Biology subjects

Georgia, S. K.

Publications and source records attributed to Georgia, S. K..

6 recordsLinked to original sources

bioRxiv2026

Bright Probes, Blurred Metabolism: Navigating Fluorescent Protein Cross-Excitation in NADH FLIM

Fluorescence lifetime imaging microscopy (FLIM) of endogenous NAD(P)H enables the label-free assessment of cellular metabolic state. Although metabolic imaging is increasingly combined with fluorescent protein (FP) reporters to enhance biological specificity, the potential cross-talk between the intrinsic and extrinsic labels remain ill-defined. Here, we systematically evaluate cross-talk from FPs in metabolic FLIM using phasor analysis of two-photon fluorescence microscopy. The results clearly show that many widely used fluorescent proteins are excited under the conditions used for NADH imaging; they emit blue-shifted, short-lifetime fluorescence that can interfere with imaging NADH metabolic signatures. This overlap persists across excitation wavelengths and FP classes, posing a significant challenge for multiplexed metabolic imaging. This cautionary tale argues against unvalidated multiplexing strategies in metabolic FLIM studies. Our study aims to identify acceptable imaging partners, offer a pipeline for assaying potential cross-talk, and provide practical guidance for experimental design. SignificanceFluorescence lifetime imaging of NADH autofluorescence is a powerful, label-free approach to map cellular metabolism in living tissues. A growing number of studies combine NADH imaging with fluorescent protein (FP) reporters to simultaneously identify specific cell types or subcellular compartments. This study reveals that many FPs, spanning the visible spectrum, are unexpectedly excited under NADH conditions. Commonly used green, yellow, and red variants produce short-lifetime, blue-shifted fluorescence that directly overlaps with metabolic NADH signals. This cross-excitation can be falsely interpreted as a shift in cellular metabolic state, posing a significant risk for multiplexed metabolic imaging studies. Our studies establish a pipeline to assess and manage this risk. We identify StayGoldE138D and mNeonGreen as the most compatible FPs for co-imaging with NADH, and provide a practical framework to guide experimental design and control strategies for multiplexed metabolic FLIM.

biophysics↗

bioRxiv2024

A Versatile Light Field Microscopy Platform for Multi-purpose Dynamic Volumetric Bio-imaging

Light field microscopy (LFM) has emerged in recent years as a unique solution for fast, scan-free volumetric imaging of dynamic biological samples. This is achieved by using a microlens array in the detection path to record both the lateral and angular information of the light fields coming from the sample, capturing a 3-dimensional (3D) volume in a single 2-dimensional (2D) snapshot. In post-acquisition, the 3D sample volume is computationally reconstructed from the recorded 2D images, thus enabling unprecedented 3D capture speed, not limited by the typical constraint of physically scanning the focal plane over the sample volume. Up to date, most published LFM imaging setups have been specialized single-purpose platforms, optimized for a narrow performance window in field of view and resolution, thus hampering widespread adoption of LFM for biomedical research. Here, we present a versatile LFM platform for fast 3D imaging across multiple scales, enabling applications from cell to system-level biology on the same imaging setup. Our multiscale LFM is built as an add-on module to a conventional commercially available wide field microscope, and the various imaging applications, with different ranges of field of view and resolution, are achieved by simply switching between the standard microscope objectives available on the wide field microscope. Importantly, we provide an open-source end-to-end software package for calculating the system performance parameters, processing the experimentally measured point spread function, and light field 3D image reconstruction. We demonstrate the performance of our multiscale LFM platform through imaging the whole-brain activity map of seizures in larval zebrafish, calcium dynamics in ex vivo mouse pancreatic islets, and subcellular protein dynamics in cultured cells.

bioengineering↗

bioRxiv2022

Systematic Bayesian posterior analysis facilitates hypothesis formation and guides investigation of pancreatic beta cell signaling

Bayesian inference produces a posterior distribution for the parameters and predictions from a mathematical model that can be used to guide the formation of hypotheses; specifically, the posterior may be searched for evidence of alternative model hypotheses, which serves as a starting point for hypothesis formation and model refinement. Previous approaches to search for this evidence are largely qualitative and unsystematic; further, demonstrations of these approaches typically stop at hypothesis formation, leaving the questions they raise unanswered. Here, we introduce a Kullback-Leibler (KL) divergence-based ranking to expedite Bayesian hypothesis formation and investigate the hypotheses it generates, ultimately generating novel, biologically significant insights. Our approach uses KL divergence to rank parameters by how much information they gain from experimental data. Subsequently, rather than searching all model parameters at random, we use this ranking to prioritize examining the posteriors of the parameters that gained the most information from the data for evidence of alternative model hypotheses. We test our approach with two examples, which showcase the ability of our approach to systematically uncover different types of alternative hypothesis evidence. First, we test our KL divergence ranking on an established example of Bayesian hypothesis formation. Our top-ranked parameter matches the one previously identified to produce alternative hypotheses. In the second example, we apply our ranking in a novel study of a computational model of prolactin-induced JAK2-STAT5 signaling, a pathway that mediates beta cell proliferation. Here, we cluster our KL divergence rankings to select only a subset of parameters to examine for qualitative evidence of alternative hypotheses, thereby expediting hypothesis formation. Within this subset, we find a bimodal posterior revealing two possible ranges for the prolactin receptor degradation rate. We go on to refine the model, incorporating new data and determining which degradation rate is most plausible. Overall, we demonstrate that our approach offers a novel quantitative framework for Bayesian hypothesis formation and use it to produce a novel, biologically-significant insight.

systems biology↗

bioRxiv2022

DNA methylation Dependent Restriction of Tyrosine Hydroxylase Contributes to Pancreatic β-cell Heterogeneity

The molecular and functional heterogeneity of pancreatic {beta}-cells is well recognized. Pancreatic islets harbor a small subset of {beta}-cells that co-express Tyrosine Hydroxylase (TH), an enzyme involved in synthesis of catecholamines that repress insulin secretion. Restriction of this sub-population within islets is essential for appropriate insulin secretion. However, the distinguishing characteristics of this subpopulation and the mechanisms that restrict TH expression in {beta}-cells are not known. Here, we define the specific molecular and metabolic characteristics of the TH+ {beta}-cells and show that TH expression in {beta}-cells is restricted by DNA methylation patterning during {beta}-cell lineage specification. Ablation of de novo DNA methyltransferase Dnmt3a in the pancreatic- and endocrine-progenitor lineages results in a dramatic increase in the proportion of TH+ {beta}-cells, while {beta}-cell specific ablation of Dnmt3a has no effect on this sub-population. We demonstrate that maintenance of Th promoter DNA methylation patterns is essential for its continued restriction in postnatal {beta}-cells, and that loss of DNA methylation dysregulates TH expression in {beta}-cells in response to chronic overnutrition, contributing to impairment of {beta}-cell identity. These data highlight the essential requirement of DNA methylation patterning in regulating endocrine cell fates, and reveal a novel role of DNA methylation in {beta}-cell heterogeneity.

developmental biology↗

bioRxiv2021

Phosphatases are predicted to govern prolactin-mediated JAK-STAT signaling in pancreatic beta cells

Patients with diabetes are unable to produce a sufficient amount of insulin to properly regulate their blood-glucose levels. One potential method of treating diabetes is to increase the number of insulin-secreting beta cells in the pancreas to enhance insulin secretion. It is known that during pregnancy, pancreatic beta cells proliferate in response to the pregnancy hormone, prolactin. Leveraging this proliferative response to prolactin may be a strategy to restore endogenous insulin production for patients with diabetes. To investigate this potential treatment, we previously developed a computational model to represent the prolactin-mediated JAK-STAT signaling pathway in pancreatic beta cells. However, this model does not account for variability in protein expression that naturally occurs between cells. Here, we applied the model to understand how heterogeneity affects the dynamics of JAK-STAT signaling. We simulated a sample of 10,000 heterogeneous cells with varying initial protein concentrations responding to prolactin stimulation. We used partial least squares regression to analyze the significance and role of each of the varied protein concentrations in producing the response of the cell. Our regression models predict that the concentrations of the cytosolic and nuclear phosphatases strongly influence the response of the cell. The model also predicts that increasing prolactin receptor strengthens negative feedback mediated by the inhibitor SOCS. These findings reveal biological targets that can potentially be used to modulate the proliferation of pancreatic beta cells to enhance insulin secretion and beta cell regeneration in the context of diabetes.

systems biology↗

bioRxiv2020

Dynamic regulation of JAK-STAT signaling through the prolactin receptor predicted by computational modeling

IntroductionHormones signal through various receptors and cascades of biochemical reactions to expand beta cell mass during pregnancy. Harnessing this phenomenon to treat beta cell dysfunction requires quantitative understanding of the signaling at the molecular level. This study explores how different regulatory elements impact JAK-STAT signaling through the prolactin receptor in pancreatic beta cells. MethodsA mechanistic computational model was constructed to describe the key reactions and molecular species involved in JAK-STAT signaling in response to the hormone prolactin. The effect of including and excluding different regulatory modules in the model structure was explored through ensemble modeling. A Bayesian approach for likelihood estimation was used to parametrize the model to experimental data from the literature. ResultsReceptor upregulation, combined with either inhibition by SOCS proteins, receptor internalization, or both, was required to obtain STAT5 dynamics matching experimental results for INS-1 cells treated with prolactin. Multiple model structures could fit the experimental data, and key findings were conserved across model structures, including faster dimerization and nuclear import rates of STAT5B compared to STAT5A. The model was validated using experimental data from rat primary beta cells not used in parameter estimation. Probing the fitted, validated model revealed possible strategies to modulate STAT5 signaling. ConclusionsJAK-STAT signaling must be tightly controlled to obtain the biphasic response in STAT5 activation seen experimentally. Receptor up-regulation, combined with SOCS inhibition, receptor internalization, or both is required to match experimental data. Modulating reactions upstream in the signaling can enhance STAT5 activation to increase beta cell mass.

systems biology↗