Biology subjects

Barry, T.

Publications and source records attributed to Barry, T..

5 recordsLinked to original sources

bioRxiv2023

3D Nuclei Segmentation by Combining GAN Based Image Synthesis and Existing 3D Manual Annotations

Nuclei segmentation is an important task in cell biology analysis that requires accurate and reliable methods, especially within complex low signal to noise ratio images with crowded cells populations. In this context, deep learning-based methods such as Stardist have emerged as the best performing solutions for segmenting nucleus. Unfortunately, the performances of such methods rely on the availability of vast libraries of ground truth hand-annotated data-sets, which become especially tedious to create for 3D cell cultures in which nuclei tend to overlap. In this work, we present a workflow to segment nuclei in 3D in such conditions when no specific ground truth exists. It combines the use of a robust 2D segmentation method, Stardist 2D, which have been trained on thousands of already available ground truth datasets, with the generation of pair of 3D masks and synthetic fluorescence volumes through a conditional GAN. It allows to train a Stardist 3D model with 3D ground truth masks and synthetic volumes that mimic our fluorescence ones. This strategy allows to segment 3D data that have no available ground truth, alleviating the need to perform manual annotations, and improving the results obtained by training Stardist with the original ground truth data.

bioinformatics↗

bioRxiv2023

Robust differential expression testing for single-cell CRISPR screens at low multiplicity of infection

Single-cell CRISPR screens (perturb-seq) link genetic perturbations to phenotypic changes in individual cells. The most fundamental task in perturb-seq analysis is to test for association between a perturbation and a count outcome, such as gene expression. We conduct the first-ever comprehensive benchmarking study of association testing methods for low multiplicity-of-infection (MOI) perturb-seq data, finding that existing methods produce excess false positives. We conduct an extensive empirical investigation of the data, identifying three core analysis challenges: sparsity, confounding, and model misspecification. Finally, we develop an association testing method -- SCEPTRE low-MOI -- that resolves these analysis challenges and demonstrates improved calibration and power.

genomics↗

bioRxiv2022

SalienceNet: an unsupervised Image-to-Image translation method for nuclei saliency enhancement in microscopy images

Automatic segmentation of nuclei in low-light microscopy images remains a difficult task, especially for high-throughput experiments where need for automation is strong. Low saliency of nuclei with respect to the background, variability of their intensity together with low signal-to-noise ratio in these images constitute a major challenge for mainstream algorithms of nuclei segmentation. In this work we introduce SalienceNet, an unsupervised deep learning-based method that uses the style transfer properties of cycleGAN to transform low saliency images into high saliency images, thus enabling accurate segmentation by downstream analysis methods, and that without need for any parameter tuning. We have acquired a novel dataset of organoid images with soSPIM, a microscopy technique that enables the acquisition of images in low-light conditions. Our experiments show that SalienceNet increased the saliency of these images up to the desired level. Moreover, we evaluated the impact of SalienceNet on segmentation for both Otsu thresholding and StarDist and have shown that enhancing nuclei with SalienceNet improved segmentation results using Otsu thresholding by 30% and using StarDist by 26% in terms of IOU when compared to segmentation of non-enhanced images. Together these results show that SalienceNet can be used as a common preprocessing step to automate nuclei segmentation pipelines for low-light microscopy images.

bioinformatics↗

bioRxiv2022

Cellular Heterogeneity of Pluripotent Stem Cell Derived Cardiomyocyte Grafts is Mechanistically Linked to Treatable Arrhythmias

BackgroundExciting pre-clinical data have confirmed that human pluripotent stem cell derived cardiomyocytes (PSC-CMs) can remuscularise the injured or diseased heart, with several clinical trials now in planning or recruitment stages worldwide. However, ventricular arrhythmias are a predictable complication following engraftment of intramyocardially injected PSC-CMs. Therefore, there is an urgent unmet need to gain mechanistic insights and treatment strategies to control or prevent these engraftment arrhythmias (EAs). MethodsWe used a porcine model of myocardial infarction and PSC-CM transplantation to investigate efficacy of pharmacologic and catheter based anti-arrhythmic strategies in mitigating EAs. Furthermore, cell doses were robustly phenotyped using single cell ribonucleic acid sequencing and high parameter flow cytometry to identify cellular characteristics predictive of arrhythmogenesis. ResultsCombination therapy with amiodarone and ivabradine significantly reduced EA rate and burden following PSC-CM transplantation. Catheter ablation was also a feasible and effective treatment strategy which could be considered in the case of pharmacologically refractory arrhythmias. In addition, we show that EAs are mechanistically linked to cellular heterogeneity in the input PSC-CM and resultant graft. Specifically, we identify atrial and pacemaker-like cardiomyocytes as culprit arrhythmogenic subpopulations. We further describe two unique surface marker signatures, SIRPA+/CD90-/CD200+ and SIRPA+/CD90-/CD200-, which identify arrhythmogenic and non-arrhythmogenic cardiomyocytes respectively. ConclusionOur data deepens mechanistic understanding of EAs and suggests that modifications to current PSC-CM production and/or selection protocols could ameliorate this problem. We further show that current clinical pharmacologic and interventional anti-arrhythmic strategies can control and potentially abolish these arrhythmias, an important safety consideration given several impending clinical trials.

cell biology↗

bioRxiv2021

Discovery of target genes and pathways of blood trait loci using pooled CRISPR screens and single cell RNA sequencing

The majority of variants associated with complex traits and common diseases identified by genome-wide association studies (GWAS) map to noncoding regions of the genome with unknown regulatory effects in cis and trans. By leveraging biobank-scale GWAS data, massively parallel CRISPR screens and single cell transcriptome sequencing, we discovered target genes of noncoding variants for blood trait loci. The closest gene was often the target gene, but this was not always the case. We also identified trans-effects networks of noncoding variants when cis target genes encoded transcription factors, such as GFI1B and NFE2. We observed that GFI1B trans-target genes were enriched for GFI1B binding sites and fine-mapped GWAS variants, and expressed in human bone marrow progenitor cells, suggesting that GFI1B acts as a master regulator of blood traits. This platform will enable massively parallel assays to catalog the target genes of human noncoding variants in both cis and trans.

genomics↗