Biology subjects

Salim, A.

Publications and source records attributed to Salim, A..

18 recordsLinked to original sources

bioRxiv2026

Chemical Interrogation and Reprogramming of ATAT1-Mediated Tubulin Acetylation

Acetylation of -tubulin K40 by -tubulin acetyltransferase 1 (ATAT1) using acetyl-coenzyme A (Ac-CoA) marks stable microtubule populations, yet chemical tools to directly measure ATAT1 ligand engagement, inhibit its activity, or visualize ATAT1-mediated modification on intact microtubules remain limited. Through the development of a quantitative binding assay, we uncovered that ATAT1 can bind unnatural cofactors but fails to efficiently use them in acyl-transfer reactions. Structure-guided mutation subsequently yielded ATAT1-L163A, which successfully installed clickable handles at the native -tubulin K40 site of synthetic tubulin peptides, -tubulin, and intact microtubules. Cu(I)-catalyzed azide-alkyne cycloaddition enabled visualization of modified microtubules by in-gel fluorescence and microscopy. Moreover, we report a p11-CoA bisubstrate inhibitor that suppressed both native acetylation and engineered acylation. Together, these tools provide chemically controlled access to ATAT1 activity and a site-verified, clickable K40 modification on intact microtubules.

biochemistry↗

bioRxiv2026

bgnorm: A Generative Statistical Framework for Background Correction, Normalisation, and Quality Control in Multiplex Spatial Proteomics

Multiplex spatial proteomics enables highly multiplexed in situ profiling but remains limited by technical variation arising from autofluorescence, non-specific antibody binding, instrument noise, and staining variability, affecting downstream biological tasks like cell typing. We present bgnorm, a statistical framework that describes fluorescence measurements using a generative mixture model of background, non-specific binding, and biological signal components. As natural statistical consequences, the model yielded three new methods: a background-correction method through probabilistic deconvolution of protein intensities, quality control metrics, and a quantile normalisation approach to unify measurements across markers, samples, and sequential slices. Across multiple multiplex imaging technologies, bgnorm improves signal separation and downstream marker positivity classification compared with existing preprocessing approaches. In expert-annotated datasets comprising over 406,000 marker positivity annotations, bgnorm achieved the highest classification performance and enabled accurate use of a single global positivity threshold across markers and samples. The method is implemented in the bgnormR and bgnormpy packages.

bioinformatics↗

bioRxiv2026

A biochemical probe for microtubule lattice integrity uncovers motor-caused lattice damage

Microtubules experience mechanical and enzymatic stresses that can compromise lattice integrity, yet where and how lattice damage forms remains poorly understood due to the lack of tools that directly visualize damage as it occurs. Existing approaches infer damage indirectly through repair events or rely on static ultrastructural snapshots, precluding dynamic analysis. Here, we introduce MT-DS (Microtubule Damage Sensor), a damage-selective fluorescent probe that directly labels microtubule lattice openings. MT-DS combines taxane-based microtubule binding with a multivalent protein scaffold to restrict intraluminal diffusion and selectively retain the probe at sites of lattice openings. Using MT-DS, we visualize intrinsic lattice defects in stabilized microtubules, uncover a strong enrichment of damage at annealing sites, and demonstrate that kinesin-1{Delta}6 actively generates de novo lattice damage during motility. By enabling direct, time-resolved detection of microtubule damage, MT-DS establishes lattice integrity as an experimentally accessible parameter and provides a chemical tool to investigate how mechanical stress reshapes the microtubule cytoskeleton.

biochemistry↗

bioRxiv2026

Tipping the balance: Synthesis and evaluation of centrinone-based degraders of polo-like kinase 4

Polo-like kinase 4 (PLK4) is a serine/threonine-protein kinase that plays a pivotal role in centriole biogenesis and, as such, represents a master regulator of centriole duplication. Due to its importance in cancer development and progression, PLK4 represents an attractive target for the development of novel therapeutics. Herein, we present a series of molecular degraders of PLK4, based on the highly selective PLK4 inhibitor centrinone, with the aim of targeting PLK4 for degradation via the ubiquitin-proteasome system. While all synthesized degraders retained low nanomolar binding affinities to the kinase domain of PLK4, large differences were found with respect to their ability to change cellular PLK4 levels. We uncover a complex pharmacological profile of the most potent degraders, D6 and D10, consisting of concomitant lowering of PLK4 levels through degradation, and enhancing PLK4 levels through inhibition of its autoregulation - dependent on its localization at the centrioles.

cell biology↗

bioRxiv2025

Identifying the Minimal Number of Protein Markers for Cell Type Annotation Using MiniMarS

Over the past decade, there has been an explosion in the characterisation and discovery of cell populations using single-cell technologies. Single-cell multi-omics data, particularly those incorporating gene and protein expression, are increasingly commonplace and can lead to more refined characterisation of cell types. A common challenge for biologists is to isolate cells of interest using a minimal number of markers for cytometry experiments. Although several methods exist for marker selection, there is limited guidance on the relative performance of these methods, and a wrapper package that combines multiple methods is lacking. The method that performs best can vary depending on the dataset and it can be challenging for researchers to test multiple methods for a given dataset. To address these issues, we present MiniMarS (Minimal Marker Selection), an R package that serves as a wrapper for 10 different algorithms. It allows users to determine the best-performing algorithm for identifying the optimal number of markers that will delineate cell populations in their dataset. MiniMarS uses pre-annotated cells with protein features from CyTOF or sequencing-based assays such as CITE-seq and Abseq as input. Outputs include 1) the minimum number of protein markers required to identify the annotated cell populations using a range of marker selection algorithms, and 2) a range of metrics to evaluate the performance of each algorithm. MiniMarS effectively differentiated populations across various datasets, including those from human blood, bone marrow, thymus, mouse spleen, and lymph nodes, even after subsampling over 41,000 cells to 2,500 cells. MiniMarS also identified 15 markers from CITE-seq data, which were then used to successfully identify the same 11 cell subsets in a CyTOF dataset (F1 score>0.9). Additionally, we showed that by appropriately combining clusters, MiniMarS improves the F1 score of a rare population identification (<1% of total cells) by 28.7%. Together, these findings highlight the broad applicability of MiniMarS in identifying appropriate markers for distinguishing cell populations.

bioinformatics↗

bioRxiv2024

SpaNorm: spatially-aware normalisation for spatial transcriptomics data

Library size normalisation is necessary to enable comparisons between observations in transcriptomic datasets. Numerous methods have been developed to normalise these effects with sample and gene specific adjustments. However, in spatial transcriptomics data, normalisation is complicated by the fact that spatial region-specific library size confounds biology. The most popular approach of adapting methods developed for single-cell RNA-seq data has been shown to excessively remove biological signals associated with spatial domains and thus results in poorer downstream domain identification. To this end, we propose the first spatially-aware normalisation method, SpaNorm. SpaNorm concurrently models spatial library size effects and the underlying smooth biology, to tease apart these effects, and thereby remove library size effects without removing biology. This is achieved through optimal decomposition of spatially smooth variation into those related and unrelated to library size and the use of location-specific scaling factors. Using 27 tissue samples from 6 datasets spanning 4 spatial platforms, we show that SpaNorm outperforms current state of the art methods at retaining biological information in the form of spatial domains and spatially variable genes (SVGs) better than 4 commonly used single-cell normalisation approaches. SpaNorm is versatile and it can be used for both spot-based and subcellular spatial transcriptomics data. Notably, the benefit of using SpaNorm is more pronounced for the latter data such as those from Xenium, STOmics and CosMx platforms for which the proportion of genes exhibiting region-specific library size effect is higher. SpaNorm works equally well with segmented cell-level data and spot-based data where each spot contains multiple cells.

bioinformatics↗

bioRxiv2024

MAssively-Parallel Flow cytometry Xplorer (MAPFX): A Toolbox for Analysing Data from the Massively-Parallel Cytometry Experiments

Massively-Parallel Cytometry (MPC) experiments allow cost-effective quantification of more than 200 surface proteins at single-cell resolution. The Infinity Flow (Inflow) analysis protocol was developed to measure highly informative protein backbone markers on all cells in all wells distributed across three 96-well plates, along with well-specific exploratory protein infinity markers. Backbone markers can be used to impute the infinity markers on cells in all other wells using machine learning methods. This protocol offers unprecedented opportunities for more comprehensive classification of cell types. However, some aspects of the protocol can be improved, including methods for background correction and removal of unwanted variation. Here, we propose MAPFX as an end-to-end toolbox that carefully pre-processes the raw data from MPC experiments, and further imputes the missing infinity markers in the wells without those measurements. Our pipeline starts by performing background correction on raw intensities to remove the noise from electronic baseline restoration and fluorescence compensation by adapting a normal-exponential convolution model. Unwanted technical variation, from sources such as well effects, is then removed using a log-normal model with plate, column, and row factors, after which infinity markers are imputed using the informative backbone markers as predictors. The completed dataset can then be used for clustering and other statistical analyses. Unique features of our approach include performing background correction prior to imputation and removing unwanted variation from the data at the cell-level, while explicitly accounting for the potential association between biology and unwanted factors. We benchmark our pipeline against alternative pipelines and demonstrate that our approach is better at preserving biological signals, removing unwanted variation, and imputing unmeasured infinity markers.

bioinformatics↗

bioRxiv2022

Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods.

Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics (SRT) data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing number of published tools designed to define SVG sets currently lack benchmarking methods to accurately assess performance. This study compares results of 6 purpose-built packages for SVG identification across 9 public and 5 simulated datasets and highlights discrepancies between results. Additional tools for generation of simulated data and development of benchmarking methods are required to improve methods for identifying SVGs.

bioinformatics↗

bioRxiv2022

Direct inference and control of genetic population structure from RNA sequencing data

RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations and the Geuvadis study, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data.

bioinformatics↗

bioRxiv2022

Decline in Environmental Quality and Spatial Dynamics of New City Area Development Metropolitan Mamminasata, South Sulawesi, Indonesia

AbstractsThe expansion of urban areas towards the development of new urban areas has an impact on changes in land use, socio-economic dynamics and a decrease in environmental quality. This study aims to analyze land use change as a determinant of environmental degradation and the spatial dynamics of metropolitan urban areas, the effect of land use change, housing development, infrastructure development, and land reclamation on the environmental degradation of the new city area, and control strategies environmental pollution and sustainable development of the new city area. This study uses a quantitative-qualitative concurrent triangulation design approach. Elaboration of data through observation, in-depth interviews, surveys, and documentation is used to describe the socio-economic community, and the decline in the environmental quality of new city area development in relation to the spatial dynamics of metropolitan urban areas. The results of the study show that the intensity of land use change coupled with an increase in socio-economic activities in the development of new city areas is positively associated with a decrease in environmental quality and segregation towards the spatial dynamics of metropolitan urban areas. Changes in land use, housing development, infrastructure development, and land reclamation simultaneously affect environmental quality degradation with a coefficient of determination of 64.96%. This study recommends strategies for controlling environmental pollution and sustainable development of new city areas for the needs of formulating urban development policies for Mamminasata Metropolitan South Sulawesi, Indonesia.

ecology↗

bioRxiv2021

RUV-III-NB: Normalization of single cell RNA-seq Data

Despite numerous methodological advances, the normalization of single cell RNA-seq (scRNA-seq) data remains a challenging task and the performance of different methods can vary greatly across datasets. Part of the reason for this is the different kinds of unwanted variation, including library size, batch and cell cycle effects, and the association of these with the biology embodied in the cells. A normalization method that does not explicitly take into account cell biology risks removing some of the signal of interest. Furthermore, most normalization methods remove the effects of unwanted variation for the cell embedding used for clustering-based analysis but not from gene-level data typically used for differential expression (DE) analysis to identify marker genes. Here we propose RUV-III-NB, a statistical method that can be used to remove unwanted variation from both the cell embedding and gene-level counts. RUV-III-NB explicitly takes into account its potential association with biology when removing unwanted variation via the use of pseudo-replicates. The method can be used for both UMI or sequence read counts and returns adjusted counts that can be used for downstream analyses such as clustering, DE and pseudotime analyses. Using five publicly available datasets that encompass different technological platforms, kinds of biology and levels of association between biology and unwanted variation, we show that RUV-III-NB manages to remove library size and batch effects, strengthen biological signals, improve differential expression analyses, and lead to results exhibiting greater concordance with independent datasets of the same kind. The performance of RUV-III-NB is consistent across the five datasets and is not sensitive to the number of factors assumed to contribute to the unwanted variation. It also shows promise for removing other kinds of unwanted variation such as platform effects. The method is implemented as a publicly available R package available from https://github.com/limfuxing/ruvIIInb.

bioinformatics↗

bioRxiv2021

Assessing and removing the effect of unwanted technical variations in microbiome data

Varying technologies and experimental approaches used in microbiome studies often lead to irreproducible results due to unwanted technical variations. Such variations, often unaccounted for and of unknown source, may interfere with true biological signals, resulting in misleading biological conclusions. In this work, we aim to characterize the major sources of technical variations in microbiome data and demonstrate how a state-of-the art approach can minimize their impact on downstream analyses. We analyzed 184 pig faecal metagenomes encompassing 21 specific combinations of deliberately introduced factors of technical and biological variations. We identify several known experimental factors, specifically storage conditions and freeze-thaw cycles, as a likely major source of unwanted variation in metagenomes. We also observed that these unwanted technical variations do not affect taxa uniformly, with freezing samples affecting taxa of class Bacteroidia the most, for example. Additionally, we benchmarked the performance of a novel batch correcting tool used in this study, RUV-III-NB (https://github.com/limfuxing/ruvIIInb/), to other popular batch correction methods, including ComBat, ComBat-seq, RUVg, and RUVs. While RUV-III-NB performed consistently robustly across our sensitivity and specificity metrics, most other methods did not remove unwanted variations optimally, with RUVg even overcorrecting and removing some of the true biological signals from the samples. Our analyses suggests that a careful consideration of possible technical confounders is critical in the experimental design of microbiome studies to ensure accurate biological reading of microbial taxa of interest, and that the inclusion of technical replicates is necessary to efficiently remove unwanted variations computationally.

bioinformatics↗

bioRxiv2019

scClassify: hierarchical classification of cells

Cell type identification is a key computational challenge in single-cell RNA-sequencing (scRNA-seq) data. To capitalize on the large collections of well-annotated scRNA-seq datasets, we present scClassify, a hierarchical classification framework based on ensemble learning. scClassify can identify cells from published scRNA-seq datasets more accurately and more finely than in the original publications. We also estimate the cell number needed for accurate classification anywhere in a cell type hierarchy.

bioinformatics↗

bioRxiv2019

Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke

Recent genome-wide association studies in stroke have enabled the generation of genomic risk scores (GRS) but the predictive power of these GRS has been modest in comparison to established stroke risk factors. Here, using a meta-scoring approach, we developed a metaGRS for ischaemic stroke (IS) and analysed this score in the UK Biobank (n=395,393; 3075 IS events by age 75). The metaGRS hazard ratio for IS (1.26, 95% CI 1.22-1.31 per standard deviation increase of the score) doubled that of previous GRS, enabling the identification of a subset of individuals at monogenic levels of risk: individuals in the top 0.25% of metaGRS had a three-fold increased risk of IS. The metaGRS was similarly or more predictive when compared to established risk factors, such as family history, blood pressure, body mass index and smoking status. For participants within accepted guideline levels for established stroke risk factors, we found substantial variation in incident stroke rates across genomic risk backgrounds. We further estimated combinations of reductions needed in modifiable risk factors for individuals with different levels of genomic risk and suggest that, for individuals with high metaGRS, achieving currently recommended risk factor levels may be insufficient to mitigate risk.

genomics↗

bioRxiv2019

Neonatal genetics of gene expression reveal the origins of autoimmune and allergic disease risk

Chronic immune-mediated diseases of adulthood often originate in early childhood. To investigate genetic associations between neonatal immunity and disease, we collected cord blood samples from a birth cohort and mapped expression quantitative trait loci (eQTLs) in resting monocytes and CD4+ T cells as well as in response to lipopolysaccharide (LPS) or phytohemagglutinin (PHA) stimulation, respectively. Cis-eQTLs were largely specific to cell type or stimulation, and response eQTLs were identified for 31% of genes with cis-eQTLs (eGenes) in monocytes and 52% of eGenes in CD4+ T cells. We identified trans-eQTLs and mapped cis regulatory factors which act as mediators of trans effects. There was extensive colocalisation of causal variants for cell type- and stimulation-specific neonatal cis-eQTLs and those of autoimmune and allergic diseases, in particular CTSH (Cathepsin H) which showed widespread colocalisation across diseases. Mendelian randomisation showed causal neonatal gene transcription effects on disease risk for BTN3A2, HLA-C and many other genes. Our study elucidates the genetics of gene expression in neonatal conditions and cell types as well as the aetiological origins of autoimmune and allergic diseases.

genetics↗

bioRxiv2019

Ferrous-glutathione coupling mediates ferroptosis and frailty in Caenorhabditis elegans

All eukaryotes require iron. Replication, detoxification, and a cancer-protective form of regulated cell death termed ferroptosis1, all depend on iron metabolism. Ferrous iron accumulates over adult lifetime in the Caenorhabditis elegans model of ageing2. Here we show that glutathione depletion is coupled to ferrous iron elevation in these animals, and that both occur in late life to prime cells for ferroptosis. We demonstrate that blocking ferroptosis, either by inhibition of lipid peroxidation or by limiting iron retention, mitigates age-related cell death and markedly increases lifespan and healthspan in C. elegans. Temporal scaling of lifespan is not evident when ferroptosis is inhibited, consistent with this cell death process acting at specific life phases to induce organismal frailty, rather than contributing to a constant ageing rate. Because excess age-related iron elevation in somatic tissue, particularly in brain3-5, is thought to contribute to degenerative disease6, 7, our data indicate that post-developmental interventions to limit ferroptosis may promote healthy ageing.

molecular biology↗

bioRxiv2019

DeepSSV: detecting somatic small variants in paired tumor and normal sequencing data with convolutional neural network

MotivationIt is of considerable interest to detect somatic mutations in paired tumor and normal sequencing data. A number of callers that are based on statistical or machine learning approaches have been developed to detect somatic small variants. However, they take into consideration only limited information about the reference and potential variant allele in both samples at a candidate somatic site. Also, they differ in how biological and technological noises are addressed. Hence, they are expected to produce divergent outputs.\n\nResultsTo overcome the drawbacks of existing somatic callers, we develop a deep learning-based tool called DeepSSV, which employs a convolutional neural network (CNN) model to learn increasingly abstract feature representations from the raw data in higher feature layers. DeepSSV creates a spatially-oriented representation of read alignments around the candidate somatic sites adapted for the convolutional architecture, which enables it to expand to effectively gather scattered evidences. Moreover, DeepSSV incorporates the mapping information of both reference-allele-supporting and variant-allele-supporting reads in the tumor and normal samples at a genomic site that are readily available in the pileup format file. Together, the CNN model can process the whole alignment information. Such representational richness allows the model to capture the dependencies in the sequence and identify context-based sequencing artifacts, and alleviates the need of post-call filters that heavily depend on prior knowledge. We fitted the model on ground truth somatic mutations, and did benchmarking experiments on simulated and real tumors. The benchmarking results demonstrate that DeepSSV outperforms its state-of-the-art competitors in overall F1 score.\n\nAvailability and Implementationhttps://github.com/jingmeng-bioinformatics/DeepSSV\n\nContacta.salim@latrobe.edu.au\n\nSupplementary informationSupplementary data are available at online.

bioinformatics↗

bioRxiv2017

DECENT: Differential Expression with Capture Efficiency AdjustmeNT for Single-Cell RNA-seq Data

Dropout is a common phenomenon in single-cell RNA-seq (scRNA-seq) data, and when left unaddressed affects the validity of the statistical analyses. Despite this, few current methods for differential expression (DE) analysis of scRNA-seq data explicitly model the dropout process. We develop DECENT, a DE method for scRNA-seq data that explicitly models the dropout process and performs statistical analyses on the inferred pre-dropout counts. We demonstrate using simulated and real datasets the superior performance of DECENT compared to existing methods. DECENT does not require spike-in data, but spike-ins can be used to improve performance when available. The method is implemented in a publicly-available R package.

genomics↗