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Abstract Integration of heterogeneous single-cell sequencing datasets generated across multiple tissue locations, time, and conditions is essential for a comprehensive understanding of the cellular states and expression programs underlying complex biological systems. Here, we present scDREAMER ( https://github.com/Zafar-Lab/scDREAMER ), a data-integration framework that employs deep generative models and adversarial training for both unsupervised and supervised (scDREAMER-Sup) integration of multiple batches. Using six real benchmarking datasets, we demonstrate that scDREAMER can overcome critical challenges including skewed cell type distribution among batches, nested batch-effects, large number of batches and conservation of development trajectory across batches. Our experiments also show that scDREAMER and scDREAMER-Sup outperform state-of-the-art unsupervised and supervised integration methods respectively in batch-correction and conservation of biological variation. Using a 1 million cells dataset, we demonstrate that scDREAMER is scalable and can perform atlas-level cross-species (e.g., human and mouse) integration while being faster than other deep-learning-based methods.

Lineage tracing studies combining CRISPR-Cas9 editing and scRNA-seq face several challenges and cannot integrate lineages from multiple individuals. Here the authors show that integration of mutation and expression leads to accurate lineage tree inference and enables the learning of a species-invariant lineage tree.

Abstract Interrupting the interplay between cancer cells and extracellular matrix (ECM) is a strategy to halt tumor progression and stromal invasion. Perlecan/heparan sulfate proteoglycan 2 (HSPG2) is an extracellular proteoglycan that orchestrates tumor angiogenesis, proliferation, differentiation and invasion. Metastatic prostate cancer (PCa) cells degrade perlecan-rich tissue borders to reach bone, including the basement membrane, vasculature, reactive stromal matrix and bone marrow. Domain IV-3, perlecan’s last 7 immunoglobulin repeats, mimics native proteoglycan by promoting tumoroid formation. This is reversed by matrilysin/matrix metalloproteinase-7 (MMP-7) cleavage to favor cell dispersion and tumoroid dyscohesion. Both perlecan and Domain IV-3 induced a strong focal adhesion kinase (FAK) dephosphorylation/deactivation. MMP-7 cleavage of perlecan reversed this, with FAK in dispersed tumoroids becoming phosphorylated/activated with metastatic phenotype. We demonstrated Domain IV-3 interacts with the axon guidance protein semaphorin 3A (Sema3A) on PCa cells to deactivate pro-metastatic FAK. Sema3A antibody mimicked the Domain IV-3 clustering activity. Direct binding experiments showed Domain IV-3 binds Sema3A. Knockdown of Sema3A prevented Domain IV-3-induced tumoroid formation and Sema3A was sensitive to MMP-7 proteolysis. The perlecan-Sema3A complex abrogates FAK activity and stabilizes PCa cell interactions. MMP-7 expressing cells destroy the complex to initiate metastasis, destroy perlecan-rich borders, and favor invasion and progression to lethal bone disease.

Abstract Single-cell sequencing enables the inference of tumor phylogenies that provide insights on intra-tumor heterogeneity and evolutionary trajectories. Recently introduced methods perform this task under the infinite-sites assumption, violations of which, due to chromosomal deletions and loss of heterozygosity, necessitate the development of inference methods that utilize finite-sites models. We propose a statistical inference method for tumor phylogenies from noisy single-cell sequencing data under a finite-sites model. The performance of our method on synthetic and experimental data sets from two colorectal cancer patients to trace evolutionary lineages in primary and metastatic tumors suggests that employing a finite-sites model leads to improved inference of tumor phylogenies.

Objective: Hepatic overexpression of the thrombospondin 2 gene (THBS2) and elevated levels of circulating thrombospondin 2 (TSP2) have been observed in patients with chronic liver disease. This study aimed to identify the specific cells expressing THBS2/TSP2 in non-alcoholic fatty liver disease (NAFLD) and investigate the underlying mechanism behind THBS2/TSP2 upregulation. Design: Comprehensive NAFLD liver gene datasets, including single-cell RNA sequencing (scRNA-seq), in-house NAFLD liver tissue, and LX-2 cells derived from human hepatic stellate cells (HSCs), were analysed using a combination of computational biology, genetic, immunological, and pharmacological approaches. Results: Analysis of the genetic dataset revealed the presence of 1433 variable genes in patients with advanced fibrosis NAFLD, with THBS2 ranked among the top 2 genes. Quantitative polymerase chain reaction (qPCR) examination of NAFLD livers showed a significant correlation between THBS2 expression and fibrosis stage (r = .349, p < .001). In support of this, scRNA-seq data and in situ hybridization demonstrated that the THBS2 gene was highly expressed in HSCs of NAFLD patients with advanced fibrosis. Pathway analysis of the gene dataset revealed THBS2 expression to be associated with the transforming growth factor beta (TGFβ) pathway and collagen gene activation. Moreover, the activation of LX-2 cells with TGFβ increased THBS2/TSP2 and collagen expression independently of the TGFβ-SMAD2/3 pathway. THBS2 gene knockdown significantly decreased collagen expression in LX-2 cells. Conclusions: THBS2/TSP2 is highly expressed in HSCs and plays a role in regulating fibrogenesis in NAFLD patients. THBS2/TSP2 may therefore represent a potential target for anti-fibrotic therapy in NAFLD. [ABSTRACT FROM AUTHOR]