Biology subjects

Paek, A. L.

Publications and source records attributed to Paek, A. L..

3 recordsLinked to original sources

bioRxiv2022

Live-Cell Imaging in Human Colonic Monolayers Reveals Erk Waves Limit the Stem Cell Compartment to Maintain Epithelial Homeostasis

The establishment and maintenance of different cellular compartments in tissues is a universal requirement across all metazoans. Maintaining the correct ratio of cell types in time and space allows tissues to form patterned compartments and perform complex functions. Patterning is especially evident in the human colon, where tissue homeostasis is maintained by stem cells in crypt structures that balance proliferation and differentiation. Here we developed a 2D patient derived organoid (PDO) screening platform to study tissue patterning and kinase pathway dynamics in single cells across hundreds of conditions. Using this system, we discovered that waves of Erk signaling induced by apoptotic cells play a critical role in maintaining tissue patterning and homeostasis. If Erk is activated acutely across all cells instead of in wavelike patterns, then tissue patterning, and stem cell maintenance are lost. Conversely, if Erk activity is inhibited, then stem cells become unrestricted and expand dramatically. This work demonstrates that the colonic epithelium requires coordinated Erk signaling dynamics to maintain patterning and tissue homeostasis. Our work reveals how Erk can antagonize stem cells yet support cell replacement and the function of the gut.

cell biology↗

bioRxiv2022

Feedback in the β-catenin destruction complex imparts bistability and cellular memory

Wnt ligands are considered classical morphogens, for which the strength of the cellular response is proportional to the concentration of the ligand. Herein, we show an emergent property of bistability arising from feedback among the Wnt destruction complex proteins that target the key transcriptional co-activator {beta}-catenin for degradation. Using biochemical reconstitution, we identified positive feedback between the scaffold protein Axin and the kinase GSK3. Theoretical modeling of this feedback between Axin and GSK3 predicted that the activity of the destruction complex exhibits bistable behavior. We experimentally confirmed these predictions by demonstrating that cellular cytoplasmic {beta}-catenin concentrations exhibit an "all-or-none" response with sustained memory (hysteresis) of the signaling input. This bistable behavior was transformed into a graded response and memory was lost through inhibition of GSK3. These findings provide a mechanism for establishing decisive, switch-like cellular response and memory upon Wnt pathway stimulation. One Sentence SummaryPositive feedback within the {beta}-catenin destruction complex gives rise to bistability and memory in response to Wnt stimulation, imparting signal transduction accuracy and insulation.

cell biology↗

bioRxiv2021

Spatio-temporal feature based deep neural network for cell lineage analysis in microscopy images

BackgroundTime-lapse microscopy has been widely used in biomedical experiments because it can visualize the molecular activities of living cells in real time. However, biomedical researchers are still conducting cell lineage analysis manually. Developing automatic lineage tracing algorithms is a challenging task. In the past two decades, deep neural networks (DNNs) became have shown outstanding performance on computer vision tasks. They can learn complex visual features, capture long-range temporal dependencies, and have the potential to be used for automatic cell lineage analysis. MethodsIn this study, we propose a multi-task spatio-temporal feature based deep neural network for cell lineages analysis (Cell-STN). The Cell-STN extracts spatio-temporal features from microscopy image sequences by leveraging our convolutional long short-term memory based core block. And the proposed Cell-STN utilized a task specific network to predict the cell location, the mitosis event, and the apoptosis event in a multi-task manner. ResultsWe evaluated the Cell-STN on three in-house datasets (MCF7, U2OS, and HCT116) and one public dataset (Fluo-N2DL-HeLa). For cell tracking, we used peak-wise precision, track-wise precision, end-peak precision, and spatial distance as metrics. The overall results showed the Cell-STN models outperform other state-of-the-art cell trackers. For mitosis and apoptosis tasks, we used accuracy, F1-score, temporal distance, and spatial distance as metrics. The Cell-STN models achieved the highest performance on all datasets. ConclusionThis study presented a novel DNNs approach for cell lineage analysis in microscopy images. The Cell-STN showed outstanding performance on the four datasets. Additionally, the Cell-STN required minimal training data and can be adapted to new biological event detection tasks by appending task-specific layers. This algorithm has the potential to be used in real-world biomedical research.

bioinformatics↗