Biology subjects

Fishman, D.

Publications and source records attributed to Fishman, D..

6 recordsLinked to original sources

bioRxiv2023

Single-cell imaging of protein dynamics of paralogs reveals mechanisms of gene retention

Gene duplication is common across the tree of life, including yeast and humans, and contributes to genomic robustness. In this study, we examined changes in the subcellular localization and abundance of proteins in response to the deletion of their paralogs originating from the whole-genome duplication event, which is a largely unexplored mechanism of functional divergence. We performed a systematic single-cell imaging analysis of protein dynamics and screened subcellular redistribution of proteins, capturing their localization and abundance changes, providing insight into forces determining paralog retention. Paralogs showed dependency, whereby proteins required their paralog to maintain their native abundance or localization, more often than compensation. Network feature analysis suggested the importance of functional redundancy and rewiring of protein and genetic interactions underlying redistribution response of paralogs. Translation of non-canonical protein isoform emerged as a novel compensatory mechanism. This study provides new insights into paralog retention and evolutionary forces that shape genomes.

systems biology↗

bioRxiv2022

ArtSeg: Rapid Artifact Segmentation and Removal in Brightfield Cell Microscopy Images

Brightfield cell microscopy is a foundational tool in life sciences. The acquired images are prone to contain visual artifacts that hinder downstream analysis, and automatically removing them is therefore of great practical interest. Deep convolutional neural networks are state-of-the-art for image segmentation, but require pixel-level annotations, which are time-consuming to produce. Here, we propose ScoreCAM-U-Net, a pipeline to segment artifactual regions in brightfield images with limited user input. The model is trained using only image-level labels, so the process is faster by orders of magnitude compared to pixel-level annotation, but without substantially sacrificing the segmentation performance. We confirm that artifacts indeed exist with different shapes and sizes in three different brightfield microscopy image datasets, and distort downstream analyses such as nuclei segmentation, morphometry and fluorescence intensity quantification. We then demonstrate that our automated artifact removal ameliorates this problem. Such rapid cleaning of acquired images using the power of deep learning models is likely to become a standard step for all large scale microscopy experiments.

bioinformatics↗

bioRxiv2021

Live-cell microscopy or fluorescence anisotropy with budded baculoviruses - which way to go with measuring ligand binding to M4 muscarinic receptors?

M4 muscarinic receptor is a G protein-coupled receptor that has been associated with alcohol and cocaine abuse, Alzheimers disease and schizophrenia which makes it an interesting drug target. For many G protein-coupled receptors, the development of high-affinity fluorescence ligands has expanded the options for high throughput screening of drug candidates and serve as useful tools in fundamental receptor research. So far, the lack of suitable fluorescence ligands has limited studying M4 receptor ligand binding. Here, we explored the possibilities of using fluorescence-based methods for studying binding affinity and kinetics to M4 receptor of both labeled and unlabeled ligands. We used two TAMRA-labeled fluorescence ligands, UR-MK342 and UR-CG072, for assay development. Using budded baculovirus particles as M4 receptor preparation and fluorescence anisotropy method, we determined the affinities and binding kinetics of both fluorescence ligands. The fluorescence ligands could also be used as reported probes for determining binding affinities of a set of unlabeled ligands. Based on these results, we took a step further towards a more natural signaling system and developed a method using live CHO-K1-hM4R cells and automated fluorescence microscopy suitable for routine determination of unlabeled ligand affinities. For quantitative image analysis, we developed random forest and deep learning-based pipelines for cell segmentation. The pipelines were integrated into the user-friendly open-source Aparecium software. Both developed methods were suitable for measuring fluorescence ligand saturation binding, association and dissociation kinetics as well as for screening binding affinities of unlabeled ligands. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/473643v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@17dfac6org.highwire.dtl.DTLVardef@1db1760org.highwire.dtl.DTLVardef@dba2a0org.highwire.dtl.DTLVardef@191f149_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2019

Segmenting nuclei in brightfield images with neural networks

Identifying nuclei is a standard first step to analysing cells in microscopy images. The traditional approach relies on signal from a DNA stain, or fluorescent transgene expression localised to the nucleus. However, imaging techniques that do not use fluorescence can also carry useful information. Here, we demonstrate that it is possible to accurately segment nuclei directly from brightfield images using deep learning. We confirmed that three convolutional neural network architectures can be adapted for this task, with U-Net achieving the best overall performance, Mask R-CNN providing an additional benefit of instance segmentation, and DeepCell proving too slow for practical application. We found that accurate segmentation is possible using as few as 16 training images and that models trained on images from similar cell lines can extrapolate well. Acquiring data from multiple focal planes further helps distinguish nuclei in the samples. Overall, our work liberates a fluorescence channel reserved for nuclear staining, thus providing more information from the specimen, and reducing reagents and time required for preparing imaging experiments.

bioinformatics↗

bioRxiv2019

PAWER: Protein Array Web ExploreR

Protein microarray is a well-established approach for characterizing activity levels of thousands of proteins in a parallel manner. Analysis of protein microarray data is complex and time-consuming, while existing solutions are either outdated or challenging to use without programming skills. The typical data analysis pipeline consists of a data preprocessing step, followed by differential expression analysis, which is then put into context via functional enrichment. Normally, biologists would need to assemble their own workflow by combining a set of unrelated tools to analyze experimental data. Provided that most of these tools are developed independently by various bioinformatics groups, making them work together could be a real challenge. Here we present PAWER, the first online tool for protein microarray analysis. PAWER enables biologists to carry out all the necessary analysis steps in one go. PAWER provides access to state-of-the-art computational methods through a user-friendly interface, resulting in publication-ready illustrations. We also provide an R package for more advanced use cases, such as bespoke analysis workflows. PAWER is freely available at https://biit.cs.ut.ee/pawer.

bioinformatics↗

bioRxiv2019

Quantitative prediction of enhancer-promoter interactions

Recent experimental and computational efforts provided large datasets describing 3-dimensional organization of mouse and human genomes and showed interconnection between expression profile, epigenetic status and spatial interactions of loci. These interconnections were utilized to infer spatial organization of chromatin, including enhancer-promoter contacts, from 1-dimensional epigenetic marks. Here we showed that predictive power of some of these algorithms is overestimated due to peculiar properties of biological data. We proposed an alternative approach, which gives high-quality predictions of chromatin interactions using only information about gene expression and CTCF-binding. Using multiple metrics, we confirmed that our algorithm could efficiently predict 3-dimensional architecture of normal and rearranged genomes.

genomics↗