performed differential gene expression analysis between HRD and HRP cases. The 2604 significantly differentially expressed genes were used to train a penalised logistic regression model. Results: IdentifiHR uses the expression of 209 genes to predict HR status in HGSC. These genes preserve the genomic damage signal, capturing known regions of HR-specific copy number alteration which impact gene expression. IdentifiHR is 85% accurate in the TCGA test set and 86% accurate in an independent cohort of 99 samples, taken from primary tumours, ascites and normal fallopian tubes. Further, IdentifiHR is 84% accurate in pseudobulked single-cell HGSC sequencing from 37 patients and outperforms existing expression-based methods to predict HR status, being BRCAness, MutliscaleHRD and expHRD. Conclusions: IdentifiHR is an accurate model to predict HR status in HGSC. It is available as an open source R package, empowering researchers to robustly classify HR status when only transcriptomic sequencing data is available.

Differential transcript usage (DTU) refers to changes in the relative abundance of transcript isoforms of the same gene between experimental conditions, even when the total expression of the gene does not change. DTU analysis requires the quantification of individual isoforms from RNA-seq data, which has a high level of uncertainty due to transcript overlap and read-to-transcript ambiguity (RTA). Popular DTU analysis methods do not directly account for the RTA overdispersion within their statistical frameworks, leading to reduced statistical power or poor error rate control, particularly in scenarios with small sample sizes. This article presents limma and edgeR analysis pipelines that account for RTA during DTU assessment. Leveraging recent advancements in the limma and edgeR Bioconductor packages, we propose DTU analysis pipelines optimized for small and large datasets with a unified interface via the diffSplice function. The pipelines make use of divided counts to remove RTA-induced dispersion from transcript isoform counts and account for the sparsity in transcript-level counts. Simulations and analyses of real data from mouse mammary epithelial cells demonstrate that the diffSplice pipelines provide greater power, improved efficiency, and improved false discovery rate control compared to existing specialized DTU methods.

Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows integrating diverse computational methods and software packages. We provide a freely accessible, open-source, continuously updated and tested online book containing reproducible code examples, datasets, and discussion about data analysis workflows for spatial omics data using Bioconductor in R, including interoperability with Python.

In spatial transcriptomics, most existing approaches for spatial domain detection analyze slide-wide gene expression patterns while integrating information from local neighborhoods or nearest neighbors. However, such global strategies can overlook tissue organization and obscure regional heterogeneity. To address this, we present scider, a framework for spatial data analysis to preserve tissue structures. scider defines spatial domains from local cellular organization and distribution, instead of relying on global clustering of gene expression and neighborhood profiles. Kernel density estimation (KDE) is used to characterize the spatial distribution of cells, enabling the robust and unbiased identification of regions of interest (ROIs) beyond histological annotations. We show that ROIs make multi-sample analysis possible and reveal spatial patterns that are undetectable in single-sample analysis. Additionally, KDE-derived contour lines define regions of similar cell density, supporting cell type composition and differential expression analyses along continuous spatial gradients.

Mass spectrometry (MS) is the technology standard for expression proteomics, but statistical analysis of the resulting data is complicated by the occurrence of missing values. Missing values remain ubiquitous in MS-based proteomics data and are especially frequent in the emerging fields of single-cell and spatial proteomics. The limpa package implements new methods for quantification and differential expression analysis of MS proteomics data, including probabilistic information recovery from missing values. limpa summarises peptide-level data to estimate an expression value for every protein in every sample. Expression values that are supported by fewer detected peptides or involve more missing values are treated as less precise and are downweighted in the differential expression analysis, maximising statistical power while avoiding false discoveries. limpa produces a linear model object suitable for downstream analysis with the limma package, allowing complex experimental designs and other downstream tasks such as the gene ontology or pathway analysis.

Differential transcript usage (DTU) refers to changes in the relative abundance of transcript isoforms of the same gene between experimental conditions, even when the total expression of the gene doesn’t change. DTU analysis requires the quantification of individual isoforms from RNA-seq data, which has a high level of uncertainty due to transcript overlap and read-to-transcript ambiguity (RTA). Popular DTU analysis methods do not directly account for the RTA overdispersion within their statistical frameworks, leading to reduced statistical power or poor error rate control, particularly in scenarios with small sample sizes. This article presents limma and edgeR analysis pipelines that account for RTA during DTU assessment. Leveraging recent advancements in the limma and edgeR Bioconductor packages, we propose DTU analysis pipelines optimized for small and large datasets with a unified interface via the diffSplice function. The pipelines make use of divided counts to remove RTA-induced dispersion from transcript isoform counts and account for the sparsity in transcript-level counts. Simulations and analysis of real data from mouse mammary epithelial cells demonstrate that the diffSplice pipelines provide greater power, improved efficiency, and improved FDR control compared to existing specialized DTU methods.

The left and right ventricles of the human heart are functionally and developmentally distinct such that genetic or acquired insults can cause dysfunction in one or both ventricles resulting in heart failure. To better understand ventricle-specific molecular changes influencing heart failure development, we first performed unbiased quantitative mass spectrometry on pre-mortem non-diseased human myocardium to compare the metabolome and proteome between the normal left and right ventricles. Constituents of gluconeogenesis, glycolysis, lipogenesis, lipolysis, fatty acid catabolism, the citrate cycle and oxidative phosphorylation were down-regulated in the left ventricle, while glycogenesis, pyruvate and ketone metabolism were up-regulated. Inter-ventricular significance of these metabolic pathways was then found to be diminished within end-stage dilated cardiomyopathy and ischaemic cardiomyopathy, while heart failure-associated pathways were increased in the left ventricle relative to the right within ischaemic cardiomyopathy, such as fluid sheer-stress, increased glutamine-glutamate ratio, and down-regulation of contractile proteins, indicating a left ventricular pathological bias.

BACKGROUND: Pregnancy imposes significant cardiovascular adaptations, including progressive increases in plasma volume and cardiac output. For most women, this physiological adaptation resolves at the end of pregnancy, but some women develop pathological dilatation and ultimately heart failure late in pregnancy or in the postpartum period, manifesting as peripartum cardiomyopathy (PPCM). Despite the mortality risk of this form of heart failure, the molecular mechanisms underlying PPCM have not been extensively examined in human hearts. METHODs: Protein and metabolite profiles from left ventricular tissue of end-stage PPCM patients (N=6–7) were compared with dilated cardiomyopathy (DCM; N=5–6) and nonfailing donors (N=7–18) using unbiased quantitative mass spectrometry. All samples were derived from the Sydney Heart Bank. Data are available via ProteomeXchange with identifier PXD055986. Differential protein expression and metabolite abundance and Kyoto Encyclopedia of Genes and Genomes pathway analyses were performed. RESULTS: Proteomic analysis identified 2 proteins, SBSPON (somatomedin B and thrombospondin type 1 domain-containing protein precursor) and TNS3 (tensin 3), that were uniquely downregulated in PPCM. SBSPON, an extracellular matrix protein, and TNS3, involved in actin remodeling and cell signaling, may contribute to impaired tissue remodeling and fibrosis in PPCM. Metabolomic analysis revealed elevated levels of homogentisate and deoxycholate and reduced levels of lactate and alanine in PPCM, indicating disrupted metabolic pathways and glucose utilization. Both PPCM and DCM shared pathways related to inflammation, immune responses, and signal transduction. However, thyroid hormone signaling was notably reduced in PPCM, affecting contractility and calcium handling through altered expression of PLN (phospholamban) and Sarcoendoplasmic Reticulum Calcium ATPase (SERCA). Enhanced endoplasmic reticulum stress and altered endocytosis pathways in PPCM suggested additional mechanisms of energy metabolism disruption. CONCLUSIONS: The present study reveals unique posttranslational molecular features of the PPCM myocardium, which mediates cellular and metabolic remodeling, and holds promise as potential targets for therapeutic intervention.