Biology subjects

Sladek, R.

Publications and source records attributed to Sladek, R..

12 recordsLinked to original sources

bioRxiv2026

Electrokinetic active grooves for liposome capture, confinement, trajectory analysis and controlled release

Extracellular vesicles (EVs) are attractive candidates for minimally invasive disease monitoring. EV diagnostic signals often reside in low-abundant subpopulations, motivating single-vesicle measurements that can report distributions rather than ensemble averages. In practice, such assays rely on transient migration of a mobile vesicle through a detection region or performing cycled processing/measurement steps on vesicles permanently immobilized on surfaces via affinity-based capture. Single vesicle analysis would benefit from technologies that can combine characteristics of transient detection and surface immobilization to enable controlled capture, fixed time analysis and release of vesicles in a marker-neutral way. Here, we present electrokinetically active (RECON) grooves that provides low-voltage, programmable capture, timed retention, and release of nanoscale vesicles within microscopic grooves under continuous optical access. The platform uses a titanium embedded electrode with thin TiO2 passivation, enabling stable actuation over multi-hour experiments. Using fluorescent, size-defined liposomes as standards, we demonstrate (1) reversible, minute-long confinement at <5 V, (2) size-dependent confinement times, and (3) optical quantification of groove-guided drift velocities. Together, these results establish RECON grooves as a reusable, electrically programmable platform for controlled capture, timed retention, and release, while providing quantitative, trajectory-derived measures of confinement and transport.

biophysics↗

bioRxiv2026

Identifying molecular instructions to hard-wire a sensory neurons synaptic connectivity

The neural circuitry underlying innate behaviours must be pre-specified using precise molecular instructions. However, it is not clear what specific molecules are required to generate one neurons wiring pattern that make it distinct from a neighboring neurons wiring pattern. Here, we repeatedly sequenced three identified Drosophila sensory neurons that each have a unique and stereotyped hard-wired synaptic connectivity, to determine the cell surface molecules that distinguish their identities. Repeated sequencing of the same neuron between different animals revealed that the variability of transcription is < 1%. We find that less than 100 cell surface molecules can distinguish between a mechanosensory neuron from a chemosensory neuron. Functional characterization of the cell surface receptors verified their different roles in axonal and synaptic targeting. Finally, we expressed combinations of the different cell surface receptors to mis-wire the chemosensory neuron and increase its axonal branching.

neuroscience↗

bioRxiv2025

SpaTM: Topic Models for Inferring Spatially Informed Transcriptional Programs

BackgroundSpatial transcriptomics has revolutionized our ability to characterize tissues and diseases by contextualizing gene expression with spatial organization. Current spatial transcriptomics pipelines require researchers to use a variety of models and tools to explore spatial domains. However, few methods provide researchers with a way to jointly analyze spatial data from both annotation-free and annotation-guided perspectives using consistent inductive biases and levels of interpretability. A single framework with consistent inductive biases ensures coherence and transferability across tasks, reducing the risks of conflicting assumptions. ResultsWe propose the Spatial Topic Model (SpaTM), a topic-modelling framework capable of annotation-guided and annotation-free analysis of spatial transcriptomics data. SpaTM can be used to learn gene programs that represent histology-based annotations while providing researchers with the ability to infer spatial domains with an annotation-free approach if manual annotations are limited or noisy. Our benchmarking experiments reveal SpaTMs competitiveness at spatial label prediction and clustering when compared to state-of-the-art methods. We also demonstrate SpaTMs interpretability with its use of topic mixtures to represent cell states and transcriptional programs in dorsolateral prefrontal cortex and ductal carcinoma samples and how its intuitive framework facilitates the integration of annotation-guided and annotation-free analyses of spatial data with downstream analyses. Finally, we demonstrate how SpaTM can be used to extend the analysis of large-scale snRNA-seq atlases with the inference of cell proximity and spatial annotations in human brains with Major Depressive Disorder. ConclusionsSpaTM provides researchers with a unified analysis framework for spatial transcriptomics data. By enabling competitive performance in a variety of tasks, SpaTM helps researchers undertake biologically-driven analyses through the identification of interpretable and biologically informed gene programs.

bioinformatics↗

bioRxiv2024

Robust fine-mapping in the presence of linkage disequilibrium mismatch

Fine-mapping methods based on summary statistics from genome-wide association studies (GWAS) and linkage disequilibrium (LD) information are widely used to identify potential causal variants. However, LD mismatch between the external LD reference panel and the GWAS population is common and can lead to compromised accuracy of fine-mapping. We developed RSparsePro, a probabilistic graphical model with an efficient variational inference algorithm, to perform robust fine-mapping in the presence of LD mismatch. In simulation studies with a varying degree of LD mismatch, RSparsePro identified credible sets with a consistently higher power and coverage than SuSiE. In fine-mapping cis-protein quantitative trait loci, RSparsePro identified credible sets with a consistently higher enrichment of variants with functional impacts and cross-study replication rates. In fine-mapping risk loci for low-density lipoprotein cholesterol in ancestry-specific GWAS, RSparsePro identified biologically relevant variants in drug target genes and implicated potential regulatory mechanisms. RSparsePro is openly available at https://github.com/zhwm/RSparsePro_LD.

bioinformatics↗

bioRxiv2024

Benchmarking Bayesian colocalization methods in validating Mendelian randomization-based target discoveries from circulating proteins for cardiometabolic diseases

Mendelian randomization (MR) is an important tool for identifying potential biomarkers and drug targets. Colocalization analysis is crucial for validating MR findings and guarding against confounding due to linkage disequilibrium. We aim to benchmark the performance of four Bayesian colocalization methods in validating MR-based target discoveries from circulating proteins for cardiometabolic traits. We assessed the associations between circulating levels of 1,535 proteins and five cardiometabolic traits, followed by colocalization analyses using coloc, coloc+SuSiE, PWCoCo and SharePro. All methods demonstrated well-controlled false discoveries. SharePro demonstrated the highest frequency in supporting 160 (79.6%) of the 201 Bonferroni-significant protein-trait associations identified by MR, compared to coloc (supporting 40.3% of these associations), coloc+SuSiE (46.8%), and PWCoCo (45.8%), and was robust to varying prior colocalization probabilities. Protein-trait associations supported by SharePro were more likely to agree with significant gene-level associations identified in exome-wide association studies and implicate known drug targets. Eight protein-trait associations were exclusively supported by SharePro, suggesting potential cardiometabolic biomarkers or drug targets, such as HSF1 and HAVCR2. In summary, SharePro most often supports statistically significant associations identified through MR for cardiometabolic traits. Combining multiple lines of evidence using different methods may substantially increase the yield of biomarker and drug target discovery programs.

genetics↗

bioRxiv2023

Accounting for genetic effect heterogeneity in fine-mapping and improving power to detect gene-environment interactions with SharePro

BackgroundCharacterizing genetic effect heterogeneity across subpopulations with different environmental exposures is useful for identifying exposure-specific pathways, understanding biological mechanisms underlying disease heterogeneity and further pinpointing modifiable risk factors for disease prevention and management. Classical gene-by-environment interaction (GxE) analysis can be used to characterize genetic effect heterogeneity. However, it can have a high multiple testing burden in the context of genome-wide association studies (GWAS) and requires a large sample size to achieve sufficient power. MethodsWe adapt a colocalization method, SharePro, to account for effect heterogeneity in finemapping and subsequently improve power for GxE analysis. Through joint fine-mapping of exposure stratified GWAS summary statistics, SharePro can greatly reduce multiple testing burden in GxE analysis. ResultsThrough extensive simulation studies, we demonstrated that accounting for effect heterogeneity can improve power for fine-mapping. With efficient joint fine-mapping of exposure stratified GWAS summary statistics, SharePro alleviated multiple testing burden in GxE analysis and demonstrated improved power with well-controlled false discovery rate. Through analyses of smoking status stratified GWAS summary statistics, we identified genetic effects on lung function modulated by smoking status mapped to the genes CHRNA3, ADAM19 and UBR1. Additionally, using sex stratified GWAS summary statistics, we characterized sex differentiated genetic effects on fat distribution and provided biologically plausible candidates for functional follow-up studies. ConclusionsWe have developed an analytical framework to account for effect heterogeneity in finemapping and subsequently improve power for GxE analysis. The SharePro software for GxE analysis is openly available at https://github.com/zhwm/SharePro_gxe.

genetics↗

bioRxiv2023

SharePro: an accurate and efficient genetic colocalization method accounting for multiple causal signals

MotivationColocalization analysis is commonly used to assess whether two or more traits share the same genetic signals identified in genome-wide association studies (GWAS), and is important for prioritizing targets for functional follow-up of GWAS results. Existing colocalization methods can have suboptimal performance when there are multiple causal variants in one genomic locus. ResultsWe propose SharePro to extend the COLOC framework for colocalization analysis. Share-Pro integrates linkage disequilibrium (LD) modelling and colocalization assessment by grouping correlated variants into effect groups. With an efficient variational inference algorithm, posterior colocalization probabilities can be accurately estimated. In simulation studies, SharePro demonstrated increased power with a well-controlled false positive rate at a low computational cost. Through a challenging case of the colocalization analysis of the circulating abundance of R-spondin 3 (RSPO3) GWAS and estimated bone mineral density GWAS, we demonstrated the utility of SharePro in identifying biologically plausible colocalized signals. Availability and ImplementationThe SharePro software for colocalization analysis is openly available at https://github.com/zhwm/SharePro_coloc and the analysis conducted in this study is available at https://github.com/zhwm/SharePro_coloc_analysis.

genetics↗

bioRxiv2022

A transcriptomic signature for single pancreatic islet leader beta cells

AimsSpatially-organised increases in cytosolic Ca2+ within pancreatic beta cells in the pancreatic islet underlie the stimulation of insulin secretion by high glucose. Recent data have revealed the existence of subpopulations of beta cells including "leaders" which initiate Ca2+ waves. Whether leader cells possess unique molecular features, or localisation, is unknown. Main methodsHigh speed confocal Ca2+ imaging was used to identify leader cells and connectivity analysis, running under MATLAB and Python, to identify highly connected "hub" cells. To explore transcriptomic differences between beta cell sub-groups, individual leaders or followers were labelled by photo-activation of the cryptic fluorescent protein PA-mCherry and subjected to single cell RNA sequencing ("Flash-Seq"). Key findingsDistinct Ca2+ wave types were identified in individual islets, with leader cells present in 73 % (28 of 38 islets imaged). Scale-free, power law-adherent behaviour was also observed in 29% of islets, though "hub" cells in these islets did not overlap with leaders. Transcripts differentially expressed (295; padj<0.05) between leader and follower cells included genes involved in cilium biogenesis and transcriptional regulation. Functionally validating these findings, cilia number and length tended to be lower in leader vs follower cells. Leader cells were also located significantly closer to delta cells in Euclidian space than were follower cells. SignificanceThe existence of both a discrete transcriptome and unique localisation implies a role for these features in defining the specialized function of leaders. Specifically, these data raise the possibility of altered signalling from delta cells towards somatostatin receptors present on leader cell cilia.

cell biology↗

bioRxiv2021

Single-cell RNA sequencing reveals a role for reactive oxygen species and peroxiredoxins in fatty-acid-induced rat β-cell proliferation

The functional mass of insulin-secreting pancreatic {beta} cells expands to maintain glucose homeostasis in the face of nutrient excess, in part via replication of existing {beta} cells. To decipher the underlying molecular mechanisms, we assessed {beta}-cell proliferation in isolated rat islets exposed to glucose and oleate or palmitate for 48 h and analyzed the transcriptional response by single-cell RNA sequencing. Unsupervised clustering of pooled {beta} cells identified subpopulations, including proliferating {beta} cells. {beta}-cell proliferation increased in response to oleate but not palmitate. Both fatty acids enhanced the expression of genes involved energy metabolism and mitochondrial activity. Comparison of proliferating vs. non-proliferating {beta} cells and pseudotime ordering suggested the involvement of reactive oxygen species (ROS) and peroxiredoxin signaling. Accordingly, the antioxidant N-acetyl cysteine and the peroxiredoxin inhibitor Conoidin A both blocked oleate-induced {beta}-cell proliferation. Our data reveal a key role for ROS signaling in {beta}-cell proliferation in response to nutrients.

cell biology↗

bioRxiv2021

A glycerol shunt functions as a glucose excess security valve in pancreatic β-cells

The recently identified glycerol-3-phosphate (Gro3P) phosphatase (G3PP) in mammalian cells, encoded by the PGP gene, was shown to regulate intermediary metabolism by hydrolyzing Gro3P and to control glucose-stimulated insulin secretion (GSIS) in {beta}-cells, in vitro. We now examined in inducible {beta}-cell specific G3PP-KO (BKO) mice, the role of G3PP in the control of insulin secretion in vivo, {beta}-cell function and glucotoxicity. BKO mice, compared to MCre controls, showed increased body weight, adiposity, fed insulinemia, GSIS, reduced plasma triglycerides and mildly altered glucose tolerance. Isolated BKO mouse islets at high (16.7 mM) but not low or intermediate glucose (3-8 mM) showed elevated GSIS, Gro3P, metabolites reflecting {beta}-cell activation, O2 consumption, ATP production and reduced glycerol release. BKO islets chronically exposed to elevated glucose showed increased apoptosis, reduced insulin content and expression of Pdx-1, MafA and Ins-2 genes. As G3PP channels glucose carbons towards glycerol formation and release, the results demonstrate that {beta}-cell are endowed with a "glycerol shunt" acting as a glucose excess security valve. We propose that the glycerol shunt plays a role in glucodetoxification, the prevention of insulin hypersecretion, acts as a defense against excess body weight gain and contributes to {beta}-cell mass preservation in the face of hyperglycemia.

biochemistry↗

bioRxiv2020

A long noncoding RNA, LOC157273, is the effector transcript at the chromosome 8p23.1-PPP1R3B metabolic traits and type 2 diabetes risk locus

AimsCausal transcripts at genomic loci associated with type 2 diabetes are mostly unknown. The chr8p23.1 variant rs4841132, associated with an insulin resistant diabetes risk phenotype, lies in the second exon of a long non-coding RNA (lncRNA) gene, LOC157273, located 175 kilobases from PPP1R3B, which encodes a key protein regulating insulin-mediated hepatic glycogen storage in humans. We hypothesized that LOC157273 regulates expression of PPP1R3B in human hepatocytes. MethodsWe tested our hypothesis using Stellaris fluorescent in-situ hybridization to assess subcellular localization of LOC157273; siRNA knockdown of LOC157273, followed by RT-PCR to quantify LOC157273 and PPP1R3B expression; RNA-seq to quantify the whole-transcriptome gene expression response to LOC157273 knockdown and an insulin-stimulated assay to measure hepatocyte glycogen deposition before and after knockdown. ResultsWe found that siRNA knockdown decreased LOC157273 transcript levels by approximately 80%, increased PPP1R3B mRNA levels by 1.7-fold and increased glycogen deposition by >50% in primary human hepatocytes. An A/G heterozygous carrier (vs. three G/G carriers) had reduced LOC157273 abundance due to reduced transcription of the A allele and increased PPP1R3B expression and glycogen deposition. ConclusionWe show that the lncRNA LOC157273 is a negative regulator of PPP1R3B expression and glycogen deposition in human hepatocytes and the causal transcript at an insulin resistant type 2 diabetes risk locus.

genomics↗

bioRxiv2019

Comparative proximity biotinylation produces an inventory of RAB18-interactions and implicates RAB18 in cholesterol mobilization

Loss of functional RAB18 causes the autosomal recessive condition Warburg Micro syndrome. To better understand this disease, we used proximity biotinylation to generate an inventory of potential RAB18 effectors. A restricted set of 28 RAB18-interactions were dependent on the binary RAB3GAP1-RAB3GAP2 RAB18-guanine nucleotide exchange factor (GEF) complex. 12 of these 28 interactions are supported by prior reports and we have directly validated novel interactions with SEC22A, TMCO4 and INPP5B. Consistent with a role for RAB18 in regulating membrane contact sites (MCSs), interactors included groups of microtubule/membrane-remodelling proteins, membrane-tethering and docking proteins, and lipid-modifying/transporting proteins. Two of the putative interactors, EBP and OSBPL2/ORP2, have sterol substrates. EBP (emopamil binding protein) is a {Delta}8-{Delta}7 sterol isomerase and OSBPL2/ORP2 is a lipid transport protein. This prompted us to investigate a role for RAB18 in cholesterol biosynthesis. We find that the cholesterol precursor and EBP-product lathosterol accumulates in both RAB18-null HeLa cells and RAB3GAP1-null fibroblasts derived from an affected individual. Further, de novo cholesterol biosynthesis is impaired in cells in which RAB18 is absent or dysregulated. Our data demonstrate that GEF-dependent Rab-interactions are highly amenable to interrogation by proximity biotinylation and may suggest that Micro syndrome is a cholesterol biosynthesis disorder.

cell biology↗