Biology subjects

Lundberg, E.

Publications and source records attributed to Lundberg, E..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

A circular engineered sortase for interrogating histone H3 in chromatin

Reversible modification of the histone H3 N-terminal tail is critical in regulating chromatin structure, gene expression, and cell states, while its dysregulation contributes to disease pathogenesis. Understanding the crosstalk between H3 tail modifications in nucleosomes constitutes a central challenge in epigenetics. Here we describe an engineered sortase transpeptidase, cW11, that displays highly favorable properties for introducing scarless H3 tails onto nucleosomes. This approach significantly accelerates the production of both symmetrically and asymmetrically modified nucleosomes. We demonstrate the utility of asymmetrically modified nucleosomes produced in this way in dissecting the impact of multiple modifications on eraser enzyme processing and molecular recognition by a reader protein. Moreover, we show that cW11 sortase is very effective at cutting and tagging histone H3 tails from endogenous histones, facilitating multiplex "cut-and-paste" middle down proteomics with tandem mass tags. This cut-and-paste proteomics approach permits the quantitative analysis of histone H3 modification crosstalk after treatment with different histone deacetylase inhibitors. We propose that these chemoenzymatic tail isolation and modification strategies made possible with cW11 sortase will broadly power epigenetics discovery and therapeutic development.

biochemistry↗

bioRxiv2024

High-Resolution In Silico Painting with Generative Models

Label-free organelle prediction presents a longstanding challenge in cellular imaging, given the promise to to circumvent the numerous drawbacks associated with fluorescent microscopy, including its high costs, cytotoxicity, and time-consuming nature. Recent advancements in deep learning have introduced numerous effective algorithms, primarily deterministic, for predicting fluorescent patterns from transmitted light microscopy images. However, existing models frequently suffer from poor performance or are limited to specific datasets, image modalities, and magnifications, thus lacking a universal solution. In this paper, we present a simplified VQGAN training scheme that is easily adapted with different input/output channels for image-to-image translation tasks. We applied the algorithm to generate multi-channel organelle staining outputs from bright field inputs, equivalent to the popular Cell Painting assay. The same algorithm also participated and placed first in the ISBI 2024 Light My Cell challenge.

bioengineering↗

bioRxiv2024

Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines

This article describes the Cell Maps for Artificial Intelligence (CM4AI) project and its goals, methods, standards, current datasets, software tools, status, and future directions. CM4AI is the Functional Genomics Data Generation Project in the U.S. National Institute of Healths (NIH) Bridge2AI program. Its overarching mission is to produce ethical, AI-ready datasets of cell architecture, inferred from multimodal data collected for human cell lines, to enable transformative biomedical AI research.

systems biology↗

bioRxiv2024

Spatial Dynamics of the Developing Human Heart

Heart development relies on a topologically defined interplay between a diverse array of cardiac cells. We finely curated spatial and single-cell measurements with subcellular imaging-based transcriptomics validation to explore spatial dynamics during early human cardiogenesis. Analyzing almost 80,000 individual cells and 70,000 spatially barcoded tissue regions between the 5.5th and 14th postconceptional weeks, we identified 31 coarse- and 72 fine-grained cell states and mapped them to highly resolved cardiac cellular niches. We provide novel insight into the development of the cardiac pacemaker-conduction system, heart valves, and atrial septum, and decipher heterogeneity of the hitherto elusive cardiac fibroblast population. Furthermore, we describe the formation of cardiac autonomic innervation and present the first spatial account of chromaffin cells in the fetal human heart. We support independent exploration of our datasets by an open-access, spatially centric interactive viewer. In summary, our study delineates the cellular and molecular landscape of the developing hearts architecture, offering links to genetic causes of heart disease.

developmental biology↗

bioRxiv2024

Biotic and abiotic drivers of ecosystem functioning differ between a temperate and a tropical region

Any single ecosystem will provide many ecosystem functions. Whether these functions tend to increase in concert or trade off against each other is a question of much current interest. Equally topical are the drivers behind ecosystem function rates. Yet, we lack large-scale systematic studies that investigate how abiotic factors can directly or indirectly -- via effects on biodiversity -- drive ecosystem functioning. In this study, we assessed the impact of climate, landscape and biotic community on ecosystem functioning and multifunctioning in the temperate and tropical zone, and investigated potential trade-offs among ecosystem functions in both zones. To achieve this, we measured a diverse set of insect-related ecosystem functions -- including herbivory, seed dispersal, predation, decomposition and pollination -- at 50 sites across Madagascar and 171 sites across Sweden, and characterized the insect community at each site using Malaise traps. We used structural equations models to infer causality of the effects of climate, landscape, and biodiversity on ecosystem functioning. For the temperate zone, we found that abiotic factors were more important than biotic factors in driving ecosystem functioning, while in the tropical zone, effects of biotic drivers were most pronounced. In terms of trade-offs among functions, in the temperate zone, only seed dispersal and predation were positively correlated, while all other functions were uncorrelated. By contrast, in the tropical zone, most ecosystem functions increased in concert, highlighting that tropical ecosystems can simultaneously provide a diverse set of functions. These correlated functions in Madagascar could for the most part be explained by similar responses to local climate, landscape, and biota. Our study suggests that the functioning of temperate and tropical ecosystems differs fundamentally in patterns and drivers. Without a better understanding of these differences, it will be impossible to correctly predict shifts in ecosystem functioning in response to environmental disturbances. To identify global patterns and drivers of ecosystem functioning, we will next need replicate sampling across biomes - as here achieved for two regions, thus paving the road and setting the baseline expectations.

ecology↗

bioRxiv2024

High-parametric protein maps reveal the spatial organization in early-developing human lung

The respiratory system, including the lungs, is essential for terrestrial life. While recent research has advanced our understanding of lung development, much still relies on animal models and transcriptome analyses. In this study conducted within the Human Developmental Cell Atlas (HDCA) initiative, we describe the protein-level spatiotemporal organization of the lung during the first trimester of human gestation. Using high-parametric tissue imaging with a 30-plex antibody panel, we analyzed human lung samples from 6 to 13 post-conception weeks, generating data from over 2 million cells across five developmental timepoints. We present a resource detailing spatially resolved cell type composition of the developing human lung, including proliferative states, immune cell patterns, spatial arrangement traits, and their temporal evolution. This represents an extensive single-cell resolved protein-level examination of the developing human lungs and provides a valuable resource for further research into the developmental roots of human respiratory health and disease.

developmental biology↗

bioRxiv2023

coRIC map: a framework for the exploration of multifaceted subcellular RNA-binding proteins

The ability of RNA-binding proteins (RBPs) to form complexes with other biomolecules underpins a broad range of functions and remarkable structural properties. Understanding the precise spatiotemporal dynamics of RBPs and their interacting partners in the steady state and upon perturbation is key to deciphering these aspects. Here, we present the coRIC (compartmentalized RNA Interactome Capture) map, an experimental resource and analytical pipeline to study the subcellular dynamics of RBPs through multimodal dataset integration and machine learning. Using this approach, we have generated an atlas comprising 1,768 RBPs distributed in a broad panel of subcellular compartments and delineated their intermolecular and intercompartmental relationships. We have also defined the hierarchy of RBP-containing complexes at multiple scales across the cell, uncovering previously unknown functions of multiple RBPs. Furthermore, we have investigated the changes in RBP complex composition and subcellular distribution in response to C9ORF72 amyotrophic lateral sclerosis/frontotemporal dementia dipeptide repeats and DNA damage stress. The coRIC map provides a valuable new approach for defining the roles of RBPs in homeostasis and disease.

cell biology↗

bioRxiv2023

Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms

The development of a reference atlas of the healthy human body requires automated image segmentation of major anatomical structures across multiple organs based on spatial bioimages generated from various sources with differences in sample preparation. We present the setup and results of the "Hacking the Human Body" machine learning algorithm development competition hosted by the Human Biomolecular Atlas (HuBMAP) and the Human Protein Atlas (HPA) teams on the Kaggle platform. We showcase how 1,175 teams from 78 countries engaged in community- driven, open-science code development that resulted in machine learning models which successfully segment anatomical structures across five organs using histology images from two consortia and that will be productized in the HuBMAP data portal to process large datasets at scale in support of Human Reference Atlas construction. We discuss the benchmark data created for the competition, major challenges faced by the participants, and the winning models and strategies.

bioinformatics↗

bioRxiv2022

ISG15-modification of the Arp2/3 complex restricts pathogen spread

The ubiquitin-like protein, ISG15, can act as a cytokine or can covalently modify host and pathogen-derived proteins. The consequences of ISG15 modification on substrate fate remain unknown. Here we reveal that ISGylation of the Arp2/3 complex slows actin filament formation and stabilizes Arp2/3 dependent structures including cortical actin and lamella. When properly controlled, this serves as an antibacterial and antiviral host defense strategy to directly restrict actin-mediated pathogen spread. However, Listeria monocytogenes takes advantage in models of dysregulated ISGylation, leading to increased mortality due to augmented spread. The underlying molecular mechanism responsible for the ISG15-dependent impact on actin-based motility is due to failed bacterial separation after division. This promotes spread by enabling the formation of multi-headed bacterial "bazookas" with stabilized comet tails that can disseminate deeper into tissues. A bacterial mutant that cannot recruit Arp2/3 or a non-ISGylatable mutant of Arp3 is sufficient to rescue slowed comet tail speed and restrict spread. Importantly, ISG15-deficient neonatal mice have aberrant epidermal epithelia characterized by keratinocytes with diffuse cortical actin, which could underlie observed defects in wound healing in human patients who lack ISG15. Ultimately, our discovery links host innate immune responses to cytoskeletal dynamics with therapeutic implications for viral infection and metastasis.

cell biology↗

bioRxiv2022

Uncovering molecular grammars of intrinsically disordered regions that organize nucleolar fibrillar centers

The nucleolus is a multilayered structure. Each layer is thought to be a compositionally distinct phase, although how these phases form and interface with one another remains unclear. Using computational, proteomics, in vitro, and in vivo studies, we uncover distinct molecular grammars within intrinsically disordered regions (IDRs) of nucleolar proteins that localize to fibrillar centers (FCs) and dense fibrillar components (DFCs). FC and DFC proteins feature two distinct types of IDRs namely those with long tracts of acidic residues and those with blocks of lysines interspersed by acid-rich-regions. We find that phase separation driven by complex coacervation in mixtures of nucleolar proteins, with their distinctive IDRs, and ribosomal DNA and RNA molecules is sufficient to drive the formation of structural facsimiles of FCs and DFCs. One-Sentence SummaryFacsimiles of core nucleolar substructures were reconstituted via phase separation of key protein and nucleic acid mixtures.

biophysics↗

bioRxiv2022

The Golgi complex is a regulatory hub for homologous recombination-mediated DNA repair.

The Golgi complex is a key organelle of the secretory pathway, but also serves as a critical hub for maintaining cellular homeostasis, orchestrating various signalling pathways and essential cellular processes such as membrane trafficking and post-translational modifications. Despite its central role, the communication between the Golgi and the cell nucleus has remained largely unexplored. To bridge this gap, we have analysed and siRNA-validated localisation data of the Human Protein Atlas, finding an unexpected and significant level of interconnectivity between the proteomes of the Golgi complex and nucleus, including an intriguing involvement in DNA repair processes. Here, we uncovered a cluster of DNA repair proteins present in distinct sub-Golgi localisations as well as in the nuclear compartment. In response to genotoxic stress these proteins redistribute dynamically between the Golgi complex and the nucleus, with the specific type of DNA injury influencing this distribution pattern. To probe this Golgi-nucleus link, we examined the Homologous Recombination (HR) regulator RAD51C and find that DNA damage triggers ATM-dependent release of a Giantin-tethered Golgi pool and Importin-{beta}-dependent nuclear import, where repair-associated nuclear foci form. Downregulation of Giantin prematurely releases RAD51C, producing RAD51C nuclear foci lacking key HR markers, reducing ATM activation, elevating genome instability, and accelerating cell proliferation. These findings support a timing-based pathway in which the Golgi acts as a spatiotemporal coordination node for HR regulators and other DDR factors. Our study sheds light on the dynamic interplay between the Golgi and the nucleus in safeguarding genomic stability while maintaining the delicate balance of cellular homeostasis.

cell biology↗

bioRxiv2022

Bento: A toolkit for subcellular analysis of spatial transcriptomics data

The spatial organization of molecules in a cell is essential for performing their functions. Spatial transcriptomics technologies have opened the door to characterization of cellular and subcellular organization. While current computational methods focus on discerning tissue architecture, cell-cell interactions and spatial expression patterns, these approaches are limited to investigating spatial variation at the multicellular scale. We present Bento, a Python toolkit that fully takes advantage of single-molecule information to enable spatial analysis at the subcellular scale. Bento ingests molecular coordinates and segmentation boundaries to perform three fundamental analyses: defining subcellular domains, annotating localization patterns, and quantifying gene-gene colocalization. To demonstrate the toolkit, we apply these methods to a variety of datasets including U2-OS cells (MERFISH), 3T3 cells (seqFISH+), and treated cardiomyocytes (Molecular Cartography). We quantify RNA localization changes in cardiomyocytes identifying mRNA depletion of critical cardiac disease-associated genes RBM20 and CACNB2 from the endoplasmic reticulum upon doxorubicin treatment. The Bento package is a member of the open-source Scverse ecosystem, enabling integration with other single-cell omics analysis tools.

bioinformatics↗

bioRxiv2022

BioImage Model Zoo: A Community-Driven Resource for Accessible Deep Learning in BioImage Analysis

Deep learning-based approaches are revolutionizing imaging-driven scientific research. However, the accessibility and reproducibility of deep learning-based workflows for imaging scientists remain far from sufficient. Several tools have recently risen to the challenge of democratizing deep learning by providing user-friendly interfaces to analyze new data with pre-trained or fine-tuned models. Still, few of the existing pre-trained models are interoperable between these tools, critically restricting a models overall utility and the possibility of validating and reproducing scientific analyses. Here, we present the BioImage Model Zoo (https://bioimage.io): a community-driven, fully open resource where standardized pre-trained models can be shared, explored, tested, and downloaded for further adaptation or direct deployment in multiple end user-facing tools (e.g., ilastik, deepImageJ, QuPath, StarDist, ImJoy, ZeroCostDL4Mic, CSBDeep). To enable everyone to contribute and consume the Zoo resources, we provide a model standard to enable cross-compatibility, a rich list of example models and practical use-cases, developer tools, documentation, and the accompanying infrastructure for model upload, download and testing. Our contribution aims to lay the groundwork to make deep learning methods for microscopy imaging findable, accessible, interoperable, and reusable (FAIR) across software tools and platforms.

bioinformatics↗

bioRxiv2022

Subcellular mapping of the protein landscape of SARS-CoV-2 infected cells for target-centric drug repurposing

The COVID-19 pandemic has resulted in millions of deaths and affected socioeconomic structure worldwide and the search for new antivirals and treatments are still ongoing. In the search for new drug target and to increase our understanding of the disease, we used large scale immunofluorescence to explore the host cell response to SARS-CoV-2 infection. Among the 602 host proteins studied in this host response screen, changes in abundance and subcellular localization were observed for 97 proteins, with 45 proteins showing increased abundance and 10 reduced abundances. 20 proteins displayed changed localization upon infection and an additional 22 proteins displayed altered abundance and localization, together contributing to diverse reshuffling of the host cell protein landscape. We then selected existing and approved small-molecule drugs (n =123) against our identified host response proteins and identified 3 compounds - elesclomol, crizotinib and rimcazole, that significantly reduced antiviral activity. Our study introduces a novel, targeted and systematic approach based on host protein profiling, to identify new targets for drug repurposing. The dataset of [~]75,000 immunofluorescence images from this study are published as a resource available for further studies.

cell biology↗

bioRxiv2022

Developmental origins of cell heterogeneity in the human lung

The lung contains numerous specialized cell-types with distinct roles in tissue function and integrity. To clarify the origins and mechanisms generating cell heterogeneity, we created a first comprehensive topographic atlas of early human lung development. We report 83 cell states, several spatially-resolved developmental trajectories and predict cell interactions within defined tissue niches. We integrated scRNA-Seq and spatial transcriptomics into a web-based, open platform for interactive exploration. To illustrate the utility of our approach we show distinct states of secretory and neuroendocrine cells, largely overlapping with the programs activated either during lung fibrosis or small cell lung cancer progression. We define the origin of uncharacterized airway fibroblasts associated with airway smooth muscle in bronchovascular bundles, and describe a trajectory of Schwann cell progenitors to intrinsic parasympathetic neurons controlling bronchoconstriction. Our atlas provides a rich resource for further research and a reference for defining deviations from homeostatic and repair mechanisms leading to pulmonary diseases.

developmental biology↗

bioRxiv2021

AI-driven Deep Visual Proteomics defines cell identity and heterogeneity

The systems-wide analysis of biomolecules in time and space is key to our understanding of cellular function and heterogeneity in health and disease1. Remarkable technological progress in microscopy and multi-omics technologies enable increasingly data-rich descriptions of tissue heterogeneity2,3,4,5. Single cell sequencing, in particular, now routinely allows the mapping of cell types and states uncovering tremendous complexity6. Yet, an unaddressed challenge is the development of a method that would directly connect the visual dimension with the molecular phenotype and in particular with the unbiased characterization of proteomes, a close proxy for cellular function. Here we introduce Deep Visual Proteomics (DVP), which combines advances in artificial intelligence (AI)-driven image analysis of cellular phenotypes with automated single cell laser microdissection and ultra-high sensitivity mass spectrometry7. DVP links protein abundance to complex cellular or subcellular phenotypes while preserving spatial context. Individually excising nuclei from cell culture, we classified distinct cell states with proteomic profiles defined by known and novel proteins. AI also discovered rare cells with distinct morphology, whose potential function was revealed by proteomics. Applied to archival tissue of salivary gland carcinoma, our generic workflow characterized proteomic differences between normal-appearing and adjacent cancer cells, without admixture of background from unrelated cells or extracellular matrix. In melanoma, DVP revealed immune system and DNA replication related prognostic markers that appeared only in specific tumor regions. Thus, DVP provides unprecedented molecular insights into cell and disease biology while retaining spatial information.

systems biology↗

bioRxiv2020

Mapping cell structure across scales by fusing protein images and interactions

ABSTRACTThe eukaryotic cell is a multi-scale structure with modular organization across at least four orders of magnitude1,2. Two central approaches for mapping this structure – protein fluorescent imaging and protein biophysical association – each generate extensive datasets but of distinct qualities and resolutions that are typically treated separately3,4. Here, we integrate immunofluorescent images in the Human Protein Atlas5 with ongoing affinity purification experiments from the BioPlex resource6 to create a unified hierarchical map of eukaryotic cell architecture. Integration involves configuring each approach to produce a general measure of protein distance, then calibrating the two measures using machine learning. The evolving map, called the Multi-Scale Integrated Cell (MuSIC 1.0), currently resolves 69 subcellular systems of which approximately half are undocumented. Based on these findings we perform 134 additional affinity purifications, validating close subunit associations for the majority of systems. The map elucidates roles for poorly characterized proteins, such as the appearance of FAM120C in chromatin; identifies new protein assemblies in ribosomal biogenesis, RNA splicing, nuclear speckles, and ion transport; and reveals crosstalk between cytoplasmic and mitochondrial ribosomal proteins. By integration across scales, MuSIC substantially increases the mapping resolution obtained from imaging while giving protein interactions a spatial dimension, paving the way to incorporate many molecular data types in proteome-wide maps of cells.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

bioRxiv2020

Mapping of the nucleolar proteome reveals spatiotemporal organization related to intrinsic protein disorder

The nucleolus is essential for ribosome biogenesis and is involved in many other cellular functions. We performed a systematic spatiotemporal dissection of the human nucleolar proteome using confocal microscopy. In total, 1,318 nucleolar proteins were identified; 287 were localized to fibrillar components, and 157 were enriched along the nucleoplasmic border, indicating a potential fourth nucleolar subcompartment (nucleoli rim). We found 65 nucleolar proteins (36 uncharacterized) to relocate to the chromosomal periphery during mitosis. Interestingly, we observed temporal partitioning into two recruitment phenotypes: early (prometaphase) and late (after metaphase), suggesting phase-specific functions. We further show that expression of MKI67 is critical for this temporal partitioning. We provide the first proteome-wide analysis of intrinsic protein disorder for the human nucleolus and show that nucleolar proteins in general, and mitotic chromosome proteins in particular, have significantly higher intrinsic disorder level compared to cytosolic proteins. In summary, this study provides a comprehensive and essential resource of spatiotemporal expression data for the nucleolar proteome as part of the Human Protein Atlas.

cell biology↗