Biology subjects

Tabaka, M.

Publications and source records attributed to Tabaka, M..

12 recordsLinked to original sources

bioRxiv2025

Identifying multimodal molecular programs with mTopic.

The simultaneous profiling of diverse molecular modalities offers unprecedented insight into complex biological processes, yet it poses significant computational challenges. Here, we introduce mTopic, a generalizable topic modeling framework for analysis of unlimited types of modalities across both single-cell and spatial contexts. mTopic enables identification of coherent multimodal molecular programs at enhanced resolution and supports investigation of cross-modality associations.

bioinformatics↗

bioRxiv2023

Ocelli: an open-source tool for the visualization of developmental multimodal single-cell data

The recent expansion of single-cell technologies has enabled simultaneous genome-wide measurements of multiple modalities in the same single cell. The potential to jointly profile such modalities as gene expression, chromatin accessibility, protein epitopes, or multiple histone modifications at single-cell resolution represents a compelling opportunity to study developmental processes at multiple layers of gene regulation. Here, we present Ocelli, a lightweight Python package for scalable visualization and exploration of developmental multimodal single-cell data. The core functionality of Ocelli focuses on diffusion-based modeling of developmental processes. Ocelli addresses common tasks in developmental single-cell data analysis, such as visualization of cells on a low-dimensional embedding that preserves the continuity of the developmental progression of cells, identification of rare and transient cell states, integration with trajectory inference algorithms, and imputation of undetected feature counts. Extensive benchmarking shows that Ocelli outperforms existing methods regarding computational time and quality of the reconstructed low-dimensional representation of developmental data.

bioinformatics↗

bioRxiv2023

spinDrop: a droplet microfluidic platform to maximise single-cell sequencing information content

Droplet microfluidic methods have massively increased the throughput of single-cell sequencing campaigns. The benefit of scale-up is, however, accompanied by increased background noise when processing challenging samples and the overall RNA capture efficiency is lower. These drawbacks stem from the lack of strategies to enrich for high-quality material or specific cell types at the moment of cell encapsulation and the absence of implementable multi-step enzymatic processes that increase capture. Here we alleviate both bottlenecks using fluorescence-activated droplet sorting to enrich for droplets that contain single viable cells, intact nuclei, fixed cells or target cell types and use reagent addition to droplets by picoinjection to perform multi-step lysis and reverse transcription. Our methodology increases gene detection rates fivefold, while reducing background noise by up to half. We harness these unique properties to deliver a high-quality molecular atlas of mouse brain development, despite starting with highly damaged input material, and provide an atlas of nascent RNA transcription during mouse organogenesis. Our method is broadly applicable to other droplet-based workflows to deliver sensitive and accurate single-cell profiling at a reduced cost.

developmental biology↗

bioRxiv2022

A newly-recognized population of residual neural crest cells in the adult leptomeninges is re-activated for vascular repair

The neural crest (NC) is a transient structure in vertebrate embryogenesis comprising highly migratory multipotent stem cells that give rise to a diverse array of cell types in organs throughout the body, including initiating neurovascular patterning. It is assumed that neural crest stem cells (NCSCs) disappear after development. Unexpectedly, using single-nucleus RNA-sequencing, we discovered residual quiescent NCSCs in the adult mouse meninges which are activated by injury and contribute to the brains homeostatic response. RNA velocity, pathway, and transcription factor analyses in a murine stroke model (combined with in vivo imaging) show that these adult NCSCs migrate towards the perivascular spaces of the infarct and undergo a perivascular stromal cell transition that is regulated by Ptp1b, Ghr, and Stat3. Loss- and gain-of-function experiments show that these "vestigial" NCSCs are required for restoring vascular endothelial barrier function via {beta}-catenin and Stat3 signaling. These findings suggest that, in the adult, an unexpected reservoir of cells -- once pivotal to embryogenesis and vascular morphogenesis -- are re-invoked for neurovascular repair.

neuroscience↗

bioRxiv2020

Cycling cancer persister cells arise from lineages with distinct transcriptional and metabolic programs

Non-genetic mechanisms have recently emerged as important drivers of therapy failure in cancer (Salgia and Kulkarni, 2018), where some cancer cells can enter a reversible drug-tolerant persister state in response to treatment (Vallette et al., 2019). While most cancer persisters, like their bacterial counterparts, remain arrested in the presence of drug, a rare subset of cancer persisters can re-enter the cell cycle under constitutive drug treatment (Sharma et al., 2010). Little is known about the non-genetic mechanisms that enable cancer persisters to maintain proliferative capacity in the presence of drug. Here, using time-lapse imaging, we found that cycling persisters emerge early in the course of treatment of EGFR-mutant lung cancer cells with the EGFR inhibitor osimertinib. To study this rare, transiently-resistant, proliferative persister population we developed Watermelon, a new high-complexity expressed barcode lentiviral library for simultaneous tracing each cells clonal origin, proliferative state, and transcriptional state. Analysis of Watermelon-transduced PC9 cells demonstrated that cycling and non-cycling persisters arise from different pre-existing cell lineages with distinct transcriptional and metabolic programs. The proliferative capacity of persisters is associated with an upregulation of antioxidant gene programs and a metabolic shift to fatty acid oxidation in specific subpopulations of tumor cells. Mitigating oxidative stress or blocking metabolic reprograming significantly alters the fraction of cycling persister cells. In human tumors, programs associated with cycling persisters were induced in malignant cells in response to multiple tyrosine kinase inhibitors. The Watermelon system enabled the identification of rare persister lineages, that are preferentially poised through specific gene programs to proliferate under drug pressure, thus exposing new vulnerabilities that can be targeted to delay or even prevent disease recurrence.

cancer biology↗

bioRxiv2020

Single cell profiling of immature human postnatal thymocytes resolves the complexity of intra-thymic lineage differentiation and thymus seeding precursors.

During postnatal life, thymopoiesis depends on the continuous colonization of the thymus by bone marrow derived hematopoietic progenitors that migrate through the bloodstream. In human, the nature of these thymus immigrants has remained unclear. Here, we employ single-cell RNA sequencing on approximately 70.000 CD34+ thymocytes to unravel the heterogeneity of the human immature postnatal thymocytes. Integration of bone marrow and peripheral blood precursors datasets identifies several putative thymus seeding precursors that display heterogeneity for currently used surface markers as revealed by CITEseq. Besides T cell precursors, we discover branches of intrathymic developing dendritic cells with predominantly plasmacytoid DCs. Trough trajectory inference, we delineate the transcriptional dynamics underlying early human T-lineage development from which we predict transcription factor modules that drive stage-specific steps of human T cell development. Thus, our work resolves the heterogeneity of thymus seeding precursors in human and reveals the molecular mechanisms that drive their in vivo cell fate.

immunology↗

bioRxiv2019

Cumulus: a cloud-based data analysis framework for large-scale single-cell and single-nucleus RNA-seq

Massively parallel single-cell and single-nucleus RNA-seq (sc/snRNA-seq) have opened the way to systematic tissue atlases in health and disease, but as the scale of data generation is growing, so does the need for computational pipelines for scaled analysis. Here, we developed Cumulus, a cloud-based framework for analyzing large scale sc/snRNA-seq datasets. Cumulus combines the power of cloud computing with improvements in algorithm implementations to achieve high scalability, low cost, user-friendliness, and integrated support for a comprehensive set of features. We benchmark Cumulus on the Human Cell Atlas Census of Immune Cells dataset of bone marrow cells and show that it substantially improves efficiency over conventional frameworks, while maintaining or improving the quality of results, enabling large-scale studies.

bioinformatics↗

bioRxiv2019

Genetic predisposition to myeloproliferative neoplasms implicates hematopoietic stem cell biology

Myeloproliferative neoplasms (MPNs) are blood cancers characterized by excessive production of mature myeloid cells that result from the acquisition of somatic driver mutations in hematopoietic stem cells (HSCs)1. While substantial progress has been made to define the causal somatic mutation profile for MPNs2, epidemiologic studies indicate a significant heritable component for the disease that is among the highest known for all cancers3. However, only a limited set of genetic risk loci have been identified, and the underlying biological mechanisms leading to MPN acquisition remain unexplained. Here, to define the inherited risk profile, we conducted the largest genome-wide association study of MPNs to date (978,913 individuals with 3,224 cases) and identified 14 genome-wide significant loci, as well as a polygenic signature that increases the odds for disease acquisition by nearly 3-fold between the top and median deciles. Interestingly, we find a shared genetic architecture between MPN risk and several hematopoietic traits spanning distinct lineages, as well as an association between increased MPN risk and longer leukocyte telomere length, collectively implicating HSC function and self-renewal. Strikingly, we find a significant enrichment for risk variants mapping to accessible chromatin in HSCs compared with other hematopoietic populations. Finally, gene mapping identifies modulators of HSC biology and targeted variant-to-function analyses suggest likely roles for CHEK2 and GFI1B in altering HSC function to confer disease risk. Overall, we demonstrate the power of human genetic studies to illuminate a previously unappreciated mechanism for MPN risk through modulation of HSC function.

genetics↗

bioRxiv2019

A cellular taxonomy of the bone marrow stroma in homeostasis and leukemia demonstrates cancer-crosstalk with stroma to impair normal tissue function

Stroma is a poorly defined non-parenchymal component of virtually every organ with key roles in organ development, homeostasis and repair. Studies of the bone marrow stroma have defined individual populations in the stem cell niche regulating hematopoietic regeneration and capable of initiating leukemia. Here, we use single-cell RNA-seq to define a cellular taxonomy of the mouse bone marrow stroma and its perturbation by malignancy. We identified seventeen stromal subsets expressing distinct hematopoietic regulatory genes, spanning new fibroblastic, and osteoblastic subpopulations. Emerging acute myeloid leukemia resulted in impaired osteogenic differentiation and reduced production of hematopoietic regulatory molecules necessary for normal hematopoiesis. Thus, cancer can affect tissue stroma in which they reside to disadvantage normal parenchymal cells. Our taxonomy of the regulatory stromal compartment provides experimental support for a model where malignant clone is not a destroyer of normal tissue but an architect of it, remodeling tissue stroma to enable emergent cancer.

cell biology↗

bioRxiv2019

scSVA: an interactive tool for big data visualization and exploration in single-cell omics

We present scSVA (single-cell Scalable Visualization and Analytics), a lightweight R package for interactive two- and three-dimensional visualization and exploration of massive single-cell omics data. Building in part of methods originally developed for astronomy datasets, scSVA is memory efficient for more than hundreds of millions of cells, can be run locally or in a cloud, and generates high-quality figures. In particular, we introduce a numerically efficient method for single-cell data embedding in 3D which combines an optimized implementation of diffusion maps with a 3D force-directed layout, enabling generation of 3D data visualizations at the scale of a million cells. To facilitate reproducible research, scSVA supports interactive analytics in a cloud with containerized tools. scSVA is available online at https://github.com/klarman-cell-observatory/scSVA.

bioinformatics↗

bioRxiv2017

Reconstruction of developmental landscapes by optimal-transport analysis of single-cell gene expression sheds light on cellular reprogramming.

Understanding the molecular programs that guide cellular differentiation during development is a major goal of modern biology. Here, we introduce an approach, WADDINGTON-OT, based on the mathematics of optimal transport, for inferring developmental landscapes, probabilistic cellular fates and dynamic trajectories from large-scale single-cell RNA-seq (scRNA-seq) data collected along a time course. We demonstrate the power of WADDINGTON-OT by applying the approach to study 65,781 scRNA-seq profiles collected at 10 time points over 16 days during reprogramming of fibroblasts to iPSCs. We construct a high-resolution map of reprogramming that rediscovers known features; uncovers new alternative cell fates including neuraland placental-like cells; predicts the origin and fate of any cell class; highlights senescent-like cells that may support reprogramming through paracrine signaling; and implicates regulatory models in particular trajectories. Of these findings, we highlight Obox6, which we experimentally show enhances reprogramming efficiency. Our approach provides a general framework for investigating cellular differentiation.

bioinformatics↗

bioRxiv2016

Genetic determinants of chromatin accessibility and gene regulation in T cell activation across human individuals

Over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, we analyzed Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq) and RNA-seq profiles from activated CD4+ T cells of up to 105 healthy donors. We found that regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution, in patterns consistent with the 3D organization of chromosomes measured by in situ Hi-C in T cells. 15% of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak through disrupting binding sites for transcription factors important for T cell differentiation and activation. These ATAC quantitative trait nucleotides (ATAC-QTNs) have the largest effects on co-accessible peaks, are associated with gene expression from the same aliquot of cells, are rarely affecting core binding motifs, and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis- regulatory elements, in isolation or in concert, to influence gene expression in primary immune cells that play a key role in many human diseases.

genomics↗