Biology subjects

Zafar, H.

Publications and source records attributed to Zafar, H..

26 records · Page 2Linked to original sources

bioRxiv2021

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 cell-state manifold and inferring trajectory and cell fate plasticity for complex topologies. We present MARGARET, a novel TI method that utilizes a deep unsupervised metric learning-based approach for inferring the cellular embeddings and employs a novel measure of connectivity between cell clusters and a graph-partitioning approach to reconstruct complex trajectory topologies. MARGARET utilizes the inferred trajectory for determining terminal states and inferring cell-fate plasticity using a scalable absorbing Markov Chain model. On a diverse simulated benchmark, MARGARET out-performed state-of-the-art methods in recovering global topology and cell pseudotime ordering. When applied to experimental datasets from hematopoiesis, embryogenesis, and colon differentiation, MARGARET reconstructed major lineages and associated gene expression trends, better characterized key branching events and transitional cell types, and identified novel cell types, and branching events that were previously uncharacterized.

genomics↗

bioRxiv2021

Pre-metastatic niche drives breast cancer invasion by modulating MSC homing and CAF differentiation

The extent to which cancer-associated alterations in extracellular matrix stiffness influences the crosstalk between cancer cells and mesenchymal stem cells (MSCs) remains unclear. By analyzing multiple singlecell RNA sequencing datasets, we establish the existence of a cell sub-population co-expressing MSC and cancer associated fibroblast (CAF) markers in highly aggressive triple-negative breast cancers in primary tumor, secondary sites, and in circulatory tumor cell clusters. Using hydrogels of varying stiffness corresponding to different stages of cancer progression, we show that on pre-metastatic stroma mimetic 2 kPa gels, MDA-MB-231 breast cancer cell secreted conditioned media drives efficient MSC chemotaxis and induces stable CAF differentiation in a TGF{beta}/contractility-dependent manner. In addition to enhancing cancer cell proliferation, 2 kPa CAFs maximally boost local invasion and confer resistance to flow-induced shear stresses. Together, our results suggest that homing of MSCs at the pre-metastatic stage and their differentiation into CAFs actively drives breast cancer invasion and metastasis.

cancer biology↗

bioRxiv2020

Feasibility and safety of a telemetric pulmonary artery pressure monitoring system in acute and chronic porcine models of pulmonary hypertension.

AimsPulmonary hypertension (PH) is associated with significant morbidity and mortality and leads to progressive right heart failure. In patients with PAH, haemodynamic parameters measured at catheterisation relate to clinical worsening events, in patients with heart failure proactive pulmonary artery pressure based therapeutic intervention reduces hospitalisation. We therefore investigated use of a novel implanted pulmonary artery (PA) pressure monitor to detect clinically relevant changes in pressure in large animal models of pulmonary hypertension (PH). Methods and ResultsPrototype pulmonary artery pressure sensors (Endotronix) were implanted using standard interventional techniques. Acute PH was induced by infusion of thromboxane A2 in domestic swine. Over a physiological range pressure monitors remained concordant to reference catheter (bias -0.43, 95%CI-5.3-4.4). Chronic PH was induced by i.p. injection of monocrotaline. Implanted pressure sensors demonstrated a gradual rise in PA pressure over 30 days (baseline: 20.7+/-0.4 vrs day-30: 31.74+/-1.4, p<0.01). Pressure sensor derived readings matched reference catheter at baseline and day-30. Pressure sensors remained stable and no adverse events were identified by clinical and histological examination. ConclusionsThe development of PA pressure monitors provide long-term haemodynamic data that identified clinically meaningful changes in pulmonary artery pressure. In addition to proactive heart failure management, such devices may be used to optimise or personalize patient therapy, investigate aspects of physiology and pathology essential to the understanding of disease and provide the opportunity to assess therapeutic interventions in clinical studies.

physiology↗

bioRxiv2020

Characteristics of Titan cell formation vary across clinical isolates of Cryptococcus neoformans

Fungal morphology significantly impacts the host response. Filamentation and tissue penetration by Candida and Aspergillus species are essential for virulence, while growth as a yeast allows the thermal dimorphic fungi Coccidiodes, Histoplasma, and Talaromyces to reside inside phagocytes and disseminate. The basidiomycete Cryptococcus neoformans exhibits an unusual yeast-to-titan transition thought to enhance pathogenicity by increasing fungal survival in the host lung and dissemination to the central nervous system. In a common laboratory strain (H99), in vitro and in vivo titan induction yields a heterogenous population including >10 m titan cells, 5-7 m yeast cells and 2-4 m titanides. Previous reports have shown that titan cells are associated with enhanced virulence and the generation of aneuploid cells that facilitate stress adaptation and drug resistance, while small (>10 m) cells are associated with increased dissemination. However, the relationship between titan cells, small cells, and titanides remains unclear. Here, we characterize titanides and small cells in H99 and three clinical isolates and show that titanides share the lipid membrane order of their titan mothers and the G0 quiescent-like DNA staining of mating spores. In addition, we show that both titanizing and non-titanizing isolates exhibit altered capsule structure and PAMP exposure over time during in vitro culture, and generate aneuploidy in vivo. Author summaryThe human fungal pathogen Cryptococcus neoformans causes 200,000 HIV-associated deaths each year. In the lung, Cryptococcus makes an unusual yeast-to-titan morphological switch that contributes to disease development by altering immune polarization and introducing aneuploidy underlying host stress and drug resistance. Specifically, a proportion of 5 um haploid yeast endoreduplicate and swell, converting to large (> 10 um) polyploid titan cells that can then produce genetically distinct daughter cells. We recently developed an in vitro protocol for inducing large titan cells and additionally observed a novel small "titanide" cell type. Here we investigate the nature and origin of these small cells, demonstrating that they emerge during both in vitro and in vivo mouse-passaged titan induction in the well characterised lab strain H99 and are also apparent in a titanizing clinical isolate, Zc8. We show that these titanide cells share features with titan mothers (lipid order) and with spores produced during heterothalic mating. Finally, we show that the capacity of clinical isolates to produce both titan and titanide cells impacts aneuploidy and the emergence of drug resistance in vivo.

microbiology↗

bioRxiv2019

A Combinatorial Approach for Single-cell Variant Detection via Phylogenetic Inference

Single-cell sequencing provides a powerful approach for elucidating intratumor heterogeneity by resolving cell-to-cell variability. However, it also poses additional challenges including elevated error rates, allelic dropout and non-uniform coverage. A recently introduced single-cell-specific mutation detection algorithm leverages the evolutionary relationship between cells for denoising the data. However, due to its probabilistic nature, this method does not scale well with the number of cells. Here, we develop a novel combinatorial approach for utilizing the genealogical relationship of cells in detecting mutations from noisy single-cell sequencing data. Our method, called scVILP, jointly detects mutations in individual cells and reconstructs a perfect phylogeny among these cells. We employ a novel Integer Linear Program algorithm for deterministically and efficiently solving the joint inference problem. We show that scVILP achieves similar or better accuracy but significantly better runtime over existing methods on simulated data. We also applied scVILP to an empirical human cancer dataset from a high grade serous ovarian cancer patient.

bioinformatics↗

bioRxiv2019

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 consensus lineage tree. To address these issues we developed a novel 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 a consensus 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.

bioinformatics↗

bioRxiv2018

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 datasets, including false-positives (FP), false-negatives (FN) due to allelic dropout and cell doublets, significantly complicate these tasks. Here, we propose a non-parametric Bayesian method that reconstructs the clonal populations as clusters of single cells, genotypes of each clone and the evolutionary relationships 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 of 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 datasets 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.

genomics↗

bioRxiv2016

SiFit: A Method for Inferring Tumor Trees from Single-Cell Sequencing Data under Finite-site Models

Single-cell sequencing (SCS) 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-site models. We propose a statistical inference method for tumor phylogenies from noisy SCS data under a finite-sites model. The performance of our method on synthetic and experimental datasets from two colorectal cancer patients to trace evolutionary lineages in primary and metastatic tumors suggest that employing a finite-sites model leads to improved inference of tumor phylogenies.

bioinformatics↗