Expanding upon the critical advancements brought forth by single-cell omics in pulmonary hypertension (PH) research, this review delves deep into how these technologies have been piloted in a new era of understanding this complex disease. By leveraging the power of single cell transcriptomics (scRNA-seq), researchers can now dissect the complicated cellular ecosystem of the lungs, examining the key players such as endothelial cells, smooth muscle cells, pericytes, and immune cells, and their unique roles in the pathogenesis of PH. This more granular view is beyond the limitations of traditional bulk analysis, allowing for the identification of novel therapeutic targets previously obscured in the aggregated data. Connectome analysis based on single-cell omics of the cells involved in pathological changes can reveal a clearer picture of the cellular interactions and transitions in the cellular subtypes. Furthermore, the review acknowledges the challenges that lie ahead, including the need for enhancing the resolution of scRNA-seq to capture even finer details of cellular changes, overcoming logistical barriers in processing human tissue samples, and the necessity of integrating diverse omics approaches to fully comprehend the molecular underpinnings of PH. The promise of these single-cell technologies is immense, offering the potential for targeted drug development and the discovery of biomarkers for early diagnosis and disease monitoring. Through these advancements, the field moves closer to realizing the goal of precision medicine for patients with PH.

Glioblastoma (GBM) is a highly heterogeneous disease with poor clinical outcomes. To comprehensively dissect the molecular landscape of GBM and heterogeneous macrophage clusters in the progression of GBM, this study integrates single-cell and bulk transcriptome data to recognize a distinct pro-tumor macrophage cluster significantly associated with the prognosis of GBM and develop a GBM prognostic signature to facilitate prior subtypes. Leveraging glioma single-cell sequencing data, we identified a novel pro-tumor macrophage subgroup, marked by S100A9, which might interact with endothelial cells to facilitate tumor progression via angiogenesis. To further benefit clinical application, a prognostic signature was established with the genes associated with pro-tumor macrophages. Patients classified within the high-risk group characterized with enrichment in functions related to tumor progression, including epithelial-mesenchymal transition and hypoxia, displays elevated mutations in the TERT promoter region, reduced methylation in the MGMT promoter region, poorer prognoses, and diminished responses to temozolomide therapy, thus effectively discriminating between the prognostic outcomes of GBM patients. Our research sheds light on the intricate microenvironment of gliomas and identifies potential molecular targets for the development of novel therapeutic approaches.

Mosaic Analysis with Double Markers (MADM) is a powerful genetic method typically used for lineage tracing and to disentangle cell autonomous and tissue-wide roles of candidate genes with single cell resolution. Given the relatively sparse labeling, depending on which of the 19 MADM chromosomes one chooses, the MADM approach represents the perfect opportunity for cell morphology analysis. Various MADM studies include reports of morphological anomalies and phenotypes in the central nervous system (CNS). MADM for any candidate gene can easily incorporate morphological analysis within the experimental workflow. Here, we describe the methods of morphological cell analysis which we developed in the course of diverse recent MADM studies. This chapter will specifically focus on methods to quantify aspects of the morphology of neurons and astrocytes within the CNS, but these methods can broadly be applied to any MADM-labeled cells throughout the entire organism. We will cover two analyses-soma volume and dendrite characterization-of physical characteristics of pyramidal neurons in the somatosensory cortex, and two analyses-volume and Sholl analysis-of astrocyte morphology.

Understanding the diversity of gastrointestinal (GI) immune cells, especially in the muscularis propria, is crucial for understanding their role in the maintenance of enteric neurons and smooth muscle and their contribution to GI motility. Here, we present a detailed protocol for isolating single immune cells from the human gastric muscularis propria. We describe steps for tissue preservation, dissection, and dissociation of the muscularis propria. We then detail procedures for magnetic sorting of CD45+ cells and single-cell RNA sequencing (scRNA-seq) analysis. For complete details on the use and execution of this protocol, please refer to Chikkamenahalli et al.1.

Cell-type-specific domains are the anatomical domains in spatially resolved transcriptome (SRT) tissues where particular cell types are enriched coincidentally. It is challenging to use existing computational methods to detect specific domains with low-proportion cell types, which are partly overlapped with or even inside other cell-type-specific domains. Here, we propose De-spot, which synthesizes segmentation and deconvolution as an ensemble to generate cell-type patterns, detect low-proportion cell-type-specific domains, and display these domains intuitively. Experimental evaluation showed that De-spot enabled us to discover the co-localizations between cancer-associated fibroblasts and immune-related cells that indicate potential tumor microenvironment (TME) domains in given slices, which were obscured by previous computational methods. We further elucidated the identified domains and found that Srgn may be a critical TME marker in SRT slices. By deciphering T cell-specific domains in breast cancer tissues, De-spot also revealed that the proportions of exhausted T cells were significantly increased in invasive vs. ductal carcinoma.

Adipose tissue immune cells are heterogeneous and dynamic, alter metabolism, and drive immune responses. Here, we present a protocol for assessment and characterization of murine adipose tissue immune cells using fluorescence-based flow cytometry and sorting into pure populations. We describe steps for isolation of the stromovascular fraction, antibody staining, and data collection by flow cytometry. We also discuss common issues and troubleshooting steps. For complete details on the use and execution of this protocol, please refer to Carey et al.1.

The mammalian kidney maintains fluid homeostasis through diverse epithelial cell types generated from nephron and ureteric progenitor cells. To extend a developmental understanding of the kidney\'s epithelial networks, we compared chromatin organization (single-nuclear assay for transposase-accessible chromatin sequencing [ATAC-seq]; 112,864 nuclei) and gene expression (single-cell/nuclear RNA sequencing [RNA-seq]; 109,477 cells/nuclei) in the developing human (10.6-17.6 weeks; n = 10) and mouse (post-natal day [P]0; n = 10) kidney, supplementing analysis with published mouse datasets from earlier stages. Single-cell/nuclear datasets were analyzed at a species level, and then nephron and ureteric cellular lineages were extracted and integrated into a common, cross-species, multimodal dataset. Comparative computational analyses identified conserved and divergent features of chromatin organization and linked gene activity, identifying species-specific and cell-type-specific regulatory programs. In situ validation of human-enriched gene activity points to human-specific signaling interactions in kidney development. Further, human-specific enhancer regions were linked to kidney diseases through genome-wide association studies (GWASs), highlighting the potential for clinical insight from developmental modeling.

The recent advances in high-throughput single-cell sequencing have created an urgent demand for computational models which can address the high complexity of single-cell multiomics data. Meticulous single-cell multiomics integration models are required to avoid biases towards a specific modality and overcome sparsity. Batch effects obfuscating biological signals must also be taken into account. Here, we introduce a new single-cell multiomics integration model, Single-cell Multiomics Autoencoder Integration (scMaui) based on variational product-of-experts autoencoders and adversarial learning. scMaui calculates a joint representation of multiple marginal distributions based on a product-of-experts approach which is especially effective for missing values in the modalities. Furthermore, it overcomes limitations seen in previous VAE-based integration methods with regard to batch effect correction and restricted applicable assays. It handles multiple batch effects independently accepting both discrete and continuous values, as well as provides varied reconstruction loss functions to cover all possible assays and preprocessing pipelines. We demonstrate that scMaui achieves superior performance in many tasks compared to other methods. Further downstream analyses also demonstrate its potential in identifying relations between assays and discovering hidden subpopulations.

BACKGROUND: Lineage tracing and trajectory inference from single-cell RNA-sequencing data hold tremendous potential for uncovering the genetic programs driving development and disease. Single cell datasets are thought to provide an unbiased view on the diverse cellular architecture of tissues. Sampling bias, however, can skew single cell datasets away from the cellular composition they are meant to represent.
RESULTS: We demonstrate a novel form of sampling bias, caused by a statistical phenomenon related to repeated sampling from a growing, heterogeneous population. Relative growth rates of cells influence the probability that they will be sampled in clones observed across multiple time points. We support our probabilistic derivations with a simulation study and an analysis of a real time-course of T-cell development. We find that this bias can impact fate probability predictions, and we explore how to develop trajectory inference methods which are robust to this bias.
METHODS: Source code for the simulated datasets and to create the figures in this manuscript is freely available in python at https://github.com/rbonhamcarter/simulate-clones. A python implementation of the extension of the LineageOT method is freely available at https://github.com/rbonhamcarter/LineageOT/tree/multi-time-clones.

Single-cell RNA sequencing (scRNA-seq) remains state-of-the-art for transcriptomic cell-mapping. Here, we provide a protocol to generate high-resolution scRNA-seq of rare cardiomyocyte populations (e.g., regenerating/dividing, etc.) from mouse and zebrafish hearts as well as induced pluripotent stem cells, collected in time to achieve detailed transcriptomic insight. We describe the serial steps of viability staining, methanol fixation, storage, and cell sorting to preserve RNA integrity suited for scRNA-seq as well as the quality assessment of the data as shown by examples. For complete details on the use and execution of this protocol, please refer to Bak et al.1.