Biology subjects

Tan, S. J.

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

6 recordsLinked to original sources

bioRxiv2025

Development of a method for large-scale single-molecule analysis of tau proteoforms

Proteins exist as diverse proteoforms resulting from a combination of genetic variation, alternative splicing, and post-translational modifications. Current methods struggle to capture this complexity at the single-molecule level. Here we introduce Iterative Mapping of Proteoforms (IMaP), a method that enables the massively-parallel interrogation of millions to billions of single-protein molecules through iterative probing with fluorescently labeled antibodies. Using 12 site-specific antibodies, the method is capable of measuring 212 (4,096) potential proteoform groups. We used IMaP to measure proteoform group profiles of the tau protein, a key player in neurodegenerative diseases, using two pan anti-tau antibodies (Tau-13, Tau-216), three isoform-specific antibodies (Anti-0N, Anti-2N, Anti-4R), and seven phosphosite-specific antibodies (Anti-pT181, Anti-pS202+pT205, Anti-pT205, Anti-pS214, Anti-pT217, Anti-pT231, and Anti-pS396). The method demonstrates high sensitivity (detecting proteoforms at 0.1% abundance), high reproducibility (median CV <5.5%), and broad dynamic range (>3 orders of magnitude), outperforming conventional techniques in resolving closely related proteoform groups. We demonstrated that the method can be used on relevant biological samples by examining various neuronal models (iNeuron cells, organoids, MiBrains, and mouse brains) and human samples. This examination revealed 130 distinct tau proteoform groups with as many as six phosphorylation events. The non-random distribution of these phosphorylation events suggests ordered and site-specific modification processes rather than random, stochastic accumulation. Certain combinations of phosphorylation events were more abundant than others; for example, pT217 preferentially co-occurred with pT181. In validating the applicability of the assay to human disease samples, we noted a specific pattern of multiple phosphorylation events in an advanced Alzheimers disease patient that suggests a sequential pathway of pathological tau modification. Iterative Mapping of Proteoforms provides insights into proteoform complexity at the single-molecule level, with significant implications for understanding protein regulation in neurodegenerative diseases and beyond.

biochemistry↗

bioRxiv2024

Macrophage migration-dependent retraction fibers and migrasomes - a structural basis for spatiotemporal cytokine secretion.

Migrating cells leave behind trails of matrix-bound retraction fibers and migrasomes, but their cell-specific physiological functions are largely unknown. We comprehensively characterize retraction fibers and migrasomes of migrating macrophages in mouse and zebrafish in vitro and in vivo models. These structures share common and macrophage-centric components of tetraspanin enriched microdomains (TEMs) including tetraspanin 4 (TSPAN4), the integrin CD11b and the pTRAP, SCIMP; like TSPAN4, we show that SCIMP expression modulates retraction fiber and migrasome formation. In immune-activated migrating macrophages, rear-positioned recycling endosomes regulate the secretory trafficking of newly synthesized inflammatory cytokines into retraction fibers and migrasomes. Transmembrane TNF is delivered via SNARE-mediated carriers to the cell surface and to the surfaces of both retraction fibers and migrasomes, from where it can be released. Retraction fibers are thus newly identified here as cytokine secretion sites, and along with migrasomes they allow migrating macrophages to secrete more cytokine compared to stationary cells. Whereas cytokine secretion has been viewed as a process of ad hoc diffusion, instead, we reveal a new mechanism of migration-dependent, localized deposition and spatiotemporal release of bioactive cytokines from RFs and migrasomes. Cytokine deployment is thus organized in a constrained manner facilitating direct influence on local inflammatory responses.

cell biology↗

bioRxiv2020

Age-related differences in the role of the prefrontal cortex in sensory-motor training gains: A tDCS study

The ability to process multiple sources of information concurrently is particularly impaired as individuals age and such age-related increases in multitasking costs have been linked to impairments in response selection. Previous neuroimaging studies with young adults have implicated the left hemisphere prefrontal cortex (PFC) as a key neural substrate of response selection. In addition, several transcranial direct current stimulation studies (tDCS) have provided causal evidence implicating this region in response selection and multitasking operations. For example, Filmer at al. (2013b) demonstrated that typically observed response selection learning/training gains in young adults were disrupted via offline transcranial direct current stimulation (tDCS) of left, but not right, PFC. Here, considering evidence of functional dedifferentiation in the brains of older adults, we assessed if this pattern of response selection learning disruption via tDCS to the left PFC is observed in older adults, testing if this region remains a key response selection node as individuals age. In a pre-registered study with 58 older adults, we applied anodal, cathodal, and sham stimulation to left and right PFC, and measured performance as participants trained on low- and high-response selection load tasks. Active stimulation did not disrupt training in older adults as compared to younger adults. However, there was evidence of enhanced training gains via tDCS, which scaled with response selection task difficulty. The results highlight age-related differences in the casual neural substrates that subserve response selection and learning.

neuroscience↗

bioRxiv2019

Regulation and dynamics of force transmission at individual cell-matrix adhesion bonds

Integrin-based adhesion complexes link the cytoskeleton to the extracellular matrix (ECM) and are central to the construction of multicellular animal tissues. How biological function emerges from the 10s-1000s of proteins present within a single adhesion complex has remained unclear. We used fluorescent molecular tension sensors to visualize force transmission by individual integrins in living cells. These measurements revealed an underlying functional modularity in which integrin class controlled adhesion size and ECM ligand specificity, while the number and type of connections between integrins and F-actin determined the force per individual integrin. In addition, we found that most integrins existed in a state of near-mechanical equilibrium, a result not predicted by existing models of cytoskeletal force transduction. A revised model that includes reversible crosslinks within the F-actin network accounts for this result, and suggests how cellular mechanical homeostasis can arise at the molecular level.

cell biology↗

bioRxiv2018

Compendiums of Cancer Transcriptome for Machine Learning Applications

BackgroundThere exist massive transcriptome profiles in the form of microarray, enabling reuse. The challenge is that they are processed with diverse platforms and preprocessing tools, requiring considerable time and informatics expertise for cross-dataset or cross-cancer analyses. If there exists a single, integrated data source consisting of thousands of samples, similar to TCGA, data-reuse will be facilitated for discovery, analysis, and validation of biomarker-based clinical strategy.\n\nFindingsWe present 11 merged microarray-acquired datasets (MMDs) of major cancer types, curating 8,386 patient-derived tumor and tumor-free samples from 95 GEO datasets. Highly concordant MMD-derived patterns of genome-wide differential gene expression were observed with matching TCGA cohorts. Using machine learning algorithms, we show that clinical models trained from all MMDs, except breast MMD, can be directly applied to RNA-seq-acquired TCGA data with an average accuracy of 0.96 in classifying cancer. Machine learning optimized MMD further aids to reveal immune landscape of human cancers critically needed in disease management and clinical interventions.\n\nConclusionsTo facilitate large-scale meta-analysis, we generated a newly curated, unified, large-scale MMD across 11 cancer types. Besides TCGA, this single data source may serve as an excellent training or test set to apply, develop, and refine machine learning algorithms that can be tapped to better define genomic landscape of human cancers.

bioinformatics↗

bioRxiv2018

Cross-platform meta-analysis reveals common matrisome variation associated with tumor genotypes and immunophenotypes in human cancers

BackgroundRecent sequencing efforts unveil genomic landscapes of the tumor microenvironment. Yet, little is known about the extent to which matrisome pattern is conserved in progressive tumors across diverse cancer types, and thus its clinical impact remains largely unexplored.\n\nFindingsUsing a newly generated, unified data resource, we conducted cross-platform assessment of a measure of altered extra-cellular matrix (ECM) composition and remodeling associated with tumor progression, termed as the matrisome index (TMI). Parallel analyses with TCGA in over 30,000 patient-derived biopsies revealed that TMI is closely associated with mutational load, tumor histopathology, and predictive of patient outcomes. We found an enrichment of specific tumor-infiltrating immune cell populations, signatures predictive of immunotherapy resistance, and several immune checkpoints in tumors with high TMI, suggesting potential role of ECM interaction with immunophenotyes and tumor immune escape mechanisms. Both epithelial cancer cells and carcinoma-associated fibroblasts are potential cellular contributors of such deregulated matrisome.\n\nConclusionsDespite wide spectrum of genetic heterogeneity and dynamic nature, matrisome abnormalities are integral to disease progression. Our resource of a curated compendium of 8,386 genome-wide profiles, molecular and clinical associations, and matrisome-tumor genotype-immunophenotype relationships identify potentially actionable immune targets that may guide personalized immunotherapy.

genomics↗