Biology subjects

Kaestner, K.

Publications and source records attributed to Kaestner, K..

6 recordsLinked to original sources

bioRxiv2026

A universal platform for simultaneous TCRα/β removal enables safer and more potent TCR therapies and autoimmune modeling

Adoptive T-cell therapies using tumour-specific T-cell receptors (TCRs) are limited by competition with endogenous receptors, which impairs efficacy and poses risks of off-target autoreactivity. Here we present a CRISPR-based platform that completely and selectively eliminates both endogenous TCR- and -{beta} chains without affecting introduced transgenic TCRs, irrespective of codon optimization. This approach achieves >90% deletion efficiency in Jurkat and primary human T cells, markedly enhancing the expression, pairing fidelity, and functional potency of transgenic receptors. Using a clinically relevant HLA-A*02:01-restricted DMF5 TCR, we show that dual TCR ablation boosts antigen-specific activation and cytotoxicity in vitro and significantly enhances tumor clearance in vivo in human immune system (HIS) mice, while preventing graft-versus-host disease (GVHD). Targeted locus amplification revealed that CRISPR-induced double-strand breaks did not alter lentiviral integration profiles, confirming genomic safety. Extending this approach to four insulin-reactive TCRs demonstrated that removal of endogenous receptors increased transduction efficiency and functional activity, with one (1E6) showing selective activation and infiltration of stem cell-derived islet grafts (SC-islets) in vivo. This study establishes a universal, safe, and scalable genome-editing platform for generating functionally precise human T cells. By integrating cancer immunotherapy and autoimmune disease modelling within a single framework, it provides a strong preclinical rationale for dual endogenous TCR removal as a route to improved specificity, safety, and therapeutic efficacy in TCR-based cell therapies.

immunology↗

bioRxiv2024

Villification of the intestinal epithelium is driven by Foxl1

The primitive gut tube of mammals initially forms as a simple cylinder consisting of the endoderm-derived, pseudostratified epithelium and the mesoderm-derived surrounding mesenchyme. During mid-gestation a dramatic transformation occurs in which the epithelium is both restructured into its final cuboidal form and simultaneously folded and refolded to create intestinal villi and intervillus regions, the incipient crypts. Here we show that the mesenchymal winged helix transcription factor Foxl1, itself induced by epithelial hedgehog signaling, controls villification by activating BMP and PDGFR as well as planar cell polarity genes in epithelial-adjacent telocyte progenitors, both directly and in a feed-forward loop with Foxo3.

developmental biology↗

bioRxiv2023

Modeling Type 1 Diabetes progression from single-cell transcriptomic measurements in human islets

Type 1 diabetes (T1D) is a chronic condition in which the insulin-producing beta cells are destroyed by immune cells. Research in the past few decades characterized the immune cells involved in disease pathogenesis and has led to the development of immunotherapies that can delay the onset of T1D by two years. Despite this progress, early detection of autoimmunity in individuals who will develop T1D remains a challenge. Here, we evaluated the potential of combining single-cell genomics and machine learning strategies as a prime approach to tackle this challenge. We used gradient-boosting-based machine learning algorithms and modeled changes in transcriptional profiles of single cells from pancreatic tissues in T1D and nondiabetic organ donors collected by the Human Pancreas Analysis Program. We assessed whether mathematical modelling could predict the likelihood of T1D development in nondiabetic autoantibody-positive organ donors. While the majority of autoantibody-positive organ donors were predicted to be nondiabetic by our model, select donors with unique gene signatures were classified with the T1D group. Remarkably, our strategy also revealed a shared gene signature in distinct T1D associated models based on different cell types including alpha cells, beta cells and acinar cells, suggesting a common effect of the disease on transcriptional outputs of these cells. Together, our strategy presents the first report on the utility of machine learning algorithms in early detection of molecular changes in T1D.

genomics↗

bioRxiv2023

Computational workflow and interactive analysis of single-cell expression profiling of islets generated by the Human Pancreas Analysis Program

Type 1 and Type 2 diabetes are distinct genetic diseases of the pancreas which are defined by the abnormal level of blood glucose. Understanding the initial molecular perturbations that occur during the pathogenesis of diabetes is of critical importance in understanding these disorders. The inability to biopsy the human pancreas of living donors hampers insights into early detection, as the majority of diabetes studies have been performed on peripheral leukocytes from the blood, which is not the site of pathogenesis. Therefore, efforts have been made by various teams including the Human Pancreas Analysis Program (HPAP) to collect pancreatic tissues from deceased organ donors with different clinical phenotypes. HPAP is designed to define the molecular pathogenesis of islet dysfunction by generating detailed datasets of functional, cellular, and molecular information in pancreatic tissues of clinically well-defined organ donors with Type 1 and Type 2 diabetes. Moreover, data generated by HPAP continously become available through a centralized database, PANC-DB, thus enabling the diabetes research community to access these multi-dimensional data prepublication. Here, we present the computational workflow for single-cell RNA-seq data analysis of 258,379 high-quality cells from the pancreatic islets of 67 human donors generated by HPAP, the largest existing scRNA-seq dataset of human pancreatic tissues. We report various computational steps including preprocessing, doublet removal, clustering and cell type annotation across single-cell RNA-seq data from islets of four distintct classes of organ donors, i.e. non-diabetic control, autoantibody positive but normoglycemic, Type 1 diabetic, and Type 2 diabetic individuals. Moreover, we present an interactive tool, called CellxGene developed by the Chan Zuckerberg initiative, to navigate these high-dimensional datasets. Our data and interactive tools provide a reliable reference for singlecell pancreatic islet biology studies, especially diabetes-related conditions.

genomics↗

bioRxiv2021

Multiomics single-cell analysis of human pancreatic islets reveals novel cellular states in health and type 1 diabetes

Type 1 diabetes (T1D) is an autoimmune disease of only partially defined etiology in which immune cells destroy insulin-producing beta cells. Using single-cell transcriptomics and an advanced analytical strategy to assess pancreatic islets of T1D, autoantibody-positive, and non-diabetic organ donors, we identified both canonical cell types and rare insulin-expressing cells with a hybrid mixture of endocrine and exocrine gene signatures within all donors. We further found elevated expression of MHC Class II pathway genes in exocrine ductal cells of T1D donors, which we confirmed through CyTOF, in situ imaging mass cytometry, and immunofluorescence analysis. Taken together, our multimodal analyses identify novel cell types and processes that may contribute to T1D immunopathogenesis and provide new cellular and molecular insights into human pancreas function.

genomics↗

bioRxiv2019

The dynamic chromatin architecture of the regenerating liver

Background & AimsThe adult liver is the main detoxification organ and is routinely exposed to environmental insults but retains the ability to restore its mass and function upon tissue damage. However, massive injury can lead to liver failure, and chronic injury causes fibrosis, cirrhosis, and hepatocellular carcinoma. Currently, the transcriptional regulation of organ repair in the adult liver is incompletely understood.\n\nMethodsWe isolated nuclei from quiescent as well as repopulating hepatocytes in a mouse model of hereditary tyrosinemia, which recapitulates the injury and repopulation seen in toxic liver injury in humans. We then performed the assay for transposase accessible chromatin with high-throughput sequencing (ATAC-seq) specifically in repopulating hepatocytes to identify differentially accessible chromatin regions and nucleosome positioning. Additionally, we employed motif analysis to predict differential transcription factor occupancy and validated the in silico results with chromatin immunoprecipitation followed by sequencing (ChIP-seq) for hepatocyte nuclear factor 4 (HNF4) and CCCTC-binding factor (CTCF).\n\nResultsChromatin accessibility in repopulating hepatocytes was increased in the regulatory regions of genes promoting proliferation and decreased in the regulatory regions of genes involved in metabolism. The epigenetic changes at promoters and liver enhancers correspond with regulation of gene expression, with enhancers of many liver function genes displaying a less accessible state during the regenerative process. Moreover, increased CTCF occupancy at promoters and decreased HNF4 binding at enhancers implicate these factors as key drivers of the transcriptomic changes in replicating hepatocytes that enable liver repopulation.\n\nConclusionsOur analysis of hepatocyte-specific epigenomic changes during liver repopulation identified CTCF and HNF4 as key regulators of hepatocyte proliferation and regulation of metabolic programs. Thus, liver repopulation in the setting of toxic injury makes use of both general transcription factors (CTCF) for promoter activation, and reduced binding by a hepatocyte-enriched factor (HNF4) to temporarily limit enhancer activity.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=66 SRC=\"FIGDIR/small/664862v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (13K):\norg.highwire.dtl.DTLVardef@cefbc3org.highwire.dtl.DTLVardef@f2edc8org.highwire.dtl.DTLVardef@d90be9org.highwire.dtl.DTLVardef@19cf7a9_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗