Biology subjects

Frankenfield, A. M.

Publications and source records attributed to Frankenfield, A. M..

5 recordsLinked to original sources

bioRxiv2026

A Deep Quantitative Proteome Turnover Platform for Human iPSC-derived Neurons

Quantitative evaluation of protein turnover in human neurons is crucial for understanding neuron homeostasis and guiding drug development for neurological diseases. However, measuring protein turnover in postmitotic neurons remains challenging due to the high dynamic range of protein half-lives and limited proteome coverage in SILAC (Stable Isotope Labeling by Amino acids in Cell culture) experiments. Despite broad applications of dynamic SILAC proteomics to measure protein turnover in rodent tissues and primary neurons, few studies have measured protein half-lives in human neurons with limited proteome coverage. Here, we established a comprehensive platform to quantify protein half-lives in human induced pluripotent stem cell (iPSC)-derived neurons. By integrating optimized dynamic SILAC labeling in human neuron cultures, extensive peptide fractionation, optimized data-dependent and data-independent LC-MS/MS acquisition methods, and a streamlined computational pipeline, we achieved deep and accurate measurement of 10,792 protein half-lives from 162,854 unique peptides. We then compared the protein turnover and abundances in iPSC-derived glutamatergic cortical neurons and spinal motor neurons, revealing globally conserved proteome dynamics alongside subtype-specific differences consistent with specialized neuronal functions. To enable broad community access, we created NeuronProfile (www.neuronprofile.com), an interactive web platform for exploring protein turnover, abundance, and subcellular location in human neurons. Together, this work provides a comprehensive analytical platform to assess human neuronal proteostasis and a foundational resource for neurological disease research and therapeutic development.

neuroscience↗

bioRxiv2024

Maintenance of neuronal TDP-43 expression requires axonal lysosome transport

TDP-43 mislocalization and pathology occurs across a range of neurodegenerative diseases, but the pathways that modulate TDP-43 in neurons are not well understood. We generated a Halo-TDP-43 knock-in iPSC line and performed a genome-wide CRISPR interference FACS-based screen to identify modifiers of TDP-43 levels in neurons. A meta-analysis of our screen and publicly available screens identified both specific hits and pathways present across multiple screens, the latter likely responsible for generic protein level maintenance. We identified BORC, a complex required for anterograde lysosome transport, as a specific modifier of TDP-43 protein, but not mRNA, levels in neurons. BORC loss led to longer half-life of TDP-43 and other proteins, suggesting lysosome location is required for proper protein turnover. As such, lysosome location and function are crucial for maintaining TDP-43 protein levels in neurons.

neuroscience↗

bioRxiv2023

Multi-modal Proteomic Characterization of Lysosomal Function and Proteostasis in Progranulin-Deficient Neurons

Progranulin (PGRN) is a lysosomal protein implicated in various neurodegenerative diseases. Over 70 mutations discovered in the GRN gene all result in reduced expression of PGRN protein. However, the detailed molecular function of PGRN within lysosomes and the impact of PGRN deficiency on lysosomal biology remain unclear. Here we leveraged multifaceted proteomic techniques to comprehensively characterize how PGRN deficiency changes the molecular and functional landscape of neuronal lysosomes. Using lysosome proximity labeling and immuno-purification of intact lysosomes, we characterized lysosome compositions and interactomes in both human induced pluripotent stem cell (iPSC)-derived glutamatergic neurons (i3Neurons) and mouse brains. Using dynamic stable isotope labeling by amino acids in cell culture (dSILAC) proteomics, we measured global protein half-lives in i3Neurons for the first time and characterized the impact of progranulin deficiency on neuronal proteostasis. Together, this study indicated that PGRN loss impairs the lysosomes degradative capacity with increased levels of v-ATPase subunits on the lysosome membrane, increased catabolic enzymes within the lysosome, elevated lysosomal pH, and pronounced alterations in neuron protein turnover. Collectively, these results suggested PGRN as a critical regulator of lysosomal pH and degradative capacity, which in turn influences global proteostasis in neurons. The multi-modal techniques developed here also provided useful data resources and tools to study the highly dynamic lysosome biology in neurons.

neuroscience↗

bioRxiv2022

How do Protein Contaminants Influence DDA and DIA Proteomics

Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are often abundant and almost impossible to avoid. For data-dependent acquisition (DDA) proteomics, exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomics data is also unclear. In this study, we established protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA and spectral libraries in all bottom-up proteomics workflow. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in different DDA and DIA data analysis platforms can be valuable resources for proteomics researchers, which are freely accessible at https://github.com/HaoGroup-ProtContLib.

systems biology↗

bioRxiv2020

Development and Comparative Evaluation of Endolysosomal Proximity Labeling-based Proteomic Methods in Human iPSC-derived Neurons

Proximity-based in situ labeling techniques offer a unique way to capture both stable and transient protein-protein and protein-organelle interactions. Combining this technology with mass spectrometry (MS)-based proteomics allows us to obtain snapshots of molecular microenvironments with nanometer resolution, facilitating the discovery of complex and dynamic protein networks. However, a number of technical challenges still exist, such as interferences from endogenously biotinylated proteins and other highly abundant bystanders, how to select the proper controls to minimize false discoveries, and experimental variations among biological/technical replicates. Here, we developed a new method to capture the proteomic microenvironment of the neuronal endolysosomal network, by knocking in (KI) an engineered ascorbate peroxidase (APEX) gene to the endogenous locus of lysosome-associated membrane protein 1 (LAMP1). We found that normalizing proximity labeling proteomics data to the endogenously biotinylated protein (PCCA) can greatly reduce variations and enable fair comparisons among different batch of APEX labeling and different APEX probes. We conducted comparative evaluation between this KI-LAMP1-APEX method and our two overexpression LAMP1-APEX probes, achieving complementary coverage of both known and new lysosomal membrane and lysosomal-interacting proteins in human iPSC-derived neurons. To summarize, this study demonstrated new analytical tools to characterize lysosomal functions and microenvironment in human neurons and filled critical gaps in the field for designing and optimizing proximity labeling proteomic experiments.

systems biology↗