Biology subjects

Thomas, B. E.

Publications and source records attributed to Thomas, B. E..

6 recordsLinked to original sources

bioRxiv2024

A single-cell atlas characterizes dysregulation of the bone marrow immune microenvironment associated with outcomes in multiple myeloma

Multiple Myeloma (MM) remains incurable despite advances in treatment options. Although tumor subtypes and specific DNA abnormalities are linked to worse prognosis, the impact of immune dysfunction on disease emergence and/or treatment sensitivity remains unclear. We established a harmonized consortium to generate an Immune Atlas of MM aimed at informing disease etiology, risk stratification, and potential therapeutic strategies. We generated a transcriptome profile of 1,149,344 single cells from the bone marrow of 263 newly diagnosed patients enrolled in the CoMMpass study and characterized immune and hematopoietic cell populations. Associating cell abundances and gene expression with disease progression revealed the presence of a proinflammatory immune senescence-associated secretory phenotype in rapidly progressing patients. Furthermore, signaling analyses suggested active intercellular communication involving APRIL-BCMA, potentially promoting tumor growth and survival. Finally, we demonstrate that integrating immune cell levels with genetic information can significantly improve patient stratification.

genomics↗

bioRxiv2023

Integrated single-nuclei and spatial transcriptomic analysis reveals propagation of early acute vein harvest and distension injury signaling pathways following arterial implantation

BackgroundVein graft failure (VGF) following cardiovascular bypass surgery results in significant patient morbidity and cost to the healthcare system. Vein graft injury can occur during autogenous vein harvest and preparation, as well as after implantation into the arterial system, leading to the development of intimal hyperplasia, vein graft stenosis, and, ultimately, bypass graft failure. While previous studies have identified maladaptive pathways that occur shortly after implantation, the specific signaling pathways that occur during vein graft preparation are not well defined and may result in a cumulative impact on VGF. We, therefore, aimed to elucidate the response of the vein conduit wall during harvest and following implantation, probing the key maladaptive pathways driving graft failure with the overarching goal of identifying therapeutic targets for biologic intervention to minimize these natural responses to surgical vein graft injury. MethodsEmploying a novel approach to investigating vascular pathologies, we harnessed both single-nuclei RNA-sequencing (snRNA-seq) and spatial transcriptomics (ST) analyses to profile the genomic effects of vein grafts after harvest and distension, then compared these findings to vein grafts obtained 24 hours after carotid-cartoid vein bypass implantation in a canine model (n=4). ResultsSpatial transcriptomic analysis of canine cephalic vein after initial conduit harvest and distention revealed significant enrichment of pathways (P < 0.05) involved in the activation of endothelial cells (ECs), fibroblasts (FBs), and vascular smooth muscle cells (VSMCs), namely pathways responsible for cellular proliferation and migration and platelet activation across the intimal and medial layers, cytokine signaling within the adventitial layer, and extracellular matrix (ECM) remodeling throughout the vein wall. Subsequent snRNA-seq analysis supported these findings and further unveiled distinct EC and FB subpopulations with significant upregulation (P < 0.00001) of markers related to endothelial injury response and cellular activation of ECs, FBs, and VSMCs. Similarly, in vein grafts obtained 24 hours after arterial bypass, there was an increase in myeloid cell, protomyofibroblast, injury-response EC, and mesenchymal-transitioning EC subpopulations with a concomitant decrease in homeostatic ECs and fibroblasts. Among these markers were genes previously implicated in vein graft injury, including VCAN (versican), FBN1 (fibrillin-1), and VEGFC (vascular endothelial growth factor C), in addition to novel genes of interest such as GLIS3 (GLIS family zinc finger 3) and EPHA3 (ephrin-A3). These genes were further noted to be driving the expression of genes implicated in vascular remodeling and graft failure, such as IL-6, TGFBR1, SMAD4, and ADAMTS9. By integrating the ST and snRNA-seq datasets, we highlighted the spatial architecture of the vein graft following distension, wherein activated and mesenchymal-transitioning ECs, myeloid cells, and FBs were notably enriched in the intima and media of distended veins. Lastly, intercellular communication network analysis unveiled the critical roles of activated ECs, mesenchymal transitioning ECs, protomyofibroblasts, and VSMCs in upregulating signaling pathways associated with cellular proliferation (MDK, PDGF, VEGF), transdifferentiation (Notch), migration (ephrin, semaphorin), ECM remodeling (collagen, laminin, fibronectin), and inflammation (thrombospondin), following distension. ConclusionsVein conduit harvest and distension elicit a prompt genomic response facilitated by distinct cellular subpopulations heterogeneously distributed throughout the vein wall. This response was found to be further exacerbated following vein graft implantation, resulting in a cascade of maladaptive gene regulatory networks. Together, these results suggest that distension initiates the upregulation of pathological pathways that may ultimately contribute to bypass graft failure and presents potential early targets warranting investigation for targeted therapies. This work highlights the first applications of single-nuclei and spatial transcriptomic analyses to investigate venous pathologies, underscoring the utility of these methodologies and providing a foundation for future investigations.

genomics↗

bioRxiv2022

Taxane chemotherapy leads to breast cancer dormancy escape by stromal injury mediated IL-6/MAP2K signaling

A major cause of cancer recurrence following chemotherapy is cancer dormancy escape. Taxane-based chemotherapy is standard of care in breast cancer treatment aimed at killing proliferating cancer cells. Here, we demonstrate that docetaxel injures stromal cells, which release protumor cytokines, IL-6 and G-CSF, that in turn invoke dormant cancer outgrowth both in vitro and in vivo. Single-cell transcriptomics shows a reprogramming of awakened cancer cells including several survival cues such as stemness, chemoresistance, as well as an altered tumor microenvironment with augmented pro-tumor immune signaling. IL-6 plays a role in cancer cell proliferation, whereas G-CSF mediates tumor immunosuppression. Pathways and differential expression analyses confirmed MEK as the key regulatory molecule in cancer cell outgrowth and survival. Antibody targeting of protumor cytokines (IL-6, G-CSF) or inhibition of cytokine signaling via MEK/ERK pathway using selumetinib prior to docetaxel treatment prevented cancer dormancy outgrowth suggesting a novel therapeutic strategy to prevent cancer recurrence.

cancer biology↗

bioRxiv2022

Single Cell RNA Sequencing Driven Characterization of Pediatric Mixed Phenotype Acute Leukemia

BackgroundMixed phenotype acute leukemia (MPAL) is a rare subgroup of leukemia characterized by blast cells that display both myeloid and lymphoid lineage features, making this cancer difficult to diagnose and treat. A deeper characterization of MPAL at the molecular level is essential to better understand similarities/differences to the more common and better-studied leukemias, acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). Therefore, we performed single-cell RNA sequencing (scRNAseq) on MPAL bone marrow (BM) samples in an attempt to develop a more granular map of the MPAL microenvironment landscape. MethodsWe analyzed [~]16,000 cells from five pediatric MPAL BM samples collected at diagnosis to generate a single-cell transcriptomic landscape of B/Myeloid (B/My) and T/Myeloid (T/My) MPAL blasts and associated microenvironment cells. Cell clusters were identified using principal component analysis and uniform manifold approximation and projection (UMAP). Unsupervised analysis was performed to determine the overall relationship among B/My MPAL, T/My MPAL, and other acute leukemias - B-ALL, T-ALL, and AML. Supervised differentially expressed gene (DEG) analysis was performed to identify B/My and T/My MPAL blast-specific signatures. MPAL sample transcriptome profiles were compared with normal BM stem and immune cells to identify MPAL-specific dysregulation. Gene set enrichment analysis (GSEA) was performed, and significantly enriched pathways were compared in MPAL subtypes. Comparative analysis was performed on diagnostic samples based on their future minimal residual disease (MRD) and relapse status. ResultsB/My MPAL and T/My MPAL blasts displayed distinct subtype-specific blast signatures. UMAP analysis revealed that B/My MPAL samples had greater overlap with B-ALL samples, while T/My MPAL samples clustered separately from other acute leukemia subtypes. Genes overexpressed in both MPAL subtypes blasts compared to other leukemias and healthy controls included PLIN2, CD81, and UBE2S. B/My MPAL blast-specific genes included IRS2, SMIM3, and HBEGF, whereas T/My MPAL blast-overexpressed genes included IER5, BOD1L1, and HPGD. Sirtuin signaling, p38 MPAK signaling, and PI3K signaling pathways were upregulated in B/My MPAL blasts while oxidative phosphorylation and Rho family GTPases signaling pathways were upregulated in T/My MPAL blasts. Transcriptomic, pathways, and cell communication level differences were observed in the MPAL samples based on future MRD and clinical outcome status. ConclusionsWe have for the first time described the single-cell landscape of pediatric MPAL and demonstrate that B/My and T/My MPAL have unique scRNAseq profiles distinct from each other as well as from ALL and AML.

genomics↗

bioRxiv2021

Single Cell Transcriptomic Landscape of Diabetic Foot Ulcers

To understand the diabetic wound healing microenvironment, we profiled 174,962 single cells from foot, forearm, and PBMCs using single-cell RNA sequencing (scRNASeq) approach. Our analysis shows enrichment of a unique population of fibroblasts overexpressing MMP1, MMP3, MMP11, HIF1A, CHI3L1, and TNFAIP6 genes and M1 macrophage polarization in the DFU patients with healing wounds. Further, scRNASeq of spatially separated samples from same patient and spatial transcriptomics (ST) revealed preferential localization of these healing associated fibroblasts toward deep wound/ulcer bed as compared to wound edge or non-wounded skin. ST also validated our findings of higher enrichment of M1 macrophages in healers and M2 macrophages in non-healers. Our analysis provides deep insights into the wound healing microenvironment, identifying cell types that could be critical in promoting DFU healing, and may inform novel therapeutic approaches for DFU treatment.

genomics↗

bioRxiv2018

A Computational Solution To Improve Biomarker Reproducibility During Long-term Projects

Biomarkers are fundamental to basic and clinical research outcomes by reporting host responses and providing insight into disease pathophysiology. Measuring biomarkers with research-use ELISA kits is universal, yet lack of kit standardization and unexpected lot-to-lot variability presents analytic challenges for long-term projects. During an ongoing two-year project measuring plasma biomarkers in cancer patients, control concentrations for one biomarker (PF) decreased significantly after changes in ELISA kit lots. A comprehensive operations review pointed to standard curve shifts with the new kits, an analytic variable that jeopardized data already collected on hundreds of patient samples. After excluding other reasonable contributors to data variability, a computational solution was developed to provide a uniform platform for data analysis across multiple ELISA kit lots. The solution (ELISAtools) was developed within open-access R software in which variability between kits is treated as a batch effect. A defined best-fit Reference standard curve is modelled, a unique Shift factor \"S\" is calculated for every standard curve and data adjusted accordingly. The averaged S factors for PF ELISA kit lots #1-5 ranged from -0.086 to 0.735, and reduced control inter-assay variability from 62.4% to <9%, within quality control limits. S factors calculated for four other biomarkers provided a quantitative metric to monitor ELISAs over the 10 month study period for quality control purposes. Reproducible biomarker measurements are essential, particularly for long-term projects with valuable patient samples. Use of research-use ELISA kits is ubiquitous and judicious use of this computational solution maximizes biomarker reproducibility.

pathology↗