The purpose of this study was to investigate changes in the lipidome of patients with sepsis to identify signaling lipids associated with poor outcomes that could be linked to future therapies. Adult patients with sepsis were enrolled within 24h of sepsis recognition. Patients meeting Sepsis‐3 criteria were enrolled from the emergency department or intensive care unit and blood samples were obtained. Clinical data were collected and outcomes of rapid recovery, chronic critical illness (CCI), or early death were adjudicated by clinicians. Lipidomic analysis was performed on two platforms, the Sciex™ 5500 device to perform a lipidomic screen of 1450 lipid species and a targeted signaling lipid panel using liquid‐chromatography tandem mass spectrometry. For the lipidomic screen, there were 274 patients with sepsis: 192 with rapid recovery, 47 with CCI, and 35 with early deaths. CCI and early death patients were grouped together for analysis. Fatty acid (FA) 12:0 was decreased in CCI/early death, whereas FA 17:0 and 20:1 were elevated in CCI/early death, compared to rapid recovery patients. For the signaling lipid panel analysis, there were 262 patients with sepsis: 189 with rapid recovery, 45 with CCI, and 28 with early death. Pro‐inflammatory signaling lipids from ω‐6 poly‐unsaturated fatty acids (PUFAs), including 15‐hydroxyeicosatetraenoic (HETE), 12‐HETE, and 11‐HETE (oxidation products of arachidonic acid [AA]) were elevated in CCI/early death patients compared to rapid recovery. The pro‐resolving lipid mediator from ω‐3 PUFAs, 14(S)‐hydroxy docosahexaenoic acid (14S‐HDHA), was also elevated in CCI/early death compared to rapid recovery. Signaling lipids of the AA pathway were elevated in poor‐outcome patients with sepsis and may serve as targets for future therapies.

Cellular communication through biochemical signaling is fundamental to every biological activity. Investigating cell signaling diffusions across cell types can further help understand biological mechanisms. In recent years, this has become an important research topic as single-cell sequencing technologies have matured. However, cell signaling activities are spatially constrained, and single-cell data cannot provide spatial information for each cell. This issue may cause a high false discovery rate, and using spatially resolved transcriptomics data is necessary. On the other hand, as far as we know, most existing methods focus on providing an ad hoc measurement to estimate intercellular communication instead of relying on a statistical model. It is undeniable that descriptive statistics are straightforward and accessible, but a suitable statistical model can provide more accurate and reliable inference. In this way, we propose a generalized linear regression model to infer cellular communications from spatially resolved transcriptomics data, especially spot-based data. Our BAyesian Tweedie modeling of COMmunications (BATCOM) method estimates the communication scores between cell types with the consideration of their corresponding distances. Due to the properties of the regression model, BATCOM naturally provides the direction of the communication between cell types and the interaction of ligands and receptors that other approaches cannot offer. We conduct simulation studies to assess the performance under different scenarios. We also employ BATCOM in a real-data application and compare it with other existing algorithms. In summary, our innovative model can fill gaps in the inference of cell–cell communication and provide a robust and straightforward result.

Background: Cholesterol metabolism is dysregulated in sepsis contributing to patient heterogeneity. Subphenotypes displaying lower lipoprotein levels and higher mortality (HYPO) or higher lipoprotein levels and lower mortality (NORMO), were described. We developed a simplified clinical algorithm for bedside subphenotype recognition. Methods: We analyzed data from four prospective studies (internal dataset), focusing on HYPO and NORMO subphenotypes. A 1,000-tree Random Forest classifier and logistic regression models were built, using clinical features to predict subphenotypes. Performance was evaluated by comparing predictions to actual subphenotypes derived from a machine learning model. The model was applied to an external dataset of 281 patients from three French studies. Results: The internal cohort consisted of 386 patients (median age 63, 46% female). Four clinical features [hepatic SOFA, cardiovascular SOFA, low (LDL-C) and high-density lipoprotein cholesterol (HDL-C)] predicted HYPO vs. NORMO subphenotypes with an AUC of 0.86, a sensitivity of 0.771 and a specificity of 0.779. In the internal dataset, 28-day mortality for HYPO vs. NORMO patients was 26% vs. 15%, and in the external cohort, 30% vs. 10%. HYPO internal vs. external dataset LDL-C levels were similar (p = 0.99), but HDL-C (p = 0.02) levels were different. Median NORMO internal vs. external dataset LDL-C (p = 0.99) and HDL-C (p = 0.12) levels were similar. HYPO patients had lower LDL-C, HDL-C and total cholesterol than NORMO patients in both internal and external datasets. Conclusions: Our simplified clinical data algorithm may allow for bedside recognition of septic patients displaying lipid dysregulation subphenotypes. External validation is needed to verify these results.

Summary: The inference of cell–cell communication is important, as it unveils the intricate cellular behaviors at the molecular level, providing crucial insights essential for understanding complex biological processes and informing targeted interventions in various pathological contexts. Here, we present TWCOM, an R package that implements a Tweedie distribution-based model for accurate cell–cell communication inference. Operating under a generalized additive model framework, TWCOM adeptly handles both single-cell resolution and spot-based spatially resolved transcriptomics data, providing a versatile tool for robust biological sample analysis. Availability and implementation: The R package TWCOM is available at https://github.com/dongyuanwu/TWCOM. Comprehensive documentation is included with the package.

Due to the development of next‐generation RNA sequencing technologies, there has been tremendous progress in research involving determining the role of genomics, transcriptomics, and epigenomics in complex biological systems. However, scientists have realized that information obtained using earlier technology, frequently called “bulk RNA‐seq” data, provides information averaged across all the cells present in a tissue. Relatively newly developed single‐cell (single‐cell RNA sequencing [scRNA‐seq]) technology allows us to provide transcriptomic information at a single‐cell resolution. Nevertheless, these high‐resolution data have their own complex natures and demand novel statistical data analysis methods to provide effective and highly accurate results on complex biological systems. In this review, we cover many such recently developed statistical methods for researchers wanting to pursue scRNA‐seq statistical and computational research as well as scientific research about these existing methods and free software tools available for their generated data. This review is certainly not exhaustive due to page limitations. We have tried to cover the popular methods starting from quality control to the downstream analysis of finding differentially expressed genes and concluding with a brief description of network analysis.This article is categorized under: Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data Statistical Models > Generalized Linear Models Software for Computational Statistics > Software/Statistical Software