Biology subjects

Wait, S. J.

Publications and source records attributed to Wait, S. J..

4 recordsLinked to original sources

bioRxiv2024

Far-red and sensitive sensor for monitoring real-time H2O2 dynamics with subcellular resolution and inmulti-parametric imaging applications.

H2O2 is a key oxidant in mammalian biology and a pleiotropic signaling molecule at the physiological level, and its excessive accumulation in conjunction with decreased cellular reduction capacity is often found to be a common pathological marker. Here, we present a red fluorescent Genetically Encoded H2O2 Indicator (GEHI) allowing versatile optogenetic dissection of redox biology. Our new GEHI, oROS-HT, is a chemigenetic sensor utilizing a HaloTag and Janelia Fluor (JF) rhodamine dye as fluorescent reporters. We developed oROS-HT through a structure-guided approach aided by classic protein structures and recent protein structure prediction tools. Optimized with JF635, oROS-HT is a sensor with 635 nm excitation and 650 nm emission peaks, allowing it to retain its brightness while monitoring intracellular H2O2 dynamics. Furthermore, it enables multi-color imaging in combination with blue-green fluorescent sensors for orthogonal analytes and low auto-fluorescence interference in biological tissues. Other advantages of oROS-HT over alternative GEHIs are its fast kinetics, oxygen-independent maturation, low pH sensitivity, lack of photo-artifact, and lack of intracellular aggregation. Here, we demonstrated efficient subcellular targeting and how oROS-HT can map inter and intracellular H2O2 diffusion at subcellular resolution. Lastly, we used oROS-HT with the green fluorescent calcium indicator Fluo-4 to investigate the transient effect of the anti-inflammatory agent auranofin on cellular redox physiology and calcium levels via multi-parametric, dual-color imaging.

bioengineering↗

bioRxiv2023

Machine Learning Ensemble Directed Engineering of Genetically Encoded Fluorescent Calcium Indicators.

Real-time monitoring of biological activity can be achieved through the use of genetically encoded fluorescent indicators (GEFIs). GEFIs are protein-based sensing tools whose biophysical characteristics can be engineered to meet experimental needs. However, GEFIs are inherently complex proteins with multiple dynamic states, rendering optimization one of the most challenging problems in protein engineering. Most GEFIs are engineered through trial-and-error mutagenesis, which is time and resource-intensive and often relies on empirical knowledge for each GEFI. We applied an alternative approach using machine learning to efficiently predict the outcomes of sensor mutagenesis by analyzing established libraries that link sensor sequences to functions. Using the GCaMP calcium indicator as a scaffold, we developed an ensemble of three regression models trained on experimentally derived GCaMP mutation libraries. We used the trained ensemble to perform an in silico functional screen on a library of 1423 novel, untested GCaMP variants. The mutations were predicted to significantly alter the fluorescent response, and off-rate kinetics were advanced for verification in vitro. We found that the ensembles predictions of novel variants biophysical characteristics closely replicated what we observed of the variants in vitro. As a result, we identified the novel ensemble-derived GCaMP (eGCaMP) variants, eGCaMP and eGCaMP+, that achieve both faster kinetics and larger fluorescent responses upon stimulation than previously published fast variants. Furthermore, we identified a combinatorial mutation with extraordinary dynamic range, eGCaMP2+, that outperforms the tested 6th, 7th, and 8th generation GCaMPs. These findings demonstrate the value of machine learning as a tool to facilitate the efficient prescreening of mutants for functional characteristics. By leveraging the learning capabilities of our ensemble, we were able to accelerate the identification of promising mutations and reduce the experimental burden associated with screening an entire library. Machine learning tools such as this have the potential to complement emerging high-throughput screening methodologies that generate massive datasets, which can be tedious to analyze manually. Overall, these findings have significant implications for developing new GEFIs and other protein-based tools, demonstrating the power of machine learning as an asset in protein engineering.

bioengineering↗

bioRxiv2022

Opto-MASS: a high-throughput engineering platform for genetically encoded fluorescentsensors enabling all optical in vivo detection of monoamines and neuropeptides

Fluorescent sensor proteins are instrumental for detecting biological signals in vivo with high temporal accuracy and cell-type specificity. However, engineering sensors with physiological ligand sensitivity and selectivity is difficult because they need to be optimized through individual mutagenesis in vitro to assess their performance. The vast mutational landscape proteins constitute an obstacle that slows down sensor development. This is particularly true for sensors that require mammalian host systems to be screened. Here, we developed a novel high-throughput engineering platform that functionally tests thousands of variants simultaneously in mammalian cells and thus allows the screening of large variant numbers. We showcase the capabilities of our platform, called Optogenetic Microwell Array Screening System (Opto-MASS), by engineering novel monoamine and neuropeptide in vivo capable sensors with distinct physiological roles at high-throughput.

bioengineering↗

bioRxiv2020

ASCL1 represses a latent osteogenic program in small cell lung cancer in multiple cells of origin

ASCL1 is a neuroendocrine-lineage-specific oncogenic driver of small cell lung cancer (SCLC), highly expressed in a significant fraction of tumors. However, ~25% of human SCLC are ASCL1-low and associated with low-neuroendocrine fate and high MYC expression. Using genetically-engineered mouse models (GEMMs), we show that alterations in Rb1/Trp53/Myc in the mouse lung induce an ASCL1+ state of SCLC in multiple cells of origin. Genetic depletion of ASCL1 in MYC-driven SCLC dramatically inhibits tumor initiation and progression to the NEUROD1+ subtype of SCLC. Surprisingly, ASCL1 loss converts tumors to a SOX9+ mesenchymal/neural-crest-stem-like state that can differentiate into RUNX2+ bone tumors. ASCL1 represses SOX9 expression, as well as WNT and NOTCH developmental pathways, consistent with human gene expression data. Together, SCLC demonstrates remarkable cell fate plasticity with ASCL1 repressing the emergence of non-endodermal stem-like fates that have the capacity for bone differentiation.

cancer biology↗