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Biology subjects

Schoof, E. M.

Publications and source records attributed to Schoof, E. M..

At least 19 recordsLinked to original sources

CD59 organizes the plasma membrane to sustain oncogenic Ras-MAPK signaling and is a targetable vulnerability in acute myeloid leukemia

Acute myeloid leukemia (AML) is a clinically heterogeneous disease. Although the genetic abnormalities associated with poor prognosis are well defined, how they drive unfavorable outcomes remains unclear. Using published gene-expression and dependency datasets, we searched for cell-surface protein-coding genes associated with poor survival and required for AML growth, prioritizing this class of proteins for its accessibility to biologics. This search identified CD59, a GPI-anchored protein with a canonical role in complement regulation, whose high mRNA expression correlates with adverse-risk genetics and stemness signatures. CD59 silencing impaired proliferation across genetically diverse AML cell lines, reduced leukemic burden, and extended survival in cell xenograft models. Moreover, CD59 expression was enriched on leukemic stem cells (LSCs), and its depletion impaired LSC self-renewal and primary AML engraftment in vivo while sparing normal hematopoiesis. Mechanistically, these effects reflected a non-canonical role for CD59 in sustaining Ras-MAPK signaling, whereby its loss depleted inner-leaflet phosphatidylserine and impaired Ras and c-Raf membrane recruitment and activation. rILYd4, a recombinant fragment of the bacterial toxin intermedilysin that binds and degrades CD59, recapitulated these effects and sensitized cells to venetoclax in vivo. These findings reveal CD59 as a critical regulator of Ras-MAPK signaling required for AML growth and nominate its rILYd4-mediated degradation as a therapeutic strategy.

cancer biology↗

Defining Quality Control Standards for Single-Cell Proteomics by Inter-Laboratory Benchmarking

Single-cell proteomics can quantify thousands of proteins from individual mammalian cells, yet the absence of community-wide quality control limits biological interpretability. Here, the HUPO Single Cell Initiative presents the first inter-laboratory single-cell proteomics benchmarking study across seven laboratories using standardized 384-well plates acquired on Orbitrap Astral and timsTOF Ultra2 instruments. Centralized analysis across six DIA software tools revealed that software choice impacts identification depth and quantitative accuracy more than instrument vendor. Multi-layered quality control enabled the detection of cell-leakage during sorting, LC misconfiguration, column degradation and site-specific pipetting failures. Inter-lab quantitative correlations were strongest between instruments of the same vendor relative to cross-platform comparisons. Sequential correction for plate identity and well position recovered clean cell-type separation for confident downstream differential expression analysis. This study provides a data-driven quality control framework spanning plate design to batch correction for reproducible single-cell proteomics across laboratories and platforms.

biochemistry↗

Capillary-based Subcellular Sampling Uncovers the Stress Granule Proteome in Single Cells

Many diseases arise from dysfunction within specific organelles or biomolecular condensates, highlighting the value of analysing proteins at subcellular resolution to uncover new biological mechanisms. We report a novel capillary-based subcellular sampling workflow coupled with liquid chromatography-mass spectrometry (LC-MS) for proteomic analysis of defined subcellular regions of individual cells. We applied this methodology to stress granules (SGs), membrane-less biomolecular condensates that form in response to cellular stress (including viral infection), and are implicated in infection, neuropathology and cancer. Comprehensive characterisation of SG protein composition remains limited by technical challenges associated with bulk purification, including loss of spatial context, dynamic behaviour and contamination from cytosolic material. Using our novel method, we identified a high-confidence set of 405 SG-associated proteins, including 46 established SG residents alongside numerous previously unreported candidates. Functional enrichment analysis revealed pathways consistent with known SG biology, while comparison with an independent cytosolic proteome dataset demonstrated minimal overlap, supporting the specificity of the sampling strategy. Selected novel SG protein candidates (AHNAK2, DDX39B, NUDT1 and FKBP2) were validated using immunofluorescence microscopy. These findings establish capillary-based subcellular sampling as a viable approach for proteomic analysis of SGs with preserved spatial context and provide a framework for analysing other subcellular compartments. Table of contentsWe report an LC-MS-based capillary sampling workflow for proteomic analysis of subcellular structures within single cells. This methodology identified 405 high-confidence stress granule-associated proteins, including 46 previously established and numerous novel candidates. The approach demonstrated high specificity and preserved spatial context, expanding the capabilities of subcellular proteomics. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=55 SRC="FIGDIR/small/724230v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1fa0bb0org.highwire.dtl.DTLVardef@1158524org.highwire.dtl.DTLVardef@1d82812org.highwire.dtl.DTLVardef@2ee4d9_HPS_FORMAT_FIGEXP M_FIG C_FIG Figure made in Biorender.com.

cell biology↗

Cholesterol Biosynthesis is a Targetable Vulnerability of CEBPA-mutant Acute Myeloid Leukemia

Bi-allelic CEBPA mutations occur in 5-15% of acute myeloid leukemia (AML) patients. The precise molecular consequences of CEBPA mutations, especially in combination with frequently co-occurring mutations in TET2, WT1, and GATA2, remain incompletely understood. Here, we present a robust human model of CEBPA-mutant AML through gene editing of healthy bone marrow-derived hematopoietic stem cells. Loss of the CEBPA-p42 isoform expressed in healthy cells with concomitant upregulation of the leukemic CEBPA-p30 isoform resulted in a myeloproliferative phenotype. Concurrent loss-of-function mutations in TET2 or WT1 drove full leukemic transformation, while GATA2 haploinsufficiency promoted erythroid precursor accumulation without overt AML. Single-cell transcriptomics and low-input proteomics revealed enhanced myeloid output, increased interferon signaling and elevated cholesterol biosynthesis in leukemic cells. Targeting cholesterol synthesis enhanced chemosensitivity, highlighting a potential therapeutic vulnerability, particularly relevant for CEBPA-mutant patients harboring co-mutations in TET2 or WT1, which have poor outcomes. Statement of significanceInduction of CEBPA-p30 by CRISPR/Cas gene editing in healthy human BM HSCs drives overt AML in vivo. TET2 and WT1 loss accelerate leukemogenesis, while GATA2 haploinsufficiency redirects differentiation toward erythroid precursors potentially driving acute erythroid leukemia. CEBPA-p30 AML exhibits cholesterol biosynthesis dependency, revealing a therapeutic vulnerability to statins.

cancer biology↗

Antibody-based signal amplification for single-cell proteomics by mass spectrometry

Single-cell proteomics by mass spectrometry (scp-MS) detects thousands of proteins per cell, yet low-abundant proteins routinely detected by antibody-based methods are challenging to quantify in the single-cell proteome. To address this, we developed antibody peptide tags (AbTags) - MS detectable tandem peptide barcodes coupled to antibodies which provide signal amplification, enable absolute quantification, and are quantified alongside endogenous proteomes by scp-MS. As proof-of-concept, AbTags quantify cytokine-induced PDL1 expression, undetectable by scp-MS.

systems biology↗

The tryptophan-binding pockets of Arabidopsis AGO1 facilitate amplified RNA interference via SGS3

ARGONAUTE (AGO) proteins associate with small RNAs to form RNA-induced silencing complexes (RISCs). Arabidopsis AGO1 effects post-transcriptional silencing by microRNAs (miRNAs) and small interfering RNAs (siRNAs) and is necessary for siRNA amplification through conversion of RISC target RNAs into double-stranded RNA by the RNA-dependent RNA Polymerase RDR6 and its mandatory cofactors SGS3 and SDE5. Many AGO proteins harbor hydrophobic pockets that interact with tryptophan residues, often surrounded by glycine (GW/WG), in intrinsically disordered regions (IDRs) of RISC cofactors. Here, we show that GW/WG dipeptides in the IDR of SGS3 and the hydrophobic pockets in AGO1 are required for fully functional RDR6-dependent siRNA amplification. We also show that this mechanism requires AGO1-specific structural elements, including positively charged residues surrounding the binding pockets, and a conserved, negatively charged patch in the IDR of SGS3. Thus, the same, conserved protein-protein interaction site is used for different purposes in distinct eukaryotic AGO proteins: the GW/WG-mediated TNRC6-Ago2 interaction is crucial for miRNA-guided silencing in metazoans whereas the GW/WG-mediated SGS3-AGO1 interaction facilitates siRNA amplification via RDR6 in plants.

molecular biology↗

Generalizable direct protein sequencing with InstaNexus

Protein-based therapeutics, such as antibodies and nanobodies, are not encoded in reference genomes, challenging their accurate characterization via standard proteomics. Current methods rely on indirect inference, fragmented outputs, and labor-intensive workflows, which hinder functional insights and routine application. Here, we present a generalizable, end-to-end workflow for direct protein sequencing, combining streamlined sample preparation, AI-driven de novo peptide sequencing, and tailored assembly to reconstruct contiguous protein sequences. A novel composite scoring framework prioritises longer assemblies and coverage, enhancing accuracy and reducing ambiguity. Validation across diverse protein modalities demonstrates its utility and ability to robustly sequence functionally critical regions of selected proteins. This workflow represents an advance in precision proteomics with promising applications in therapeutic discovery, immune profiling, and protein science.

bioinformatics↗

Structural Identification of Major Molecular Determinants for Phosphotyrosine Recognition in Tyrosine Kinases Reveals Tumour Promoting and Suppressive Functions

Protein tyrosine kinases activate signaling pathways by catalyzing the phosphorylation of tyrosine residues in their substrates. Mounting evidence suggests that, in addition to recognizing phosphorylated tyrosine (pTyr) residues through specific phosphobinding modules, many protein kinases selectively recognize pTyr directly adjacent to the tyrosine residue they phosphorylate and catalyze the formation of twin pTyr-pTyr sites. Here, we demonstrate the importance of this phosphopriming-driven twin pTyr signaling in promoting cell cycle progression through the cell cycle-inhibitory protein p27Kip1. We identify, structurally resolve, and tune two distinct molecular determinants driving the selective recognition of pTyr directly N- and C-terminal to the target phospho-acceptor tyrosine site. We further show structural and biochemical conservation in this recognition, and identify cancer-associated alterations to these determinants that are unable to recognize phosphoprimed substrates. Finally, using an in vivo mouse model of leukemia we show that Bcr-Abl mutants unable to recognize phosphoprimed substrates paradoxically result in enhanced tumor development and progression. These data indicate that Bcr-Abl, like other proto-oncogenes such as Ras or Myc, engages both pro- and anti-oncogenic programs - but in the case of Bcr-Abl, this is accomplished through a mechanism involving traditional and phosphoprimed substrate recognition.

biochemistry↗

Informed Data-Independent Acquisition Enables Targeted Quantification of Key Regulatory Proteins in Cell Fate Decision at Single-Cell Resolution

Low-abundance regulatory proteins, including transcription factors (TFs), remain largely inaccessible to direct quantification in single cells despite their central roles in cellular state transitions. Although single-cell proteomics by mass spectrometry (scp-MS) enables broad proteome profiling, current approaches often lack the sensitivity required to quantify these regulators at the protein level robustly. Here, we present informed data-independent acquisition (iDIA), a cross-instrument MS acquisition framework combining sensitive targeted measurements with global proteome profiling of the same cell. Thereby, iDIA significantly improves the sensitivity for predefined regulatory proteins, while preserving global proteome coverage. Applied to hematopoietic stem and progenitor cells, iDIA quantified 12 lineage-associated TFs, including GATA1 and SPI1, alongside the global proteome from single cells. Integrated analysis reconstructed the differentiation hierarchy and revealed protein-level states of early granulocytic-monocytic lineage priming and coordinated changes between erythroid TF abundance and cell-cycle progression. Thus, iDIA opens scp-MS to the regulatory architecture of cell state transitions.

systems biology↗

InstaNovo-P: A de novo peptide sequencing model for phosphoproteomics

Phosphorylation, a crucial post-translational modification (PTM), plays a central role in cellular signaling and disease mechanisms. Mass spectrometry-based phosphoproteomics is widely used for system-wide characterization of phosphorylation events. However, traditional methods struggle with accurate phosphorylated site localization, complex search spaces, and detecting sequences outside the reference database. Advances in de novo peptide sequencing offer opportunities to address these limitations, but have yet to become integrated and adapted for phosphoproteomics datasets. Here, we present InstaNovo-P, a phosphorylation specific version of our transformer-based InstaNovo model, fine-tuned on extensive phosphoproteomics datasets. InstaNovo-P significantly surpasses existing methods in phosphorylated peptide detection and phosphorylated site localization accuracy across multiple datasets, including complex experimental scenarios. Our model robustly identifies peptides with single and multiple phosphorylated sites, effectively localizing phosphorylation events on serine, threonine, and tyrosine residues. We experimentally validate our model predictions by studying FGFR2 signaling, further demonstrating that InstaNovo-P uncovers phosphorylated sites previously missed by traditional database searches. These predictions align with critical biological processes, confirming the models capacity to yield valuable biological insights. InstaNovo-P adds value to phosphoproteomics experiments by effectively identifying biologically relevant phosphorylation events without prior information, providing a powerful analytical tool for the dissection of signaling pathways.

bioinformatics↗

Improving proteomic dynamic range with Multiple Accumulation Precursor Mass Spectrometry (MAP-MS)

Orbitrap (OT) -based mass spectrometer platforms are a gold standard in high-resolution mass spectrometry, where their primary disadvantage is slower-scanning speed in comparison to time-of-flight or linear ion trap mass analyzers. In this study, we utilize long OT transients to extend the precursor dynamic range by modifying the selected ion monitoring method to multiplex several precursor m/z ranges from 400 to 1000 m/z into a single scan called "Multiple Accumulation Precursor Mass Spectrometry" (MAP-MS). Our approach requires no software or hardware modifications and hides the additional ion accumulation steps during the time it takes to make other Orbitrap measurements, producing precursor spectra with nearly 2x dynamic range and essentially no consequences. We collected data using both data-dependent acquisition (DDA) and data-independent acquisition (DIA) methods to evaluate a range of approaches. With DDA, MAP-MS precursor quantification improves with higher quality measurements. At the same time, DIA detection is enhanced by up to 11% when combining precursor and tandem mass spectra for peptide detection.

biochemistry↗

Distinct gene regulatory networks govern hematopoietic and leukemia stem cells

The underlying gene regulatory networks (GRN) that govern leukemia stem cells (LSC) in acute myeloid leukemia (AML) and hematopoietic stem cells (HSC) are not well understood. Here, we identified GRNs by integrating gene expression (GE) and chromatin accessibility data derived from functionally defined cell populations enriched for HSC and LSC. We analyzed n=32 LSC+ and n=32 LSC-cell fractions from n=22 AML patients, along with n=7 stem and n=10 progenitor enriched cell populations sorted from human umbilical cord blood (hUCB), producing a database of n{approx}17,000 transcription factor (TF) regulatory interactions for hUCB-HSPC and AML. We developed an iterative algorithm that associates the degree of chromatin openness with TF binding preferences, and the GE of candidate TF and target genes within 100kb upstream of transcription start sites. A putative regulatory structure was found to be enriched in HSC-enriched cell populations, comprising TF-target gene interactions between ETS1, EGR1, RUNX2, and ZNF683 oriented in a self-reinforcing configuration. A regulatory loop comprising FOXK1 and MEIS1, rather than the 4-factor HSC subnetwork, was detected in the LSC-specific GRN. The core HSC and LSC TF networks were extended using protein-protein interaction (PPI) data to determine connectivity with interacting genes whose expression strongly associated with LSC/HSC frequency estimates, producing a database of n=103,516 PPI target pathways. The effect of perturbing genes along the identified pathways on functional HSC and LSC frequency was predicted based on statistical regression analyses. To validate GRN predictions, we used pharmacologic and CRISPR targeting, in addition to re-examining published functional data associated with several network nodes that were predicted to impact stemness. Notably, we found that inhibition of CDK6 in AML samples markedly reduced LSC numbers as assessed in de novo serial xenotransplantation studies (fold change {approx} 10), as predicted by the LSC GRN model. Additionally, in-house CRISPR-based knockdown of ETS1 resulted in a significant decrease in HSC quiescence-associated microRNA-126 expression, and increased HSC frequency. Taken together, our models provide a comprehensive view of the underlying regulatory structures governing functional human HSC and LSC. This approach has translational potential as it can be used as a high-throughput in-silico screening tool for the systematic identification of gene targets for LSC elimination and HSC expansion.

bioinformatics↗

Quantitative Label-Free Single-Cell Proteomics on the Orbitrap Astral MS

Single-cell proteomics by mass spectrometry (scp-MS) holds the potential to provide unprecedented insights into molecular features directly linked to the cellular phenotype, while deconvoluting complex organisms into their basic building blocks. Tailored sample preparation that maximizes the extracted amount of material that is introduced into the mass spectrometer has rapidly propelled the field forward. However, the measured signal is still at the lower edge of detection approaching the sensitivity boundary of current instrumentation. Here, we investigate the capacity of the enhanced sensitivity of the Orbitrap Astral mass spectrometer to facilitate deeper proteome profiles from low-input to single-cell samples. We carry out a comprehensive data acquisition method survey to pinpoint which parameters provide most sensitivity. Furthermore, we explore the quantitative accuracy of the obtained measurements to ensure that the obtained abundances are in line with expected ground truth values. We culminate our technical exploration by generating small datasets from two cultured cell lines and a primary bone marrow sample, to showcase obtainable proteome coverage differences from different source materials. Finally, as a proof of concept we explore protein covariation to showcase how information on known protein complexes is captured inherently in our scp-MS data.

biochemistry↗

Mapping the human hematopoietic stem and progenitor cell hierarchy through integrated single-cell proteomics and transcriptomics

Single-cell transcriptomics (scRNA-seq) has enabled the characterization of cell state heterogeneity and recapitulation of differentiation trajectories. However, since proteins are the main functional entities in cells, the exclusive use of mRNA measurements comes at the risk of missing important biological information. Here we leverage recent technological advances in single-cell proteomics by Mass Spectrometry (scp-MS) to generate the first scp-MS dataset of an in vivo differentiation hierarchy encompassing over 2,500 human CD34+ hematopoietic stem and progenitor cells. Through integration with scRNA-seq, we identify proteins that are important for stem cell quiescence, which were not indicated by their mRNA transcripts, and demonstrate functional expression covariance during differentiation that is only detectable on protein level. Finally, we show that modeling translation dynamics can infer cell progression during differentiation and explain 45% more protein variation from mRNA than linear correlation. Our work serves as a framework for future single-cell multi-omics studies across biological systems.

cell biology↗

Unraveling the proteome landscape of mouse hematopoietic stem and progenitor compartment with high sensitivity low-input proteomics

Proteins play a key role in defining cellular phenotypes, yet comprehensive proteomic analysis often requires substantial input material, posing challenges in studying rare populations in complex cell systems. Here, we present an accessible, label-free low-input proteomics workflow that allows for comprehensive proteome coverage reminiscent of classical bulk samples from only 500 cells and showcase its application in murine hematopoiesis. With this approach, we construct a proteomic map of hematopoietic stem and progenitor cell (HSPC) populations isolated by fluorescence-activated cell sorting (FACS) from the bone marrow of a single mouse, identifying approximately 7,000 proteins per cell population. Our study recapitulates the differentiation trajectories along the megakaryocytic-erythroid and granulocytic-monocytic lineages. We specifically focus on the dynamics of transcriptional regulators and provide insights into both known and novel population-specific factors. Furthermore, we extend our exploration to the most primitive stem and progenitor compartment, and identify ADP-Ribosyltransferase ART4 (CD297) as a novel cell surface marker that can potentially be used to enrich for long-term hematopoietic stem cells (LT-HSC). The low-input proteomics workflow presented here holds promise for overcoming the challenges associated with analyzing proteomes of rare cell populations, thereby paving the way for broader applications in biomedical research.

cell biology↗

Refining Spatial Proteomics by Mass Spectrometry: An Efficient Workflow Tailored for Archival Tissue

BackgroundFormalin-fixed, paraffin-embedded (FFPE) tissue remains the gold standard for extensively archiving biological specimens, providing biobanks with large repositories of retrospective potential. However, while formalin crosslinking is effective at preserving tissue, it poses significant challenges for extracting molecular information, including the proteome. Traditionally, this process required high levels of input material, which, in turn, limited the ability to preserve cell-type heterogeneity and spatial information. To address these limitations, we developed an easily adaptable and highly efficient workflow for extracting deep proteomes from low-input materials, such as biopsies used in routine histopathological diagnostics. MethodsWe compared the extraction efficiency of pancreatic acinar cells identified in FFPE tissue samples stained with conventional hematoxylin-eosin (H&E) against that of cells isolated from tissue samples immunostained for the epithelial cell adhesion molecule (EpCAM) across material inputs ranging from 1,166 to 800,000 {micro}m2 (estimated to 2 to 1,310 cells in volume). Cells were isolated using laser capture microdissection and subsequently analyzed using Liquid Chromatography-Tandem Mass Spectrometry. ResultsSimilar yields for both methods were observed, with EpCAM-positive cells yielding slightly higher results--approximately 1,200 unique protein groups at the lowest input and up to 5,900 at the highest. In cells isolated from H&E-stained tissue, [~]900 to [~]5,200 protein groups were identified. We decided that the optimal balance for our workflow, ensuring maximum protein identification while minimizing input material, lies within the range of approximately 50,000 to 100,000 {micro}m2. With these results, we tested spatial capabilities and biological relevance by isolating cancer cells from biopsies of pancreatic cancer, lung cancer, or glioblastoma, with the first two being stained with EpCAM and the latter being stained against the tumor-suppressor protein p53. We successfully identified tissue-specific protein expressions and observed prominent clustering of all cell populations. DiscussionOur results highlight the feasibility of performing spatial proteomics on FFPE tissue using minimal input material. This adaptable methodology opens up possibilities for investigating cell-type-specific biology while preserving spatial and histological information.

bioengineering↗

CLIPPER 2.0: Peptide level annotation and data analysis for positional proteomics

Positional proteomics methodologies have transformed protease research, and have brought mass spectrometry (MS)-based degradomics studies to the forefront of protease characterization and system-wide interrogation of protease signaling. Considerable advancements in sensitivity and throughput of liquid chromatography (LC)-MS/MS instrumentation enable generation of enormous positional proteomics datasets of natural and protein termini and neo-termini of cleaved protease substrates. However, such progress has not been observed to the same extent in data analysis and post-processing steps, which arguably constitute the largest bottleneck in positional proteomics workflows. Here, we present a computational tool, CLIPPER 2.0, that builds on prior algorithms developed for MS-based protein termini analysis, facilitating peptide level annotation and data analysis. CLIPPER 2.0 can be used with several sample preparation workflows and proteomics search algorithms, and enables fast and automated database information retrieval, statistical and network analysis, as well as visualization of terminomic datasets. We demonstrate our tool by analyzing GluC and MMP9 cleavages in HeLa lysates. CLIPPER 2.0 is available at https://github.com/UadKLab/CLIPPER-2.0. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=192 SRC="FIGDIR/small/569335v1_ufig1.gif" ALT="Figure 1"> View larger version (83K): org.highwire.dtl.DTLVardef@16d603borg.highwire.dtl.DTLVardef@1c8d9d0org.highwire.dtl.DTLVardef@1540246org.highwire.dtl.DTLVardef@614c9a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

Lipid levels correlate with neuronal and dopaminergic markers during the differentiation of SH-SY5Y cells

Parkinsons Disease (PD) is characterized by the loss of dopaminergic neurons and the deposition in the remaining cells of protein inclusions called Lewy Bodies (LBs). LBs are heterogeneous structures composed of protein and lipid molecules and their main constituent is the presynaptic protein -synuclein. SH-SY5Y cells are neuroblastoma cells commonly used to model PD because they express dopaminergic markers and -synuclein and they can be differentiated into neuronal cells using established protocols. Despite increasing evidence pointing towards a role of lipids in the initiation of PD, limited knowledge is available on the lipidome of undifferentiated and differentiated SH-SY5Y cells. In this study, we show that the levels and chemical properties of negatively charged phospholipids, diacyl glycerol and sphingolipids are specifically altered along the differentiation process of SH-SY5Y cells and that the levels of these lipids species correlate with those of dopaminergic and neuronal markers. These results are supported by proteomic data showing that the main biological processes affected by the differentiation of SH-SY5Y cells are lipid metabolism and processes associated with neuron maturation. Finally, our results show that electrophysiological activity can be detected in differentiated SH-SY5Y cells at a stage where most of the lipid changes have reached their maximal value. These results provide the first complete and quantitative characterisation of the changes in lipidome associated with the differentiation of SH-SY5Y cells into more neuronal and dopaminergic-like phenotype and serve as a basis for further characterisation of lipid disruptions in association with PD and its risk factors in this dopaminergic-like neuronal cell model.

cell biology↗