Biology subjects

Bianco, S.

Publications and source records attributed to Bianco, S..

4 recordsLinked to original sources

bioRxiv2018

Single-cell chromatin interactions reveal regulatory hubs in dynamic compartmentalized domains

The promoters of mammalian genes are commonly regulated by multiple distal enhancers, which physically interact within discrete chromatin domains. How such domains form and how the regulatory elements within them interact within single cells is not understood. To address this we developed Tri-C, a new Chromosome Conformation Capture (3C) approach to identify concurrent chromatin interactions at individual alleles within single cells. The heterogeneity of interactions observed between such cells shows that CTCF-mediated formation of chromatin domains and interactions within them are dynamic processes. Importantly, our analyses reveal higher-order structures involving simultaneous interactions between multiple enhancers and promoters within individual cells. This provides a structural basis for understanding how multiple cis-elements act together to establish robust regulation of gene expression.

genetics↗

bioRxiv2018

Complementary chromosome folding by transcription factors and cohesin

The spatial organisation of interphase chromosomes is known to affect genomic function, yet the principles behind such organisation remain elusive. Here, we first compare and then combine two well-known biophysical models, the transcription factor (TF) and loop extrusion (LE) models, and dissect their respective roles in organising the genome. Our results suggest that extrusion and transcription factors play complementary roles in folding the genome: the former are necessary to compact gene deserts or \"inert chromatin\" regions, the latter are sufficient to explain most of the structure found in transcriptionally active or repressed domains. Finally, we find that to reproduce interaction patterns found in HiC experiments we do not need to postulate an explicit motor activity of cohesin (or other extruding factors): a model where co-hesin molecules behave as molecular slip-links sliding diffusively along chromatin works equally well.

biophysics↗

bioRxiv2017

Active deep learning reduces annotation burden in automatic cell segmentation

The relationship between cellular architecture and cellular state and function is apparent, but not yet completely understood. Precise characterization of cellular state is important in many fields, from pathology to synthetic biology. High-content high-throughput microscopy is now more than ever accessible to researchers. This allows for collection of large amount of cellular images. Naturally, the analysis of this data cannot be left to manual investigation and needs to resort to the use of efficient computing algorithms for cellular detection, segmentation, and tracking. Annotation is required for building high quality algorithms. Medical professionals and researchers spend a lot of effort and time in annotating cells. This task has proved to be very repetitive and time consuming. The experts time is valuable and should be used effectively. Our hypothesis is that active deep learning will help to share some of the burden that researchers face in their everyday work. In this paper, we focus specifically on the problem of cellular segmentation.\n\nWe approach the segmentation task using a classification framework. Each pixel in the image is classified based on whether the patch around it resides on the interior, boundary or exterior of the cell. Deep convolutional neural networks (CNN) are used to perform the classification task. Active learning is the method used to reduce the annotation burden. Uncertainty sampling, a popular active learning framework is used in conjunction with CNN to segment the cells in the image. Three datasets of mammalian nuclei and cytoplasm are used for this work. We show that active deep learning significantly reduces the number of training samples required and also improves the quality of segmentation.

bioinformatics↗

bioRxiv2017

Quantius: Generic, high-fidelity human annotation of scientific images at 105-clicks-per-hour

We describe Quantius, a crowd-based image annotation platform that provides an accurate alternative to task-specific computational algorithms for difficult image analysis problems. We use Quantius to quantify a variety of computationally challenging medium-throughput tasks with ~50x and 30x savings in analysis time and cost respectively, relative to a single expert annotator. We show equivalent deep learning performance for Quantius- and expert-derived annotations, bridging towards scalable integration with tailored machine-learning algorithms.

bioinformatics↗