Biology subjects

Hamprecht, F. A.

Publications and source records attributed to Hamprecht, F. A..

8 recordsLinked to original sources

bioRxiv2024

From spots to cells: Cell segmentation in spatial transcriptomics with BOMS

Imaging-based Spatial Transcriptomics methods enable the study of gene expression and regulation in complex tissues at subcellular resolution. However, inaccurate cell segmentation procedures lead to misassignment of mRNAs to individual cells which can introduce errors in downstream analysis. Current methods estimate cell boundaries using auxiliary DAPI/Poly(A) stains. These stains can be difficult to segment, thus requiring manual tuning of the method, and not all mRNA molecules may be assigned to the correct cells. We describe a new method, based on mean shift, that segments the cells based on the spatial locations and the gene labels of the mRNA spots without requiring any auxiliary images. We evaluate the performance of BOMS across various publicly available datasets and demonstrate that it achieves comparable results to the best existing method while being simple to implement and significantly faster in execution. Open-source code is available at https://github.com/sciai-lab/boms.

genomics↗

bioRxiv2024

Visualizing single-cell data with the neighbor embedding spectrum

The two-dimensional embedding methods t-SNE and UMAP are ubiquitously used for visualizing single-cell data. Recent theoretical research in machine learning has shown that, despite their very different formulation and implementation, t-SNE and UMAP are closely connected, and a single parameter suffices to interpolate between them. This leads to a whole spectrum of visualization methods that focus on different aspects of the data. Along the spectrum, this focus changes from representing local structures to representing continuous ones. In single-cell context, this leads to a trade-off between highlighting rare cell types or continuous variation, such as developmental trajectories. Visualizing the entire spectrum as an animation can provide a more nuanced understanding of the high-dimensional dataset than individual visualizations with either t-SNE or UMAP.

bioinformatics↗

bioRxiv2022

Temporal control of the integrated stress response by a stochastic molecular switch

Stress granules (SGs) are formed in the cytosol as an acute response to environmental cues and activation of the integrated stress response (ISR), a central signaling pathway controlling protein synthesis. Using chronic virus infection as stress model, we previously uncovered a unique temporal control of the ISR resulting in recurrent phases of SG assembly and disassembly. Here, we elucidate the molecular network generating this fluctuating stress response, by integrating quantitative experiments with mathematical modeling, and find that the ISR operates as a stochastic switch. Key elements controlling this switch are the cooperative activation of the stress-sensing kinase PKR, the ultrasensitive response of SG formation to the phosphorylation of the translation initiation factor eIF2, and negative feedback via GADD34, a stress-induced subunit of protein phosphatase 1. We identify GADD34 mRNA levels as the molecular memory of the ISR that plays a central role in cell adaptation to acute and chronic stress.

cell biology↗

bioRxiv2021

Seipin forms a flexible cage at lipid droplet formation sites

Lipid droplets (LDs) form in the endoplasmic reticulum by phase separation of neutral lipids. This process is facilitated by the seipin protein complex, which consists of a ring of seipin monomers, with yet unclear function. Here, we report a structure of yeast seipin based on cryo-electron microscopy and structural modeling data. Seipin forms a decameric, cage-like structure with the lumenal domains forming a stable ring at the cage floor and transmembrane segments forming the cage sides and top. The transmembrane segments interact with adjacent monomers in two distinct, alternating conformations. These conformations result from changes in switch regions, located between the lumenal domains and the transmembrane segments, that are required for seipin function. Our data suggest a model for LD formation in which a closed seipin cage enables TG phase separation and subsequently switches to an open conformation to allow LD growth and budding.

molecular biology↗

bioRxiv2021

ISTDECO: In Situ Transcriptomics Decoding by Deconvolution

In Situ Transcriptomics (IST) is a set of image-based transcriptomics approaches that enables localisation of gene expression directly in tissue samples. IST techniques produce multiplexed image series in which fluorescent spots are either present or absent across imaging rounds and colour channels. A spots presence and absence form a type of barcoded pattern that labels a particular type of mRNA. Therefore, the expression of a gene can be determined by localising the fluorescent spots and decode the barcode that they form. Existing IST algorithms usually do this in two separate steps: spot localisation and barcode decoding. Although these algorithms are efficient, they are limited by strictly separating the localisation and decoding steps. This limitation becomes apparent in regions with low signal-to-noise ratio or high spot densities. We argue that an improved gene expression decoding can be obtained by combining these two steps into a single algorithm. This allows for an efficient decoding that is less sensitive to noise and optical crowding. We present IST Decoding by Deconvolution (ISTDECO), a principled decoding approach combining spectral and spatial deconvolution into a single algorithm. We evaluate ISTDECO on simulated data, as well as on two real IST datasets, and compare with state-of-the-art. ISTDECO achieves state-of-the-art performance despite high spot densities and low signal-to-noise ratios. It is easily implemented and runs efficiently using a GPU. ISTDECO implementation, datasets and demos are available online at: github.com/axanderssonuu/istdeco

bioinformatics↗

bioRxiv2020

Integration of Cell Growth and Asymmetric Division During Lateral Root Initiation In Arabidopsis thaliana

Lateral root formation determines to a large extent the ability of plants to forage their environment and thus their growth. In Arabidopsis thaliana and other angiosperms, lateral root initiation requires radial cell expansion and several rounds of anticlinal cell divisions that give rise to a central core of small pericycle cells, which express different markers than the larger surrounding cells. These small central cells then switch their plane of divisions to periclinal, and give rise to seemingly morphologically similar daughter cells that have different identities and establish the different cell types of the new root. Although the execution of these two types of divisions is tightly regulated and essential for the correct development of the lateral root, we know little about their geometrical features. Here we analyse a four-dimensional reconstruction of the first stages of lateral root formation and analyze the geometric features of the anticlinal and periclinal divisions. We identify that the periclinal divisions of the small central cells are morphologically dissimilar and asymmetric. We show that mother cell volume is different when looking at anticlinal versus periclinal divisions and the repeated anticlinal divisions do not lead to reduction in cell volume although cells are shorter. Finally, we show that cells undergoing a periclinal division are characterized by a strong cell expansion. Our results indicate that cells integrate growth and division to precisely partition their volume upon division during the first two stages of lateral root formation.

plant biology↗

bioRxiv2020

Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera

Emergence of the novel pathogenic coronavirus SARS-CoV-2 and its rapid pandemic spread presents numerous questions and challenges that demand immediate attention. Among these is the urgent need for a better understanding of humoral immune response against the virus as a basis for developing public health strategies to control viral spread. For this, sensitive, specific and quantitative serological assays are required. Here we describe the development of a semi-quantitative high-content microscopy-based assay for detection of three major classes (IgG, IgA and IgM) of SARS-CoV-2 specific antibodies in human samples. The possibility to detect antibodies against the entire viral proteome together with a robust semi-automated image analysis workflow resulted in specific, sensitive and unbiased assay which complements the portfolio of SARS-CoV-2 serological assays. The procedure described here has been used for clinical studies and provides a general framework for the application of quantitative high-throughput microscopy to rapidly develop serological assays for emerging virus infections.

microbiology↗

bioRxiv2020

Accurate And Versatile 3D Segmentation Of Plant Tissues At Cellular Resolution

Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, and acquisition settings. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.

plant biology↗