Biology subjects

Helin, J. S.

Publications and source records attributed to Helin, J. S..

3 recordsLinked to original sources

bioRxiv2025

Biomolecular tracking by FIRESCAPE reveals distinct modes of clearance, damage induction and cellular uptake for extracellular histone H3

In attempting to observe the behaviour of a protein of interest in vivo, akin to other observer effects, current techniques require modifications or interventions that inherently alter the protein or its host, leaving one uncertain as to whether natural behaviour remains unperturbed. The study of chromatin has mostly been restricted to defining its function in the nucleus, where histone proteins fulfil vital roles in packaging genomic DNA and regulating transcription. However, chromatin components can be released into the extracellular space, either intentionally via cellular secretion or during disease-induced cell death. These extracellular chromatin components, depending on the context, can consist of: free histones, free DNA, intact nucleosomes (histone octamers wrapped by DNA), or heterogeneous, higher order structures such as neutrophil extracellular traps (NETs). They have been associated with diverse pathologies such as inflammation, cancer, and sepsis, and distinct toxic effects. However, there is a widely acknowledged lack of methods that distinguish between them and between their unique functions. Here, we now address protein observer effects to explore the fate and function of extracellular free histones by utilizing FIRESCAPE ([18F]-Fluorine Isotopic Radiolabeling Enabling Scanning of Clearance After Proteolytic Events), a novel radiolabeling concept that leverages the unique, high-sensitivity properties of the radioisotope fluorine-18, 18F, and residue-specific protein editing chemistry. By installing close and then true 18F-containing protein sidechain mimics site-specifically, FIRESCAPE enables the hierarchical in vivo scanning of the half-lives, proteolytic susceptibility and clearance of single residues in a protein of interest, and at microdoses far below toxic levels (low nanomole). These radioequivalent proteins bearing near-zero-size, zero-background labels now precisely reveal the strikingly distinct distribution, half-lives, damage-inducing abilities and accumulation of free extracellular histones in cellulo and in vivo compared to intact nucleosomes. Free extracellular histone H3 is rapidly cleared from circulation, mediated first by proteolysis of the histone tail. By contrast, direct injection of free histones vs nucleosomes into tissue that is unprotected by such proteolysis (brain), provokes a starkly different response; free histones exhibited limited diffusion and swiftly promoted damage both in cell culture and in vivo, whilst intact nucleosomes were essentially passive and benign. Remarkably, synthetic extracellular histone H3 was observed to enter cells and integrate into chromatin, indistinguishable from native H3 in both localization and post-translational modification (PTM) accumulation, yet paired with cellular and tissue damage. The exploratory studies described here now provide much needed clarity to the distinct fates and effects of extracellular histones vs nucleosomes, in particular the strongly damaging effects of free histones, their rapid uptake into cells, and an associated histone-specific proteolysis pathway via the removal of the histone tail.

biochemistry↗

bioRxiv2025

Isostere 18F-protein post-translational editing enables dynamic tracking of neurodegeneration biomarkers

The neurofilament light chain protein (NfL) is a suggested general marker for neuronal loss. Its release from brain parenchyma into cerebral spinal fluid, and presumed detection in blood has seen it established as a first blood-based marker of disease activity and drug efficacy in multiple sclerosis (MS) and in the presymptomatic diagnosis and assessment of disease course for other neurodegenerative disorders.1 However, the lack of characterisation of its behaviour in circulation, largely due to its antibody-dependent measurement, have hampered the biological interpretation of these measurements, especially after acute injury such as in MS relapse or head trauma.2 Here, we describe a strategy for exploiting positron emission tomography (PET) imaging using isosteric protein mimics following the installation of a fluorine-18 label that is benign enough to provide sensitive, real-time information on the dynamics and trafficking of NfL protein. This circumvents the limits of current methods that integrate 18F into proteins through the bio-conjugation of bulky, unnatural groups, which we show perturb NfLs assembly and functional properties from those in the natural state. We use a visible-light-driven reaction to access radioactive isostere proteins that are unperturbed and so closely resemble their native form. In this way, generation of [18F]fluoroalkyl radicals that can be rapidly reacted at pre-defined sites on proteins creates mimics of proteinogenic side chains bearing near-zero-size labels to probe proteins in functionally true form. These prosthetic-free, protein radiotracers can be generated in excellent radiochemical yield (up to 67%) via a semi-automated protocol in just 15 mins. High associated molar activities (precursor up to 102 GBq mol-1) allowed high sensitivity dynamic observations in blood, brain and cerebrospinal fluid, enabling even the first unambiguous observations of spinal flow kinetics using proteins. These dynamics, including the high rate of spinal flow (on the order of mm per min) and drainage of NfL from CSF into sacral lymph nodes, now provides evidence that the slow fall rate of antibody-detected markers that is observed after acute neural insults is not due to a long half-life, but rather reflects sustained neuronal loss. This discovery will now help to better correlate clinical and radiological features of disease with NfL blood levels. Our methodology now demonstrates the broad potential of a near-zero-size labelling method for the functional study of proteins in whole organisms without interfering with their biological activity and native assembly.

synthetic biology↗

bioRxiv2023

SEGMENTATION OF DYNAMIC TOTAL-BODY -FDG PET IMAGES USING UNSUPERVISED CLUSTERING

AO_SCPLOWBSTRACTC_SCPLOWClustering time activity curves of PET images has been used to separate clinically relevant areas of the brain or tumours. However, PET image segmentation in multi-organ level is much less studied due to the available total-body data being limited to animal studies. Now the new PET scanners providing the opportunity to acquire total-body PET scans also from humans are becoming more common, which opens plenty of new clinically interesting opportunities. Therefore, organ level segmentation of PET images has important applications, yet it lacks sufficient research. In this proof of concept study, we evaluate if the previously used segmentation approaches are suitable for segmenting dynamic human total-body PET images in organ level. Our focus is on general-purpose unsupervised methods that are independent of external data and can be used for all tracers, organisms, and health conditions. Additional anatomical image modalities, such as CT or MRI, are not used, but the segmentation is done purely based on the dynamic PET images. The tested methods are commonly used building blocks of the more sophisticated methods rather than final methods as such, and our goal is to evaluate if these basic tools are suited for the arising human total-body PET image segmentation. First we excluded methods that were computationally too demanding for the large datasets from human total-body PET scanners. This criteria filtered out most of the commonly used approaches, leaving only two clustering methods, k-means and Gaussian mixture model (GMM), for further analyses. We combined k-means with two different pre-processings, namely principal component analysis (PCA) and independent component analysis (ICA). Then we selected a suitable number of clusters using 10 images. Finally, we tested how well the usable approaches segment the remaining PET images in organ level, highlight the best approaches together with their limitations, and discuss how further research could tackle the observed shortcomings. In this study, we utilised 40 total-body [18F]fluorodeoxyglucose PET images of rats to mimic the coming large human PET images and a few actual human total-body images to ensure that our conclusions from the rat data generalise to the human data. Our results show that ICA combined with k-means has weaker performance than the other two computationally usable approaches and that certain organs are easier to segment than others. While GMM performed sufficiently, it was by far the slowest one among the tested approaches, making k-means combined with PCA the most promising candidate for further development. However, even with the best methods the mean Jaccard index was slightly below 0.5 for the easiest tested organ and below 0.2 for the most challenging organ. Thus, we conclude that there is a lack of accurate and computationally light general-purpose segmentation method that can analyse dynamic total-body PET images. Key pointsO_LIMajority of the considered clustering methods were computationally too intense even for our total-body rat images. The coming total-body human images are 10-fold bigger. C_LIO_LIHeterogeneous VOIs like brain require more sophisticated segmentation method than the basic clustering tested here. C_LIO_LIPCA combined with k-means had the best balance between performance and running speed among the tested methods, but without further preprocessing, it is not accurate enough for practical applications. C_LI FundingResearch of both first authors was supported by donation funds of Faculty of Medicine at University of Turku. JCH reports funding from The Academy of Finland (decision 317332), the Finnish Cultural Foundation, the Finnish Cultural Foundation Varsinais-Suomi Regional Fund, the Diabetes Research Foundation of Finland, and State Research Funding/Hospital District of Southwest Finland. KAV report funding from The Academy of Finland (decision 343410), Sigrid Juselius Foundation and State Research Funding/Hospital District of Southwest Finland. JH reports funding from The Finnish Cultural Foundation Varsinais-Suomi Regional Fund. These funding sources do not present any conflict of interest. Data availabilityThe codes used in this study are available from Github page https://github.com/rklen/Dynamic_FDG_PET_clustering. The example data used in this study have not been published at the time of writing.

bioinformatics↗