Biology subjects

Lam, P. Y.

Publications and source records attributed to Lam, P. Y..

5 recordsLinked to original sources

bioRxiv2023

Parkinson's VPS35 mutation induces LRRK2 mediated lysosomal association of RILPL1 and TMEM55B

The Parkinsons VPS35[D620N] mutation causes lysosome dysfunction enhancing LRRK2 kinase activity. We find the VPS35[D620N] mutation alters expression of [~]350 lysosomal proteins and stimulates LRRK2 phosphorylation of Rab proteins at the lysosome. This recruits the phosphoRab effector protein RILPL1 to the lysosome where it binds to the lysosomal integral membrane protein TMEM55B. We identify highly conserved regions of RILPL1 and TMEM55B that interact and design mutations that block binding. In mouse fibroblasts, brain, and lung, we demonstrate that the VPS35 [D620N] mutation reduces RILPL1 levels, in a manner reversed by LRRK2 inhibition. Knock-out of RILPL1 enhances phosphorylation of Rab substrates and knock-out of TMEM55B increases RILPL1 levels. The lysosomotropic agent LLOMe, also induced LRRK2 kinase mediated association of RILPL1 to the lysosome, but to a lower extent than the D620N mutation. Our study uncovers a pathway through which dysfunctional lysosomes resulting from the VPS35[D620N] mutation recruit and activate LRRK2 on the lysosomal surface, driving assembly of the RILPL1-TMEM55B complex.

neuroscience↗

bioRxiv2023

Benchmarking robust spatial transcriptomics approaches to capture the molecular landscape and pathological architecture of archived cancer tissues

AbtractsApplying spatial transcriptomics (ST) to explore a vast amount of formalin-fixed paraffin-embedded (FFPE) archival cancer tissues has been highly challenging due to several critical technical issues. In this work, we optimised ST protocols to generate unprecedented spatial gene expression data for FFPE skin cancer. Skin is among the most challenging tissue types for ST due to its fibrous structure and a high risk of RNAse contamination. We evaluated tissues collected from ten years to two years ago, spanning a range of tissue qualities and complexity. Technical replicates and multiple patient samples were assessed. Further, we integrated gene expression profiles with pathological information, revealing a new layer of molecular information. Such integration is powerful in cancer research and clinical applications. The data allowed us to detect the spatial expression of non-coding RNAs. Together, this work provides important technical perspectives to enable the applications of ST on archived cancer tissues.

pathology↗

bioRxiv2020

Spatially resolved transcriptome profiles of mammalian kidneys illustrate the molecular complexity of functional nephron segments, cell-to-cell interactions and genetic variants.

Understanding the molecular mechanisms underlying mammalian kidney function requires transcriptome profiling of the interplay between cells comprising nephron segments. Traditional transcriptomics requires cell dissociation, resulting in loss of the spatial context of gene expression within native tissue. To address this problem, we performed spatial transcriptomics (ST) to retain the spatial context of the transcriptome in human and mouse kidneys. The generated ST data allowed spatially resolved differential gene expression analysis, spatial identification of functional nephron segments, cell-to-cell interaction analysis, and chronic kidney disease-associated genetic variant calling. Novel ST thus provides an opportunity to enhance kidney diagnostics and knowledge, by retaining the spatial context of gene expression within intact tissue.

molecular biology↗

bioRxiv2020

stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues

Spatial Transcriptomics is an emerging technology that adds spatial dimensionality and tissue morphology to the genome-wide transcriptional profile of cells in an undissociated tissue. Integrating these three types of data creates a vast potential for deciphering novel biology of cell types in their native morphological context. Here we developed innovative integrative analysis approaches to utilise all three data types to first find cell types, then reconstruct cell type evolution within a tissue, and search for tissue regions with high cell-to-cell interactions. First, for normalisation of gene expression, we compute a distance measure using morphological similarity and neighbourhood smoothing. The normalised data is then used to find clusters that represent transcriptional profiles of specific cell types and cellular phenotypes. Clusters are further sub-clustered if cells are spatially separated. Analysing anatomical regions in three mouse brain sections and 12 human brain datasets, we found the spatial clustering method more accurate and sensitive than other methods. Second, we introduce a method to calculate transcriptional states by pseudo-space-time (PST) distance. PST distance is a function of physical distance (spatial distance) and gene expression distance (pseudotime distance) to estimate the pairwise similarity between transcriptional profiles among cells within a tissue. We reconstruct spatial transition gradients within and between cell types that are connected locally within a cluster, or globally between clusters, by a directed minimum spanning tree optimisation approach for PST distance. The PST algorithm could model spatial transition from non-invasive to invasive cells within a breast cancer dataset. Third, we utilise spatial information and gene expression profiles to identify locations in the tissue where there is both high ligand-receptor interaction activity and diverse cell type co-localisation. These tissue locations are predicted to be hotspots where cell-cell interactions are more likely to occur. We detected tissue regions and ligand-receptor pairs significantly enriched compared to background distribution across a breast cancer tissue. Together, these three algorithms, implemented in a comprehensive Python software stLearn, allow for the elucidation of biological processes within healthy and diseased tissues.

bioinformatics↗

bioRxiv2019

Cell type specific gene expression profiling reveals a role for the complement component C3A in neutrophil migration to tissue damage

Following acute injury, leukocytes rapidly infiltrate into tissues. For efficient recruitment, leukocytes must sense and respond to signals from both from the damaged tissue and from one another. However, the cell type specific transcriptional changes that influence leukocyte recruitment and wound healing have not been well characterized. In this study, we performed a large-scale translating ribosome affinity purification (TRAP) and RNA sequencing screen in larval zebrafish to identify genes differentially expressed by neutrophils, macrophages, and epithelial cells in the context of wounding. We identified the complement pathway and c3a.1, homologous to the C3A component of human complement, as significantly increased in neutrophils in response to a wound. We report that c3a.1-/- zebrafish larvae have impaired neutrophil responses to both tail wounds and localized bacterial infections, as well as increased susceptibility to infection due to a neutrophil-intrinsic function of C3A. We further show that C3A enhances migration of human primary neutrophils to IL-8 and that c3a.1-/- larvae have impaired neutrophil migration in vivo, and a decrease in neutrophil directed migration speed early after wounding. Together, our findings suggest a role for C3A in mediating efficient neutrophil migration to damaged tissues and support the power of TRAP to identify cell-specific changes in gene expression associated with wound-associated inflammation.

immunology↗