Biology subjects

Tabib, T.

Publications and source records attributed to Tabib, T..

10 recordsLinked to original sources

bioRxiv2026

High cRel-expressing germinal center B cells favor precursor plasma cell generation

Germinal centers (GCs) are the site of antibody affinity maturation by the process of somatic hypermutation (SHM) and produce long-lived plasma cells (PCs). GC B cells circulate between two distinct zones: the light zone (LZ) and the dark zone (DZ). It has been demonstrated that the transcription factor NF{kappa}B cRel is required for B cell proliferation and GC B cell maintenance. cRel-deleted GC B cells show reduced SHM and affinity maturation. In contrast, transgenic overexpression of cRel does not affect SHM and results in little or no increase in affinity maturation. Therefore, the function of cRel in regulating SHM and GC B cell-derived PC generation remains unknown. To understand the function of cRel in GC B cell selection, we have used cRel fluorescence reporter mice, which provide insight into cRel function in GC B cells at the level of natural expression variation. We found that about 6-9% of LZ cells increased cRel expression compared to DZ cells, and high cRel-expressing LZ cells showed increased SHM and increased expression of IRF4 and cyclin D2, though cMyc expression remained similar. Combining single-cell RNA-Seq and flow cytometry, we revealed that high cRel-expressing LZ cells are enriched for precursor PCs (pre-PCs), not precursor memory B cells (pre-MBCs). Our findings provide insight into the physiologic function of cRel in regulating GC B cell output.

immunology↗

bioRxiv2024

Altered AP-1, RUNX, and EGR chromatin dynamics drive fibrotic lung disease

Pulmonary fibrosis, including systemic sclerosis-associated interstitial lung disease (SSc-ILD), involves myofibroblasts and SPP1hi macrophages as drivers of fibrosis. Single-cell RNA sequencing has delineated fibroblast and macrophages transcriptomes, but limited insight into transcriptional control of profibrotic gene programs. To address this challenge, we analyzed multiomic snATAC/snRNA-seq on explanted SSc-ILD and donor control lungs. The neural network tool ChromBPNet inferred increased TF binding at single base pair resolution to profibrotic genes, including CTHRC1 and ADAM12, in fibroblasts and SPP1 and CCL18 in macrophages. The novel algorithm HALO confirmed AP-1, RUNX, and EGR TF activity controlling profibrotic gene programs and established TF-regulatory element-gene networks. This TF action atlas provides comprehensive insights into the transcriptional regulation of fibroblasts and macrophages in healthy and fibrotic human lungs.

systems biology↗

bioRxiv2022

SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains

Modern multi-omic technologies can generate deep multi-scale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make the analyses and integration of high-dimensional omic datasets challenging. Here, we present Significant Latent factor Interaction Discovery and Exploration (SLIDE), a first-in-class interpretable machine learning technique for identifying significant interacting latent factors underlying outcomes of interest from high-dimensional omic datasets. SLIDE makes no assumptions regarding data-generating mechanisms, comes with theoretical guarantees regarding identifiability of the latent factors/corresponding inference, outperforms/performs at least as well as state-of-the-art approaches in terms of prediction, and provides inference beyond prediction. Using SLIDE on scRNA-seq data from systemic sclerosis (SSc) patients, we first uncovered significant interacting latent factors underlying SSc pathogenesis. In addition to accurately predicting SSc severity and outperforming existing benchmarks, SLIDE uncovered significant factors that included well-elucidated altered transcriptomic states in myeloid cells and fibroblasts, an intriguing keratinocyte-centric signature validated by protein staining, and a novel mechanism involving altered HLA signaling in myeloid cells, that has support in genetic data. SLIDE also worked well on spatial transcriptomic data and was able to accurately identify significant interacting latent factors underlying immune cell partitioning by 3D location within lymph nodes. Finally, SLIDE leveraged paired scRNA-seq and TCR-seq data to elucidate latent factors underlying extents of clonal expansion of CD4 T cells in a nonobese diabetic model of T1D. The latent factors uncovered by SLIDE included well-known activation markers, inhibitory receptors and intracellular regulators of receptor signaling, but also honed in on several novel naive and memory states that standard analyses missed. Overall, SLIDE is a versatile engine for biological discovery from modern multi-omic datasets.

systems biology↗

bioRxiv2022

Early events marking lung fibroblast transition to profibrotic state in idiopathic pulmonary fibrosis

BackgroundIdiopathic Pulmonary Fibrosis (IPF) is an age-associated progressive lung disease with accumulation of scar tissue impairing gas exchange. Previous high-throughput studies elucidated the role of cellular heterogeneity and molecular pathways in advanced disease. However, critical pathogenic pathways occurring in the transition of fibroblasts from normal to profibrotic have been largely overlooked. MethodsWe used single cell transcriptomics (scRNA-seq) from lungs of healthy controls and IPF patients (lower and upper lobes). We identified fibroblast subclusters, genes and pathways associated to early disease. Immunofluorescence assays validated the role of MOXD1 early in fibrosis. FindingsWe identified four distinct fibroblast subgroups, including one marking the normal-to-profibrotic state transition. Our results show for the first time that global downregulation of ribosomal proteins and significant upregulation of the majority of copper-binding proteins, including MOXD1, mark the IPF transition. We find no significant differences in gene expression in IPF upper and lower lobe samples, which were selected to have low and high degree of fibrosis, respectively. InterpretationEarly events during IPF onset in fibroblasts include dysregulation of ribosomal and copper-binding proteins. Fibroblasts in early stage IPF have already acquired a profibrotic phenotype while hallmarks of advanced disease, including fibroblast foci and honeycomb formation, are still not evident. The new transitional fibroblasts we discover could prove very important for studying the role of fibroblast plasticity in disease progression and help develop early diagnosis tools and therapeutic interventions targeting earlier disease states.

genomics↗

bioRxiv2022

An integrated cell atlas of the human lung in health and disease

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human Cell Atlas.

cell biology↗

bioRxiv2022

Single-nucleus chromatin accessibility identifies a critical role for TWIST1 in idiopathic pulmonary fibrosis myofibroblast activity

In idiopathic pulmonary fibrosis (IPF) myofibroblasts are key effectors of fibrosis and architectural distortion by excessive deposition of extracellular matrix and their acquired contractile capacity. Single-cell RNA-sequencing has precisely defined the IPF myofibroblast transcriptome, but identifying critical transcription factor activity by this approach is imprecise. We performed and integrated snATAC-seq and scRNA-seq from human IPF and donor control explants to identify differentially accessible chromatin regions and enriched transcription factor motifs within lung cell populations. TWIST1 and other E-box transcription factor motifs were significantly enriched in IPF myofibroblasts compared to both IPF non-myogenic and control fibroblasts. TWIST1 expression was also selectively upregulated in IPF myofibroblasts. Overexpression of Twist1 in lung COL1A2-expressing fibroblasts in bleomycin-injured mice was associated with increased collagen synthesis. Our studies utilizing human multiomic single-cell analyses combined with in vivo murine disease models confirm a critical regulatory function for TWIST1 in IPF myofibroblast activity in the fibrotic lung. Understanding the global process of opening TWIST1 and other E-box TF motifs that govern myofibroblast differentiation may identify new therapeutic interventions for fibrotic pulmonary diseases.

molecular biology↗

bioRxiv2021

An instance-specific causal framework for learning intercellular communication networks that define microenvironments of individual tumors

Cells within a tumor microenvironment (TME) dynamically communicate and influence each others cellular states through an intercellular communication network (ICN). In cancers, intercellular communications underlie immune evasion mechanisms of individual tumors. We developed an individualized causal analysis framework for discovering tumor-specific ICNs. Using head and neck squamous cell carcinoma (HNSCC) tumors as a testbed, we first mined single-cell RNA-sequencing data to discover gene expression modules (GEMs) that reflect the states of transcriptomic processes within tumor and stromal single cells. By deconvoluting bulk transcriptomes of HNSCC tumors profiled by The Cancer Genome Atlas (TCGA), we estimated the activation states of these transcriptomic processes in individual tumors. Finally, we applied individualized causal network learning to discover an ICN within each tumor. Our results show that cellular states of cells in TMEs are coordinated through ICNs that enable multi-way communications among epithelial, fibroblast, endothelial, and immune cells. Further analyses of individual ICNs revealed structural patterns that were shared across subsets of tumors, leading to the discovery of 4 different subtypes of networks that underlie disparate TMEs of HNSCC. Patients with distinct TMEs exhibited significantly different clinical outcomes. Our results show that the capability of estimating individual ICNs reveals heterogeneity of ICNs and sheds light on the importance of intercellular communication in impacting disease development and progression.

cancer biology↗

bioRxiv2021

Myofibroblast transcriptome indicates SFRP2+ fibroblast progenitors in systemic sclerosis skin

Skin and lung fibrosis in systemic sclerosis (SSc) is driven by myofibroblasts, alpha-smooth muscle actin expressing cells that arise from a variety of cell types in murine fibrosis models. Utilizing single cell RNA-sequencing to examine the transcriptome changes, we show that SSc dermal myofibroblasts arise from an SFRP2/DPP4-expressing progenitor fibroblast population that globally upregulates expression of transcriptome markers, such as PRSS23 and THBS1. Only a fraction of SSc fibroblasts differentiate into myofibroblasts, as shown by expression of additional markers, SFRP4 and FNDC1. The myofibroblast transcriptome implicates upstream transcription factors that drive myofibroblast differentiation.

cell biology↗

bioRxiv2020

Divergent cancer etiologies drive distinct B cell signatures and tertiary lymphoid structures

Current immunotherapy paradigms aim to reinvigorate CD8+ T cells, but the contribution of humoral immunity to antitumor immunity remains understudied1,2. Head and neck squamous cell carcinoma (HNSCC) is caused by either human papillomavirus (HPV+) or environmental carcinogens (i.e. tobacco and alcohol; HPV-)3,4. Here, we demonstrate that HPV+ HNSCC patients have transcriptional signatures of germinal center (GC) tumor infiltrating B cells (TIL-Bs) and spatial organization of immune cells consistent with GC-like tertiary lymphoid structures (TLS), both of which correlate with favorable outcomes in HNSCC patients. Further, our single-cell RNAseq data also indicate that GC TIL-Bs are characterized by distinct waves of gene expression consistent with dark zone, light zone and a transitional state of GC B cells. High-dimensional spectral flow cytometry permitted in depth characterization of activated, memory and GC TIL-Bs. Further, single cell RNAseq analysis and subsequent protein validation identified a role for semaphorin 4a (Sema4a) in the differentiation of GC TIL-Bs and indicated that expression of Sema4a was enhanced on GC TIL-Bs and within GC-like TLS in the TME. Thus, in contrast to some reports on the detrimental role of TIL-Bs in human tumors, our findings suggest that TIL-Bs play an instrumental role in antitumor immunity5,6. Novel therapeutics to enhance TIL-B responses in HNSCC should be prioritized as a compliment to current T-cell mediated immunotherapies.

immunology↗

bioRxiv2018

BAMM-SC: A Bayesian mixture model for clustering droplet-based single cell transcriptomic data from population studies

The recently developed droplet-based single cell transcriptome sequencing (scRNA-seq) technology makes it feasible to perform a population-scale scRNA-seq study, in which the transcriptome is measured for tens of thousands of single cells from multiple individuals. Despite the advances of many clustering methods, there are few tailored methods for population-scale scRNA-seq studies. Here, we have developed a BAyesiany Mixture Model for Single Cell sequencing (BAMM-SC) method to cluster scRNA-seq data from multiple individuals simultaneously. Specifically, BAMM-SC takes raw data as input and can account for data heterogeneity and batch effect among multiple individuals in a unified Bayesian hierarchical model framework. Results from extensive simulations and application of BAMM-SC to in-house scRNA-seq datasets using blood, lung and skin cells from humans or mice demonstrated that BAMM-SC outperformed existing clustering methods with improved clustering accuracy and reduced impact from batch effects. BAMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/~Cwec47/singlecell.html.

bioinformatics↗