Biology subjects

Piddini, E.

Publications and source records attributed to Piddini, E..

5 recordsLinked to original sources

bioRxiv2021

Deep learning-enhanced morphological profiling predicts cell fate dynamics in real-time in hPSCs

Predicting how stem cells become patterned and differentiated into target tissues is key for optimising human tissue design. Here, we established DEEP-MAP - for deep learning-enhanced morphological profiling - an approach that integrates single-cell, multi-day, multi-colour microscopy phenomics with deep learning and allows to robustly map and predict cell fate dynamics in real-time without a need for cell state-specific reporters. Using human pluripotent stem cells (hPSCs) engineered to co-express the histone H2B and two-colour FUCCI cell cycle reporters, we used DEEP-MAP to capture hundreds of morphological- and proliferation-associated features for hundreds of thousands of cells and used this information to map and predict spatiotemporally single-cell fate dynamics across germ layer cell fates. We show that DEEP-MAP predicts fate changes as early or earlier than transcription factor-based fate reporters, reveals the timing and existence of intermediate cell fates invisible to fixed-cell technologies, and identifies proliferative properties predictive of cell fate transitions. DEEP-MAP provides a versatile, universal strategy to map tissue evolution and organisation across many developmental and tissue engineering contexts.

cell biology↗

bioRxiv2021

PECAn, a pipeline for image processing and statistical analysis of complex mosaic 3D tissues

Investigating organ biology requires sophisticated methodologies to induce genetically distinct clones within a tissue. Microscopic analysis of such samples produces information-rich 3D images. However, the 3D nature and spatial anisotropy of clones makes sample analysis challenging and slow and limits the amount of information that can be extracted manually. Here we have developed a pipeline for image processing and statistical data analysis which automatically extracts sophisticated parameters from complex multi-genotype 3D images. The pipeline includes data handling, machine-learning-enabled segmentation, multivariant statistical analysis, and graph generation. This enables researchers to run rigorous analyses on images and videos at scale and in a fraction of the time, without requiring programming skills. We demonstrate the power of this pipeline by applying it to the study of Minute cell competition. We find an unappreciated sexual dimorphism in Minute competition and identify, by statistical regression analysis, tissue parameters that model and predict competitive death.

cell biology↗

bioRxiv2021

Xrp1 and Irbp18 trigger a feed-forward loop of proteotoxic stress to induce the loser status

Cell competition induces the elimination of less-fit "loser" cells by fitter "winner" cells. In Drosophila, cells heterozygous mutant in ribosome genes, Rp/+, known as Minutes, are eliminated via cell competition by wild-type cells. Rp/+ cells display proteotoxic stress and the oxidative stress response, which drive the loser status. Minute cell competition also relies on the activities of the transcription factors Irbp18 and Xrp1, however how these contribute to the loser status is partially understood. Here, we show that Irbp18 and Xrp1 induce the loser status by promoting proteotoxic stress. We find that Xrp1 is necessary for Rp/+ - induced proteotoxic stress and is sufficient to induce proteotoxic stress in otherwise wild-type cells. Xrp1 is also induced downstream of proteotoxic stress and required for the competitive elimination of cells suffering from proteotoxic stress. Our data suggests that a feed-forward loop between Xrp1, proteotoxic stress, and Nrf2 drives Minute cells to become losers.

genetics↗

bioRxiv2021

Ribosome protein mutant cells rely on the GR64 cluster of gustatory receptors for survival and proteostasis in Drosophila

Mutations in ribosome protein (Rp) genes and ribosome biogenesis factors result in debilitating diseases known as ribosomopathies. Recent studies in Drosophila have shown that cells heterozygous mutant for Rp genes (Rp/+) exhibit proteotoxic stress and aggregates, which drive stress pathway activation and apoptosis. Understanding how Rp/+ cells fend off proteotoxic stress could suggest mechanisms to ameliorate these and other conditions caused by proteotoxic stress. Here we find that Rp/+ epithelial cells express all six Gustatory Receptor 64 (Gr64) genes, a cluster of sugar receptors involved in taste sensation. We show that Rp/+ cells depend on Gr64 for survival and that loss of Gr64 autonomously exacerbates stress pathway activation and proteotoxic stress by negatively effecting autophagy and proteasome function in Rp/+ cells. This work identifies a non-canonical role in proteostasis maintenance for a family of gustatory receptors known for their function in neuronal sensation.

developmental biology↗

bioRxiv2021

Multiplexed live visualization of cell fate dynamics in hPSCs at single-cell resolution

Live imaging can provide powerful insights into developmental and cellular processes but availability of multiplexable reporters has been limiting. Here we describe ORACLE, a cell fate reporter class in which fluorescent proteins fused with the nucleoporin POM121 are driven by promoters of transcription factors of interest. ORACLEs nuclear rim localisation therefore enables multiplexing with conventional nuclear reporters. We applied ORACLE to investigate the dynamics of pluripotency exit at single-cell level, using human pluripotent stem cells (hPSCs) imaged by multi-day time-lapse high-content microscopy. Using an ORACLE-OCT4 pluripotency marker we reveal that G1 phase length and OCT4 level are strongly coupled and that spatial location in a colony impacts the timing of pluripotency exit. Combining ORACLE-OCT4 and an ORACLE-SOX1 early neuronal differentiation marker, we visualize in real-time the dynamics of cell fate transition between pluripotency and early neural fate, and show that pluripotency exit and differentiation onset are likely not tightly coupled in single-cells. Thus ORACLE is a powerful tool to enable quantitative studies of spatiotemporal cell fate control.

cell biology↗