Biology subjects

Linder, A.

Publications and source records attributed to Linder, A..

12 recordsLinked to original sources

bioRxiv2025

High-Resolution Time-Lapse Imaging of Droplet-Cell Dynamics via Optimal Transport and Contrastive Learning

Single-cell analysis is essential for uncovering heterogeneous biological functions that arise from intricate cellular interaction. Microfluidic droplet arrays enable precise dynamic data collection through cell encapsulation in picoliter volumes. The time-lapse imaging of these arrays can reveal functional kinetics and cellular fates, but accurate tracking of cell identities across time frames remains challenging when droplets move significantly. Specifically, existing machine learning methods often depend on labeled data or require neighboring cells as reference; without them, these methods struggle to track identical objects across long distances with complex movements. To address these limitations, we developed a pipeline combining visual object detection, feature extraction via contrastive learning, and optimal transport-based object matching, which minimizes reliance on labeled training data. Our approach was validated across various experimental conditions and was able to track thousands of water-in-oil microfluidic droplets over large distances and long (> 30 min) time-separated frames. We achieved high precision in previously untraceable scenarios, tracking small, medium and large movements (corresponding to ~126, ~800 and ~10,000 {micro}m, respectively) with a success rate of correctly tracked droplets of > 90% for average movements within 212 object diameters, and > 60% for average movements of > 100 object diameters. This workflow lays the foundation for high-resolution, dynamic analysis of droplets and cells in both spatial and temporal dimensions without relying on visual labeling, allowing high-accuracy tracking in samples, where the uniqueness of the sample makes repeating experiments infeasible.

bioinformatics↗

bioRxiv2024

Inferring the composition of the blood plasma proteome by a human proteome distribution atlas

The plasma proteome is maintained by the influx and efflux of proteins from surrounding organs and cells. To quantify the extent different organs and cells contribute to the plasma proteome composition, we developed a mass spectrometry-based proteomics strategy to infer the origin of proteins detected in human plasma in health and disease. First, we constructed an extensive human proteome atlas from 18 vascularized organs and the most abundant cell types in blood. Second, the atlas was interfaced with previous RNA/protein atlases to objectively define proteome wide protein-organ associations to enable both the inference of origin and the reproducible quantification of organ-specific proteins in plasma. We demonstrate that the resource can determine disease-specific quantitative changes of organ-enriched protein panels in three separate patient cohorts with infection, pancreatitis, and myocardial injury. The strategy can be extended to other diseases to advance our understanding of the processes contributing to plasma proteome dynamics.

systems biology↗

bioRxiv2023

Single-B cell analysis correlates high-lactate secretion with stress and increased apoptosis

While cellular metabolism was proposed to be a driving factor of the activation and differentiation of B cells and the function of the resulting antibody-secreting cells (ASCs), the study of correlations between cellular metabolism and functionalities has been difficult due to the absence of technologies enabling the parallel measurement. Herein, we performed single-cell transcriptomics and introduced a direct concurrent functional and metabolic flux quantitation of individual murine B cells. Our transcriptomic data identified lactate metabolism as dynamic in ASCs, but antibody secretion did not correlate with lactate secretion rates (LSRs). Instead, our study of all splenic B cells during an immune response linked increased lactate metabolism with acidic intracellular pH and the upregulation of apoptosis. T cell-dependent responses increased LSRs, and added TLR4 agonists affected the magnitude and boosted LSRhigh B cells in vivo, while resulting in only a few immunoglobulin-G secreting cells (IgG-SCs). Therefore, our observations indicated that LSRhigh cells were not differentiating into IgG-SCs, and were rather removed due to apoptosis.

immunology↗

bioRxiv2023

Stimulation-induced cytokine polyfunctionality as a dynamic concept

Cytokine polyfunctionality is a well-established concept in immune cells, especially T cells, and their ability to concurrently produce multiple cytokines has been associated with better immunological disease control and subsequent effectiveness during infection and disease. To date, only little is known about the secretion dynamics of those cells, masked by the widespread deployment of mainly time-integrated endpoint measurement techniques that do not easily differentiate between concurrent and sequential secretion. Here, we employed a single-cell microfluidic platform capable of resolving secretion dynamics of individual PBMCs. To study the dynamics of poly-cytokine secretion, as well as the dynamics of concurrent and sequential polyfunctionality, we analyzed the response at different time points after ex vivo activation. Firstly, we observed simultaneous secretion of cytokines over the measurement time for most stimulants in a subpopulation of cells only. Secondly, polyfunctionality generally decreased with prolonged stimulation times and revealed no correlation with the concentration of secreted cytokines in response to stimulation. However, we observed a general trend towards higher cytokine secretion in polyfunctional cells, with their secretion dynamics being distinctly different from mono-cytokine secreting cells. This study provided insights into the distinct secretion behavior of heterogenous cell populations after stimulation with well-described agents and such a system could provide better understanding for various immune dynamics in therapy and disease.

immunology↗

bioRxiv2022

Wikipedia as a tool for contemporary history of science: A case study on CRISPR

Rapid developments and methodological divides hinder the study of how scientific knowledge accumulates, consolidates and transfers to the public sphere. Our work proposes using Wikipedia, the online encyclopedia, as a historiographical source for contemporary science. We chose the high-profile field of gene editing as our test case, performing a historical analysis of the English-language Wikipedia articles on CRISPR. Using a mixed method approach, we qualitatively and quantitatively analyzed its text, sections and references, alongside 50 affiliated articles. These, we found, documented CRISPRs maturation from a fundamental scientific discovery to a biotechnological revolution with vast social and cultural implications. We developed automated tools to support such research generically and demonstrated its applicability on two other scientific fields we have previously studied - COVID-19 and Circadian clocks. This method makes use of Wikipedia as a digital and free archive, documenting the incremental growth of knowledge and the manner scientific research accumulates and translates into public discourse. Using Wikipedia in this manner compliments and overcomes some issues with contemporary histories and can also augment existing bibliometric research.

scientific communication and education↗

bioRxiv2022

Generalized peakgroup scoring boosts identification rates and accuracy in mass spectrometry based discovery proteomics

The statistical validation of peptide and protein identifications in mass spectrometry proteomics is a critical step in the analytical workflow. This is particularly important in discovery experiments to ensure only confident identifications are accumulated for downstream analysis and biomarker consideration. However, the inherent nature of discovery proteomics experiments leads to scenarios where the search space will inflate substantially due to the increased number of potential proteins that are being queried in each sample. In these cases, issues will begin to arise when the machine learning algorithms that are trained on an experiment specific basis cannot accurately distinguish between correct and incorrect identifications and will struggle to accurately control the false discovery rate. Here, we propose an alternative validation algorithm trained on a curated external data set of 2.8 million extracted peakgroups that leverages advanced machine learning techniques to create a generalizable peakgroup scoring (GPS) method for data independent acquisition (DIA) mass spectrometry. By breaking the reliance on the experimental data at hand and instead training on a curated external dataset, GPS can confidently control the false discovery rate while increasing the number of identifications and providing more accurate quantification in different search space scenarios. To first test the performance of GPS in a standard experimental environment and to provide a benchmark against other methods, a novel spike-in data set with known varying concentrations was analyzed. When compared to existing methods GPS increased the nunmber of identifications by 5-18% and was able to provide more accurate quantification by increasing the number of ratio validated identifications by 24-74%. To evaluate GPS in a larger search space, a novel data set of 141 blood plasma samples from patients developing acute kidney injury after sepsis was searched with a human tissue spectral library (10000+ proteins). Using GPS, we were able to provide a 207-377% increase in the number of candidate differentially abundant proteins compared to the existing methods while maintaining competitive numbers of global identifications. Finally, using an optimized human tissue library and workflow we were able to identify 1205 proteins from the 141 plasma samples and increase the number of candidate differentially abundant proteins by 70.87%. With the addition of machine learning aided differential expression, we were able to identify potential new biomarkers for stratifying subphenotypes of acute kidney injury in sepsis. These findings suggest that by using a generalized model such as GPS in tandem with a massive scale spectral library it is possible to expand the boundaries of discovery experiments in DIA proteomics. GPS is open source and freely available on github at (https://github.com/InfectionMedicineProteomics/gscore).

bioinformatics↗

bioRxiv2022

A pharmacoproteomic landscape of organotypic intervention responses in gram-negative sepsis

Sepsis is the major cause of mortality across intensive care units globally, yet details of accompanying pathological molecular events remains unclear. This knowledge gap has resulted in ineffective development of sepsis-specific biomarkers and therapies, and suboptimal treatment regimens to prevent or reverse organ damage. Here, we used pharmacoproteomics to score treatment effects in a murine Escherichia coli sepsis model based on changes in the organ, cell, and plasma proteome landscapes. A combination of pathophysiological read-outs and time-resolved proteome maps of organs and blood enabled us to define time-dependent and organotypic proteotypes of dysfunction and damage upon administration of several combinations of the broad-spectrum beta-lactam antibiotic meropenem (Mem) and/or the immunomodulatory glucocorticoid methylprednisolone (Gcc). Three distinct response patterns were identified, defined as intervention-specific reversions, non-reversions, and specific intervention-induced effects, which depended on the underlying proteotype and varied significantly across organs. In the later stages of the disease, Gcc enhanced some positive treatment effects of Mem with superior reduction of the inflammatory response in the kidneys and partial restoration of sepsis-induced metabolic dysfunction. Unexpectedly, Mem introduced sepsis-independent perturbations in the mitochondrial proteome that were to some degree counteracted by Gcc. In summary, this study provides a pharmacoproteomic resource describing the time-resolved septic organ failure landscape across organs and blood, coupled to a novel scoring strategy that captures unintended secondary drug effects as an important criterion to consider when assessing therapeutic efficacy. Such information is critical for quantitative, objective, and organotypic assessment of benefits and unintended effects of candidate treatments in relationship to dosing, timing, and potential synergistic combinations in murine sepsis models.

systems biology↗

bioRxiv2021

Injury risk functions for the four primary knee ligaments

The purpose of this study was to develop Injury Risk Functions (IRFs) for the Anterior- and Posterior Cruciate Ligament (ACL and PCL, respectively) and the Medial- and Lateral Collateral Ligament (MCL and LCL, respectively) in the knee joint. The IRFs were based on Post-Mortem Human Subject (PMHS) tensile failure strains of either Bone-Ligament-Bone (BLB) or dissected LIGament (LIG) preparations. Due to insufficient sample sizes of the experimental data points available in the current literature, statistically-generated failure strains (virtual values) based on the reported mean- and standard deviation were used to accommodate for the unprovided specimen-specific results. All virtual and specimen-specific values were then categorized into groups of static and dynamic rates, respectively, and tested for the best fitting theoretical distribution to derive a ligament IRF. Ten IRFs were derived (3 for ACL, 2 for PCL, 2 for MCL and 3 for LCL). These IRFs are, to the best of the authors knowledge, the first attempt of knee ligament injury prediction tools based on PMHS data. For future improvements of the knee ligament IRFs, upcoming experiments need comparable testing and strain measurements. More emphasis on a clear definition of failure and transparent reporting of each specimen-specific result is necessary.

bioengineering↗

bioRxiv2021

Protective immune trajectories in early viral containment of non-pneumonic SARS-CoV-2 infection

The immune system of most SARS-CoV-2 infected individuals limits viral spread to the upper airways without pulmonary involvement. This prevents the development of pneumonic COVID-19. However, the protective immunological responses causative of successful viral containment in the upper airways remain unclear. Here, we combine longitudinal single-cell RNA sequencing, proteomic profiling, multidimensional flow cytometry, RNA-Seq of FACS-sorted leukocyte subsets and multiplex plasma interferon profiling to uncover temporally resolved protective immune signatures in non-pneumonic and ambulatory SARS-CoV-2 infected patients. We compare host responses in a high-risk patient population infected with SARS-CoV-2 but without pulmonary involvement to patients with COVID-19 pneumonia. Our data reveal a distinct immunological signature of successful viral containment, characterized by an early prominent interferon stimulated gene (ISG) upregulation across immune cell subsets. In addition, reduced cytotoxic potential of Natural Killer (NK) and T cells, as well as a monocyte phenotype with immune-modulatory potential are hallmarks of protective immunity. Temporal resolution across disease trajectories highlights ISG upregulation as particularly prominent early in the disease and confirms increased expression also in comparison to healthy controls. We validate this distinct temporal ISG signature by in-depth RNA-seq of FACS-sorted leukocyte subsets in a large prospective ambulatory SARS-CoV-2 infected cohort confirming early and robust ISG upregulation particularly in monocytes and T cells. In conclusion, our data demonstrate a protective ISG phenotype in patients with successful containment of SARS-CoV-2 infection without progression to COVID-19. This early protective interferon response might be exploited as a therapeutic approach and for disease course prediction.

immunology↗

bioRxiv2020

Cerebrospinal fluid proteome maps detect pathogen-specific host response patterns in meningitis.

Meningitis is a potentially life-threatening infection characterized by the inflammation of the leptomeningeal membranes. Many different viral and bacterial pathogens can cause meningitis, with differences in mortality rates, risk of developing neurological sequelae and treatment options. Here we constructed a compendium of digital cerebrospinal fluid (CSF) proteome maps to define pathogen-specific host response patterns in meningitis. The results revealed a drastic and pathogen-type specific influx of tissue-, cell- and plasma proteins in the CSF, where in particular a large increase of neutrophil derived proteins in the CSF correlated with acute bacterial meningitis. Additionally, both acute bacterial and viral meningitis result in marked reduction of brain-enriched proteins. Generation of a multi-protein LASSO regression model resulted in an 18-protein panel of cell and tissue associated proteins capable of classifying acute bacterial meningitis and viral meningitis. The same protein panel also enabled classification of tick-borne encephalitis, a subgroup of viral meningitis, with high sensitivity and specificity. The work provides insights into pathogen specific host response patterns in CSF from different disease etiologies to support future classification of pathogen-type based on host response patterns in meningitis.

microbiology↗

bioRxiv2019

Sweet revenge - Streptococcus pyogenes showcases first example of immune evasion through specific IgG glycan hydrolysis

Streptococcus pyogenes (Group A streptococcus, GAS) is an important human pathogen responsible for a wide variety of diseases from uncomplicated tonsillitis to life-threatening invasive infections. GAS secretes EndoS, an endoglycosidase able to specifically cleave the conserved N-glycan on human IgG antibodies. In vitro, removal of this glycan impairs IgG effector functions but its relevance to GAS infection in vivo is unclear. Using targeted mass spectrometry, we were able to characterize the effects of EndoS on host IgG glycosylation during the course of natural infections in human patients. We found substantial IgG glycan hydrolysis locally at site of infection as well as systemically in the most severe cases. Using these findings we were able to set up appropriate model systems to demonstrate decreased resistance to phagocytic killing of GAS lacking EndoS in vitro, as well as decreased virulence in a mouse model of invasive infection. This study represents the first described example of specific bacterial IgG glycan hydrolysis during infection and highlights the importance of IgG glycan hydrolysis for streptococcal pathogenesis. We thereby offer new insights into the mechanism of immune evasion employed by this pathogen with clear implications for treatment of severe GAS infections and future efforts at vaccine development.

microbiology↗

bioRxiv2017

Streptococcus pyogenes infection and the human proteome with a special focus on the IgG-cleaving enzyme IdeS

Infectious diseases are characterized by a complex interplay between host and pathogen, but how these interactions impact the host proteome is unclear. Here we applied a novel mass spectrometry based proteomics strategy to investigate how the human proteome is transiently modified by the pathogen Streptococcus pyogenes, with a particular focus on bacterial cleavage of IgG in vivo. In invasive diseases, S. pyogenes evokes a massive host response in blood, whereas superficial diseases are characterized by a local leakage of several blood plasma proteins at the site of infection including IgG. S. pyogenes produces IdeS, a protease cleaving IgG in the lower hinge region and we find highly effective IdeS-cleavage of IgG in samples from local IgG poor microenvironments. The results show that IdeS contributes to the adaptation of S. pyogenes to its normal ecological niches. Additionally, the work identifies novel clinical opportunities for in vivo pathogen detection.

microbiology↗