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Article ; Online: Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET.

Pandey, Kushagra / Zafar, Hamim

2022 Volume 50, Issue 15, Page(s) e86

Abstract: ... cell-fate plasticity for complex topologies. Here, we present MARGARET (https://github.com/Zafar-Lab ...

Abstract	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.
MeSH term(s)	Cell Differentiation ; Cell Lineage ; Colitis, Ulcerative/pathology ; Colon/cytology ; Colon/pathology ; Hematopoiesis ; Humans ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods ; Software
Language	English
Publishing date	2022-05-31
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkac412
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Article ; Online: scDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier.

Shree, Ajita / Pavan, Musale Krushna / Zafar, Hamim

Nature communications

2023 Volume 14, Issue 1, Page(s) 7781

Abstract: ... Zafar-Lab/scDREAMER ), a data-integration framework that employs deep generative models and adversarial ...

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.
MeSH term(s)	Humans ; Animals ; Mice ; Ascomycota ; Benchmarking ; Single-Cell Analysis
Language	English
Publishing date	2023-11-27
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-43590-8
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Article ; Online: scDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier

Ajita Shree / Musale Krushna Pavan / Hamim Zafar

Nature Communications, Vol 14, Iss 1, Pp 1-

2023 Volume 19

Abstract: ... github.com/Zafar-Lab/scDREAMER ), a data-integration framework that employs deep generative models and ...

Abstract	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.
Keywords	Science ; Q
Subject code	004
Language	English
Publishing date	2023-11-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Stiffness-dependent MSC homing and differentiation into CAFs - implications for breast cancer invasion.

Saxena, Neha / Chakraborty, Soura / Dutta, Sarbajeet / Bhardwaj, Garvit / Karnik, Nupur / Shetty, Omshree / Jadhav, Sameer / Zafar, Hamim / Sen, Shamik

Journal of cell science

2024 Volume 137, Issue 1

Abstract: 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 ... ...

Abstract	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 TGFβ (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.
MeSH term(s)	Humans ; Female ; Breast Neoplasms/genetics ; Breast Neoplasms/pathology ; Cancer-Associated Fibroblasts ; Triple Negative Breast Neoplasms ; Mesenchymal Stem Cells ; Cell Differentiation ; Gels ; Tumor Microenvironment/genetics ; Cell Line, Tumor
Chemical Substances	Gels
Language	English
Publishing date	2024-01-15
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2993-2
ISSN	1477-9137 ; 0021-9533
ISSN (online)	1477-9137
ISSN	0021-9533
DOI	10.1242/jcs.261145
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Article ; Online: Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data

Hamim Zafar / Chieh Lin / Ziv Bar-Joseph

Nature Communications, Vol 11, Iss 1, Pp 1-

2020 Volume 14

Abstract: 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 ... ...

Abstract	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.
Keywords	Science ; Q
Language	English
Publishing date	2020-06-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data.

Zafar, Hamim / Lin, Chieh / Bar-Joseph, Ziv

Nature communications

2020 Volume 11, Issue 1, Page(s) 3055

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Algorithms ; Animals ; Brain/cytology ; CRISPR-Cas Systems ; Caenorhabditis elegans/embryology ; Caenorhabditis elegans/genetics ; Cell Differentiation/genetics ; Cell Lineage/genetics ; Data Interpretation, Statistical ; Embryo, Nonmammalian/cytology ; Gene Expression Profiling/methods ; Gene Expression Profiling/statistics & numerical data ; Likelihood Functions ; Mutation ; Single-Cell Analysis/methods ; Single-Cell Analysis/statistics & numerical data ; Zebrafish/genetics
Language	English
Publishing date	2020-06-16
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-020-16821-5
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Article ; Online: Comments on the model parameters in "SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models".

Zafar, Hamim / Tzen, Anthony / Navin, Nicholas / Chen, Ken / Nakhleh, Luay

Genome biology

2019 Volume 20, Issue 1, Page(s) 95

MeSH term(s)	Computational Biology ; Humans ; Models, Genetic ; Neoplasms ; Trees
Language	English
Publishing date	2019-05-16
Publishing country	England
Document type	Letter ; Comment
ZDB-ID	2040529-7
ISSN	1474-760X ; 1465-6914 ; 1465-6906
ISSN (online)	1474-760X ; 1465-6914
ISSN	1465-6906
DOI	10.1186/s13059-019-1692-5
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Article ; Online: SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data.

Zafar, Hamim / Navin, Nicholas / Chen, Ken / Nakhleh, Luay

Genome research

2019 Volume 29, Issue 11, Page(s) 1847–1859

Abstract: 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) ... ...

Abstract	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.
MeSH term(s)	Bayes Theorem ; Clone Cells ; Genotype ; Humans ; Neoplasms/genetics ; Phylogeny ; Point Mutation ; Single-Cell Analysis/methods
Language	English
Publishing date	2019-10-18
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1284872-4
ISSN	1549-5469 ; 1088-9051 ; 1054-9803
ISSN (online)	1549-5469
ISSN	1088-9051 ; 1054-9803
DOI	10.1101/gr.243121.118
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Article ; Online: Comments on the model parameters in “SiFit

Hamim Zafar / Anthony Tzen / Nicholas Navin / Ken Chen / Luay Nakhleh

Genome Biology, Vol 20, Iss 1, Pp 1-

inferring tumor trees from single-cell sequencing data under finite-sites models”

2019 Volume 2

Keywords	Biology (General) ; QH301-705.5 ; Genetics ; QH426-470
Language	English
Publishing date	2019-05-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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

Edrisi, Mohammadamin / Valecha, Monica V / Chowdary, Sunkara B V / Robledo, Sergio / Ogilvie, Huw A / Posada, David / Zafar, Hamim / Nakhleh, Luay

Bioinformatics (Oxford, England)

2022 Volume 38, Issue Suppl 1, Page(s) i195–i202

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Genome, Human ; High-Throughput Nucleotide Sequencing/methods ; Humans ; Nucleotides ; Phylogeny ; Sequence Analysis, DNA
Chemical Substances	Nucleotides
Language	English
Publishing date	2022-07-05
Publishing country	England
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btac254
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