Biology subjects

Laddach, A.

Publications and source records attributed to Laddach, A..

4 recordsLinked to original sources

bioRxiv2026

An integrated single cell and spatial omics atlas of human prenatal development

Single cell genomics has enabled analysis of human prenatal development at unprecedented resolution. However, most studies have relied on dissociated tissues during restricted windows of development, limiting insights into how spatially distributed networks of cells, and multicellular niches emerge and adapt to distinct organ microenvironments in situ. Moreover, existing human developmental atlases have not yet been harmonised, and we thus lack a comprehensive catalogue of known cell types in the developing human body. Here, we introduce the Human Developmental Cell Atlas (HDCA), a unified structural, cellular and molecular resource for prenatal human development. The HDCA integrates published and unpublished single cell/nucleus RNAseq atlases across prenatal organs, and includes a newly generated, spatially resolved, multimodal cell atlas of intact human embryos. Spanning 4-22 post conceptional weeks, capturing embryonic and early to mid fetal stages, the HDCA contains [~]4.6 million cells/nuclei which resolve into [~]450 cell types, explorable with a bespoke web portal. For a global overview of the human embryos multicellular communities, we applied unsupervised deep learning to our intact human embryo spatial data, charting 114 tissue niches that are structural and signalling hubs for the cellular interactions of the embryo. Guided by these niches, we profiled cellular networks over space and time, not examinable using single-organ atlases. In so doing, we revealed tissue-specific fibroblast patterning from previously undescribed mesenchyme progenitors, early diversification of organ-specific blood capillaries and lymphatic vasculature, emergence of neural crest cell fates, the formation of placode-and neural crest-derived peripheral sensory neurons, and how tissue niches guide peripheral neuron maturation and axonal migration. The HDCA thus serves as a comprehensive step towards a comprehensive understanding of human prenatal development, and a template towards unravelling the biology of congenital disorders.

developmental biology↗

bioRxiv2019

Missense variants in health and disease target distinct functional pathways and proteomics features

1Missense variants are present amongst the healthy population, but some of them are causative of human diseases. Therefore, a classification of variants associated with \"healthy\" or \"diseased\" states is not always straightforward. A deeper understanding of the nature of missense variants in health and disease, the cellular processes they may affect, and the general molecular principles which underlie these differences, is essential to better distinguish pathogenic from population variants. Here we quantify variant enrichment across full-length proteins, their domains and 3D-structure defined regions. We integrate this with available transcriptomic and proteomic (protein half-life, thermal stability, abundance) data. Using this approach we have mined a rich set of molecular features which enable us to understand the differences underlying pathogenic and population variants: pathogenic variants mainly affect proteins involved in cell proliferation and nucleotide processing, localise to protein cores and interaction interfaces, and are enriched in more abundant proteins. In terms of their molecular properties, we find that common population variants and pathogenic variants show the greatest contrast. Additionally, in contrary to other studies, we find that rare population variants display features closer to common than pathogenic variants. This study provides molecular details into how different proteins exhibit resilience and/or sensitivity towards missense variants. Such details could be harnessed to predict variant deleteriousness, and prioritise variant-enriched proteins and protein domains for therapeutic targeting and development. The ZoomVar database, which we created for this study, is available at http://fraternalilab.kcl.ac.uk/ZoomVar. It allows users to programmatically annotate a large number of missense variants with protein structural information, and to calculate variant enrichment in different protein structural regions.\n\nSignificance StatementOne of the greatest challenges in understanding the genetic basis of diseases is to discriminate between likely harmless and potentially disease-causing sequence variants. To better evaluate the pathogenic potential of missense variants, we developed a strategy to quantitatively measure the enrichment of both disease and non disease-related variants within a protein based on its structural and domain organisation. By integrating available transcriptomics and proteomics data, our approach distinguishes pathogenic from population variants far more clearly than previously possible, and reveals hitherto unknown details of how different proteins exhibit resilience and/or sensitivity towards genetic variants. Our results will help to prioritise variant-enriched proteins for therapeutic targeting; we have created the ZoomVar database, accessible at http://fraternalilab.kcl.ac.uk/ZoomVar, for programmatic mapping of user-defined variants to protein structural and domain information.

bioinformatics↗

bioRxiv2018

Short loop motif profiling of protein interaction networks in acute myeloid leukaemia

Recent advances in biotechnologies for genomics and proteomics have expanded our understanding of biological components which play crucial roles in complex mechanisms related to cancer. However, it is still challenging to extract from the available knowledge reliable targets to use in a translational setting. The reasons for this are manifold, but essentially distilling real biological signal from heterogeneous \"big data\" collections is the major hurdle. Here, we aim to establish an in-silico pipeline to explore mutations and their effects on protein-protein interactions, with a focus on acute myeloid leukaemia (AML), one of the most common blood cancers with the highest mortality rate. Our method, based on cyclic interactions of a small number of proteins topologically linked in the network (short loop network motifs), highlights specific protein-protein interactions (PPIs) and their functions in AML when compared with other leukaemias. We also developed a new property named short loop commonality to measure indirect PPIs occurring via common short loop interactions. This new method detects \"modules\" of PPI networks (PPINs) enriched with common biological functions which have proteins that contain mutation hotspots. We further perform 3D structural modelling to extract atomistic details, which shows that such hotspots map to PPI interfaces as well as active sites. Thus, our study proposes a framework for the macroscopic and microscopic investigation of PPINs, their relation to cancers, and highlights important functional modules in the network to be exploited in targeted drug screening.

bioinformatics↗

bioRxiv2018

Identifying the genetic basis of variation in cell behaviour in human iPS cell lines from healthy donors

Large cohorts of human iPSCs from healthy donors are potentially a powerful tool for investigating the relationship between genetic variants and cellular phenotypes. Here we integrate high content imaging, gene expression and DNA sequence datasets for over 100 human iPSC lines to identify the genetic basis of inter-individual variability in cell behaviour. By applying a dimensionality reduction approach, Probabilistic Estimation of Expression Residuals (PEER), we identified genes that correlated in expression with intrinsic (genetic) and extrinsic (ECM) factors. However, variation in mRNA levels could not account for outlier cell behaviour. Instead, we identified rare, deleterious SNVs in the coding sequence of genes involved in ECM adhesion that occurred in cell lines that were outliers for one or more phenotypes such as cell spreading. These also correlated with altered germ layer differentiation on micropatterned surfaces. Our study thus establishes a strategy for integrating genetic and cell biological measurements for high-throughput analysis.

cell biology↗