Biology subjects

Bassler, K.

Publications and source records attributed to Bassler, K..

12 recordsLinked to original sources

bioRxiv2023

Donor tissue-resident memory-like T and NK cells generate a transient peripheral chimerism in lung transplant recipients, potentially protective from chronic lung allograft dysfunction

Lung transplantation (LTx) is the only definite treatment option of patients suffering from end- stage lung disease. Long-term outcome is hampered by chronic allograft dysfunction resulting from poorly defined immune mechanisms. In this study of 97 lung recipients, we demonstrate dynamic changes of T, B and NK cell subsets early after lung transplantation with a selective decrease in memory CD4+ and CD8+ T cells accompanied by a relative increase in NK cells. Simultaneously, donor-derived T and NK cells were detected in recipient blood (n=44) immediately after LTx, persisting for three weeks. Donor T and NK cells displayed a CD69+ but CD103-CD49a- CD25- phenotype, which was shared by T and NK cells in lung perfusion solutions. In order to uncover the origin of these donor T and NK cells, the immune compartment of human lung explant tissue, i.e. trachea and parenchyma, was analyzed and it revealed three major subsets: classical circulating CD69-CD103-CD49a- T and NK cells, CD69+CD103+CD49a+ tissue-resident memory (TRM) T and NK cells and CD69+CD103-CD49a- TRM-like T and NK cells. Single-cell RNA sequencing confirmed the presence of TRM-like subsets with unique features, which reflected the phenotypes of donor T and NK cells and created a transient chimerism in recipient blood. Higher frequencies of donor T cells within the first three weeks showed a tendency for protection from chronic lung allograft dysfunction (CLAD) two years after transplantation although the correlation analyses did not reach statistical significance. To the best of our knowledge, we show for the first time that a transient chimerism is established within the first weeks after lung transplantation by donor TRM- like T and NK cells, which may contribute to protection from chronic lung allograft dysfunction (CLAD) development. Single Sentence SummaryDonor TRM-like T and NK cells cause a transient chimerism in lung recipients and potentially contribute to protection from CLAD.

immunology↗

bioRxiv2023

Fetal liver macrophages contribute to the hematopoietic stem cell niche by controlling granulopoiesis

During embryogenesis, the fetal liver becomes the main hematopoietic organ, where stem and progenitor cells as well as immature and mature immune cells form an intricate cellular network. Hematopoietic stem cells (HSCs) reside in a specialized niche, which is essential for their proliferation and differentiation. However, the cellular and molecular determinants contributing to this fetal HSC niche remain largely unknown. Macrophages are the first differentiated hematopoietic cells found in the developing liver, where they are important for fetal erythropoiesis by promoting erythrocyte maturation and phagocytosing expelled nuclei. Yet, whether macrophages play a role in fetal hematopoiesis beyond serving as a niche for maturing erythroblasts remains elusive. Here, we investigate the heterogeneity of macrophage populations in the fetal liver to define their specific roles during hematopoiesis. Using a single-cell omics approach combined with spatial proteomics and genetic fate-mapping models, we found that fetal liver macrophages cluster into distinct yolk sac-derived subpopulations and that long-term HSCs are interacting preferentially with one of the macrophage subpopulations. Fetal livers lacking macrophages show a delay in erythropoiesis and have an increased number of granulocytes, which can be attributed to transcriptional reprogramming and altered differentiation potential of long-term HSCs. Together, our data provide a detailed map of fetal liver macrophage subpopulations and implicate macrophages as part of the fetal HSC niche.

developmental biology↗

bioRxiv2022

Single-cell analysis reveals dynamics of human B cell differentiation and identifies novel B and antibody-secreting cell intermediates

Differentiation of B cells into antibody-secreting cells (ASCs) is a key process to generate protective humoral immunity. A detailed understanding of the cues controlling ASC differentiation is important to devise strategies to modulate antibody formation. Here, we dissected differentiation trajectories of human naive B cells into ASCs using single-cell RNA sequencing. By comparing transcriptomes of B cells at different stages of differentiation from an in vitro model with ex vivo B cells and ASCs, we uncovered a novel pre-ASC population present ex vivo in lymphoid tissues. For the first time, a germinal-center-like population is identified in vitro from human naive B cells and possibly progresses into a memory B cell population through an alternative route of differentiation, thus recapitulating in vivo human GC reactions. Our work allows further detailed characterization of human B cell differentiation into ASCs or memory B cells in both healthy and diseased conditions.

immunology↗

bioRxiv2022

Apolipoprotein E controls Dectin-1-dependent development of monocyte-derived alveolar macrophages upon pulmonary β-glucan-induced inflammatory adaptation

The lung is constantly exposed to the outside world and optimal adaptation of immune responses is crucial for efficient pathogen clearance. However, mechanisms which lead to the functional and developmental adaptation of lung-associated macrophages remain elusive. To reveal such mechanisms, we developed a reductionist model of environmental intranasal {beta}-glucan exposure, allowing for the detailed interrogation of molecular mechanisms of pulmonal macrophage adaptation. Employing single-cell transcriptomics, high dimensional imaging and flow cytometric characterization paired to in vivo and ex vivo challenge models, we reveal that pulmonary low-grade inflammation results in the development of Dectin-1 - Card9 signaling-dependent monocyte-derived macrophages (MoAM). MoAMs expressed high levels of CD11b, ApoE, Gpnmb and Ccl6, were glycolytic and produced large amounts of interleukin 6 upon restimulation. Myeloid cell specific ApoE ablation inhibited monocyte to MoAM differentiation dependent on M-CSF secretion, promoting MoAM cell death thus impeding MoAM maintenance. In vivo, {beta}-glucan-elicited MoAMs limited the bacterial burden of Legionella pneumophilia post infection and ameliorated fibrosis severity in a murine fibrosis model. Collectively these data identify MoAMs that are generated upon environmental cues and ApoE as an important determinant for lung immune resilience.

immunology↗

bioRxiv2021

Induction of peri-implantation stage synthetic embryos using reprogramming paradigms in ESCs

Blastocyst-derived stem cell lines were shown to self-organize into embryo-like structures in 3D cell culture environments. Here, we provide evidence that synthetic embryo-like structures are generated solely based on transcription factor-mediated molecular reprogramming of embryonic stem cells in a simple 3D co-culture system. ESCs in these cultures self-organize into elongated, compartmentalized synthetic embryo-like structures over the course of reprogramming exhibiting anterior visceral endoderm formation and symmetry breaking. Single-cell RNA-Seq reveals transcriptional profiles resembling epiblast, visceral endoderm, and extraembryonic ectoderm of early murine embryos around E4.5-E5.5. Within the epiblast, compartment marker gene expression supports primordial germ cell specification. After transplantation, synthetic embryo-like structures implant in uteri and initiate the formation of decidual tissues. This system allows for fast and reproducible generation of synthetic embryo-like structures, providing further insights into synthetic embryology.

synthetic biology↗

bioRxiv2020

Alterations of multiple alveolar macrophage states in chronic obstructive pulmonary disease

Despite the epidemics of chronic obstructive pulmonary disease (COPD), the cellular and molecular mechanisms of this disease are far from being understood. Here, we characterize and classify the cellular composition within the alveolar space and peripheral blood of COPD patients and control donors using a clinically applicable single-cell RNA-seq technology corroborated by advanced computational approaches for: machine learning-based cell-type classification, identification of differentially expressed genes, prediction of metabolic changes, and modeling of cellular trajectories within a patient cohort. These high-resolution approaches revealed: massive transcriptional plasticity of macrophages in the alveolar space with increased levels of invading and proliferating cells, loss of MHC expression, reduced cellular motility, altered lipid metabolism, and a metabolic shift reminiscent of mitochondrial dysfunction in COPD patients. Collectively, single-cell omics of multi-tissue samples was used to build the first cellular and molecular framework for COPD pathophysiology as a prerequisite to develop molecular biomarkers and causal therapies against this deadly disease.

systems biology↗

bioRxiv2020

Integrated analyses of single-cell atlases reveal age, gender, and smoking status associations with cell type-specific expression of mediators of SARS-CoV-2 viral entry and highlights inflammatory programs in putative target cells

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, creates an urgent need for identifying molecular mechanisms that mediate viral entry, propagation, and tissue pathology. Cell membrane bound angiotensin-converting enzyme 2 (ACE2) and associated proteases, transmembrane protease serine 2 (TMPRSS2) and Cathepsin L (CTSL), were previously identified as mediators of SARS-CoV2 cellular entry. Here, we assess the cell type-specific RNA expression of ACE2, TMPRSS2, and CTSL through an integrated analysis of 107 single-cell and single-nucleus RNA-Seq studies, including 22 lung and airways datasets (16 unpublished), and 85 datasets from other diverse organs. Joint expression of ACE2 and the accessory proteases identifies specific subsets of respiratory epithelial cells as putative targets of viral infection in the nasal passages, airways, and alveoli. Cells that co-express ACE2 and proteases are also identified in cells from other organs, some of which have been associated with COVID-19 transmission or pathology, including gut enterocytes, corneal epithelial cells, cardiomyocytes, heart pericytes, olfactory sustentacular cells, and renal epithelial cells. Performing the first meta-analyses of scRNA-seq studies, we analyzed 1,176,683 cells from 282 nasal, airway, and lung parenchyma samples from 164 donors spanning fetal, childhood, adult, and elderly age groups, associate increased levels of ACE2, TMPRSS2, and CTSL in specific cell types with increasing age, male gender, and smoking, all of which are epidemiologically linked to COVID-19 susceptibility and outcomes. Notably, there was a particularly low expression of ACE2 in the few young pediatric samples in the analysis. Further analysis reveals a gene expression program shared by ACE2+TMPRSS2+ cells in nasal, lung and gut tissues, including genes that may mediate viral entry, subtend key immune functions, and mediate epithelial-macrophage cross-talk. Amongst these are IL6, its receptor and co-receptor, IL1R, TNF response pathways, and complement genes. Cell type specificity in the lung and airways and smoking effects were conserved in mice. Our analyses suggest that differences in the cell type-specific expression of mediators of SARS-CoV-2 viral entry may be responsible for aspects of COVID-19 epidemiology and clinical course, and point to putative molecular pathways involved in disease susceptibility and pathogenesis.

bioinformatics↗

bioRxiv2019

A rule-based data-informed cellular consensus map of the human mononuclear phagocyte cell space

Single-cell genomic techniques are opening new avenues to understand the basic units of life. Large international efforts, such as those to derive a Human Cell Atlas, are driving progress in this area; here, cellular map generation is key. To expedite the inevitable iterations of these underlying maps, we have developed a rule-based data-informed approach to build next generation cellular consensus maps. Using the human dendritic-cell and monocyte compartment in peripheral blood as an example, we performed computational integration of previous, partially overlapping maps using an approach we termed backmapping, combined with multi-color flow-cytometry and index sorting-based single-cell RNA-sequencing. Our general strategy can be applied to any atlas generation for humans and other species.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC=\"FIGDIR/small/658179v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (18K):\norg.highwire.dtl.DTLVardef@c1b7b2org.highwire.dtl.DTLVardef@32b68org.highwire.dtl.DTLVardef@16e5fcorg.highwire.dtl.DTLVardef@1552028_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDefining a consensus of the human myeloid cell compartment in peripheral blood\nC_LIO_LI3 monocytes subsets, pDC, cDC1, DC2, DC3 and precursor DC make up the compartment\nC_LIO_LIDistinguish myeloid cell compartment from other cell spaces, e.g. the NK cell space\nC_LIO_LIProviding a generalizable method for building consensus maps for the life sciences\nC_LI

immunology↗

bioRxiv2019

Memory-driven computing accelerates genomic data processing

Next generation sequencing (NGS) is the driving force behind precision medicine and is revolutionizing most, if not all, areas of the life sciences. Particularly when targeting the major common diseases, an exponential growth of NGS data is foreseen for the next decades. This enormous increase of NGS data and the need to process the data quickly for real-world applications requires to rethink our current compute infrastructures. Here we provide evidence that memory-driven computing (MDC), a novel memory-centric hardware architecture, is an attractive alternative to current processor-centric compute infrastructures. To illustrate how MDC can change NGS data handling, we used RNA-seq assembly and pseudoalignment followed by quantification as two first examples. Adapting transcriptome assembly pipelines for MDC reduced compute time by 5.9-fold for the first step (SAMtools). Even more impressive, pseudoalignment by near-optimal probabilistic RNA-seq quantification (kallisto) was accelerated by more than two orders of magnitude with identical accuracy and indicated 66% reduced energy consumption. One billion RNA-seq reads were processed in just 92 seconds. Clearly, MDC simultaneously reduces data processing time and energy consumption. Together with the MDC-inherent solutions for local data privacy, a new compute model can be projected pushing large scale NGS data processing and primary data analytics closer to the edge by directly combining high-end sequencers with local MDC, thereby also reducing movement of large raw data to central cloud storage. We further envision that other data-rich areas will similarly benefit from this new memory-centric compute architecture.

bioinformatics↗

bioRxiv2018

Diagnostic value of blood gene expression-based classifiers as exemplified for acute myeloid leukemia

ABSTRACTAcute Myeloid Leukemia (AML) is a severe, mostly fatal hematopoietic malignancy. Despite nearly two decades of promising results using gene expression profiling, international recommendations for diagnosis and differential diagnosis of AML remain based on classical approaches including assessment of morphology, immunophenotyping, cytochemistry, and cytogenetics. Concerns about the translation of whole transcriptome profiling include the robustness of derived predictors when taking into account factors such as study- and site-specific effects and whether achievable levels of accuracy are sufficient for practical use. In the present study, we sought to shed light on these issues via a large-scale analysis using machine learning methods applied to a total of 12,029 samples from 105 different studies. Taking advantage of the breadth of data and the now much improved understanding of high-dimensional modeling, we show that AML can be predicted with high accuracy. High-dimensional approaches - in which multivariate signatures are learned directly from genome-wide data with no prior biological knowledge - are highly effective and robust. We explore also the relationship between predictive signatures, differential expression and known AML-related genes. Taken together, our results support the notion that transcriptome assessment could be used as part of an integrated genomic approach in cancer diagnosis and treatment to be implemented early on for diagnosis and differential diagnosis of AML.\n\nOne Sentence SummaryBlood gene expression data and machine learning were used to develop robust and accurate classifiers for diagnosis and differential diagnosis of acute myeloid leukemia based on analysis of more than 12,000 samples derived from more than 100 individual studies

genomics↗

bioRxiv2018

FASTGenomics: An analytical ecosystem for single-cell RNA sequencing data

Recent technological advances enable genomics of individual cells, the building blocks of all living organisms. Single cell data characteristics differ from those of bulk data, which led to a plethora of new analytical strategies. However, solutions are only useful for experts and currently, there are no widely accepted gold standards for single cell data analysis. To meet the requirements of analytical flexibility, ease of use and data security, we developed FASTGenomics (https://fastgenomics.org) as a powerful, efficient, versatile, robust, safe and intuitive analytical ecosystem for single-cell transcriptomics.

bioinformatics↗

bioRxiv2017

Cellular reprogramming of human monocytes is regulated by time-dependent IL4 signalling and NCOR2

The clinical and therapeutic value of human in vitro generated monocyte-derived dendritic cell (moDC) and macrophages is well established. However, in line with recent findings regarding myeloid cell ontogeny and due to our limited understanding of their physiological counterparts, transcriptional regulation and heterogeneity, the full potential of these important cellular systems is still underestimated.\n\nIn this study, we use cutting edge high-dimensional analysis methods to better understand the transcriptional organization, phenotypic heterogeneity and functional differences between human ex vivo isolated and in vitro generated mononuclear phagocytes with the aim to better realize their full potential in the clinic.\n\nWe demonstrate that human monocytes activated by MCSF or GMCSF most closely resemble inflammatory macrophages identified in vivo, while IL4 signalling in the presence of GMCSF generates moDCs resembling inflammatory DCs in vivo, but not steady state cDC1 or cDC2. Moreover, these reprogramming regimes lead to activated monocytes that present with profoundly different transcriptomic, metabolic, phenotypic and functional profiles. Furthermore, we demonstrate that CD14+ monocytes are integrating multiple exogenous activation signals such as GMCSF and IL4 in a combinatorial and temporal fashion, resulting in a high-dimensional cellular continuum of reprogrammed monocytes dependent on the mode and timing of cytokine exposure. Utilizing nanostraw-based knockdown technology, we demonstrate that the IL4-dependent generation of moDCs relies on the induction, nuclear localization and function of the transcriptional regulator NCOR2.\n\nFinally, we unravel unappreciated heterogeneity within the clinically moDCs population and propose a novel high-dimensional phenotyping strategy to better tailor clinical quality control strategies for patient need and culture conditions to enhance therapeutic outcome.

immunology↗