Biology subjects

Lehmann, K.

Publications and source records attributed to Lehmann, K..

13 recordsLinked to original sources

bioRxiv2026

A draft atlas of the human plasma proteome at isoform resolution

Key plasma protein, including albumin, complement system proteins, and coagulation factors circulate in biochemically and functionally distinct isoforms, or proteoforms. Despite the importance of these proteoforms being widely accepted, they are challenging to resolve systematically, and as a consequence, we still lack a more complete picture of the total proteoform diversity of the human plasma proteome. Here, we generated 3,114 intact protein fractions by extensively separating plasma according to isoelectric point and molecular weight. We then analysed these fractions by liquid chromatography data-independent acquisition mass spectrometry (DIA-MS) upon tryptic digestions, and combined the resultant data into a multidimensional, proteoform resolved atlas of the human plasma proteome. We report that 5,077 unequivocally detected plasma proteins circulate at least 179,049 distinct proteoforms, of which we detect 44,083, with a median of 11 proteoforms per protein, at high confidence. Substantially increasing the number of experimentally confirmed proteoforms, our atlas implies that the canonical protein forms account for less than half of the total protein mass in plasma. We detect the greatest proteoform diversity among the most abundant plasma proteins and those functioning in nutrient transport, complement and coagulation. Finally, our data clarifies discrepancies between the Olink and SomaScan proteomic platforms, as according to our atlas, these techniques provide correlated values for relatively abundant proteins with low to medium proteoform diversity, but struggle in the quantification of low-abundant proteins and on those which exist in many proteoforms. In sum, our data reveals extensive proteoform diversity of the human plasma proteome, and implies that proteoform diversity represents an enormous untapped reservoir for biomedical research and biomarker discovery.

biochemistry↗

bioRxiv2026

TLR-mediated activation of synovial fibroblasts from osteoarthritis patients promotes chondrocyte dysfunction

Objective: Toll like receptor (TLR) activation by cartilage derived damage-associated molecular patterns contributes to osteoarthritis (OA) pathogenesis, but the role of synovial fibroblasts in this process remains incompletely understood. We investigated the TLR responsiveness of human OA synovial fibroblasts and determined how TLR activation influences fibroblast-mediated regulation of chondrocyte function. Design: Primary synovial fibroblasts isolated from OA patients were characterized for TLR expression and stimulated with agonists targeting TLR1/2 to TLR9. Inflammatory mediator production, matrix metalloproteinase expression, mitochondrial respiration, and transcriptomic responses were assessed. Functional effects on cartilage homeostasis were evaluated using autologous chondrocyte spheroid co-cultures. Results: Approximately 50% of cells within OA synovial membranes were fibroblasts (CD45-CD31-PDPN+). Synovial fibroblasts expressed multiple TLRs at both the transcript and protein levels. Activation of TLR1/2, TLR4, TLR5, and TLR2/6 induced robust expression of IL-6, IL-8, G-CSF, MMP3, and MMP10, with minimal effects on mitochondrial respiratory function. Transcriptomic analysis revealed activation of inflammatory pathways together with enrichment of antigen presentation and protein translation programs. In autologous co-culture, TLR1/2-activated synovial fibroblasts promoted inflammatory and catabolic gene expression, suppressed anabolic gene expression, and impaired chondrocyte spheroid growth. Conclusions: Human OA synovial fibroblasts are highly responsive to TLR activation and acquire a pro-inflammatory, cartilage-degrading phenotype that directly impairs chondrocyte homeostasis. These findings identify synovial fibroblasts as key effectors of innate immune signalling within the OA joint and support targeting shared TLR-mediated pathways in joint-resident cells as a potential disease-modifying therapeutic strategy.

immunology↗

bioRxiv2025

Stable characteristics of intrapopulation heterogeneity in virus-specific Th1 cells during chronic viral challenge infection

Virus-specific CD4+ T cells typically undergo T helper (Th) 1 differentiation and contribute to a type 1 immune response in infection with lymphocytic choriomeningitis virus (LCMV). Using this model pathogen, we performed an in-depth analysis of the quantitative expression stability of the Th1 key transcription factor T-bet. Previously, it was shown that virus-specific Th1 cells arising in acute infections expressed T-bet at distinct intensities and maintained their T-bet expression differences after viral clearance as memory cells for weeks in the steady state. However, it was unclear whether differential T-bet expression was associated with heterogeneity inside the Th1 population and if the quantitative T-bet memory, particularly of those cells expressing T-bet at low levels, could withhold the strong and continuous stimulation present during chronic infection. Using T-bet-ZsGreen reporter mice, virus-specific Th1 cells were characterized phenotypically at protein, RNA, and DNA/chromatin accessibility levels. The Th1 cells arising during acute LCMV Armstrong infection showed a continuous spectrum of T-bet expression, ranging from cells with very high T-bet to cells with low T-bet. Even though the cells with low T-bet expression clearly possessed Th1 characteristics, they additionally showed certain Tfh-like features at protein and RNA level. When virus-specific Th1 cells were sorted according to T-bet-ZsGreen reporter expression intensity, adoptively transferred, and rechallenged by infecting the host animals with the chronic Clone 13 strain of LCMV, they maintained quantitative differences in T-bet reporter and IFN-{gamma} expression levels. A subpopulation of the progeny of the former T-betlow cells still showed a mild Tfh-associated phenotype. Independent of their past and present T-bet expression level, all virus-reactive CD4+ T cells acquired phenotypic signs of exhaustion as characterized by upregulation of PD-1, LAG3, and TOX and vast absence of effector cytokine co-expression in the chronic infection environment. Collectively, our findings highlight the heterogeneity of T-bet+ antiviral CD4+ T cells and the stability of quantitative differences in individual virus-specific CD4+ T cells during chronic viral challenge infection.

immunology↗

bioRxiv2025

Balancing Act: Groundwater microbiome''s resilience and vulnerability to hydroclimatic extremes

Groundwater health is increasingly threatened by climate change, which alters precipitation patterns, leading to groundwater recharge shifts. These shifts impact subsurface microbial communities, crucial for maintaining ecosystem functions. In this decade-long study of carbonate aquifers, we analyzed 815 bacterial 16S rRNA gene datasets, 226 dissolved organic matter (DOM) profiles, 387 metabolomic datasets, and 174 seepage microbiome sequences. Our findings reveal distinct short- and long-term temporal patterns of groundwater microbiomes driven by environmental fluctuations. Microbiomes of hydrologically connected aquifers exhibit lower temporal stability due to stochastic processes and greater susceptibility to surface disturbances, yet they demonstrate remarkable resilience. Conversely, isolated aquifer microbiomes show resistance to short-term changes, governed by deterministic processes, but exhibit reduced stability under prolonged stress. Variability in seepage-associated microorganisms, DOM, and metabolic diversity further drive microbiome dynamics. These findings highlight the dual vulnerability of groundwater systems to acute and chronic pressures, emphasizing the critical need for sustainable management strategies to mitigate the impacts of hydroclimatic extremes.

microbiology↗

bioRxiv2023

Single cell analysis reveals CD45RClow/-TNFR2+CD29lowCD8+ Tregs with superior activity

Although described in the 70s, CD8+ regulatory T cells (Tregs) remain incompletely understood and to date, although several markers are used to define them, they remain poorly defined. The identification of reliable and consistent markers, as it was done for CD4+ Tregs, remains an urgent task and a challenge to advance our understanding. Herein, we analyzed total CD8+ T cells using single cell CITEseq and VDJ T cell receptor sequencing utilizing markers used previously to identify Tregs, in particular CD45RC described by our team and others to divide pro-inflammatory (CD45RChigh) and pro-regulatory (CD45RClow/-) CD8+ T cells in rat, mice and human. 7000 freshly isolated, non-stimulated CD8+ T lymphocytes of four healthy volunteers were analyzed. Combining at a single cell level transcriptome and protein expression data led for the first time to the characterization and definition of three subsets of regulatory CD8+ T cells. Further in vitro functional analysis based on three markers highlighted the superior suppressive activity of the CD8+CD45RClow/-TNFR2+CD29low Tregs subset. To our knowledge, this is the largest characterization of human CD8+ Tregs to date. This data resource will help improve our understanding of CD8+ T cells heterogeneity and will help to translate CD8+ Tregs to the clinic.

immunology↗

bioRxiv2022

The Children's Brain Tumor Network (CBTN) - Accelerating Research in Pediatric Central Nervous System Tumors through Collaboration and Open Science

Pediatric brain tumors are the leading cause of cancer-related death in children in the United States and contribute a disproportionate number of potential years of life lost compared to adult cancers. Moreover, survivors frequently suffer long-term side effects, including secondary cancers. The Childrens Brain Tumor Network (CBTN) is a multi-institutional international clinical research consortium created to advance therapeutic development through the collection and rapid distribution of biospecimens and data via open-science research platforms for real-time access and use by the global research community. The CBTNs 32 member institutions utilize a shared regulatory governance architecture at the Childrens Hospital of Philadelphia to accelerate and maximize the use of biospecimens and data. As of August 2022, CBTN has enrolled over 4,700 subjects, over 1,500 parents, and collected over 65,000 biospecimen aliquots for research. Additionally, over 80 preclinical models have been developed from collected tumors. Multi-omic data for over 1,000 tumors and germline material is currently available with data generation for > 5,000 samples underway. To our knowledge, CBTN provides the largest open-access pediatric brain tumor multi-omic dataset annotated with longitudinal clinical and outcome data, imaging, associated biospecimens, child-parent genomic pedigrees, and in vivo and in vitro preclinical models. Empowered by NIH-supported platforms such as the Kids First Data Resource and the Childhood Cancer Data Initiative, the CBTN continues to expand the resources needed for scientists to accelerate translational impact for improved outcomes and quality of life for children with brain and spinal cord tumors.

cancer biology↗

bioRxiv2021

SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing

MotivationSeveral recently developed single-cell DNA sequencing technologies enable whole-genome sequencing of thousands of cells. However, the ultra-low coverage of the sequenced data (< 0.05x per cell) mostly limits their usage to the identification of copy number alterations in multi-megabase segments. Many tumors are not copy number-driven, and thus single-nucleotide variant (SNV)-based subclone detection may contribute to a more comprehensive view on intra-tumor heterogeneity. Due to the low coverage of the data, the identification of SNVs is only possible when superimposing the sequenced genomes of hundreds of genetically similar cells. Thus, we have developed a new approach to efficiently cluster tumor cells based on a Bayesian filtering approach of relevant loci and exploiting read overlap and phasing. ResultsWe developed Single Cell Data Tumor Clusterer (SECEDO, lat. to separate), a new method to cluster tumor cells based solely on SNVs, inferred on ultra-low coverage single-cell DNA sequencing data. We applied SECEDO to a synthetic dataset simulating 7,250 cells and eight tumor subclones from a single patient, and were able to accurately reconstruct the clonal composition, detecting 92.11% of the somatic SNVs, with the smallest clusters representing only 6.9% of the total population. When applied to four real single-cell sequencing datasets from a breast cancer patient, each consisting of {approx}2,000 cells, SECEDO was able to recover the major clonal composition in each dataset at the original coverage of 0.03x, achieving an ARI score of {approx}0.6. The current state-of-the-art SNV-based clustering method achieved an ARI score of {approx}0, even after increasing the coverage in silico by a factor of 10, and was only able to match SECEDOs performance when pooling data from all four datasets, in addition to artificially increasing the sequencing coverage by a factor of 7. Variant calling on the resulting clusters recovered more than twice as many SNVs as would have been detected if calling on all cells together. Further, the allelic ratio of the called SNVs on each subcluster was more than double relative to the allelic ratio of the SNVs called without clustering, thus demonstrating that calling variants on subclones, in addition to both increasing sensitivity of SNV detection and attaching SNVs to subclones, significantly increases the confidence of the called variants. AvailabilitySECEDO is implemented in C++ and is publicly available at https://github.com/ratschlab/secedo.

bioinformatics↗

bioRxiv2020

SCIM: Universal Single-Cell Matching with Unpaired Feature Sets

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSMotivationC_ST_ABSRecent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. ResultsWe propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an auto-encoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 93% and 84% cell-matching accuracy for each one of the samples respectively. Availabilityhttps://github.com/ratschlab/scim

bioinformatics↗

bioRxiv2019

Antigen-driven PD1+Tox+Eomes+ and PD1+Tox+BHLHE40+ synovial T lymphocytes regulate chronic inflammation in situ

Introduction/AbstractT lymphocytes accumulate in inflamed tissues of patients with chronic inflammatory diseases (CIDs) and express pro-inflammatory cytokines upon re-stimulation in vitro1-29. Further, a significant genetic linkage to MHC genes suggests that T lymphocytes play an important role in the pathogenesis of CIDs including juvenile idiopathic arthritis (JIA)30-33. However, the functions of T lymphocytes in established disease remain elusive. Here we dissect the heterogeneity of synovial T lymphocytes in JIA patients by single cell RNA-sequencing. We identify subpopulations of T lymphocytes expressing genes reflecting recent activation by antigen in situ. A PD-1+TOX+EOMES+ population of CD4+ T lymphocytes expressed immune regulatory genes and chemoattractant genes for myeloid cells. A PD-1+TOX+BHLHE40+ population of CD4+, and a mirror population of CD8+ T lymphocytes expressed genes driving inflammation, and genes supporting B lymphocyte activation. This analysis points out that multiple types of T lymphocytes have to be targeted for therapeutic regeneration of tolerance in arthritis.

immunology↗

bioRxiv2019

Discrete populations of isotype-switched memory B lymphocytes are maintained in murine spleen and bone marrow

Here we describe tissue-resident memory B lymphocytes of spleen and bone marrow. Single cell transcriptomes and B cell receptor repertoires identify several exclusive populations of isotype-switched memory B cells (Bsm) in murine spleen and bone marrow, and one interconnected population of 10-20%. A population of marginal zone-like Bsm is located exclusively in the spleen while a novel population of quiescent Bsm is located exclusively in the bone marrow. Cells of two further populations, present in both, spleen and bone marrow, differ in repertoire between the two organs, i.e. are resident as well. Finally, another interconnected population of Bsm of the B1 lineage is present in spleen and bone marrow. In the bone marrow, all Bsm individually dock onto VCAM1+ stromal cells, resting in terms of activation, proliferation and mobility. The discrete B cell memory of bone marrow may be key to rapid secondary humoral responses to systemic antigens.

immunology↗

bioRxiv2019

A Bayesian Nonparametric Approach to Discover Clinico-Genetic Associations across Cancer Types

MotivationPersonalized medicine aims at combining genetic, clinical, and environmental data to improve medical diagnosis and disease treatment, tailored to each patient. This paper presents a Bayesian nonparametric (BNP) approach to identify genetic associations with clinical/environmental features in cancer. We propose an unsupervised approach to generate data-driven hypotheses and bring potentially novel insights about cancer biology. Our model combines somatic mutation information at gene-level with features extracted from the Electronic Health Record. We propose a hierarchical approach, the hierarchical Poisson factor analysis (H-PFA) model, to share information across patients having different types of cancer. To discover statistically significant associations, we combine Bayesian modeling with bootstrapping techniques and correct for multiple hypothesis testing.\n\nResultsUsing our approach, we empirically demonstrate that we can recover well-known associations in cancer literature. We compare the results of H-PFA with two other classical methods in the field: case-control (CC) setups, and linear mixed models (LMMs).

bioinformatics↗

bioRxiv2019

Social hierarchy regulates ocular dominance plasticity in adult male mice.

We here show that social rank, as assessed by competition for a running wheel, influences ocular dominance plasticity in adult male mice. Dominant animals showed a clear ocular dominance shift after four days of MD, whereas their submissive cage mates did not. NMDA receptor activation, reduced GABA inhibition, and serotonin transmission were necessary for this plasticity, but not sufficient to explain the difference between dominant and submissive animals. In contrast, prefrontal dopamine concentration was higher in dominant than submissive mice, and systemic manipulation of dopamine transmission bidirectionally changed ocular dominance plasticity. Thus, we could show that a social hierarchical relationship influences ocular dominance plasticity in the visual cortex via higher-order cortices, most likely the medial prefrontal cortex. Further studies will be needed to elucidate the precise mechanisms by which this regulation takes place.

neuroscience↗

bioRxiv2017

A pan cancer analysis of promoter activity highlights the regulatory role of alternative transcription start sites and their association with noncoding mutations

Most human protein-coding genes are regulated by multiple, distinct promoters, suggesting that the choice of promoter is as important as its level of transcriptional activity. While the role of promoters as driver elements in cancer has been recognized, the contribution of alternative promoters to regulation of the cancer transcriptome remains largely unexplored. Here we infer active promoters using RNA-Seq data from 1,188 cancer samples with matched whole genome sequencing data. We find that alternative promoters are a major contributor to context-specific regulation of isoform expression and that alternative promoters are frequently deregulated in cancer, affecting known cancer-genes and novel candidates. Our study suggests that a highly dynamic landscape of active promoters shapes the cancer transcriptome, opening many opportunities to further explore the interplay of regulatory mechanism and noncoding somatic mutations with transcriptional aberrations in cancer.

genomics↗