Thrombospondin 2 is a key determinant of fibrogenesis in non-alcoholic fatty liver disease.

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 (TGFß) pathway and collagen gene activation. Moreover, the activation of LX-2 cells with TGFß increased THBS2/TSP2 and collagen expression independently of the TGFß-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.

Stiffness-dependent MSC homing and differentiation into CAFs - implications for breast cancer invasion.

Cellular heterogeneity and extracellular matrix (ECM) stiffening have been shown to be drivers of breast cancer invasiveness. Here, we examine how stiffness-dependent crosstalk between cancer cells and mesenchymal stem cells (MSCs) within an evolving tumor microenvironment regulates cancer invasion. By analyzing previously published single-cell RNA sequencing datasets, we establish the existence of a subpopulation of cells in primary tumors, secondary sites and circulatory tumor cell clusters of highly aggressive triple-negative breast cancer (TNBC) that co-express MSC and cancer-associated fibroblast (CAF) markers. By using hydrogels with stiffnesses of 0.5, 2 and 5 kPa to mimic different stages of ECM stiffening, we show that conditioned medium from MDA-MB-231 TNBC cells cultured on 2 kPa gels, which mimic the pre-metastatic stroma, drives efficient MSC chemotaxis and induces stable differentiation of MSC-derived CAFs in a TGFß (TGFB1)- and contractility-dependent manner. In addition to enhancing cancer cell proliferation, MSC-derived CAFs on 2 kPa gels maximally boost local invasion and confer resistance to flow-induced shear stresses. Collectively, our results suggest that homing of MSCs at the pre-metastatic stage and their differentiation into CAFs actively drives breast cancer invasion and metastasis in TNBC.

scDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier.

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.

Molecular mechanism of synovial joint site specification and induction in developing vertebrate limbs.

The vertebrate appendage comprises three primary segments, the stylopod, zeugopod and autopod, each separated by joints. The molecular mechanisms governing the specification of joint sites, which define segment lengths and thereby limb architecture, remain largely unknown. Existing literature suggests that reciprocal gradients of retinoic acid (RA) and fibroblast growth factor (FGF) signaling define the expression domains of the putative segment markers Meis1, Hoxa11 and Hoxa13. Barx1 is expressed in the presumptive joint sites. Our data demonstrate that RA-FGF signaling gradients define the expression domain of Barx1 in the first presumptive joint site. When misexpressed, Barx1 induces ectopic interzone-like structures, and its loss of function partially blocks interzone development. Simultaneous perturbations of RA-FGF signaling gradients result in predictable shifts of Barx1 expression domains along the proximo-distal axis and, consequently, in the formation of repositioned joints. Our data suggest that during early limb bud development in chick, Meis1 and Hoxa11 expression domains are overlapping, whereas the Barx1 expression domain resides within the Hoxa11 expression domain. However, once the interzone is formed, the expression domains are refined and the Barx1 expression domain becomes congruent with the border of these two putative segment markers.

Temporal recording of mammalian development and precancer.

Key to understanding many biological phenomena is knowing the temporal ordering of cellular events, which often require continuous direct observations [1, 2]. An alternative solution involves the utilization of irreversible genetic changes, such as naturally occurring mutations, to create indelible markers that enables retrospective temporal ordering [3-8]. Using NSC-seq, a newly designed and validated multi-purpose single-cell CRISPR platform, we developed a molecular clock approach to record the timing of cellular events and clonality in vivo , while incorporating assigned cell state and lineage information. Using this approach, we uncovered precise timing of tissue-specific cell expansion during murine embryonic development and identified new intestinal epithelial progenitor states by their unique genetic histories. NSC-seq analysis of murine adenomas and single-cell multi-omic profiling of human precancers as part of the Human Tumor Atlas Network (HTAN), including 116 scRNA-seq datasets and clonal analysis of 418 human polyps, demonstrated the occurrence of polyancestral initiation in 15-30% of colonic precancers, revealing their origins from multiple normal founders. Thus, our multimodal framework augments existing single-cell analyses and lays the foundation for in vivo multimodal recording, enabling the tracking of lineage and temporal events during development and tumorigenesis.

Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data.

MOTIVATION: Single-nucleotide variants (SNVs) are the most common variations in the human genome. Recently developed methods for SNV detection from single-cell DNA sequencing data, such as SCIΦ and scVILP, leverage the evolutionary history of the cells to overcome the technical errors associated with single-cell sequencing protocols. Despite being accurate, these methods are not scalable to the extensive genomic breadth of single-cell whole-genome (scWGS) and whole-exome sequencing (scWES) data. RESULTS: Here, we report on a new scalable method, Phylovar, which extends the phylogeny-guided variant calling approach to sequencing datasets containing millions of loci. Through benchmarking on simulated datasets under different settings, we show that, Phylovar outperforms SCIΦ in terms of running time while being more accurate than Monovar (which is not phylogeny-aware) in terms of SNV detection. Furthermore, we applied Phylovar to two real biological datasets: an scWES triple-negative breast cancer data consisting of 32 cells and 3375 loci as well as an scWGS data of neuron cells from a normal human brain containing 16 cells and approximately 2.5 million loci. For the cancer data, Phylovar detected somatic SNVs with high or moderate functional impact that were also supported by bulk sequencing dataset and for the neuron dataset, Phylovar identified 5745 SNVs with non-synonymous effects some of which were associated with neurodegenerative diseases. AVAILABILITY AND IMPLEMENTATION: Phylovar is implemented in Python and is publicly available at https://github.com/NakhlehLab/Phylovar.

Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET.

Despite recent advances in inferring cellular dynamics using single-cell RNA-seq data, existing trajectory inference (TI) methods face difficulty in accurately reconstructing the cell-state manifold and cell-fate plasticity for complex topologies. Here, we present MARGARET (https://github.com/Zafar-Lab/Margaret) for inferring single-cell trajectory and fate mapping for diverse dynamic cellular processes. MARGARET reconstructs complex trajectory topologies using a deep unsupervised metric learning and a graph-partitioning approach based on a novel connectivity measure, automatically detects terminal cell states, and generalizes the quantification of fate plasticity for complex topologies. On a diverse benchmark consisting of synthetic and real datasets, MARGARET outperformed state-of-the-art methods in recovering global topology and cell pseudotime ordering. For human hematopoiesis, MARGARET accurately identified all major lineages and associated gene expression trends and helped identify transitional progenitors associated with key branching events. For embryoid body differentiation, MARGARET identified novel transitional populations that were validated by bulk sequencing and functionally characterized different precursor populations in the mesoderm lineage. For colon differentiation, MARGARET characterized the lineage for BEST4/OTOP2 cells and the heterogeneity in goblet cell lineage in the colon under normal and inflamed ulcerative colitis conditions. Finally, we demonstrated that MARGARET can scale to large scRNA-seq datasets consisting of ∼ millions of cells.

Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data.

Recent studies combine two novel technologies, single-cell RNA-sequencing and CRISPR-Cas9 barcode editing for elucidating developmental lineages at the whole organism level. While these studies provided several insights, they face several computational challenges. First, lineages are reconstructed based on noisy and often saturated random mutation data. Additionally, due to the randomness of the mutations, lineages from multiple experiments cannot be combined to reconstruct a species-invariant lineage tree. To address these issues we developed a statistical method, LinTIMaT, which reconstructs cell lineages using a maximum-likelihood framework by integrating mutation and expression data. Our analysis shows that expression data helps resolve the ambiguities arising in when lineages are inferred based on mutations alone, while also enabling the integration of different individual lineages for the reconstruction of an invariant lineage tree. LinTIMaT lineages have better cell type coherence, improve the functional significance of gene sets and provide new insights on progenitors and differentiation pathways.

SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data.

Accumulation and selection of somatic mutations in a Darwinian framework result in intra-tumor heterogeneity (ITH) that poses significant challenges to the diagnosis and clinical therapy of cancer. Identification of the tumor cell populations (clones) and reconstruction of their evolutionary relationship can elucidate this heterogeneity. Recently developed single-cell DNA sequencing (SCS) technologies promise to resolve ITH to a single-cell level. However, technical errors in SCS data sets, including false-positives (FP) and false-negatives (FN) due to allelic dropout, and cell doublets, significantly complicate these tasks. Here, we propose a nonparametric Bayesian method that reconstructs the clonal populations as clusters of single cells, genotypes of each clone, and the evolutionary relationship between the clones. It employs a tree-structured Chinese restaurant process as the prior on the number and composition of clonal populations. The evolution of the clonal populations is modeled by a clonal phylogeny and a finite-site model of evolution to account for potential mutation recurrence and losses. We probabilistically account for FP and FN errors, and cell doublets are modeled by employing a Beta-binomial distribution. We develop a Gibbs sampling algorithm comprising partial reversible-jump and partial Metropolis-Hastings updates to explore the joint posterior space of all parameters. The performance of our method on synthetic and experimental data sets suggests that joint reconstruction of tumor clones and clonal phylogeny under a finite-site model of evolution leads to more accurate inferences. Our method is the first to enable this joint reconstruction in a fully Bayesian framework, thus providing measures of support of the inferences it makes.

Matrilysin/MMP-7 Cleavage of Perlecan/HSPG2 Complexed with Semaphorin 3A Supports FAK-Mediated Stromal Invasion by Prostate Cancer Cells.

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.

SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models.

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.

Monovar: single-nucleotide variant detection in single cells.

Current variant callers are not suitable for single-cell DNA sequencing, as they do not account for allelic dropout, false-positive errors and coverage nonuniformity. We developed Monovar (https://bitbucket.org/hamimzafar/monovar), a statistical method for detecting and genotyping single-nucleotide variants in single-cell data. Monovar exhibited superior performance over standard algorithms on benchmarks and in identifying driver mutations and delineating clonal substructure in three different human tumor data sets.